Repository: Sygil-Dev/sygil-webui
Branch: master
Commit: 15a337cd4377
Files: 290
Total size: 21.8 MB

Directory structure:
gitextract_lx9je00i/

├── .dockerignore
├── .env_docker.example
├── .gitattributes
├── .github/
│   ├── FUNDING.yml
│   ├── ISSUE_TEMPLATE/
│   │   └── bug_report.yml
│   ├── PULL_REQUEST_TEMPLATE.md
│   ├── dependabot.yml
│   └── workflows/
│       ├── deploy.yml
│       └── test-deploy.yml
├── .gitignore
├── .gitmodules
├── .pre-commit-config.yaml
├── .streamlit/
│   └── config.toml
├── CONTRIBUTING.md
├── Dockerfile
├── Dockerfile_base
├── Dockerfile_runpod
├── LICENSE
├── README.md
├── Stable_Diffusion_v1_Model_Card.md
├── Web_based_UI_for_Stable_Diffusion_colab.ipynb
├── _config.yml
├── babel.config.js
├── blog/
│   ├── 2022-10-20/
│   │   └── 1.Textual inversion usage competitio.md
│   └── authors.yml
├── configs/
│   ├── autoencoder/
│   │   ├── autoencoder_kl_16x16x16.yaml
│   │   ├── autoencoder_kl_32x32x4.yaml
│   │   ├── autoencoder_kl_64x64x3.yaml
│   │   └── autoencoder_kl_8x8x64.yaml
│   ├── blip/
│   │   ├── bert_config.json
│   │   ├── caption_coco.yaml
│   │   ├── med_config.json
│   │   ├── nlvr.yaml
│   │   ├── nocaps.yaml
│   │   ├── pretrain.yaml
│   │   ├── retrieval_coco.yaml
│   │   ├── retrieval_flickr.yaml
│   │   ├── retrieval_msrvtt.yaml
│   │   └── vqa.yaml
│   ├── latent-diffusion/
│   │   ├── celebahq-ldm-vq-4.yaml
│   │   ├── cin-ldm-vq-f8.yaml
│   │   ├── cin256-v2.yaml
│   │   ├── ffhq-ldm-vq-4.yaml
│   │   ├── lsun_bedrooms-ldm-vq-4.yaml
│   │   ├── lsun_churches-ldm-kl-8.yaml
│   │   └── txt2img-1p4B-eval.yaml
│   ├── retrieval-augmented-diffusion/
│   │   └── 768x768.yaml
│   ├── stable-diffusion/
│   │   ├── v1-inference.yaml
│   │   ├── v2-inference-v.yaml
│   │   ├── v2-inference.yaml
│   │   ├── v2-inpainting-inference.yaml
│   │   ├── v2-midas-inference.yaml
│   │   └── x4-upscaling.yaml
│   └── webui/
│       ├── webui.yaml
│       ├── webui_flet.yaml
│       └── webui_streamlit.yaml
├── daisi_app.py
├── data/
│   ├── img2txt/
│   │   ├── artists.txt
│   │   ├── flavors.txt
│   │   ├── mediums.txt
│   │   ├── movements.txt
│   │   ├── sites.txt
│   │   ├── subreddits.txt
│   │   ├── tags.txt
│   │   └── techniques.txt
│   ├── scn2img_examples/
│   │   ├── cat_at_beach.scn2img.md
│   │   ├── corgi_3d_transformation.scn2img.md
│   │   ├── corgi_at_beach.scn2img.md
│   │   ├── corgi_at_beach_2.scn2img.md
│   │   └── landscape_3d.scn2img.md
│   └── tags/
│       ├── config.json
│       ├── danbooru.csv
│       ├── e621.csv
│       ├── key_phrases.json
│       └── thumbnails.json
├── docker-compose.yml
├── docs/
│   ├── 1.Installation/
│   │   ├── 1.one-click-installer.md
│   │   ├── 2.windows-installation.md
│   │   ├── 3.linux-installation.md
│   │   └── 4.docker-guide.md
│   ├── 2.Streamlit/
│   │   └── 1.streamlit-interface.md
│   ├── 3.Gradio/
│   │   └── 2.gradio-interface.md
│   ├── 4.post-processing.md
│   ├── 5.concepts-library.md
│   └── 6.custom-models.md
├── docusaurus.config.js
├── entrypoint.sh
├── environment.yaml
├── frontend/
│   ├── .eslintrc.js
│   ├── __init__.py
│   ├── css/
│   │   ├── custom.css
│   │   ├── docs_custom.css
│   │   ├── no_progress_bar.css
│   │   ├── streamlit.main.css
│   │   └── styles.css
│   ├── css_and_js.py
│   ├── dists/
│   │   ├── concept-browser/
│   │   │   └── dist/
│   │   │       ├── assets/
│   │   │       │   ├── index.3ab9729b.css
│   │   │       │   └── index.b5b962e4.js
│   │   │       └── index.html
│   │   └── sd-gallery/
│   │       └── dist/
│   │           ├── assets/
│   │           │   ├── index.4194368f.css
│   │           │   └── index.aeaed602.js
│   │           └── index.html
│   ├── frontend.py
│   ├── image_metadata.py
│   ├── index.html
│   ├── job_manager.py
│   ├── js/
│   │   └── index.js
│   ├── package.json
│   ├── src/
│   │   ├── Component.vue
│   │   ├── app.vue
│   │   ├── env.d.ts
│   │   ├── main.ts
│   │   └── streamlit/
│   │       ├── StreamlitVue.ts
│   │       ├── WithStreamlitConnection.vue
│   │       └── index.ts
│   ├── tsconfig.json
│   ├── tsconfig.node.json
│   ├── ui_functions.py
│   └── vite.config.ts
├── horde_bridge.cmd
├── horde_bridge.sh
├── installer/
│   ├── create_installers.sh
│   ├── install.bat
│   └── install.sh
├── ldm/
│   ├── __init__.py
│   ├── data/
│   │   ├── __init__.py
│   │   ├── base.py
│   │   ├── coco_karpathy_dataset.py
│   │   ├── flickr30k_dataset.py
│   │   ├── imagenet.py
│   │   ├── lsun.py
│   │   ├── nlvr_dataset.py
│   │   ├── nocaps_dataset.py
│   │   ├── personalized.py
│   │   ├── personalized_file.py
│   │   ├── pretrain_dataset.py
│   │   ├── util.py
│   │   ├── video_dataset.py
│   │   └── vqa_dataset.py
│   ├── devices/
│   │   ├── __init__.py
│   │   └── devices.py
│   ├── lr_scheduler.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── autoencoder.py
│   │   ├── blip.py
│   │   ├── blip_itm.py
│   │   ├── blip_nlvr.py
│   │   ├── blip_pretrain.py
│   │   ├── blip_retrieval.py
│   │   ├── blip_vqa.py
│   │   ├── diffusion/
│   │   │   ├── __init__.py
│   │   │   ├── classifier.py
│   │   │   ├── ddim.py
│   │   │   ├── ddpm.py
│   │   │   ├── dpm_solver/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── dpm_solver.py
│   │   │   │   └── sampler.py
│   │   │   ├── kdiffusion.py
│   │   │   ├── plms.py
│   │   │   └── sampling_util.py
│   │   ├── med.py
│   │   ├── nlvr_encoder.py
│   │   └── vit.py
│   ├── modules/
│   │   ├── __init__.py
│   │   ├── attention.py
│   │   ├── diffusionmodules/
│   │   │   ├── __init__.py
│   │   │   ├── model.py
│   │   │   ├── openaimodel.py
│   │   │   ├── upscaling.py
│   │   │   └── util.py
│   │   ├── distributions/
│   │   │   ├── __init__.py
│   │   │   └── distributions.py
│   │   ├── ema.py
│   │   ├── embedding_manager.py
│   │   ├── encoders/
│   │   │   ├── __init__.py
│   │   │   └── modules.py
│   │   ├── image_degradation/
│   │   │   ├── __init__.py
│   │   │   ├── bsrgan.py
│   │   │   ├── bsrgan_light.py
│   │   │   └── utils_image.py
│   │   ├── losses/
│   │   │   ├── __init__.py
│   │   │   ├── contperceptual.py
│   │   │   └── vqperceptual.py
│   │   ├── midas/
│   │   │   ├── api.py
│   │   │   ├── midas/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── base_model.py
│   │   │   │   ├── blocks.py
│   │   │   │   ├── dpt_depth.py
│   │   │   │   ├── midas_net.py
│   │   │   │   ├── midas_net_custom.py
│   │   │   │   ├── transforms.py
│   │   │   │   └── vit.py
│   │   │   └── utils.py
│   │   └── x_transformer.py
│   └── util.py
├── optimizedSD/
│   ├── ddpm.py
│   ├── diffusers_txt2img.py
│   ├── openaimodelSplit.py
│   ├── optimUtils.py
│   ├── optimized_img2img.py
│   ├── optimized_txt2img.py
│   ├── samplers.py
│   ├── splitAttention.py
│   └── v1-inference.yaml
├── package.json
├── requirements.txt
├── runpod_entrypoint.sh
├── scripts/
│   ├── APIServer.py
│   ├── ModelManager.py
│   ├── Settings.py
│   ├── __init__.py
│   ├── barfi_baklavajs.py
│   ├── bridgeData_template.py
│   ├── clip_interrogator.py
│   ├── convert_original_stable_diffusion_to_diffusers.py
│   ├── custom_components/
│   │   ├── draggable_number_input/
│   │   │   ├── __init__.py
│   │   │   └── main.js
│   │   └── sygil_suggestions/
│   │       ├── __init__.py
│   │       ├── main.css
│   │       ├── main.js
│   │       └── parent.css
│   ├── diffusers_textual_inversion_2.py
│   ├── home.py
│   ├── hydrus_api/
│   │   ├── __init__.py
│   │   └── utils.py
│   ├── img2img.py
│   ├── img2txt.py
│   ├── imglab.py
│   ├── logger.py
│   ├── merge.py
│   ├── modeldownload.py
│   ├── perlin.py
│   ├── pipelines/
│   │   └── stable_diffusion/
│   │       └── no_check.py
│   ├── post_processing.py
│   ├── prune-ckpt.py
│   ├── relauncher.py
│   ├── scn2img.py
│   ├── sd_concept_library.py
│   ├── sd_concepts_library_downloader.py
│   ├── sd_utils/
│   │   ├── __init__.py
│   │   └── bridge.py
│   ├── sd_utils_old.py
│   ├── stable_diffusion_pipeline.py
│   ├── textual_inversion.py
│   ├── txt2img.py
│   ├── txt2vid.py
│   ├── webui.py
│   ├── webui_streamlit.py
│   └── webui_streamlit_new.py
├── setup.py
├── sidebars.js
├── streamlit_webview.py
├── webui/
│   ├── flet/
│   │   ├── assets/
│   │   │   └── manifest.json
│   │   ├── scripts/
│   │   │   ├── __init__.py
│   │   │   ├── flet_asset_manager.py
│   │   │   ├── flet_canvas.py
│   │   │   ├── flet_file_manager.py
│   │   │   ├── flet_gallery_window.py
│   │   │   ├── flet_messages.py
│   │   │   ├── flet_property_manager.py
│   │   │   ├── flet_settings_window.py
│   │   │   ├── flet_titlebar.py
│   │   │   ├── flet_tool_manager.py
│   │   │   └── flet_utils.py
│   │   └── webui_flet.py
│   └── streamlit/
│       ├── frontend/
│       │   └── css/
│       │       └── streamlit.main.css
│       └── scripts/
│           ├── APIServer.py
│           ├── ModelManager.py
│           ├── Settings.py
│           ├── barfi_baklavajs.py
│           ├── custom_components/
│           │   ├── dragable_number_input/
│           │   │   └── index.html
│           │   ├── draggable_number_input/
│           │   │   ├── __init__.py
│           │   │   └── main.js
│           │   └── sygil_suggestions/
│           │       ├── __init__.py
│           │       ├── main.css
│           │       ├── main.js
│           │       └── parent.css
│           ├── img2img.py
│           ├── img2txt.py
│           ├── post_processing.py
│           ├── sd_concept_library.py
│           ├── sd_utils/
│           │   ├── __init__.py
│           │   └── bridge.py
│           ├── textual_inversion.py
│           ├── txt2img.py
│           ├── txt2vid.py
│           └── webui_streamlit.py
├── webui.cmd
├── webui.sh
└── webui_legacy.cmd

================================================
FILE CONTENTS
================================================

================================================
FILE: .dockerignore
================================================
outputs/
src/
configs/webui/userconfig_streamlit.yaml


================================================
FILE: .env_docker.example
================================================
# Force miniconda to attempt to update on every container restart
# instead only when changes are detected
CONDA_FORCE_UPDATE=false

# Validate the model files on every container restart
# (useful to set to false after you're sure the model files are already in place)
VALIDATE_MODELS=true

#Automatically relaunch the webui on crashes
WEBUI_RELAUNCH=true

#Pass cli arguments to webui.py e.g:
#WEBUI_ARGS=--gpu=1 --esrgan-gpu=1 --gfpgan-gpu=1
WEBUI_ARGS=

================================================
FILE: .gitattributes
================================================
* text=auto
*.{cmd,[cC][mM][dD]} text eol=crlf
*.{bat,[bB][aA][tT]} text eol=crlf
*.sh text eol=lf


================================================
FILE: .github/FUNDING.yml
================================================
github: [ZeroCool940711]
patreon: zerocool94
ko_fi: zerocool94
open_collective: sygil_dev
custom: ["https://paypal.me/zerocool94"]


================================================
FILE: .github/ISSUE_TEMPLATE/bug_report.yml
================================================
name: 🐞 Bug Report
description: File a bug report
title: "[Bug]: "
labels: ["bug", "triage"]
assignees:
  - octocat
body:
  - type: markdown
    attributes:
      value: |
        Thanks for taking the time to fill out this bug report!
  - type: textarea
    id: what-happened
    attributes:
      label: What happened?
      description: Also tell us, what did you expect to happen?
      placeholder: Tell us what you see!
      value: "A bug happened!"
    validations:
      required: true
  - type: dropdown
    id: version
    attributes:
      label: Version
      description: What version of our software are you running?
      options:
        - 0.0.1 (Default)
    validations:
      required: true
  - type: dropdown
    id: browsers
    attributes:
      label: What browsers are you seeing the problem on?
      multiple: true
      options:
        - Firefox
        - Chrome
        - Safari
        - Microsoft Edge
  - type: dropdown
    id: os
    attributes:
      label: Where are you running the webui?
      multiple: true
      options:
        - Windows
        - Colab
        - Linux
        - MacOS
  - type: textarea
    id: settings
    attributes:
      label: Custom settings
      description: If you are running the webui with specifi settings, please paste them here for reference (like --nitro)
      render: shell
  - type: textarea
    id: logs
    attributes:
      label: Relevant log output
      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
      render: shell
  - type: checkboxes
    id: terms
    attributes:
      label: Code of Conduct
      description: By submitting this issue, you agree to follow our [Code of Conduct](https://docs.github.com/en/site-policy/github-terms/github-community-code-of-conduct)
      options:
        - label: I agree to follow this project's Code of Conduct
          required: true


================================================
FILE: .github/PULL_REQUEST_TEMPLATE.md
================================================
# Description

Please include:
* relevant motivation
* a summary of the change
* which issue is fixed.
* any additional dependencies that are required for this change.

Closes: # (issue)

# Checklist:

- [ ] I have changed the base branch to `dev`
- [ ] I have performed a self-review of my own code
- [ ] I have commented my code in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation


================================================
FILE: .github/dependabot.yml
================================================
# To get started with Dependabot version updates, you'll need to specify which
# package ecosystems to update and where the package manifests are located.
# Please see the documentation for all configuration options:
# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates

version: 2
updates:
  - package-ecosystem: "pip" # See documentation for possible values
    directory: "/" # Location of package manifests
    target-branch: "dev"

    schedule:
      interval: "daily"


================================================
FILE: .github/workflows/deploy.yml
================================================
name: Deploy to GitHub Pages

on:
  push:
    branches:
      - master
    # Review gh actions docs if you want to further define triggers, paths, etc
    # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#on

jobs:
  deploy:
    name: Deploy to GitHub Pages
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - uses: actions/setup-node@v3
        with:
          node-version: 18
          cache: yarn

      - name: Install dependencies
        run: yarn install
      - name: Build website
        run: yarn build

      # Popular action to deploy to GitHub Pages:
      # Docs: https://github.com/peaceiris/actions-gh-pages#%EF%B8%8F-docusaurus
      - name: Deploy to GitHub Pages
        uses: peaceiris/actions-gh-pages@v3
        with:
          github_token: ${{ secrets.GITHUB_TOKEN }}
          # Build output to publish to the `gh-pages` branch:
          publish_dir: ./build
          # The following lines assign commit authorship to the official
          # GH-Actions bot for deploys to `gh-pages` branch:
          # https://github.com/actions/checkout/issues/13#issuecomment-724415212
          # The GH actions bot is used by default if you didn't specify the two fields.
          # You can swap them out with your own user credentials.
          user_name: github-actions[bot]
          user_email: 41898282+github-actions[bot]@users.noreply.github.com


================================================
FILE: .github/workflows/test-deploy.yml
================================================
name: Test deployment

on:
  pull_request:
    branches:
      - master
    # Review gh actions docs if you want to further define triggers, paths, etc
    # https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#on

jobs:
  test-deploy:
    name: Test deployment
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - uses: actions/setup-node@v3
        with:
          node-version: 18
          cache: yarn

      - name: Install dependencies
        run: yarn install
      - name: Test build website
        run: yarn build


================================================
FILE: .gitignore
================================================
# OS-generated
# ------------
.DS_Store*
[Tt]humbs.db
[Dd]esktop.ini

# Programming - general
*.log


# =========================================================================== #
# 	Python-related
# =========================================================================== #
# src: https://github.com/github/gitignore/blob/master/Python.gitignore

# JetBrains PyCharm / Rider
.idea/

# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
.env_docker
.env_updated
condaenv.*.requirements.txt

# Visual Studio directories
.vs/
.vscode/

# =========================================================================== #
# 	Repo-specific
# =========================================================================== #
/configs/webui/userconfig_streamlit.yaml
/configs/webui/userconfig_flet.yaml
/custom-conda-path.txt
!/src/components/*
!/src/pages/*
/src/*
/outputs
/model_cache
/log/**/*.png
/log/webui/*
/log/log.csv
/flagged/*
/gfpgan/*
/models/*
/webui/flet/assets/uploads/
/webui/flet/assets/outputs/

z_version_env.tmp
scripts/bridgeData.py
/user_data/*

# Dependencies
/node_modules

# Production
/build

# Generated files
.docusaurus
.cache-loader

# Misc
.DS_Store
.env.local
.env.development.local
.env.test.local
.env.production.local

npm-debug.log*
yarn-debug.log*
yarn-error.log*


================================================
FILE: .gitmodules
================================================


================================================
FILE: .pre-commit-config.yaml
================================================
# See https://pre-commit.com for more information
# See https://pre-commit.com/hooks.html for more hooks
ci:
  autofix_prs: true
  autoupdate_branch: 'dev'
  autoupdate_commit_msg: '[pre-commit.ci] pre-commit autoupdate'
  autoupdate_schedule: weekly

repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.4.0
    hooks:
      - id: trailing-whitespace
      - id: end-of-file-fixer
      - id: check-yaml
      - id: check-added-large-files

  - repo: https://github.com/astral-sh/ruff-pre-commit
    rev: "v0.0.278"
    hooks:
      - id: ruff
        args: [--fix, --exit-non-zero-on-fix]

  - repo: https://github.com/psf/black
    rev: 23.7.0
    hooks:
      - id: black


================================================
FILE: .streamlit/config.toml
================================================
[global]
disableWatchdogWarning = false
showWarningOnDirectExecution = true
dataFrameSerialization = "arrow"

[logger]
level = "info"
messageFormat = "%(asctime)s %(message)s"

[client]
caching = true
displayEnabled = true
showErrorDetails = true

[runner]
magicEnabled = true
installTracer = false
fixMatplotlib = true
postScriptGC = true
fastReruns = false

[server]
folderWatchBlacklist = []
fileWatcherType = "auto"
cookieSecret = ""
headless = false
runOnSave = false
port = 8501
baseUrlPath = ""
enableCORS = true
enableXsrfProtection = true
maxUploadSize = 200
maxMessageSize = 200
enableWebsocketCompression = false

[browser]
gatherUsageStats = false
serverPort = 8501

[mapbox]
token = ""

[deprecation]
showfileUploaderEncoding = true
showPyplotGlobalUse = true

[theme]
base = "dark"


================================================
FILE: CONTRIBUTING.md
================================================
# Contribution Guide

All Pull Requests are opened against `dev` branch which is our main development branch.

There are two UI systems that are supported currently:

* **Gradio** — entry point is in the `/scripts/webui.py` you can start from there. Check out [Gradio documentation](https://gradio.app/docs/) and their [Discord channel](https://discord.gg/Qs8AsnX7Jd) for more information about Gradio.
* **Streamlit** — entry point is in the `/scripts/webui_streamlit.py`. Documentation on Streamlit is [located here](https://docs.streamlit.io/).

### Development environment

`environment.yaml` can be different from the one on `master` so be sure to update before making any changes to the code.

The development environment is currently very similar to the one in production, so you can work on your contribution in the same conda env. Optionally you can create a separate environment.

### Making changes

If you're working on a fix please post about it in the respective issue. If the issue doesn't exist create it and then mention it in your Pull Request.

If you're introducing new features please make the corresponding additions to the documentation with an explanation of the new behavior. The documentation is located in `/docs/`. Depending on your contribution you may edit the existing files in there or create a new one.

### Opening a Pull Request

Prior to opening a request make sure your Web UI works locally with your changes and that your branch is up-to-date with the main repository. Finally, open a new PR against `dev` branch.


================================================
FILE: Dockerfile
================================================
ARG IMAGE=tukirito/sygil-webui:base

# Use the base image
FROM ${IMAGE}

# Set the working directory
WORKDIR /workdir

# Use the specified shell
SHELL ["/bin/bash", "-c"]

# Set environment variables
ENV PYTHONPATH=/sd

# Expose the required port
EXPOSE 8501

# Copy necessary files and directories
COPY ./entrypoint.sh /sd/
COPY ./data/DejaVuSans.ttf /usr/share/fonts/truetype/
COPY ./data /sd/data
COPY ./images /sd/images
COPY ./scripts /sd/scripts
COPY ./ldm /sd/ldm
COPY ./frontend /sd/frontend
COPY ./configs /sd/configs
COPY ./configs/webui/webui_streamlit.yaml /sd/configs/webui/userconfig_streamlit.yaml
COPY ./.streamlit /sd/.streamlit
COPY ./optimizedSD /sd/optimizedSD

# Set the entrypoint
ENTRYPOINT ["/sd/entrypoint.sh"]

# Create .streamlit directory and set up credentials.toml
RUN mkdir -p ~/.streamlit \
    && echo "[general]" > ~/.streamlit/credentials.toml \
    && echo "email = \"\"" >> ~/.streamlit/credentials.toml


================================================
FILE: Dockerfile_base
================================================
ARG PYTORCH_IMAGE=hlky/pytorch:1.12.1-runtime

FROM ${PYTORCH_IMAGE}
SHELL ["/bin/bash", "-c"]

WORKDIR /install

RUN apt-get update && \
    apt-get install -y wget curl git build-essential zip unzip nano openssh-server libgl1 libsndfile1-dev && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

COPY ./requirements.txt /install/
COPY ./setup.py /install/

RUN /opt/conda/bin/python -m pip install -r /install/requirements.txt

RUN /opt/conda/bin/conda clean -ya


================================================
FILE: Dockerfile_runpod
================================================
ARG IMAGE=tukirito/sygil-webui:base

FROM ${IMAGE}

WORKDIR /workdir

SHELL ["/bin/bash", "-c"]

ENV PYTHONPATH=/sd

EXPOSE 8501
COPY ./runpod_entrypoint.sh /sd/entrypoint.sh
COPY ./data/DejaVuSans.ttf /usr/share/fonts/truetype/
COPY ./configs/ /sd/configs/
copy ./configs/webui/webui_streamlit.yaml /sd/configs/webui/userconfig_streamlit.yaml
COPY ./data/ /sd/data/
COPY ./frontend/ /sd/frontend/
COPY ./gfpgan/ /sd/gfpgan/
COPY ./images/ /sd/images/
COPY ./ldm/ /sd/ldm/
COPY ./models/ /sd/models/
copy ./optimizedSD/ /sd/optimizedSD/
COPY ./scripts/ /sd/scripts/
COPY ./.streamlit/ /sd/.streamlit/
ENTRYPOINT /sd/entrypoint.sh

RUN mkdir -p ~/.streamlit/
RUN echo "[general]"  > ~/.streamlit/credentials.toml
RUN echo "email = \"\""  >> ~/.streamlit/credentials.toml


================================================
FILE: LICENSE
================================================
                    GNU AFFERO GENERAL PUBLIC LICENSE
                       Version 3, 19 November 2007

 Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
 Everyone is permitted to copy and distribute verbatim copies
 of this license document, but changing it is not allowed.

                            Preamble

  The GNU Affero General Public License is a free, copyleft license for
software and other kinds of works, specifically designed to ensure
cooperation with the community in the case of network server software.

  The licenses for most software and other practical works are designed
to take away your freedom to share and change the works.  By contrast,
our General Public Licenses are intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users.

  When we speak of free software, we are referring to freedom, not
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  Developers that use our General Public Licenses protect your rights
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  An older license, called the Affero General Public License and
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  The precise terms and conditions for copying, distribution and
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================================================
FILE: README.md
================================================
# <center>Web-based UI for Stable Diffusion</center>

## Created by [Sygil.Dev](https://github.com/sygil-dev)

## Join us at Sygil.Dev's Discord Server [![Generic badge](https://flat.badgen.net/discord/members/ttM8Tm6wge?icon=discord)](https://discord.gg/ttM8Tm6wge)

## Installation instructions for:

- **[Windows](https://sygil-dev.github.io/sygil-webui/docs/Installation/windows-installation)**
- **[Linux](https://sygil-dev.github.io/sygil-webui/docs/Installation/linux-installation)**

### Want to ask a question or request a feature?

Come to our [Discord Server](https://discord.gg/gyXNe4NySY) or use [Discussions](https://github.com/sygil-dev/sygil-webui/discussions).

## Documentation

[Documentation is located here](https://sygil-dev.github.io/sygil-webui/)

## Want to contribute?

Check the [Contribution Guide](CONTRIBUTING.md)

[Sygil-Dev](https://github.com/Sygil-Dev) main devs:

* ![ZeroCool940711's avatar](https://avatars.githubusercontent.com/u/5977640?s=40&v=4)[ZeroCool940711](https://github.com/ZeroCool940711)
* ![Kasiya13's avatar](https://avatars.githubusercontent.com/u/26075839?s=40&v=4)[Kasiya13](https://github.com/Kasiya13)

### Project Features:

* Built-in image enhancers and upscalers, including GFPGAN and realESRGAN

* Generator Preview: See your image as its being made

* Run additional upscaling models on CPU to save VRAM

* Textual inversion: [Reaserch Paper](https://textual-inversion.github.io/)

* K-Diffusion Samplers: A great collection of samplers to use, including:

  - `k_euler`
  - `k_lms`
  - `k_euler_a`
  - `k_dpm_2`
  - `k_dpm_2_a`
  - `k_heun`
  - `PLMS`
  - `DDIM`

* Loopback: Automatically feed the last generated sample back into img2img

* Prompt Weighting & Negative Prompts: Gain more control over your creations

* Selectable GPU usage from Settings tab

* Word Seeds: Use words instead of seed numbers

* Automated Launcher: Activate conda and run Stable Diffusion with a single command

* Lighter on VRAM: 512x512 Text2Image & Image2Image tested working on 4GB (with *optimized* mode enabled in Settings)

* Prompt validation: If your prompt is too long, you will get a warning in the text output field

* Sequential seeds for batches: If you use a seed of 1000 to generate two batches of two images each, four generated images will have seeds: `1000, 1001, 1002, 1003`.

* Prompt matrix: Separate multiple prompts using the `|` character, and the system will produce an image for every combination of them.

* [Gradio] Advanced img2img editor with Mask and crop capabilities

* [Gradio] Mask painting 🖌️: Powerful tool for re-generating only specific parts of an image you want to change (currently Gradio only)

# SD WebUI

An easy way to work with Stable Diffusion right from your browser.

## Streamlit

![](images/streamlit/streamlit-t2i.png)

**Features:**

- Clean UI with an easy to use design, with support for widescreen displays
- *Dynamic live preview* of your generations
- Easily customizable defaults, right from the WebUI's Settings tab
- An integrated gallery to show the generations for a prompt
- *Optimized VRAM* usage for bigger generations or usage on lower end GPUs
- *Text to Video:* Generate video clips from text prompts right from the WebUI (WIP)
- Image to Text: Use [CLIP Interrogator](https://github.com/pharmapsychotic/clip-interrogator) to interrogate an image and get a prompt that you can use to generate a similar image using Stable Diffusion.
- *Concepts Library:* Run custom embeddings others have made via textual inversion.
- Textual Inversion training: Train your own embeddings on any photo you want and use it on your prompt.
- **Currently in development: [Stable Horde](https://stablehorde.net/) integration; ImgLab, batch inputs, & mask editor from Gradio

**Prompt Weights & Negative Prompts:**

To give a token (tag recognized by the AI) a specific or increased weight (emphasis), add `:0.##` to the prompt, where `0.##` is a decimal that will specify the weight of all tokens before the colon.
Ex: `cat:0.30, dog:0.70` or `guy riding a bicycle :0.7, incoming car :0.30`

Negative prompts can be added by using  `###` , after which any tokens will be seen as negative.
Ex: `cat playing with string ### yarn` will negate `yarn` from the generated image.

Negatives are a very powerful tool to get rid of contextually similar or related topics, but **be careful when adding them since the AI might see connections you can't**, and end up outputting gibberish

**Tip:* Try using the same seed with different prompt configurations or weight values see how the AI understands them, it can lead to prompts that are more well-tuned and less prone to error.

Please see the [Streamlit Documentation](docs/4.streamlit-interface.md) to learn more.

## Gradio [Legacy]

![](images/gradio/gradio-t2i.png)

**Features:**

- Older UI that is functional and feature complete.
- Has access to all upscaling models, including LSDR.
- Dynamic prompt entry automatically changes your generation settings based on `--params` in a prompt.
- Includes quick and easy ways to send generations to Image2Image or the Image Lab for upscaling.

**Note: the Gradio interface is no longer being actively developed by Sygil.Dev and is only receiving bug fixes.**

Please see the [Gradio Documentation](https://sygil-dev.github.io/sygil-webui/docs/Gradio/gradio-interface/) to learn more.

## Image Upscalers

---

### GFPGAN

![](images/GFPGAN.png)

Lets you improve faces in pictures using the GFPGAN model. There is a checkbox in every tab to use GFPGAN at 100%, and also a separate tab that just allows you to use GFPGAN on any picture, with a slider that controls how strong the effect is.

If you want to use GFPGAN to improve generated faces, you need to install it separately.
Download [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth) and put it
into the `/sygil-webui/models/gfpgan` directory.

### RealESRGAN

![](images/RealESRGAN.png)

Lets you double the resolution of generated images. There is a checkbox in every tab to use RealESRGAN, and you can choose between the regular upscaler and the anime version.
There is also a separate tab for using RealESRGAN on any picture.

Download [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth) and [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth).
Put them into the `sygil-webui/models/realesrgan` directory.

### LSDR

Download **LDSR** [project.yaml](https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1) and [model last.cpkt](https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1). Rename `last.ckpt` to `model.ckpt` and place both under `sygil-webui/models/ldsr/`

### GoBig, and GoLatent *(Currently on the Gradio version Only)*

More powerful upscalers that uses a separate Latent Diffusion model to more cleanly upscale images.

Please see the [Post-Processing Documentation](https://sygil-dev.github.io/sygil-webui/docs/post-processing) to learn more.

-----

### *Original Information From The Stable Diffusion Repo:*

# Stable Diffusion

*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*

[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)
[Robin Rombach](https://github.com/rromb)\*,
[Andreas Blattmann](https://github.com/ablattmann)\*,
[Dominik Lorenz](https://github.com/qp-qp)\,
[Patrick Esser](https://github.com/pesser),
[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)

**CVPR '22 Oral**

which is available on [GitHub](https://github.com/CompVis/latent-diffusion). PDF at [arXiv](https://arxiv.org/abs/2112.10752). Please also visit our [Project page](https://ommer-lab.com/research/latent-diffusion-models/).

[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion
model.
Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.
Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),
this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.
With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.
See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).

## Stable Diffusion v1

Stable Diffusion v1 refers to a specific configuration of the model
architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet
and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and
then finetuned on 512x512 images.

*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present
in its training data.
Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion).

## Comments

- Our code base for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
  and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).
  Thanks for open-sourcing!

- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).

## BibTeX

```
@misc{rombach2021highresolution,
      title={High-Resolution Image Synthesis with Latent Diffusion Models},
      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
      year={2021},
      eprint={2112.10752},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```


================================================
FILE: Stable_Diffusion_v1_Model_Card.md
================================================
# Stable Diffusion v1 Model Card
This model card focuses on the model associated with the Stable Diffusion model, available [here](https://github.com/CompVis/stable-diffusion).

## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [Proprietary](LICENSE)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**

      @InProceedings{Rombach_2022_CVPR,
          author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
          title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
          booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
          month     = {June},
          year      = {2022},
          pages     = {10684-10695}
      }

# Uses

## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include

- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.

Excluded uses are described below.

 ### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.


The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.

## Limitations and Bias

### Limitations

- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
  [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
  and is not fit for product use without additional safety mechanisms and
  considerations.

### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.


## Training

**Training Data**
The model developers used the following dataset for training the model:

- LAION-2B (en) and subsets thereof (see next section)

**Training Procedure**
Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,

- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.

We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
which were trained as follows,

- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
  194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
  515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).


- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant

## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
steps show the relative improvements of the checkpoints:

![pareto](assets/v1-variants-scores.jpg)

Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution.  Not optimized for FID scores.
## Environmental Impact

**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.

- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
    @InProceedings{Rombach_2022_CVPR,
        author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
        title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
        month     = {June},
        year      = {2022},
        pages     = {10684-10695}
    }

*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*


================================================
FILE: Web_based_UI_for_Stable_Diffusion_colab.ipynb
================================================
{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5RoIM-5IPZJ"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Sygil-Dev/sygil-webui/blob/main/Web_based_UI_for_Stable_Diffusion_colab.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-Bx4AsEoPU-"
      },
      "source": [
        "# README"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z4kQYMPQn4d-"
      },
      "source": [
        "###<center>Web-based UI for Stable Diffusion</center>\n",
        "\n",
        "## Created by [Sygil-Dev](https://github.com/Sygil-Dev)\n",
        "\n",
        "## [Visit Sygil-Dev's Discord Server](https://discord.gg/gyXNe4NySY) [![Discord Server](https://user-images.githubusercontent.com/5977640/190528254-9b5b4423-47ee-4f24-b4f9-fd13fba37518.png)](https://discord.gg/gyXNe4NySY)\n",
        "\n",
        "## Installation instructions for:\n",
        "\n",
        "- **[Windows](https://sygil-dev.github.io/sygil-webui/docs/1.windows-installation.html)**\n",
        "- **[Linux](https://sygil-dev.github.io/sygil-webui/docs/2.linux-installation.html)**\n",
        "\n",
        "### Want to ask a question or request a feature?\n",
        "\n",
        "Come to our [Discord Server](https://discord.gg/gyXNe4NySY) or use [Discussions](https://github.com/Sygil-Dev/sygil-webui/discussions).\n",
        "\n",
        "## Documentation\n",
        "\n",
        "[Documentation is located here](https://sygil-dev.github.io/sygil-webui/)\n",
        "\n",
        "## Want to contribute?\n",
        "\n",
        "Check the [Contribution Guide](CONTRIBUTING.md)\n",
        "\n",
        "[Sygil-Dev](https://github.com/Sygil-Dev) main devs:\n",
        "\n",
        "* ![hlky's avatar](https://avatars.githubusercontent.com/u/106811348?s=40&v=4) [hlky](https://github.com/hlky)\n",
        "* ![ZeroCool940711's avatar](https://avatars.githubusercontent.com/u/5977640?s=40&v=4)[ZeroCool940711](https://github.com/ZeroCool940711)\n",
        "* ![codedealer's avatar](https://avatars.githubusercontent.com/u/4258136?s=40&v=4)[codedealer](https://github.com/codedealer)\n",
        "\n",
        "### Project Features:\n",
        "\n",
        "* Two great Web UI's to choose from: Streamlit or Gradio\n",
        "\n",
        "* No more manually typing parameters, now all you have to do is write your prompt and adjust sliders\n",
        "\n",
        "* Built-in image enhancers and upscalers, including GFPGAN and realESRGAN\n",
        "\n",
        "* Run additional upscaling models on CPU to save VRAM\n",
        "\n",
        "* Textual inversion 🔥: [info](https://textual-inversion.github.io/) - requires enabling, see [here](https://github.com/hlky/sd-enable-textual-inversion), script works as usual without it enabled\n",
        "\n",
        "* Advanced img2img editor with Mask and crop capabilities\n",
        "\n",
        "* Mask painting 🖌️: Powerful tool for re-generating only specific parts of an image you want to change (currently Gradio only)\n",
        "\n",
        "* More diffusion samplers 🔥🔥: A great collection of samplers to use, including:\n",
        "  \n",
        "  - `k_euler` (Default)\n",
        "  - `k_lms`\n",
        "  - `k_euler_a`\n",
        "  - `k_dpm_2`\n",
        "  - `k_dpm_2_a`\n",
        "  - `k_heun`\n",
        "  - `PLMS`\n",
        "  - `DDIM`\n",
        "\n",
        "* Loopback ➿: Automatically feed the last generated sample back into img2img\n",
        "\n",
        "* Prompt Weighting 🏋️: Adjust the strength of different terms in your prompt\n",
        "\n",
        "* Selectable GPU usage with `--gpu <id>`\n",
        "\n",
        "* Memory Monitoring 🔥: Shows VRAM usage and generation time after outputting\n",
        "\n",
        "* Word Seeds 🔥: Use words instead of seed numbers\n",
        "\n",
        "* CFG: Classifier free guidance scale, a feature for fine-tuning your output\n",
        "\n",
        "* Automatic Launcher: Activate conda and run Stable Diffusion with a single command\n",
        "\n",
        "* Lighter on VRAM: 512x512 Text2Image & Image2Image tested working on 4GB\n",
        "\n",
        "* Prompt validation: If your prompt is too long, you will get a warning in the text output field\n",
        "\n",
        "* Copy-paste generation parameters: A text output provides generation parameters in an easy to copy-paste form for easy sharing.\n",
        "\n",
        "* Correct seeds for batches: If you use a seed of 1000 to generate two batches of two images each, four generated images will have seeds: `1000, 1001, 1002, 1003`.\n",
        "\n",
        "* Prompt matrix: Separate multiple prompts using the `|` character, and the system will produce an image for every combination of them.\n",
        "\n",
        "* Loopback for Image2Image: A checkbox for img2img allowing to automatically feed output image as input for the next batch. Equivalent to saving output image, and replacing input image with it.\n",
        "\n",
        "# Stable Diffusion Web UI\n",
        "\n",
        "A fully-integrated and easy way to work with Stable Diffusion right from a browser window.\n",
        "\n",
        "## Streamlit\n",
        "\n",
        "![](https://github.com/aedhcarrick/sygil-webui/blob/patch-2/images/streamlit/streamlit-t2i.png?raw=1)\n",
        "\n",
        "**Features:**\n",
        "\n",
        "- Clean UI with an easy to use design, with support for widescreen displays.\n",
        "- Dynamic live preview of your generations\n",
        "- Easily customizable presets right from the WebUI (Coming Soon!)\n",
        "- An integrated gallery to show the generations for a prompt or session (Coming soon!)\n",
        "- Better optimization VRAM usage optimization, less errors for bigger generations.\n",
        "- Text2Video - Generate video clips from text prompts right from the WEb UI (WIP)\n",
        "- Concepts Library - Run custom embeddings others have made via textual inversion.\n",
        "- Actively being developed with new features being added and planned - Stay Tuned!\n",
        "- Streamlit is now the new primary UI for the project moving forward.\n",
        "- *Currently in active development and still missing some of the features present in the Gradio Interface.*\n",
        "\n",
        "Please see the [Streamlit Documentation](docs/4.streamlit-interface.md) to learn more.\n",
        "\n",
        "## Gradio\n",
        "\n",
        "![](https://github.com/aedhcarrick/sygil-webui/blob/patch-2/images/gradio/gradio-t2i.png?raw=1)\n",
        "\n",
        "**Features:**\n",
        "\n",
        "- Older UI design that is fully functional and feature complete.\n",
        "- Has access to all upscaling models, including LSDR.\n",
        "- Dynamic prompt entry automatically changes your generation settings based on `--params` in a prompt.\n",
        "- Includes quick and easy ways to send generations to Image2Image or the Image Lab for upscaling.\n",
        "- *Note, the Gradio interface is no longer being actively developed and is only receiving bug fixes.*\n",
        "\n",
        "Please see the [Gradio Documentation](docs/5.gradio-interface.md) to learn more.\n",
        "\n",
        "## Image Upscalers\n",
        "\n",
        "---\n",
        "\n",
        "### GFPGAN\n",
        "\n",
        "![](https://github.com/aedhcarrick/sygil-webui/blob/patch-2/images/GFPGAN.png?raw=1)\n",
        "\n",
        "Lets you improve faces in pictures using the GFPGAN model. There is a checkbox in every tab to use GFPGAN at 100%, and also a separate tab that just allows you to use GFPGAN on any picture, with a slider that controls how strong the effect is.\n",
        "\n",
        "If you want to use GFPGAN to improve generated faces, you need to install it separately.\n",
        "Download [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth) and put it\n",
        "into the `/sygil-webui/models/gfpgan` directory.\n",
        "\n",
        "### RealESRGAN\n",
        "\n",
        "![](https://github.com/aedhcarrick/sygil-webui/blob/patch-2/images/RealESRGAN.png?raw=1)\n",
        "\n",
        "Lets you double the resolution of generated images. There is a checkbox in every tab to use RealESRGAN, and you can choose between the regular upscaler and the anime version.\n",
        "There is also a separate tab for using RealESRGAN on any picture.\n",
        "\n",
        "Download [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth) and [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth).\n",
        "Put them into the `sygil-webui/models/realesrgan` directory.\n",
        "\n",
        "\n",
        "\n",
        "### LSDR\n",
        "\n",
        "Download **LDSR** [project.yaml](https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1) and [model last.cpkt](https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1). Rename last.ckpt to model.ckpt and place both under `sygil-webui/models/ldsr/`\n",
        "\n",
        "### GoBig, and GoLatent *(Currently on the Gradio version Only)*\n",
        "\n",
        "More powerful upscalers that uses a seperate Latent Diffusion model to more cleanly upscale images.\n",
        "\n",
        "\n",
        "\n",
        "Please see the [Image Enhancers Documentation](docs/6.image_enhancers.md) to learn more.\n",
        "\n",
        "-----\n",
        "\n",
        "### *Original Information From The Stable Diffusion Repo*\n",
        "\n",
        "# Stable Diffusion\n",
        "\n",
        "*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*\n",
        "\n",
        "[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>\n",
        "[Robin Rombach](https://github.com/rromb)\\*,\n",
        "[Andreas Blattmann](https://github.com/ablattmann)\\*,\n",
        "[Dominik Lorenz](https://github.com/qp-qp)\\,\n",
        "[Patrick Esser](https://github.com/pesser),\n",
        "[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>\n",
        "\n",
        "**CVPR '22 Oral**\n",
        "\n",
        "which is available on [GitHub](https://github.com/CompVis/latent-diffusion). PDF at [arXiv](https://arxiv.org/abs/2112.10752). Please also visit our [Project page](https://ommer-lab.com/research/latent-diffusion-models/).\n",
        "\n",
        "[Stable Diffusion](#stable-diffusion-v1) is a latent text-to-image diffusion\n",
        "model.\n",
        "Thanks to a generous compute donation from [Stability AI](https://stability.ai/) and support from [LAION](https://laion.ai/), we were able to train a Latent Diffusion Model on 512x512 images from a subset of the [LAION-5B](https://laion.ai/blog/laion-5b/) database.\n",
        "Similar to Google's [Imagen](https://arxiv.org/abs/2205.11487),\n",
        "this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts.\n",
        "With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.\n",
        "See [this section](#stable-diffusion-v1) below and the [model card](https://huggingface.co/CompVis/stable-diffusion).\n",
        "\n",
        "## Stable Diffusion v1\n",
        "\n",
        "Stable Diffusion v1 refers to a specific configuration of the model\n",
        "architecture that uses a downsampling-factor 8 autoencoder with an 860M UNet\n",
        "and CLIP ViT-L/14 text encoder for the diffusion model. The model was pretrained on 256x256 images and\n",
        "then finetuned on 512x512 images.\n",
        "\n",
        "*Note: Stable Diffusion v1 is a general text-to-image diffusion model and therefore mirrors biases and (mis-)conceptions that are present\n",
        "in its training data.\n",
        "Details on the training procedure and data, as well as the intended use of the model can be found in the corresponding [model card](https://huggingface.co/CompVis/stable-diffusion).\n",
        "\n",
        "## Comments\n",
        "\n",
        "- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)\n",
        "  and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch).\n",
        "  Thanks for open-sourcing!\n",
        "\n",
        "- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories).\n",
        "\n",
        "## BibTeX\n",
        "\n",
        "```\n",
        "@misc{rombach2021highresolution,\n",
        "      title={High-Resolution Image Synthesis with Latent Diffusion Models},\n",
        "      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},\n",
        "      year={2021},\n",
        "      eprint={2112.10752},\n",
        "      archivePrefix={arXiv},\n",
        "      primaryClass={cs.CV}\n",
        "}\n",
        "\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iegma7yteERV"
      },
      "source": [
        "# Config options for Colab instance\n",
        "> Before running, make sure GPU backend is enabled. (Unless you plan on generating with Stable Horde)\n",
        ">> Runtime -> Change runtime type -> Hardware Accelerator -> GPU (Make sure to  save)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OXn96M9deVtF"
      },
      "outputs": [],
      "source": [
        "#@title  { display-mode: \"form\" }\n",
        "#@markdown WebUI repo (and branch)\n",
        "repo_name = \"Sygil-Dev/sygil-webui\" #@param {type:\"string\"}\n",
        "repo_branch = \"dev\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown Mount Google Drive\n",
        "mount_google_drive = True #@param {type:\"boolean\"}\n",
        "save_outputs_to_drive = True #@param {type:\"boolean\"}\n",
        "#@markdown Folder in Google Drive to search for custom models\n",
        "MODEL_DIR = \"sygil-webui/models\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown Folder in Google Drive to look for custom config file (streamlit.yaml)\n",
        "CONFIG_DIR = \"sygil-webui\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown Enter auth token from Huggingface.co\n",
        "#@markdown >(required for downloading stable diffusion model.)\n",
        "HF_TOKEN = \"\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown Select which models to prefetch\n",
        "STABLE_DIFFUSION = True #@param {type:\"boolean\"}\n",
        "WAIFU_DIFFUSION = False #@param {type:\"boolean\"}\n",
        "TRINART_SD = False #@param {type:\"boolean\"}\n",
        "SD_WD_LD_TRINART_MERGED = False #@param {type:\"boolean\"}\n",
        "GFPGAN = True #@param {type:\"boolean\"}\n",
        "REALESRGAN = True #@param {type:\"boolean\"}\n",
        "LDSR = True #@param {type:\"boolean\"}\n",
        "BLIP_MODEL = False #@param {type:\"boolean\"}\n",
        "\n",
        "#@markdown Save models to Google Drive for faster loading in future (Be warned! Make sure you have enough space!)\n",
        "SAVE_MODELS = False #@param {type:\"boolean\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZjJSr-WPNxB"
      },
      "source": [
        "# Setup\n",
        "\n",
        ">Runtime will crash when installing conda. This is normal as we are forcing a restart of the runtime from code.\n",
        "\n",
        ">Just hit \"Run All\" again. 😑"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eq0-E5mjSpmP"
      },
      "outputs": [],
      "source": [
        "#@title Make sure we have access to GPU backend\n",
        "!nvidia-smi -L"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cDu33xkdJ5mD"
      },
      "outputs": [],
      "source": [
        "#@title Install miniConda (mamba)\n",
        "!pip install condacolab\n",
        "import condacolab\n",
        "condacolab.install_from_url(\"https://github.com/conda-forge/miniforge/releases/download/4.14.0-0/Mambaforge-4.14.0-0-Linux-x86_64.sh\")\n",
        "\n",
        "import condacolab\n",
        "condacolab.check()\n",
        "# The runtime will crash here!!! Don't panic! We planned for this remember?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pZHGf03Vp305"
      },
      "outputs": [],
      "source": [
        "#@title Clone webUI repo and download font\n",
        "import os\n",
        "REPO_URL = os.path.join('https://github.com', repo_name)\n",
        "PATH_TO_REPO = os.path.join('/content', repo_name.split('/')[1])\n",
        "!git clone {REPO_URL}\n",
        "%cd {PATH_TO_REPO}\n",
        "!git checkout {repo_branch}\n",
        "!git pull"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dmN2igp5Yk3z"
      },
      "outputs": [],
      "source": [
        "#@title Install dependencies\n",
        "!mamba install cudatoolkit=11.3 git numpy=1.22.3 pip=20.3 python=3.8.5 pytorch=1.11.0 scikit-image=0.19.2 torchvision=0.12.0 -y\n",
        "!python --version\n",
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nxaxfgo_F8Am"
      },
      "outputs": [],
      "source": [
        "#@title Install localtunnel to openGoogle's ports\n",
        "!npm install localtunnel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pcSWo9Zkzbsf"
      },
      "outputs": [],
      "source": [
        "#@title Mount Google Drive (if selected)\n",
        "if mount_google_drive:\n",
        "    # Mount google drive to store outputs.\n",
        "    from google.colab import drive\n",
        "    drive.mount('/content/drive/', force_remount=True)\n",
        "\n",
        "if save_outputs_to_drive:\n",
        "    # Make symlink to redirect downloads\n",
        "    OUTPUT_PATH = os.path.join('/content/drive/MyDrive', repo_name.split('/')[1], 'outputs')\n",
        "    os.makedirs(OUTPUT_PATH, exist_ok=True)\n",
        "    os.symlink(OUTPUT_PATH, os.path.join(PATH_TO_REPO, 'outputs'), target_is_directory=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vMdmh81J70yA"
      },
      "outputs": [],
      "source": [
        "#@title Pre-fetch models\n",
        "%cd {PATH_TO_REPO}\n",
        "# make list of models we want to download\n",
        "model_list = {\n",
        " 'stable_diffusion': f'{STABLE_DIFFUSION}',\n",
        " 'waifu_diffusion': f'{WAIFU_DIFFUSION}',\n",
        " 'trinart_stable_diffusion': f'{TRINART_SD}',\n",
        " 'sd_wd_ld_trinart_merged': f'{SD_WD_LD_TRINART_MERGED}',\n",
        " 'gfpgan': f'{GFPGAN}',\n",
        " 'realesrgan': f'{REALESRGAN}',\n",
        " 'ldsr': f'{LDSR}',\n",
        " 'blip_model': f'{BLIP_MODEL}'}\n",
        "download_list = {k for (k,v) in model_list.items() if v == 'True'}\n",
        "\n",
        "# get model info (file name, download link, save location)\n",
        "import yaml\n",
        "from pprint import pprint\n",
        "with open('configs/webui/webui_streamlit.yaml') as f:\n",
        "    dataMap = yaml.safe_load(f)\n",
        "models = dataMap['model_manager']['models']\n",
        "existing_models = []\n",
        "\n",
        "# copy script from model manager\n",
        "import requests, time, shutil\n",
        "from requests.auth import HTTPBasicAuth\n",
        "\n",
        "if MODEL_DIR != \"\":\n",
        "    MODEL_DIR = os.path.join('/content/drive/MyDrive', MODEL_DIR)\n",
        "else:\n",
        "    MODEL_DIR = '/content/drive/MyDrive'\n",
        "\n",
        "def download_file(file_name, file_path, file_url):\n",
        "    os.makedirs(file_path, exist_ok=True)\n",
        "    link_path = os.path.join(MODEL_DIR, file_name)\n",
        "    full_path = os.path.join(file_path, file_name)\n",
        "    if os.path.exists(link_path):\n",
        "        print( file_name + \" found in Google Drive\")\n",
        "        if not os.path.exists(full_path):\n",
        "            print( \"  creating symlink...\")\n",
        "            os.symlink(link_path, full_path)\n",
        "        else:\n",
        "            print( \"  symlink already exists\")\n",
        "    elif not os.path.exists(full_path):\n",
        "        print( \"Downloading \" + file_name + \"...\", end=\"\" )\n",
        "        token = None\n",
        "        if \"huggingface.co\" in file_url:\n",
        "            token = HTTPBasicAuth('token', HF_TOKEN)\n",
        "        try:\n",
        "            with requests.get(file_url, auth = token, stream=True) as r:\n",
        "                starttime = time.time()\n",
        "                r.raise_for_status()\n",
        "                with open(full_path, 'wb') as f:\n",
        "                    for chunk in r.iter_content(chunk_size=8192):\n",
        "                        f.write(chunk)\n",
        "                        if ((time.time() - starttime) % 60.0) > 2 :\n",
        "                            starttime = time.time()\n",
        "                            print( \".\", end=\"\" )\n",
        "                print( \"done\" )\n",
        "                print( \"  \" + file_name + \" downloaded to \\'\" + file_path + \"\\'\" )\n",
        "            if SAVE_MODELS and os.path.exists(MODEL_DIR):\n",
        "                shutil.copy2(full_path,MODEL_DIR)\n",
        "                print( \"  Saved \" + file_name + \" to \" + MODEL_DIR)\n",
        "        except:\n",
        "            print( \"Failed to download \" + file_name + \".\" )\n",
        "            return\n",
        "    else:\n",
        "        print( full_path + \" already exists.\" )\n",
        "    existing_models.append(file_name)\n",
        "\n",
        "# download models in list\n",
        "for model in download_list:\n",
        "    model_name = models[model]['model_name']\n",
        "    file_info = models[model]['files']\n",
        "    for file in file_info:\n",
        "        file_name = file_info[file]['file_name']\n",
        "        file_url = file_info[file]['download_link']\n",
        "        if 'save_location' in file_info[file]:\n",
        "            file_path = file_info[file]['save_location']\n",
        "        else:\n",
        "            file_path = models[model]['save_location']\n",
        "        download_file(file_name, file_path, file_url)\n",
        "\n",
        "# add custom models not in list\n",
        "CUSTOM_MODEL_DIR = os.path.join(PATH_TO_REPO, 'models/custom')\n",
        "if os.path.exists(MODEL_DIR):\n",
        "    custom_models = os.listdir(MODEL_DIR)\n",
        "    custom_models = [m for m in custom_models if os.path.isfile(MODEL_DIR + '/' + m)]\n",
        "    os.makedirs(CUSTOM_MODEL_DIR, exist_ok=True)\n",
        "    print( \"Custom model(s) found: \" )\n",
        "    for m in custom_models:\n",
        "        if m in existing_models:\n",
        "            continue\n",
        "        full_path = os.path.join(CUSTOM_MODEL_DIR, m)\n",
        "        if not os.path.exists(full_path):\n",
        "            print( \"  \" + m )\n",
        "            os.symlink(os.path.join(MODEL_DIR , m), full_path)\n",
        "\n",
        "# get custom config file if it exists\n",
        "if CONFIG_DIR != \"\":\n",
        "    CONFIG_FILE = os.path.join('/content/drive/MyDrive', CONFIG_DIR, 'userconfig_streamlit.yaml')\n",
        "    config_location = os.path.join(PATH_TO_REPO, 'configs/webui/userconfig_streamlit.yaml')\n",
        "    if os.path.exists(CONFIG_FILE) and not os.path.exists(config_location):\n",
        "        os.symlink(CONFIG_DIR, config_location)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjIjiCuJysJI"
      },
      "source": [
        "# Launch the web ui server\n",
        "### (optional) JS to prevent idle timeout:\n",
        "Press 'F12' OR ('CTRL' + 'SHIFT' + 'I') OR right click on this website -> inspect. Then click on the console tab and paste in the following code.\n",
        "```js,\n",
        "function ClickConnect(){\n",
        "console.log(\"Working\");\n",
        "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
        "}\n",
        "setInterval(ClickConnect,60000)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-WknaU7uu_q6"
      },
      "outputs": [],
      "source": [
        "#@title Press play on the music player to keep the tab alive (Uses only 13MB of data)\n",
        "%%html\n",
        "<b>Press play on the music player to keep the tab alive, then start your generation below (Uses only 13MB of data)</b><br/>\n",
        "<audio src=\"https://henk.tech/colabkobold/silence.m4a\" controls>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5whXm2nfSZ39"
      },
      "outputs": [],
      "source": [
        "#@title Run localtunnel and start Streamlit server. ('Ctrl' + 'left click') on link in the 'link.txt' file. (/content/link.txt)\n",
        "!npx localtunnel --port 8501 &>/content/link.txt &\n",
        "!streamlit run scripts/webui_streamlit.py --theme.base dark --server.headless true 2>&1 | tee -a /content/log.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QhazvrFG97zX"
      },
      "source": [
        "Run Streamlit through cloudflare."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jjjjjjjjjjjjjj"
      },
      "outputs": [],
      "source": [
        "#@title Run Streamlit through cloudflare.\n",
        "!wget https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64.deb\n",
        "!dpkg -i cloudflared-linux-amd64.deb\n",
        "\n",
        "import subprocess\n",
        "import threading\n",
        "import time\n",
        "import socket\n",
        "import urllib.request\n",
        "\n",
        "def iframe_thread(port):\n",
        "  while True:\n",
        "      time.sleep(0.5)\n",
        "      sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
        "      result = sock.connect_ex(('127.0.0.1', port))\n",
        "      if result == 0:\n",
        "        break\n",
        "      sock.close()\n",
        "\n",
        "  p = subprocess.Popen([\"cloudflared\", \"tunnel\", \"--url\", \"http://127.0.0.1:{}\".format(port)], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
        "  for line in p.stderr:\n",
        "    l = line.decode()\n",
        "    if \"trycloudflare.com \" in l:\n",
        "      print(\"This is the URL to access Sygil WebUI:\", l[l.find(\"http\"):], end='')\n",
        "\n",
        "\n",
        "threading.Thread(target=iframe_thread, daemon=True, args=(8501,)).start()\n",
        "\n",
        "!streamlit run scripts/webui_streamlit.py --theme.base dark --server.headless true"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "5-Bx4AsEoPU-",
        "xMWVQOg0G1Pj"
      ],
      "private_outputs": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}


================================================
FILE: _config.yml
================================================
plugins:
  - jekyll-relative-links
  - jekyll-readme-index
  - jemoji
  - jekyll-remote-theme
relative_links:
  enabled: true
  collections: true
source: docs
remote_theme: jekyll/minima
minima:
  skin: dark
include:
  - CONTRIBUTING.md


================================================
FILE: babel.config.js
================================================
module.exports = {
  presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
};


================================================
FILE: blog/2022-10-20/1.Textual inversion usage competitio.md
================================================
# Textual inversion usage competition

We are hosting a competition where the community can showcase their most inventive use of textual inversion concepts in text-to-image or text-to-video.

Our compute cluster; `Nataili`, currently comprises of 3 nodes, two have 3090, the other has 2 x A5000.

We estimate `Nataili` can handle 12 concepts per hour, and we can add more workers if there is high demand.

Hopefully demand will be high, we want to train **hundreds** of new concepts!

# Schedule

2022/10/20 - Stage 1 begins, train concept command opened for usage

2022/10/22 12AM UTC - Stage 2 begins, text to image command opened for usage

2022/10/22 12PM UTC - Stage 1 ends, train concept command closed

2022/10/24 12PM UTC - Stage 2 ends, no more entries will be accepted

2022/10/24 6-12PM UTC - Winners announced


# What does `most inventive use` mean?

Whatever you want it to mean! be creative! experiment!

There are several categories we will look at:

* anything that's particularly creative, ~ artistic ~ or a e s t h e t i c

![20221019203426_00000](https://user-images.githubusercontent.com/106811348/197045193-d6f9c56b-9989-4f1c-b42a-bb02d62d77cd.png)

* composition; meaning anything related to how big things are, their position, the angle, etc

* styling;

![image](https://user-images.githubusercontent.com/106811348/197045629-029ba6f5-1f79-475c-9ce7-969aaf3d253b.png)

* `The Sims(TM): Stable Diffusion edition`

## So I can trai-

* Yes, as long as it's sfw

## `The Sims(TM): Stable Diffusion edition` ?

For this event the theme is “The Sims: Stable Diffusion edition”.

So we have selected a subset of [products from Amazon Berkely Objects dataset](https://github.com/sd-webui/abo).

Any other object is welcome too these are just a good source of data for this part of the competition.

Each product has images from multiple angles, the train concept command accepts up to 10 images, so choose the angles and modify backgrounds, experiment!

The goal with this category is to generate an image using the trained object, and the other categories apply, your imagination is the only limit! style a couch, try to make a BIG couch, try to make a couch on top of a mountain, try to make a vaporwave couch, anything!

# How do I train a concept using the discord bot?

Type `/trainconcept` then press tab to go through the fields

`Concept name` is just a name for your concept, it doesn't have to be a single word

`Placeholder` is what you will use in prompts to represent your concept
Add `<` and `>` so it is unique, multiple words should be hyphenated

`Initializer` is used as the starting point for training your concept, so this should be a single word that represents your concept

Minimum 2 images. Squareish aspect ratios work best

![Untitled-2](https://user-images.githubusercontent.com/106811348/197035834-cc973e29-31f8-48de-be2d-788fbe938b2e.png)
![image](https://user-images.githubusercontent.com/106811348/197035870-b91ef2a8-0ffd-47e1-a8df-9600df26cd6b.png)

# How do I use the trained concept?

## Prompting with concepts

When your concept is trained you can use it in prompts.

`a cute <nvidiafu> as an astronaut`:

![image](https://user-images.githubusercontent.com/106811348/197037250-044ea241-72a5-4caa-b772-35034245b4b6.png)

or `a green <green-couch> sitting on top of a floor, a 3D render, trending on polycount, minimalism, rendered in cinema4d`:

![image](https://user-images.githubusercontent.com/106811348/197037344-7ce72188-9129-4ba2-8a28-cba5fd664a9c.png)

## Using concepts in the webui

The discord bot will give you a link to a `.zip` file, download this, extract it, and put the folder in `stable-diffusion-webui/models/custom/sd-concepts-library`

![image](https://user-images.githubusercontent.com/106811348/197037892-ce53bea4-d1db-4b25-bb7c-7dfe4d71b2b1.png)


================================================
FILE: blog/authors.yml
================================================
endi:
  name: Endilie Yacop Sucipto
  title: Maintainer of Docusaurus
  url: https://github.com/endiliey
  image_url: https://github.com/endiliey.png

yangshun:
  name: Yangshun Tay
  title: Front End Engineer @ Facebook
  url: https://github.com/yangshun
  image_url: https://github.com/yangshun.png

slorber:
  name: Sébastien Lorber
  title: Docusaurus maintainer
  url: https://sebastienlorber.com
  image_url: https://github.com/slorber.png


================================================
FILE: configs/autoencoder/autoencoder_kl_16x16x16.yaml
================================================
model:
  base_learning_rate: 4.5e-6
  target: ldm.models.autoencoder.AutoencoderKL
  params:
    monitor: "val/rec_loss"
    embed_dim: 16
    lossconfig:
      target: ldm.modules.losses.LPIPSWithDiscriminator
      params:
        disc_start: 50001
        kl_weight: 0.000001
        disc_weight: 0.5

    ddconfig:
      double_z: True
      z_channels: 16
      resolution: 256
      in_channels: 3
      out_ch: 3
      ch: 128
      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
      num_res_blocks: 2
      attn_resolutions: [16]
      dropout: 0.0


data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 12
    wrap: True
    train:
      target: ldm.data.imagenet.ImageNetSRTrain
      params:
        size: 256
        degradation: pil_nearest
    validation:
      target: ldm.data.imagenet.ImageNetSRValidation
      params:
        size: 256
        degradation: pil_nearest

lightning:
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 1000
        max_images: 8
        increase_log_steps: True

  trainer:
    benchmark: True
    accumulate_grad_batches: 2


================================================
FILE: configs/autoencoder/autoencoder_kl_32x32x4.yaml
================================================
model:
  base_learning_rate: 4.5e-6
  target: ldm.models.autoencoder.AutoencoderKL
  params:
    monitor: "val/rec_loss"
    embed_dim: 4
    lossconfig:
      target: ldm.modules.losses.LPIPSWithDiscriminator
      params:
        disc_start: 50001
        kl_weight: 0.000001
        disc_weight: 0.5

    ddconfig:
      double_z: True
      z_channels: 4
      resolution: 256
      in_channels: 3
      out_ch: 3
      ch: 128
      ch_mult: [ 1,2,4,4 ]  # num_down = len(ch_mult)-1
      num_res_blocks: 2
      attn_resolutions: [ ]
      dropout: 0.0

data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 12
    wrap: True
    train:
      target: ldm.data.imagenet.ImageNetSRTrain
      params:
        size: 256
        degradation: pil_nearest
    validation:
      target: ldm.data.imagenet.ImageNetSRValidation
      params:
        size: 256
        degradation: pil_nearest

lightning:
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 1000
        max_images: 8
        increase_log_steps: True

  trainer:
    benchmark: True
    accumulate_grad_batches: 2


================================================
FILE: configs/autoencoder/autoencoder_kl_64x64x3.yaml
================================================
model:
  base_learning_rate: 4.5e-6
  target: ldm.models.autoencoder.AutoencoderKL
  params:
    monitor: "val/rec_loss"
    embed_dim: 3
    lossconfig:
      target: ldm.modules.losses.LPIPSWithDiscriminator
      params:
        disc_start: 50001
        kl_weight: 0.000001
        disc_weight: 0.5

    ddconfig:
      double_z: True
      z_channels: 3
      resolution: 256
      in_channels: 3
      out_ch: 3
      ch: 128
      ch_mult: [ 1,2,4 ]  # num_down = len(ch_mult)-1
      num_res_blocks: 2
      attn_resolutions: [ ]
      dropout: 0.0


data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 12
    wrap: True
    train:
      target: ldm.data.imagenet.ImageNetSRTrain
      params:
        size: 256
        degradation: pil_nearest
    validation:
      target: ldm.data.imagenet.ImageNetSRValidation
      params:
        size: 256
        degradation: pil_nearest

lightning:
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 1000
        max_images: 8
        increase_log_steps: True

  trainer:
    benchmark: True
    accumulate_grad_batches: 2


================================================
FILE: configs/autoencoder/autoencoder_kl_8x8x64.yaml
================================================
model:
  base_learning_rate: 4.5e-6
  target: ldm.models.autoencoder.AutoencoderKL
  params:
    monitor: "val/rec_loss"
    embed_dim: 64
    lossconfig:
      target: ldm.modules.losses.LPIPSWithDiscriminator
      params:
        disc_start: 50001
        kl_weight: 0.000001
        disc_weight: 0.5

    ddconfig:
      double_z: True
      z_channels: 64
      resolution: 256
      in_channels: 3
      out_ch: 3
      ch: 128
      ch_mult: [ 1,1,2,2,4,4]  # num_down = len(ch_mult)-1
      num_res_blocks: 2
      attn_resolutions: [16,8]
      dropout: 0.0

data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 12
    wrap: True
    train:
      target: ldm.data.imagenet.ImageNetSRTrain
      params:
        size: 256
        degradation: pil_nearest
    validation:
      target: ldm.data.imagenet.ImageNetSRValidation
      params:
        size: 256
        degradation: pil_nearest

lightning:
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 1000
        max_images: 8
        increase_log_steps: True

  trainer:
    benchmark: True
    accumulate_grad_batches: 2


================================================
FILE: configs/blip/bert_config.json
================================================
{
  "architectures": [
    "BertModel"
  ],
  "attention_probs_dropout_prob": 0.1,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "type_vocab_size": 2,
  "vocab_size": 30522,
  "encoder_width": 768,
  "add_cross_attention": true
}


================================================
FILE: configs/blip/caption_coco.yaml
================================================
image_root: '/export/share/datasets/vision/coco/images/'
ann_root: 'annotation'
coco_gt_root: 'annotation/coco_gt'

# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'

# size of vit model; base or large
vit: 'base'
vit_grad_ckpt: False
vit_ckpt_layer: 0
batch_size: 32
init_lr: 1e-5

# vit: 'large'
# vit_grad_ckpt: True
# vit_ckpt_layer: 5
# batch_size: 16
# init_lr: 2e-6

image_size: 384

# generation configs
max_length: 20
min_length: 5
num_beams: 3
prompt: 'a picture of '

# optimizer
weight_decay: 0.05
min_lr: 0
max_epoch: 5


================================================
FILE: configs/blip/med_config.json
================================================
{
  "architectures": [
    "BertModel"
  ],
  "attention_probs_dropout_prob": 0.1,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "type_vocab_size": 2,
  "vocab_size": 30524,
  "encoder_width": 768,
  "add_cross_attention": true
}


================================================
FILE: configs/blip/nlvr.yaml
================================================
image_root: '/export/share/datasets/vision/NLVR2/'
ann_root: 'annotation'

# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth'

#size of vit model; base or large
vit: 'base'
batch_size_train: 16
batch_size_test: 64
vit_grad_ckpt: False
vit_ckpt_layer: 0
max_epoch: 15

image_size: 384

# optimizer
weight_decay: 0.05
init_lr: 3e-5
min_lr: 0


================================================
FILE: configs/blip/nocaps.yaml
================================================
image_root: '/export/share/datasets/vision/nocaps/'
ann_root: 'annotation'

# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'

vit: 'base'
batch_size: 32

image_size: 384

max_length: 20
min_length: 5
num_beams: 3
prompt: 'a picture of '


================================================
FILE: configs/blip/pretrain.yaml
================================================
train_file: ['/export/share/junnan-li/VL_pretrain/annotation/coco_karpathy_train.json',
             '/export/share/junnan-li/VL_pretrain/annotation/vg_caption.json',
             ]
laion_path: ''

# size of vit model; base or large
vit: 'base'
vit_grad_ckpt: False
vit_ckpt_layer: 0

image_size: 224
batch_size: 75

queue_size: 57600
alpha: 0.4

# optimizer
weight_decay: 0.05
init_lr: 3e-4
min_lr: 1e-6
warmup_lr: 1e-6
lr_decay_rate: 0.9
max_epoch: 20
warmup_steps: 3000


================================================
FILE: configs/blip/retrieval_coco.yaml
================================================
image_root: '/export/share/datasets/vision/coco/images/'
ann_root: 'annotation'
dataset: 'coco'

# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

# size of vit model; base or large

vit: 'base'
batch_size_train: 32
batch_size_test: 64
vit_grad_ckpt: True
vit_ckpt_layer: 4
init_lr: 1e-5

# vit: 'large'
# batch_size_train: 16
# batch_size_test: 32
# vit_grad_ckpt: True
# vit_ckpt_layer: 12
# init_lr: 5e-6

image_size: 384
queue_size: 57600
alpha: 0.4
k_test: 256
negative_all_rank: True

# optimizer
weight_decay: 0.05
min_lr: 0
max_epoch: 6


================================================
FILE: configs/blip/retrieval_flickr.yaml
================================================
image_root: '/export/share/datasets/vision/flickr30k/'
ann_root: 'annotation'
dataset: 'flickr'

# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth'

# size of vit model; base or large

vit: 'base'
batch_size_train: 32
batch_size_test: 64
vit_grad_ckpt: True
vit_ckpt_layer: 4
init_lr: 1e-5

# vit: 'large'
# batch_size_train: 16
# batch_size_test: 32
# vit_grad_ckpt: True
# vit_ckpt_layer: 10
# init_lr: 5e-6

image_size: 384
queue_size: 57600
alpha: 0.4
k_test: 128
negative_all_rank: False

# optimizer
weight_decay: 0.05
min_lr: 0
max_epoch: 6


================================================
FILE: configs/blip/retrieval_msrvtt.yaml
================================================
video_root: '/export/share/dongxuli/data/msrvtt_retrieval/videos'
ann_root: 'annotation'

# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

# size of vit model; base or large
vit: 'base'
batch_size: 64
k_test: 128
image_size: 384
num_frm_test: 8


================================================
FILE: configs/blip/vqa.yaml
================================================
vqa_root: '/export/share/datasets/vision/VQA/Images/mscoco/' #followed by train2014/
vg_root: '/export/share/datasets/vision/visual-genome/'  #followed by image/
train_files: ['vqa_train','vqa_val','vg_qa']
ann_root: 'annotation'

# set pretrained as a file path or an url
pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'

# size of vit model; base or large
vit: 'base'
batch_size_train: 16
batch_size_test: 32
vit_grad_ckpt: False
vit_ckpt_layer: 0
init_lr: 2e-5

image_size: 480

k_test: 128
inference: 'rank'

# optimizer
weight_decay: 0.05
min_lr: 0
max_epoch: 10


================================================
FILE: configs/latent-diffusion/celebahq-ldm-vq-4.yaml
================================================
model:
  base_learning_rate: 2.0e-06
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.0015
    linear_end: 0.0195
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: image
    image_size: 64
    channels: 3
    monitor: val/loss_simple_ema

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 64
        in_channels: 3
        out_channels: 3
        model_channels: 224
        attention_resolutions:
        # note: this isn\t actually the resolution but
        # the downsampling factor, i.e. this corresnponds to
        # attention on spatial resolution 8,16,32, as the
        # spatial reolution of the latents is 64 for f4
        - 8
        - 4
        - 2
        num_res_blocks: 2
        channel_mult:
        - 1
        - 2
        - 3
        - 4
        num_head_channels: 32
    first_stage_config:
      target: ldm.models.autoencoder.VQModelInterface
      params:
        embed_dim: 3
        n_embed: 8192
        ckpt_path: models/first_stage_models/vq-f4/model.ckpt
        ddconfig:
          double_z: false
          z_channels: 3
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 2
          - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity
    cond_stage_config: __is_unconditional__
data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 48
    num_workers: 5
    wrap: false
    train:
      target: taming.data.faceshq.CelebAHQTrain
      params:
        size: 256
    validation:
      target: taming.data.faceshq.CelebAHQValidation
      params:
        size: 256


lightning:
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 5000
        max_images: 8
        increase_log_steps: False

  trainer:
    benchmark: True


================================================
FILE: configs/latent-diffusion/cin-ldm-vq-f8.yaml
================================================
model:
  base_learning_rate: 1.0e-06
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.0015
    linear_end: 0.0195
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: image
    cond_stage_key: class_label
    image_size: 32
    channels: 4
    cond_stage_trainable: true
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 32
        in_channels: 4
        out_channels: 4
        model_channels: 256
        attention_resolutions:
        #note: this isn\t actually the resolution but
        # the downsampling factor, i.e. this corresnponds to
        # attention on spatial resolution 8,16,32, as the
        # spatial reolution of the latents is 32 for f8
        - 4
        - 2
        - 1
        num_res_blocks: 2
        channel_mult:
        - 1
        - 2
        - 4
        num_head_channels: 32
        use_spatial_transformer: true
        transformer_depth: 1
        context_dim: 512
    first_stage_config:
      target: ldm.models.autoencoder.VQModelInterface
      params:
        embed_dim: 4
        n_embed: 16384
        ckpt_path: configs/first_stage_models/vq-f8/model.yaml
        ddconfig:
          double_z: false
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 2
          - 2
          - 4
          num_res_blocks: 2
          attn_resolutions:
          - 32
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity
    cond_stage_config:
      target: ldm.modules.encoders.modules.ClassEmbedder
      params:
        embed_dim: 512
        key: class_label
data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 64
    num_workers: 12
    wrap: false
    train:
      target: ldm.data.imagenet.ImageNetTrain
      params:
        config:
          size: 256
    validation:
      target: ldm.data.imagenet.ImageNetValidation
      params:
        config:
          size: 256


lightning:
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 5000
        max_images: 8
        increase_log_steps: False

  trainer:
    benchmark: True


================================================
FILE: configs/latent-diffusion/cin256-v2.yaml
================================================
model:
  base_learning_rate: 0.0001
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.0015
    linear_end: 0.0195
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: image
    cond_stage_key: class_label
    image_size: 64
    channels: 3
    cond_stage_trainable: true
    conditioning_key: crossattn
    monitor: val/loss
    use_ema: False

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 64
        in_channels: 3
        out_channels: 3
        model_channels: 192
        attention_resolutions:
        - 8
        - 4
        - 2
        num_res_blocks: 2
        channel_mult:
        - 1
        - 2
        - 3
        - 5
        num_heads: 1
        use_spatial_transformer: true
        transformer_depth: 1
        context_dim: 512

    first_stage_config:
      target: ldm.models.autoencoder.VQModelInterface
      params:
        embed_dim: 3
        n_embed: 8192
        ddconfig:
          double_z: false
          z_channels: 3
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 2
          - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity

    cond_stage_config:
      target: ldm.modules.encoders.modules.ClassEmbedder
      params:
        n_classes: 1001
        embed_dim: 512
        key: class_label


================================================
FILE: configs/latent-diffusion/ffhq-ldm-vq-4.yaml
================================================
model:
  base_learning_rate: 2.0e-06
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.0015
    linear_end: 0.0195
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: image
    image_size: 64
    channels: 3
    monitor: val/loss_simple_ema
    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 64
        in_channels: 3
        out_channels: 3
        model_channels: 224
        attention_resolutions:
        # note: this isn\t actually the resolution but
        # the downsampling factor, i.e. this corresnponds to
        # attention on spatial resolution 8,16,32, as the
        # spatial reolution of the latents is 64 for f4
        - 8
        - 4
        - 2
        num_res_blocks: 2
        channel_mult:
        - 1
        - 2
        - 3
        - 4
        num_head_channels: 32
    first_stage_config:
      target: ldm.models.autoencoder.VQModelInterface
      params:
        embed_dim: 3
        n_embed: 8192
        ckpt_path: configs/first_stage_models/vq-f4/model.yaml
        ddconfig:
          double_z: false
          z_channels: 3
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 2
          - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity
    cond_stage_config: __is_unconditional__
data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 42
    num_workers: 5
    wrap: false
    train:
      target: taming.data.faceshq.FFHQTrain
      params:
        size: 256
    validation:
      target: taming.data.faceshq.FFHQValidation
      params:
        size: 256


lightning:
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 5000
        max_images: 8
        increase_log_steps: False

  trainer:
    benchmark: True


================================================
FILE: configs/latent-diffusion/lsun_bedrooms-ldm-vq-4.yaml
================================================
model:
  base_learning_rate: 2.0e-06
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.0015
    linear_end: 0.0195
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: image
    image_size: 64
    channels: 3
    monitor: val/loss_simple_ema
    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 64
        in_channels: 3
        out_channels: 3
        model_channels: 224
        attention_resolutions:
        # note: this isn\t actually the resolution but
        # the downsampling factor, i.e. this corresnponds to
        # attention on spatial resolution 8,16,32, as the
        # spatial reolution of the latents is 64 for f4
        - 8
        - 4
        - 2
        num_res_blocks: 2
        channel_mult:
        - 1
        - 2
        - 3
        - 4
        num_head_channels: 32
    first_stage_config:
      target: ldm.models.autoencoder.VQModelInterface
      params:
        ckpt_path: configs/first_stage_models/vq-f4/model.yaml
        embed_dim: 3
        n_embed: 8192
        ddconfig:
          double_z: false
          z_channels: 3
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 2
          - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity
    cond_stage_config: __is_unconditional__
data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 48
    num_workers: 5
    wrap: false
    train:
      target: ldm.data.lsun.LSUNBedroomsTrain
      params:
        size: 256
    validation:
      target: ldm.data.lsun.LSUNBedroomsValidation
      params:
        size: 256


lightning:
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 5000
        max_images: 8
        increase_log_steps: False

  trainer:
    benchmark: True


================================================
FILE: configs/latent-diffusion/lsun_churches-ldm-kl-8.yaml
================================================
model:
  base_learning_rate: 5.0e-5   # set to target_lr by starting main.py with '--scale_lr False'
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.0015
    linear_end: 0.0155
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    loss_type: l1
    first_stage_key: "image"
    cond_stage_key: "image"
    image_size: 32
    channels: 4
    cond_stage_trainable: False
    concat_mode: False
    scale_by_std: True
    monitor: 'val/loss_simple_ema'

    scheduler_config: # 10000 warmup steps
      target: ldm.lr_scheduler.LambdaLinearScheduler
      params:
        warm_up_steps: [10000]
        cycle_lengths: [10000000000000]
        f_start: [1.e-6]
        f_max: [1.]
        f_min: [ 1.]

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 32
        in_channels: 4
        out_channels: 4
        model_channels: 192
        attention_resolutions: [ 1, 2, 4, 8 ]   # 32, 16, 8, 4
        num_res_blocks: 2
        channel_mult: [ 1,2,2,4,4 ]  # 32, 16, 8, 4, 2
        num_heads: 8
        use_scale_shift_norm: True
        resblock_updown: True

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: "val/rec_loss"
        ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
        ddconfig:
          double_z: True
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult: [ 1,2,4,4 ]  # num_down = len(ch_mult)-1
          num_res_blocks: 2
          attn_resolutions: [ ]
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity

    cond_stage_config: "__is_unconditional__"

data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 96
    num_workers: 5
    wrap: False
    train:
      target: ldm.data.lsun.LSUNChurchesTrain
      params:
        size: 256
    validation:
      target: ldm.data.lsun.LSUNChurchesValidation
      params:
        size: 256

lightning:
  callbacks:
    image_logger:
      target: main.ImageLogger
      params:
        batch_frequency: 5000
        max_images: 8
        increase_log_steps: False


  trainer:
    benchmark: True


================================================
FILE: configs/latent-diffusion/txt2img-1p4B-eval.yaml
================================================
model:
  base_learning_rate: 5.0e-05
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.00085
    linear_end: 0.012
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: image
    cond_stage_key: caption
    image_size: 32
    channels: 4
    cond_stage_trainable: true
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    use_ema: False

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 32
        in_channels: 4
        out_channels: 4
        model_channels: 320
        attention_resolutions:
        - 4
        - 2
        - 1
        num_res_blocks: 2
        channel_mult:
        - 1
        - 2
        - 4
        - 4
        num_heads: 8
        use_spatial_transformer: true
        transformer_depth: 1
        context_dim: 1280
        use_checkpoint: true
        legacy: False

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
          double_z: true
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 2
          - 4
          - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity

    cond_stage_config:
      target: ldm.modules.encoders.modules.BERTEmbedder
      params:
        n_embed: 1280
        n_layer: 32


================================================
FILE: configs/retrieval-augmented-diffusion/768x768.yaml
================================================
model:
  base_learning_rate: 0.0001
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.0015
    linear_end: 0.015
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: jpg
    cond_stage_key: nix
    image_size: 48
    channels: 16
    cond_stage_trainable: false
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_by_std: false
    scale_factor: 0.22765929
    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 48
        in_channels: 16
        out_channels: 16
        model_channels: 448
        attention_resolutions:
        - 4
        - 2
        - 1
        num_res_blocks: 2
        channel_mult:
        - 1
        - 2
        - 3
        - 4
        use_scale_shift_norm: false
        resblock_updown: false
        num_head_channels: 32
        use_spatial_transformer: true
        transformer_depth: 1
        context_dim: 768
        use_checkpoint: true
    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        monitor: val/rec_loss
        embed_dim: 16
        ddconfig:
          double_z: true
          z_channels: 16
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 1
          - 2
          - 2
          - 4
          num_res_blocks: 2
          attn_resolutions:
          - 16
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity
    cond_stage_config:
      target: torch.nn.Identity


================================================
FILE: configs/stable-diffusion/v1-inference.yaml
================================================
model:
  base_learning_rate: 1.0e-04
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 64
    channels: 4
    cond_stage_trainable: false   # Note: different from the one we trained before
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    use_ema: False

    scheduler_config: # 10000 warmup steps
      target: ldm.lr_scheduler.LambdaLinearScheduler
      params:
        warm_up_steps: [ 10000 ]
        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
        f_start: [ 1.e-6 ]
        f_max: [ 1. ]
        f_min: [ 1. ]

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 32 # unused
        in_channels: 4
        out_channels: 4
        model_channels: 320
        attention_resolutions: [ 4, 2, 1 ]
        num_res_blocks: 2
        channel_mult: [ 1, 2, 4, 4 ]
        num_heads: 8
        use_spatial_transformer: True
        transformer_depth: 1
        context_dim: 768
        use_checkpoint: True
        legacy: False

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
          double_z: true
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 2
          - 4
          - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity

    cond_stage_config:
      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder


================================================
FILE: configs/stable-diffusion/v2-inference-v.yaml
================================================
model:
  base_learning_rate: 1.0e-4
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    parameterization: "v"
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 64
    channels: 4
    cond_stage_trainable: false
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    use_ema: False # we set this to false because this is an inference only config

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        use_checkpoint: True
        use_fp16: True
        image_size: 32 # unused
        in_channels: 4
        out_channels: 4
        model_channels: 320
        attention_resolutions: [ 4, 2, 1 ]
        num_res_blocks: 2
        channel_mult: [ 1, 2, 4, 4 ]
        num_head_channels: 64 # need to fix for flash-attn
        use_spatial_transformer: True
        use_linear_in_transformer: True
        transformer_depth: 1
        context_dim: 1024
        legacy: False

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
          #attn_type: "vanilla-xformers"
          double_z: true
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 2
          - 4
          - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity

    cond_stage_config:
      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
      params:
        freeze: True
        layer: "penultimate"


================================================
FILE: configs/stable-diffusion/v2-inference.yaml
================================================
model:
  base_learning_rate: 1.0e-4
  target: ldm.models.diffusion.ddpm.LatentDiffusion
  params:
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 64
    channels: 4
    cond_stage_trainable: false
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    use_ema: False # we set this to false because this is an inference only config

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        use_checkpoint: True
        use_fp16: True
        image_size: 32 # unused
        in_channels: 4
        out_channels: 4
        model_channels: 320
        attention_resolutions: [ 4, 2, 1 ]
        num_res_blocks: 2
        channel_mult: [ 1, 2, 4, 4 ]
        num_head_channels: 64 # need to fix for flash-attn
        use_spatial_transformer: True
        use_linear_in_transformer: True
        transformer_depth: 1
        context_dim: 1024
        legacy: False

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
          #attn_type: "vanilla-xformers"
          double_z: true
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
          - 1
          - 2
          - 4
          - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity

    cond_stage_config:
      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
      params:
        freeze: True
        layer: "penultimate"


================================================
FILE: configs/stable-diffusion/v2-inpainting-inference.yaml
================================================
model:
  base_learning_rate: 5.0e-05
  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
  params:
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 64
    channels: 4
    cond_stage_trainable: false
    conditioning_key: hybrid
    scale_factor: 0.18215
    monitor: val/loss_simple_ema
    finetune_keys: null
    use_ema: False

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        use_checkpoint: True
        image_size: 32 # unused
        in_channels: 9
        out_channels: 4
        model_channels: 320
        attention_resolutions: [ 4, 2, 1 ]
        num_res_blocks: 2
        channel_mult: [ 1, 2, 4, 4 ]
        num_head_channels: 64 # need to fix for flash-attn
        use_spatial_transformer: True
        use_linear_in_transformer: True
        transformer_depth: 1
        context_dim: 1024
        legacy: False

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
          #attn_type: "vanilla-xformers"
          double_z: true
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
            - 1
            - 2
            - 4
            - 4
          num_res_blocks: 2
          attn_resolutions: [ ]
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity

    cond_stage_config:
      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
      params:
        freeze: True
        layer: "penultimate"


data:
  target: ldm.data.laion.WebDataModuleFromConfig
  params:
    tar_base: null  # for concat as in LAION-A
    p_unsafe_threshold: 0.1
    filter_word_list: "data/filters.yaml"
    max_pwatermark: 0.45
    batch_size: 8
    num_workers: 6
    multinode: True
    min_size: 512
    train:
      shards:
        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -"
        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -"
        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -"
        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -"
        - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -"  #{00000-94333}.tar"
      shuffle: 10000
      image_key: jpg
      image_transforms:
      - target: torchvision.transforms.Resize
        params:
          size: 512
          interpolation: 3
      - target: torchvision.transforms.RandomCrop
        params:
          size: 512
      postprocess:
        target: ldm.data.laion.AddMask
        params:
          mode: "512train-large"
          p_drop: 0.25
    # NOTE use enough shards to avoid empty validation loops in workers
    validation:
      shards:
        - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - "
      shuffle: 0
      image_key: jpg
      image_transforms:
      - target: torchvision.transforms.Resize
        params:
          size: 512
          interpolation: 3
      - target: torchvision.transforms.CenterCrop
        params:
          size: 512
      postprocess:
        target: ldm.data.laion.AddMask
        params:
          mode: "512train-large"
          p_drop: 0.25

lightning:
  find_unused_parameters: True
  modelcheckpoint:
    params:
      every_n_train_steps: 5000

  callbacks:
    metrics_over_trainsteps_checkpoint:
      params:
        every_n_train_steps: 10000

    image_logger:
      target: main.ImageLogger
      params:
        enable_autocast: False
        disabled: False
        batch_frequency: 1000
        max_images: 4
        increase_log_steps: False
        log_first_step: False
        log_images_kwargs:
          use_ema_scope: False
          inpaint: False
          plot_progressive_rows: False
          plot_diffusion_rows: False
          N: 4
          unconditional_guidance_scale: 5.0
          unconditional_guidance_label: [""]
          ddim_steps: 50  # todo check these out for depth2img,
          ddim_eta: 0.0   # todo check these out for depth2img,

  trainer:
    benchmark: True
    val_check_interval: 5000000
    num_sanity_val_steps: 0
    accumulate_grad_batches: 1


================================================
FILE: configs/stable-diffusion/v2-midas-inference.yaml
================================================
model:
  base_learning_rate: 5.0e-07
  target: ldm.models.diffusion.ddpm.LatentDepth2ImageDiffusion
  params:
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 64
    channels: 4
    cond_stage_trainable: false
    conditioning_key: hybrid
    scale_factor: 0.18215
    monitor: val/loss_simple_ema
    finetune_keys: null
    use_ema: False

    depth_stage_config:
      target: ldm.modules.midas.api.MiDaSInference
      params:
        model_type: "dpt_hybrid"

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        use_checkpoint: True
        image_size: 32 # unused
        in_channels: 5
        out_channels: 4
        model_channels: 320
        attention_resolutions: [ 4, 2, 1 ]
        num_res_blocks: 2
        channel_mult: [ 1, 2, 4, 4 ]
        num_head_channels: 64 # need to fix for flash-attn
        use_spatial_transformer: True
        use_linear_in_transformer: True
        transformer_depth: 1
        context_dim: 1024
        legacy: False

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
          #attn_type: "vanilla-xformers"
          double_z: true
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
            - 1
            - 2
            - 4
            - 4
          num_res_blocks: 2
          attn_resolutions: [ ]
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity

    cond_stage_config:
      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
      params:
        freeze: True
        layer: "penultimate"


================================================
FILE: configs/stable-diffusion/x4-upscaling.yaml
================================================
model:
  base_learning_rate: 1.0e-04
  target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
  params:
    parameterization: "v"
    low_scale_key: "lr"
    linear_start: 0.0001
    linear_end: 0.02
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 128
    channels: 4
    cond_stage_trainable: false
    conditioning_key: "hybrid-adm"
    monitor: val/loss_simple_ema
    scale_factor: 0.08333
    use_ema: False

    low_scale_config:
      target: ldm.modules.diffusionmodules.upscaling.ImageConcatWithNoiseAugmentation
      params:
        noise_schedule_config: # image space
          linear_start: 0.0001
          linear_end: 0.02
        max_noise_level: 350

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        use_checkpoint: True
        num_classes: 1000  # timesteps for noise conditioning (here constant, just need one)
        image_size: 128
        in_channels: 7
        out_channels: 4
        model_channels: 256
        attention_resolutions: [ 2,4,8]
        num_res_blocks: 2
        channel_mult: [ 1, 2, 2, 4]
        disable_self_attentions: [True, True, True, False]
        disable_middle_self_attn: False
        num_heads: 8
        use_spatial_transformer: True
        transformer_depth: 1
        context_dim: 1024
        legacy: False
        use_linear_in_transformer: True

    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        ddconfig:
          # attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though)
          double_z: True
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult: [ 1,2,4 ]  # num_down = len(ch_mult)-1
          num_res_blocks: 2
          attn_resolutions: [ ]
          dropout: 0.0

        lossconfig:
          target: torch.nn.Identity

    cond_stage_config:
      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
      params:
        freeze: True
        layer: "penultimate"


================================================
FILE: configs/webui/webui.yaml
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

# UI defaults configuration file. Is read automatically if located at configs/webui/webui.yaml, or specify path via --defaults.

txt2img:
  prompt:
  ddim_steps: 50
  # Adding an int to toggles enables the corresponding feature.
  #  0:  Create prompt matrix (separate multiple prompts using |, and get all combinations of them)
  #  1:  Normalize Prompt Weights (ensure sum of weights add up to 1.0)
  #  2:  Save individual images
  #  3:  Save grid
  #  4:  Sort samples by prompt
  #  5:  Write sample info files
  #  6:  write sample info to log file
  #  7:  jpg samples
  #  8:  Filter NSFW content
  #  9:  Fix faces using GFPGAN
  #  10:  Upscale images using RealESRGAN
  toggles: [1, 2, 3, 4, 5]
  sampler_name: k_lms
  ddim_eta: 0.0  # legacy name, applies to all algorithms.
  n_iter: 1
  batch_size: 1
  cfg_scale: 7.5
  # Leave blank for random seed:
  seed:
  height: 512
  width: 512
  # Textual inversion embeddings file path:
  fp:

img2img:
  prompt:
  ddim_steps: 50
  # Adding an int to toggles enables the corresponding feature.
  # 0: Create prompt matrix (separate multiple prompts using |, and get all combinations of them)
  # 1: Normalize Prompt Weights (ensure sum of weights add up to 1.0)
  # 2: Loopback (use images from previous batch when creating next batch)
  # 3: Random loopback seed
  # 4: Save individual images
  # 5: Save grid
  # 6: Sort samples by prompt
  # 7: Write sample info files
  # 8: jpg samples
  # 9: Color correction
  # 10: Filter NSFW content
  # 11: Fix faces using GFPGAN
  # 12: Upscale images using Real-ESRGAN
  toggles: [1, 4, 5, 6, 7]
  sampler_name: k_lms
  ddim_eta: 0.0
  n_iter: 1
  batch_size: 1
  cfg_scale: 5.0
  denoising_strength: 0.75
  # 0: Keep masked area
  # 1: Regenerate only masked area
  mask_mode: 0
  # 0: Just resize
  # 1: Crop and resize
  # 2: Resize and fill
  resize_mode: 0
  # Leave blank for random seed:
  seed:
  height: 512
  width: 512
  # Textual inversion embeddings file path:
  fp:

gfpgan:
  strength: 100


================================================
FILE: configs/webui/webui_flet.yaml
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

# UI defaults configuration file. It is automatically loaded if located at configs/webui/webui_flet.yaml.
# Any changes made here will be available automatically on the web app without having to stop it.
# You may add overrides in a file named "userconfig_flet.yaml" in this folder, which can contain any subset
# of the properties below.

# any section labeled '_page' will get it's own tab in settings
# any section without that suffix will still be read by parser and stored in session
#
# display types
#           -- every display type must have 'value: '
#           -- to do: add 'tooltip : ' to every display type
#                   --(make optional, not everything needs one.)
#   bool
#       -value
#   dropdown
#       -value
#       -option_list
#   slider
#       -value
#       -value_type  'int', 'float'
#       -min
#       -max
#       -step
#   textinput
#       -value
#
# list of value types
#   !!bool      boolean     'true' 'false'
#   !!float     float       '0.01'
#   !!int       integer     '23'
#   !!str       string      'foo' 'bar'
#   !!null      None

webui_page:
    default_theme:
        display: dropdown
        value: 'dark'
        option_list:
            - !!str 'dark'
            - !!str 'light'
    default_text_size:
        display: slider
        value: !!int '20'
        value_type: 'int'
        min: !!int '10'
        max: !!int '32'
        step: !!float '2.0'
    max_message_history:
        display: slider
        value: !!int '20'
        value_type: 'int'
        min: !!int '1'
        max: !!int '100'
        step: !!int '1'
    theme_color:
        display: textinput
        value: !!str 'blue'

general_page:
    huggingface_token:
        display: textinput
        value: !!str ''
    stable_horde_api:
        display: textinput
        value: !!str '0000000000'
    global_negative_prompt:
        display: textinput
        value: !!str " "
    default_model:
        display: textinput
        value: !!str "Stable Diffusion v1.5"
    base_model:
        display: textinput
        value: !!str "Stable Diffusion v1.5"
    default_model_config:
        display: textinput
        value: !!str "configs/stable-diffusion/v1-inference.yaml"
    default_model_path:
        display: textinput
        value: !!str "models/ldm/stable-diffusion-v1/Stable Diffusion v1.5.ckpt"
    use_sd_concepts_library:
        display: bool
        value: !!bool 'true'
    sd_concepts_library_folder:
        display: textinput
        value: !!str "models/custom/sd-concepts-library"
    GFPGAN_dir:
        display: textinput
        value: !!str "./models/gfpgan"
    GFPGAN_model:
        display: textinput
        value: !!str "GFPGANv1.4"
    LDSR_dir:
        display: textinput
        value: !!str "./models/ldsr"
    LDSR_model:
        display: textinput
        value: !!str "model"
    RealESRGAN_dir:
        display: textinput
        value: !!str "./models/realesrgan"
    RealESRGAN_model:
        display: textinput
        value: !!str "RealESRGAN_x4plus"
    upscaling_method:
        display: textinput
        value: !!str "RealESRGAN"

output_page:
    outdir:
        display: textinput
        value: !!str 'outputs'
    outdir_txt2img:
        display: textinput
        value: !!str "outputs/txt2img"
    outdir_img2img:
        display: textinput
        value: !!str "outputs/img2img"
    outdir_img2txt:
        display: textinput
        value: !!str "outputs/img2txt"
    save_metadata:
        display: bool
        value: !!bool true
    save_format:
        display: dropdown
        value: !!str "png"
        option_list:
            - !!str 'png'
            - !!str 'jpeg'
    skip_grid:
        display: bool
        value: !!bool 'false'
    skip_save:
        display: bool
        value: !!bool 'false'
    #grid_quality: 95
    #n_rows: -1
    #update_preview: True
    #update_preview_frequency: 10

performance_page:
    gpu:
        display: dropdown
        value: !!str ''
        option_list:
            - !!str '0:'
    gfpgan_cpu:
        display: bool
        value: !!bool 'false'
    esrgan_cpu:
        display: bool
        value: !!bool 'false'
    extra_models_cpu:
        display: bool
        value: !!bool 'false'
    extra_models_gpu:
        display: bool
        value: !!bool 'false'
    gfpgan_gpu:
        display: textinput
        value: !!int 0
    esrgan_gpu:
        display: textinput
        value: !!int 0
    keep_all_models_loaded:
        display: bool
        value: !!bool 'false'
    no_verify_input:
        display: bool
        value: !!bool 'false'
    #no_half: False
    #use_float16: False
    #precision: "autocast"
    #optimized: False
    #optimized_turbo: False
    #optimized_config: "optimizedSD/v1-inference.yaml"
    #enable_attention_slicing: False
    #enable_minimal_memory_usage: False

server_page:
    hide_server_setting:
        display: bool
        value: !!bool 'false'
    hide_browser_setting:
        display: bool
        value: !!bool 'false'

textual_inversion:
    pretrained_model_name_or_path: "models/diffusers/stable-diffusion-v1-5"
    tokenizer_name: "models/clip-vit-large-patch14"


================================================
FILE: configs/webui/webui_streamlit.yaml
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

# UI defaults configuration file. It is automatically loaded if located at configs/webui/webui_streamlit.yaml.
# Any changes made here will be available automatically on the web app without having to stop it.
# You may add overrides in a file named "userconfig_streamlit.yaml" in this folder, which can contain any subset
# of the properties below.
general:
    version: 1.24.6
    streamlit_telemetry: False
    default_theme: dark
    huggingface_token: ''
    stable_horde_api: '0000000000'
    gpu: 0
    outdir: outputs
    default_model: "Stable Diffusion v1.5"
    base_model: "Stable Diffusion v1.5"
    default_model_config: "configs/stable-diffusion/v1-inference.yaml"
    default_model_path: "models/ldm/stable-diffusion-v1/Stable Diffusion v1.5.ckpt"
    use_sd_concepts_library: True
    sd_concepts_library_folder: "models/custom/sd-concepts-library"
    GFPGAN_dir: "./models/gfpgan"
    GFPGAN_model: "GFPGANv1.4"
    LDSR_dir: "./models/ldsr"
    LDSR_model: "model"
    RealESRGAN_dir: "./models/realesrgan"
    RealESRGAN_model: "RealESRGAN_x4plus"
    upscaling_method: "RealESRGAN"
    outdir_txt2img: outputs/txt2img
    outdir_img2img: outputs/img2img
    outdir_img2txt: outputs/img2txt
    gfpgan_cpu: False
    esrgan_cpu: False
    extra_models_cpu: False
    extra_models_gpu: False
    gfpgan_gpu: 0
    esrgan_gpu: 0
    keep_all_models_loaded: False
    save_metadata: True
    save_format: "png"
    skip_grid: False
    skip_save: False
    grid_quality: 95
    n_rows: -1
    no_verify_input: False
    show_percent_in_tab_title: True
    enable_suggestions: True
    no_half: False
    use_float16: False
    precision: "autocast"
    use_cudnn: False
    optimized: False
    optimized_turbo: False
    optimized_config: "optimizedSD/v1-inference.yaml"
    enable_attention_slicing: False
    enable_minimal_memory_usage: False
    update_preview: True
    update_preview_frequency: 10

admin:
    hide_server_setting: False
    hide_browser_setting: False
    global_negative_prompt: ""

debug:
    enable_hydralit: False

txt2img:
    prompt:
    width:
        value:      512
        min_value:  64
        max_value:  2048
        step:       64

    height:
        value:      512
        min_value:  64
        max_value:  2048
        step:       64

    cfg_scale:
        value:      7.5
        min_value:  1.0
        step:       0.5

    seed: ""
    batch_count:
        value:      1

    batch_size:
        value:      1

    sampling_steps:
        value:      30
        min_value:  10
        max_value:  250
        step:       10

    LDSR_config:
        sampling_steps: 50
        preDownScale: 1
        postDownScale: 1
        downsample_method: "Lanczos"

    default_sampler: "k_euler"
    separate_prompts: False
    update_preview: True
    update_preview_frequency: 10
    normalize_prompt_weights: True
    save_individual_images: True
    save_grid: True
    group_by_prompt: False
    save_as_jpg: False
    use_GFPGAN: False
    use_RealESRGAN: False
    use_LDSR: False
    RealESRGAN_model: "RealESRGAN_x4plus"
    use_upscaling: False

    variant_amount:
        value:      0.0
        min_value:  0.0
        max_value:  1.0
        step:       0.01

    variant_seed: ""
    write_info_files: True

txt2vid:
    default_model: "runwayml/stable-diffusion-v1-5"
    custom_models_list: ["runwayml/stable-diffusion-v1-5", "Sygil/Sygil-Diffusion", "CompVis/stable-diffusion-v1-4", "hakurei/waifu-diffusion"]
    prompt:
    width:
        value:      512
        min_value:  64
        max_value:  2048
        step:       64

    height:
        value:      512
        min_value:  64
        max_value:  2048
        step:       64

    cfg_scale:
        value:      7.5
        min_value:  1.0
        step:       0.5

    batch_count:
        value:      1

    batch_size:
        value:      1

    sampling_steps:
        value:      30
        min_value:  10
        max_value:  250
        step:       10

    num_inference_steps:
        value:      200
        min_value:  10
        max_value:  500
        step:       10

    seed: ""
    default_sampler: "k_euler"
    scheduler_name: "klms"
    separate_prompts: False
    update_preview: True
    update_preview_frequency: 10
    dynamic_preview_frequency: True
    normalize_prompt_weights: True
    save_individual_images: True
    save_video: True
    save_video_on_stop: False
    group_by_prompt: True
    write_info_files: True
    do_loop: False
    use_lerp_for_text: False
    save_as_jpg: False
    use_GFPGAN: False
    use_RealESRGAN: False
    RealESRGAN_model: "RealESRGAN_x4plus"
    use_upscaling: False
    variant_amount:
        value:      0.0
        min_value:  0.0
        max_value:  1.0
        step:       0.01

    variant_seed: ""

    beta_start:
        value:      0.00085
        min_value:  0.00010
        max_value:  0.03000
        step:       0.00010
        format:     "%.5f"

    beta_end:
        value:      0.01200
        min_value:  0.00010
        max_value:  0.03000
        step:       0.00010
        format:     "%.5f"

    beta_scheduler_type: "scaled_linear"
    max_duration_in_seconds: 30
    fps: 30

    LDSR_config:
        sampling_steps: 50
        preDownScale: 1
        postDownScale: 1
        downsample_method: "Lanczos"

img2img:
    prompt:
    sampler_name: "k_euler"
    denoising_strength:
        value:      0.75
        min_value:  0.0
        max_value:  1.0
        step:       0.01
    # 0: Keep masked area
    # 1: Regenerate only masked area
    mask_mode: 0
    noise_mode: "Matched Noise"
    mask_restore: False
    # 0: Just resize
    # 1: Crop and resize
    # 2: Resize and fill
    resize_mode: 0
    # Leave blank for random seed:
    seed: ""
    ddim_eta: 0.0
    width:
        value:      512
        min_value:  64
        max_value:  2048
        step:       64

    height:
        value:      512
        min_value:  64
        max_value:  2048
        step:       64

    cfg_scale:
        value:      7.5
        min_value:  1.0
        step:       0.5

    batch_count:
        value:      1

    batch_size:
        value:      1

    sampling_steps:
        value:      30
        min_value:  10
        max_value:  250
        step:       10

    num_inference_steps:
        value:      200
        min_value:  10
        max_value:  500
        step:       10

    find_noise_steps:
        value:      100
        min_value:  100
        step:       100

    LDSR_config:
        sampling_steps: 50
        preDownScale: 1
        postDownScale: 1
        downsample_method: "Lanczos"

    loopback: True
    random_seed_loopback: True
    separate_prompts: False
    update_preview: True
    update_preview_frequency: 10
    normalize_prompt_weights: True
    save_individual_images: True
    save_grid: True
    group_by_prompt: False
    save_as_jpg: False
    use_GFPGAN: False
    use_RealESRGAN: False
    RealESRGAN_model: "RealESRGAN_x4plus"
    use_upscaling: False
    variant_amount: 0.0
    variant_seed: ""
    write_info_files: True

img2txt:
    batch_size: 2000
    blip_image_eval_size: 512
    keep_all_models_loaded: False

post_processing:
        save_original_images: True
        outdir_post_processing: outputs/post_processing

concepts_library:
    concepts_per_page: 12

gfpgan:
  strength: 100

textual_inversion:
    pretrained_model_name_or_path: "models/diffusers/stable-diffusion-v1-5"
    tokenizer_name: "models/clip-vit-large-patch14"


daisi_app:
    running_on_daisi_io: False

model_manager:
    models:
        stable_diffusion:
            model_name:     "Stable Diffusion v1.5"
            save_location:  "./models/ldm/stable-diffusion-v1"
            files:
                model_ckpt:
                    file_name:  "Stable Diffusion v1.5.ckpt"
                    download_link:  "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt"

        gfpgan:
            model_name:     "GFPGAN"
            save_location:  "./models/gfpgan"
            files:
                gfpgan_1_4:
                    file_name:  "GFPGANv1.4.pth"
                    download_link:  "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
                resnet_50:
                    file_name: "detection_Resnet50_Final.pth"
                    save_location:  "./gfpgan/weights"
                    download_link: "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth"
                parsing_parsenet:
                    file_name: "parsing_parsenet.pth"
                    save_location:  "./gfpgan/weights"
                    download_link: "https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth"


        realesrgan:
            model_name:     "RealESRGAN"
            save_location:  "./models/realesrgan"
            files:
                x4plus:
                    file_name: "RealESRGAN_x4plus.pth"
                    download_link:  "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"
                x4plus_anime_6b:
                    file_name: "RealESRGAN_x4plus_anime_6B.pth"
                    download_link:  "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth"


        waifu_diffusion:
            model_name:     "Waifu Diffusion v1.3"
            save_location:  "./models/custom"
            files:
                waifu_diffusion:
                    file_name: "Waifu-Diffusion-v1-3 Full ema.ckpt"
                    download_link:  "https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-full.ckpt"


        trinart_stable_diffusion:
            model_name:     "TrinArt Stable Diffusion v2"
            save_location:  "./models/custom"
            files:
                trinart:
                    file_name: "trinart.ckpt"
                    download_link:  "https://huggingface.co/naclbit/trinart_stable_diffusion_v2/resolve/main/trinart2_step95000.ckpt"

        sd_wd_ld_trinart_merged:
            model_name:     "SD1.5-WD1.3-LD-Trinart-Merged"
            save_location:  "./models/custom"
            files:
                sd_wd_ld_trinart_merged:
                    file_name: "SD1.5-WD1.3-LD-Trinart-Merged.ckpt"
                    download_link:  "https://huggingface.co/ZeroCool94/sd1.5-wd1.3-ld-trinart-merged/resolve/main/SD1.5-WD1.3-LD-Trinart-Merged.ckpt"

        stable_diffusion_concept_library:
            model_name:     "Stable Diffusion Concept Library"
            save_location:  "./models/custom/sd-concepts-library/"
            files:
                concept_library:
                    file_name: ""
                    download_link:  "https://github.com/Sygil-Dev/sd-concepts-library"

        blip_model:
            model_name:     "Blip Model"
            save_location:  "./models/blip"
            files:
                blip:
                    file_name: "model__base_caption.pth"
                    download_link:  "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth"

        ldsr:
            model_name: "Latent Diffusion Super Resolution (LDSR)"
            save_location:  "./models/ldsr"
            files:
                project_yaml:
                    file_name:     "project.yaml"
                    download_link:  "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"

                ldsr_model:
                    file_name:     "model.ckpt"
                    download_link:  "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"

        sygil:
            model_name:     "Sygil Diffusion 0.4"
            save_location:  "./models/custom"
            files:
                sygil_diffusion:
                    file_name: "sygil-diffusion-v0.4.ckpt"
                    download_link:  "https://huggingface.co/Sygil/Sygil-Diffusion/resolve/main/sygil-diffusion-v0.4.ckpt"


================================================
FILE: daisi_app.py
================================================
import os, subprocess
import yaml

print(os.getcwd)

try:
    with open("environment.yaml") as file_handle:
        environment_data = yaml.safe_load(file_handle, Loader=yaml.FullLoader)
except FileNotFoundError:
    try:
        with open(os.path.join("..", "environment.yaml")) as file_handle:
            environment_data = yaml.safe_load(file_handle, Loader=yaml.FullLoader)
    except:
        pass

try:
    for dependency in environment_data["dependencies"]:
        package_name, package_version = dependency.split("=")
        os.system("pip install {}=={}".format(package_name, package_version))
except:
    pass

try:
    subprocess.run(
        [
            "python",
            "-m",
            "streamlit",
            "run",
            os.path.join("..", "scripts/webui_streamlit.py"),
            "--theme.base dark",
        ],
        stdout=subprocess.DEVNULL,
    )
except FileExistsError:
    subprocess.run(
        [
            "python",
            "-m",
            "streamlit",
            "run",
            "scripts/webui_streamlit.py",
            "--theme.base dark",
        ],
        stdout=subprocess.DEVNULL,
    )


================================================
FILE: data/img2txt/artists.txt
================================================
A B Jackson
A J Casson
A R Middleton Todd
A. B. Jackson
A. J. Casson
A. R. Middleton Todd
A.B. Frost
A.D.M. Cooper
AB Frost
ADM Cooper
Aaron Bohrod
Aaron Douglas
Aaron Jasinski
Aaron Miller
Aaron Nagel
Abbott Handerson Thayer
Abdullah Gërguri
Abdur Rahman Chughtai
Abidin Dino
Abraham Begeyn
Abraham Bloemaert
Abraham Bosschaert
Abraham Hondius
Abraham Mignon
Abraham Storck
Abraham Willaerts
Abraham de Vries
Abraham van Beijeren
Abraham van Calraet
Abraham van den Tempel
Abram Arkhipov
Achille Leonardi
Ada Gladys Killins
Ada Hill Walker
Adam Bruce Thomson
Adam Chmielowski
Adam Dario Keel
Adam Elsheimer
Adam Manyoki
Adam Marczynski
Adam Marczyński
Adam Paquette
Adam Pijnacker
Adam Rex
Adam Saks
Adam Szentpétery
Adam Willaerts
Adolf Bierbrauer
Adolf Born
Adolf Dietrich
Adolf Fényes
Adolf Hirémy-Hirschl
Adolf Hölzel
Adolf Schrödter
Adolf Ulric Wertmüller
Adolf Wölfli
Adolfo Müller-Ury
Adolph Gottlieb
Adolph Menzel
Adolphe Willette
Adriaen Brouwer
Adriaen Coorte
Adriaen Hanneman
Adriaen Isenbrant
Adriaen van Ostade
Adriaen van de Velde
Adriaen van de Venne
Adriaen van der Werff
Adrian Zingg
Adrienn Henczné Deák
Adélaïde Labille-Guiard
Adélaïde Victoire Hall
Aelbert Cuyp
Aert de Gelder
Aert van der Neer
Aertgen van Leyden
Afewerk Tekle
Affandi
Agnes Lawrence Pelton
Agnes Martin
Agnolo Bronzino
Agnolo Gaddi
Agostino Carracci
Aguri Uchida
Agustín Fernández
Ahmed Karahisari
Ahmed Yacoubi
Ai Weiwei
Ai Xuan
Ai-Mitsu
Aileen Eagleton
Aimé Barraud
Akihiko Yoshida
Akira Toriyama
Akseli Gallen-Kallela
Al Feldstein
Al Williamson
Aladár Körösfoi-Kriesch
Aladár Körösfői-Kriesch
Alain Tasso
Alan Bean
Alan Davis
Alan Lee
Alan Pollack
Alan Sutherland
Alasdair Grant Taylor
Alasdair Gray
Albert Anker
Albert Bertelsen
Albert Bierstadt
Albert Dorne
Albert Dubois-Pillet
Albert Edelfelt
Albert Gleizes
Albert Guillaume
Albert Henry Krehbiel
Albert Irvin
Albert J Welti
Albert J. Welti
Albert Joseph Moore
Albert Joseph Pénot
Albert Keller
Albert Kotin
Albert Marquet
Albert Namatjira
Albert Nemethy
Albert Paris Gütersloh
Albert Swinden
Albert Welti
Alberto Burri
Alberto Giacometti
Alberto Morrocco
Alberto Seveso
Alberto Sughi
Alberto Vargas
Albin Egger-Lienz
Albrecht Altdorfer
Albrecht Dürer
Aldus Manutius
Alejandro Burdisio
Alejandro Obregón
Aleksander Gierymski
Aleksander Gine
Aleksander Kobzdej
Aleksander Kotsis
Aleksander Orlowski
Aleksander Orłowski
Aleksandr Gerasimov
Aleksandr Ivanovich Laktionov
Aleksi Briclot
Alena Aenami
Alessandro Allori
Alessandro Galli Bibiena
Alesso Baldovinetti
Alex Grey
Alex Horley
Alex Horley-Orlandelli
Alex Katz
Alex Petruk APe
Alex Ross
Alex Toth
Alexander Archipenko
Alexander Bogen
Alexander Brook
Alexander Calder
Alexander Carse
Alexander Deyneka
Alexander Fedosav
Alexander Ivanov
Alexander Jansson
Alexander Johnston
Alexander Kanoldt
Alexander Kucharsky
Alexander Litovchenko
Alexander Mann
Alexander McQueen
Alexander Milne Calder
Alexander Nasmyth
Alexander Robertson
Alexander Rodchenko
Alexander Roslin
Alexander Runciman
Alexander Scott
Alexander Sharpe Ross
Alexander Stirling Calder
Alexander V Kuprin
Alexander V. Kuprin
Alexandre Benois
Alexandre Cabanel
Alexandre Falguière
Alexandre-Évariste Fragonard
Alexei Kondratyevich Savrasov
Alexej von Jawlensky
Alexey Merinov
Alexey Venetsianov
Alexis Grimou
Alexis Simon Belle
Alfons Karpinski
Alfons Karpiński
Alfons Walde
Alfons von Czibulka
Alfonse Mucha
Alfred Charles Parker
Alfred East
Alfred Edward Chalon
Alfred Eisenstaedt
Alfred Freddy Krupa
Alfred Janes
Alfred Jensen
Alfred Krupa
Alfred Kubin
Alfred Leslie
Alfred Leyman
Alfred Manessier
Alfred Richard Gurrey
Alfred Sisley
Alfred Thompson Bricher
Alfred Wallis
Alfredo Volpi
Algernon Talmage
Alice Bailly
Alice Mason
Alice Neel
Alice Prin
Alice Rahon
Alison Debenham
Alison Geissler
Alison Kinnaird
Alison Watt
Allaert van Everdingen
Allan Brooks
Allan Linder
Allan Ramsay
Allen Butler Talcott
Allen Jones
Allen Tupper True
Alma Thomas
Almada Negreiros
Almeida Júnior
Alonso Vázquez
Aloysius O'Kelly
Alphonse Legros
Alphonse Mucha
Alson S Clark
Alson S. Clark
Altichiero
Alton Tobey
Altoon Sultan
Alvan Fisher
Alén Diviš
Amadeo de Souza Cardoso
Amalia Lindegren
Amanda Sage
Ambreen Butt
Ambrose McCarthy Patterson
Ambrosius Benson
Ambrosius Bosschaert
Ambrosius Bosschaert II
Ambrosius Holbein
Amedeo Modigliani
Amelia Peláez
Amelia Robertson Hill
Americo Makk
Amir Zand
Ammi Phillips
Amos Ferguson
Amos Sewell
Amy Weber
Amédée Ozenfant
An Gyeon
An Zhengwen
Anato Finnstark
Ancell Stronach
Anders Zorn
Andor Basch
Andre Derain
Andre de Krayewski
Andrea Kowch
Andrea Mantegna
Andrea Orcagna
Andrea Pozzo
Andrea del Sarto
Andrea del Verrocchio
Andreas Achenbach
Andreas Gursky
Andreas Rocha
Andrei Kolkoutine
Andrei Rublev
Andrei Ryabushkin
Andrew Allan
Andrew Bell
Andrew Boog Faithfull
Andrew Domachowski
Andrew Ferez
Andrew Geddes
Andrew Henderson
Andrew Law
Andrew Loomis
Andrew Robertson
Andrew Robinson
Andrew Stevovich
Andrew Wyeth
Andrey Esionov
Andrey Yefimovich Martynov
Andries Both
Andries Stock
Andries Stock Dutch Baroque painter
Android Jones
Andrzej Pronaszko
Andrzej Wróblewski
André Bauchant
André Beauneveu
André Castro
André Charles Biéler
André Derain
André François
André Kertész
André Lhote
André Masson
André Pijet
André Thomkins
Andrée Ruellan
Andy Goldsworthy
Andy Warhol
Aneurin Jones
Angelica Kauffman
Aniello Falcone
Anish Kapoor
Anita Kunz
Anita Malfatti
Anka Zhuravleva
Ann Thetis Blacker
Anna Ancher
Anna Boch
Anna Dittmann
Anna Findlay
Anna Füssli
Anna Haifisch
Anna Hotchkis
Anna Katharina Block
Anna Maria Barbara Abesch
Anna Mary Robertson Moses
Anna and Elena Balbusso
Annabel Eyres
Annabel Kidston
Anne Dunn
Anne Geddes
Anne Nasmyth
Anne Redpath
Anne Rigney
Anne Ryan
Anne Said
Anne Savage
Anne Stokes
Anni Albers
Annibale Carracci
Annie Abernethie Pirie Quibell
Annie Leibovitz
Annie Rose Laing
Ansel Adams
Anselm Kiefer
Anson Maddocks
Antanas Sutkus
Anthony Angarola
Anthony Devas
Anthony Palumbo
Anthony S Waters
Anthony van Dyck
Antoine Blanchard
Antoine Ignace Melling
Antoine Le Nain
Antoine Verney-Carron
Antoine Wiertz
Anton Ažbe
Anton Fadeev
Anton Graff
Anton Lehmden
Anton Mauve
Anton Möller
Anton Otto Fischer
Anton Pieck
Anton Räderscheidt
Anton Solomoukha
Antonello da Messina
Antoni Brodowski
Antoni Pitxot
Antoni Tàpies
Antonie Palamedesz
Antonin Artaud
Antonio Canova
Antonio Cavallucci
Antonio Ciseri
Antonio Donghi
Antonio Galli Bibiena
Antonio Mancini
Antonio Rotta
Antonio Saura
Antonio de la Gandara
Antonín Chittussi
Antonín Slavícek
Antonín Slavíček
Antônio Parreiras
Aníbal Villacís
Apelles
Apollinary Vasnetsov
Apollonia Saintclair
Aquirax Uno
Arabella Rankin
Araceli Gilbert
Aramenta Dianthe Vail
Archibald Motley
Archibald Robertson
Archibald Skirving
Archibald Standish Hartrick
Arcimboldo
Arent Arentsz
Arie Smit
Arik Brauer
Aristide Maillol
Arkhip Kuindzhi
Arlington Nelson Lindenmuth
Armand Guillaumin
Armin Baumgarten
Armin Hansen
Arnie Swekel
Arnold Blanch
Arnold Bronckhorst
Arnold Brügger
Arnold Böcklin
Arnold Franz Brasz
Arnold Mesches
Arnold Newman
Arshile Gorky
Art & Language
Art Brenner
Art Fitzpatrick
Art Frahm
Art Green
Art Spiegelman
Art of Brom
Artemisia Gentileschi
Artgerm
Arthur Adams
Arthur B Carles
Arthur B. Carles
Arthur Boyd
Arthur Burdett Frost
Arthur Dove
Arthur Garfield Dove
Arthur Hughes
Arthur Lismer
Arthur Melville
Arthur Merric Boyd
Arthur Pan
Arthur Quartley
Arthur Rackham
Arthur Sarkissian
Arthur Streeton
Arthur Webster Emerson
Artur Grottger
Artur Tarnowski
Arturo Rivera
Arvid Nyholm
Asaf Hanuka
Asai Chu
Asai Chū
Asger Jorn
Asher Brown Durand
Atey Ghailan
Attila Meszlenyi
Aubrey Beardsley
Audrey Kawasaki
August Friedrich Schenck
August Lemmer
August Macke
August Querfurt
August Sander
Auguste Baud-Bovy
Auguste Herbin
Augustin Meinrad Bächtiger
Augustus Dunbier
Augustus Earle
Augustus John
Augustus Vincent Tack
Augustyn Mirys
Aurél Bernáth
Auseklis Ozols
Austin Briggs
Austin English
Austin Osman Spare
Avgust Cernigoj
Avgust Černigoj
Avigdor Arikha
Awataguchi Takamitsu
Ay-O
Aya Goda
Ayako Rokkaku
Ayami Kojima
Ayshia Taskin
Ayshia Taşkın
Baioken Eishun
Baiōken Eishun
Balcomb Greene
Balthasar van der Ast
Balthus
Balázs Diószegi
Banksy
Bapu
Barbara Balmer
Barbara Greg
Barbara Longhi
Barbara Nasmyth
Barbara Nessim
Barclay Shaw
Barent Fabritius
Barkley Hendricks
Barnett Newman
Barron Storey
Barthel Bruyn the Elder
Barthel Bruyn the Younger
Bartholomeus Breenbergh
Bartholomeus Strobel
Bartholomeus van Bassen
Bartholomeus van der Helst
Barthélemy Menn
Barthélemy d'Eyck
Bartolomeo Cesi
Bartolomeo Vivarini
Bartolomé Esteban Murillo
Bascove
Basil Blackshaw
Bastien L Deharme
Bastien L. Deharme
Bastien Lecouffe-Deharme
Basuki Abdullah
Bauhaus
Bayard Wu
Beatrice Ethel Lithiby
Beatrice Huntington
Beatrix Potter
Beauford Delaney
Bedwyr Williams
Beeple
Beksinski
Bela Cikoš Sesija
Bela Čikoš Sesija
Ben Enwonwu
Ben Nicholson
Ben Shahn
Ben Stahl
Ben Templesmith
Ben Thompson
Ben Zoeller
Bencho Obreshkov
Benito Quinquela Martín
Benjamin Block
Benjamin Franklin
Benjamin Gerritsz Cuyp
Benjamin Marra
Benjamin West
Benjamin Williams Leader
Benoit B Mandelbrot
Benoit B. Mandelbrot
Bernard Accama
Bernard Buffet
Bernard D’Andrea
Bernard Fleetwood-Walker
Bernard Meninsky
Bernard van Orley
Bernardino Mei
Bernardo Bellotto
Bernardo Cavallino
Bernardo Daddi
Bernardo Strozzi
Bernat Sanjuan
Bernd Fasching
Bernie D’Andrea
Bernie Wrightson
Bernt Tunold
Bert Hardy
Bert Stern
Bertalan Karlovszky
Bertalan Pór
Bertalan Székely
Berthe Morisot
Bertram Brooker
Bessie Wheeler
Beta Vukanovic
Beta Vukanović
Bettina Heinen-Ayech
Betty Churcher
Betty Merken
Betye Saar
Bholekar Srihari
Bhupen Khakhar
Bian Jingzhao
Bian Shoumin
Bikash Bhattacharjee
Bill Lewis
Bill Sienkiewicz
Bill Traylor
Bill Ward
Bill Watterson
Billie Waters
Billy Childish
Bjørn Wiinblad
Blanche Hoschedé Monet
Bob Byerley
Bob Eggleton
Bob Ringwood
Bob Ross
Bob Singer
Bob Thompson
Boetius Adamsz Bolswert
Bogi Fabian
Bohumil Kubista
Boleslaw Cybis
Bonnard Pierre
Boris Kustodiev
Boris Vallejo
Boris Vladimirski
Bouchta El Hayani
Bourgeois
Božidar Jakac
Bracha L Ettinger
Bracha L. Ettinger
Brad Holland
Brad Kunkle
Bradley Walker Tomlin
Brassaï
Brenda Chamberlain
Brett Whiteley
Breyten Breytenbach
Brian 'Chippy' Dugan
Brian Alfred
Brian Bolland
Brian Despain
Brian Dunlop
Brian Fies
Brian Froud
Brian Snøddy
Brian Thomas
Brian and Wendy Froud
Briana Mora
Brice Marden
Bridget Bate Tichenor
Bridget Riley
Brigette Barrager
Brigid Derham
Brom
Brooke Shaden
Brothers Hildebrandt
Bruce Davidson
Bruce Gilden
Bruce McLean
Bruce Munro
Bruce Nauman
Bruce Onobrakpeya
Bruce Pennington
Bruce Timm
Bruno Liljefors
Bryan Organ
Buckminster Fuller
Bunny Yeager
Byeon Sang-byeok
Byron Galvez
Bálint Kiss
Béla Apáti Abkarovics
Béla Czóbel
Béla Iványi-Grünwald
Béla Kondor
Béla Nagy Abodi
Béla Pállik
Béni Ferenczy
Caesar Andrade Faini
Caesar van Everdingen
Cafer Bater
Cagnaccio di San Pietro
Cam Sykes
Camille Bombois
Camille Bouvagne
Camille Corot
Camille Pissarro
Camille Souter
Camille-Pierre Pambu Bodo
Camilo Egas
Camilo Mori
Canaletto
Candido Bido
Candido Portinari
Cao Buxing
Cao Zhibai
Caravaggio
Carel Fabritius
Carel Weight
Carel Willink
Carey Morris
Carl Arnold Gonzenbach
Carl Barks
Carl Critchlow
Carl Eugen Keel
Carl Eytel
Carl Frederik von Breda
Carl Gustaf Pilo
Carl Heinrich Bloch
Carl Hoppe
Carl Larsson
Carl Morris
Carl Rahl
Carl Spitzweg
Carl Walter Liner
Carl-Henning Pedersen
Carla Wyzgala
Carle Hessay
Carles Delclaux Is
Carlo Carlone
Carlo Carrà
Carlo Crivelli
Carlo Galli Bibiena
Carlo Maderna
Carlo Martini
Carlo Mense
Carlo Randanini
Carlos Berlanga
Carlos Catasse
Carlos Enríquez Gómez
Carlos Francisco Chang Marín
Carlos Saenz de Tejada
Carlos Schwabe
Carlos Trillo Name
Carne Griffiths
Caro Niederer
Carol Bove
Carol Sutton
Caroline Chariot-Dayez
Caroline Gotch
Caroline Lucy Scott
Caroline Mytinger
Carpoforo Tencalla
Carrie Mae Weems
Caspar David Friedrich
Caspar Netscher
Caspar Wolf
Caspar van Wittel
Cassandra Austen
Cassius Marcellus Coolidge
Caterina Tarabotti
Catrin G Grosse
Catrin Welz-Stein
Cecil Beaton
Cecile Walton
Cecilia Beaux
Cecily Brown
Cedric Peyravernay
Cedric Seaut
Cedric Seaut (Keos Masons)
Ceferí Olivé
Celia Fiennes
Celia Frances Bedford
Ceri Richards
Cerith Wyn Evans
Chafik Charobim
Chagall
Chaim Soutine
Chang Dai-chien
Chantal Joffe
Charles Addams
Charles Alphonse du Fresnoy
Charles Alston
Charles Angrand
Charles Billich
Charles Bird King
Charles Blackman
Charles Codman
Charles Conder
Charles Crodel
Charles Cundall
Charles Dana Gibson
Charles Demuth
Charles E Burchfield
Charles E. Burchfield
Charles Ellison
Charles Fremont Conner
Charles Furneaux
Charles Ginner
Charles Gleyre
Charles H Woodbury
Charles H. Woodbury
Charles Harold Davis
Charles Haslewood Shannon
Charles Hinman
Charles Hopkinson
Charles Joshua Chaplin
Charles Le Brun
Charles Le Roux
Charles Mahoney
Charles Marion Russell
Charles Martin
Charles Maurice Detmold
Charles McAuley
Charles Mozley
Charles Ragland Bunnell
Charles Rennie Mackintosh
Charles Ricketts
Charles Roka
Charles Rollier
Charles S Kaelin
Charles S. Kaelin
Charles Schulz
Charles Thomson
Charles Uzzell-Edwards
Charles W Bartlett
Charles W. Bartlett
Charles Williams
Charles Willson Peale
Charles-Amédée-Philippe van Loo
Charles-André van Loo
Charles-François Daubigny
Charlie Bowater
Charlotte Harding
Charlotte Nasmyth
Charly Amani
Charmion von Wiegand
Chase Stone
Chaïm Soutine
Chen Chi
Chen Chun
Chen Daofu
Chen Hong
Chen Hongshou
Chen Jiru
Chen Lin
Chen Lu
Chen Rong
Chen Yifei
Cheng Jiasui
Cheng Shifa
Cheng Zhengkui
Cherryl Fountain
Chesley Bonestell
Chica Macnab
Chiharu Shiota
Chiho Aoshima
Childe Hassam
Chinwe Chukwuogo-Roy
Chip Zdarsky
Chippy
Chizuko Yoshida
Choi Buk
Chris Cold
Chris Foss
Chris Friel
Chris LaBrooy
Chris Moore
Chris Rahn
Chris Rallis
Chris Spollen
Chris Ware
Christabel Dennison
Christen Dalsgaard
Christen Købke
Christian August Lorentzen
Christian Hilfgott Brand
Christian Jane Fergusson
Christian Krohg
Christian Rohlfs
Christian W Staudinger
Christian W. Staudinger
Christo
Christoffel van den Berghe
Christoffer Wilhelm Eckersberg
Christoph Amberger
Christoph Ludwig Agricola
Christophe Vacher
Christopher Balaskas
Christopher Moeller
Christopher Perkins
Christopher Rush
Christopher Williams
Christopher Wood
Christopher Wren
Chuck Close
Cicely Hey
Cicely Mary Barker
Cimabue
Cindy Sherman
Cindy Wright
Claes Corneliszoon Moeyaert
Claes Jansz Visscher
Claes Jansz. Visscher
Claire Dalby
Claire Falkenstein
Claire Hummel
Clara Miller Burd
Clara Peeters
Clara Weaver Parrish
Clarence Holbrook Carter
Clarice Beckett
Clark Voorhees
Claude Bonin-Pissarro
Claude Cahun
Claude Lorrain
Claude Monet
Claude Rogers
Cleon Peterson
Cleve Gray
Cliff Childs
Clifford Ellis
Clifford Ross
Clint Cearley
Clovis Trouille
Clyde Caldwell
Clyfford Still
Clément Serveau
Coby Whitmore
Coles Phillips
Colijn de Coter
Colin Campbell Cooper
Colin Gill
Colin Hayes
Colin McCahon
Colin Middleton
Colin Moss
Conrad Marca-Relli
Conrad Roset
Conroy Maddox
Constance Copeman
Constance Gordon-Cumming
Constance-Anne Parker
Constant
Constant Permeke
Constantin Hansen
Constantine Andreou
Coppo di Marcovaldo
Cor Melchers
Corneille
Cornelia MacIntyre Foley
Cornelia Parker
Cornelis Anthonisz
Cornelis Bisschop
Cornelis Claesz van Wieringen
Cornelis Dusart
Cornelis Engebrechtsz
Cornelis Pietersz Bega
Cornelis Saftleven
Cornelis Verbeeck
Cornelis de Heem
Cornelis de Man
Cornelis van Haarlem
Cornelis van Poelenburgh
Cornelisz Hendriksz Vroom
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Trending on artstation
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Utagawa Hiroshige II
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Utagawa Kunimasa
Utagawa Kunimasu
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Utagawa Kunisada II
Utagawa Kunisada III
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V DeGrailly
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Victor Higgins
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Victor HugoDraughtsman
Victor Huguet
Victor Ivanov
Victor Louis
Victor Meirelles
Victor Moscoso
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Victor Mottez
Victor Nehlig
Victor Nizovtsev
Victor Noble Rainbird
Victor Pasmore
Victor Prezio
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Victor Surbek
Victor Vasarely
Victor Wang
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Victoria Crowe
Victoria Francés
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Victoria Poyser
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Viola Frey
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Virginia Carten
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Vladimir Makovsky
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Vlady Kibalchich Russakov
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Vuolvinio
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Václav Brožík
Víctor Manuel García Valdés
W Eugene Smith
W Heath Robinson
W Kinnard
W Lester Stevens
W Lindsay Cable
W Oram
W. Lindsay Cable
WE Atkinson
WH Brown
WLOP
WM Keesey
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Wagguno
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Walid RaadLebanese
Walker Evans
Wallace Berman Assemblage
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Wallerant Vaillant
Wally Brants
Wally Wood
Walt Disney
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Walt Reed
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Walter Battiss
Walter Bayes
Walter Beach Humphrey
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Walter Dendy Sadler
Walter Dexel
Walter Dorwin Teague
Walter Dusenbery
Walter E Bohl
Walter Ellison
Walter Emerson Baum
Walter Ernest Webster
Walter Fitch
Walter Franklin Lansil
Walter Frederick Osborne
Walter Gay
Walter Geikie
Walter Granville Smith
Walter Greaves
Walter Griffin
Walter Gropius
Walter Haskell Hinton
Walter Howell Deverell
Walter Humphrey
Walter I Cox
Walter J Phillips
Walter John Trower
Walter L Greene
Walter Langley
Walter Launt Palmer
Walter Leighton Clark
Walter Murch
Walter Osborne
Walter Percy Day
Walter Quirt
Walter Richard Sickert
Walter Rosenblum
Walter Schnackenberg
Walter Shirlaw
Walter Sickert
Walter Stuempfig
Walter Ufer
Walter Velez
Walter William Ouless
Walter Withers
Walther Jervolino
Walton Ford
Wanda Gag
Wang Chunjie
Wang Dongling
Wang Duo
Wang E
Wang Fu
Wang Geyi
Wang Guangyi
Wang Guxiang
Wang Huaiqing
Wang Hui
Wang Jian
Wang Jiqian (CC Wang)
Wang Lü
Wang Meng
Wang Mengqi
Wang Mian
Wang Shenglie
Wang Shimin
Wang Shishen
Wang Wei
Wang Wen
Wang Wu
Wang Wuxie (Wucius Wong)
Wang Ximeng
Wang Xuetao
Wang Yi
Wang Yingchun
Wang Yuan
Wang Yuangi
Wang Yuanqi
Wang Yun
Wang Zhen
Wang Zhenpeng
Wang Zhongyu
Wangechi Mutu
Ward Kimball
Warhol
Warren Buffet
Warren Criswell
Warren Ellis
Warren Eugene Brandon
Warren Mahy
Warren P Lombard
Warren W Sheppard
Warrington Colescott
Warwick Goble
Washington Allston
Washington Bogart Cooper
Washington Friend
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Watanabe Kazan
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Weegee
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Weiwei
Wen Bao
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Wen Jia
Wen Lou
Wen Tong
Wen Zhengming
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Wenceslaus HollarBohemian
Wendell Castle
Wendell Minor
Wendy Froud
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Wenzel Peter
Wenzel von Olmutz
Werner Andermatt
Werner Drewes
Werner Gutzeit
Werner Herzog
Werner Tübke
Werner van den Valckert
Wes Anderson
Wes Benscoter
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Wesley Anderegg
Wesley Burt
Whitney Darrow
Whitney Hubbard
Whitney Sherman
Wieslaw Walkuski
Wifredo Lam
Wifredo LamFrench
Wijnand Nuyen
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Wilfredo Lam
Wilfrid de Glehn
Wilhelm Bendz
Wilhelm Freddie
Wilhelm Hammershøi
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Wilhelm Lehmbruck
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Wilhelm List
Wilhelm Marstrand
Wilhelm Sasnal
Wilhelm Schnarrenberger
Wilhelm Schubert van Ehrenberg
Wilhelm Trubner
Wilhelm Trübner
Wilhelm Wagenfeld
Wilhelm von Kaulbach
Wilhelm von Kobell
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Wilhelmina Weber Furlong
Wiligelmo
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Will Eisner
Will Ellis
Will Hicok Low
Will Howe Foote
Will James
Willard Leroy Metcalf
Willard Metcalf
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Willem Claesz Heda
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Willem Drost
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Willem Hofhuizen
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Willem Jacobsz Delff
Willem Kalf
Willem Key
Willem Koekkoek
Willem Labeij
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Willem Martens
Willem Pieterszoon Buytewech
Willem Roelofs
Willem Vrelant
Willem de Kooning
Willem de Poorter
Willem van Aelst
Willem van Haecht
Willem van Mieris
Willem van Tetrode
Willem van de Velde the Elder
Willem van de Velde the Younger
Willem van der Vliet
Willi Baumeister
William A Walker
William A Wilson
William Aikman
William Alexander
William Alfred Delamotte
William Allan
William Allen Wall
William Andrews Nesfield
William Armstrong
William Artaud
William Auerbach-Levy
William B Eaton
William B Ritchie
William Bailey
William Barak Aboriginal
William Baxter Closson
William Baziotes
William Beckman
William Behnes
William Bell Scott
William Berra
William Birnie Rhind
William Blair Bruce
William Blake
William Blake Richmond
William Bliss Baker
William Bonnar
William Bonnell
William Bowyer
William Bradford
William Brodie
William Bromley
William Brymner
William Burges
William Bygrave
William Callow
William Chadwick
William Charles Thomas Dobson
William Christenberry
William Coldstream
William Collins
William Congdon
William Conor
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William Cooper
William Copley
William Couper
William Crosbie
William Crozier
William Cruickshank
William Crutchfield
William Daniell
William Dargie
William Davis
William DeLeftwich Dodge
William Degouve de Nuncques
William Derby
William Didier-Pouget
William Dixon
William Dobell
William Dobson
William Doughty
William Dring
William DunlapWriter
William Dyce
William Edmondson
William Edouard Scott
William Edward Frost
William Edward West
William Eggleston
William Etty
William Evan Charles Morgan
William Evelyn Osborn
William Faithorne
William Fettes Douglas
William Fleetwood Varley
William Ford
William Forsyth
William Foster
William Frank Calderon
William Franklin Draper
William Frater
William Frederick Witherington
William Frederick Yeames
William Frome Smallwood
William G Yorke
William GR Hind
William Gale
William Gear
William Geissler
William George Gillies
William George Reindel
William Gerard Barry
William Glackens
William Gorman Wills
William Gould
William Gouw Ferguson
William Grant Stevenson
William Gropper
William Guy Wall
William H Johnson
William H Mosby
William H. Mosby
William Hahn
William Hamilton
William Harnett
William Hart
William Harvey
William Havell
William Heath
William Henry Bartlett
William Henry Brooke
William Henry Clapp
William Henry Dethlef Koerner
William Henry Fox Talbot
William Henry Hunt
William Henry Jackson
William Henry Luscomb
William Henry Margetson
William Henry Pyne
William Henry Rinehart
William Herbert Dunton
William Herman Rau
William Hicks
William Hilton the Younger
William Hoare
William Hodges
William Hogarth
William Holbrook Beard
William Holman Hunt
William Holmes Sullivan
William Home Lizars
William Horace Littlefield
William Howard Yorke
William Huggins
William J McCloskey
William Jacob Baer
William Jacob Hays
William James Bennett
William James Glackens
William James Laidlay
William James Muller
William Jennys
William Jewett
William John Hennessy
William John Huggins
William John Thomson
William Jones
William K Hartmann
William Kay Blacklock
William Keith
William Kentridge
William King
William Klein
William Kurelek
William Lamb Picknell
William Langson Lathrop
William Larkin
William Lee-Hankey
William Lees Judson
William Leighton Leitch
William Leroy Flint
William Lescaze
William Lindsay Windus
William Linton
William Lionel Wyllie
William Locke Jr of Norbury
William Logsdail
William Louis Sonntag
William MacTaggart
William Marlow
William Mason Brown
William Matthew Prior
William Maw Egley
William McGregor Paxton
William McTaggart
William Merritt Chase
William Meyerowitz
William Michael Harnett
William Miller
William Morris
William Morris Hunt
William Mossman
William Mulready
William Mustart Lockhart
William Nicholson
William Nicoll Cresswell
William Notman
William Oliver
William Owen
William Oxer
William P Sherlock
William Page
William Palmer Robins
William Parrott
William Pars
William Partridge Burpee
William Patten
William Payne
William Perehudoff
William Pether
William Powell Frith
William Powhida
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William R Leigh
William Richardson
William Rickarby Miller
William Rimmer
William Ritschel
William Robert Colton
William Robert Symonds
William Roberts
William Robinson
William Rush
William Russell Birch
William Russell Flint
William S Burroughs
William Salter
William Samuel Horton
William Schumacher
William Scott
William Sergeant Kendall
William Shakespeare Burton
William Shayer Sr
William Sidney Mount
William Simpson
William Sommer
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William Stearns
William Steig
William Stott
William Stout
William Strang
William Strutt
William Taverner
William Theed
William Thon
William Timlin
William Tombleson
William Tomkins
William Trost Richards
William TuckerEgyptian
William Turnbull
William Turner
William Turner of Oxford
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William Tylee Ranney
William Van Alen
William Vincent Cahill
William Wegman
William Wendt
William Westall
William Wetmore Story
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William Wiley
William Williams
William Winstanley
William Wolfram
William Wood
William Woodward
William Wray
William Yellowlees
William York Macgregor
William Young Ottley
William Zorach
William de Morgan
William-Adolphe Bouguereau
Willian Murai
Willie Cole
Willie Doherty
Willie Ito
Willy Bo Richardson
Willy Finch
Willy Pogany
Wilson Irvine
Wim Delvoye
Wim Wenders
Winfield Scott Clime
Winifred Knights
Winifred Nicholson
Winold Reiss
Winona Nelson
Winslow Homer
Winsor McCay
Winston Churchill
Winston Spencer Churchill
Winthrop Chandler
Witold Pruszkowski
Witold Pruszkówski
Witold Wojtkiewicz
Wlad Safronow
Wladimir Baranoff-Rossine
Wladyslaw Czachórski
Wladyslaw Malecki
Wladyslaw Podkowinski
Wladyslaw Slewinski
Wladyslaw Strzeminski
Wlastimil Hofman
Wlodzimierz Tetmajer
Wo Zha
Wojciech Gerson
Wojciech Korneli Stattler
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Wojciech Siudmak
Wojciech Weiss
Wolf Huber
Wolf Kahn
Wolf Vostell
Wolfgang Laib
Wolfgang Letti
Wolfgang Lettl
Wolfgang Paalen
Wolfgang Suschitzky
Wolfgang Tillmans
Wolfgang Zelmer
Wong Kar-wai
Woody Crumbo
Wordsworth Thompson
Worthington Whittredge
Wouter Johannes van Troostwijk
Wouter Pietersz Crabeth
Wouterus Verschuur
Wright Barker
Wright Morris
Wu Biduan
Wu Bin
Wu Changshi
Wu Changshuo
Wu Daozi
Wu Guanzhong
Wu Hong
Wu Hufan
Wu Jiayou
Wu Jingting
Wu Li
Wu Shixian
Wu Wei
Wu Zhen
Wu Zuoren
Wuzhun Shifan
Wyatt Eaton
Wyke Bayliss
Wylie Beckert
Wyndham Lewis
Wäinö Aaltonen
Władysław Czachórski
Władysław Malecki
Władysław Podkowiński
Władysław Ślewiński
Xanthus Russell Smith
Xanti Schawinsky
Xavier Blum Pinto
Xavier Martinez
Xavier Mellery
Xavier Veilhan
Xenophantos
Xi Gang
Xia Chang
Xia Gui
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Xia Yong
Xiang Shengmo
Xiao Haichun
Xiao Yuncong
Xiaofei Yue
Xie He
Xie Huan
Xie Shichen
Xie Sun
Xu Beihong
Xu Bing
Xu Daoning
Xu Lei
Xu Lele
Xu Wei
Xu Xi
Xuande Emperor
Xugu
Xul Solar
Ya Ming
Yaacov Agam
Yael Bartana Video
Yael Kanarek
Yahoo Kusama
Yamagata Hiro
Yamaguchi Kayo
Yamakawa Shuho
Yamamoto Shoun
Yamamoto Shōun
Yan Han
Yan Hui
Yan Liben
Yanagawa Nobusada
Yanagawa Shigenobu
Yanagawa Shigenobu II
Yang Borun
Yang Buzhi
Yang Fudong Video
Yang J
Yang Jin
Yang Jingsheng
Yang Kewu
Yang Keyang
Yang Lizhou
Yang Nawei
Yang Shanshen
Yang Weizhen
Yang Yong
Yang Zhiguang
Yanjun Cheng
Yao Tingmei
Yasar Vurdem
Yasuhiro Nightow
Yasumasa Morimura
Yasuo Kuniyoshi
Yasushi Nirasawa
Yasushi Sugiyama
Yasutomo Oka
Yayoi Kusama
Yayou Kusama
Ye Minren
Ye Xin
Yehia Dessouki
Yeong-Hao Han
Yerkaland
Yervand Kochar
Yi Inmun
Yi Insang
Yi Jaegwan
Yi Yuanji
Yiannis Moralis
Yigal Ozeri
Yildiray Cinar
Yin Rongsheng
Yinka Shonibare
Yinka ShonibareNigerian Textile
Ynez Johnston
Yoann Lossel
Yoh Nagao
Yohann Schepacz
Yoji Shinkawa
Yoko Ono MusicianConceptual
Yokoyama Taikan
Yolanda Lopez
Yosa Buson
Yosa BusonWriter
Yoshida Hanbei
Yoshifuji
Yoshihiko Wada
Yoshijiro Urushibara
Yoshikawa Kanpo
Yoshikazu Yasuhiko
Yoshio Markino
Yoshitaka Amano
Yoshitomo Nara
Yoshitoshi ABe
Yoshitoshi Mori
Yoshiyuki Tomino
Yosl Bergner
Youqua
Youri Messen-Jaschin
Youssef Howayek
Yousuf Karsh
Yozo Hamaguchi
Yreina Cervantez
Yu Ben
Yu Youren
Yu Zhiding
Yuan Jiang
Yuan Yao
Yue Minjun
Yuki Katsura
Yuki Ogura
Yukihiko Yasuda
Yukimasa Ida
Yuko Shimizu
Yuko Tatsushima
Yumihiko Amano
Yun Du-seo
Yun Ge
Yun Shouping
Yun-Fei Ji
Yurakusai Nagahide
Yuri Ivanovich Pimenov
Yuri Shwedoff
Yuriko Yamaguchi
Yuumei
Yves Brayer
Yves Gaucher
Yves Klein
Yves Tanguay
Yves Tanguy
Yvonne Coomber
Yvonne Jacquette
Zacharias Martin Aagaard
Zacharie Astruc
Zack Snyder
Zack Stella
Zaha Hadid
Zaha HadidIraqi
Zahari Zograf
Zama V Helder
Zanobi Machiavelli
Zanobi Strozzi
Zao Wou-Ki
Zbigniew Nowosadzki
Zdenek Burian
Zdeněk Burian
Zdzislaw Beksinski
Zdzisław Beksiński
Zeen Chin
Zelma Blakely
Zena Holloway
Zeng Chuangxing
Zeng Jing
Zeng Mi
Zha Shibiao
Zhan Jianjun
Zhan Ziqian
Zhang Daqian
Zhang Dazhuang
Zhang Han
Zhang Hong
Zhang Huan
Zhang Hui
Zhang Jingna
Zhang Kechun
Zhang Lu
Zhang Sengyao
Zhang Shengwen
Zhang Shunzi
Zhang Shuqi
Zhang Wang
Zhang Wo
Zhang Xiaogang
Zhang Xuan
Zhang Yan
Zhang Yi
Zhang Yin
Zhang Zeduan
Zhang Zongcang
Zhao Chungxiang
Zhao Mengfu
Zhao Shou
Zhao Yannian
Zhao Yong
Zhao Yuan
Zhao Zhiqian
Zhao Zhiquan
Zhao Zuo
Zheng Guogu
Zheng Wu
Zheng Wuchang
Zheng Xie
Zhichao Cai
Zhong Weisheng
Zhou Changjiang
Zhou Chen
Zhou Fang
Zhou Jichang
Zhou Luyun
Zhou Shuqiao
Zhou Shuxi
Zhou Sicong
Zhou Wenjing
Zhu Da
Zhu Derun
Zhu Qizhan
Zhu Rui
Zhu Xinghua
Zinaida Serebriakova
Ziraldo
Zlatyu Boyadzhiev
Zoan Andrea
Zoe Buckman
Zoe Leonard
Zofia Stryjenska
Zoltan Boros
Zoltan KemenySwiss
Zoltán Joó
Zoltán Nuridsány
Zoran Music
Zoran Mušic
Zoran Mušič
Zou Yigui
Zou Zhe
Zoë Mozert
Zsolt Bodoni
Zsuzsa Máthé
Zvest Apollonio
Zwelethu Mthethwa
Zygmunt Vogel
Zygmunt Waliszewski
adrian ghenie
albert aublet
amano
amy sol
andrei riabovitchev
andrey ryabovichev
artist
ashley wood
casey baugh
charles vess
david rubín
derek zabrocki
disney
ferdinand knab
gaston bussiere
gustav dore
hajime sorayama
helen huang
hr giger
irakli nadar
james christensen
jean moebius giraud
jeonseok lee
jessica rossier
joseph christian leyendecker
karlkka
karolis strautniekas
katsuya terada
ken kelly
loish
lovecraft
mads berg
mark keathley
maxim verehin
mckadesinsanity
miles johnston
mucha
national geographic
nicoletta ceccoli
reyna rochin
ridley scott
roger deakins
rossdraws
senior artist
senior character artist
senior environment artist
sparth
stanley artgerm
stephen bliss
sung choi
sylvain sarrailh
teamLab
the Brothers Hildebrandt
theCHAMBA
tomasz alen kopera
wlop
wolfgang lettl
yoji shinkawa
zdzisław beksinski
Ángel Botello
Ásgrímur Jónsson
Édouard Debat-Ponsan
Édouard Detaille
Édouard Manet
Édouard Vuillard
Élisabeth Vigée Le Brun
Émile Bernard
Étienne Aubry
Évariste Vital Luminais
Óscar Domínguez
Ödön Márffy
Þórarinn B Þorláksson
Þórarinn B. Þorláksson
Ștefan Luchian


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moebius
! apocalypse landscape!!
! baron harkonnen!
! dream
! dream artgerm
! haute couture!
! holding in his hand !
! low contrast!
! monster anatomy!
! movie scene
! photorealistic
! split hair dye!
! two tone hair! dye
!! half and half hair dye!!
!! haute couture!!
!! looking at the camera!!
!! low contrast!!
!! muted colors!!
!! takeshi amano!!
!! very coherent!!
!!! cat!!!
!!! colored photography
!!! haute couture!!!
!!! shallow depth of field!!!
!!! very coherent!!! vector art
!!!!
!!!! cat!!!!
!!!! very coherent!!!!
!!!!! human eyes!!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!mila kunis
!!!anthropomorphic!!!
!!!award-winning!!!
!!!edward hopper!!! surrealism
!!!esao andrews!!!
!!award-winning!!
!!beautiful!!
!!full body portrait!!
!!highly detailed
!!highly detailed!!
!!highly detalied
!!natural beauty!!
!!no glasses
!!playing chess!!
!!posing_as_last_supper
!!wearing glasses!!
!!wearing modern glasses!!
!5 three eyed goddesses
!8k!
!anthropomorphic!
!dramatic !face
!dream abandoned rocket ship
!dream concept art
!dream los angeles
!duke nukem 3d
!face
!female
!pencil
!seductive eyes and face!
!subtle smiling!
!updo hair
#
# 0 1 7 9 6 f
# 1 digital painting of all time
# 2 2 3 3 e 6
# 4 2 0
# 6 6 9 3 fs
# 7 3 c 2 fb
# 9 8 fb 9 8
# character design
# de 9 5 f 0
# digital 2 d
# dystopian art
# e 4 e 6 2 0
# e 5 3 7 1 b
# film
# fututistic
# glazersout
# macro
# mecha
# mechanical design
# micro
# mist # horror # magic # spell
# myportfolio
# nofilter
# oc
# pixelart
# rococo
# sci - fi
# screenshotsaturday
# smokeweedeveryday
# vfxfriday
#1 digital painting of all time
#cameldeath
#conceptart
#green
#illustration
#macro
#micro
#oc
#pixelart
#pixelart:3
#screenshotsaturday
#trending
#vfxfriday
$100000000
& a dark
& a river
& all the world around you
& even a little mouse
& her expression is solemn
& jeehyung lee & wlop
& the artist has used bright
& the eyes & mouth are closed
& they all look terrified
' lost in a lucid dream
' ramona flowers '
''dragon breathing fire
''wallpaper of a phoenix
''wallpaper of a phoenix resting
'0 0 s nostalgia
'20
'2d graphic design
'9 0 s
'a human head made of glass
'action lines '!!!
'action lines'!!!
'dark sun'- campaign setting
'friends' episode screenshot
'friends' tv show episode
'groovy'
'he knows what you did '
'i'm so tired
'if all can begin again
'lone dark figure'!!
'silent hill '
'the red citadel
'untitled 9 '
'wherever you go
'white background'!!!
( ( ( ( ( bomb
( ( ( ( ( dan mumford ) ) ) ) )
( ( ( ( ( garbage truck
( ( ( ( ( jaguar e - type car
( ( ( ( ( knight rider kitt
( ( ( ( ( vermeer ) ) ) ) )
( ( ( ( ( warhammer 4 0 k
( ( ( ( 3 d render ) ) ) )
( ( ( ( kauai ) ) ) )
( ( ( ( volumetric light ) ) ) )
( ( ( alien ) ) )
( ( ( anime ) ) )
( ( ( aquarium bed ) ) ) )
( ( ( art deco ) ) )
( ( ( bauhaus ) ) )
( ( ( black witch hat ) ) ) )
( ( ( buses
( ( ( colorful ) ) )
( ( ( copper ) ) ) wire whiskers
( ( ( edward hopper ) ) )
( ( ( el dia los muertos ) ) )
( ( ( grant wood ) ) )
( ( ( horror art ) ) )
( ( ( in a dark
( ( ( jenny saville ) ) )
( ( ( kauai ) ) )
( ( ( koi colors ) ) )
( ( ( mickey mouse ) ) )
( ( ( rainbow ) ) )
( ( ( skeleton ) ) )
( ( ( soviet tank ) ) )
( ( ( surrealism ) ) )
( ( ( synthwave ) ) )
( ( ( volumetric lighting ) ) )
( ( ( wearing jeans ) ) )
( ( ( yoshinari yoh ) ) )
( ( 3 d render ) )
( ( abstract ) )
( ( barclay shaw ) )
( ( bauhaus ) )
( ( bit flip | god of ai art ) )
( ( brown skin ) )
( ( brush stroke ) )
( ( character concept art ) )
( ( cinematic ) ) concept art
( ( colored pen ) )
( ( cyberpunk ) )
( ( dark green
( ( dark skin ) )
( ( deep black eyes ) )
( ( dia de los muertos ) )
( ( dieter rams ) )
( ( dither ) )
( ( dithered ) )
( ( dr sues ) )
( ( emma lindstrom ) )
( ( extreme detail ) )
( ( eye glasses ) )
( ( fantasy plants ) )
( ( film noirs ) )
( ( fractal waves ) )
( ( generative ) )
( ( glitch art ) )
( ( glitch art pixel sorting ) )
( ( god king of ai art ) )
( ( habitat 6 7 background ) )
( ( illustration
( ( isometric ) )
( ( konstantin razumov ) )
( ( large black hat ) )
( ( mads berg ) )
( ( makoto shinkai ) )
( ( mask ) )
( ( misa amane # ) )
( ( misty atmosphere ) )
( ( photograph ) )
( ( railings ) )
( ( risograph ) )
( ( robot cyborgs ) )
( ( ruins of ancient rome ) )
( ( spiderwoman ) )
( ( stage lights ) )
( ( stippled gradients ) )
( ( strong dither ) )
( ( strong stippled shadows ) )
( ( sunbeams ) )
( ( theatrical ) )
( ( thomas kinkade ) )
( ( unreal engine ) )
( ( vibrating colors ) )
( ( victo ngai ) )
( ( wearing a long coat ) )
( 3 1
( alexander archipenko )
( all seeing eye )
( apocalyptic ) 8 k
( art deco )
( art fitzpatrick )
( art nouveau )
( asaf hanuka )
( bear wearing sunglasses
( beksinski )
( bladerunner 2 0 4 9
( boris johnson )
( brown skin )
( brush stroke )
( castle in the background )
( collage )
( colorful )
( conceptual art )
( cyberpunk )
( cyberpunk 2 0 7 7
( dia de los muertos )
( dieter rams )
( dog ) jumps from mountain
( dog ) jumps over hill
( dog ) looks like elephant
( el dia los muertos )
( eos 5 ds r
( evangelion )
( ferris wheel )
( few clouds )
( film noirs )
( gears of war )
( geometric )
( golden hour )
( golden ratio )
( golden ratio ) details
( greg rutkowski )
( h. r. giger )
( herman nitsch
( highly detailed figure )
( ilya kuvshinov )
( ivory black )
( land )
( leonardo da vinci )
( low key light )
( matte painting
( mechanical )
( moebius )
( norman rockwell oil painting )
( octane render )
( pirate with a bandanna )
( pixar ) and disney animation
( queen chess piece
( ray of sunlight )
( ready - made )
( redhead
( rembrandt )
( sasquatch
( shy smiling face )
( side ) profile
( sony a 7 r iv
( steampunk )
( streamline moderne )
( symmetrical )
( the blood meridian
( thomas kincade )
( ultra realistic
( ultra realistic )
( vibrating colors )
( visually stunning
( waitress ) girl
( watercolor )
(((((((((an overgrown forest
((((((((night)))))))) day time
(((((((no glow)))))))
(((((high tech
((((dirt brick road))))
((((exotic artifacts))))
((((library interior))))
((((lightning))))
((((occult art))))
((((octane render
((((unreal engine))))
((((volumetric lighting))))
(((alphonse mucha)))
(((colorful clouds)))
(((conglomerate!
(((dark colors)))
(((dragonfly)))
(((edward hopper)))
(((flowers on hair)))
(((forest)))
(((francis bacon)))
(((greek))) romanian
(((high tech
(((intricate)))
(((knight)))
(((low light)))
(((luke chueh)))
(((lumnious)))
(((mad))) elf princess
(((mist)))
(((neon palette)))
(((rusty)))
(((thomas kinkade)))
(((trees)))
(((underwater lights)))
(((unreal engine)))
(((volumetric lighting)))
(((yellow)))
((a beautiful fantasy empress))
((alphonse mucha))
((at the kid choice awards))
((blue))
((chains))
((dark fantasy
((dark fantasy))
((fish eye))
((forest))
((gears))
((greenish blue tones))
((gta 5 screenshot house))
((habitat 67 background))
((in a super market costco))
((intricate))
((messy))
((mist))
((monolith))
((monster))
((neon colors))
((octane render
((octane render))
((oversaturated))
((photorealistic))
((pink))
((portrait))
((purple))
((raytracing))
((red)) baggy eyes
((restrained))
((robot))
((rocks))
((rust))
((sharp focus))
((skull))
((space nebula background))
((still life))
((sunset))
((synthwave))
((tatsuro yamashita))
((the mona lisa))
((tiger))
((trees))
((unreal engine))
((water color))
((waves
((wearing aristocrat robe))
((woodblock))
((yellow magic orchestra))
((zerator))
(1 as december
(12x) extremely pale white skin
(3 are winter
(38 years old)
(a bowl of fruit)!!!!!!!!!
(abstract)
(aesthetics)
(akira 1988) highly detailed 8k
(alphonse mucha)
(anime girl)
(anthropology of wonder)
(art nouveau)
(art station)
(attack on titans anime)
(beautiful eyes)
(beautiful) girl
(bee)
(bolts)
(books)
(brave police j decker)
(by tom purvis)
(canon
(cheese)
(colonial expedition)
(colour) eyes
(cybernetic)
(cyberpunk 2077)
(dark shorter curly hair)
(dia de los muertos)
(digital art)
(doctor)
(empty black void)
(eos 5ds r
(ethereal)
(exotic artifacts)
(extremely detailed
(fantasy violin)
(fantasy)
(fire)
(fish eye)
(flowers)
(french bande dessinée)
(gears)
(golden hour)
(good looking )
(grin)
(h.r giger)
(heart)
(high contrast)
(hr giger)
(inktober)
(jean jules linden)
(light orange mist)
(mist filters)
(mist)
(monster)
(moon in the background)
(neon colors)
(neotokyo)
(night)
(octane render
(octane render) fantasy style
(oil) painting
(pen)
(pink colors)
(rust)
(screen print)
(sfw) safe for work
(sharp)
(smoke)
(snow)
(snowy new york in background)
(sony a7r iv
(steampunk)
(synthwave)
(tom cruise!!!!!) jester costume
(tropicalism)
(unreal engine)
(vines)
(waterfall)
(webcomic)
(winking)
) ominous vibes
* * *
* * * * *
* colour splash *
**cinematic
+++ +++ quality j.c. leyendecker
++++ super veiny hands
+81 magazine
-
- - ar 9 : 1 6
- 12p
- 6
- 8
- 8 k
- 9
- 9 9 9 mm macro lens
- 9 9 mm macro lens
- ar 1 6 : 9
- g
- h 1 0 2 4
- h 6 4 0
- h 7 0 4
- h 7 6 8
- h 768
- h 8 0 4
- h 8 5 0 - w 6 0 0
- i
- n 4
- n 5
- n 6
- n 8
- n 9
- photorealistic
- s 1 5 0
- signature
- w 1 0 2 4
- w 7 6 8
--ar 16:9
--n 6
--width 1600
-4
-640
-h 1024
-h 512
-h 640
-h 704
-h 768
-h 960
-n 2
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-n4
-n9
-step 50
-w 1024
-w 512
-w 768
. background: jungle river
. dramatic angle
. ethereal lights
. iridescent accents
.. if only i could sleep
..'
.ai
.eps
.highly realistic
/!\ the sorceress
/r/earthporn
/r/pixelart
/r/razer
0
0 0 0
0 0 0 bc
0 0 0 years ago
0 0 s movie
0 brainsucker render
00% artistic
000
000 battle
000 years ago
000 — википедия
1
1 - bit
1 / 1 0 0 0 sec shutter
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1 / 2 headshot
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1 / 4 headshot
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1 / 6 katsuya terada
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1 0
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1 0 / 1 0
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1 3 3 4 building
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1 3 5 mm nikon f / 2
1 3 5 mm nikon portrait
1 3 5 mm!!
1 3 century style
1 3 ghosts movie artwork
1 3 mm film
1 3 mm film color grading
1 4 4 p
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1 5 th century
1 5 th century spanish soldier
1 6 - 3 5 mm
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1 6 0 0 s
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1 6 6 7. mandelbulb 3 d
1 6 9 5
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1 6 : 9 ratio
1 6 bit
1 6 bit colors
1 6 bits
1 6 century style
1 6 colors
1 6 k
1 6 k hd
1 6 k resolution
1 6 mm
1 6 mm f 1. 4 lens
1 6 mm film
1 6 mm film vignette
1 6 mm lens
1 6 mm wideangle lens
1 6 th century
1 6 x 1 6
1 6 years old
1 7 - year - old anime goth girl
1 7 - year - old boy thin face
1 7 - year - old goth girl
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1 7 th century duchess
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1 8 th century manor
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1 8 th century south america
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1 8 th century. oil on canvas
1 8 year limit
1 8 yo
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1 : 1 album artwork
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1 : 1 brutal design
1 : 1 hyper illustration
1 as february)
1 as january
1 female
1 figure only
1 glowing bridge crossing river
1 male
1 petapixel image
1 red shoe 1 blue shoe
1 st place
1 st winner
1 staircase
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10
10 bit colour
10 mm
10 years old
10% surreal
100
100 mm lens
100 mm sigma art
100-300mm canon f/5.6
1001 nights
100k
100lb cardstock
100mm
100mm lens
100mm sigma lens
1024x1024
1032106751
105mm
108 megapixels
1080p
1080p 4k resolution
1080s
10k
10k resolution
10mm
10mm camera
10mm fisheye
10mm lens
10th doctor who
11 years old
1120mm lens
1128x191 resolution
12
12 figures
12 megapixels
12 years old
120 black and white film
120 degree view
1200 dpi
120k resolution
120mm
120mm black and white photograph
123456789
1285445247]
128k
12in action figure
12k
12k ursa
12mm
12mm wide-angle
12mpx
12p
12th century apothecary shop
1309988900
130mm
135 mm
135mm
135mm camera lens
135mm lens
135mm nikon f/2
136412351
14 yo berber boy
1400s
144p
144x144 canvas
1450
14k gold wire
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150 mm lens
1500
1500's
1500s oil portrait
1505
15081959 21121991 01012000 4k
1508195921121991010120004k
150mm
150mm lens
150mp
155 cm tall
15mm
15mm lens
15mm wide angle close up
15th century
15th century european fine art
16 bit
16 bits
16 bits graphic
16 colors
16 thousand
16 thousand - n 6
16-bit
16-bit rgb
1600 iso
1600 south azusa avenue
1614572159
16384k film
1647
165 cm tall
1666
1683
16:9
16bit
16bits
16bits videogame
16k
16k 3d
16k hd
16k hyper realistic photograph
16k render
16k resolution
16k resolution:0.6|people
16k resolution:0.8
16k uhd
16k ultra-realistic 3d
16k upscaled image
16mm
16mm f/1.4
16mm film
16mm film live soft color
16mm film soft color
16mm grain
16mm lens
16mm photography
16mm soft light
16mm wide angle lens
16th century
16th century painting
1700s
1759
1775 oil painting
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1787
1789
1790
1792
17mm
17th century
17th century dutch attire
17th century european fine art
17th century oil painting
17th century painting
17th-century
18 mm lens
18 years old
1800
1800 photograph
1800s
1800s painting
1800s photo of a deformed
1801446143
180mm f/1.8
1811
1820
1824
1825-1829
1840
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1840580735
1840s
1848
1849
1850
1850s
1852
1856
1857
1860
1860 photograph
1862
1863
1865
1866
1868
1870s
1871 punch magazine cartoon
1872
1875
1878
1880
1880 photograph
1882. epic
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1883
1884
1885
1889
1890
1890 photo
1890s
1894
1895
1896
1898
18mm
18mm lens
18th century
18th century art
18th century drawing
18th century oil painting
18th century painting
18th century photograph
19 century
19-year-old girl
1900
1900 illustration artwork
1900's
1900's photography
1900s
1900s photo
1900s photograph
1900s photography
1900s picture
1900’s photo”
1901
1902
1904
1905
1907
1908
1910
1910 photography
1910 polaroid photo
1910 polaroid photography
1910s
1910s architecture
1910s film scene
1910s film still
1910s paris
1911
1913
1914
1915
1916
1918
1919
1920
1920 style
1920'
1920's london street
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1920s geisha
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1920s photograph
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1920s style
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1921
1923
1924
1925
1929
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1930s
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1930s film still
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1930s style clothing
1932
1933
1934
1935
1935 bmw m1 stratos
1936
1939
1940
1940's musician playing drums
1940's photography
1940s
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1940s propaganda poster
1942
1943
1944
1945
1946
1947
1948
1950
1950's
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1950s family
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1950s photograph
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1950s retro cactus robot
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1950’s
1951
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1954
1955
1956
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1958 ferrari 250 gt
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1960's
1960's olympics
1960's olympics footage
1960's rankin bass stop motion
1960s
1960s advertisement
1960s american world's fair
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1960s color photograph
1960s flower power hippy
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1960s spy
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1960s technicolor
1960s-era
1961
1961”
1962
1962 soviet
1963
1964
1964 jerry lewis nutty professor
1965
1966
1967
1968
1968 cut out collage
1968 psychedelic
1968 science fiction tarot card
1968 soviet
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1969
1970
1970 furniture
1970 photo from live magazine
1970 poster
1970'
1970's
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1970s cinema camera
1970s comic art style
1970s fashion
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1970s philippines
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1971
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1972 soviet
1973
1973 photo from life magazine
1974
1974 porsche 911
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1976
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1976 poster
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1978 color fellini film
1978 cut out collage
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1979 omni magazine
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1980
1980 cars
1980 cinematography
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1980s photo
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1981
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1982
1982 color fellini film
1983
1984
1985
1985 cheverlot k20 c10
1985 vector w8 twin turbo
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1987 photograph
1987 pulp art
1987 video game boxart
1988
1988 video game screenshot
1989
1990
1990 manga panel
1990 photograph
1990 video game screenshot
1990's
1990's diner full of zombies
1990's photo
1990's sears portrait photo
1990s
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1990s 1992 sega genesis box art
1990s anime
1990s arcade machine
1990s bedroom
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1990s photograph
1991
1991 video game screenshot
1992
1992 video game concept art
1993
1993 anime
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1994
1994 vhs quality
1994 video game screenshot
1995
1995 movie
1996
1996)
1997
1997 )
1998
1998 photo
1999
1999 photograph
19th century
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19th century painting
19th century wood-engraving
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19th-century
19xx
19xx :2 akira movie style : 8
1:1 album artwork
1:87
1k hd
1km tall
1x
2
2 - bit
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2 0
2 0 % pearlescent detailing
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2 0 0 1 : a space odyssey
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2 0 1 2 watermelon diner
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2 0 1 9 anime
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2 0 2 1 formula 1 audi car
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2 0 2 2 anime style
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2 0 2 3 senior graduation shirt
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2 0 2 5 popstar comeback single
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2 0 5 0 scary movie
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2 2 year old mary louise brooks
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2 4 mm anamorphic lens
2 4 mm iso 8 0 0
2 4 mm iso 8 0 0 color
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2 4 mm lenses
2 4 mm wide angle
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2 5 th anniversary music video
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2 8 mm color
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2 8 mm macro photo
2 8 year old mary louise brooks
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2 angels
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2 arms
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2 arms and 2 legs!
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2 d depth map
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2 d full body lion
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2 wings
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2.35:1 ratio
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20 century photography
20 megapixels
20 years old
20% pearlescent detailing
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200 mm
200 mm lens
2000
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2000s
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2001
2001 a space odissey
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2004 photograph
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2005 blog
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2006 photograph
2006 youtube video
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2007 blog
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2008 cinematography
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2009 cinematography
2009)
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200mm
200mm f/2.0
200mm focal length
200mm focus
200mm lens
200mm prime lens
200mm telephoto
200mm wide shot
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2010s
2011
2011 comic-con
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2013
2013 scion tc
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2015
2015 alfa romeo 4c
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2017
2018
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2019 anime screenshot
2019 bmw m5 wagon
2019 trending photo
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2020 video game screenshot
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2023 4k
2024
2025
2025 popstar comeback single
2029
2030
2030s
2040
2045
2049
2050
2063300223
2070
2070s
2077
2099 neo-tokyo
20b parameters
20k
20mm
20mm camera
20mm film
20mm lens
20th century
20yo
21 savage
21 years old
210mm ƒ / 64 zeiss tessar
21:9
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21st century monk
22 megapixels
22 year old mary louise brooks
22 years old
2223194009
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22mm
22mm lens
23 years old
24 - year - old man
24 35 mm
24 mm
24 mm lens
24 mm shot
24 year old female model
2400 dpi
240p
240p footage
2435mm
24k
24mm
24mm anamorphic lens
24mm f/1.4
24mm f8 aperture
24mm film
24mm hasselblot photography
24mm iso 800
24mm iso 800 color
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24mm lens
24mm lenses
24mm shot
24mm tilt-shift
24mm wide angle anamorphic
24mp
25 years old
250 mm sharp lens
2506921471
250mm sharp lens
250mp
256 colors
256435456k film
256x256
256x256 pixels
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25mm f/1.7 asph lens
25mm f/1.8
25mm film grain
25mm lens
25yo
26 year old man on a sailboat
2654465279
268435456k film
26mm
27
27 years old
2700k
2708519935
2717433015
278122496
28 year old mary louise brooks
28 years old
2806927615
28mm
28mm dramatic photo
28mm heroic scale
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28mm macro photo
28mm scale
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2b from nier automata
2b nier automata
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2dcg
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2k
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3
3 - d
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3 / 4 view
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3 / 4 view of woman with wings
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3 / 4 wide shot
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3 0 0 0 ( dr. john a. zoidberg )
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3 5 mm!!!!!! lens
3 5 year brazilian mother
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3 actors on stage
3 am
3 are spring
3 are summer and 3 are autumn
3 are summer and 3 are autumn)
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30 mm
30 mm lens
30 year old man
30 year old man :: athletic
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30-year-old french woman
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300 dpi
300 mm depth of field bokeh
300 the movie
300mm
300mm depth of field bokeh
300mm lens
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300mm telephoto lens
303
30mm
30mm film
30mm lens
30mm photography
30s
3127318783
3156350975
32 megabit
32 megapixel
32 years old
32-bit
32-bit pixel art
32-year - old man
3237315913
3274589695
3278750727
3284479999
32k
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32mm
32x32
3389861569
33mm
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35 mm
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35 mm lens
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35 mm product photo”
35 years old
350mm f/1.4l lens
3580780586
35840k film
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35mm film!!!!!!!!
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35mm!!! 1990
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360*
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36864k film
38 years old
3840 x 2160
3840x2160
38mm photograhpy
38mm photography
3945074687
39mm lens
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84mm
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8k highly detailed ❤️‍🔥 🔥 💀 🤖 🚀
8k hq
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8k!
8k!!
8k!!!!!
8k''
8k))
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8l
8mm
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8mm fisheye lens
8mm photography
9
9 / 1 1
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9 0 - s
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9 0 degrees fov
9 0 mm
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9 0 s
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9 0 s bedroom
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9 0 s polaroid
9 0 s romantic movie
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9 0's
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9 4
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9 9 9 9 inside of a dark cave
9 9 9 mm lens
9 9 designs
9 brilliant peacock tails
9 k
9 peacock tails
9 steel barrels in a graveyard
9 years old
9/11
90 - s
90 - s fashion
90 60 90
90 degree spotlighting
90 degrees fov
90's
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90's photo
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90mm
90mm lens
90s
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9126k film
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985530902
99 designs
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999 mm lens
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9gag
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: 5 stylish
: :
: jaiye farrell art
: kanye west wearing vr goggles
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: psychedelic ski resort
:10 gas grenades
:14 80s anime style
:14 flcl + akira
:14 flcl + jet grind radio
:3
:6
:6 gas grenades
:6 smoke grenades
:9 detailed face: 8
::
:: madison beer
:: morning
:: nixri
:: octane render
:: rossdraws
:native american shamen fantasy
:psychedelic ski resort
; visual novel
; weekly shonen jump issue 1 4
; weekly shonen jump issue 14
; wide shot
;cinematic lighting
;weekly shonen jump issue 14
< full body robot >
<3d eco brutalist
<hd
<mmorpgs scene
<perfect face>
<pointé pose>;open mouth
<wearing brown leather armor>
?black apples
@ cronobreak on twitter. com
@ zbrush
@zbrush
I cant believe how beautiful this is
Silent Hill
[ 4 k digital art
[ 4 k digital art ]!!
[ 4 k photorealism ]
[ 4 k photorealism ]!!
[ 4 k photorealism ]!!!
[ 4 k synthwave art style ]!!
[ [ award winning ] ]
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[ bioluminescent colors ]!!
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[ digital art ]!!
[ everything is floating ]!!!
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[ floating ]!!
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[ overhead view ]!!
[ overhead view of a table ]!!
[ realistic photo ]!!
[ realistic photography ]
[ red dead ]
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[ scifi
[ shards
[ synthwave art style ]!!
[ synthwave art style ]!! ]!!
[ tarot card ]!!!!!
[ theatrical ]
[ trending on cgsociety ]!!
[ western film ]
[ zero - gravity ]!!
[32k hd]^10
[[[[grinning evily]]]]
[[blood]]
[[empty warehouse]] background
[[fantasy]]
[bioluminescense
[epic
[explosions and fire]
[sculpture] and [hyperrealism]
[sirius]
\ 4 \ k
\!cinestill 50d! film photo
\'emulador\'
\'obey\'
\'the end
\'the soul creates
_3d-terms_
a 15 foot tall
a 18th century
a 1980s goth nightclub in soho
a 35mm photo
a ancient crashed spaceship
a backpack
a baddass dragon
a bald
a barn at an iowan farm
a beautiful
a beautiful artwork illustration
a beautiful buddhist mandala
a beautiful detailed orixa
a beautiful fantasy empress
a beautiful female wizard
a beautiful fox lady
a beautiful kitsune woman
a beautiful land art of a large
a beautiful landscape
a beautiful mine
a beautiful painting
a beautiful pathway in a forest
a beautiful pole dancing fairie
a beautiful sorceress
a beautiful teen-aged girl
a beautiful tree
a beautiful victorian woman
a beautiful woman
a beautiful woman in white
a beautiful woman warrior
a beautiful woman with long
a beautiful young woman
a beuatiful matte painting
a black choker
a black cloak
a black dieselpunk policewoman
a black man with long curly hair
a black tshirt. high key
a blond
a blonde emerald warrior
a blue dress
a blue skirt
a book
a bot in the game super mario 64
a boy
a boy made out of gold
a brick cabin in the woods
a bright
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a brightly coloured
a broad shouldered
a bulletproof vest
a bustling magical town
a busy arcade
a cake with all planets on it
a cannon mounted on his back
a car
a cat
a cat is smiling
a cat sipping coffee
a cat sitting in a chair
a cat swimming in water
a cat wearing a spiked helmet
a cathedral under an overpass
a centaur
a character based on a haggis
a christian jedi
a city in the clouds
a clockwork orange
a close up shot
a close up shot of a crying eye
a close-up
a clown floating in space
a cold
a collage
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a colorful
a confident smile
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a cpu with human brains
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a cyborg cat
a cyborg meditating
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a dark phoenix
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a daub of cold blue
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a detailed
a devilish grin on his face
a digital dreamscape
a dingo mascot
a disembodied
a disembodied head screaming
a diver is under the sea
a dog
a door you must never open
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a dragon made of clouds
a dragon made of machine parts
a dragon statue made of jade
a dragon with a hat
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a druid
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a face
a fantasy comic book style
a fantasy forest
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a fat
a ferret as superman
a few roses
a field full of crows
a firetruck at night
a fish climbing a tree
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a flaming forest of trees
a folding knife
a forest with bunnies
a friend in need
a friendly wisp
a frontal portrait of a delicate
a fruit basket
a full portrait of nordic female
a full-color airbrushed
a gangly british man
a george miller film
a ghetto in germany
a gigantic
a gigantic wall
a girl playing tennis
a girl with blonde hair
a glaceon ice princess
a glowing halo
a goat
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a green
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a group of people
a group photo of a seal
a hand
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a handsome man，black short hair
a happy meal toy of hatsune miku
a harmonious integration
a head-on
a high angle shot
a highly detailed
a hooded killer a cane
a hookah smoking caterpillar
a horned
a huge
a huge glass tank
a huge radar
a human
a human bard
a human head
a human male paladin
a human-like juicy peach
a humanoid mosquito
a humanoid mosquito wolf
a humanoid thistle monster
a hyper realistic
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a lake between mountains
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a laser rifle
a liminal space
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a lonely woman
a long shot
a long-shot
a long-shot from front
a magician's chamber
a maid in a magical forest
a majestic gothic dragon
a majestic gothic indian dragon
a male elf
a man
a man sitting on a jetty
a man wearing a backpack
a man wearing a black jacket
a man wearing golden mask
a massive cathedral in a forrest
a massive celestial giant god
a massive tornado approaching
a master piece of storytelling
a masterpiece of storytelling
a mcdonalds gundam
a medieval
a medieval castle
a medieval city
a medieval keep
a medium shot
a mermaid in the sea
a messy
a mid-century modern collage
a middle aged elf
a middle-shot from front
a minotaur wolf
a monumental
a moogle in the legend of korra
a mountain look like a women
a mouse in a gothic atelier
a multidimensional cozy tavern
a muscular
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a mysterious landscape
a mystic river
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a mystical temple
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a painting of a cat
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a pair of ribbed
a palace with a thousand long
a pale skin
a palm tree
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a paper cutout garden
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a park
a person at a music festival
a person standing in front of a
a person with a raccoon head
a phoenix
a photo of a disheveled man
a photo of a frog with a hat
a photo of a lake on a sunny day
a photo of a man
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a photo of an old opened camera
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a picture of a klingon musical
a picture of a klingon opera
a pig
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a pirate
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a plaster on her cheek
a pool inside the giant palace
a portal to the depths
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a portrait of @hypnos_onc
a portrait of a blue eye girl
a portrait of a british man
a portrait of a girl
a portrait of a male elf
a portrait of a man
a portrait of a plump woman
a portrait of a suicidal girl
a portrait of a woman
a portrait of an android
a portrait of an elf
a portrait of dwayne johnson
a portrait of gordon ramsay
a portrait of issey miyake
a portrait of josh brolin
a portrait of judy hopps
a portrait of junkrat
a portrait of lana del rey
a portrait of link
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a portrait of the character
a potrait of a beautiful
a propaganda
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a purple and white dress uniform
a purple fish
a purple suit jacket
a python programmer's despair
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a ragdoll cat windsurfing
a red bearded guy driving a gt3s
a red bow in her hair
a red cape
a red dragon
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a retropunk naturewave defender
a river flowing with waterfall
a road leading to the lighthouse
a robot
a robot dog
a robot reading a book in a park
a room
a round minimalist behind
a rubik's cube
a rustic album cover of a clock
a sad
a sad cheese puppet
a salt&pepper goatee
a screenshot of a rusty
a sea battle
a seance
a sexy blonde warrior
a sexy maid in a magical forest
a shaman holding up the universe
a sharknado
a ship lost in a storm
a ship on a deserted island
a short and average built
a sick cat laying in a bedroom
a silver haired mad
a single
a skeleton smoking a cigar
a skeleton winning a marathon
a skull of an alien creature
a small
a small medieval village
a snowy day in the forest
a soldier aiming a gun
a sorcerer casting a fireball
a sorceress casting a ice ball
a spaceship through the nebula
a spotted dove flying
a steam wheeler from 1880s
a steampunk beautiful goddess
a steampunk city
a steampunk store
a steampunk tyrannosaurus
a still life of a microwave
a still life of a robot
a still life of a rusty robot
a still of a happy
a still of an ethereal
a still of kowloon
a stunning
a stunning masterpiece
a stunning portrait of a goddess
a stunning young ethereal figure
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a sunny bedroom
a super-smart
a surreal
a surreal dream landscape
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a sword stuck in the ground
a table full of candy
a tall
a tall tree
a tattoo of a fox
a teen biopunk cyborg
a teen black cyborg
a teen cyberpunk cyborg
a teenage girl cheerleader
a telephone receiver in hand
a temple
a thin mouth
a tiara with emeralds
a tiger
a tim burton film
a train in red dead redemption 2
a translucency
a tshirt. blender art
a tvr sagaris
a unicorn
a very beautiful berserker woman
a very macular woman in white
a very sad man
a very tall
a violent storm at sea
a wanderer on a mountain
a wandering mind
a weird circus
a white wolf
a wide full shot
a wide open courtyard in an epic
a wide shot
a wide-angle
a wild
a witch
a wizard battling a tiger
a wizard's apothecary
a woman
a woman floats in midair
a woman holding an orb
a woman walking
a woman wearing red high heels
a woman's face
a woman's profile
a wooden
a yellow sun shining down
a young asian woman
a young female shaman
a young female wizard
a young man
a young woman
a young woman as genghis khan
a-1 pictures
a-channel
a-pose
a24
a24 cinematography
a24 film cinematography
a24!film cinematography
a24film cinematography
aaa game
aaa graphics
aaaa achievement collection
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aang
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aardman animation
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abaddon and magali villeneuve
abalone
abandon the ego
abandoned
abandoned asylum
abandoned barn in the background
abandoned building
abandoned buildings
abandoned bus stop
abandoned car garage
abandoned cars
abandoned castle
abandoned castle in the sky
abandoned circus
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abandoned city with graffiti
abandoned cottage
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abandoned dirty tiny rocket ship
abandoned factory
abandoned flying future city
abandoned gas station
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abandoned hospital
abandoned hospital room
abandoned house
abandoned house interior
abandoned in a desert
abandoned japaense village
abandoned laboratory
abandoned mall at night
abandoned mall in the 1 9 8 0 s
abandoned night hangar
abandoned on mars
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abandoned photograph
abandoned places
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abandoned railroads
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abandoned ruins landscape
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abbey lee kershaw as emma frost
abbey warhammer battle
abbeys
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abcdefghijklmnopqrstuvwxyz
abdomen
abdominal muscles
abduction
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ability image
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aboriginal art
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aboriginal engraving
about
about 3 5 years old
about 35 years old
about to consume you
about to enter doorframe
about to step on you
above a forest
above a village
above city
above lighting
above low layered clouds
above lush garden and hot spring
above meaning
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above the clouds
above the family room
above the waist
above view
above water
abraham lincoln
abraham teerlink
abram games
abrupt
abs
absence makes heart grow fonder
absent father
absent friends
absinthe
absolute chad
absolute peace and quiet
absolute peace and quiet ground
absolutely
absolutely gorgeous
absolutely nothing
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absolutely outstanding image
abstract
abstract 3 d artwork
abstract 3d artwork
abstract 3d rendering
abstract acrylic painting
abstract album cover
abstract and / or modernism
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abstract art representing data
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abstract background
abstract black hole in space
abstract black leather
abstract black oil
abstract blocks
abstract brush strokes
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abstract colors
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abstract flat colour
abstract forms and shapes
abstract fractal art
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abstract!
abstract!!
abstract!!!
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abundant fruition seeds
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abzu
acab
acacia trees
academia
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academic clothing
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academician prokhor zakharov
academicism
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acanthus
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accent lighting
accent lighting : : peugot onyx
accent white lighting
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accents of red
accentuated feminine features
accentuated hips
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acceptance
accessible for the disabled
accessories
accident
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accompany cory chase
accompany hybrid
accompanying cory chase
accompanying hybrid
according to european art
accretion disk
accurate
accurate anatomy
accurate and detailed
accurate animal imagery
accurate bodies
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accurate boroque
accurate brown eyes
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accurate court
accurate depiction
accurate detail
accurate detailed face
accurate details
accurate face
accurate faces
accurate facial detailing
accurate facial details
accurate features
accurate features )
accurate fictional proportions
accurate fingers
accurate geography
accurate green eyes
accurate hands
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accurate jimin face
accurate klaus schwab face
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accurate portrait of a bob dylan
accurate portrayal
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accurate roads
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accurate skin textures
accurate textures
accurate to egyptian tradition
accurate ultra realistic faces
accurate walk cycle
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accursed
ace attorney
ace attorney style
ace card
ace combat
ace of spades
acecombat
acescg colorspace
achenbach
achille mauzan
achingly beautiful
achluophobia
acid
acid bleeding deep colors
acid color smoke
acid colors
acid graphics
acid graphix
acid house
acid leaking from mouth
acid pixie
acid pixie acid pixie
acid pooling inside
acid pooling on the floor
acid rain
acid rain effect
acid rains
acid rains. the sacred nipple
acid rich colors
acid trip
acid wash layering
acid-green sneakers
acids
acidwave
acne
acnh
acorns
acoustic guitar
acoustic information
acrace catoon
acrilic paint
acrion
acrnym
acrobat
acrobatic
acrobatic moveset
acrobatic pose
acronym
acronym p31-ds pants
acrophobia
across an alien landscape
across holding a hand
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acroteria
acryclics
acryl on canvas
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actias luna
acting
acting headshot
action
action - adventure
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action photograph
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action pose : : spotlight
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activision blizzard
activision blizzard style
activity play centre
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acton figure
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actor liam hemsworth
actors
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actual photo
acupuncture treatment
acurate
ad
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adafruit
adam
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adam and eve inside the brain
adam and eve versus godzilla
adam burn
adam driver
adam driver as a centaur warrior
adam driver as master chief
adam driver behind
adam duff lucidpixul
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adam miller
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adam narozanski
adam ondra
adam sandler
adam smasher
adam varga
adam warlock
adam west as batman 2022
adamantium
adamantly defined abs
adaptation
adapted to a drier climate
adaptive armor
adar darnov
add a glow around subj. edge
add text: auto repair
addams
added detail
addiction
addidas
adebanji alade
adelaide labille - guiard
adele
adeptus astartes
adeptus custodes centurion
adeptus mechanicus
adeptus mechanicus!
adeptus mechanicus!!!
adhd
adi granov
adi meyers
adidas
adidas painting
adim kashin
adinkra symbols
adjacent hallways
adjustment of temporal matrix
admiring her own reflection
adobe
adobe - illustrator
adobe after effects
adobe eps
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adobe lightroom
adobe photoshop
adobe premier pro
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adolf hiremy - hirschl
adolf hitler
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adolf wolfli
adolf wolfli and donato giancola
adolf wölfli and donato giancola
adolfo hohenstein
adolphe bouguereau
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adonis belt
adopt
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adorable appearance!!!
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adorable friendly robot
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adorable outfit
adorable!!!
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adorned pillars
adorned with all kind of plants
adorned with demon skulls
adorned with precious stones
adornment
adornments
adoryanti
adreas rocha
adrian
adrian borda
adrian monk
adrian smith
adrian smith concept art
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adrian tomine
adrian wilkins
adriana chechik
adriana dxim
adriana lima
adriano celentano as terminator
adrien brody
adrien girod
adrien henri tanoux
ads
adularia
adult
adult character
adult dog
adult man
adult pair of twins
adult swim
adult swim style
adult video store
adut akech
advance civilization
advanced
advanced anime digital art
advanced art
advanced automobile
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advanced digital anime art ”
advanced digital art
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advanced technology flight suit
advanced warfare
adventure
adventure game
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adventure hyper realistic render
adventure movie
adventure playground
adventure playground accident
adventure time
adventure time cartoon
adventure time colour palette
adventure time style
adventurer
adventurer outfit large cloak
adventuring
adventuring gear
adventuring party
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adversitement
advert
advert logo
advertisement
advertisement photo
advertisement photography
advertisement poster
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advertising lighting
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adward winning
ae 8 6
ae 86
aela the huntress
aenaluck
aenami alena
aeon flux
aeon flux style
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aerial combat
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aerial photograph of skyscraper
aerial photography
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aerial shot from the drone
aerial silk
aerial spaces
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aerial view cinestill 800t 18mm
aerial view from above
aerial view of a city
aerial view of a cyberpunk city
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aerial view of moscow kremlin
aerial view top down
aerial view. hand stitching
aerial viewyoji shinkawa
aerith gainsborough
aero dynamic
aerochrome eyes
aerodynamic
aerodynamic body
aerodynamic design
aerodynamic frame
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aerodynamic!!!!!!
aerogel ios ui concept
aeromorph
aeron alfrey
aeroplane
aestetic
aesthehic
aesthetic
aesthetic / a riot in mars
aesthetic art
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aesthetic clouds in the sky
aesthetic color palette
aesthetic cute with flutter
aesthetic cyberpunk
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aesthetic face
aesthetic film grain
aesthetic lighting
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aesthetic octane render
aesthetic octane render!
aesthetic portrait
aesthetic shot
aesthetic siamese cat
aesthetic ”
aesthetic!
aesthetic!!
aesthetic!!!
aesthetic!!!!
aesthetic!!!!!
aesthetic!!!!!!
aesthetic!!!!!! female genie
aesthetic!!!!!!!
aesthetic!!!!!!!!
aesthetic!!!!!!!!!!
aesthetically pleasing
aestheticfemale genie
aestheticly inspired
aesthetics
aetherpunk
aetherpunk airbrush digital art
aetstationhd
afar
affable ， wenjun lin
affinity photo
afghan girl
afghan warrior
afofuturism
afp
afraid
afremov
afremov leonid
africa
african
african aaron paul
african ameera al taweel
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african american elegant girl
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african argonian! body! in full
african argonianbodyin full
african arts
african arts magazine
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african elephants in the jungle
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african fractals
african god mask
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african jesse pinkman portrait
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african steppe
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afro
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afro comb
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afro hair
afro made of flowers
afro samurai anime style
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afshar
after a battle
after a riot
after all
after creating humans
after effects
after hours
after life
after rain
after rain and no girls
after the rain
after the storm
aftereffects
afterlife
aftermarket parts
aftermath
aftermath of a huge battle
afternoon
afternoon hangout
afternoon light
afternoon lighting
afternoon lights
afternoon sun
afternoon sunlight
afternoon sunshine
afternoon time
again and again
against a deep black background
against a red curtain
against a stormy sky
against a winter garden
against all odds
against dark background
against light
against the backdrop of trees
agartha
agate
age
age 2 0
age 3 0
age 3 5
age 30
age lines
age marks
age of empires
age of empires 2
age of empires 3
age of empires ii
age of sigma art
aged
aged 13
aged 2 5
aged 25
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aged and weathered
aged armor plating
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aged photo
aged shaggy ex military soldier
aged turtle
agent 47 in a scene from naruto
agent cooper
agent orange
agent pekka
agents from the matrix movie
agfa akurit
agfa film
agfa isopan iso 2 5
agfa isopan iso 25
agfa photo
agfa vista 800 film
aggresive
aggresive strokes
aggression
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aggressive head
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agharta
agile
aging
agitated
agnes cecile
agnes pelton
agonizing
agony
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agoraphobia
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agressive
agrigento
ah puch
ah yes
ahegao
ahegao face
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ahhhhhhh
ahmad merheb
ahri
ahri from league of legends
ahsoka tano
ai
ai - generated art
ai art
ai art is souless
ai assisted digital painting
ai biodiversity
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ai limbo
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ai self portrait
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ai yazawa
aida muluneh
aiden gillen
aiden pearce
aikido
aiming
aiming a bow and arrow
aincrad
ainz ooal gown
aion
air
air and fire
air and tornado theme
air brush
air brush art
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air bubbles
air conditioner
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air force jumpsuit
air is being pushed around him
air jordan 1 high
air particles
air pollution
air ships
air shot
air support
air technology
airbnb
airborn studios
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aircraft
aircraft carrier
aircraft carrier scene
aircraft wings on back
airforce gear
airing in 2 0 2 3
airplane
airplane cockpit
airplane hanger background
airplane in the sky
airplane view
airplane window view
airplanes
airplanes bombing the beach
airpods
airport
airscape
airship
airship in steampunk
airship in the sky
airships
airships in the sky
airships on background
airsoft close quarter combat
airsoft cqb
airsoft electric pistol
airsoft gun
airsoft handgun
airy
airy colors
airy landscape
airy midnight theme
airy theme
aishwarya rai
aisles of aquariums
aivazovsky
aizome patterns
aji de gallina
ajna chakra
ak - 4 7
ak 1 2
ak 12
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ak 47
ak45
ak47
aka dream
akaka falls
akali
akali from league of legends
akane owari danganronpa
akashic
akasuki brightmind
akasuki voidstar
akatsuki akira
akehiko inoue and ross tran
aketan
akihabara
akihabara style
akihiko yoshida
akihiko yoshida 8 k
akihiko yoshida 8k
akihiko yoshida!
akihiko yoshida. nomax
akihiko yoshida. unreal engine
akihiko yoshida”
akihito tsukushi
akihito yoshida
akikazu mizuno
akiko takase
akiman
akimino kamijyo
akio watanabe
akira
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akira cgi movie stills
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akira from chinese mythology
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akira kurosawa
akira moto
akira motorcycle
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akira toriyama style
akira toriyama 📹
akira vibes
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akira's motorcycle
akiras motorcycle
akiri toriyama
akitipe studios
akiyuki shinbo
akiyuki shinbou
akseli gallen kallela
akt photography
al - qadim
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al pacino scarface hot tub scene
alabama
alabaster gothic cathedral
aladdin
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alain delon as monster hunter
alain delon vampire hunter
alamy stock photo
alan grey
alan jackson
alan lee and marc simonetti
alan moore is angry about you
alan moore portrait
alan turing
alan watts
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alanis guillen
alarcon caricaturista
alarm clock
alaska
alaskan glacier on fire
albedo
albedo from overlord
albedo from the anime overlord
albedo texture
albert bierdstadt
albert bierdstat
albert bierstadt 4 k
albert bierstadt 4k
albert bierstadt and artgerm
albert bierstadt greg rutkowski
albert einstein
albert einstein as a teenager
albert kiefer
albert lynch
albert ramon puig
albert uderzo
albert watson
albert wesker
albert wesker and chris redfield
albert wesker full body portrait
alberto mielgo
alberto seveso and dan mccaw
albino
albino dwarf
albino hair
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albino white pale skin
albion
albrecht durer
albrecth durer
album
album art
album art cover
album art for a trance dj
album art for char zulu
album art young thug
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album cover art
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album cover design
album cover style
album cover!
album is called tripmachine
album photo
albumen silver print
albuquerque
alchemic
alchemical
alchemical diagram
alchemical equipment
alchemical objects on the side
alchemical still
alchemical still made from clay
alchemical symbols
alchemist
alchemist bottles
alchemist girl
alchemist lab
alchemist library background
alchemy
alchemy concept
alchemy laboratory
alchemy tavern
alcohol
alcohol ink art
alcohol ink painting
alcohol inks on parchment
alcohol with blue delirium skies
alduin
alebrijes aesthetic
alec soth : : love
alegria
alegria style
aleister crowley
aleister crowley with baphomet
alejandro
alejandro burdisio art
alejandro burdiso style art
alejandro inarritu
alejandro jodorowsky
alejandro mirabal
alejandro olmedo art
aleksander rostov
aleksandra waliszewska
alembic
alen kopera
alena
alena aemani digital art
alena aenami and android jones
alena aenami and artgerm
alena aenami and lilia alvarado
alena and bluth
aleriia _ v ( lerapi )
alert
alert brown eyes
alert eyes
alessandra ambrosio
alessandro barbucci
alessandro bavari
alessio albi
alessio albi and shin jeongho
alex
alex and his droogs
alex andreev
alex boyd
alex colville
alex flores
alex from a clockwork orange
alex gray
alex grey and beksinski
alex grey and dan hillier
alex grey and gustave dore
alex grey and romero ressendi
alex grey and tim hildebrandt
alex grey art
alex grey cat
alex grey style
alex gross
alex heywood
alex horley and jimmy nelson
alex horley and jimmy nelson!
alex jones
alex jones with frog legs
alex kanevsky
alex kiesling
alex maksiov and john pugh
alex malveda
alex miranda
alex pardee
alex ries
alex ries zdzisław beksinski
alex ross and richard corben
alex ross style
alex trebek
alex yanes
alex yanes and lisa frank
alexa 65
alexa grace
alexa mini
alexa65
alexander
alexander abdulov
alexander archipenko
alexander hamilton style
alexander jansson style
alexander lukashenko in scarface
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alexander minze thumler
alexander the great
alexander trufanov
alexander wells
alexandra daddario
alexandra daddario face
alexandra daddario face!!
alexandra fomina
alexandra fomina artstation
alexandre bourlet
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alexandre ferra
alexandre ferra mecha
alexandre ferra white mecha
alexandria ocasio - cortez
alexandria ocasio cortez
alexandria ocasio-cortez
alexandria's genesis
alexandria\'s genesis
alexandrias genesis
alexandros pyromallis
alexei savrasov
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alexis ohanian as a wizard
alexstrasza
alf
alfa romeo giulia
alfa romeo project car
alfio presotto
alfons
alfons maria mucha
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alfons mucha and greg rutkowski
alfons mucha ， from china
alfonse mucha detailed
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alfred cheney johnston
alfred dehodencq
alfred elmore
alfred ghisoland
alfred hitchcock
alfred kelsner
alfredo and the afterlife
alfric overguard
algae
algae feet
algebra
algorithm
algorithmic
aliased
aliasing visible
alice
alice attends mad tea party
alice cooper
alice cooper as a goat
alice cooper as marilyn manson
alice cullen
alice from alice in wonder land
alice goes down the rabbit hole
alice hunt and peter hurd
alice in the wonderland
alice in wonderland
alice in wonderland 3 d
alice in wonderland cyberpunk
alice in wonderland style
alice in wonderland syndrome
alice in wonderland theme
alice russell glenny
alice x. zhang
alice's sister
alicia vikander
alicization
alien
alien  1979
alien  1984
alien ( 1 9 7 9 )
alien ( 1 9 8 4 )
alien - style
alien 1 9 7 9
alien 1979
alien abduction
alien antenna
alien architecture
alien armor
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alien art
alien autopsy
alien base
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alien breathtaking landscape
alien buildings
alien capsules
alien castle in background
alien church
alien city
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alien colorful greenery
alien colors
alien creature
alien creatures
alien creatures scattered around
alien desert
alien design
alien dream worlds
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alien eyes
alien fabric
alien faces
alien fauna
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alien flora
alien flora and fauna
alien flower
alien flowers
alien foliage plants
alien food
alien forest
alien forest in background
alien forms
alien frog
alien futuristic city
alien girl
alien glyphs
alien god
alien goo
alien grey
alien ground
alien habitat
alien head
alien helmet
alien hive
alien homeworld
alien hunter
alien hybrids
alien in the form of mantis
alien invasion
alien invasion. 2 4 mm
alien isolation
alien jungle forest
alien landscape
alien language
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alien like
alien looking
alien man with alien woman
alien monster
alien mothership
alien mothership in the sky
alien mountains
alien mouth
alien movie
alien movie screenshot
alien musician
alien nature
alien owl
alien planet
alien planet covered in water
alien planet in the sky faraway
alien planet landscape
alien plant
alien plant from alpha centauri
alien plants
alien plants and animals
alien portrait
alien princess
alien queen
alien racing drivers
alien relic
alien robot luffy
alien room
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alien ruins
alien sedimentary schematic
alien ship wreck
alien shop
alien skin
alien skull
alien skulls
alien sky
alien space knight
alien space ship flying in space
alien space ships
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alien spaceship in the sky
alien species
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alien trees
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alien vs predator
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alien woman
alien world
alien worlds
alien writing
alien xenomorph
alien! alien isolation
alien-like
alienating
alienation
aliens
aliens arriving dune
aliens in the background
aliens in the last supper
aliens invading earth
aliens land here
aliens movie
aliens vivid
aliens vs predator
alienware
aligned xenomorph nesting dolls
alina ivanchenko
alison brie
alison brie as black widow
alison hannigan
alison williams
alita
alita battle angel
alive
alive colors
alizarin curtains
all - seeing eye
all alone
all around
all at once
all black cyberpunk clothes
all black matte product
all body
all body! shot
all bodyshot
all buildings on bridge
all cast members
all colors
all dark blue metal
all dirty with severed limbs
all enclosed in a circle
all face covered with a fire
all from the group nct
all growing inside an enormous
all in focus
all in the amazing outdoors view
all inside an enormous cavern
all inside frame
all looking at camera
all manner of beasts
all marble
all mimsy were the borogoves
all overly excited
all parts
all red
all religions combined
all rights reserved
all roads lead to rome
all robots are half human
all seeing eye
all skeletons
all teeth
all terrain vehicle race
all the people are wearing suits
all these worlds are yours
all things cease to exist
all white
all white render
all-metal
alla prima
alla prima oil painting
allan houser
allan lee
allegorical
allegorical painting
allegorical realism
allegorical style
allegory of metamorphosis
allegory of winter
allen anderson
allen williams
alley
alleys
alleyway
alliance
alligator
alligator shaman
alligators
alluring
alluring and terrifying
alluring elf princess knight
alluring eyes
alluring mesmer woman
alluring plus sized model
alluring tiefling druid
alma tadema
almond blossom
almost black
almost night
almost smiling
alone
alone dramatic
alone gorgeous latin woman
alone in a nebula
alone!!
aloof
alosaurus
aloy
aloy from horizon zero dawn
aloy from horizon: zero dawn
alp
alpaca
alpaca sculpture
alpes
alpha
alpha movie
alpha wolf head
alphabet soup
alphonce mucha
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alphonse
alphonse fly
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alphonse mucha 8k
alphonse mucha : :
alphonse mucha and alena aenami
alphonse mucha and ayami kojima
alphonse mucha and craig mullins
alphonse mucha and rossdraws
alphonse mucha background
alphonse mucha cgsociety
alphonse mucha frank fanzzeta
alphonse mucha j. c. leyendecker
alphonse mucha johannes voss
alphonse mucha magali villeneuve
alphonse mucha meet beksinski
alphonse mucha poster
alphonse mucha style
alphonse mucha''
alphonse mucha.
alphonse mucha. highly detailed
alphonse mucha. rich colors
alphonse murac
alphonse musha
alphonse-mucha
alphonso azpiri
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alpine
alpine architecture
alpine climate
alpine landscape with a cottage
alpine pond with water lilies
alpine scenery
alpine tundra wildfire
alps
also known as artemis or selene
also known as artemis the selene
also one tomato slice
also spelled ka'bah or kabah
also spelled kabah or kabah
also symmetrical
also tomato
also very detailed
alsphonse mucha
alt art
alt fashion
altar
alter
alterd carbon
altered carbon
altered carbon series
altered carbon style
altered states
alternate album cover
alternate angle
alternate dimension
alternate history
alternate reality ceramic age
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alternative metal
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alternative reality mirrors
alternative rock
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alton kelley
altostratus clouds
alucard
aluminium
aluminum
aluminum sheen
alvah angelrune
alvar aalto
alvaro siza
alvin schwartz
always sunny in philadelphia
always the sun
aly fell
aly fell and artgerm
alyssa monks
am a jean giraud
am a naranbaatar ganbold
amad
amagaitaro
amalgamation of embers
amalgamation of magical stars
amanda clarke
amanda lilleston
amanita
amanita muscaria
amanita myscaria
amano and karol bak
amano and olivier ledroit
amano yoshitaka
amaro
amaterasu
amateur
amateur photo
amateur photograph
amateur photography
amaze art
amazed
amazement
amazing
amazing 8k character concept art
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amazing and immaculate scale
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amazing artwork
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amazing background
amazing background theme
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amazing blue background theme
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amazing colorful background
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amazing colours
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amazing concept painting
amazing concrete sculpture
amazing contrasting background
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amazing d & d art
amazing d & d dark sun art
amazing d d art
amazing d d dark sun art
amazing depth
amazing detail
amazing detail digital art
amazing detail. colored
amazing details
amazing digital art
amazing ditial art
amazing effect
amazing exquisite matte painting
amazing eyes
amazing fantasy art
amazing film quality
amazing fire art
amazing flower tiara
amazing food illustration
amazing food photography
amazing graphics
amazing landscape
amazing landscape in background
amazing light
amazing lighting
amazing lighting effect
amazing lightning
amazing lightning art
amazing likeness
amazing line work
amazing mmorpg
amazing octane render
amazing painting
amazing photo
amazing photography
amazing photorealistic graphics
amazing portrait of viego
amazing professional picture
amazing quality
amazing shades
amazing shading
amazing short
amazing shot
amazing simple composition
amazing sky
amazing space creature 4 k
amazing splashscreen artwork
amazing textured brush strokes
amazing value control
amazing values
amazing view
amazing volcanic eruption
amazing wallpaper
amazing water art
amazing weather
amazing!
amazingly composed image
amazingly detailed
amazingly detailed d & d art
amazingly detailed d d art
amazingly detailed semirealism
amazingly epic visuals
amazon
amazon forest burning
amazon in the background
amazon indian peoples in brazil
amazon jungle
amazon milk frog
amazon queen
amazon rainforest background
amazon warrior
amazonian
ambeint
amber
amber and blue color scheme
amber and chaos
amber eyes
amber glasses
amber glow
amber heard
amber heard as captain america
amber heard squatting on a bed
amber jewelry
amber jewels
ambiance
ambience
ambience lighting
ambient
ambient amber light
ambient art
ambient cave lighting
ambient diffusion
ambient fog
ambient glow
ambient green light
ambient key art
ambient lain
ambient lifting
ambient light
ambient light background
ambient lighting
ambient lighting at night
ambient lighting from top
ambient lighting highly detailed
ambient lighting!
ambient lightning
ambient lights
ambient occlusion
ambient occlusion render
ambient occlusion:3
ambient oclusion
ambient reflective occlusion
ambient style
ambient teal light
ambient vibe
ambient volumetric lighting
ambients
ambiguous gender
ambitious
ambrosius bosschaert the elder
ambrotype
ambulance
ameera al taweel
ameera al-taweel
amelie poulain
amen beats jungle party
america
america 2 0 9 8
american
american astronaut
american astronaut in the forest
american canteen
american city
american civil war
american civil war trench battle
american dream
american exceptionalism
american flag
american flag in background
american flag on background
american flags
american gods
american gothic
american gothic interior
american gothic style
american indian headdress
american man
american mcgee's alice
american mcgees alice
american oil painting
american postcard art style
american progress
american propaganda
american psycho
american realism style
american realist
american realist style
american school
american shot
american soldiers invaded mars
american suburb
american top manager
american total portrait
american velvet painting
american veteran gi
american west scenery
americana
americana architecture
americana vibrant colors
americanoize
amethyst
amethyst citrine opal
amethyst mineral quartz
amethyst opal citrine
amethyst stained glass
amicable
amid nature
amidst nature
amidst of nature fully covered
amigurumi
amityville
ammo bandolier
ammo belt
ammo bullets
ammolite
ammonite
ammonites
amnesia
amogus
amogus photo - realistic
amoled
amoled wallpaper
among foxes and deer
among golden fireflies
among heavenly sunlit clouds
among ravens
among the clouds
among the stars
among us
among us character
among us crewmate
among us imposter
among us impostor
among us video game
among wonderful golden fireflies
amongst coffee beans and flowers
amongst foliage
amorphous
amour
amouranth
amouranth as a super villain
amphibians
amphora
ample lighting
amplified ritual engine
amr elshamy
amsterdam
amulet
amused
amused facial expression
amusement park
amusement park attractions
amusement park buildings
amusement park interior design
amusing
amy adams
amy adams as morticia addams
amy judd
amy pond
amy rose red dress
amy sol in the style of
amy yasbeck
an 80 year old man
an 80's bedroom
an 8k resolution
an 9 4
an 94
an abandonded courtyard
an abandoned
an abandoned dystopian city
an abandoned old
an abandoned rococo salon
an abandoned rusted train
an abstract
an abstract spiritual background
an abstract tropical landscape
an absurdly beautiful
an achingly beautiful
an adorable kitten
an aerial tennis court
an aesthetic
an aesthetic field of flowers
an aesthetic!
an aesthetically pleasing
an affable devil among demons
an afghan male type
an african wild dog sitting up!
an ahoge stands up on her head
an ai generated image
an alchemical art illustration
an alchemist
an alchemist gnome
an alien
an alien autopsy
an alien battle in space
an alien city
an alien cityscape
an alien drinking horchata
an alien fruit
an alien jungle
an alien landscape
an alien landscape view
an alien mushroom with tentacles
an alien musician lost in space
an alien planet
an alien robot naughty nurse
an alien working on a computer
an alien. angled jaw
an all white horse
an all white human
an alley in paris in winter
an almighty warlock
an altar of a temple
an amalgamation of a snake
an amazing landscape image
an amazing photo
an american propaganda
an american romanticism painting
an amusement park in old egypt
an ancient
an ancient city on fire
an ancient creature
an ancient greek statue
an ancient greek trireme
an ancient greek vase
an ancient land
an ancient male bearded face
an ancient path
an angel
an angel of the dawn light
an angel standing still
an angel with boxing gloves
an angry
an angry expression
an angry lemon
an angry muscular army general
an animation cel of dana scully
an anime girl
an anime large mecha robot
an anime nendoroid of elon musk
an anime nendoroid of karl marx
an anime nendoroid of snoop dogg
an anime nendoroid of son goku
an anime portrait of cirno
an anime portrait of david bowie
an anthro cat
an anthro fox
an anthro wolf
an anthropomorphic blue hedgehog
an anthropomorphic cyberpunk fox
an anthropomorphic deer
an anthropomorphic fox
an anthropomorphic gangster rat
an anthropomorphic rat
an anthropomorphic stomach
an anthropomorphic turtle
an anthropomorphic wolf
an antique
an apple
an apple doing crossfit
an approaching shadow
an arab standing watching over
an arcane wizard casting a spell
an architectural
an archway
an arcylic art
an area rug
an armored core on the ground
an armored core v
an armored core v on the ground
an army of evil
an army recruitment poster
an art deco
an art nouveau
an artistic pose
an asian woman
an astronaut
an astronaut floating in space
an astronaut giving a peace sign
an astronaut holding a beachball
an astronaut in space
an astronaut is under the sea
an astronaut lost in the ocean
an astronaut relaxing in space
an astronaut riding a horse
an atom bomb explosion in mumbai
an attractive
an australian summer landscape
an aviator jacket and jorts
an avocado armchair
an avocado chair
an award winning
an award winning digital render
an award winning masterpiece
an award winning photo
an axe elemental
an axolotl
an aztec city in a island lake
an aztec warrior
an beautiful
an concept art of the tau queen
an delightfully crazy
an diver is under the sea
an dramatic
an eagle
an eagle flying
an earring
an eclipse
an ecological gothic scene
an edgy teen assassin
an eerie
an eerie cave
an eerie whirlpool
an egg
an elderly
an eldritch entity
an elegant
an elegant couple
an elegant gothic princess
an elegant green
an elegant woman in black dress
an elephant
an elephant doing yoga poses
an elephant in the savannah
an elephant king
an elephant octopus chimera
an elephant wearing a tutu
an elf
an elf in a suit
an elf queen
an emo girl
an empty backroom at night
an empty brutalist chamber
an empty hallway
an empty liminal space
an empty office hallway
an enchanted forest
an endless waterfall
an english man
an enormous
an enormous elephant king
an enormous silver tree
an entity
an epic
an epic anime of a energy man
an epic anime of tuff luck
an epic fantastic realism
an epic fantasy
an epic landscape
an epic love affair with doubt
an epic majestical degen trader
an epic non - binary model
an epic painting
an epic painting minion
an epic painting of an artist
an epic portrayal of the sandman
an epic scifi movie still
an epic space battle
an epic space ship scene
an epic viking battle scene
an epic western
an escalating violent firefight
an escape room in a small
an estate agent listing photo
an eternal library
an ethereal
an evil
an evil alien astronaut
an evil catholic priest
an evil forest
an evil nun
an evil sword
an evil villain's lair
an evil villains lair
an ewok eating a lollipop
an example of saul leiter's work
an exhausted deity
an exoskeleton
an expansive grassy plain
an expansive view of the sun
an explosion
an explosion of colors
an expressive digital painting
an expressive oil painting
an exquisite 3 d map
an extreme closeup shot
an extreme long shot wide shot
an extremely angry
an extremely detailed building
an extremely high quality hd
an extremely large cave
an eye
an film still
an film still of mr bean
an giant evil
an ice volcano
an icelandic landscape
an idea seep's into infinity
an idealistic man
an idyllic vineyard
an illustration
an illustration of a bar/lounge
an image of a beautiful cyborg
an image of a moonlit forest
an image of a tornado
an immense floating castle
an impeccable beauty
an imperial agent from star wars
an imposingly tall
an impossibly huge pirate ship
an independent psycho
an indifferent face
an indigo bunting
an indigo forest in japan
an indonesian family portrait
an inform organism
an insane
an instrument
an intact ancient alien ship
an interdimensional being
an interesting color scheme
an interior of room
an intricate
an intricate art nouveau frame
an intricate dress
an intruder
an inventor
an iron man concept yacht
an island
an island floating in the air
an island made of caviar
an island made of food
an island made of red caviar
an isolated hydrangea plant
an isometric fantasy map
an king kong rage on street
an obese
an ocean
an octopus fighting an astronaut
an octopus in a car repair shop
an octopus lost in a garage
an octopus'garden
an odyssey beyond reality
an oil painting of a kitten
an oil painting. wave
an oil paiting of a sparrow
an old
an old abandoned mansion
an old balck and white photo
an old cinema
an old elven wood
an old lady
an old lady with red skin
an old man
an old man with 7 yellow birds
an old man with a ginger beard
an old tv
an old twisted tree
an oldman
an olive skinned
an ominous fantasy illustration
an ominous haze
an open book
an open eye in its center
an open field
an optical illusion
an optical illusion drawing
an opulent favela environment
an orange
an orange cat
an orange fire in the background
an orc
an orc smiling into the camera
an orchid flower
an orgy of colorful
an ork
an outdoor festival stage
an overgrown
an overgrown forest
an overgrown library
an overpopulated
an oversized beret
an overweight
an owl
an ox
an retro anime image
an scp anomalous object
an the first xbox
an ugly giant spider
an ultra high definition
an ultra realistic
an ultra realistic 8k octa photo
an ultra realistic photo
an ultrafine detailed photo
an ultrafine painting
an umbrella top
an undead desert lich pharaoh
an underslung jaw
an underwater alien ocean
an underwater city
an unknown ethnographic object
an upright lightbulb
an upside down urinal
an victorian city
an young urban explorer woman
ana de armas
ana de armas as joan of arc
ana de armas portrait
ana de la reguera portrait
ana from overwatch
anachronism
anachronistic fantasy
anaconda
anaesthetic
anaglyph
anaglyph effect
anaglyph effect ayami kojima
anaglyph filter
anaglyph lighting
anakin skywalker
analog
analog camera
analog data processing plant
analog film
analog filter
analog glitch art
analog horror
analog photo
analog photograph
analog photography
analogic flight controls
analogic photograph
analogic synthsizer
analogous colors
analogous texture
analogue
analogue horror
analogue interferences
analogue photo
analogue photo low quality
analogue photo quality
analogue photography
analogue texture
analysis city
analysis report
anamorph lenses
anamorphic
anamorphic 24 mm lens
anamorphic 35 mm lens
anamorphic 5 0 mm lens
anamorphic 50 mm lens
anamorphic 50mm lens
anamorphic 80mm lens
anamorphic bokeh
anamorphic bokeh and lens flares
anamorphic cinematography
anamorphic film
anamorphic flares
anamorphic illustration
anamorphic lens
anamorphic lens flare
anamorphic lens flares
anamorphic lens flares 4k
anamorphic lenses
anamorphic lenses 2 4 mm
anamorphic lenses 24 mm
anamorphic lenses. high quality
anamorphic shot lens
anamorphic wide angle lens
anamorphic widescreen
anamorpic lens
anarcho - communist heaven
anarcho - communist hordes
anarchy
anastasia ovchinnikova
anato finnstark
anato finnstark and alena aenami
anato finnstark and kelogsloops
anato finnstark!!
anato finnstark. 3 5 mm
anato finnstark. 5 0 mm
anato finnstark. front view
anato finnstark. hyper detailed
anato finnstark. perfect faces
anato finnstark. pixar
anatomic
anatomic description
anatomic!!
anatomical
anatomical accuracy
anatomical art
anatomical chart
anatomical cutaway
anatomical details
anatomical diagram
anatomical drawing
anatomical drawing of a minion
anatomical drawing of saitama
anatomical figure
anatomical fractal
anatomical heart
anatomical in an astronaut suit
anatomical notes with labels
anatomical perfection
anatomically accurate
anatomically accurate face
anatomically accurate skeleton
anatomically correct
anatomically correct android
anatomically correct body
anatomically correct equine
anatomically correct features
anatomically correct hands
anatomically correct hands!!!!!!
anatomically correct heart
anatomically correct proportions
anatomically correct skeleton
anatomically correct vulpine
anatomically correct!!
anatomically incorrect
anatomically perfect
anatomically proportional
anatomy
anatomy artifacts
anatomy book
anatomy features
anatomy for beginners
anatomy for sculptors
anatomy of a bird
anatomy of a dragon
anatomy of the terminator
anatomy portrait
anatomy skills
anatomy studies
anatomy study
ancalagon the black
ancestors
ancestors and future
anchient
anchor goatee
ancien chinese tower
ancienct
ancient
ancient alien head stone statues
ancient alien jungle
ancient alien portal
ancient alien portral
ancient alien tools and houses
ancient aliens
ancient antler deity
ancient architecture
ancient armor
ancient art
ancient asian dynasty princess
ancient astronauts
ancient atmosphere
ancient battlefield
ancient biblical
ancient biomechanical temple
ancient blacksmith god
ancient brass dwemer ruins
ancient buildings
ancient catacombs
ancient catedral behind her
ancient cave
ancient caves
ancient china
ancient china art style
ancient chinese architecture
ancient chinese beauties
ancient chinese goddess
ancient chinese ornate
ancient chinese princess
ancient city
ancient city landscape
ancient city of white stone
ancient city ruins
ancient city streets behind her
ancient civilisation
ancient civilization
ancient civilizations
ancient coffee machine
ancient creature
ancient crown
ancient cyberpunk 8k resolution
ancient demon summoning
ancient dream
ancient dungeon
ancient egypt
ancient egypt art
ancient egypt painting
ancient egyptian
ancient egyptian art
ancient egyptian mural
ancient eldritch horror cthulhu
ancient epic tower in the desert
ancient era
ancient evil
ancient evil letters
ancient fairy dust
ancient fantasy regional map
ancient fish
ancient forest
ancient forest like fanal forest
ancient futuristic
ancient garden behind her
ancient god
ancient goddess
ancient gods
ancient greece
ancient greek
ancient greek assassin mask
ancient greek city
ancient greek ornamented armor
ancient greek painting
ancient greek pottery
ancient greek ruins
ancient greek sculpture
ancient greek statue
ancient greek style
ancient greek temple
ancient greek temple ruins
ancient high tech
ancient india
ancient indonesia
ancient interior tent background
ancient irish
ancient japan
ancient japanese
ancient japanese architecture
ancient japanese monk
ancient japanese painting
ancient japanese samurai
ancient jungle
ancient keys
ancient kings in white robes
ancient library
ancient libu princess
ancient libu young girl
ancient magic
ancient magical overgrown ruins
ancient magus
ancient majestic
ancient map
ancient marble city
ancient mediterranean city
ancient mediterranean village
ancient megastructure pyramid
ancient mesopotamia
ancient mesopotamian warrior
ancient mongolian elon musk
ancient monument waterfall
ancient mystic time lord
ancient neon monster portrait
ancient numidian cavalryman
ancient oak forest
ancient ochre palette
ancient overgrown ruins
ancient overgrown! ruins
ancient overgrownruins
ancient persian city
ancient persian temple dungeon
ancient photograph
ancient plants
ancient pyramid
ancient queen emma watson
ancient roman mosaic
ancient roman painting
ancient roman setting
ancient roman style
ancient rome
ancient rome man
ancient ruins
ancient ruins and waterfalls
ancient ruins background
ancient ruins behind
ancient ruins behind her
ancient ruins favela
ancient ruins in the background
ancient ruins in the forest
ancient ruins under the desert
ancient runes
ancient russian architecture
ancient sci - fi city
ancient sci - fi elements
ancient science fiction art
ancient silver tower of the moon
ancient slavs
ancient soldier
ancient space ship
ancient steampunk city
ancient stone tiling
ancient swirls
ancient symbol behind it
ancient symbols
ancient technology
ancient temple
ancient temple ruins
ancient times
ancient tombs in the background
ancient tree
ancient trees
ancient tribe
ancient viking warrior
ancient warrior
ancient white dress
ancient wood environment
ancient world
ancient writing
ancient yet futuristic
ancient. atmospheric lighting
ancient”
and
and beautiful
and blue
and burbled as it came
and hypnotic
and intricate
and ornated
and realistic
and saw my life like a long shot
and the uncertainty\'
and what rough beast
and you fade away just enough
anders petersen
anderson
andes
andes mountain forest
andi rusu
andnorman rockwell
ando tadao
andor kollar
andre breton
andre le notre
andre masson
andre shulze
andre the giant
andrea rocha
andrea savchenko
andreas
andreas achenbac
andreas franke
andreas m wiese
andreas marschall
andreas rocha and john howe
andreas rocha style
andreas rochas
andree wallin
andrei markin
andrei riabovitchev symmetrical
andrei riabovitchevy
andrei ryabovichev
andrei tarkovski
andrei tarkovsky
andrei tarkovsky and hr giger
andrei tarkovsky scene
andres gursky
andres rios
andrew atroshenko
andrew bernstein
andrew dickson
andrew garfield
andrew gonzalez
andrew krivulya
andrew newell wyeth
andrew tate
andrew tate driving a bugatti
andrew thomas huang
andrew wyeth artwork
andrew's base
andrews base
andrews esao artstyle
andrey gordeev
andrey remnev
andrey surnov
andrey tarkovsky
andro
androgyn beauty
androgynous
androgynous face
androgynous male
androgynous person
androgynous vampire
androgyny
android
android 18
android body
android cameraphone
android close to camera
android coffee shop
android eyes
android format
android girl in egyptian ruins
android heroine
android james
android jones and atey ghailan
android jones and chris dyer
android jones and rhads
android mystic
android netrunner
android phones
androids
andromeda
andromeda galaxy
andromorphic
andrzej duda
andrzej marszalek
andrzej sykut
andré le nôtre
andrée wallin
andy catling
andy milonakis
andy milonakis as a goat
andy morin
andy park
andy richter
andy samberg
andy shaw
andy warhol style
andy worhol
anemone
anemones
anemones and starfish
anfas portrait of a mech warrior
anfree wallin
angel
angel doing yoga in temple
angel face
angel falling to andromeda
angel girl
angel halo
angel in hell
angel in plastic armor
angel in white linen
angel is split in two with smoke
angel knight girl
angel knight gothic girl
angel of death
angel of grief
angel of verdun
angel protecting man
angel protecting woman
angel relief
angel sitting on a rock
angel spirit guide
angel statues
angel themed
angel versus devil
angel watching demon
angel wings
angel with black wings
angel's trumpet
angel-themed
angela merkel
angela merkel as a banana
angela merkel as thanos
angela merkel in gta v
angela sarafyan
angela white
angelarium
angelawhite
angelheaded hipsters
angelic
angelic and unsettling
angelic face
angelic features
angelic golden armor
angelic halo
angelic light
angelic photograph
angelic pretty
angelic purity
angelic wings
angelic wings on her back
angelical
angelina jolie
angelina jolie portrait
angelina stroganova
angeline jolie
angels
angels and demons
angels in the sky
angels in white gauze dresses
angels protecting a praying man
angels vs demons
angemon l sitting at the thrown
anger
anger + wrath
anger. hyper detailed
angered
angewomon from digimon
angie glocka
angiewolf
angkor
angkor thon
angkor wat
angle
angle face
angle profile portrait
angle view
angled
angled shot
angled view
angled walls
angler fish
angler fish sculpture
angles
angry
angry 8 k
angry ali khamenei
angry and bored
angry and helpless
angry and pointing
angry anime face
angry at mirror
angry batman
angry bear
angry bob ross
angry cat
angry character wielding a sword
angry complexion
angry expression
angry eyes
angry face
angry facial expression
angry facial expressions
angry female alien
angry female cyborg
angry frown
angry gecko english gentelman
angry god anubis
angry high moral sexy werewolf
angry light
angry look
angry look in his face
angry looking
angry looking at camera
angry man
angry sasquatch
angry scarlett johansson
angry smile
angry video game nerd
angst-inducing
anguish
angular
angular 16-color
angular and altermodern
angular asymmetrical design
angular background elements
angular design
angular dynamic white rock floor
angular eyebrows
angular face
angular features
angular jawline
angular metal
angular metal armor
angular minimalism
angular shapes
angus mcbride
angus mckie
ani
anigirl batman
anima
animal
animal - shaped bread
animal - shaped cake
animal - shaped statue
animal caricaturist
animal crossing
animal crossing character
animal crossing characters
animal crossing new horizons
animal crossing style
animal drawing
animal ears
animal eyes
animal face
animal hat
animal horn
animal kingdom
animal monster
animal nose
animal painting
animal photography
animal portrait
animal shapes
animal skins
animal skull
animal skull made of rifles
animal skulls
animal sloth
animal style head
animal transformation
animal wearing a hat
animal world
animalia
animals
animals chasing
animals creepy everywhere
animals in the streets
animals mating
animals ruling the world
animals running along
animals wildlife
animated
animated cartoon series
animated character design
animated cinematography
animated disney movie inking
animated disney movie palette
animated episode still
animated film
animated film kubo
animated film still
animated movie
animated movie scene
animated movie shot
animated movie still
animated series
animated still
animated style
animatic
animation
animation cel
animation cel for anime movie
animation character
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animation concept art
animation film
animation film still
animation illustrative style
animation key shot
animation model
animation pixar (2018)
animation pixar style
animation portrait concept art
animation printed poster
animation still
animation still screencap
animation style
animation style render
animatrix
animatronic
animatronic angelina jolie
animatronic bill murray
animatronic john c. reilly
animatronic mark zuckerberg
animatronics
anime
anime / manga
anime 3 d art
anime 4 k
anime 4k
anime abstract art
anime action figure
anime aesthetic
anime and manga
anime and manga style
anime art
anime art nouveau
anime art nouveau cosmic display
anime art style
anime art vehicle concept art
anime art wallpaper 4 k
anime art wallpaper 4k
anime art wallpaper 8 k
anime artbook
anime arts
anime artstyle
anime artwork
anime asthetic
anime atmospheric
anime background
anime background art
anime background key visual
anime barbie doll
anime barbie in white
anime barbie in white stockings
anime beautiful peace scene
anime best girl
anime boy
anime bullet vfx
anime car wrap
anime cartoon
anime cat
anime cat girl in a maid costume
anime catgirl
anime cel
anime cels
anime cgi
anime cgi style
anime character
anime character anatomy
anime character art
anime character design
anime character portrait
anime character reference sheet
anime character; full body art
anime chibi
anime city lineart
anime clouds
anime color palette
anime colours
anime concept art
anime concept hdr anime macmanus
anime convention
anime cosplay
anime countryside landscape
anime cover
anime cyberpunk
anime cyberpunk art
anime cyborg
anime digital art
anime drawing
anime ears
anime elon musk
anime elvis presley
anime epic artwork
anime eyes
anime face
anime fantasy artwork
anime fantasy illustration
anime fencer
anime fight
anime figma figure
anime figure
anime figurine
anime film still
anime food
anime frames
anime full body illustration
anime girl
anime girl cosplay
anime girl desktop background
anime girl drinks energy drink
anime girl in a maid costume
anime girl in ascii art style
anime girl in real life
anime girl named lucy
anime girl of the future
anime girl portrait
anime girl portrait profile
anime girl walking on water
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anime girls
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anime key visual”
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anime manga robot!! anime girl
anime manga robot!! cat tattoo
anime manga robotcat tattoo
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anime shading)
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anime style”
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anime!!!!!!!!!!!!!!!
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animorphs
animorphs book cover
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anish kapoor black
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anja millen
anjali mudra
ankh
ankh necklace
ankh pendant
ankh symbol
ankh symbol around the neck
anmi
ann long
ann stokes
ann takamaki
ann takamaki from persona 5
anna
anna dittman
anna dittmann alberto vargas
anna forsterling
anna kendrick
anna kovalevskaya
anna nikonova
anna nikonova aka newmilky
anna podedworna
annabeth chase
annasophia robb
annasophia robb as aphrodite
annasophia robb as joan of arc
annasophia robb as link
annato finnstark
anne hathaway
annibale siconolfi
annie from league of legends
annie lebovetz
annie lebowitz
annie leibovitz and zack snyder
annie leibovitz photography
annie leibovitz photorealistic
annie leibovitz. detailed photo
annie leibowit
annie leibowitz
annie leonhart
annie leonhart in a neon city
annie liebovitz
annie liebovitz photography
annie liebovitz. gustave dore
annie liebowitz
annie stegg gerard
annie swynnerton
annihilation
anno 1404
anno 1602
anno 2070
annotated
annotated highly
annotations
annotations and scratches
annotations handwritten
annoyance
annoyed
annoyed expression
annoyed facial expression
annoying sister vibes
annual award winner
anomalisa
anomaly
anomander rake
anomorphic lens flare
anon 5 0 mm
anon 50 mm
anonymous
anonymous as a car
anonymous as a sausage
anonymous lion face
anonymous mask
anor londo
anorexic figure
anorld render
another planet
another universe
another world
anri cartier bresson
ansel ]
ansel adams photograph
ansel adams. intricate
ansel!!! adams!!!. intricate
ant
ant alien
ant farm simulator
ant humanoid
ant humanoid mechanic
ant life
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ant pov from the floor
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antarctic mountains
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antediluvian
antediluvian occult cosmology
antelope canyon
antenna
antennae
antennae on a hestiasula head
antennas
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anthem game
anthem game inspiration
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anthony mcbain
anthony moravian
anthony pafford
anthracite
anthro
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anthro gecko
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anthropologist
anthropology
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anthropology photo
anthropology photography
anthropology photo”
anthropomorphic
anthropomorphic _ humanoid
anthropomorphic alligator
anthropomorphic badger
anthropomorphic beaver
anthropomorphic bird
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anthropomorphic cat
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anthropomorphic character
anthropomorphic cheese wedge
anthropomorphic coyote male
anthropomorphic crab
anthropomorphic creature
anthropomorphic deer
anthropomorphic deer female
anthropomorphic dog
anthropomorphic dog cleaning
anthropomorphic dog eating
anthropomorphic dragon
anthropomorphic edible piechart
anthropomorphic female
anthropomorphic female cat
anthropomorphic female deer
anthropomorphic fox
anthropomorphic furry art
anthropomorphic furry otter
anthropomorphic gangster rat
anthropomorphic hedgehog
anthropomorphic horse
anthropomorphic humanoid
anthropomorphic large maine coon
anthropomorphic lynx
anthropomorphic machine
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anthropomorphic otter in costume
anthropomorphic penguin
anthropomorphic praying mantis
anthropomorphic rabbit
anthropomorphic racoon
anthropomorphic rat
anthropomorphic raven knight
anthropomorphic robot  thing
anthropomorphic robot [ thing ]
anthropomorphic samurai bear
anthropomorphic shark
anthropomorphic shiba inu
anthropomorphic silhouette
anthropomorphic sloth
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anthropomorphic turtle
anthropomorphic turtle hero
anthropomorphic turtle humanoid
anthropomorphic warrior piglet
anthropomorphic wolf
anthropomorphic wolf male
anthropomorphic!!
anthropomorphism
anthropomorphized
anthropomorphized chicken
anthroposcen
anti - aliasing
anti - communist
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antibodies
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antichrist dancing at studio 54
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antifa
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antlers
antlers on her head
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anton
anton fadeev 8 k
anton fadeev 8k
anton fadeev and dan mumford
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anton semenov
anton semonov
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antoni tapies and cy twombly
antonio j. manzanedo
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ants
ants eye view
antview
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anubis head
anubis reptilian
anubis-reptilian
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anxious eyes
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anxious. highly detailed
any racial background
anya forger
anya from spy x family
anya taylor - joy
anya taylor - joy and emma stone
anya taylor - joy vampire queen
anya taylor joy
anya taylor-joy
anya_taylor-joy
anything
anything that comes into my mind
ao dai
aoc
aoe2
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aorta
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aoshima chiho
ap
ap art
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ap news photograph
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apartment
apartment building on the moon
apartment complex made of tubes
apartment design
apartment hallway
apartment of an art student
apartment set in the near future
apartment with black walls
apartments
ape
ape teaching pepe
apeling
aperture
aperture f 1. 2
aperture f 2. 8
aperture f 3. 2
aperture f/11
aperture f/9
aperture f1.2
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aperture f12
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aperture gradient
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aperture science
aperture science test chamber
apes hanging from vines
apex
apex legends
apex legends armor
apex legends character
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aphrodite
aphrodite goddess of love
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apocaliptic contamination
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apocalypse art!!!!
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apocalypse landscape!!!!!
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apocalypse now film still
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apocalyptic scenery
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apollo 1 1
apollo 11
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app
app design
app icon
appa from the last airbender
appalachian mountains
appalling
apparent brush strokes
apparent tooth
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apparitions
appealing
appealing long hair
appearing from the background
appearing in a shopping mall
appears as the fire goddess
appeasing
appetizing
appgamekit
apple
apple - store
apple blossoms
apple car
apple design
apple logo
apple orange
apple pie
apple skull
apple store
apple tree
apple trees
applepencil
apples
apples on the ground
applied thick
applied to tone
apprehensive mood
approaching earth
april
apron
apsaras warrior
apteryx mantelli
apulia
aqua
aqua from konosuba
aqua glass eyes
aqua volumetric lights
aquaman
aquaman aesthetic
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aquamarine color palette
aquamarine eyes
aquamarine windows
aquapunk
aquarel
aquarel painting
aquarelle
aquarelle painting
aquarion
aquarium
aquarium bed
aquarium life
aquarius
aquasixio
aquatic
aquatic creature
aquatic devices
aquatic life
aquatic plants
aquatical plants
aqueduct and arches
aqueducts
aquiline facial features
aquiline features
aquiline nose
aquiline nose!!
ar
ar 16:9
ar glasses
arab
arab ameera al taweel
arab ameera al-taweel
arab inspired
arab man
arab man light beard
arab princess
arab young monica belluci
arabella mistsplitter
arabia
arabian
arabian art
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arabian calligraphy
arabian features
arabian night
arabian nights
arabian nights inspired
arabian princess
arabian samurai
arabian sword
arabian wrestling woman
arabic
arabic architecture
arabic calligraphy
arabic orc
arabic pronunciation: [kaʕ.bah])
arabic pronunciation: kaʕ.bah
arabic!
arabica style
arachne
arachnid
arachnophobia
aragorn
aragorn in an anime world
aragorn medieval armor
aragorn with medieval armour
araki
araki nobuyoshi
araki style
aramaki shinji
arasaka
arasaka mech
arbeitsrat fur kunst
arbor
arboreal
arborescent architecture
arc
arc de triomphe full of graffiti
arca album cover
arcade
arcade cabinet
arcade cabinet in background
arcade game
arcade machine
arcade machines
arcadia
arcain style
arcana
arcane
arcane : league of legends
arcane art style
arcane books
arcane clothing
arcane colors
arcane concept art
arcane from netflix
arcane glow
arcane jayce
arcane jinx
arcane league of legends
arcane magic
arcane netflix
arcane patterns
arcane ritual
arcane style
arcane style bomb
arcane style viking battleship
arcane style!!!!!
arcane tarot
arcane vi
arcane wizard x boba fett
arch
arch enemy
archaeology
archaic
archan nair
archangel
archangel michael
archaon the everchosen
archdaily
arched back
arched ceiling
arched doorway
archeologial dig setting
archeological discover
archeological find
archeology
archeology journal cover
archer
archer bow
archer boy
archers
archery
arches
arches adorned pillars
archfey
archie andrews
archie comic style
archie comics
archille superbi
archillect
archillect concept art
archimboldo
archimboldo!!!
archimedes
arching milkyway
archipelago
architect
architect lebbeus woods
architect studio
architectural
architectural 3 d render
architectural 3d render
architectural and tom leaves
architectural concept
architectural concept diagram
architectural concepts
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architectural detailed
architectural digest
architectural digest photo
architectural digest!!!
architectural drawing
architectural finishes
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architecture
architecture and more
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architecture carved for a titan
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architecture magazine
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architecture of purification
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architecture render ”
architecture technical blueprint
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archival
archival footage
archival photo
archival photography
archival pigment print
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archive
archive material
archive photo
archive photograph
archive photography
archive pieces
archived photograph
archmage
archons
archs
archs and columns
archs and walls
archvis
archviz
archway
archways
archways between stalagtites
archways made of lush greenery
archwizzard in a hat
arcimboldo giuseppe
arcs
arcs of fiery neon light
arcs of flame
arctic
arctic fish
arcylic
arduino
are bure boke
are-bure-boke
are-bure-boke!!!!!!!!
area 3
area 51
area n
areas rugs
arena
arena background
ares
ares with heavy armor and sword
areuz
argand lamp
argentic
argentina
argentina flag behind
argentina flags behind
argentina presidential rally
argentine flags behind
argerm
argonian
argos
arguing
arham horror
arhitectural shot
ari aster
arial shot
arian
arian mark
ariana grande
ariana grande as a mermaid
ariana grande as a sith
ariana grande in gta v
ariana grande in star wars
ariana grande on a tricycle
ariana grande photography
ariana grande portrait painting
arid ecosystem
arid mountains and palm forest
arid planet
ariel perez
ariel the little mermaid
ariel view
aries constellation
aries fiery ram tarot
arik roper
aristocrat
aristocrat cloak
aristocrat robe
aristocratic
aristocratic appearance
aristocratic clothing
arizona
arizona desert
ark survival
ark survival evolved
arkane lyon
arkham
arkham city
arkham horror
arkhip kuindzhi painting
arknights
arm
arm around her neck
arm reaching out of thick fog
arm tattoo
arm tattoos
arm wrestling
arma 3
arma 3 screenshot
armadillo patriot potus
armageddon
armani
armature wire
armchair
armchairs
armed
armed and dangerous
armed with edged weapons
armfold pose
armfold pose!
armillary rings jewelry
armitage
armor
armor and robes
armor angle with wing
armor elements
armor focus on face
armor girl
armor made of bark
armor made of ice
armor made of steel
armor piercing missiles
armor plate
armor plating
armor style of giger
armored
armored cat
armored core
armored core 4
armored core style mecha
armored core v
armored dieselpunk wardrobe
armored duck
armored feline companion
armored knight
armored sword blade
armored units
armored warrior
armored!
armour
armour and crown
armour and fallen crown
armour merging with body
armour! fantasy
armoured warrior
armpit
arms
arms akimbo pose
arms and legs
arms behind back
arms covered in gang tattoo
arms crossed
arms crossed on chest
arms down
arms extended
arms folded
arms held high in triumph
arms made out of spaghetti
arms of lemons
arms open
arms out
arms outstretched
arms raised
arms spread wide
arms stretched out
arms stretched wide
arms to side
armstrong rolf
army
army boots
army girl outfit
army of big robots
army of robotic penguins
army of robotic space penguins
army of robots
army parade glorious march
army uniform
arney fretag
arney freyag
arney freytag
arney freytag!!!
arnold
arnold 3
arnold 3 d render
arnold 3 materials
arnold 3d render
arnold and zbrush
arnold armitage
arnold bocklin
arnold gpu + octane workflow
arnold maya render
arnold render
arnold renderer
arnold rendering
arnold schwarzenegger
arny freytag
aron wiesenfeld
aron wiesenfeld dark fantasy
around 1 9 years old
around 20 yo
around a neck
around everlasting darkness
around the city
around the throne
around the world
around thousand of stars
around tree babies running
arper's bazaar
arrakeen
arrakis
arranged in the golden ratio
array of crt televisions
arrays
arrendajo in avila pinewood
arrested
arri
arri 3 5 mm cinema
arri 35 mm cinema
arri alexa
arri alexa 65
arri alexa mini lf
arri alfa anamorphic lens
arriflex
arriflex 3 5
arriflex 35
arriflex 35 bl camera
arriflex lens
arrival ( film )
arrogant
arrogant and sinister attitude
arrogant look
arrow
arrow shaped
arrowed longbow
arrows
arsen lupin as a paladin
arsitektur nusantara
arson
arstation
arstation and beeple highly
arstation trending
arstationhd
arstationhq
art
art - deco
art - deco style
art - station
art : : professional photograph
art alien
art alphonse mucha
art alphonse mucha and loish
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art book
art brought to life
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art chris rahn and jung park
art chuck close
art colouring : roberto bernardi
art concept
art concept for a book cover
art contest winner on behance
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art deco architecture
art deco background
art deco borders
art deco bowling alley
art deco buildings
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art deco era
art deco era)
art deco factory
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art deco illustration
art deco influence
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art deco intricate ripples
art deco medieval style
art deco motifs
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art deco office buildings
art deco outrun anime aesthestic
art deco painting
art deco pattern
art deco patterns
art deco portrait
art deco poster
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art deco shapes
art deco stadium
art deco stripe pattern
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art deco!
art deco!!
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art decoration
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art digital art
art digital formations
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art direction
art director alphonse mucha
art drawings
art du monde
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art exhibition
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art for the film in color
art for the fool tarot card
art for the game
art foreground : eloy morales
art forms in nature
art francis bacon
art from harry potter
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art gallery
art gallery quality
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art genevieve gauckler
art germ
art giger
art greg rutkowski
art gta 5 comics
art gta 5 cover
art high renaissance art
art history
art history 8 k
art history 8k
art house
art house aesthetic
art house film aesthetic
art illustration
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art in 4k
art in a noir crime novel style
art in an absurdist style
art in the style of disney
art in the style of joshy sly
art in the style of paula rego
art in the style of terry moore
art installation
art jacek malczewski
art jiro matsumoto
art lens
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art nouveaucyberpunkstyle
art nouveaux
art nouveaux colored
art nouveau”
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art of aaron hain
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art station”
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art study of a body
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art style of dark cloud 2
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art style of rune factory 5
art styles mix
art supplies
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art tom bagshaw
art toy
art toys
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artstyle andree wallin
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artstyleunknown
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aruba
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arvalis
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arya stark
arya stark as venus
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as a strict school teacher )
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as far as the eye can see
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as well as scratches
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asaf hanuka
asao urata
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ascended
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ascii
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aseprite
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ash
ash blond greyish hair
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ash williams from the evil dead
ashe
ashe from overwatch
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ashes
ashes and smoke columns
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ashford black marble
ashford black marble sculpture
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ashley greene
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ashmole bestiary
ashoka tano
ashteroth
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asia
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asimov
asking for change
aslan the lion
asleep
asmodeus
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aspect ratio
aspect ratio 16:9
aspect ratio 1:3
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assault rifle
assemble
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assembly drawing
assembly instructions
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asset pack
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asset store
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asterix
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astronauts waving hello
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asuka suit under clothes!
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asylum
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asymmetrical!!
asymmetry
asymmetry!!
at - at
at - at imperial walkers
at - st
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at 8k
at a 5 0 s diner
at a bar
at a beach
at a beach party in ibiza
at a beautiful sunset
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at a city street
at a clearing
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at a dinner table
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at a mall
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at a skate park
at a slightly elevated level
at a tropical beach
at a volcano
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at an angle
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at dawn
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at golden hour
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at highgate cemetery
at home
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at instagram
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at night
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at sunset
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at the hospital in patient gown
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atari logo
atef
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athena goddess of wisdom
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athens in the background
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attack of the eldritch dragon
attack on titan
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attacked submarine in background
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attention
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awakening
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award-winning crisp details”
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award-winning details”
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award-winning masterpiece
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award-winning photo uhd
award-winning photo!!!!
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award-winning”
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awareness
awe
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awesome
awesome and moody
awesome art
awesome bright color palette
awesome cat
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awesome composition rules
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awesome trendy color palette
awesomenes
awestriking
awestruck
awful
awful experiments
awingawat
awkward
awkward and anxious
awkward situation
awkward smile
awkwardly holding red solo cup
awnings
aww
awww
awww aspiring
awwwards
awwwww
ax
axe
axe in hand
axes
axolotl
axolotl creature
axon
axonometric
axonometric drawings
axonometric exploded view
axsens
axure sky
aya takano
aya takano color style
ayahausca
ayahuasca
ayahuasca ceremony
ayahuasca shaman
ayaka
ayaka cosplay
ayaka game genshin impact
ayaka genshin impact
ayami
ayami koj ima
ayami kojima amano
ayami kojima and greg rutkowski
ayami kojima and lyde caldwell
ayami kojima and yoshitaka amano
ayamin kojima
ayan nag
ayanami
ayanamikodon and irakli nadar
aykut aydogdu
aykut aydogdu eric zener
aykut aydogdu zener
ayne haag
azamat khairov
azathoth
azctec warrior
azimov
azo yellow
azores
aztec
aztec architecture
aztec astronaut
aztec bathing suit
aztec empress
aztec fantasy
aztec god
aztec hair
aztec iconography
aztec mythology
aztec princess fights barbarians
aztec princess portrait
aztec queen
aztec skull
aztec skull with crown of hops
aztec street fashion
aztec sun goddess
aztec warrior
aztec warrior goddess
aztec warrior with jaguar mask
aztec yaotl warrior
aztodio
azulejo
azur blue eyes
azur lane style
azure
azure and red tones
azure blue eyes
azure blue sky
azure blue water
azure blue watery eyes
azure cape
azure color bleed
azure eyes
azure ocean
azure sky
azure sky tones
azure tones
azure water
azure waves of water
azure. detailed hair
azurite
b
b & w
b & w detailed sharp photo
b - 2 bomber
b - roll
b movie
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b. b king
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b3d
baader u venus filter
baars
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babbling brook
babe
babel
babies in her lap
baboon wearing a suit and tie
back
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back alley
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back breaker
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back lit vertigo fear of heights
back of emma stone in beige coat
back of hand on the table
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back portrait of a rugged ranger
back pose
back room
back rooms
back shark fin
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back slit
back tattoo
back to back
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back to school comedy
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back to the future
back to us
back towards camera
back turned
back view
back view also
back view of the grim reaper
back view »
back view!!
back view. nuri iyem
back yard
backbone
backdrop
backdrop of dawn
backed on kickstarter
backfacing
background
background  smoke
background ( dark _ smoke )
background ( dark _ smokiness )
background ( smoke
background 1970s office
background : diego fazio
background = library
background a gym
background aerial battle
background art
background art deco palace
background art nouveau
background artwork
background atmospheric effects
background bar
background basilica! sacre coeur
background basilicasacre coeur
background battlefield
background blur
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background blur bokeh!!
background blurred
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background canon
background chaotic flowers
background colorful
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background cyberpunk spaceship
background dark
background depicting a temple
background depth
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background explosion
background flames
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background fortress
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background galaxy
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background hogwarts
background hyper detailed
background image
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background is a city in ruins
background is a low light museum
background is a slum
background is an alien galaxy
background is data server room
background is disneyland castle
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background is made of stars
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background is purple
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background is the ruins
background is white
background is white and blank
background jet ground radio
background jungle
background lush vegetation
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background megastructure
background mountains
background natural flower
background of a golden ballroom
background of a lava river
background of classic red cloth
background of digital greebles
background of flowery hill
background of invading army
background of poison apples
background of resident evil game
background of stars and galaxies
background of the galaxy
background out of focus
background pastel
background removed
background sand dunes
background sharp earth
background soft blue
background space
background space station
background urban cityscape
background vibrant
background white
background with complex patterns
background with neon lighting
background yellow and blue
background(solid)
background: assam tea garden
background: battle scene
backgroundsolid
backlight
backlight body
backlight glow
backlight green leaves
backlight leaves
backlight photo sample
backlight studio lighting
backlight sunset sky
backlighted
backlighting
backlighting sunset golden hour
backlit
backlit beautiful face
backlit ears
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backlit fog
backlit fur
backlit girl in raincoat
backlit glow
backlit golden hour
backlit lighting
backlit portrait
backlit shot girl in parka
backlit stained glass
backlit sunset
backlit!!
backlite
backmouth
backpack
backpfeifengesicht
backroom background
backrooms
backrooms abandoned mall
backrooms liminal space
backrooms office space
backround dark
backscatter orbs
backshot
backside of scenography elements
backstreet boys
backup vocalists
backveiw picture of elf rogue
backview
backwards
backwater bayou
backyard
backyard garden
backyard wrestling
bacon
bacon lettuce and tomatos
bacteria
bad
bad acid trip
bad art
bad attitude
bad boy
bad bunny
bad camera footage
bad camrea
bad composition
bad dream
bad drone camera
bad graphics
bad guy
bad light
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bad selfie
bad skin
bad teeth
bad trip
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bad vhs quality
bad vibes
bad weather
bad weather approaching
badass
badass anime 8 k
badass batmobile car design
badass beard
badass clothing
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badge
badge on collar
badger
badger badger
badlands
badly drawn
badminton
bae suzy
bag
bag - valve mask
bag over the waist
bagdasarian productions
bagels
baggy
baggy black pants
baggy clothing and hat
baggy eyes
baggy jeans
baggy pants
baggy purple overall shorts
bags
bags of money
bags on ground
bags under eyes
bags under his eyes
bagshaw art style
bahamas
baked bean skin texture
baked beans
bakelite rocky mountains
bakemonogatari
bakery
baki
baki style
baki the grappler
baking a cake
baking artwork
baking cookies
baking french baguette
balaclava
balaclava covering face
balaclava mask
balance
balance and proportional
balance beams
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balance rendered
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balances
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balaskas
balayage
balconies
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bald
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bald lines
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bald orc mechanic
bald patch
bald with short beard
balding
balding hair
balding older cyborg repairing
baldur's gate
baldur's gate character portrait
baldurs gate
baldurs gate character portrait
baleful
baleful young
balenciaga
balenciaga fashion
bali
ball
ball jointed doll
ball of yarns all around
ball shaped accordion sleeve
ballard
ballerina
ballerina dance in smoke
ballet
ballet dancer
ballet performance photography
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ballistic
balloon
balloons
ballpoint
ballpoint pen
ballpoint pen art
ballroom
ballroom background
balls of fire
balls of light for eyes
balls of rice
balmy
baloons
balrog
balrog concept art
baltermants
bam margera
bamboo
bamboo forest
bamboo forest in the background
bamboo huts
bamboo wood
banal but mysterious
banal object on a pedestal
banana
banana color
banana hat
banana plants drawing
banana trees
bananas
bananas weapon
banchou
band
band merchandise
band name is tripmachine
band of brothers
band of gold round his breasts
band playing
band playing instruments
band promo
band promo photo
bandage
bandage on arms
bandage taped fists
bandaged nose
bandages
bandai
bandai box art
bandai box art. realistic
bandai macross box art
bandana
bandanas
bandname is tripmachine
bandolier
bandoliers
banff national park
bang olufsen
bangalore
bangkok
bangkok townsquare
bangkuart
bangladesh
bangles
bangs
bangs and wavy hair
banish fiction and illusion
banjo kazooie
bank
bank robbery
bank robbery movie
bank robbery movies
banknote
banknotes flying from barrel
banksy graffiti
banner
banners
banners with lenin
banpresto
banshee
bao pham
bao phan
bao pnan
baobab tree
baobab trees
baobab trees in distance
baotou china
baphomet
baptism
baptiste from overwatch
bar
bar background
bar charts
bar in background
bar tosz domiczek
bara
barabas miklos and hollosy simon
barack obama
barack obama in attack on titan
barack obama in gta v
barack obama wearing sunglasses
baraka
barbara canepa
barbara hammer
barbara hammer 4 k
barbara hammer 4k
barbara kruger
barbara palvin
barbarella
barbarian
barbarian celebrate his birthday
barbarian class
barbarian on mars
barbarian pelt
barbarian warrior
barbarian warrior woman
barbarians
barbatos gundam
barbatos mobile suit
barbecue
barbecuing chewing gum
barbed wire
barbie
barbie cyborg
barbie doll
barbie doll in panties and bra
barbie or ken doll
barbra streisand no makeup
barbra streisand sitting
barcelo tomas
barcelona
barcelona in 1 9 1 8
barcelona in 1918
barclay shaw
barclay shaw 8 k photorealistic
barclay shaw 8k photorealistic
barcode
barcodes
bard
bard jester character sheet
bare
bare back
bare bark
bare bodybuilder shoulders. kohl
bare chest
bare feet
bare feet in grass
bare foot
bare forehead
bare hairy chest
bare leg
bare legs
bare midriff
bare room
bare shoulders
bare thighs
bare thighs!!!
bare torso
bare trees
barechest
bared shoulders
bared teeth
barefeet
barefoot
barefoot beautiful girl alien
barefoot in sandals
barely any walls inside
barely lit warm violet red light
barely seen women
barely visible
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bargello
bargello pattern
baris yesilbas
bark
bark for skin
barlow
barlowe
barlowe wayne
barn
barn in background
barn in the background
barn owl face
barn owl mask
barn owl symbol in chest
barnacle
barnet
barney and friends
barney the dinosaur
barney the purple dinosaur
barocco
barometric projection
baron harkonnen
barong family
barong family member
baroque
baroque acrylic painting
baroque aesthetics
baroque and rococo ornaments
baroque architecture
baroque art
baroque art jewelry
baroque art style
baroque cloth
baroque color palette
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baroque curls
baroque dark art
baroque detailed
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baroque digital painting
baroque dress
baroque element
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baroque environment
baroque frame border
baroque gold headpiece
baroque hair
baroque hibiscus queen
baroque interior
baroque landscape painting
baroque marble and gold in space
baroque object
baroque objects
baroque oil painting
baroque ornament detail
baroque ornament details
baroque painting
baroque painting. star lit sky
baroque portrait
baroque portrait painting
baroque renaissance. medium shot
baroque style
baroque style painting
baroque style painting backdrop
baroque symmetry
baroque vaporwave statue
baroque visual kei decora art
baroque wallpaper
baroque winding cobbled streets
baroque!
barracks
barrel chested
barrel fires and tents
barreleye
barreleye fish
barrels
barren
barren desert landscape
barren dry land
barren earth
barren landscape
barren moon landscape
barren sands
barren tundra
barren wasteland
barret frymire
barrier integrity
barriers
barron trump
barroque armor
barroque painting
barrows
barry chuckle
barry qindsor-smith
barry smith
barry windsor smith
bars on the windows
barsoom
bart simpson
bart simpson in real life
bartending
bartlomiej gawel
bartolome ros
barycentric subdivision
bas - relief
bas relief
base
baseball
baseball bat
baseball cap
baseball player mike trout
baseball stadium
based on a puma
based on bumblebee
based on child's drawing
based on childs drawing
based on geographical map
baselitz
basement
bashful
bashful expression
basia tran
basic
basic background
basic clothing
basic photo
basic shapes
basic steel sword
basic white background
basil
basil flying
basil gogos
basil leaves flying all over
basil leaves instead of leaves
basil wolverton
basilisk
basketball
basketball ball
basketball court
basketball sneaker concept art
basquiat
basquiat  afrofuturism
basquiat + francis bacon
basquiat style
basquiat | afrofuturism
basquiat!!
bass
bass clef
bass drum
bass music
bass sound waves on circuitry
bass wood
bassist
bast
bastet
bastien grivet
bastien lecouffe - deharme
bastien lecouffe deharme
bastien yoshitaka amano
bastion
bastion from overwatch
bastion game
bat
bat claws
bat ears
bat head
bat nose
bat wings
batgirl
bath
bath like style
bathed in golden light
bathed in light
bathed in moonlight
bathed in the the glow
bathed in the the glow of a fire
bathhouse
bathing in a waterfall
bathing in light
bathing inside the tar pit
bathing suit
bathrobe
bathroom
bathtub
bathtub with golden faucet
batik
batleground
batman
batman as a zombie
batman as a zombie!!!
batman as a zombie!!!!!!!
batman as the joker
batman beyond
batman dressed as darth vader
batman eating pizza
batman forever 1 9 9 5
batman horror movie
batman mecha
batman portrait
batman t shirt
batman wielding axe
batman with guns
batmobile
batoidea shape
bats
bats flying away from castle
bats flying over tombstones
bats in sky
bats pop art
bats splash painting
battalion
battered
batteries not included
battery
battery and wires
battle
battle - weary
battle action
battle action pose
battle action shot
battle angel
battle angel alita
battle angel manga
battle armor
battle armour
battle axe
battle between angels and demons
battle between good and evil
battle chasers
battle damage
battle damaged
battle damaged iron man suit
battle field
battle for whiterun
battle for whiterun city
battle garments
battle hardened
battle landscape
battle mage
battle map prop
battle mech
battle of 1453
battle of cape ecnomus
battle of geonosis
battle of good versus evil
battle of helmand moebius
battle of kings
battle of waterloo
battle pose
battle position
battle ram
battle rap
battle ready
battle scar
battle scars
battle scars across body
battle scene
battle shoes
battle stance
battle stand
battle standing
battle the dragon
battle toast
battle turtles
battle warrior
battle weary
battle with dragon
battle worn
battle worn armour
battle-damaged
battle-scarred
battle-worn
battleaxe
battlefield
battlefield 1
battlefield 4
battlefield 5
battlefield background
battlefield backround
battlefield in background
battlefield landscape
battlefield scene
battlefield war zone
battlefront
battleground background
battlehammer
battlemech
battlements
battlements with soldiers
battleship
battlestar galactica
battletech
battletech style
battling demons
battroid mode
batwings
batwoman
baugh casey
bauhaus
bauhaus architecture
bauhaus art
bauhaus poster
bauhaus style
bauhaus style painting
bauhause
bautiful
bay
bay area
bay window
bay window sofa
bayc
bayeux tapestry
baymax
baymax from big hero 6
bayonetta
bayou
bbc
bbc documentary
bbc earth
bbc news
bbc promotional artwork
bbloom
bbw
bbwchan
bc
bcy. net
be concept art
be not afraid
be occult
be real
be running up that hill
beach
beach aesthetic
beach and tropical vegetation
beach background
beach bar
beach in the foreground
beach is between the two valleys
beach landscape
beach on the outer rim
beach party
beach pic
beach sand background
beach scene
beach setting
beach setting medium shot
beach sunset background
beach surreal photography
beach tennis
beach trees in the background
beached submarine
beaches
beachfront
beachfront mansion
beaching
beachwood treehouse
bead and reel
bead necklace
beaded curtains
beaded embroidery
beads
beads cross onbare chest
beads of sweat
beady black eyes
beak
beak of an eagle
beaked mask
beaker
beakers
beakers full of liquid
beakers of colored liquid
beaks
beam
beam glowing eye
beam of light
beaming shining light
beams
beams of golden light
beams of light
beams of light from sky
beams of sunlight
beanie
beanie hat
beans
beans in his eyes sockets
bear
bear legs
bear rug
bear with scales
bearbrick
beard
beard stubble
bearded
bearded and built
bearded beautiful man
bearded cowboy
bearded man
bearded man seated on a throne
bearded skull
beardless
beardsley
bearing a large mad grin
bears
beast
beast wars
beastars legoshi
beastly
beastman concept
beaten
beaten city. neo noir style
beaten tech. neo noir style
beaten. unreal engine
beatiful
beatiful backgrounds
beatiful house
beatiful lights
beatiful mountain background
beatiful smiling face
beatiful vivid lights
beatifull lightning
beatifully lit
beatle
beatrice blue
beats
beatufil painting
beauitful
beauriful
beauteous sumptuous
beautful view
beautifier
beautiful
beautiful  face
beautiful - n 9
beautiful 2000s phone-camera
beautiful 3 d concept art
beautiful 3 d rendering
beautiful 3d concept art
beautiful 3d render
beautiful 3d rendering
beautiful acrylic fluid portrait
beautiful adult book fairy
beautiful adult fairy
beautiful adult fairy queen
beautiful adult library fairy
beautiful aerith gainsborough
beautiful aesthetic
beautiful aesthetic art
beautiful aesthetic design
beautiful aesthetic face
beautiful aesthetic lighting
beautiful aged and rustic finish
beautiful alien landscape
beautiful alison brie magician
beautiful alluring anime teen
beautiful alluring anime woman
beautiful alluring female cyborg
beautiful alluring teen
beautiful alluring women
beautiful ambiance
beautiful ambience
beautiful ambient light
beautiful ancient forest
beautiful ancient frost witch
beautiful ancient garden behind
beautiful ancient ruins behind
beautiful ancient trees
beautiful and aesthetic
beautiful and cinematic lighting
beautiful and colorful
beautiful and creepy
beautiful and cute
beautiful and detailed eyes
beautiful and elegant
beautiful and elegant elf queen
beautiful and elegant female elf
beautiful and graceful
beautiful and horrible
beautiful and mysterious
beautiful and ominous
beautiful and realistic
beautiful and realistic face
beautiful and realistic faces
beautiful and smiling
beautiful and smooth soft light
beautiful and spectacular dusk
beautiful and symmetrical face
beautiful and terrifying
beautiful androgynous girl
beautiful androgynous prince
beautiful android woman
beautiful angel
beautiful angel girl portrait
beautiful angelic wings
beautiful angle
beautiful animal pearl queen
beautiful anime
beautiful anime art
beautiful anime art style
beautiful anime artwork
beautiful anime catgirl
beautiful anime face
beautiful anime girl
beautiful anime girl squatting
beautiful anime high school girl
beautiful anime portrait
beautiful anime scene
beautiful anime scenery
beautiful anime style
beautiful anime woman
beautiful arab woman
beautiful architecture
beautiful armor
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beautiful art uhd 4 k
beautiful artist rendering
beautiful artwork
beautiful as the moon
beautiful asian girl
beautiful asian woman
beautiful asian woman sitting
beautiful asuka from evangelion
beautiful atmosphere
beautiful atmospheric lighting
beautiful audrey hepburn
beautiful autumn spirit
beautiful avatar pictures
beautiful background
beautiful backlight
beautiful backlit
beautiful beach
beautiful being
beautiful big symmetric eyes
beautiful biomechanical djinn
beautiful black blue yellow
beautiful black hair
beautiful blonde girl
beautiful blonde hair
beautiful blonde woman
beautiful blue - eyes
beautiful blue armet helmet
beautiful blue eyes
beautiful blue glowing eyes
beautiful blue haired girl
beautiful blue lights
beautiful blueish eyes
beautiful body
beautiful body and face
beautiful bone structure
beautiful border
beautiful boy
beautiful brand labels
beautiful bright lighting
beautiful brown eyes
beautiful brush stroke rendering
beautiful brush strokes
beautiful burqa's woman
beautiful burqas woman
beautiful caitriona balfe
beautiful cameraphone
beautiful cameraphone 2000s
beautiful castle
beautiful cat
beautiful cat feet
beautiful celestial mage
beautiful character painting
beautiful charlize theron
beautiful cheekbones
beautiful cinematic
beautiful cinematic light
beautiful cinematic lighting
beautiful cinematic poster
beautiful cinematography
beautiful city
beautiful city black woman only
beautiful city of the future
beautiful cityscape
beautiful cityscape background
beautiful cleopatra
beautiful clothes
beautiful cloud
beautiful clouds
beautiful clouds in the sky
beautiful cloudy atmosphere
beautiful cloudy deep blue sky
beautiful cloudy sky
beautiful code
beautiful coherent professional
beautiful color
beautiful color art!
beautiful color composition
beautiful color pallet
beautiful color pallette
beautiful color theme
beautiful colorful
beautiful colorful corals
beautiful colorful lights
beautiful colorful tilework
beautiful colors
beautiful colour palette
beautiful colours
beautiful comic art
beautiful composition
beautiful composition 3 - d 4 k
beautiful composition 3-d 4k
beautiful concept art
beautiful confident eyes
beautiful cosmic neural network
beautiful costume
beautiful cowboy witch
beautiful craftsmanship
beautiful creative space behind
beautiful creature
beautiful crying female android!
beautiful crying! android woman
beautiful crystals
beautiful curves
beautiful curvy female
beautiful cute
beautiful cyberpunk city
beautiful cyberpunk girl face
beautiful cyborg angel girl
beautiful cyborg girl
beautiful cyborg girl pinup
beautiful cyborg priestess
beautiful cyborg woman
beautiful dappled lighting
beautiful dark beach landscape
beautiful dark chaos
beautiful dark creepy landscape
beautiful dark elf countess
beautiful dark forest landscape
beautiful dark landscape
beautiful dark magician girl
beautiful day
beautiful daylight
beautiful decay
beautiful deep colors
beautiful deep colours
beautiful delicate face
beautiful depiction
beautiful design
beautiful detail
beautiful detail and color
beautiful detailed
beautiful detailed background
beautiful detailed blue eyes
beautiful detailed body and face
beautiful detailed concept art
beautiful detailed dress
beautiful detailed elegant
beautiful detailed eyes
beautiful detailed face
beautiful detailed faces
beautiful detailed fantasy
beautiful detailed figure
beautiful detailed illustration
beautiful detailed intricate
beautiful detailed miniature
beautiful detailed pixel art
beautiful detailed scene
beautiful details
beautiful digital art
beautiful digital artwork
beautiful digital illustration
beautiful digital painting
beautiful dog head
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beautiful drapes
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beautiful drawing style
beautiful dreamy breathtaking
beautiful dreamy landscape
beautiful dreamy lighting
beautiful dress
beautiful dusk
beautiful dusk sky
beautiful dynamic
beautiful dynamic dramatic
beautiful dynamic lighting
beautiful dynamic shadows
beautiful eden garden
beautiful elegant demon queen
beautiful elegant dryad
beautiful elf with ornate robes
beautiful elf with violet skin
beautiful elsa
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beautiful english countryside
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bench
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big wide broad strong physique |
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big!!!!!!!!!!!!
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billions of details
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binding of isaac
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bioluminescent skin!
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bioluminiscent fireflies
biolusence
biomachine war against humanity
biomaterial
biomech
biomechanic
biomechanical
biomechanical and intricate
biomechanical armor
biomechanical bodysuit
biomechanical corals
biomechanical cyborg
biomechanical details
biomechanical dress
biomechanical elements
biomechanical fluid
biomechanical gaia
biomechanical garment
biomechanical open chewing mouth
biomechanical oppai
biomechanical pattern
biomechanical railroad
biomechanical sculpture
biomechanical xenomorph
biomechanical xray
biomechanics
biomechanoid
biometric humanoid robot
biomimetic
biomorphic
biomutant
bionic
bionic armor
bionic arms and eyes
bionic cyborg implants
bionic exploded drawing
bionic implants
bionic scifi alexandre ferra
bionicle
bionics
bioorganic concept
biophilia
biophilia mood
biopic
biopods
biopunk
biopunk armor
biopunk art
biopunk clothes
biopunk cyborg wasp
biopunk environment
biopunk futuristic wardrobe
biopunk knight
biopunk style
biopunk suit
biopunk toys made in china
bioremediation
bios chip
bioshock
bioshock art style
bioshock big daddy
bioshock concept art
bioshock infinite
bioshock infinite art style
bioshock infinite style
bioshock pop art
bioshock pop art style
bioshock screenshot
bioshock style
biotech
biotechnology
bioware
bip bippadotta
biped
bipedal
biplanes
biplanes flying
bipolar
birb
birch
birch forest clearing
birch swamp
birch trees
birches
bird
bird - eye view
bird eye view
bird feathers
bird flocks
bird flocks!!
bird flying out of water
bird head
bird legs
bird mask
bird nightingale as subject
bird on his shoulder
bird poo on head
bird sight
bird tattoo
bird view
bird wings
bird's - eye view
bird's eye
bird's eye overhead shot
bird's eye view
bird's eye view of a city
bird's view
bird's-eye view
bird\'s eye view
birdeye
birds
birds - eye view
birds and butterflies
birds and trees
birds are all over the ground
birds eye
birds eye overhead perspective
birds eye overhead shot
birds eye photograph
birds eye view
birds f cgsociety
birds flying
birds flying away
birds flying away from explosion
birds flying in the distance
birds flying in the sunlight
birds in flight
birds in the distance
birds in the sky
birds in the sunlight
birds of paradise
birds on cherry tree
birds on sky
birds overhead
birds-eye view
birdseye view
birkenstock sandals
biroremediation
biroremediation plant
birth
birth and death
birth of the universe
birth of venus
birthday
birthday cake
birthday cake on the ground
birthday card
birthday painting
birthday party
birthday wrapped presents
bisexual lighting
bishounen
bisley
bismuth
bismuth art
bismuth beautiful well designed
bismuth cups
bismuth metal
bismuth rainbow metal
bison god
bispo do rosario
bisti badlands
bitches brew
bitcoin
bitcoin evil
bite
bite her lip
biting
biting lip
bitmap
bits of broken light
bitter
bittersweet
bixbite
bizarre
bizarre alien creature
bizarre compositions
bizarrrrre
bizzare
bizzare adventures of jojo
bizzare color pallete
bizzare landscape
bizzaro
bjarke ingels
bjd
bjork
bjork aesthetic
bjork smiling
björk
bjørn
bjørn skalldrasson
bl
black
black & white
black & white art
black & white photo
black & white photograph
black + white
black - and - white photograph
black - and - white photography
black - haired mage
black 3 d cuboid device
black 3d cuboid device
black aarakocra eagle warlord
black african princess
black ambient background
black an white
black and aqua colors
black and auburn colour pallet
black and blue
black and blue and gold jewelry
black and blue and purple scheme
black and blue color scheme
black and blue eyes
black and blue scheme
black and brown
black and brown colors
black and cyan color scheme
black and gold
black and gold armor
black and gold colors
black and gold palette
black and gold rich color
black and gold wires
black and golden armor
black and green
black and green eyes
black and green scheme
black and grey
black and grey tattoo style
black and orange
black and orange coat
black and orange colour palette
black and pink dress
black and purple
black and purple hair
black and purple rose petals
black and red
black and red armor
black and red background
black and red color scheme
black and red colors
black and red colour palette
black and red dress
black and red fantasy sword
black and red hair
black and red hair hair
black and red jacket
black and red only
black and red only!!!
black and red reflected armor
black and red scheme
black and red silk clothing
black and red suit
black and red tones
black and red tuxedo
black and reddis
black and reddish color armor
black and silver
black and teal paper
black and terracotta
black and violet costume
black and white
black and white and gold”
black and white and red
black and white and red colors
black and white artistic photo
black and white artwork
black and white background
black and white cat
black and white charcoal sketch
black and white checkered floor
black and white cinematography
black and white clothes
black and white color
black and white color aesthetic
black and white color only
black and white color palette
black and white color photograph
black and white color scheme
black and white color scheme!!!
black and white coloring
black and white colors
black and white detailed sketch
black and white drawing
black and white engraving
black and white film
black and white film grain
black and white film photograph
black and white film photography
black and white filter
black and white fur
black and white gainy film
black and white grainy film
black and white graphite drawing
black and white hair
black and white illustration
black and white image
black and white ink
black and white ink '
black and white ink on paper
black and white ink style
black and white line art
black and white logo
black and white manga
black and white manga comic
black and white manga page
black and white manga panel
black and white manga style
black and white matte painting
black and white monochrome
black and white old photo
black and white old photograph
black and white only
black and white paint
black and white painting
black and white palette
black and white photo
black and white photo.iso200
black and white photograph
black and white photography
black and white picture
black and white polaroid
black and white portrait
black and white press photograph
black and white scheme
black and white sketch
black and white spots
black and white style
black and white tattoo
black and white television still
black and white tones
black and white vector
black and white vector art
black and white vintage photo
black and white watercolor
black and white with hearts
black and white with mixed noise
black and white with red hearts
black and white zig zag floor
black and white)
black and white. high contrast
black and whitehighly detailed
black and white”
black and yellow
black and yellow and red scheme
black and yellow color scheme
black and yellow colors
black and yellow scheme
black and yellow tracksuit
black angel wings
black anime manga girl
black anime pupils in her eyes
black armchair
black armor
black armor with yellow accents
black armored uniform
black armour
black art
black atmospheric background
black backdrop
black backdrop!
black background
black background hyperrealism
black background pinterest
black background with stars
black background!!!!!
black background)
black backgrounds
black backround. inkscape
black ball pen on white paper
black bandage on arms
black bandana mask
black bangs
black beanie
black bear samurai
black beard
black beret
black beret with a red star
black bikini
black black black woman
black blindfold
black blue green
black bob cut hair
black bob hair
black bob haircut
black body
black bodysuit
black bomber jacket
black boots
black border
black border: 0.75
black bow tie
black bowtie
black box on the field flowers
black boy shorts
black bra
black bull samurai
black business suit
black butterflies
black canary
black canvas
black cap
black cape
black car
black cat
black cat at her feet
black cat in a city made of yarn
black cat taking a selfie
black cats
black centered pupil
black chair
black chalk
black choker
black choker necklace
black circle
black cloak
black cloak from neck to ankles
black cloak hidden in shadows
black cloth
black clothes
black clothing
black clouds
black coat
black color
black color background
black color on white background
black color scheme
black colors
black cowboy
black crown
black crows
black crows flying
black curly beard
black curly hair
black cyan gold and aqua colors
black cyberlox
black cybernetic enhancements
black darkness
black denim pants
black depths
black design
black details
black dog
black domes and spires
black dots
black draconic - leather
black dragon
black dress
black dress : : symmetrical face
black dress with belt
black dynamic hair
black ears
black edges
black elegant hair
black elon musk
black emma watson as egyptian
black energy
black extremely tight jeans
black eye makeup
black eye mask
black eye patch over left eye
black eye shadow
black eyebrows
black eyed
black eyed kids
black eyeliner
black eyepatch
black eyes
black eyes and large forehead
black eyes and sclera
black eyes!!!
black eyeshadow
black face
black facemask
black feathers
black feathers instead of hair
black fedora
black female
black fine lines
black fine lines on warm brown
black fir
black fire color reflected armor
black flat background
black floor
black flowers
black fluid simulation
black fog
black forest
black fork
black fractal filigree
black full plate armor
black fur
black fur armor
black furniture
black gears
black gems
black girl
black glasses
black glossy xenomorph
black gloves
black gloves!! and boots
black gold
black gold color scheme
black gold ink dripping
black gold light blue
black gold silver
black gold smoke ink
black goo
black gothic jacket
black gothic lolita dress
black gotic letter m
black gown
black gradient background
black graffiti
black greatcoat
black habit
black hair
black hair and brown eyes
black hair and large eyes
black hair and white bangs
black hair black cat ears
black hair in a rough shag
black hair in braids
black hair ribbons
black hair with purple streaks
black haired girl wearing hoodie
black haired yoongi
black hairs
black halo
black halter top
black hands with black claws
black harness
black hat
black haute couture
black head
black heavy armor
black heavy armor with gold trim
black heavy eyeliner
black helicopters
black hime cut hair
black hole
black hole event horizon
black hole in space
black hole rising above city
black hole sun
black hole time portal
black hole with accretion disс
black holes
black hollow eyes
black hood
black hoodie
black hoodie techie
black horns
black house
black human spine
black in
black ink
black ink & copic markers
black ink copic markers
black ink drawing
black ink illustration
black ink line art
black ink lines
black ink on paper
black ink on textured paper
black ink on white background
black ink on white paper
black ink outline
black ink outlines
black ink painting
black ink rough sketch
black intense eyes
black interface
black iron armour
black iron crown
black iron suit of armour
black iron sword
black iron tiara
black jacket
black jacket  shiny
black jacket | shiny
black jeans
black jersey
black jesus
black jewellery
black jewelry
black jewerly
black knee - length skirt
black lace
black lace dress
black lacquer
black lagoon
black lagoon manga
black latex
black latex female balaclava
black latex sculpt
black latex suit
black leahter gloves
black leather
black leather accents
black leather armor
black leather armour
black leather boots
black leather bra
black leather choker
black leather costume
black leather garment
black leather gloves
black leather harness
black leather jacket
black leather robes! intricate
black leather samurai garment
black leather shiny jeans
black leather shorts
black leather slim clothes
black leather vest is open
black leggings
black leggins
black light
black light movie poster
black light rave
black light velvet poster
black lightning
black line art
black lines
black lion with peacock wings
black lips
black lipstick
black loafers
black long curly hair
black long hair
black long hair!
black lotus
black lung detail
black luxurious suit
black magic
black magic crystal ring
black magic spells
black magician girl
black main color
black makeup
black man
black man with afro hair
black marble
black marble and gold
black marker
black mask
black matte finish
black mecha
black mesa
black messy hair
black metal
black metal aesthetics
black metal album cover
black metal band font
black metal band logo
black metal concert flyer
black metal face paint
black metal logos
black metal rococo
black metal style
black micron pen illustration
black mirror
black mirror series
black mist
black mold
black moons
black mos eisley
black mountains
black nails
black neon lights
black night sky
black nose
black octane render
black oil
black oil bath
black oled background
black on black
black on black. intricate
black on red
black on white
black on white background
black on white line art
black on white only
black on white paper
black ooze
black opal
black opals
black orb of fire
black outfit
black outline
black outlines
black overcoat
black oversized clothes
black paint
black paint drops
black paint flows down
black panther
black pants
black paper
black paper tarot
black pearls
black pearls and golden gems
black pen drawn edges
black peonies
black people
black pixie undercut haircut
black plastic
black plate armor
black pointed hat
black ponytail
black ponytail hair
black pulcinella mask
black pulcinella masquerade mask
black purple studio background
black quick
black rainbow opal
black ray - bans
black red long hair
black red long hair!
black red white clothes
black reflect robe
black resin
black rimmed glasses
black rims
black road
black robe
black robes
black rock statue material
black rocks
black rococo
black roof
black rose
black rose frame. d&d
black roses
black roses in hair
black round hole
black round hood and cloak
black rubber suit
black sand
black scales
black scales and cyborg tech
black scarf
black scars on her face
black scheme
black sclera
black sclera eyes
black sclera white pupil
black sclera! eyes
black scribbles and wiggles
black sea
black shadows
black shiny armor
black shiny bob haircut
black shiny eyes
black shirt
black shirt underneath armor
black shirt with red star
black shirt with red suspenders
black shoes
black short curtain haircut
black short hair
black shorts
black show room
black silhouette
black silky hair
black skeleton sword guard
black sketch
black skin
black skin!!!
black skirt
black sky
black sky background
black sky full of stars
black sky with stars
black slime
black smoke
black smoke particles
black smoke surrounds the area
black soggy pants
black sokkel
black space
black spandex
black splashes
black spot over left eye
black square glasses
black squares on 4 corners
black stars
black steel buildings
black steel with red trim
black stencil
black stetson and coat
black stetson hat
black stone
black straight hair
black stripes
black strokes
black studio background
black studio background color
black suit
black suit pants
black sullen eyes
black sun
black sun hat
black sun with purple eclipse
black sunglasses
black supremacy
black swan
black swans
black swat vest
black sweater
black sweatpants
black swimsuit
black t - shirt
black t shirt
black t-shirt
black tactical gear
black tank top
black tanktop
black tar particles
black teenage boy
black teenage girl
black tendrils
black textured
black theme
black thor
black tie
black to blue fade hairstyle
black to light pink fade hair
black to purple fade hair color
black to purple fade hairstyle
black tones
black top
black top hat
black traveling cloak
black trousers
black tunic
black turtle neck shirt
black turtleneck
black turtleneck lace dress
black umbrella
black undersuit
black underwear
black veil
black veins
black velvet
black velvet dress
black velvet painting
black vertical slatted timber
black vest
black victorian dress
black void
black volcano afar
black walls
black water
black watercolour
black wavy hair
black wavy hair with bangs
black wheel rims
black whispy smoke
black white
black white and red colors
black white gold
black white pastel pink
black white photo
black white pink
black white purple
black white red
black widow
black windows
black wings
black wings instead of arms
black wings slightly burnt
black wired cables
black witch hat
black witch hat and broomstick
black wolf
black wolf guarding
black woman
black wool sweater
black young woman
black! and white colors
black!!!!! background
black+velvet+red+turquoise
black-and-white
black-and-white photography
black-crimson color scheme
black-hair
black-hair pretty face
black-water-background
black-white retro photo 1910
black-white skintight robes
black-white skintight robes!
black. airports
black. yellow
blackboard
blackboard in background
blackened clouds cover sky
blackened space
blackening effect
blackhair
blackhole
blackhole sun
blacklight
blacklight aesthetic
blacklight neon colors
blacklight poster
blacklight reacting
blacklight reactive
blackness
blackout
blackpink
blackpink jennie
blacks and blues
blacksad
blacksmith
blacksmith apron
blacksmith product design
blacksmith's outfit
blacksmiths outfit
blackwork
blade
blade and soul
blade design
blade of grass
blade runned color palette
blade runner
blade runner 1 9 8 2 city
blade runner 1982
blade runner 1982 city
blade runner 2 0 4 9
blade runner 2 0 4 9 concept art
blade runner 2 0 4 9 manual
blade runner 2 0 9 9
blade runner 2049
blade runner 2049 colors
blade runner 2049 concept art
blade runner 2049 film
blade runner 2049 lighting color
blade runner 2049 manual
blade runner 2049 style
blade runner 2099
blade runner aesthetic
blade runner artifacts
blade runner background
blade runner city
blade runner city background
blade runner color palette
blade runner color scheme
blade runner concept art
blade runner eyes
blade runner feel
blade runner film
blade runner film style
blade runner lighting
blade runner movie
blade runner set
blade runner style
blade runner tech
blade runner vibes
bladed wings lace wear
bladee from drain gang
bladerunner
bladerunner 2 0 4 7
bladerunner 2 0 4 9
bladerunner 2 0 4 9 )
bladerunner 2047
bladerunner 2049
bladerunner apartment
bladerunner car
bladerunner city landscape
bladerunner in the rain
bladerunner scene
bladerunner street
bladerunner street alley
bladerunner style
bladerunner2049
blades
blades in the dark
blair armitage
blair witch
blair witch project
blake lively
blake rottinge
blake stone
blame
blame manga
blame!
blame! brutalist architecture
blanca alvarez
blanche
blanco y negro
bland
blank
blank background
blank expression
blank expression on her face
blank faces
blank paper
blank stare
blank stare”
blanket
blanket of fog
blankets of fog pockets
blasphemous
blast
blast of mystical energy
blaster fire bright as neon
blastoise
blazing engines
blazing fire
blazing fire and glowing lava
blazing fire tornadoes
blazing infero
blcak hair
bleach
bleach blonde hair
bleach manga style
bleached
bleached blonde short hair
bleached colors
bleached colours
bleached strong lights
bleak
bleak and oppressive atmosphere
bleak apocalyptic environment
bleak apocalyptic style
bleak atmosphere
bleak cityscape background
bleak color
bleak lighting
bleak tone
bleak. big stones
bleed
bleeding
bleeding audience
bleeding colors
bleeding decaying colors
bleeding decaying colors!
bleeding in the bath
blend elements
blended
blended colors
blended colors!!!!!
blended palette
blender
blender 3 d
blender 3 d 8 k
blender 3d
blender 8k uhd
blender and photoshop
blender art
blender cycles
blender cycles render
blender design assets
blender donut
blender donut tutorial
blender eevee render
blender guru
blender model
blender npr
blender render
blender render
blender rendering
blender screenshot
blender-hard-surface
blender3d
blending
blessed
blessing
blessing hands
blessing palms
blessing the soil at night
bliblical
blimp
blimps
blind
blind brown man
blind eyes
blindfold
blindfolded
blinding backlight
blinding backlight evening sun
blinding bright sun
blinding red orange sky
blinding sun
blinding white hair
bling
bling airpods
bling earbuds
blingee
blink
blinking lights
blippi
bliss
blissful
blissful atmosphere
blissful journey
blissful landscape
blizzard
blizzard cinematic
blizzard concept art
blizzard concept artist
blizzard concept artists
blizzard entertainment
blizzard entertainment 8 k
blizzard game concept art
blizzard hearthstone concept art
blizzard in the mountains
blizzard north 1997
blizzard sharp dynamic lights
blizzard storm
blizzard studio
blizzard warcraft
blizzard warcraft artwork
blizzard warcraft card game
bloated
blob
blob anime
blobs
block head
block party
block print
blockbuster
blockbuster ghostbusters movie
blockbuster movie
blockbuster movie art
blockchain
blockchain vault
blocked drains
blocking the sun
blockout
blocks
blocktober
blocky
blocky like minecraft
blocky shape
blog-photo
blond
blond beard
blond bob haircut
blond boy
blond brown long hair
blond brown stubble thin beard
blond curly hair
blond fur
blond furr
blond hair
blond hair blue eyes
blond hair green eyes
blond hair with pigtails
blond hair. ponytail
blond long hair
blond ltd
blonde
blonde - haired princess
blonde and attractive features
blonde and auburn two toned hair
blonde and silver hair
blonde anime girl with long hair
blonde beautiful young woman
blonde boy with yellow eyes
blonde braided hair
blonde braids and blue eyes
blonde british man
blonde crea
blonde cream
blonde curly hair
blonde elvish empress
blonde female jedi
blonde flowing hair
blonde girl
blonde girl in a cosmic dress
blonde goddess
blonde guy
blonde hair
blonde hair and blue eyes
blonde hair and large eyes
blonde hair blue eyes
blonde hair human head
blonde hair with a pony tail
blonde hair with pink highlights
blonde hot woman in wine cellar
blonde long hair
blonde man
blonde medium hair
blonde reddish hair
blonde shaggy hair
blonde short hair
blonde straight hair
blonde swedish woman
blonde woman
blonde women
blood
blood and body parts
blood and bone
blood and bones
blood and dark red highlights
blood and dead soldiers
blood and dust in the air
blood and guts
blood around the lips
blood aura red light
blood cells
blood collection vials
blood drenched
blood dripping
blood dripping down the head
blood dripping from eyes
blood dripping from mouth
blood drop
blood droplets
blood everywhere
blood for the blood god
blood in the seahighly
blood in the water
blood knight
blood letter
blood lines
blood meridian
blood moon
blood moon background
blood moon eclipse
blood moon tentacles!!
blood on body
blood on clothes and face
blood on floors
blood on his face
blood on sand
blood on tennis court
blood on the floor
blood on the streets
blood puddles on the floor
blood red
blood red armor
blood red background
blood red colored sky
blood red cresent moon
blood red eyes
blood red leaves
blood red moon
blood red sky
blood rivers
blood seeping from eyes
blood smear
blood spatter
blood splatter
blood splatter background
blood splatter on the sides
blood splatters
blood spray
blood stains
blood stains on shirt
blood stains on the walls
blood tears
blood temple
blood thirsty
blood trails
blood vesels
blood vessels
blood; dark cinematic lighting
bloodbath battlefield
bloodbath battlefield background
bloodborn
bloodborne
bloodborne art
bloodborne boss
bloodborne cathedral
bloodborne concept art
bloodborne diagrams
bloodborne monster
bloodborne style
bloodbourne
blooded
bloodlust
bloodshot eyeballs
bloodshot eyes
bloodthirsty
bloodwave
bloody
bloody + concept art
bloody battlefield
bloody eyes
bloody face
bloody knife
bloody knife in right hand
bloody ocean
bloody river in hell
bloody runes
bloody scene
bloody sunset
bloody tears
bloody walls
bloom
bloom and flowers in background
bloom effect
bloom effect 8 k
bloom effect 8k
bloom flowers
bloom lighting
bloom. fantasy
bloom. high fantasy
bloomed lighting
blooming
blooming effect
blooming flowers
blooming tropical flowers
bloomy
bloons td 6 dart monkey
blooodborne
blossom
blossom sakura
blossom wheat fields
blossoming
blossoming path to heaven
blossoming rhythm
blossoms
blotch of spray paint in corner
blotter art
blow my mind
blowing bubblegum
blowing hair
blowing out smoke
blowing sands
blowing smoke
blown glass
blown glass figure
blows smoke
blu - ray
blu - ray transfer 5 k
blu ray cover usa
blu-ray
blu-ray transfer
blue
blue - eyes
blue - green tones
blue - grey gear
blue - petals
blue - print
blue - ray screenshot
blue - turquoise fog in the void
blue - yellow sky
blue / grey background
blue / grey eyes
blue accent lighting
blue accents
blue adornements
blue and black
blue and black color scheme
blue and black color scheme))
blue and black scheme
blue and clear sky
blue and cyan colors
blue and cyan scheme
blue and gold
blue and gold color scheme
blue and gold palette
blue and gray colors
blue and green
blue and green and red tones
blue and green color palette
blue and green color scheme
blue and green colours
blue and green light
blue and green rainbow fire
blue and green water
blue and grey
blue and grey color scheme
blue and grey theme
blue and grey tones
blue and ice silver color armor
blue and orange
blue and orange color scheme
blue and orange lighting
blue and orange palette
blue and orange rim lights
blue and orange tones
blue and pink
blue and pink accents
blue and pink bonsai tree
blue and pink color scheme
blue and pink colors
blue and pink colour splash
blue and pink highlights
blue and pink lighting
blue and pink shift
blue and purle lighting
blue and purple
blue and purple and green
blue and purple colour scheme
blue and purple fur
blue and purple glowing hair
blue and purple hair
blue and purple lighting
blue and purple plants
blue and purple scheme
blue and purple tones
blue and purple vapor
blue and red
blue and red color palette
blue and red color scheme
blue and red glowing lights
blue and red hair
blue and red lighting
blue and red lights
blue and red tattoo
blue and red tones
blue and red two - tone
blue and silver
blue and silver colors
blue and violet
blue and violet color scheme
blue and white
blue and white and gold
blue and white and red mist
blue and white color palette
blue and white color scheme
blue and white colour scheme
blue and white tones
blue and yellow
blue and yellow color scheme
blue and yellow color theme
blue and yellow fauna
blue and yellow glowing lights
blue and yellow gradient
blue and yellow lighting
blue and yellow pythons intwined
blue and yellow ribbons
blue and yellow spiderman
blue and yellow theme
blue android eyes
blue arara
blue archive
blue armor
blue atmosphere
blue aura
blue aurora
blue backdrop
blue backdrop. hd
blue background
blue background colour
blue backgroung
blue backlight
blue ballpoint pen
blue bikini
blue bioluminescence
blue bioluminescent plastics
blue bird
blue black gold
blue black pink
blue blood
blue blurred
blue body
blue body paint
blue bonsai
blue border
blue braided hair
blue brain
blue cape
blue checkerboard background
blue checkerboard dress
blue chrome top hippo body
blue circular hologram
blue clear skies
blue clear sky
blue cloak
blue clothes
blue clothing
blue cloudy sky
blue coat
blue cobblestones
blue color
blue color bleed
blue color grading
blue color palette
blue color scheme
blue color theme
blue colored
blue colored traditional wear
blue colors
blue colors with red accents
blue colour scheme
blue colour splash
blue colours
blue corset
blue crashing waves
blue crystal exploding
blue crystals
blue cyborg
blue cyborg eyes
blue delphinium
blue diamond
blue diamonds
blue djinn
blue dog
blue door
blue dragon
blue dreadlocks
blue dress
blue drips
blue edge
blue elf
blue energy
blue eye
blue eye and green eye
blue eye. anime
blue eyeball
blue eyed
blue eyes
blue eyes and a wide face
blue eyes and blond hair
blue eyes and large forehead
blue eyes blue iris
blue eyes on the face
blue eyes white dragon
blue eyes!
blue eyes!!
blue eyes!!!!
blue eyes. deep focus
blue face unreal engine
blue faces
blue feathers
blue fedora
blue filter
blue fire
blue fire everywhere
blue fire powers
blue fire!
blue fireball
blue fireflies
blue flame
blue flames
blue flames surrounding
blue flower field
blue flowers
blue flowers accents
blue flowers bloomed all over
blue fog
blue forest
blue fur
blue fur with white spots
blue glacier
blue glacier volcano eruption
blue glass dreadlocks
blue gloves
blue glow
blue glowing eyes
blue gold and black
blue gold suit
blue gradient
blue grass
blue gray
blue grey and white color scheme
blue grey eyes
blue ground. fantasy
blue hair
blue hairs
blue hairstyle
blue haze
blue head
blue headlights
blue holographic face
blue hooded cloak
blue hoodie
blue hour
blue hour lighting
blue hour photography
blue hour stars
blue hue
blue hues
blue ice
blue image
blue indygo thunder lightning
blue ink
blue ink pen
blue iris
blue iris eyes
blue jacket
blue jay with a pile of acorns
blue jeans
blue jeans and grey sneakers
blue jeans. unreal 5
blue jellyfish
blue jewellery
blue lamps on the ceiling
blue leather armor
blue leds
blue lens airsoft mask
blue light
blue light accents
blue light saber
blue lighting
blue lighting. fantasy
blue lightning
blue lightnings
blue lights
blue lights and purple lights
blue lightsaber
blue lips
blue lipstick
blue liquid and snow
blue long hair
blue long pants and red shoes
blue magic
blue marble
blue mcdonalds
blue metal
blue mist
blue mohawk
blue mohawk hairstyle
blue monochromatic
blue mood
blue moon
blue moon light
blue moon ray tracing
blue moonlight
blue nebula
blue neon
blue neon accents
blue neon details
blue neon light
blue neon lighting
blue neon lights
blue night
blue ocean
blue or red
blue orange
blue outfit
blue overalls
blue paint on top
blue paint splash
blue palette
blue pants
blue parka
blue particles
blue penguin
blue period
blue phoenix bird
blue piercing eyes
blue planet
blue planet still
blue print
blue pupil
blue purple aqua colors
blue purple gradient
blue rays
blue rays from tv
blue rays of light
blue realistic 3 d render
blue realistic 3d render
blue reflections
blue reflective eyes
blue rim light
blue rim lighting
blue rimmed glasses
blue river in the middle
blue robe
blue robes
blue room
blue rose
blue sand
blue scaled dragon
blue scales
blue scales covering her chest
blue scales with white spots
blue scales. playing in the snow
blue scheme
blue sea
blue sea. by makoto shinkai
blue shadows
blue shark
blue shining eyes
blue shiny eyes
blue shiny lighting
blue shirt
blue shoes
blue short hair
blue shorts
blue shutters on windows
blue siberian forest cat
blue silk dress
blue silver and black
blue skies
blue skin
blue skintight closed longcoat
blue sky
blue sky above
blue sky and green grassland
blue sky and white clouds
blue sky at sunset
blue sky background
blue sky background with moon
blue sky in the background
blue sky with a few clouds
blue sky with beautiful clouds
blue sky with clouds
blue sky with colorful clouds
blue sky with dramatic clouds
blue sky with some clouds
blue sky without clouds
blue slide park
blue smoke
blue soft background
blue soft details
blue soft light
blue spiky hair
blue steel
blue storm clouds
blue submarine no 6
blue submarine no. 6
blue suit
blue sunglasses
blue sunny sky
blue sunset
blue sunshine
blue surcoat
blue sweater
blue swirling dress
blue symmetric eyes 24yo
blue tank top
blue text that says 3kliksphilp
blue theme
blue theme and yellow accents
blue themed
blue tiara
blue tie
blue tiefling
blue tight tshirt
blue tint
blue tint ektachrome film
blue tint expired film
blue tinted
blue tone
blue tone light
blue toned
blue tones
blue torn fabric
blue translucent resin
blue transparent jelly
blue trees
blue tunic
blue tunic and robes
blue turtleneck
blue undergarments
blue unicorn
blue uniform
blue v2 rocket in space
blue veins
blue velvet
blue waffle cone
blue wall
blue walls
blue water
blue waters
blue watery eyes
blue whale
blue white colors
blue witch hat
blue wizard hat
blue wolf
blue woodcut print
blue woodcut print cartoon
blue! and white colors
blue!! with orange details
blue'snappy gifts'plush doll
blue-black
blue-eyed
blue-eyed man
blue-eyes
blue-fabric
blue-green fish skin
blue-skinned elf
blue-white dress
blue-white hair
blue: 0.25
blue: 0.5
blueberries
blueberries on the side
blueberry
blueberry and orange and teal
blueish
blueish moonlight
blueish tones
bluejay
blueprint
blueprint diagram
blueprint of a laser space gun
blueprint of a spaceship
blueprint of spaceship
blueprint red ink
blueprint schematics
blueprint style
blueprints
blueray
blues
blues. beautiful
blueshift render
bluesnappy giftsplush doll
bluestone walls
bluewith orange details
bluey
bluish and cream tones
bluish face
blunt
blunt bangs fall on her forehead
blunt borders
blur
blur : - 2
blur background
blur cinematic
blur dreamy outdoor
blur effect
blur effect face
blur focus
blur studio
blur: -4
blur: -5
blur:-1
bluray
bluray image
blured background
blurred
blurred and dreamy illustration
blurred background
blurred backround
blurred detail
blurred environment background
blurred face
blurred forest background
blurred lost edges
blurred photo
blurred space
blurry
blurry and dreamy illustration
blurry and glitchy
blurry and sharp focus
blurry background
blurry background of the library
blurry backround
blurry distant background
blurry face
blurry focus
blurry footage
blurry image
blurry photo
blurry photography
blurry plume smoke at background
blush
blushing
bluth
bluth!
bmw
bmw and mercedes concept cars
bmw e 3 0
bmw e 30
bmw e30
bmw i 8
bmw m 1
bmw m1
bmw m1 lincoln continental
bo bartlett
bo burnham
bo chen
bo feng
bo xun ling
board game
board game cover art
board games
board games on a table
boarded up
boardgamegeek
boards of canada
boards of canada album cover
boardwalk
boat
boat dock
boat in foreground
boat with lamp
boats
boats in the water
bob
bob clampett
bob cut
bob cut hair
bob dylan
bob haircut
bob larkin
bob larkin and tomer hanuka
bob marley
bob odenkirk
bob pepper
bob ross
bob ross as 007
bob ross as captain america
bob ross as scarface
bob ross as superman
bob ross dressed as a superhero
bob ross painting
bob ross painting bob ross
bob ross style
bob's burgers
boba fett
bobcat standing alone on a log
bobs burgers
bocage
bocages
boch
bodhisattva
bodies
bodies on the ground
body
body - horror
body and face
body and head in frame
body and head in view
body and headshot
body and torso
body armor
body armour
body breaking apart
body builder
body building blacksmith
body centered
body complet
body covered in floral tattoos
body covers with neon crystals
body covers with neon flowers
body draped in moss
body features
body fit
body fitted dart manipulation
body format
body full
body full glowing vacuum tubes
body golden armor
body hair
body harness
body horror
body in water
body is strong and upright
body made of fire
body made of glass
body made out of macaroni
body meshes
body modification
body of a robot
body of pure fire
body of water
body paint
body painted with black fluid
body parts
body parts everywhere
body portrait
body ribs meshes
body shot
body swelling about to explode
body symmetrical anatomy
body transmogrify
body with black and red lava
body-horror
bodybuilder
bodybuilder body
bodybuilder ernest khalimov
bodybuilder physique
bodybuilder posing
bodybuilder superhero bikini
bodybuilding woman
bodycam
bodycam footage
bodycon dress
bodyhorror
bodyless
bodypaint
bodypainting
bodysuit
boe jiden
boeing 737 cabin
boeing concept art
boeing concept art painting
bog
bog oak
bogdan rezunenko
bogna gawrońska
bohek
bohemian
bohemian digitals
bohemian fashion
bohemian mystics
bohemian style
boho
boho - chic
boho chic
boho chic   very anime
boho chic | | very anime!!!
boho chic!!
boho floral vines
boho neutral colors
boho poncho and long maxi dress
boho style
bohr model
boichi
boiler room
boiling
boiling imagination in a bowl
boissb - blanca. j
boisterous
boixcar style
bojack horseman
bojack horseman in real life
boka
boke
bokeh
bokeh )
bokeh + dof + 8k
bokeh backdrop
bokeh background
bokeh blur
bokeh chrome accents
bokeh color background
bokeh depth of field
bokeh details
bokeh dof
bokeh dof sky
bokeh effect
bokeh filter
bokeh focus
bokeh forest background
bokeh glow
bokeh in the background only
bokeh intricate details
bokeh iridescent accents
bokeh light
bokeh light from top
bokeh lights
bokeh macro lens
bokeh on background
bokeh on side of the river
bokeh paris in background
bokeh photo
bokeh photograph
bokeh photography
bokeh soft
bokeh top cinematic lighting
bokeh volumetric lighting
bokeh ”
bokeh!
bokeh!!
bokeh!! watercolor
bokeh!!!!
bokeh!!!!!
bokeh. brian spilner
bokeh. chrome accents
bokeh. debadged
bokeh. i
bokeh. iridescent accents
bokeh. rule of thirds
bokehlicious
bokehs
bokehwatercolor
bokeh”
boken
bokh
boku no hero academia style
bold
bold 3 d
bold and colourful
bold architecture
bold black lines
bold bright colors
bold brushstrokes
bold brushwork
bold color
bold color scheme
bold colored walls
bold colors
bold colour
bold colours
bold complementary colors
bold complementary colours
bold complimentary colors
bold composition
bold contour lines
bold crimson ornate robes
bold face type
bold graffiti
bold graphic graffiti
bold graphics
bold intricate ink illustration
bold lamborghini style
bold lighthouse in horizon
bold lighting
bold line art
bold lineart
bold lines
bold moody colors
bold natural colors
bold pastel colors
bold psychedelic colors
bold rave outfit
bold serious expression
bold shapes
bold simple shapes
bold strokes
bold warm and cool colours
bold warm colours
bold. intricate
boldly-inked gouache artwork
bolero
bolivian cholitas
bollywood
bolsonaro
bolt action rifle
bolt pistol in one hand
bolts
bolts of bright yellow fish
bolts of lightning
bomb
bomb explosion
bombastic ostentatious
bomber jacket
bomber planes
bomberman
bombs
bombs are falling from the sky
bombshell
bond girl
bondi beach in the background
bone
bone and ivory
bone armor
bone crown
bone dress
bone jewellery
bone jewelry
bone plants
bone scepter
bone to bone
bone wastes
bone wings
boneface
bones
bones and veins
bones joints
bones lying on the ground
bones on the ground
bones that were very dry
bonestell
boney
bonfire
bong
bongos
bonnard
bonnie maclean
bonsai
bonsai anatomy atlas
bonsai skeleton anatomy atlas
bonsai tree
bonsai tree house
bonsai tree on roof
bonsai trees
bony
bony face
boogiepop phantom
book
book case
book cover
book cover art
book cover design
book cover illustration
book cover!!!!!!!!!!!!
book illustration
book in hand
book library studying
book of kells
book of the dead
book portrait
book shelf
book shelf small library
book shelves
book title visible
books
books all over the place
books and flowers
books cave
books covered in jewels
books flying around
books messy about the room
books on side table
bookshelf
bookshelves
bookshelves on sides
bookshops
boom
boombox
boomhauer
booru
booster
booster flares
boot camp
booth. pm
boots
boots!!!!!!
bootsy collins
booty shorts
booze
borat is a chimp
bordalo
border
border collie
border collie dog on book cover
border pattern
bordered
borderlands
borderlands 3
borderlands 3 style
borderlands 4k render
borderlands art style
borderlands style
borders
boreal forest
borealis
bored
bored ape
bored ape nft
bored ape yacht club
bored expression
borg
borg cube
borgia
boring
boris artzybasheff and hr giger
boris johnson
boris johnson as captain britain
boris johnson as doctor strange
boris johnson as dr. strange
boris johnson as hulk
boris johnson as iron man
boris johnson as neo from matrix
boris johnson as punisher
boris johnson as rambo
boris johnson as rick sanchez
boris johnson as thanos
boris johnson as the joker
boris johnson as thor
boris johnson as wolverine
boris johnson gigachad
boris johnson hairstyle
boris johnson in gta v
boris johnson skydiving
boris valejo
boris valejo. octopath traveler
boris vallego
boris vallejo
boris vallejo and ilya kuvshinov
boris vallejo and julie bell
boris vallejo and tom bagshaw
boris vallejo artstyle
boris vallejo style
boris vallejo)))
boris villajo
boris villejo
borja
born survivor
born this way
born this way album
born under a bad sign
borne space library artwork
boromir in an 80\'s anime world
boromir in an 80s anime world
boromir in an anime world
boroque
bosch
bosnian
boss
boss battle
boss fight
bossfight
bossons vintage chalkware
boston
boston celtics
boston celtics ironman suit
boston dynamics
boston dynamics robots
boston massachusetts
botanic
botanic foliage
botanic garden
botanic watercolors
botanical
botanical anatomy
botanical art
botanical artwork
botanical background
botanical drawing
botanical fractal structures
botanical garden
botanical herbarium
botanical herbarium paper
botanical illustration
botanical photo
botanical poster
botanical print
botanical rainbow backdrop
botanicals
botany
botany bones
both bright and earth colors
both faces visible
both have red lips
both laughing
both men and cattle
both smiling for the camera
both wearing victorian clothes
boticelli
botticelli
botticelli and victo ngai
botticelli style
bottle
bottle of vodka
bottle of wine
bottlebrush
bottles
bottles covered in wax
bottles of alcohol next to him
bottles of spices
bottom - up perspective
bottom - view
bottom angle
bottom body close up
bottom half wood!!!!! carved
bottom lighting
bottom made half wood
bottom of ocean
bottom of the ocean
bottom of the sea
bottom shot
bottom up green lighting
bottom up lighting
bottom viev
bottom view
bottom view ， bladerunner
bottom viewa
bottomless void
botw
botw style
boucheron
boucheron style
boudoir
boudoir photography
bougainvillea
bougeureau
bouguereau
bouguereau and bowater
bouguereau and mucha
bouguereau style pose
bouguereau. full-shot
boulder chase
boulders
boulevard
boulevard of broken dreams
bounce light
bounce lighting
bounced light
bouncing
bouncy belly
boundary of two lands
boundless and bare
bountiful crafts
bounty hunter
bouquet
bouquets
bourgeoise
boutinela bikini
bovine
bow
bow and arrow
bow ashigaru
bow tie
bowater charlie
bowater charlie and brom gerald
bowknot
bowl
bowl filled with food
bowl haircut
bowl of fruit
bowling
bowling alley carpet
bows
bowser
bowser nintendo
bowtie
box
box art
box braids
box cutter
box office hit
boxart
boxcar on the railroad
boxer
boxing
boxing gloves
boxing match
boxing ring
boxing stance
boy
boy and girl
boy and girl are the focus
boy girl traditional romance
boy hair
boy has short black hair
boy scout troop
boy shorts
boy staring at the window
boy thin face
boy with cat ears and tail
boy with neutral face
boy's room
boyish
boyish face
boys
boys and girls
bozo the clown. circus motif
bozo the clown. clown motif
bra
bra and shorts streetwear
bra strap
braavos
bracelets
bracelets and necklaces
brachiosaurus
bracts
brad
brad kunkle detailed
brad kunkle elson peter
brad pitt
brad pitt is leon s. kennedy
brad pitt portrait
brad rigney
bradbuilds
bradley
bradley cooper
bradley cooper in finding nemo
bradley james and colin morgan
bradley wright
braid hairstyle
braided beard
braided beard redhead dreadlocks
braided brown hair
braided cable
braided hair
braided hair loops
braided hair with roses
braided hair. nightime
braided hairstyle
braided intricate hair
braids
braids in hair
brain
brain anatomy
brain city art
brain computer interface
brain connected to computer
brain from crystals
brain in a vat
brain in a vat!!
brain in a vat!!!
brain interface
brain stars
brain surgery
brain tree eye holy grail
brain visible
brainbow
brains
brainstorm
brainwashed
brainwashing
bralette
bram sels
branches
branches and foliage
branches and ivy
branches and twigs
branches composition abstract
branches growing as hair
branches sprouting from her head
branches wrapped
branching
branching hallways
brancusi
brand
brand colours are green and blue
brand colours are red and blue
brand identity
brand mark
brand new lego set ( 2 0 2 1 )
brandi love
branding
brandishing a gun
brandishing a powerful sword
brandishing chainsword
brandishing cosmic weapon
brandishing futuristic sword
brandishing halberd
brandishing naginata haldberds
brandishing powerful sword
brandishing sword
brandmark
brandon
brandon sanderson
brandon woelfel
brandywine school
brash
brasil
brass
brass and copper
brass and steam technology
brass and wood mechanisms
brass armor
brass beak
brass bracelets
brass colored armor coat
brass copper
brass debris
brass equipment and computers
brass goggles
brass horns
brass instruments
brass metal armor
brass pipes on the walls
brass plated
brass plates
brass semi - mechanical woman
brass sheet
brass victorian goggles
brass wheels
brass woman
brave
brave police j decker face
brave young girl
brave. by makoto shinkai
bravely default inspired
bravery
brawl
brawl stars
brawny
braziers
brazil
brazil carnival
brazilan supermodel
brazilian
brazilian flag
brazilian ronaldo
breach and clear
bread
bread type pokemon
breads
break of dawn
break of dawn on jupiter
break of dawn on neptun
break of dawn on pluto
break of dawn on venus
breakcore
breakdancing
breakdown
breakfast
breakfast at las pozas
breakfast buffet
breaking
breaking apart
breaking bad
breaking bad as a manga
breaking bad scene
breaking news
breaking news footage
breaking statue of girl
breaking the waves
breakthrough is iminent
breastplate
breastplate ) ) )
breasts covered and sfw
breath - taking beautiful trees
breath condensation
breath of the wild
breath of the wild art style
breath of the wild screenshot
breath of the wild style
breath of the wilde
breath taking
breath taking beautiful
breath-taking beautiful beach
breath-taking beautiful trees
breathe
breathing
breathing blue fire
breathing fire
breathtaking
breathtaking art
breathtaking clouds
breathtaking colors
breathtaking composition
breathtaking detail
breathtaking detailed
breathtaking digital art
breathtaking eyes
breathtaking face
breathtaking fantasy art
breathtaking flower tiara
breathtaking himalayan landscape
breathtaking ilya kuvshinov
breathtaking landscape
breathtaking lights
breathtaking lights shining
breathtaking look
breathtaking masterpiece of art
breathtaking mountains
breathtaking quality
breathtaking realistic
breathtaking render
breathtaking scenery
breathtaking shot
breathtaking stars
breathtaking wave
breeches
breed corgi and doodle mix
breed russian brown toy terrier
breeding
breezy background
breitling
brendan fraser
brenizer method
brent hollowell
brethtaking lighting
breton cap
brett amory
brett goldstein
brewing potion in witch hut
brexit
brian froud juan gimenez
brian froud style
brian griffin
brian ingram
brian jacques
brian jacques redwall woodland
brian kesinger
brian miller
brian pulido
brian sum
brian wilson
briareus
brick
brick building
brick debris
brick wall
brick wall background
brick wall texture
brick walls
bricks
bricks flying
bricks flying outwards
briclot
bride
bride and groom
bride of chucky
bride of frankenstein
bridge
bridge between the worlds
bridge city
bridge over the water
bridge to terabithia
bridges
bridges and railings
bridges crossing the gap
briefcase
brienne of tarth
brigandine
bright
bright - light photography
bright accurate green eyes
bright amber eyes
bright ambient lighting
bright and beautiful
bright and colorful
bright and colourful
bright and contrasting colors
bright and energetic
bright and fun colors
bright and happy
bright and moody
bright and saturated palette
bright and sunny
bright and sunny day
bright art
bright atmosphere
bright atmospheric lighting
bright background
bright backlit
bright bloom
bright blue
bright blue eyes
bright blue future
bright blue glowing water
bright blue shiny lingerie
bright blue sky
bright blue smoke
bright bold colors
bright brown eyes
bright camera flash
bright camera flash enabled
bright castleton green
bright cinematic backlit
bright cinematic key lighting
bright cinematic lighting
bright city lights
bright clouds
bright color
bright color palette
bright color scheme
bright colored streaks of hair
bright colorful
bright colorful colors
bright colors
bright colors highly detailed
bright colors oil on canvas
bright colors ultrawide lens
bright colors with red hues
bright colour
bright colour tone
bright coloured streaks of hair
bright colours
bright construction materials
bright contrast
bright cyberpunk glow
bright dappled golden sunlight
bright day
bright day light sun
bright daylight
bright daylight indoor photo
bright daylight moody
bright deep blue
bright demonic glowing eyes
bright depth oil colors
bright details
bright diffuse lighting
bright divine lighting
bright explosion
bright eye makeup
bright eyes
bright fauna
bright fire eyes
bright flare
bright flash
bright fluorescent light
bright fluorescent lights
bright forest
bright fuchsia skin
bright future
bright glowing eyes
bright glowing eyes as leds
bright glowing eyes like leds
bright glowing instruments
bright glowing translucent aura
bright glowing veins
bright godrays
bright gold eyes
bright gold long hair
bright green dark orange
bright green eyes
bright green hair
bright green swirls coming up it
bright ground
bright hair
bright halo
bright happy atmosphere
bright headlights
bright high tech lights
bright hopeful atmosphere
bright in colour
bright internal glow
bright internal light
bright iridescent light
bright it interiors
bright landscape
bright led lights
bright lens flare
bright light
bright light masterpiece
bright lighting
bright lights
bright limited color palette
bright lit interiors
bright macro view pixar
bright microscopic view realism
bright modern style
bright mood
bright moon
bright moonlight
bright moonlight and stars
bright morning
bright multiple glowing eyes
bright natural light
bright neon
bright neon city lights
bright neon color palette
bright neon colors
bright neon colours
bright neon highlights
bright neon lighting
bright neon lights
bright neon lights from the city
bright neon signs
bright neon solar flares
bright nordic forest
bright on black
bright orange camp fire
bright orange eyes
bright orange hair
bright pale blue eyes
bright palette
bright pastel color
bright pastel colors
bright piercing brown eyes
bright pink highlights
bright pink purple lights
bright pink streaks of hair
bright psychedelic color
bright psychedelic colors
bright rainbow nimbus
bright rays of light
bright red
bright red cape on her back
bright red desert sands
bright red eyes
bright red hair
bright red lipstick
bright render
bright rim light
bright room
bright saturated colors
bright saturated colours
bright scene
bright screen
bright sea
bright shining eyes
bright signage
bright sky
bright smile
bright smiles
bright soft colors
bright soulful eyes
bright sparks
bright spells
bright spotlights
bright stars
bright studio lighting
bright studio setting
bright style
bright summer day
bright sun
bright sun bleached ground
bright sun ground
bright sunlight
bright sunny day
bright sunny day blue sky
bright sunny summer day
bright sunny time
bright sunshine
bright thin lasers
bright thin wires
bright tones
bright towels
bright trouser suit for a rave
bright uniform background
bright vibrant color
bright vibrant colors
bright vivid color hues:1
bright vivid colors
bright vivid colours
bright vivid lighting
bright volumetric lighting
bright volumetric sunlight
bright white
bright white castle stones
bright white hair
bright white light
bright white porcelain
bright white realistic
bright window lit kitchen
bright witch
bright yellow and red sun
bright yellow color scheme
bright yet cool colors
bright ”
bright:
brightly colored
brightly colored buildings
brightly colored flowers
brightly coloured
brightly coloured hair
brightly coloured oil on canvas
brightly coloured smoke
brightly glowing eyes
brightly lit
brightly lit - style atmosphere
brightly lit blue room
brightly lit pink room
brightly lit purple room
brightly lit room
brightly lit!
brightly-lit
brightvibrant
brigitte
brigitte bardot
brigitte from overwatch
brilliant
brilliant aesthetic lighting
brilliant blue flowing hair
brilliant cold lighting
brilliant colors
brilliant composition
brilliant daylight
brilliant daylight vr os ux
brilliant detail
brilliant glittering hair
brilliant glow
brilliant highlights
brilliant lighting
brilliant lights
brilliant modern colors
brilliant museum sculpture
brilliant peaks
brilliant photorealism
brilliant pink ball gown
brilliant reflections
brilliant royal blue
brilliant sunset
brilliant symmetry
brilliantly colored
brilliantly coloured
brimming with energy
brimstone
brine lacrosse king v gloves
brine lacrosse stick
bringing people together
bristlecone pine trees
bristling
brit marling style 3/4
britain
british
british columbia
british dragons
british features
british flag
british gang member
british museum
british pathe archive
british propaganda poster
british stopmotion
british street background
britney spears
britt marling style
britt marling style 3 / 4
britt marling style 3/4
brittle.
brittle. highly detailed
brittney lee
broad
broad brush
broad brush strokes
broad brush strokes!
broad daylight
broad detail
broad light
broad lighting
broad shoulder
broad shoulders
broad strokes
broad sword
broadshouldered
broadsword
broadway
brocade
brocade dress
brocade robes
broccoli
brochure
brock hofer
brock lesnar
brock lesnar riding a donkey
brocken spectre
brockholes
broke machines
broken
broken antenna
broken armor
broken beautiful female android!
broken bones
broken bottles
broken bridges
broken buildings
broken camera
broken camera colors
broken cars
broken composition
broken computers and cables
broken down
broken down grey wall
broken fluorescent lighting
broken forests
broken furniture
broken glass
broken glass photo
broken glasses
broken gold shackles
broken google maps
broken heart
broken hearted
broken hearts
broken landscape
broken laptop screen
broken leaking cell wall
broken lights
broken machinery
broken mask
broken mirror
broken mirrors composition
broken neon signs
broken panes of glass
broken parts
broken pieces
broken pipes
broken rainbow
broken reality
broken road
broken ruin armored core trunk
broken ruin pacific rim jaeger
broken shackles
broken signs
broken stained glass windows
broken statues
broken teeth
broken throne
broken tiles
broken toilets
broken toys
broken toys are scattered around
broken tv
broken vase
broken vending machines
broken wheelchair
broken windows
broken wires
broken-stained-glass
broly
brom art
brom digital art
brom gerald
brom redshift
bromeliads
bronze
bronze - skinned
bronze age
bronze armor
bronze biomechanical
bronze biomechanical garment
bronze brown hair
bronze dwarven breastplate
bronze face
bronze greek armor
bronze headdress
bronze headset
bronze material
bronze poli
bronze sculpture of medusa
bronze skin
bronze skinned
bronze statue
bronze statue and silver
bronze!! (eos 5ds r
brood spreading
brooding
brooding clouds
brooding clouds'
broody
brook
brooke ashling
brooke shaden detailed
brooke shields
brooklyn
brooklyn at night neon boards
brooklyn background
brooklyn museum
broomstick
brotherhood
brothers
brown
brown ) )
brown almond-shaped eyes
brown and cream color scheme
brown and cyan blue color scheme
brown and cyan color scheme
brown and gold
brown and gold color palette
brown and magenta color scheme
brown and pink color scheme
brown and white color scheme
brown armor
brown atmospheric lighting
brown background
brown bangs
brown bear
brown beard
brown bob haircut
brown body
brown boots
brown bread with sliced salo
brown buzzcut
brown canyon background
brown cloak
brown clothes
brown cobble stones
brown color
brown color palette
brown colored long hair
brown colors
brown colours
brown corset
brown curly hair
brown dark hair
brown durand
brown exoskeleton
brown eyes
brown eyes and white skin
brown eyes with red eye markers
brown flowers
brown flowing hair
brown fluffy hair
brown fringe
brown fur
brown gravy
brown hair
brown hair and a perfect body
brown hair and bangs
brown hair and large eyes
brown hair flow
brown hair fringe
brown hair in a ponytail
brown hair in two buns
brown hair with bangs
brown hair with light blue ends
brown hair!
brown haired
brown hat
brown heavy armor
brown holes
brown hooded cloak
brown jacket
brown jacket with long sleeves
brown jedi robe
brown leather armor
brown leather jacket
brown long and straight hair
brown long hair
brown long hair with bangs
brown medium hair
brown medium length hair
brown messy hair
brown mist
brown mud
brown neat hair
brown palette
brown pants
brown paper
brown red and gold ”
brown red blue
brown red long hair
brown reddish hair
brown resin
brown robes
brown sauce
brown scales
brown shirt
brown short hair
brown skin
brown skin like soil
brown skin man egyptian prince
brown skin man with a giant grin
brown skin. light makeup
brown skinned
brown smiling eyes
brown stubble
brown suit vest
brown sweater
brown tail
brown tones
brown tuffle coat
brown water
brown watery eyes
brown wavy hair
brown wood cabinets
brown-blond-hair pretty face
brown-red-hair pretty face
brown:-2
brownish colors
brownish fossil
brownish old fossil remnant
browns and whites
browny
bruce davidson photography
bruce kaiser
bruce lee
bruce penington
bruce springsteen
bruce timm and vladimir kush
bruce weber
bruce willis
bruce willis as samurai
bruegel
bruegels the tower of babel
brught tones
bruh moment
bruised
bruised face
bruises
brujeria
brunette
brunette boy and redhead boy
brunette elf with fairy wings
brunette fairy woman
brunette fairy woman stretching
brunette hair
brunette with dyed blonde hair
brunette woman
brunettes
brush
brush art
brush hard
brush marks
brush paint
brush painting
brush pen
brush stroke
brush stroke oil painting
brush strokes
brush strokes oil painting
brush texture
brush work
brushed
brushed aluminum
brushed metal
brushed red and blue paint
brushed rose gold car paint
brushed white and blue paint
brushes
brushes her teeth
brushstroke - laden
brushstrokes
brushwork
brussels
brutal
brutal archi
brutal battle
brutal black clothes
brutal blooded symmetrical face
brutal bloody and sluty make up
brutal bloody sluty make up
brutal concrete architecture
brutal concrete interiors
brutal depiction
brutal fight
brutal human detailed
brutal human detailed!!!
brutal joyful face expression
brutal modern sculpure
brutal shapes
brutal shapes high detail 8k
brutal shapes stormy sky
brutal violence
brutalism architecture
brutalism buildings
brutalism style
brutalism style stool
brutalist
brutalist alien civilization
brutalist appearance
brutalist architecture
brutalist architecture building
brutalist architecture buildings
brutalist aztec architecture
brutalist base scifi
brutalist buildings
brutalist buildings tower over
brutalist city
brutalist city architecture
brutalist courtyard
brutalist dark fantasy
brutalist design
brutalist environment
brutalist fantasy
brutalist fashion show
brutalist futuristic interior
brutalist illustration
brutalist international futurist
brutalist liminal architecture
brutalist office buildings
brutalist painting
brutalist shiro
brutalist space ship
brutalist style
brutalist waterfall environment
brutalistic environment
brutality
brute
brutish
brutus
bryan christie
bryan cranston
bryan cranston as gordon freeman
bryan cranston as thor
bryan lee o'malley
bryan skerry
bryan sola
bryce 3 d
bryce 3 d style
bryce 3d
bryce dallas howard
bt 2 1
bt 21
bt21
bts
bubble
bubble background
bubble bath
bubble chamber
bubble goth
bubble goth makeup
bubble gum
bubble head
bubble letters
bubblegum
bubblegum body
bubblegum crisis
bubblegum face
bubblegum hair
bubblegum pop
bubbles
bubbles everywhere
bubbles in the air
bubbles of the impossible
bubbles rising
bubbles vfx
bubbles ”
bubbling cauldron
bubbling cauldron!
bubbling geysers
bubbling liquids
bubbling ooze covered serious
bubbling skin
bubbly
bubbly scenery
bubbly underwater scenery
buck rogers
buck studios artwork
buck teeth
bucket hat
buckethead
bucklebury ferry
bucolic
bud spencer
budapest
budapest street background
buddha
buddhism
buddhist
buddhist architecture
buddhist art
buddhist monastery on mars
buddhist monk
buddhist monk meditating
buddhist temple
buds
buenos aires
buff
buff man
buff painting
buffalo
buffalo chase
buffalo hunt movie
buffed
buffy the vampire slayer
bug
bug life inside human body
bug like face
bugatti
bugatti veyron
bugbear
bugbear ranger
buggy
bugs
bugs bunny
build in a forest near of a lake
building
building along a river
building anticipation
building blocks
building cover with plant
building crumbling
building destroyed
building facing
building in the distance
building mushrooms
building on fire
building plans
buildings
buildings and smoke
buildings are flying
buildings carved out of stone
buildings collapsed
buildings covered in black tar
buildings covered with greebles
buildings in the distance
buildings made out of glass
buildings on fire
buildings photorealism
built
built and strong
built around ocean
built in the egyptian desert
built into trees and stone
built on a small
built on a steep hill
buizel and human fusion
bulbapedia
bulbasaur
bulbous
bulbous nose
bulgari
bulgari style
bulging eyes
bulging muscles
bulging veins
bulky
bulky armor
bulky build
bull
bull head
bull netch floating around
bullet hell
bullet holes
bullet holes and blood splatter
bullet proof vest
bullet shells flying
bullet time
bullet tracers
bullet trails
bullet train
bulletproof vest
bullets
bullets inside
bullets whizzing past
bullets whizzing past camera
bulli
bullpup
bullrushes
bully maguire
bullying
bulma from dragon ball
bum
bumblebee
bumblebee pug
bump in form of hand
bump map
bump mapped
bump mapping
bumped three-dimensional fetures
bumper cars
bumps
bumpy
bumpy mottled skin
bumpy nose
bumpy skin
bun
bun )
bungie
bunker
bunkers
bunnies
bunny
bunny ears
bunny girl
bunny head
bunny leg
bunny suit
bunny with helmet and sword
bunnypunk
burberry
burberry hoody
burdisio
bureau of engraving and printing
burger
burger face
burger king
burger on a plate
burger with a mouth
burger with human eyes
burger with human mouth
buried in sand
burka
burlap
burlesque
burlesque elf
burlesque psychobilly
burly
burly man
burn
burn scar left cheek
burne hogarth
burned
burned cars
burned city
burned forest
burning
burning battlefield background
burning bright
burning building
burning buildings
burning bus crash inferno
burning bus inferno
burning cars
burning city
burning city background
burning city in background
burning clouds
burning down
burning embers
burning eyes
burning fire
burning fire embers
burning fires
burning flames
burning forest
burning halo
burning hands
burning houses
burning in hell
burning inferno
burning man
burning man nevada
burning ocean
burning overgrowth
burning pulse
burning red desert horizon
burning scene in the background
burning ships
burning trees
burning village
burning village in background
burning water
burning white eyes
burning wings
burning world
burning wrecked mercedes 1 2 4
burning wrecked mercedes 124
burnout
burnt
burnt armor
burnt edges
burnt forest
burnt huts
burnt paper
burnt sienna
burnt sienna and cerulean blue
burnt sienna and venetian red
burnt umber
burnt umber and blue
burried in snow at night
burst of colour
burst of powders
burst of power
bursting neon stars
bursting with blue light
bursting with holy light
bursting with muscles
bursting with positive energy
bursts of color
burt reynolds
burtonesque
bus
bus station
bus station exploded
bus stop
bus stop on a rainy day
buses
bushes
bushes and leafs
bushes in the background
bushes in the foreground
bushes of blueberry
bushfire
bushveld background
bushy beard
bushy eyebrows
bushy grey eyebrows
bushy moustache
bushy tail
bushy white beard
business
business attire
business card
business clothes
business logo
business meeting
business men
business outfit
business suit
business surrounding
business woman
bussiere
bussiere and roberto ferri
bussiere rutkowski andreas rocha
bust
bust of hercules
bust portrait
bust portrait of a knight
bust shot
bust view
bust with a beautiful neck
bust with a long beautiful neck
bust with a very long neck
bust with face of emma watson
bust with intricate details
busted
busted crushed face
buster sword
bustling
bustling city
bustling with people
busty
busy
busy background
busy but lonely
busy city
busy city on background
busy cityscape
busy crowds
busy cyberpunk metropolis
busy market
busy night
busy people
busy restaurant
busy room
busy small town street
busy street
busy streets
busy streets filled with people
busy wet street at night
busy with people
busytown
but a stern look about her
but as a dragon
but as a photograph
but as a real life photograph
but as an anthropomorphic dragon
but as high contrast photography
but as macro photography
but as macro!!!!! photography
but as photography
but can only be burned
but eerie
but futuristic food
but is mostly wood
but minimalist
but monster is dendritic
but not pretty
but not quite
but resolute
but slower in changing
but the armor covers her face
but very good looking”
but what do you mean
but who's to judge
but with thin
butcher
butcher angry rotting zombie
butcher billy
butcher billy style
butress tree roots
butter
butter sculpture
buttercup eating pizza
buttercups
butterflies
butterflies and birds
butterflies and sunrays
butterflies and worms
butterflies floating in the sky
butterflies flying
butterflies in the foreground
butterfly
butterfly embroidery
butterfly in jar
butterfly jewelry
butterfly lighting
butterfly pop art
butterfly roller skates
butterfly squid
butterfly stroke
butterfly wings
butterflyfish
button - up shirt
button eyes
button nose
button potenciometers
button up shirt
buttons
buttress tree roots
buttresses
buttshape
buxom
buxom chested
buying beers in the british bar
buzz cut
buzz cut gray hair
buzz cut hair
buzz lightyear
buzz lightyear  film
buzz lightyear ( film )
buzzed hair
buzzed hair on temple
buzzed short dark hair
buzzed sides
buzzing fluorescent lights
bvlgari
bvlgari jewelry
bw
bw 3 d render
bw 3d render
bw close - up profile face
bw photo
by :5
by :5 sexy: 7
by claude monet
by david lynch
by delphin anjolras
by emmanuel lubezki
by greg rutkowski
by jake parker
by james jean
by jean giraud
by jim bush and ed repka
by joseph binder
by m. c. escher
by makoto shinkai
by rainer hosch
by rossdraws
by ruan jia and stanley artgerm
by shirow masamune
by studio ghibli
by thomas kinkade
by tite kubo
by tom bagshaw
by tom bagshaw and boris vallejo
by tom purvis
by wes wilson
byzantine
byzantine mosaic
byzantine painting
byzantine ruins
béla tarr
c 10.0
c 12.0
c 4 d
c 4 d style
c 4 d ”
c anon 5 d 5 0 mm lens
c anon 5 d 50 mm lens
c. m. coolidge
c. r. stecyk iii
c1970
c1976
c3po
c4
c4 explosive
c4d
ca
ca. 1990
ca. 2001
caaats
cabbage trees
cabin
cabin in the woods
cabin lights
cabinet furniture
cabinet of curiosities
cabinets
cable
cable electric wires
cable plugged in
cable plugged into cyberdeck
cable wire implants
cable wires
cable wires as hair
cables
cables and monitors
cables and tubes
cables and wires
cables connected to head
cables everywhere
cables hanging
cables hanging from ceiling
cables on floor
cables on her body
cables on walls
cables out of the ears
cacodemon
cacti
cacti everywhere
cactus
cactus adjacent
cactus and flowers
cactus and pearls over the head
cad
cad cam cae
cad design of lawnmower
cadmium red
caernarfon castle
caesar
caesar victorious
cafe
cafe for felted animals
cafe for mice
cafe in the clouds
cafe interior
cafe lighting
cafe racer
cafe tables
cage
cages
cahaba river alabama
cai guo-qiang
cai xukun
caidychen
cain
caio santos
cairo
cait miers
caitlyn from league of legends
cake
cake art
cake in hand
cake sculpture
cakes
cal kestis lightsaber wallpaper
cal-arts
calamity
calanque
calarts
calatrava
calavera
calcite
calculus
calcutta
caleb from critical role
caleb worcester
caledonian forest
calendar says monday
calf
calico
calico cat
california
california coast
california roll
california;
caligrafiturism style
caligraphy
call now
call of cthulhu
call of duty
call of duty zombies
called 'the last supper'
called elysion
called the last supper
calligraphic poetry
calligraphy
calligraphy border
calligraphy formula
callouts
calm
calm afternoon
calm and contented painting
calm and mysterious vibes
calm and serene
calm atmosphere
calm clouds
calm colors
calm environment
calm evening
calm expression
calm eyes
calm face
calm feeling
calm lighting
calm night. digital illustration
calm night. over shoulder shot
calm ocean
calm ocean landscape
calm sea
calm sea and beach background
calm seas
calm serene atmosphere
calm serene relaxed
calm vivid colors
calm water
calm waves
calm weather
calming
calmly conversing 8k
calotype
calude monet style
calvin and hobbes
calvin klein
calvin klein photograph
calvin klein photography
calzone zone
cam
cam de leon
cam de leon & john j. park
cam de leon john j. park
camaraderie
cambodia
cambrian
camcorder
camcorder effect
camel
camel god
camel statue
camelot
camels
cameo
camera
camera angle from below
camera angle looking up at her
camera bloom
camera close to the legs
camera close up
camera face
camera flash
camera flash is so bright
camera flash on
camera footage
camera from a low angle
camera glare in oil style
camera looking down into the maw
camera looking down upon
camera looking up
camera looking up at her
camera low
camera obscura
camera on the ground
camera photo
camera pov
camera pulled back far
camera shaking
camera shutter iris
cameraphone
cameraphone 2002
cameras
cameras lenses
cameron diaz portrait
camilla luddington
camille vivier
camilo gc
cammy
camo
camo made of out teeth
camo made of teeth
camouflage
camouflage made of love
camouflage scheme
camouflage uniform
camouflaged gear
camp
camp fire
camp half-blood
camp x-ray
campau mike style
campbell's soup can
camper
campfire
campfire background
campfire in background
camping
camps in the background
campsites
campy
campy and colorful
campy color scheme
campy. cow costume with udders
can
can basdogan
can of paint
can of shark spam
canada
canada goose
canadian goose with a funny hat
canadian maple leaves
canal
canals
canary wharf
candid
candid flash photography
candid photo
candid photo of gal gadot
candid photograph
candid photography
candid picture
candid portrait
candid portrait of joe biden
candid portrait photo
candid portrait photograph
candid shot
candid street photography
candid!! dark background
candies
candle
candle dripping wax
candle dripping white wax
candle light
candle lighting
candle lightning
candle lights
candle lit
candle volumetric
candle wax
candlelight
candlelit
candlelit catacombs
candlelit restaurant table
candles
candles dripping wax
candles in foreground
candles!
candles. baroque elements
candy
candy - coated
candy apple
candy brains and broken bones
candy canes
candy colors
candy decorations
candy forest
candy girl
candy hospital room
candy land
candy pastel
candy rush
candy shop in a mall
candy treatments
candy worms
candyland
candypunk character design
cane
canine
canines sports photo
cannabis
cannabis - sativa - field
cannabis leaves
cannabis paranoia
cannabis!
canned dog food on a plate
cannes
cannon
cannon ef 6 5 mm f / 2. 8
cannon ef 65 mm f / 2. 8
cannon mounted on back
cannon photo
cannon snow covered trees
cannonballs
cannons
canoe
canon
canon 1 - dx
canon 1 0 0 mm
canon 1 0 0 mm f / 2. 8
canon 1 d
canon 100 mm
canon 100 mm f / 2. 8
canon 2 0 0 mm
canon 2 0 mm
canon 2 0 mm lens
canon 2 4 mm f / 1. 4 lens
canon 20 mm
canon 20 mm lens
canon 200 mm
canon 20mm
canon 20mm lens
canon 24mm
canon 24mm f11 aperture
canon 24mm lens
canon 3 0 0 mm
canon 3 5 mm photography
canon 300 mm
canon 300mm
canon 35 mm photography
canon 35mm lens
canon 35mm lens f8 aperture
canon 40mm view
canon 5 0 mm
canon 5 0 mm lens
canon 5 d
canon 5 d 5 0 mm lens
canon 5 d 5 0 mm lens kodachrome
canon 5 d 50 mm lens
canon 50 mm
canon 50mm
canon 50mm lens
canon 5d
canon 5d 50 mm lens
canon 5d mark iii
canon 5d mark iii photo
canon 7 d
canon 8 0 d
canon 8 0 mm
canon 8 5 mm
canon 8 5 mm f 1. 2 photograph
canon 80 d
canon 80 mm
canon 85 mm
canon 85 mm f 1. 2 photograph
canon a 1
canon a1
canon c300
canon camera
canon dslr
canon ef 85mm f/1.4l is usm
canon eos
canon eos - 1 d mark ii
canon eos - 1 d x mark iii
canon eos 1 0 0 0 d
canon eos 1000d
canon eos 5 d mark iv
canon eos 5 d mark iv camera
canon eos 5d mark 2
canon eos 5d mark iv
canon eos 6d
canon eos 7 d
canon eos 7d mark ii
canon eos 90d
canon eos c 3 0 0
canon eos c 300
canon eos c300
canon eos digital rebel xti
canon eos ef 5 0 mm
canon eos ef 50 mm
canon eos m50
canon eos r 3
canon eos r 5
canon eos r 6
canon eos r3
canon eos r5
canon eos r7 2006
canon eos rebel
canon eos-1d mark ii
canon f / 1. 8 g af - s dx
canon lens
canon m 5 0
canon m 50
canon macro lens
canon mark ii
canon nikon
canon on chest
canon photo
canon portrait
canon pro
canon r 3
canon r 5
canon rf 8 0 0 mm f / 5. 6 l
canon rf 800 mm f / 5. 6 l
canon shot
canon ts-e 17 mm
canon ts-e 17mm
canon ts-e 17mm f/4
canon wide - angle lens
canon- 70-200mm lens
canopee
canopies
canopy
cans
cant believe it is real
cantered
canva
canvas
canvas art
canvas art print
canvas coated with red paint
canvas oil painting
canvas painting
canvas print
canvas texture
canyon
canyon background
canyon topography
canyons
cap
capacitors
capacitors and coils inside
caparison
caparisons
capcom
capcom official media
cape
capillaries
capirote
capital
capital city
capital of estonia
capital plaza
capital sin of pride
capitalism
capitalism realism
capitalist
capitalist clown
capitol building
capitol hill
capitol riot
capoeira
cappuccino
capri coast
capricious
caps sideways
capsule hotel
captain
captain america
captain falcon
captain hat
captain jack sparrow
captain jean-luc picard
captain kirk
captain marvel
captain picard
captivating
captivating 8 k hdr
captivating 8k hdr
captivating and enticing
captivating eyes
captivating glowing lights
captivating look
captura
capture
captured in bottles
captured in low light
captured on canon eos r 6
captured on iphone
captured with sony a3 camera
captures emotion and movement
capybara
capybara pirate
capybaras in tokyo
car
car accident
car chase
car chase scene
car commercial photograph
car concept
car concept art
car crash
car crash test
car design
car drifting
car engine
car engine concept
car garage
car jump
car jumping
car lights
car made of meat
car moving fast
car on fire
car on highway
car on sale
car paint
car parts concept
car photography
car shot
car sketch
car tesla 3
car trading game
car traffic
car vs knight
car with holographic paint
car wreck
cara delevigne
cara delevingne
cara delevingne standing
caracal
caracal cyborg
caracal head
caracter with brown hat
caramel
caramel. rugged
caran d'ache luminance
caran dache luminance
carapace
caravaggesque style
caravaggio and artgerm
caravaggio and moebius
caravaggio style
caravagio
caravan
carbide lamp
carbon
carbon blac and antique gold
carbon black and antique gold
carbon fiber
carbon fibers
carcass carrion covered in flies
carcassonne
card
card art
card back template
card frame
card game
card game illustration
card template
cardboard
cardboard cutout
cardboard cutout of tentacles
cardboard pinhole camera
cardboard tunnels
cardboards ; mess
cardcaptor sakura
cardi b
cardiac anatomy
cardigan
cardistry
cards
cardstock
carefree
carefully crafted
carefully designed
carefully drawn
careless
caretaker
carey price goaltender
cargo pants
cargo pants. cyberpunk city
cargo shorts
cargo spaceships
caribbean
caricatural
caricature
caricature illustration
caricature style
caricature!!!
caricaturist
carice van houten
caring
caring alien life form
caring fatherly wide forehead
carl critchlow. moody
carl friedrich
carl friedrich deiker
carl gustav
carl gustav carus
carl sagan
carl spitzweg:7
carl wahlbom
carl zeiss
carl zeiss 8 5 mm vintage lens
carles dalmau
carlos huante
carlos huante and doug chiang
carlos ortega elizalde
carlos samuel araya
carlos shwabe
carmelo blandino
carmen dell  orefice
carmen dell ’ orefice
carmen electra
carmen kass
carmen sings beautifully
carmilla vampire
carnage
carnage fangs
carnal  wet
carnal ) wet
carnation
carnaval de barranquilla
carne griffiths
carne griffiths'
carneval
carnival
carnival background
carnival mask
carnival on the background
carnivore
carnivore dinosaur
carnivorous
carnivorous plant
carnivorous plants
carola rubio
carole feuerman
carolina reaper
caroline foster
caroline gariba
carousel
carpathian mountains
carpe diem
carpenter
carpet
carpet at the floor
carpeted floor
carpool karaoke
carrara marble
carravaggio
carravaggion
carriage
carriage full of computers
carriage made of pumpkins
carriages with horses
carribean turquoise water
carribean white sand
carrington
carrion
carrot
carrots
carroty hair
carrying a bottle of perfume
carrying a guitar
carrying a gun
carrying a laser gun
carrying a magical staff
carrying a rifle
carrying a saddle bag
carrying a scoped hunting rifle
carrying a sword
carrying a tray
carrying big sack
carrying flowers
carrying guns
carrying mosin on back
carrying survival gear
carrying swords on his back
carrying two barbells
cars
cars and people
cars crashed
cars on the road
cars parked
cars parked underneath
cars pixar movie style
cars portraits
carson ellis
cart
cart wheels
cartier
cartier style
cartographic
cartography
cartography map art
cartoon
cartoon art
cartoon art style
cartoon artstyle
cartoon brew
cartoon carticature
cartoon character
cartoon concept art
cartoon creature
cartoon digital art
cartoon digital painting
cartoon drawing
cartoon eyes
cartoon face
cartoon fantasy spaceship
cartoon from the 90's
cartoon illustration
cartoon image
cartoon look
cartoon moody scene
cartoon network
cartoon network stillframe
cartoon network style
cartoon painting
cartoon paper coffee cup
cartoon pixar style
cartoon portrait
cartoon proportions
cartoon shading
cartoon still
cartoon strip
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cartoon style illustration
cartoon turtles
cartoonish
cartoonish and simplistic
cartoonish cute
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cartoonish style
cartoonish vector style
cartoonist
cartoons
cartoony
cartoony lighting
cartoony shaders
cartouche
carvaggio
carvagio
carved
carved black marble
carved bone ruff
carved floor
carved from sapphire stone
carved in marble
carved in stone
carved in white marble
carved in wood
carved into the side of a tree
carved ivory
carved marble
carved marble statue
carved marble texture silk cloth
carved soap
carved stone
carved white marble
carved wood
carving
carvings
cas
cascade
cascade helmet
cascadia
cascadian
cascading
cascading hair
cascading highrise
cascading iridescent waterfalls
cascading waterfalls
case
caseless ammunition
casette futurism
casey baugh and james jean
casey cooke
casey weldon
cash
cash on a sidetable
cash register
casimir art
casino
casiopea
casket
caspar david
caspar friedrich
caspar james gurney
casper the friendly ghost
cassandra cain
cassandra cain in satin
cassette
cassette futurism
cassette futurist
cassette tape
cast
cast across the night sky
cast glass
cast glass eyes
cast in epoxy
cast iron material
cast shadows
castelvania
casting
casting a fire spell
casting a flame spell
casting a magic spell
casting a multi colored spell
casting a powerful spell
casting a protection spell
casting a root spell
casting a spell
casting a spell on a potion
casting an evil spell
casting chaos magic
casting dark magic
casting demonic magic
casting epic spell
casting evil spell
casting fire spell
casting fireball
casting light magic
casting long shadows
casting magic
casting magic spell
casting nature magic
casting shadows
casting spell
casting spells
castle
castle background
castle core
castle great hall
castle in background
castle in distance
castle in the background
castle in the clouds
castle in the distance
castle in the middle
castle in the sky
castle in the sky style
castle library
castle made of clouds
castle on the background
castle on the mountain
castle ruins
castle scene manga
castle setting
castle towers
castle town
castle wall
castles
castles and temple details
castles floating in mid air
castlevania
castlevania inspired
castlevania sotn
castlevania witch
casual
casual black clothing
casual business outfit
casual clothes
casual clothing
casual clothing style
casual game
casual green clothing
casual modern clothing
casual outfit
casual photography
casual playrix games
casual pose
casual streetwear
casual summer clothes
casual white garment
casually dressed
cat
cat  playing a piano
cat - like scarlet eyes
cat - superman
cat [ playing a piano ]
cat and dog licking each other
cat as super mario
cat attacking tokyo
cat bunny
cat cat dreamcats
cat design
cat detailed
cat dog hybrid
cat dog teddy mix
cat donald rumsfeld
cat drinking milk
cat ears
cat ears and cat tail!
cat ears and tail
cat ears on her head
cat eating
cat eating pizza
cat eye
cat eyes
cat face
cat female with a whit and chest
cat from the void
cat furry
cat giraffe hybrid
cat girl
cat head
cat in space
cat in the forest
cat in the hat
cat is floating in air
cat masterpiece
cat merged elephant head cow
cat on a windowsill
cat on the table
cat on the table in front of her
cat on top of dog
cat photo
cat photography
cat playing chess looking wise
cat playing lute in the garden
cat portrait
cat portrait painting
cat riding a bicycle
cat robot
cat seahorse fursona
cat seahorse shapeshifter
cat silhouette
cat sleeping
cat smoking a cigar
cat smoking a cigarette
cat summoning a spaceship
cat summons a spaceship
cat tail
cat the assassin
cat theme banner
cat theme logo
cat tower
cat warrior
cat wearing a top hat
cat witch
cat with a hat
cat with a pearl earring
cat with laser eyes
cat with lute
cat woman
cat. digital painting
cataclysm
cataclysmic
catacomb in background
catacombs
catalog
catalog exhibition
catalog photo
catalog print
catalogue photo
catalogue photography
catalogue raisonne
catapult
catapults
cataract. subsurface scattering
catarthic
catastrophe
catastrophe ballet
catastrophic
catchlight in the eyes
catchlight on the eyes
catchlight on the eyes!
catdog
cate blanchet
cate blanchett
cate blanchett as galadriel
cate blanchett as medieval queen
category b film poster
catgirl
cathedral
cathedral background
cathedral ceiling
cathedral in the background
cathedral of sun
cathedral!!!!!
cathedrals
cathedrals and abbeys
catherine nolin
cathode ray tube
cathode ray tube distortion
catholic
catholic icon
catholic punk
catholic religious art
catholicpunk
cathrin machin
catia
catman
catnet
cats
cats and plants
cats cat dreamcats
cats musical 1 9 9 8
cats on her side
cats party
cats playing chess
cats playing poker
cats sleeping
cats! are around
catsare around
catscatscats
catss
catsuit
cattie - brie of mithril hall
catwalk
catwalk photo
catwalks
catwoman
caucasian
caucasian eyes
caught
caught in 4 k
caught in 4k
caught in the flow of time
caught on camera
caught on security camera
caught on trail cam
cauldrons
caulfield
caulicoli
caustic
caustic light
caustic lighting
caustic lights
caustic projection
caustic shadows
caustic shadows lighting
caustics
caustics and refraction
caustics effects
caustics lighting from above
caustics reflections
caustics shadows
cautious
cavalier king charles spaniel
cavalry
cavalry charge
cave
cave art
cave art style
cave background
cave crystals
cave drawings
cave entrance
cave exploration
cave glowing stones
cave lighting
cave like teeth
cave painting
cave painting of mona lisa
cave painting of the mona lisa
cave paintings
cave prison
cave reflecting in the lava lake
cave setting
cave system
cave town
cave wall
cave with waterfall
cavern
cavern ceiling visible
cavernous
cavernous interior wide shot
caves
caves of qud
caves!
cavewoman
cavities
caza
cccp
cctv
cctv - c 8
cctv camera footage
cctv footage
cctv footage of a movie set
cd
cd cover artwork
cd jacket
cds
cdx
ceaseless watcher
cecco bravo
cedar
cedric payravernay
cedric peyranavernay
cedric peyravernay. sharp focus
ceiling
ceiling fluorescent lighting
ceiling hides in the dark
ceiling to floor windows
ceilings
cel - shaded
cel - shaded art style
cel - shading
cel animation
cel illustration
cel shad
cel shade
cel shaded
cel shaded anime
cel shaded pbr
cel shaded vector art
cel shaded!!!
cel shaded:15
cel shading
cel shadow
cel-shaded
cel-shaded:15
cel-shaded:17
cel-shading
cel-shading style
celadon glaze
celebrate goal
celebrating
celebrating a birthday
celebrating a king being crowned
celebrating an illegal marriage
celebrating day of the dead
celebration
celebration costume
celebration of coffee products
celebrities
celebrity
celery man
celestia
celestial
celestial asthetics
celestial aura
celestial background
celestial bodies
celestial collision
celestial color scheme
celestial cosmos
celestial gardens
celestial goddess
celestial light
celestial red flowers vibe
celestial regulator
celestialpunk
cell
cell - shaded
cell - shading
cell animation
cell automata
cell bars
cell biology
cell cover style
cell journal
cell phone
cell phone photo
cell phones
cell shaded
cell shaded adult animation
cell shaded anime intricate
cell shaded art
cell shaded cartoon
cell shaded graphics
cell shaded!!!
cell shader shading
cell shading
cell shading.  rb 6 s
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cell-shaded
cell-shading
cellar
cello
cellphone
cells
cellshaded
cellshading
cellular
cellular automata
cellular structures
celluloid
celshaded
celshaded art
celshading
celtic
celtic and cyberpunk armor
celtic antlered god
celtic art
celtic art style
celtic braid
celtic culture
celtic designs
celtic druid
celtic fantasy
celtic fantasy art
celtic golden symbols
celtic hair braid
celtic knot
celtic knots
celtic neon runes
celtic norse frankish
celtic symbols
celtic vegetation
celtics
cement
cement brick wall background
cementary
cementary of skulls
cemetery
censored
centaur
centaur from greek mythology
centaur horse
center
center angle
center composition
center focus
center focus on table
center focused
center frame
center frame medium shot
center frame portrait
center framing
center model buildings
center of focus
center of frame
center of image
center of interest
center of picture
center of sky
center of the frame
center of the universe
center parted bangs
center parted brown hair
center parted curtain bangs
center punched
center spotlight
center straight composition
center universe
center view
centerd
centered
centered and symmetrical
centered close-up
centered composition
centered design
centered dot pupil
centered elven
centered eye
centered face
centered face shot
centered focus
centered full body
centered full body pose
centered full body rear-shot
centered full body shot
centered full-body shot
centered headdress
centered horizon
centered hyperrealistic
centered image
centered in a frame
centered in frame
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centered in panel
centered in picture
centered in portrait
centered in the frame
centered mass
centered on face
centered painted portrait
centered portrait
centered position
centered radial design
centered rim lighting
centered shot
centered shoulders up view
centered subject
centered symmetrical
centered thin ring iris
centered torii gate
centered wide framed window
centered!!
centered!!!
centered!!!!
centered!!!!!
centerede
centerfold
centerframe
centerpiece
centerpiece symmetry
centimeters away from my face
centipede
central california
central circular composition
central composition
central farm
central focus
central hub
central park
central parted fringe
central pork
central shot
central station in sydney
central subject
central symmetrical composition
central tree
centralised
centralized
centralized head and upper torso
centralized urban fantasy
centre composition
centre image
centred
centred award winning 4k
centred composition
centred in image
centred position
centred symmetrical portrait
ceo
ceo of microsoft gaming ( xbox )
ceoncept art
cephalopod
ceramic
ceramic base
ceramic cyborg
ceramic looking face
ceramic pot
ceramic statue
ceramics
cerberus
cereal
cereal box
cereal mascot
cerebellar peduncle
cerebral corpus callosum
cerebri movie poster
ceremonial
ceremonial clouds
ceremonial portrait
ceremonial ritual
ceremony
cernunnos
cerpuscular rays
cerulean
cerulean blue
cerulean blue and burnt sienna
cervix awakening
cesede
cessna
cessna glider plane
cevagraf comics
cezanne
cfd
cfg = 3
cfg _ scale 1 5
cfg=50
cg
cg animation
cg architects
cg art
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cg awards gallery
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cga
cga graphics scheme
cgartists
cgartists trending
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cgi
cgi 8k
cgi animated
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cgi clay sculpture
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cgsociety )
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chad
chaim soutine and frank auerbach
chain
chain mail
chained skeleton
chainmail
chains
chains and red fluid background
chains broken on hands
chains dangling from the ceiling
chains hanging from ceiling
chains on hands
chainsaw
chainsaw attached to hand
chainsaw man
chainsaw man manga
chainsaw sword
chainsaw sword katana
chainsword in the other
chair
chairlifts
chairman mao
chairs
chairs and tables
chakra diagram
chakra diagram face
chakras
chalet
chalk
chalk art
chalk cliffs above
chalk digital art
chalk drawing
chalk texture on canvas
chalk white skin
chalkboard
chalked
chalky
chamber
chambers
chambliss giobbi
chameleon
chamomile
chamonix
champagne
champagne commercial
champagne on the table
champion
chandelier
chandeliers
chandler bing
chanel
chanel boots
change
changeless
changelingcore
changquan
channel
channel 4
channeling mana
channeling mystical energy
channeling swirling energy
channeling third eye energy
chaos
chaos beauty
chaos comics
chaos is visible
chaos magick
chaos marine
chaos nightmare ❄️ amour
chaos nightmare ❄️ amour venom
chaos space marine
chaos!!!
chaotic
chaotic and brutal
chaotic atmopshere
chaotic atmosphere
chaotic cinematic space rift
chaotic composition
chaotic fractal patterns
chaotic gold leaf flowers
chaotic landscape
chaotic patterns
chaotic punk
chaotic revenge
chaotic riots in 2022
chaotic sea setting
chaotic storm of liquid smoke
chaotic swirling ferrofluids
chaotic teenage bedroom
chappie
chappie in an adidas track suit
chappie the robot
char
char aznable album art
charachter design
character
character - portrait
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character album cover
character animation
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character art of maple story
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character close-up
character cohesion
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character concept art
character concept art portrait
character concept artwork
character concept design
character concept exploration
character concept explorations
character concept model
character concept portrait
character concept portrait of me
character concept reference art
character concepts
character creation
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character design : : gothic
character design art
character design concept
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character design contest winner
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character design from d & d
character design humanoid
character design police man
character design police man!!
character design portrait
character design sheet
character design sketch
character designs
character designs painting
character dragonball
character drawing
character exploration
character face study
character faces
character focus
character from king of fighters
character from mortal kombat
character full body portrait
character headshot concept art
character headshot portrait
character icon
character illustration
character illustrations
character is covered in liquid
character is flying
character is half in the water
character is in all its glory
character is in her natural pose
character is standing
character mashup
character model
character model sheet
character model sheet turnaround
character modeling
character painting
character photography
character portrait
character portrait art
character portrait closeup
character portrait of godzilla
character portrait of me
character posing
character posing for concept art
character poster
character profile art
character reference
character reference sheet
character render
character reveal
character sculpt
character select portrait
character selection screen
character sheet
character sheets on table
character silhouette
character splash art
character study
character turnaround
character with a hat
characteristics of golden curve
characters
characters 8k symmetrical
characters from azur lane
characters from machinarium
characters merged
characters reference sheet
characters walk
charactor
charcoal
charcoal and champagne
charcoal and silver color scheme
charcoal and yellow leather
charcoal art
charcoal color skin
charcoal drawing
charcoal line art
charcoal on paper
charcoal painting
charcoal pencil
charcoal portrait
charcoal sketch
charcoal skin
charcoal smudges
charge explosives
charging plug in the chest
charging through a dragons lair
charging through city
charicature
charioscuro
chariot
charismatic
charismatic beautiful
charizard
charizard dog hybrid animal
charizard flying above new york
charles
charles baudelaire
charles bowater
charles burchfield art painting
charles burns
charles dulac. very large
charles maurice
charles monet
charles o. perry
charles perry
charles sheeler
charles sillem lidderdale
charles theodore frere
charles vess and thomas kinkade
charles vess. high detail
charli bowater
charli bowater and artgeem
charli xcx
charlie
charlie bowater and artgeem
charlie bowater and artgerm
charlie bowater and mark brooks
charlie bowater and tom bagshaw
charlie bowater art style
charlie bowater character art
charlie bowater rich deep colors
charlie chaplin dancing
charlie cox
charlie day
charlie immer
charlie immer and jenny saville
charlize
charlize theron
charmander
charmed sexy look
charming
charming black eyes
charming expression gesicht
charming eyes
charming sly smile
charming smile
charon
charon the ferryman of hades
charred
charred desert
chartpak ad markers
chartres cathedral
chartreuse and orange and cyan
chartreuse color scheme
charts
charybdis
chase
chase scene
chasing a kangaroo
chasing a robber down a street
chasing action scene
chasm
chaste
chateau de cheverny
chateau de versailles
chateau frontenac
chaumet
chaumet style
chauvet
chauvet cave
chauvet cave art
chav
chawick the fail wizard
che guevara
cheap
cheap abundant
cheap design
cheburashka
checkerboard background
checkerboard horizon
checkerboard pattern underwater
checkered floor
checkered motiffs
checkered pattern
checkered spiked hair
checking her cell phone
checking her phone
cheddar
cheekbones
cheeks
cheeky
cheeky devil
cheeky smile
cheeky smile with red lips
cheeky!!!
cheerful
cheerful ambient lighting
cheerful atmosphere
cheerful colors
cheerful colours
cheerful expression
cheering
cheering crowd
cheering crowds
cheerios
cheerless
cheers
cheery
cheeryblossom
cheese
cheese and pepperoni
cheese and salami on the table
cheese revolution
cheeseburger
cheeses
cheesy
cheesy iron maiden motifs
cheetah
cheetah running over clouds
chef hat
chef platypus
chef saitama one punch man
chef table
chefs table
chelate appendages
chelicerae
chell
chell from portal
chemical plant
chemical substances
chemical woekshop
chemically damaged
chemically propelled
chemicals
chemistry
chemisty
chemrail
chengwei pan
chengwei pan on artstation
chengyou liu
cheongsam
chequered cape
cherenkov radiation
cherished trees
cherries
cherry
cherry blosom trees
cherry blossom
cherry blossom background
cherry blossom falling
cherry blossom forest
cherry blossom petals
cherry blossom rain everywhere
cherry blossom tree
cherry blossom trees
cherry blossoms
cherry blossoms in the wind
cherry blossums
cherry explosion
cherry tree in the background
cherry trees
cherry-blossom-tree
cherub
cherubic
cheshire cat
cheshire cat death tarot card
cheshire cat drinking tea
chesley
chess
chess game
chess knight
chess motiffs
chess pawn
chess piece
chess set
chess tournament
chessboard scientist
chest
chest and face
chest coverd
chest covered with palm leaves
chest guard
chest hair
chest high
chest legs
chest plate with ferrari logo
chest up bust shot
chest up human lower half snake
chesterfield
chestnut hair
chestplate
cheval michael
chewbacca
chewbacca robbing a mcdonald's
chewing
chewing on a graphic card
chewing on a video card
chewing tobacco
chi-gong
chiaki nanami
chiaki nanami from danganronpa
chianti molotov cocktails
chiaoscuro
chiaroscuro
chiaroscuro anime key visual
chiaroscuro lighting
chiaroscuro oil painting
chiaroscuro painting
chiaroscuro portrait
chiaroscuro soft lighting
chiaroscuro!!
chiascuro
chiba prefecture
chibi
chibi anime
chibi anime girl
chibi art
chibi girl
chibi monster girl
chibi naruto biting into a pizza
chibi proportions
chibi style
chic
chicago
chicago skyline
chicano airbrush art
chicken
chicken dressed as an inmate
chicken face morphed fish head
chicken feather armor
chicken feathers
chicken nuggets
chicken on top of a car
chicken sandwich
chickens
chie yoshii
chief keef
chief keef in the garden of eden
chiffon
chihiro! fujisaki
chiho
chiho aoshima color scheme
chiho ashima
chihuahua
chihuahua holding a camera
chihuly
chilaquiles
child
child drawing
child hybrid
child like
child of dark
child of light
child's drawing
childhood
childhood friend
childhood friend vibes
childhood memory
childish
childish gambino
childish look
childlike
children
children book illustration
children born as ghosts
children drawing
children drawing with pencils
children illustration
children painting
children playground
children playing at the beach
children playing with pogs
children's
children's animated films
children's artwork
children's book
children's book art
children's book cover
children's book drawing
children's book illustration
children's book style
children's cartoon
children's drawing
children's illustration
children's storybook
children's toy
children's toy advertisement
children's tv show
children\'s book drawing
children\'s illustration
childrenbook illustration
childrens
childrens art in artstation
childrens book
childrens book art
childrens book cover
childrens book illustration
childrens book style
childrens cartoon
childrens drawing
childrens illustrated storybook
childrens illustration
childrens toy
childrens toy advertisement
children’s book illustration
children’s drawing
childs bedroom
childs drawing
chile
chilean
chili
chill
chill feeling
chill out
chill summer
chill time. good view
chill vibe
chill vibes
chilled out smirk on face
chillhop
chillin at the club together
chilling
chilling 4 k
chilling on a leaf
chillwave
chilly
chilly dark mood
chimera
chimeric
chimeric organism
chimerical
chimney
chimney with smoke
chimneys
chimneys on buildings
chimpanzee
chin-length hair
chin-length purple hair
china
china blue eyes
china doll face
china silk 3d dragon
china town
china town blade runner
chinatown
chinatown bar
chinchilla animal
chinese
chinese architecture
chinese armor
chinese art
chinese artist
chinese artist cai guo qiang
chinese blue and white porcelain
chinese brush pen
chinese brush pen illustration
chinese building
chinese calligraphic painting
chinese costume
chinese crested powderpuff dog
chinese dragon
chinese dragon concept art
chinese dragon engrave
chinese dragons fighting
chinese dress
chinese empress
chinese fantasy
chinese girl
chinese heritage
chinese ink and wash painting
chinese ink brush
chinese ink painting
chinese landscape
chinese lanterns
chinese men in a prison
chinese mongolian script
chinese mythology
chinese new year in shanghai
chinese painting
chinese painting style
chinese palace
chinese president
chinese president with bananas
chinese princess
chinese ribbon dance
chinese style
chinese style painting
chinese surgery operating table
chinese surrealism
chinese temple
chinese text
chinese three kingdoms
chinese village
chinese warrior
chinese water color
chinese watercolor
chinese watercolor style
chinese woman
chinnese mafia
chinoiserie pattern
chinoiserie wallpaper
chip 'n dale
chip n dale
chipped paint
chips
chique
chiral lighting
chirico and rene magritte
chiron
chiroptera ears
chiroptera head
chiroptera nose
chiseled
chiseled abs
chiseled architecture
chiseled features
chiseled formations
chiseled good looks
chiseled jaw
chiseled jawline
chiseled muscles
chitin
chitty chitty bang bang
chloe bennet
chloe grace moretz
chloe moretz
chloe price
chloe price from life is strange
chloë grace moretz
chocolate
chocolate art
chocolate candy bar packaging
chocolate city
chocolate frosting
chocolate river
chocolate sauce
chocolate.
chocolate. highly detailed
chocolate. intricate background
chocolate. rugged
choi
choir
choke smirk smile grin
choker
choker around neck
choker necklace
choker on neck
chomatic aberration
chonker cat
choo choo
chopping hands
chopsticks
choreographed
choreographed fight scene
chorizo sausage
chozo
chris achilleos
chris bachalo
chris bachalo comic art
chris bangle
chris bonura
chris bourassa brooks gordon
chris chan
chris cunningham
chris cunninham
chris dyer
chris evans
chris evans as a bodybuilder
chris evans as a powerlifter
chris evans hatches from an egg
chris evans peeks out of an egg
chris evans portrait
chris evans with a beer belly
chris farley
chris foss
chris haas and bernini
chris hemsworth
chris hemsworth portrait
chris hemsworth underwear ad
chris knight
chris mars
chris martin from coldplay
chris mcgrath
chris moore. artgerm
chris moore”
chris nolan movie
chris pine
chris pratt
chris pratt ancient corsican
chris pratt as a potato chip
chris pratt as mario
chris pratt as super mario
chris redfield
chris rock slapping will smith
chris saunders
chris scalf
chris tulloch mccabe
chris van allsburg
chriss foss
christ the redeemer
christalized
christian
christian art
christian bale
christian bale as the joker
christian cline
christian dimitrov
christian dior style
christian horner portrait
christian macnevin
christian mysticism
christian orrillo
christian saint
christian saint rosace
christian weston chandler
christianism
christianity
christina applegate
christina hendricks
christina kritkou
christina ricci
christmas
christmas lights
christmas night
christmas tree
christof romuald
christoper nolan
christoph waltz
christoph wohlgemuth
christophe szpajdel
christophe young
christophe young eytan zana
christopher
christopher alexander
christopher c. lee
christopher cao
christopher lloyd as belial
christopher lloyd as the joker
christopher morris photography
christopher nolan
christopher nolan film style
christopher shy
christopher walken
christopher walken super saiyan
chroma
chroma color
chroma green background
chromal aberration
chromatic
chromatic 3d geometry
chromatic abberation
chromatic abberations
chromatic aberration
chromatic aberration!!!!!
chromatic aberrations
chromatic abrasion
chromatic color
chromatic color scheme
chromatic colors
chromatic colours
chromatic distortion
chromatic filament
chromatic gradient
chromatic iridescence
chromatic material
chromatic skin
chrome
chrome accents
chrome and carbon
chrome and viscera and bone
chrome armor
chrome art
chrome bob haircut
chrome body
chrome bodysuit
chrome buildings
chrome cathedrals
chrome dance pole
chrome dino
chrome face symmetry
chrome facial piercings
chrome hearts
chrome mask
chrome metal material
chrome military base
chrome motorcycle parts
chrome outfit
chrome parts
chrome plated
chrome red
chrome reflect
chrome reflections
chrome robot
chrome silver
chrome skeksis
chrome skin
chrome spheres on a red cube
chrome tubes
chrome vortex
chromed metal
chrometype
chrometype aesthetics
chromostereopsis
chronicles of narnia
chrono trigger
chrono trigger guilty gear style
chrysalis
chrysanthemum
chrysanthemum and hyacinth
chrysanthemum and tulips
chrysanthemum eos-1d
chrysanthemums
chrysler building
chthonic
chtulhu
chuck
chuck close and richard corben
chuck close:7
chuck e cheese
chuck e. cheese
chuck e. cheese head
chuck jones
chuck norris
chuck roast norris
chucky
chucky style
chun - li
chun li
chun li at the gym
chun-li
chungking express color palette
chunk teeth
chunky
chunky build
chunky gauntlets
chunky!!!
chupacabra
church
church background
church background!
church cathedral
church chandelier
church in the background
church in the wood
church interior
church painting
church window
churches
churchyard
churning
chuvabak
château de cheverny
cia
cibercity
ciberpunk
cicada wings
cider - man
cider-man
cigar
cigarette
cigarette advertising
cigarette dangling
cigarette in hand
cigarette in his mouth
cigarette in mouth
cigarette smoke
cigarettes
cigarrette boxes at the table
cigars
cimbri
cimematic
cinamtic
cinder blocks
cinderella
cindmatic
cindy avelino
cine colors
cine still
cineastic
cinecolor
cinegraphic
cinelux asa 1 0 0
cinelux asa 100
cinema
cinema 4 d
cinema 4 d art
cinema 4 d cinematica
cinema 4 d octane
cinema 4 d render
cinema 4 d sharp focus
cinema 4d
cinema 4d :9 detailed face: 8
cinema 4d bright light render
cinema 4d cinematic
cinema 4d cinematic render
cinema 4d colorful render
cinema 4d multi-pass ray traced
cinema 4d ray traced
cinema 4d render
cinema c 9. 0
cinema effect
cinema lighting
cinema poster
cinema quality
cinema still
cinema studio lights
cinema verite
cinema-4d
cinema4d
cinema4d render
cinemascope
cinemascope panorama
cinematatic lighting
cinemathic lights
cinemati
cinematic
cinematic   very anime
cinematic  concept art
cinematic - lighting
cinematic 3d render
cinematic 4 k
cinematic 4 k wallpaper
cinematic 4k
cinematic 4k blu-ray
cinematic 4k blu-ray render
cinematic 4k octane render
cinematic 4k wallpaper
cinematic 8 k
cinematic 8 k hd movie shot
cinematic 8k
cinematic 8k hd movie shot
cinematic 8k hdr
cinematic 8k uhd
cinematic accent lighting
cinematic action
cinematic action scene
cinematic action shot
cinematic aesthetic
cinematic ambient lighting
cinematic and atmospheric
cinematic and cold atmospheric
cinematic and dramatic
cinematic and dramatic red light
cinematic angle
cinematic angles
cinematic architectural scale
cinematic art
cinematic art shot
cinematic arthouse
cinematic atmospher
cinematic atmosphere
cinematic atmospheric
cinematic atmospheric lighting
cinematic atmospheric photo
cinematic aura lighting
cinematic back lit lighting
cinematic background
cinematic backlighting
cinematic backlit lighting
cinematic battle
cinematic battlefield
cinematic beautiful natural skin
cinematic beeple
cinematic beer
cinematic blue and gold
cinematic blue lighting
cinematic body shot
cinematic bright sunny daylight
cinematic bust portrait
cinematic bust shot
cinematic camera
cinematic centered camera
cinematic cgsociety
cinematic character
cinematic character concept art
cinematic chiaroscuro
cinematic cinematography
cinematic close shot
cinematic close-up bust shot
cinematic closeup
cinematic closeup!!
cinematic color
cinematic color grade
cinematic color grading
cinematic color palette
cinematic color scheme
cinematic colors
cinematic colors lighting
cinematic colors sharp
cinematic colour grading
cinematic colour palette
cinematic composistion
cinematic composition
cinematic composition 8 k
cinematic composition 8k
cinematic composition hdr
cinematic compositions
cinematic compositon
cinematic concept
cinematic concept art
cinematic contrast
cinematic contrasted lighting
cinematic counter light
cinematic dark lighting
cinematic depth
cinematic depth of field
cinematic design
cinematic detail
cinematic detailed
cinematic diane arbus
cinematic diffuse lighting
cinematic digital art
cinematic digital painting
cinematic dramatic
cinematic dramatic atmosphere
cinematic dramatic light
cinematic dramatic lighting
cinematic dramatic lights
cinematic dull colours
cinematic dynamic lighting
cinematic eastman 5 3 8 4 film
cinematic eastman 5384 film
cinematic effect
cinematic effects vfx
cinematic environment
cinematic epic
cinematic epic shot
cinematic establishing shot
cinematic fantasy painting
cinematic feel
cinematic feeling
cinematic film
cinematic film still
cinematic filter
cinematic flavor
cinematic focus
cinematic focus dof
cinematic footage
cinematic forest lighting
cinematic frame
cinematic framing
cinematic framing rule of thirds
cinematic front lightning
cinematic front shot
cinematic full body shot
cinematic full character
cinematic full shot
cinematic goddess body shot
cinematic goddess close shot
cinematic goddess shot
cinematic grade
cinematic grain
cinematic gritty lighting
cinematic headshot portrait
cinematic highlights
cinematic highly detailed
cinematic hologram
cinematic horror
cinematic ilumination
cinematic image
cinematic imax shot
cinematic industrial lighting
cinematic jennifer lawrence
cinematic keyframe
cinematic lain fractal lain
cinematic landscape
cinematic lens
cinematic lens flare
cinematic level shot
cinematic lighitng
cinematic light
cinematic light and reflections
cinematic light dramatic light
cinematic light effect
cinematic light effects
cinematic light shadows
cinematic light. medium shot
cinematic lightening
cinematic lighthing
cinematic lighting
cinematic lighting + masterpiece
cinematic lighting 4 k
cinematic lighting 4k
cinematic lighting 8k
cinematic lighting and shadows
cinematic lighting at night
cinematic lighting color scheme
cinematic lighting h 704
cinematic lighting high details
cinematic lighting masterpiece
cinematic lighting photography
cinematic lighting tri - x
cinematic lighting ”
cinematic lighting!!
cinematic lighting”
cinematic lightning
cinematic lightning - h 7 6 8
cinematic lightning - h 768
cinematic lightning and colors
cinematic lights
cinematic lights hyperdetailed
cinematic ligting
cinematic long shot
cinematic look
cinematic low shot
cinematic lut
cinematic luts
cinematic masterpiece
cinematic matte illustration
cinematic matte painting
cinematic medium shot
cinematic mid shot fight
cinematic minecraft
cinematic mode
cinematic mood
cinematic mood lighting
cinematic moody
cinematic moody colors
cinematic moody lighting
cinematic morning light
cinematic movie
cinematic movie image
cinematic movie photo
cinematic movie photograph
cinematic movie poster
cinematic movie scene
cinematic movie shot
cinematic movie still
cinematic neon highlights
cinematic neon matte painting
cinematic neon uplighting
cinematic night lighting
cinematic opening shot
cinematic outdoor lighting
cinematic outfit photo
cinematic paint
cinematic painting
cinematic panavision 5384 film
cinematic paris
cinematic pastel lighting
cinematic perspective
cinematic photo
cinematic photo 4k
cinematic photo shoot
cinematic photograph
cinematic photograph concept art
cinematic photography
cinematic photogtaphy
cinematic photoshooting
cinematic pinterest style
cinematic portrait
cinematic pose
cinematic post - processing
cinematic post process
cinematic post-processing
cinematic poster
cinematic promo material
cinematic quality
cinematic realistic
cinematic realistic photo
cinematic realistic portrait
cinematic reality
cinematic red lighting
cinematic render
cinematic render unreal engine
cinematic rendered
cinematic rendering
cinematic rim light
cinematic rim lighting
cinematic romantic magical
cinematic rule of thirds
cinematic scene
cinematic scenery
cinematic scifi shot
cinematic screen
cinematic screenshot gta 5
cinematic shading
cinematic shadows
cinematic shoot
cinematic shot
cinematic shot ar 9:16 -n 6 -g
cinematic shot epic portraits
cinematic shot!
cinematic shots
cinematic silk road lanscape
cinematic sky
cinematic smooth
cinematic smooth stone
cinematic smooth unreal engine
cinematic soft
cinematic soft lighting
cinematic space scenery
cinematic specular lighting
cinematic still
cinematic still frame
cinematic still in adam  unity
cinematic still in adam | unity
cinematic still in westworld
cinematic still of westworld
cinematic stillframe
cinematic stormy sky
cinematic studio ghibli still
cinematic studio light
cinematic studio light! windy
cinematic studio lighting
cinematic studio lightwindy
cinematic studio shot
cinematic style
cinematic style photograph
cinematic summer vibrancy
cinematic sunset
cinematic tone
cinematic top lighting
cinematic toplight
cinematic trailer
cinematic unreal 5
cinematic unreal 6
cinematic unreal engine
cinematic unreal engine 5
cinematic very crisp
cinematic view
cinematic view from lower angle
cinematic view!!!
cinematic volume lighting
cinematic volumentric lighting
cinematic volumetric
cinematic volumetric light
cinematic volumetric lighting
cinematic volumetric lights
cinematic wallpaper
cinematic wide angle
cinematic wide shot
cinematic widescreen shot
cinematic | | very anime
cinematic | | very anime!!!
cinematic —ar 16:9
cinematic ， - h 7 6 8
cinematic ， - h 768
cinematic!
cinematic!!
cinematic-lighting
cinematic-shot
cinematic. art deco
cinematic. atmospheric
cinematic. beksinski painting
cinematic. by leng jun
cinematic. cinematic composition
cinematic. futurism
cinematic. white
cinematic. ”
cinematic:0.9unreal engine
cinematic:0.9|unreal engine
cinematica
cinematical composition
cinematics
cinematics lighting
cinematic”
cinematik
cinematix
cinematografic
cinematographic
cinematographic atmosphere
cinematographic composition
cinematographic light
cinematographic lighting
cinematographic photo
cinematographic shot
cinematographic wide angle shot
cinematography
cinematography 4k
cinematography atmosphere
cinematography composition
cinematography lighting
cinematography photo
cinematography roger deakins
cinematography”
cinematográfic
cinematográfica
cinematrographic
cinematrography
cinemática
cineovision
cinestill
cinestill 4 0 0 t film
cinestill 400 t film
cinestill 400t
cinestill 400t film
cinestill 5 0 0
cinestill 5 0 d
cinestill 50 d
cinestill 500
cinestill 50d
cinestill 8 0 0
cinestill 8 0 0 t
cinestill 8 0 0 t 2 0 0 mm
cinestill 8 0 0 t 3 5 mm
cinestill 8 0 0 t 3 5 mm 1 9 8 9
cinestill 8 0 0 t 3 5 mm b & w
cinestill 8 0 0 t 5 0 mm
cinestill 8 0 0 t film
cinestill 8 0 0 t technicolor
cinestill 8 0 0 tungsten
cinestill 8 0 0 tungsten 3 5 mm
cinestill 800
cinestill 800 t
cinestill 800 t 200 mm
cinestill 800 t 35 mm
cinestill 800 t 35 mm b w
cinestill 800 t 50 mm
cinestill 800 t film
cinestill 800 tungsten
cinestill 800 tungsten 35 mm
cinestill 800t
cinestill 800t 10mm 4k
cinestill 800t 10mm eastmancolor
cinestill 800t 18mm
cinestill 800t 18mm 4k
cinestill 800t 18mm heavy grain
cinestill 800t 35mm
cinestill 800t 35mm eastmancolor
cinestill 800t 35mm full-hd
cinestill 800t 35mm technicolor
cinestill 800t 50mm
cinestill 800t 50mm eastmancolor
cinestill 800t 8k
cinestill 800t agfacolor
cinestill 800t eastmancolor
cinestill 800t film
cinestill 800t film photo
cinestill 800t technicolor
cinestill cinematography
cinestill cinematrography
cinestill colour
cinestill colour cinematography
cinestill eastmancolor
cinestill hasselblad 2 0 0 mm
cinestill hasselblad 200 mm
cinestill hasselblad 8 5 mm
cinestill hasselblad 8 mm
cinestill hasselblad 85 mm
cinestill!
cinestill!!
cinimatic
cinimatic composition
cinmatic shot
cinnabar
cinnamon
cinnamon #b57e59 skin color
cinnamon skin color
cinq terre
cinémascope
cinématique”
circa 1 6 0 0
circa 1 8 5 4
circa 1 8 5 8
circa 1 8 6 1
circa 1 8 6 3
circa 1 9 2 4
circa 1 9 3 9
circa 1 9 6 9
circa 1 9 7 9
circa 1 9 8 4
circa 1 9 9 9
circa 1615
circa 1912
circa 1924
circa 1939
circa 1940s
circa 1958
circa 1969
circa 1970
circa 1982
circa 1984
circa 1992
circa 2 0 0 8
circa the year 2994
circe
circle
circle beard
circle design
circle eyes
circle face
circle forms
circle iris detailed structure
circle of the crone
circle pit
circle pit demons
circles
circlet
circuit
circuit bent synthesis
circuit board
circuit board artifacts
circuit board processor
circuit board processor block
circuit boards
circuit boards)))))
circuitry
circuitry visible in head
circuitry. 8k 3d geology
circuits
circular
circular cardboard eyes
circular face
circular gate in a white wall
circular glasses
circular logo
circular planet
circular planet behind it
circular shape
circular sunglasses
circular tattoo
circular towers
circular white full moon
circular windows
circural
circus
circus background
circus clowns
circus performance
ciri
ciri from the witcher
ciri holding a cat
cirno
cirno from touhou
cirno touhou
cirrus clouds
citadel
citadel colour
citadel of erbil
cities
cities flourishing
cities of mesopotamia
citizens
citrinitas
city
city + graffiti background
city 1 7
city 17
city apartment
city apartment cozy calm
city at night
city at night in the rain
city backdrop
city background
city background in silhouette
city bay bridge aqueduct
city below
city birds eye view
city buildings on top of trees
city depth of field
city destruction
city docks
city environment
city fog
city high-rise
city hunter
city in background
city in backround
city in desert
city in the background
city in the clouds
city in the distance
city in the distant on fire
city in the sky
city in the style of cyberpunk
city is pure wasteland
city landscape
city led lights
city light reflections
city lighting
city lights
city lights bokeh
city lights in the background
city lights made of lush trees
city lights on the horizon
city like hong kong
city map
city midnight mist
city midnight mist lights
city midnight neon lights
city mist softlight
city morning
city neon light
city night
city of armenia quindio
city of atlantis
city of golden shadows
city of industry
city of munich
city of munich!!!
city of pristine colors
city of the future
city of the future in russia
city of the jungle
city of towers
city on a hillside
city on fire
city on fire background
city on the background
city on the moon
city panorama
city park
city park with flowers
city pop
city quality lighting
city reflection
city reflections
city rooftop
city ruins background
city ruins in the background
city scape
city scene
city skyline
city skyline in the background
city skyline in the backround
city skyline on background
city snowing with a lot of snow
city square
city street
city street at dusk
city street cinematic lighting
city street lights
city street on the moon
city street view background
city streets
city streetscape
city sunset
city sunset mist lights
city sunset mist neon lights
city sunset night
city twilight landscape
city view
city views
city wall
city walls
cityscape
cityscape background
cityscape in style of basquiat
cityscape in the window
cityscape ruins in the distance
civ ghandi
civil engineer
civil war style
civilization
civilized
cl
clad in robes
clad in vines
claes oldenburg
claiming vengance
clair obscur
claire forlani
claire wendling
clamp
clamp shell lighting
clamp style
clannad
clap. scifi
clara oswald
clarice starling
clarity
claroscuro
clash
clash of clans
clash of clans style
clash royal style characters
clash royale
clash royale goblin
clasps his bangs in one hand
class hunter
class tank
classic
classic 3 d model of molecule
classic 3d model of molecule
classic 8 0 s flick )
classic alien
classic art
classic beauty
classic bond iconic shot
classic car
classic car magazine
classic cars
classic children's illustration
classic children's illustrations
classic childrens illustrations
classic chrome
classic cinema
classic dancer striking a pose
classic disney mood
classic fantasy
classic film noir scene
classic gem
classic greek art
classic greek mythology
classic guitar
classic iconic rambo pose
classic kabuki
classic kimono
classic mayan mythology
classic oil painting
classic painting
classic portrait
classic product design
classic realism
classic roman mythology
classic sculpture
classic shoujo
classic style
classic vibes
classic-cult-comic-style
classical
classical ancient greek art
classical animation
classical antiquities
classical antiquities on display
classical architecture
classical art
classical artwork
classical greece
classical japanese art
classical landscape painting
classical lighting
classical marble sculpture
classical oil painting
classical ornament
classical ornamental design
classical painting
classical portrait
classical robes
classical sculpture
classical statue
classical stone columns
classical style
classical witch
classical witch robe
classicism
classicism artstyle
classicism oil painting
classicism style
classified
classified government archive
classified photo
classroom
classroom background
classroom doodle
classroom in background
classy
classy yukata clothing
clathrus - ruber
claude joseph vernet
claude monet
claude monet)
claudia black
claudia schiffer
claudio bravo
claustrophobia
claustrophobic
claustrophobic and futuristic
claustrophobic room
claw
claw blades in fingers
claws
claws are up
clay
clay amulets
clay animation
clay art
clay material
clay model
clay render
clay sculpture
clay texture
clay wilson
claymation
claymation character
claymation style
claymore
claymore anime background
claymorphism
claymotion
clayton crain
clean
clean - shaven
clean 3 d render
clean 3d render
clean 4 k
clean 4k
clean aesthetic
clean and clear face
clean and empty
clean and neat
clean and organized
clean and pristine design
clean and simple
clean and simple design
clean and textures
clean anime art
clean anime outlines
clean architecture
clean artstyle
clean background
clean background trending
clean beautiful symmetrical face
clean black and white print
clean black outlines
clean black pen drawing
clean blonde hair
clean blue sky
clean borders
clean borders ; photorealistic
clean bright happy
clean brush stroke
clean brutal blooded face
clean cel shaded
clean cel shaded vector art
clean clear face
clean coloring book page
clean colors
clean composition
clean concept art
clean crisp graphics
clean design
clean detail
clean detail 4k
clean detailed
clean detailed anime art
clean detailed anime style
clean detailed art
clean details
clean digital art
clean digital render
clean edges
clean elegant painting
clean energy
clean environment
clean face
clean face and body skin
clean from scene from twin peaks
clean glow
clean graphic design
clean hair
clean horizon
clean image
clean ink
clean ink detailed line drawing
clean lighting
clean line
clean line art
clean lineart
clean lineart and color
clean lineart and flat color
clean lines
clean lines in dark pen
clean linework
clean logo
clean logo design
clean long lines
clean medical environment
clean minimalist design
clean perfect symmetrical face
clean photo
clean photorealistic realistic
clean post process
clean render
clean room
clean scan
clean shapes
clean shaved
clean shaven
clean shaven face
clean shaven wide face
clean shaven wide wide wide face
clean shaven!!!!
clean soft lighting
clean spot color
clean streets
clean strokes
clean style
clean symmetrical face
clean symmetrical faces
clean thick line
clean vector art
clean white background
clean white lab background
clean white paper background
cleaned up
cleanest image
cleanest posse
cleaning future
cleanshaven
cleanshaven!
clear
clear [[bronze]] face [mask]
clear [bronze] face
clear and focused
clear and sunny
clear as crystal
clear atmosphere
clear background
clear beautiful sky
clear blue eyes
clear blue skies
clear blue sky
clear blue sky vintage style
clear blue water
clear clean
clear clean face
clear color
clear colors
clear colours
clear cross hatching
clear curvy details
clear cute face
clear dark background
clear day
clear definition
clear detail
clear detailed view
clear details
clear edges
clear eyed
clear eyes
clear eyes concept art
clear eyes looking into camera
clear eyes. 2 4 mm
clear eyes. 24 mm
clear face
clear face and eyes
clear faces
clear facial details
clear facial features
clear figures
clear focus
clear focused details
clear gaze
clear geometry
clear glass
clear glass wall
clear green eyes
clear hd image
clear image
clear julian lage face
clear lighting
clear line
clear lines
clear lines and clear shapes
clear lines and shapes
clear lines!!
clear lips and high quality
clear liquid
clear makeup
clear night sky
clear outfit design
clear painting and good lighting
clear parts
clear photo
clear photography
clear portrait
clear portrait of demi rose
clear portrait of emma watson
clear refined
clear reflection
clear resolution
clear seas
clear shapes
clear sharp face of todd solondz
clear sharp focus
clear sharp image
clear sharp todd solondz face
clear shot
clear silhouette
clear skies
clear skies in the distance
clear skin
clear sky
clear sky above
clear sky and background
clear sky background
clear smooth face
clear subject
clear summer sky background
clear sunny day
clear symmetrical eyes
clear symmetrical face
clear todd solondz face
clear type
clear vector
clear view
clear water
clear weather
cleared forest
clearing
clearing. full shot
clearly defined outlines
clearly visible face
cleary see face
cleavage
cleaver
cleft chin
clematis design
clematis in the deep sea
clematis like stars in the sky
clematis theme banner
clematis theme logo
clemens ascher
clement hurd
clemente
clenched fist
clenched fists
clenching
clenching teeth
cleopatra
cleopatra in her palace
cleopatra portrait
cleric
cleveland
clever
clever monkey with a long knife
clickbait
clicks of eyelids
cliff
cliff chiang
cliff edge
cliff side
cliff side at dusk
cliffjumper
clifford the big red dog
cliffs
cliffs of dover
cliffside
cliffside ocean scene
cliffside town
climate change
climax
climber
climbing
climbing a tree
climbing mountain in washington
climbing up a cliffside
clinical
clinically depressed
clint clearley
clint eastwood
clint eastwood as wolverine
clip art
clip stadio
clip studio paint
clip-art
clipart
clipart icon
clipboard
clippings of a fashion magazine
clipstudio
clive barker
cloak
cloak covering face
cloak flittering in the wind
cloak with hood
cloak. extremely high details
cloaked
cloaked dark night
cloaked dark winter night
cloaked woman
clock
clock iconography
clock tower
clockface
clockpunk
clocks
clocktower
clockwork
clockwork automaton
clockwork horror
clockwork mechanical
clockwork orange
clockwork steampunk
clockwork woman
cloese-up
clogs
cloicsonne
cloisonne
cloisonne cat head sculpture
cloissonne
clone laboratory
clone wars
clones
cloning spell
close
close - fitting nurse costume
close - ip shot
close - shot
close - up
close - up bokeh
close - up face
close - up face portrait
close - up face portrait from up
close - up of face
close - up of the faces
close - up on detailed
close - up on face
close - up photo
close - up photograph
close - up photography
close - up portrait
close - up portrait shot
close - up profile
close - up profile face
close - up shot
close - up shot of eyes
close - up shots of faces
close - up studio photo
close - up view
close - ups
close angle
close body shot
close camera
close detailed face
close establishing shot
close face view
close full body shot
close full figure
close look
close macro photo. studio photo
close medium shot
close photo
close portrait
close portrait of beautiful
close river bank
close scene
close shot
close to defeat
close to night
close to the camera
close together
close up
close up 1 9 9 0
close up angle
close up at face
close up bokeh hiperrealistic
close up bust shot
close up camera angle
close up camera on bonfire level
close up cat
close up character
close up details
close up dslr photo
close up dslr studio photograph
close up face
close up face detail
close up face female portrait
close up face portrait
close up food photography
close up front view
close up guns and roses
close up half body shot
close up head shot
close up high detailed
close up image
close up iwakura lain
close up macro photography
close up of a blonde woman
close up of a young anime girl
close up of face
close up of iwakura lain
close up of lain iwakura
close up of single sugar crystal
close up photo
close up photograph
close up photography
close up portrait
close up portrait bust of woman
close up portrait of a beautiful
close up portrait of woman
close up portrait photo
close up portrait polaroid
close up portrait shot
close up potrait
close up shot
close up shot a rugged
close up shot from the side
close up shot from the top
close up shot of an amulet
close up shots
close up subtle shadows
close up to a skinny
close up to the screen
close up view
close up wide angle lens
close up. macro. hyper realistic
close ups
close view
close-up
close-up 35mm macro shot
close-up fight
close-up from above
close-up imagery
close-up of a robot sitting down
close-up of face
close-up of thin soft hand
close-up on legs
close-up perfect face
close-up photo
close-up photograph
close-up photography
close-up portrait
close-up portrait film still
close-up portrait goddess skull
close-up portrait of cyborg
close-up print of fractured
close-up product photo
close-up professional portrait
close-up shoot
close-up shot
close-up shot from behind
close-up shot taken from behind
close-up view
close-up!!!!!
close-up!!!!!!
close-ups
closed - mouth smile
closed ecosystem
closed eyes
closed hands
closed limbo room
closed lips
closed mouth
closed mouth showing no teeth
closed mouth smile
closed swimsuit
closed up portrait
closed visor
closed-eyes
closed-mouth smile
closer view
closes her eye
closeup
closeup - view
closeup 4k
closeup at the face
closeup at the faces
closeup at the food
closeup character portrait
closeup cinematic aquatic scene
closeup detailed
closeup face
closeup faces
closeup fantasy with water magic
closeup giant dahlia flower head
closeup headshot
closeup headshot portrait
closeup of a butcher working
closeup of a crow
closeup of a snow owls face
closeup of an adorable
closeup of arms
closeup of car engine
closeup of face
closeup of face melting
closeup of face melting in agony
closeup of fist
closeup of hand
closeup of magic water gate
closeup of sweating forehead
closeup of the face
closeup on face
closeup painted portrait
closeup painting of bee
closeup photo
closeup photo at nyc comicon
closeup photograph
closeup portrait
closeup portrait of an artificer
closeup portrait of an mage
closeup portrait shot
closeup shot
closeup shot of face
closeup view
closeup!!
closeup!!!!!
closeup!!!!!!
closeup. mouth open
closing eyes
closing her eyes
closup
cloth
cloth accessories
cloth and metal
cloth banners
cloth head wrap
cloth jerkin
cloth physics
cloth sim
cloth simulation
cloth simulation with houdini
cloth wraps
clothed
clothed holy body
clothed in ancient
clothed in ancient battle armor
clothed in ancient street wear
clothed in ancient suit
clothed in armor
clothed in battle armor
clothed in cyber armour
clothed in ethereal armor
clothed in ethereal battle armor
clothed in hooded
clothed in military armor
clothed in old samurai uniform
clothed in sci-fi military armor
clothed in silk
clothed in space suit
clothed in stealth armor
clothed in summer
clothed in vines
clothed in white shirt
clothed in worrier armor
clothed non - nude portrait
clothed!
clothed! oil on canvas
clothed!! lucien levy - dhurmer
clothed!! oil on canvas
clothed.1990s
clothedlucien levy - dhurmer
clothes
clothes floating
clothes from the future
clothes fully on
clothes high detail
clothes in military armor
clothes in the style of 1 9 9 0
clothes in the style of 1990
clothes made out of flower
clothes made out of veins
clothes themed on a peacock mage
clothes torn apart
clothes!
clothing
clothing concept
clothing design
clothing drop
clothing photography
cloud
cloud and sky color scheme
cloud and thunder
cloud background
cloud day
cloud forest
cloud forest in background
cloud goddess
cloud hair
cloud in the shape of a dragon
cloud in the sky
cloud iridescence
cloud jumper
cloud mage robes
cloud nebula
cloud of sand
cloud of smoke
cloud ominous day background
cloud palace
cloud server
cloud storage
cloud strife
cloud vortex
cloud with eye
cloud-like white hair
clouded
clouded sky
cloudless blue sky
cloudless sky
cloudless-crear-sky
cloudpunk
clouds
clouds and corrupted city behind
clouds and fields in background
clouds and sky
clouds and waves
clouds and wings and waves
clouds around
clouds background
clouds in background
clouds in the background
clouds in the sky
clouds mystic hue
clouds of smoke
clouds of vivid horse-hair wigs
clouds on ground!!!!!
clouds on surface
clouds on the sky
clouds outside the windows
clouds swirling
clouds visible
clouds. fantasy
cloudscape
cloudy
cloudy air
cloudy and mystical
cloudy atmosphere
cloudy background
cloudy dark sky
cloudy day
cloudy eyes
cloudy grey hair
cloudy night
cloudy night sky
cloudy overcast sky
cloudy skies
cloudy sky
cloudy sky background
cloudy sky in the background
cloudy sunset
cloudy weather
clouse up face portrait
cloven feet and horns
clover
cloverfield movie
clown
clown darth vader hybrid
clown face
clown fish
clown frog king
clown girl
clown makeup
clown makeup and rainbow wig
clown nose
clown waving hello
clown world
clowns
clowns boxing
club
club lighting
club penguin
club photography
clubs
clumps of bananas
clumps of hair
clustered
clutch yo - yo
clutching a sword
clutter
cluttered
cluttered medium shot
cluttered room
clyde aspevig
clyde waldwell
cmyk
cmyk color scheme
cmyk portrait
cn tower
cnn
cnn news footage
coachella
coal
coal dust
coarse canvas
coarse paper
coarse texture
coast
coast as the background
coastal
coastal cliffs
coastline
coat
coat decollete jodhpurs
coat for a rave
coat for a rave with fur
coat of arms
coat pleats
coated pleats
coating a stubborn core.'
cobalt
cobalt blue
cobalt blue and pyrrol red
cobalt blue hair
cobalt coloration
coban
cobbled together nissan r34 gtr
cobblestone
cobblestone floors
cobblestone road
cobblestone roads
cobblestone street
cobblestone streets
cobblestones
cobblestones on the ground
cobra head mascot facing right
cobweb
cobwebs
cobwebs and dust
coca cola bottle
coca cola in camel head
coca cola logo
cocaine
cockatoo holding a trumpet
cockpit
cockpit view
cockroach
cockroach character
cockroach lady
cocktail bar
cocktail in an engraved glass
cocky
cocky expression
cocky smirk
coconut palms
coconut trees
coconuts
cocroach
cod
codachrome
code
code geass
code vein
codex
codex gigas
codex seraphinianus
coding
coding time
coerent face
coerent face and body
coffee
coffee and musical instrument
coffee and stars background
coffee art
coffee beans
coffee cup
coffee cups
coffee machine
coffee shop
coffee smell
coffee stain
coffee stain on napkin
coffin comics
cognition
cognitive cohesion coherence
cognitive transcendence
cognitohazard
cogs
cogs and gears
cogs and springs and jewels
cogs and wheels
cogs:1000
cogwheel
coherence
coherent
coherent and smooth
coherent body
coherent composition
coherent design
coherent eye
coherent eyes
coherent face
coherent hands
coherent image
coherent like dall-e 2
coherent painting
coherent photo
coherent portraits
coherent professional
coherent symmetrical artwork
coherent symmetrical eyes
coherent symmetrical faces
coherent symmetry
coherent text english characters
coherent!!! art directed
coherentart directed
cohesive
cohesive composition
cohesive!
cohesive.69:1
coiffed brown hair
coiled realistic serpents
coilgun
coin
coin of secret society
coin with the letter n
coins
coka-cola advertisement
coke and chips on table
col price
colchians painting
cold
cold ambient light
cold as ice! 🧊
cold as ice🧊
cold atmosphere
cold beer
cold blood
cold blue colors
cold blue light
cold blue light from the window
cold blue lighting
cold blue tones
cold breath
cold brew coffee
cold brew coffee )
cold but beautiful
cold color
cold color palate
cold color palette
cold color scheme
cold colors
cold colors. insanely detailed
cold colour temperature
cold colour temperture
cold colours
cold drinks
cold environment
cold freezing nights
cold grey eyes
cold hue's
cold hues
cold light
cold light from the window
cold lighting
cold lights soft painting
cold metallic atmosphere
cold mist
cold mist black background
cold mood
cold neon light
cold neon lighting
cold place
cold pure color background
cold scene
cold secondary colors
cold shades
cold snow outside
cold snowy
cold stormy wind
cold studio lighting
cold sunny weather
cold sunset
cold sunshine through window
cold temperature
cold texture
cold tone
cold tone gradient background
cold tones
cold top lighting
cold war
cold war era 1 9 6 0
cold weather
cold winter
colin campbell cooper speedart
colin farrell
colin farrell with jessica alba
colin greenwood (bass)
colin hay
colin searle
coliseum
coliseum backdrop
coliseum background
coliseum of rome
collaboration
collaborative
collaborative artwork
collaborative painting
collage
collage art
collage art background
collage artwork
collage effect
collage of styles
collage paper and tape
collage style joseba elorza
collaged
collages
collapse
collapsed brutalist architecture
collapsed building
collapsed buildings
collapsed ceiling
collapsed floors
collapsed water tower
collapsing
collapsing stars
collapsing stars and supernovae
collar
collar and leash
collar around neck
collar on neck
collared shirt
collateral
collectible
collectible action figure
collectible card art
collection
collection of louvre
collection product
collective civilization tree
collective consciousness
collectors
colleen moore 2 2 years old
colleen moore 2 5 years old
colleen moore 2 8 years old
colleen moore 22 years old
colleen moore 25 years old
colleen moore 28 years old
college
college girls
college party
college students
collodion photograph
collodion photography
collosal mech
coloful
colombia
colombia en los años 60
colombian
colombian jungle
colombo sri lanka cityscape
colombo sri lankan city street
colonel sanders
colonial
colonial era street
colonial expedition
colonial exploration
colonial house in background
colonial marines
colonial style
colonisation
colonnade
colonnade ornate headdress
colony
colony on mars
color
color ( sony a 7 r iv
color - out - of - space
color 1970
color 3 5 mm
color accents
color accurate
color aerial photo drone
color airbrush
color and contrast corrected
color balance
color balance harmony
color bleed
color bleeding
color block
color blocking
color blocking!
color blocks
color burn
color chart
color comic
color comics style
color contrast
color corrected
color correction
color degragation
color dispersion
color displacement
color dnd illustration
color drawing
color edition
color ektachrome photograph
color explosion
color field painting
color field painting. 8k
color film
color film camera
color film expired film
color film grain
color film photography
color film photography 1970s
color film still
color film still 1 9 7 7
color footage
color gel
color glow
color grade
color graded
color gradient
color gradients
color grading
color graflex macro photograph
color grain 3 5 mm
color grain 35mm
color harmony
color highway
color hues
color illustration
color image
color ink
color ink explosion
color ink pen illustration
color interference
color kodak
color kodak film stock
color kodak stock
color kodakcrhome 64
color leaves
color light
color light waves
color lights
color limit
color manga panel
color negative
color out of space
color overlay
color page
color painterly
color painting
color palette
color palette is dark orange
color palette of gold
color pencil
color pencil art
color pencil drawing
color pencil sketch
color photo
color photo of josip broz tito
color photograph
color photograph portrait 4k
color photography
color photos
color picture
color pigments spread out in air
color polaroid photo
color pop
color portrait
color red
color restoration
color rich
color scheme
color screenprint
color slide
color slide film
color smoke
color spectrum refraction
color splash
color splashes
color splotches
color spread
color studio portrait
color study
color tearing
color theory
color triads
color vector
color vhs picture quality
color vibe
color video footage
color vintage
color wheel
color-key painting
colorado
colorado mountains
colorbleed
colored
colored accurately
colored album art
colored analog photography
colored background
colored concept art
colored daguerreotype
colored dots
colored drawing
colored elephant art
colored feathers
colored flowers
colored fog
colored fruit stand
colored gel lighting
colored gels
colored gems
colored hair
colored illustration
colored illustration for tattoo
colored in
colored ink
colored ink drawing
colored lasers
colored layers
colored light
colored lighting
colored lights
colored lineart
colored manga
colored manga art
colored manga panel
colored marble
colored marker
colored market stand
colored milk tea
colored neons
colored paint
colored paper collage
colored pencil
colored pencil art
colored pencil art on paper
colored pencil drawing
colored pencil illustration
colored pencil on paper
colored pencil sketch
colored pencils
colored photo
colored photography
colored projections
colored screentone
colored sketch
colored sketch anime manga panel
colored smoke
colored smoke clouds
colored spotlights
colored spots
colored vibrantly
colored woodblock print
colored woodcut
colored zbrush render
colored-pen drawing
colorful
colorful - patterns
colorful 4k
colorful 8 k
colorful accents
colorful acrylic
colorful adornments
colorful aesthetic
colorful airsoft gun
colorful alien flora
colorful ancient egyptian city
colorful and bright
colorful and dark
colorful and grayish palette
colorful and psychedelic
colorful and vibrant
colorful anime movie background
colorful architectural drawing
colorful architecture
colorful art
colorful art nouveau
colorful aura
colorful autumn trees
colorful backdrop
colorful background
colorful bandana
colorful ben day dots
colorful bird with a long
colorful birds
colorful bottles and plants
colorful building
colorful buildings
colorful caparisons
colorful character faces
colorful chromatic abberation
colorful city
colorful city in ancient egypt
colorful clear sharp focus
colorful clothes
colorful clothing
colorful clouds
colorful coctail
colorful colors
colorful comics style
colorful composition
colorful computer screen
colorful concept art
colorful contrast
colorful copper brass accents
colorful coral
colorful coral reef
colorful crowd
colorful crystals
colorful dark vector
colorful detailed projections
colorful details
colorful digital art
colorful digital painting
colorful digital screens) xf iq4
colorful doodle art
colorful dots
colorful dramatic puffy clouds
colorful drawing
colorful dream
colorful dress
colorful dystopian futurism
colorful energetic brush strokes
colorful explosion
colorful eyes
colorful fashion
colorful feathers
colorful ferrofluid armor
colorful fire
colorful fish
colorful flat design
colorful flat surreal
colorful flat surreal design
colorful flat surreal ethereal
colorful flowers
colorful galaxy theme colors
colorful gems
colorful generative art
colorful geometric pattern
colorful glass art
colorful glass wall
colorful glow
colorful graffiti
colorful hair
colorful hairs
colorful haze
colorful hd picure
colorful high contrast
colorful high contrast hd
colorful hilt
colorful house
colorful houses
colorful hyperbolic background
colorful hyperrealism
colorful ideas
colorful illustration
colorful image
colorful impasto brush strokes
colorful in colour
colorful ink
colorful intricate masterpiece
colorful iridescent and playful
colorful kids book illustration
colorful kitsune city
colorful landscape painting
colorful lanterns
colorful leaves
colorful lens flares
colorful lenses
colorful light
colorful light leather armor
colorful lighting
colorful lightning cinematic
colorful lights
colorful magic
colorful magic effects
colorful mandala
colorful medical equipment
colorful melting human head
colorful modern
colorful mold
colorful mood
colorful mosaic
colorful mural on walls
colorful muscular eldritch
colorful muscular robot
colorful mushrooms
colorful nebula
colorful nebula background
colorful nebulas
colorful neon lighting
colorful neon lights
colorful neon signs
colorful night sky
colorful octane render
colorful oil painting
colorful otherworldly trees
colorful outfit
colorful paint
colorful painting
colorful palette
colorful palette illustration
colorful paper lanterns
colorful pastel
colorful patterns
colorful pentax camera
colorful photo
colorful photograph
colorful picture
colorful pigtail
colorful pixel art
colorful plasma hairs
colorful plastic
colorful plumage
colorful projections
colorful rave makeup
colorful ravine
colorful redshift render
colorful reflective eyes
colorful refracted sparkles
colorful refractive adornments
colorful retrofutur
colorful rich fantasy clothing
colorful robes
colorful scene
colorful scheme
colorful sci-fi steampunk
colorful signs
colorful sketch
colorful skies
colorful sky
colorful smoke
colorful smoke in the background
colorful spells
colorful splatters
colorful stars
colorful static
colorful striped pavillions
colorful sunset
colorful sunset!!
colorful swirls of paint
colorful swirly magic ripples
colorful swirly magical clouds
colorful swirly magical ripples
colorful swirly ripples
colorful swirly ripples of magic
colorful tiled architecture
colorful torn fabric
colorful torn nebulas
colorful trees
colorful tropical plants
colorful twirling smoke trails
colorful umbrella
colorful uniforms
colorful vapor
colorful vector illustration
colorful vegetation
colorful vines
colorful vivid octane render
colorful warcraft architecture
colorful watercolor
colorful watercolor painting
colorful wildflowers
colorful wires
colorful with red hues
colorful!
colorful! character design
colorful!!
colorful!!!
colorful]”
colorfull
colorfull digital fantasy art
colorfull illustration
colorfull painting
colorfull sky
colorfully
colorfully background
colorfully ominous background
colorful”
colorgrading
coloring book
coloring book outline
coloring book page
coloring book style
coloring page
coloring pages
colorised
colorized
colorized 1 9 0 4 photo
colorized background
colorized neon lights
colorized pencil
colorized pencil sketch
colorized photo
colorized photograph
colorized photon
colorkey scene
colorless
colorless and silent
colors
colors : yellow sunflowers
colors are strong but calm
colors of jamaica
colors of mark rothko
colors of tron legacy
colors orange
colors red
colors red white blue and black
colors reflecting on lake
colors white
colors white!!
colors with gold and dark blue
colors: yellow
colors: yellow sunflowers
colorsmoke
colorul
color”
colossal
colossal dragon as background
colossal dragon in background
colossal fluffy tardigrade
colossal frankenstein
colossal lightning
colossal rat
colossal robot
colossal scale
colossal titan
colossal tower
colossale interstellar portal
colosseo
colosseum
colossus
colossus of rhodes
colour
colour blocking
colour corrected
colour eyes
colour film street photography
colour hd photography
colour led
colour photo
colour photograph
colour photography
colour polaroid photo
colour portrait photograph
colour print
colour shift
colour splash
coloured
coloured background
coloured comic
coloured film photography
coloured gel studio light
coloured in blueberra and orange
coloured in orange fire
coloured in teal and orange
coloured line art
coloured lineart
coloured lithograph engraving
coloured manga scan
coloured marker
coloured pencil
coloured photo
coloured polaroid photograph
coloured with lots of colour
coloured woodcut
colourful
colourful 3 d crystals
colourful 3 d crystals and gems
colourful 3d crystals and gems
colourful 4 k hd
colourful apples
colourful artwork
colourful background
colourful biomorphic opart
colourful biomorphic temple
colourful books
colourful close up shot
colourful clothes
colourful clothing
colourful cupcake
colourful digital art
colourful dramatic lighting
colourful drawing
colourful explosion
colourful flat surreal design
colourful flowers
colourful flowers bouquet
colourful hand drawing
colourful jungle
colourful light
colourful lighting
colourful long hair
colourful magazine collage
colourful movie art
colourful palette
colourful pastel
colourful roses and gladioli
colourful sky
colourful slime
colourful!! highly detailed
colourfull
colouring
colouring - in sheet
colouring page
colouring pages
colourised
colourized
colours
colours of the sunset
colours red and green
colt
columbo
columns
colurful
combat
combat and adventure photography
combat armor
combat boots
combat drone
combat drone strike war footage
combat gear
combat goggles
combat googles
combat helmet
combat mech clothes
combat photography
combat pose
combat scene
combat setting
combat stance
combat suit
combat vest
combination art
combine
combover
combustion engine
come
come on
comedian is funny
comedic
comedy
comet
comets
comfort
comfort posse
comfortable
comfortable atmosphere
comforting
comforting and familiar
comfy
comfy ambience
comfy chairs
comfy lighting
comfycore
comic
comic art
comic art style
comic art ”
comic artstyle
comic artwork
comic book
comic book art
comic book art style
comic book artist moebius
comic book arzach style
comic book black lines
comic book character
comic book cover
comic book cover art
comic book cover style
comic book cover visual style
comic book design
comic book drawing
comic book hero
comic book illustration
comic book page
comic book panel
comic book panels
comic book panels background
comic book realism
comic book style
comic book style art
comic book style battlemech
comic book style!!
comic book texture
comic book thick outline
comic book visual style
comic book's cover
comic book:.2
comic book:.2 | unreal engine:.3
comic books
comic books style
comic card style
comic character
comic character design
comic concept art
comic cover
comic cover art
comic cover painting
comic digital art
comic drawing
comic drawing style
comic illustration
comic inks
comic lines
comic page
comic paint
comic panel
comic panels
comic pinup style
comic shading
comic strip
comic strip style
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comic styled
comical
comicbook
comicbook art
comicbook cover
comicbook style
comicpunk
comics
comics accurate design
comics art
comics gta 5 cover
comics illustration
comics sandman
comics style
comics style art
comicstyle
coming ashore
coming down the stairs
coming out of a black hole
coming out of the ocean
coming to life
comix wave films
comixology
comixwave films
comiс style
command presence
commander
commander riker
commander shepard
commanding
comme des garcon campaign
commercial
commercial banner
commercial billboard
commercial illustration
commercial lighting
commercial photo
commercial photo shoot
commercial photograph
commercial photography
commercial product photography
commercial shot
commercial studio lighting
commercial washroom hand dryer
commercially ready
comming
comminism
commission
commission art
commission for
commission for high res
commission of freddy fazbear
commission on furaffinity
commissioned
commodore 6 4
commodore 64
common street
commoner
communication
communism
communist
communist city
communist clown
communist clown painting
communist clown portrait
communist draven
communist hammer and sickle
communist propaganda
communist propaganda poster
communist starfish
communist states of america
community
community celebration
compact buildings
company logo
compass
compass energy flowing
compassion
compassionate
compelling
competent
competition
competition highlights
competition winning
complaints
complementary color
complementary color scheme
complementary colors
complementary colour
complementary colour palette
complementary colour scheme
complementary colours
complementary contrast
complementary lighting
complementary palette
complementary rim lights
complementing colors
complete body
complete body view
complete body!
complete darkness
complete darkness background
complete detailed body
complete disregard
complete figure
complete house
complete light occlusion
complete model
complete scene
complete whole lion body
complete window!
completely blue eyes
completely coherent
completely consisting of fire
completely dark
completely empty
completely filled space
completely flooded
completely framed
completely golden eyes
completely metal
completely new
completely overgrown with vines
complex
complex 3 d render
complex 3d render
complex 3d scene
complex alien fractal structure
complex and desaturated
complex and detailed
complex and intricate
complex architecture
complex armour
complex artistic style
complex background
complex beings
complex braided hair
complex buildings
complex ceiling
complex city
complex composition
complex composition!!
complex computations
complex contraption
complex cybernetic beings
complex design
complex design hair
complex detailed environment
complex details
complex drawing
complex emotion
complex fantasy character
complex features
complex geometry
complex layered composition
complex layered composition!!
complex lighting
complex lights
complex machinery
complex massive detail
complex outfit
complex pattern
complex patterns
complex redhead braided hair
complex rotary airplane engine
complex scene
complex shading
complex shapes
complex structure
complex structures
complex swirling accents
complex system of order
complex tarot card background
complex vortex
complex!!
complexity
complexity from simplicity
complexity of the image
complexity of the picture
complexly detailed
complicated
complicated circuits and wires
complicated dynamic composition
complimentary - colors
complimentary color
complimentary color palette
complimentary color scheme
complimentary colors
complimentary colours
complimentary contrast
complimentary eyeliner
complimenting color scheme
composed
composed of random limbs
composer
composite
compositing
composition
composition of and futuristic
composition study
composition william s boroughs
compound eyes
comprehensive 2 d render
comprehensive 2d render
comprehensive art
comprehensive artwork
compressed
compressed jpeg
compression
compulsion
computation
compute shader
computer
computer - generated
computer aesthetic
computer cgi
computer chips
computer code
computer components
computer desk
computer drawing
computer equipment
computer game
computer game art
computer generated
computer glitch
computer glitches
computer made out of human flesh
computer monitor
computer monitors
computer mouse
computer music
computer nerd
computer network
computer painting
computer parts
computer render
computer science
computer screens
computer setup
computer vision
computer wallpaper
computers
computers and digital monitors
computers and holograms
conan
conan o'brien
conan obrien
conan the barbarian
conan the barbarian illustration
conan the destroyer 1 9 8 5
conan the destroyer 1985
concentrated
concentrated buildings
concentrated look
concentration
concentric circles
concep art
concept
concept - art
concept ar
concept ard
concept are
concept armor
concept art
concept art  artgerm
concept art  beeple
concept art  dyson
concept art  feng zhu
concept art  rhads
concept art  rutkowski
concept art 2022
concept art 4 k
concept art 4k
concept art 8 k
concept art 8 k resolution
concept art 8k
concept art 8k resolution
concept art artwork masterpiece
concept art character
concept art character modeling
concept art character sheet
concept art design illustration
concept art digital illustration
concept art for a video game
concept art for movie
concept art for movies
concept art futuristic norse
concept art h 8 0 0
concept art h 800
concept art highly detailed
concept art illustration
concept art in 3 d
concept art like ernest khalimov
concept art magical highlight
concept art matte painting
concept art octane render
concept art of a dark forest
concept art of a monk
concept art of a warrior
concept art of chris foss
concept art of comiket cosplay
concept art of god
concept art of h. r. giger
concept art of love
concept art of omegamon
concept art of pikachu
concept art of single boy
concept art of tony stark
concept art oil painting
concept art painting
concept art render
concept art rendering
concept art scene
concept art sketch
concept art stunning atmosphere
concept art style
concept art wallpaper 4k
concept art world
concept art zippo lighter
concept art | artgerm
concept art | beeple
concept art | dyson
concept art | feng zhu
concept art | rhads
concept art | rutkowski
concept art ”
concept art ， highly rendered
concept art!
concept art!!
concept art-h 640
concept art. 8 k
concept art. 8k
concept art. epic landscape
concept art. smoke
concept artbook
concept arti
concept arts
concept artstyle
concept artwork
concept art”
concept automobile design
concept boat
concept car
concept car design
concept character
concept character art
concept character sheet
concept creature character art
concept design
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concept design art octane render
concept digital art
concept drawing
concept drawing my syd mead
concept eclectic
concept fantasy
concept headshot art
concept house
concept illustartion
concept illustration
concept image
concept matte
concept model
concept painting
concept photo
concept photos
concept photoset
concept piece
concept pixelart
concept portrait
concept render
concept robot
concept sheet
concept sketch
concept sketches
concept style
concept tattoo design
concept world
concept world art
concept-art
conceptart
conceptart. com
conceptart.com
conceptartworld
conceptual
conceptual art
conceptual installation
conceptual mystery pokemon
conceptual rendering
concern
concerned
concerned expression
concert
concert documentary
concert footage
concert hall
concert light
concert lighting
concert lights
concert photo
concert photography
concert photography from the 70s
concert poster
concert poster retro
conch shell
concise lines
concpet art
concrete
concrete )
concrete _ wall ) ]
concrete architecture
concrete art style
concrete balcony
concrete brick background
concrete building
concrete hitech interior
concrete housing
concrete jungle
concrete pillars
concrete poetry
concrete steel glass
concrete wall
concrete walls
conde nast traveler photo
condensation
condensation drip
condensation droplet render
condescending
condorito
conductor
cone
cone heads
cone shaped
conecpt art
coneheads
conept art
confetti
confidant
confidence
confident
confident action pose
confident expression
confident facial expression
confident heroic pose
confident holding vegetables
confident knight's outfit
confident looking
confident personality
confident pose
confident poses
confident posse
confident relaxed pose
confident shaded eyes
confident smile
confident smirk
confident stance
confidential documents
confidently
conflict
conflicted emotions
conformity
confortable atmosphere
confounding
confucius and jury trial
confused
confused expression
confused facial expression
confusing
confusing optical illusion
confusing perspective
confusion
congas
conglomerate
conglomerate!
congruent
coniferous forest
conjoined twins
conjure devils
conjuring a demon
conjuring psychedelic background
connected
connected to heart machines
connected to nature via vines
connected to wires
connected trough wired
connected with glowing tubes 8 k
connected with glowing tubes 8k
connected with hanging bridge
connected with hanging bridge!!
connectedness
connecting life
connecting lines
connection rituals
connections
connectivity
connector
conniving
connor hibbs
conor mcgregor
conor walton
conquering imbalance
conquest
conquistador
conrad roset and makoto shinkai
consciousness
consciousness projection
consciousness rising
consectetur adipiscing elit
conservative
conservatively dressed
consist of shadow
consistency
consistent
consistent face
console
console and computer
conspiracy
conspiracy theory
constant green background
constantine
constellations
constrast
constructed upon salar de uyuni
construction
construction equipment 1 9 9 0
construction site
construction yard
constructive solid geometry
constructivism
constructivism style
constructivist
contact sheet
contain
container ship
containing a hidden portal
containing tables and walls
containment pod
contaminated
contaning tables and walls
contemplating
contemplating existence
contemplation
contemplative
contemporary
contemporary 3d art
contemporary aesthetic
contemporary architecture
contemporary art
contemporary art aesthetic
contemporary art gallery
contemporary art illustration
contemporary art style
contemporary ceramics
contemporary collage
contemporary dance
contemporary dance poses
contemporary design
contemporary digital art
contemporary fashion shoot
contemporary house
contemporary masterpiece
contemporary painting
contemporary sculpture
contemporary surrealism
content
content balance proportion
content!!!
contented
contented female bodhisattva
contentment
conteporary art
contest
contest award winner
contest winner
contest winner 2021
contest winning masterpiece
continents
continuous line drawing
contorted
contorted limbs
contour
contour drawing
contour hatching
contour light effect!! 8 k
contourless
contours
contra
contracept
contracting colors mood
contrails
contraposto
contrapposto
contrast
contrast and shadows
contrast colors
contrast icon
contrast light
contrast lighting
contrast lightning
contrast of light and shadows
contrast picture
contrast shading
contrast shadows
contrast side light
contrast volumetric light
contrast. surreal
contrasted
contrasted color
contrasted colors
contrasted light
contrasted lighting and shadows
contrasting
contrasting color scheme
contrasting colors
contrasting colours
contrasting light
contrasting lighting
contrasting shadows
contrastive colors
contrasts
contrasty
contre - jour
contre jour
contrejour
contrejour lighting
control
control art
control panel
control panels
control the game
control the soul
controller
controller deck
controllers
controlling shadows
controls
controversial
controversial poster art
convenience store
convention photo
conversano
conversation pit
converse
convertable
convertible
convex
conveyor belts
convincing
convoluted
convoluted halls
conway's game of life
cook
cookbook photo
cookbook photo 1 9 8 2
cooke 8 5 mm f / 1. 2
cooke 85 mm f / 1. 2
cooke anamorphic / i lenses
cooke lens feel
cooke prime 25mm
cooke prime 50mm
cooke varotal 20-100 t3.1
cooke varotal 20-100mm t3.1
cooked
cooked to perfection
cookie monster
cookie monster as a bond villain
cookies
cooking
cooking it up
cooking oil
cooking pizza
cooking show
cooky
cool
cool 3d visualisation
cool and bright tint
cool background
cool blue and green colors
cool blue night color palette
cool clothes
cool color palette
cool color scheme
cool colors
cool dad vibes
cool desert
cool dynamic lighting
cool face
cool glasses
cool hair
cool hairstyle
cool japanese street fashion
cool light
cool lighting
cool lights
cool looking
cool marketing photo
cool mecha style
cool night color
cool offset colors
cool palette
cool pallet
cool pose
cool purple grey lighting
cool purple lighting
cool purple slate blue lighting
cool red jacket
cool saturated colours
cool sepia tone colors
cool shading
cool skydome
cool smooth space colours
cool style
cool sunglasses
cool temperature
cool tint
cool tone
cool tone pastel rainbow colors
cool toned
cool tones
cool tousled hair
cool twilight
cool vibes
cool warm lighting
cool white
cool white color temperature
cooling
cop
coper cladding
copic marker
copic markers
copious jewelry
copper
copper and brass
copper and deep teal mood
copper and emerald
copper and emerald inlay
copper and emerald jewelry
copper cup
copper details
copper elements
copper goat skulls
copper hair
copper oxide and rust materials
copper oxide material
copper patina
copper pipers
copper short hair
copper spiral hair decorations
copper veins
copper wire
copperplate print style
copyright time magazine
copyright tmz
copyright tsr 1989
coral
coral brown hair
coral headdress
coral lipstick
coral red
coral reef
coral reefs
coral sea bottom
coral snakes grow from her head
coral underwater colorful
coral-like pebbles
coraline
corals
corals are gemstones
cords
cords and wires
corduroy
corduroy road
corel painter
corel paintshop pro
corgi
corgi and cat fixing the website
corgi cosmonaut
corgi dressed as captain america
corgi with  angelic wings
corgi with [ angelic wings ]!!
corgis in no man's sky
corgis in no mans sky
corinne day
coriolios rpg art style
corn
corn chess board game
corn floating in ocean
corn on a cob everywhere
cornelia geppert
cornell
corner
corner assault rifle
corner office background
cornered like an animal
corners
cornrows
cornrows braids
cornucopia
cornwall
coroflot
corona
corona render
corona rendered
corona renderer
coronal storm
coronarender
coronation
coronation of the flower prince
coronavirus
coronavirus as a stuffed toy
corot
corporate
corporate animation style
corporate boss
corporate business
corporate flow chart
corporate holograms
corporate logo
corporate logo of an eye
corporate memphis
corporate memphis style
corporate phone app icon
corporate photo
corporate portait
corporate portrait
corporate style
corps scattered on the ground
corpse
corpse bride art style
corpse bride style
corpse bridegroom of the spring
corpsepaint
corpses
corpses and shadows
corpses and shadows!
corpses come to life
corpses floor
correct anatomy
correct details
correct eye placement
correct eyes proportion
correct face
correct faces
correct proportions
correct robot face
correct wolf muzzle
corrected
corrected faces
corrected hand
corrected hands
correctly anatomy
corridor
corrugated hose
corrupted
corrupted armor
corrupted data
corrupted file
corruption
corset
corsets
cortana
cortana from halo
coruscant
corvette c2 1969
corvette c21969
corvo attano
cory
cory behance hd
cory chase
cory chase as an atlantean
cory loftis
cosmetics
cosmic
cosmic ambient
cosmic and colorful
cosmic apocalypse
cosmic architecture
cosmic artifacts
cosmic atmosphere
cosmic atmosphere light flares
cosmic background
cosmic bjork
cosmic butterfly nebula
cosmic cataclysm
cosmic color scheme
cosmic colorful ultima moongate
cosmic colors
cosmic crystals
cosmic destruction
cosmic dynamic lighting
cosmic energy
cosmic energy wires
cosmic enlightenment
cosmic entity
cosmic fantasy
cosmic girl
cosmic goddess
cosmic horror
cosmic horror concept art
cosmic horror creature
cosmic horror elements
cosmic horror entity
cosmic horror entity with wings
cosmic horror illustration
cosmic horror painting
cosmic horror style
cosmic horror!!
cosmic horror!!!
cosmic imagery
cosmic integration
cosmic landscape
cosmic lighting
cosmic lsd poster art
cosmic microwave background
cosmic nebula
cosmic nebulae
cosmic night background
cosmic purple space
cosmic purple space!
cosmic royalty
cosmic scale
cosmic skies
cosmic skies. by makoto shinkai
cosmic sky
cosmic soul
cosmic space
cosmic starry sky
cosmic stars
cosmic style
cosmic sun in the background
cosmic survival
cosmic tiefling d&d
cosmic tiger
cosmic tree
cosmic tree of life
cosmic universe
cosmic void background
cosmic weapons
cosmic wind
cosmo kramer as dr. strange
cosmology
cosmology of kyoto landscape
cosmonaut
cosmopolitan
cosmoport
cosmos
cosmos backdrop
cosmos exploration
cosmos in the background
cospay
cosplay
cosplay award winner
cosplay journal cover
cosplay of a catboy! maid! dress
cosplay on black harley queen
cosplay photo
cosplayer
cosplayer dressed like a crab
cost
costa blanca
costume
costume desig
costume design
costume design made with love
costume weird
costume weird creepy
costume with blue accents
costumes
costumes from peaky blinders
cosy
cosy atmoshpere
cosy atmosphere
cosy enchanted scene
cosy fireplace
cosy vibes
cottage
cottage close up
cottage core
cottage decor
cottage hippie naturalist
cottage in the forest
cottage in the woods
cottage town
cottagecore
cottagecore flower garden
cottagecore hippie
cottagecore!!
cottagecore!! fitness body
cottages
cotton
cotton candy
cotton candy bushes
cotton candy clouds
cotton candy trees
cotton cloud mage robes
cotton clouds
cotton fabric
cotton texture
cotw
couch
couch desk
couches melting
couches vibrating and melting
cougar
cougar in forest at night
coughing
coulson
count dracula
count of monte cristo
countach
countdown
counter
counter strike
counterfeit mickey mouse head
country
country landscape
country road
country style
countryside
countryside city scene
countryside in japan
couple
couple dancing
couple kissing
couple on bed
couple portrait
couple pose
couple walking hand in hand
couples portrait
courage
courage the cowardly dog
courageous
courbet
courful illustration
court archive images
court drawing
court images
court jester
court jester in renaissance era
court politics
court room
court room sketch
court session images
court sketch
court sketch art
courtesy mbari
courtesy of centre pompidou
courtesy of mbari
courtesy of moma
courtroom
courtroom drawing
courtroom scene
courtroom sketch
courtroom the bench
courtyard
courtyard walkway
couture
coventry city centre
cover
cover art
cover art design
cover art of graphic novel
cover corp
cover game art
cover girl
cover illustration
cover image
cover magazine
cover manga
cover of a magazine
cover of gta v
cover of life magazine
cover of magazine
cover of national geographic
cover of new york times
cover of new yorker magazine
cover of surfer magazine
cover of time magazine
cover of vogue
cover page
cover photo portrait of du juan
cover shot
cover story
cover with a lot of red water
cover with blood
cover. photo : david roemer
covered
covered breasts
covered bridge
covered chest
covered face
covered head
covered in
covered in bandages
covered in blood
covered in circuitry
covered in clouds
covered in coral
covered in coral and barnacles
covered in crystals and glitter
covered in dirt
covered in dust
covered in fallen leaves
covered in feathers
covered in flame porcelain vine
covered in flowers
covered in full leather armor
covered in full metal armor
covered in full silver armor
covered in human eyes
covered in ice
covered in jewels
covered in ketchup
covered in leaves
covered in matted fur
covered in moss
covered in moss and birds
covered in mud
covered in oil
covered in oil painting
covered in organic flesh meat
covered in pink flesh
covered in plants
covered in red slime
covered in robes
covered in runes
covered in salt
covered in sand
covered in slime
covered in slime!!
covered in snow
covered in soft fur
covered in sprinkles
covered in sprinkles and crumbs
covered in transparent cloth
covered in vines
covered in water drops
covered in white flour
covered outdoor stage
covered sfw huge breasts
covered solar system
covered with black goo
covered with blanket
covered with blood
covered with cobwebs and dust
covered with ferrofluid. dslr
covered with flowers
covered with liquid tar. dslr
covered with organic flesh
covered with pink marzipan
covered with roots
covered with snow
covered with tar
covered with tar. dslr
covered with tentacles
covered with vegetation
covered with wires
covered!
covered!!
covered!! hair
covering the ground
covert art
covert military pants
covet death
coveted
coveted alive
covid
covid-19 as a human
cow
cow hoof feet
cow horns
cow skull
cow-girl
coward
cowardly
cowboy
cowboy bebop
cowboy bebop anime
cowboy bebop anime style
cowboy bebop art style
cowboy boots
cowboy cats
cowboy dream
cowboy hat
cowboy hat and glasses
cowboy on the range
cowboy plague doctors
cowboy portrait
cowboy portrait male
cowboy shot
cowboy style
cowboy themed
cowboys
cowgirl
cowl
coworkers
cows
coxcomb
coy
coy expression wearing intricate
coy smile
cozy
cozy 1 9 5 0's
cozy 1950s
cozy 9 0 s bedroom retrofuturism
cozy aesthetic
cozy and calm
cozy and peaceful atmosphere
cozy arm chairs
cozy atmosphere
cozy atmospheric
cozy bathhouse hidden in a cave
cozy bed
cozy cafe background
cozy calm! fabrics textiles
cozy calmfabrics textiles
cozy candlelight
cozy contemporary living room
cozy dark 1920s speakeasy bar
cozy environment
cozy home background
cozy lighting
cozy lights
cozy living room
cozy living room background
cozy living room interior
cozy night fireflies
cozy place
cozy room
cozy setting
cozy studio photography
cozy treehouse bedroom
cozy under a blanket
cozy vibe
cozy wallpaper
cozy warm tint
cp2077
cpl filter
cpu
cpu gpu wafer
cr3
crab
crab monster
crab on beach on sand
crabcore
crabs
crack head
crack pipe
cracked
cracked and faded in places
cracked and faded photo paper
cracked body full of scars
cracked brick background
cracked brick wall
cracked dry lake bed
cracked earth
cracked glass
cracked handmade pottery vase
cracked japanese pottery vase
cracked mirror
cracked mud
cracked paint
cracked picture window
cracked plastic wrap
cracked porcelain face
cracked steel
cracked varnish
cracked walls
cracked windows
cracked. biomechanical cyborg
cracking glass through reality
crackles
crackling black lightning
crackling blue lightning
crackling green lightning
crackling lighting magic
crackling with lightning
cracks
cracks in the armor
cracks of magma
craft
crafting
crafting a nightmare
crafting spells
crafts
crafts and more
crafts and souvenirs
craftsman home
craftsmanship
craggy
craggy mountains
craig
craig mallism
craig miller
craig mulins
craig mullens
craig mullin
craig mullins 8 k
craig mullins 8k
craig mullins alphonse mucha
craig mullins and artgerm
craig mullins and greg rutkowski
craig mullins dappled lighting
craig mullins dark key
craig mullins greg rutkowski
craig mullins nekro
craig mullins style
craig mullinshyper
craig russell
craig wylie
craigslist photo
craigville
craiyon
cramel hair
cramped
cramped new york apartment
cranberry helmet
cranberry statue
cranbow jenkins
crane
crane shot
cranes
crap fish
crash
crash bandicoot
crash bandicoot artstyle
crash landing
crashcart
crashed
crashed in roswell
crashed in the ground
crashed spaceship
crashed spaceship!!
crashed ufo
crashing waves
crashing waves and sea foam
crass
crass records
crater
crater lake
craters
crates and parts on the ground
craving
crawling
crawling along a bed of moss
crawling humanoid monsters
crawling in a wet sewer pipe
crawling on the ground
crawling out of a dark room
crawling towards the camera
craxula
crayon
crayon and cut up
crayon drawing
crayon face
crayon lines
crayon on paper
crayons
crayons and watercolor sketch
crayons on paper
crayons. high details
crazed
craziness
crazy
crazy angle
crazy atmosphere
crazy cat lady
crazy clothes
crazy colors
crazy colors 1 0 %
crazy colors 10 %
crazy detail
crazy detailed
crazy detailed and complex
crazy expression
crazy eyes
crazy eyes wide open
crazy fashion catwalk
crazy fish
crazy fun
crazy hacker girl
crazy hair
crazy hate face
crazy high definition
crazy looking rocks
crazy racer spinning
crazy seductive smile
crazy white hair
cream
cream - colored room
cream and blue color scheme
cream and white color scheme
cream colored blouse
cream colored peasant shirt
cream dripping on face
cream of the crop
cream paper
cream white background
creamy
creamy skin
creary
created in adobe illustrator
created in octane render
created in unreal engine 5
created m.c. escher and picasso
creating a soft
creating a thin monolith
creating an ominous presence
creating an optical illusion
creating the false christian god
creation
creation of adam
creation of the universe
creation of the world
creative
creative and cool
creative art
creative book cover
creative coder with a computer
creative coding
creative color choice
creative colorfull - makeup
creative commons attribution
creative composition
creative design
creative fashion
creative interpretation
creative lighting
creative makeup
creative mash up
creative photo manipulation
creative photoshop
creative vfx
creativity
creativity in fashion design
creature
creature bust
creature concept art
creature design
creature from the black lagoon
creature god
creature head
creature in view
creatures
creatures dancing
credit esa
credit nasa
creek
creeks
creep
creeper
creeper world
creepers
creeping forward
creeping gloom
creeptastic
creepy
creepy aesthetic
creepy ambiance
creepy ambience
creepy and dark feelings
creepy and dramatic atmosphere
creepy and eerie athmosphere
creepy and unsettling
creepy art
creepy astronaut
creepy atmosphere
creepy background
creepy backrooms
creepy black figure standing
creepy carved expression
creepy child
creepy clown girl
creepy creature
creepy cute creatures everywhere
creepy dungeon
creepy evil smile
creepy explorer sketch
creepy fantasy ambience
creepy forest
creepy gothic portrait
creepy hd 4k
creepy horror
creepy kawaii
creepy lighting
creepy lightning
creepy liminal interior of re7
creepy macro photo
creepy mannequin
creepy marionette puppet
creepy masked marionette puppet
creepy monster
creepy mood
creepy mutant flesh creature
creepy pasta
creepy photo
creepy photography
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creepy shadows
creepy sigma 75mm
creepy skies
creepy smile
creepy smiles
creepy surrealism
creepy themed
creepy uncanny valley
creepy vibe
creepy!!
creepy!!!
creepypasta
crenellated balconies
crepax
crepuscular
crepuscular ray
crepuscular rays
crepuscular!!
crepuscule
crescendo
crescent moon
crescent moon in background
crescent wrench
crestfallen
cresting waves and seafoam
creterion collection
crew cut
crew cut hair
crewdson
crewson photography
crib goch!!!!!!!!!!! ridge
crib gochridge
crime
crime photos
crime scene
crime scene photo
crime scene photography
crimes
criminal
criminal appearance
criminal mugshot
criminals
crimped hair in warhammer
crimson
crimson - black bee army behind
crimson - black beehive
crimson - black color scheme
crimson accents
crimson and ecru color scheme
crimson and grey color scheme
crimson and white color scheme
crimson attire
crimson clothes
crimson clouds
crimson color blood tears
crimson darkness
crimson fire head
crimson fire palette
crimson fork
crimson gradient
crimson hair
crimson halo
crimson heavy armor
crimson highlights
crimson led
crimson peek
crimson rain sought flower
crimson red aura
crimson red hair and red eyes
crimson single french braid bun
crimson themed
crimson tide
cringe
crips
crips details
crisp
crisp 8 k line art
crisp 8 k line work
crisp 8k line art
crisp 8k line work
crisp and clear
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crisp clear resolution
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crisp colors
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crisp contour lines
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crisp face
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cristi balanescu
cristiano ronaldo
cristiano ronaldo as batman
cristiano ronaldo as pope
cristo redentor
criterion collection
critical moment
critical moment photograph
critical role
critically acclaimed
critically condemned
critters
critters - movie
croatian coastline
crochet
crochet octopus eating sushi
crochet skin
crocodile
crocodile - like teeth
crocodile god lair
crocodile loki
crocodile reptilian motifs
cronenberg
cronenberg automata
cronenberg bookshop
cronenburg
crono
crono portrait
cronobreaker
crooked legs
crooked nose
crooked smile
crooked teeth
crop circles
crop shirt and strong abs
crop top
crop yoga short
cropped red yoga short
cropped shirt with jacket
cropped wide sleeve
croptop
croptop and shorts
cross
cross - hatching
cross composition
cross contour
cross contour drawing
cross haching
cross hatch
cross hatched
cross hatching
cross section
cross section of mannequin head
cross-eyed
cross-hatchings
crossbow
crossbow arrow
crossbreed
crossed arms
crossed eyes
crossed legs
crosses
crosshatch
crosshatch shading
crosshatch sketch gradient
crosshatching
crossing road
crossing the blue horizon
crossing the desert
crossing the line
crossing the river
crossover
crosswalk
crosswalks
crouching
crouching humanoid
crow
crow head
crow in cyber armour
crow portrait
crow portrait!!!!!
crow skull knight helmet
crowd
crowd cheering
crowd cheers him
crowd hails him
crowd of androids
crowd of cool people dancing
crowd of longhairs
crowd of people
crowded
crowded airport
crowded and populated
crowded beach
crowded city
crowded inn in the background
crowded people
crowded place
crowded silhouettes
crowded square
crowded stands
crowded street
crowded streets
crowds
crowds of people
crowds of people praying
crowds panic
crowdy
crown
crown and gown
crown made of bones
crown made of fabric
crown made of felt
crown of (((white lasers)))
crown of (pink lasers)
crown of (white lasers)
crown of blue flowers
crown of blue gears and diamonds
crown of body length feathers
crown of bones
crown of fire
crown of giant diamonds
crown of giant rubies
crown of mechanical peach roses
crown of peach roses
crown of pink lasers
crown of red steampunk lasers
crown of roses
crown of skulls
crown of snakes
crown of the gods
crown of thorns
crown of very long feathers
crown of white diamonds
crown of white lasers
crown on head
crown with pins
crown!! oil painting
crown!!!!!!
crowned
crownoil painting
crowns
crows
crows as a symbol of death
crows beautiful
crows feet
crows on the oak tree
crpg
crt
crt filter
crt monitor
crt screen
crt screens in background
crt television
crt tubes
crt tv mounted
crucifix
crucifixion
crucifixion of conor mcgregor
crude
crude crayon scribbles
cruel
cruel green-eyed
cruel korean goth girl
cruella devil
cruelty
cruise control
cruise missile strikes
crumbled wall in jerusalem
crumbling
crumbling ancient skyscrapers
crumbling buildings
crumbling masonry
crumbling ruins
crumpled newspaper as a texture
crunch time on uranus
crunchyroll
crusade
crusader kings
crusader kings 3
crushed
crushed quality
crushing despair
crust
crustacean
crustacean head
crusty electronics
crutches
cry
cry engine
crybernetic
cryengine
cryengine 8 k
cryengine 8 k uhd
cryengine 8k
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cryengine octane
cryengine octane blender
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cryengine render 8 k uhd
cryengine render 8k uhd
cryenging 8 k uhd
cryenging 8k uhd
crying
crying and puking
crying and reaching with her arm
crying and smiling franticly
crying and weeping
crying beautiful female android
crying beautiful female android!
crying big blue tears
crying blood
crying cyborg woman
crying engine
crying eyes closed
crying eyes closed!
crying fashion model
crying makeup
crying many tears
crying one single tear
crying queen of feathers
crying softly and humbly
crying tears
crying tears of blood
crying! android! woman
crying. scifi
cryingandroidwoman
cryo engine
cryogenic pods
crypt
crypt lurker
crypt lurker!!
cryptic
cryptid
cryptidcore
cryptids
crypto
crypto valut
cryptoblades
cryptocurrency
cryptocurrency in the background
cryptonomicon
cryptopunk
cryptopunk with pit vipers
crysis
crysis nanosuit
crystal
crystal amber eyes
crystal ball
crystal blue eyes
crystal castles
crystal clear
crystal clear blue water
crystal clear neon water
crystal clear sea
crystal clear water
crystal clusters
crystal color
crystal column
crystal crown
crystal desert
crystal dragon
crystal encrustations
crystal encrusted
crystal face
crystal forest
crystal formation
crystal holographic undertones
crystal incrustations
crystal lake
crystal lighting
crystal lights
crystal maiden
crystal material
crystal nodes
crystal palace
crystal particles
crystal refraction of light
crystal ruff
crystal shards
crystal skull
crystal teeth
crystal water
crystal waters
crystal-clear-focus
crystal-embellished
crystaline celtic
crystalized
crystalized scales
crystalized time warps
crystall
crystallic
crystallic sunflowers
crystalline
crystalline edges
crystalline masterpiece implants
crystalline rock
crystalline skin
crystalline translucent hair
crystalline!!
crystallized
crystallized human silhouette
crystals
crystals and diamonds
crystals enlight the scene
cs lewis
cs:go screenshot
csgo
csgo sticker
csgo team sticker
csok istvan
csp
csók istván
csók istván and da vinci
ctane 3 d rendered
cthonic resonance
cthulhu
cthulhu eating toast
cthulhu mythos
cthulhu rising from the ocean
cthulhu samurai
cthulhu squid
cthulu
cthulu portrait
cthylla's laboratory!
cthyllas laboratory
cuastics
cuaxolotl
cub
cuba
cuban revolution
cuban setting
cuban women in havana
cube
cube portals
cube shaped
cube shaped irises
cubensis
cuberpunk
cubes
cubes of ice around
cubes on table
cubic
cubic and right angles
cubic blocks
cubic blocks mix stripes cuts
cubic blocks stripes
cubic blocks stripes cuts
cubic crystals
cubic minerals
cubical meeting room office
cubicles
cubism
cubism and / or romanticism
cubism brutalism architecture
cubism painting of a tree
cubism style
cubist
cubist painting
cubist picasso
cubist style
cubists love it
cubo - futurism
cucumber
cucumbers
cuddling
cuddling her gremlings
cuddly
cuddly fur
cuirass
cuisine
culinary art photography
cult
cult - classic - comic
cult - classic - comic - style
cult horror
cult leader
cult of technology
cult of the lamb
cult-classic-comic-style
cultist
cultists watching
cultivator
culture
culture wars
cultured
cumulonimbus
cumulonimbus clouds
cumulonimbus clouds in the sky
cumulus
cumulus cloud tattoos
cumulus clouds
cumulus tattoos
cunning
cunning expression
cunning smile
cup of coffee
cup of death
cupboards
cupcake
cupertino
cuphead
cuphead’s art style
cupid
cups and balls
curated collection
curated collections
cure
curiosities
curiosities carnival
curiosities carnival fallout
curiosity
curious
curious expression
curious eyes
curl noise
curled beard
curled perspective
curled silver hair
curled slightly at the ends
curled up on a book
curled up under the covers
curls
curls and curves
curls hair
curls on top
curls on top of his head
curly
curly afro
curly and short top hair
curly bangs
curly bangs and ponytail
curly black beard
curly black hair
curly blond
curly blond hair
curly blonde hair
curly blonde hair  d d
curly blonde hair | d & d
curly brown hair
curly copper colored hair
curly dark hair
curly dirty blonde hair
curly hair
curly haired
curly long hair
curly messy high bun hairstyle
curly middle part haircut
curly pink hair
curly pixie cut hair
curly pixie hair
curly red hair
curly white hair
currency symbols printed
currents
cursed
cursed baroque with ebony inlay
cursed image
cursed imagery
cursed images
cursed oil painting
cursed photograph
cursed photography
cursed polaroid
curtain
curtain bangs
curtains
curvaceous
curvaceous. detailed
curvaceous. detailed expression
curvacious
curvature
curve
curved
curved blades on each hand
curved body
curved bridge
curved furniture
curved hallways
curved horned dragon!
curved horns
curved horns!
curved lines
curved perspective
curved red arrow
curved sword
curved trees
curved. studio lighting
curves
curves!!
curvilinear
curvilinear architecture
curvilinear pencil marks
curvilinear perspective
curving
curving black
curving geometric arches
curvy
curvy accentuated booty
curvy and bow
curvy body
curvy build
curvy crossfit build
curvy figure
curvy hourglass figure
curvy model
cushart
cushart kenz
cushart krentz
cushart krenz
cushart krenz key art feminine
cushions
custer's last stand
custom
custom brush
custom computer
custom design
custom headlights
custom modern castle
custom scooter
customer
customers
cut
cut and paste
cut and paste collage
cut into the side of a mountain
cut lines
cut out
cut out collage
cut out of cardboard
cut paper texture
cut style
cut up collage
cut-away
cut-out paper collage
cut-scene
cuta anime
cutaway
cute
cute - fine - face
cute 3 d render
cute adorable
cute and adorable
cute and cuddly
cute and friendly eyes
cute and funny
cute and lovely
cute animal
cute animals
cute anime
cute anime catgirl
cute anime face
cute anime girl
cute anime girl portrait
cute anime girl portraits
cute anime style
cute anime waifu in a nice dress
cute anthropomorphic bunny
cute art
cute art style
cute artwork
cute astronaut sticker art
cute axolotl
cute bakery shop
cute bandaid on nose
cute bandaid on nose!!
cute beagle
cute beautiful
cute bikini
cute black dress
cute boy
cute boys
cute but determined
cute caracal
cute caracal in bathtub
cute cartoon
cute cartoon character
cute cartoon style
cute casual streetwear
cute cat
cute cat in a sunflower field
cute cat photo
cute cats
cute character
cute characters
cute checkerboard sundress
cute chibi rat wizard
cute colorful adorable
cute complexion
cute corgi
cute coronavirus creatures
cute coronavirus creatures!
cute creature
cute creatures
cute cthulhu
cute cyber gods
cute cyborg girl
cute decapodiformes
cute detailed artwork
cute detailed digital art
cute details
cute digital art
cute dog
cute drawing
cute dress
cute earrings.
cute earrings. highly detailed
cute ears
cute elaborate epic robot
cute elegant pose
cute elephant
cute emo guy
cute expression
cute eyes
cute face
cute face big eyes and smiley
cute face. dark fantasy
cute faces
cute features
cute fine face
cute fluffy caracal
cute forest creature
cute fox
cute freckles
cute frog
cute fumo chibi plush imp
cute fumo plush bunny girl
cute fumo plush fox girl
cute funny figurine wooden
cute funny ghost
cute furry needs your help
cute future vehicles
cute girl
cute girl wearing tank suit
cute girl with short pink hair
cute girls
cute goose
cute happy face
cute hats
cute horns
cute huge pockets
cute huge pockets hardware
cute humanoid robot
cute illustration
cute kawaii girl
cute kitchen
cute kitten
cute kittens
cute korean actress
cute large eyes
cute lion
cute little creature
cute little dragon
cute little girl
cute little troll
cute looking
cute miniature resine figure
cute monster
cute monster character design
cute monster skateboarding
cute monsters
cute mouse pokemon
cute natural anime face
cute nose
cute otter
cute otter sailor eat cake
cute outfit
cute owl
cute panda
cute panties
cute photo
cute photograph
cute pictoplasma
cute pilot girl
cute pixar character
cute pizza monster
cute pocelain doll
cute pockets
cute portrait
cute pose
cute pout
cute rave outfit
cute robot
cute robot with grass hair
cute robot wooden
cute room
cute round green slanted eyes
cute round slanted eyes
cute scene
cute schoolgirl
cute seductive smile
cute silly face
cute single animal
cute skeleton
cute slightly nerdy smile
cute smile
cute smiling face
cute sportswear
cute storybook illustration
cute style
cute style garden
cute sundress
cute teeth
cute top
cute toy
cute woman
cute young girl
cute young man
cute young redhead girl
cute young woman
cute!
cute! biomechanical axolotl
cute! c4d
cute!! chibi!!! catgirl
cute!! chibi!!! schoolgirl
cute!!!
cute-fine-face
cute:2
cutebiomechanical axolotl
cutec4d
cutechibischoolgirl
cutecore
cutecore clowncore
cuteness
cutesexyrobutts
cutest
cutie
cutie mark
cutlery
cutout
cuts
cutscene
cutscene footage
cutting a salad
cuttlefish
cwc
cyan
cyan and gold scheme
cyan and green
cyan and magenta
cyan and orange
cyan and orange palette. vivid
cyan atmosphere
cyan chinese dragon fantasy
cyan colors
cyan corset
cyan dimensional light
cyan fog
cyan gold blank light
cyan headlights
cyan lightning
cyan mist
cyan photographic backdrop
cyan shutters on windows
cyangmou
cyanotype
cyarine
cyber
cyber - punk
cyber - punk background
cyber aesthetic
cyber architecture
cyber armor
cyber armour
cyber augmentation implant
cyber background
cyber city
cyber copper spiral decorations
cyber embellishment
cyber fight armor
cyber future jacket
cyber goggles
cyber implants
cyber installation
cyber japan armor
cyber japan samurai armor
cyber japan style armor
cyber led neon lighting
cyber mech
cyber monkey in the scifi forest
cyber mushroom city
cyber necklace
cyber neon lighting
cyber neon lightings
cyber neon lights
cyber noir
cyber patterns
cyber penguin in fallout 4
cyber punk
cyber punk setting
cyber school girl
cyber security polygon
cyber skeleton
cyber skeletons
cyber songman
cyber space
cyber space cowboy
cyber space forest scene
cyber steampunk
cyber steampunk 8 k 3 d
cyber steampunk 8k 3 d
cyber style
cyber suit
cyber universe
cyber universe style
cyber war
cyber wear
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cybercat
cybercore
cyberdeck computer terminal
cyberdemon on omaha beach
cyberface
cyberfunk
cybergods
cybergoth
cyberlox
cybermagnetosphere
cybermosque interior
cybernatic
cybernetic
cybernetic and
cybernetic and highly detailed
cybernetic bionic ancient cyborg
cybernetic body
cybernetic body parts
cybernetic city background
cybernetic civilisations
cybernetic civilizations
cybernetic components
cybernetic components!!
cybernetic culture research unit
cybernetic cyborg warrior
cybernetic demon dreaming
cybernetic dreadlocks
cybernetic enhancement
cybernetic enhancements
cybernetic eye
cybernetic eyes implants
cybernetic faces
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cybernetic fire armor
cybernetic flame armor
cybernetic future perfect
cybernetic glowing
cybernetic hands
cybernetic head
cybernetic implant
cybernetic implant h 768
cybernetic implants
cybernetic legs
cybernetic limbs
cybernetic machine female face
cybernetic machines
cybernetic neck implant
cybernetic organism
cybernetic parts
cybernetic scifi
cybernetic shrine
cybernetic wallpaper
cybernetic webs
cybernetic wonderland
cybernetically enhanced
cybernetics
cyberpsycho
cyberpunc
cyberpunk
cyberpunk 2 0 2 0
cyberpunk 2 0 2 0 manual
cyberpunk 2 0 4 8
cyberpunk 2 0 4 9
cyberpunk 2 0 7 7
cyberpunk 2 0 7 7 )
cyberpunk 2 0 7 7 character art
cyberpunk 2 0 7 7 night city
cyberpunk 2 0 8 8
cyberpunk 2 0 y. o model girl
cyberpunk 2020 manual
cyberpunk 2048
cyberpunk 2049
cyberpunk 2070
cyberpunk 2077
cyberpunk 2077 color
cyberpunk 2077 night city
cyberpunk 2077 rossdraws
cyberpunk 2077)
cyberpunk 2077”
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cyberpunk angry gorgeous goddess
cyberpunk anime art
cyberpunk anime girl
cyberpunk anime girl in hoodie
cyberpunk anime girl mech
cyberpunk apartment
cyberpunk apocalyptic city
cyberpunk architecture
cyberpunk armor
cyberpunk art 2077
cyberpunk art nouveau
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cyberpunk art ultrarealistic 8k
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cyberpunk assassin
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cyberpunk background
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cyberpunk basement
cyberpunk batman
cyberpunk beautiful girl
cyberpunk bedroom at night
cyberpunk bee
cyberpunk black metal band
cyberpunk blade runner art
cyberpunk bodysuit
cyberpunk brackets
cyberpunk building
cyberpunk buildings
cyberpunk bus stop
cyberpunk car
cyberpunk castle
cyberpunk cat
cyberpunk cathedral
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cyberpunk childrens bedroom
cyberpunk chinese ancient castle
cyberpunk church
cyberpunk city
cyberpunk city abandoned
cyberpunk city at night
cyberpunk city backdrop
cyberpunk city backgeound
cyberpunk city background
cyberpunk city in background
cyberpunk city in the background
cyberpunk city in the distance
cyberpunk city landscape
cyberpunk city on background
cyberpunk city on the background
cyberpunk city street
cyberpunk city street background
cyberpunk cityscape
cyberpunk cloisters
cyberpunk clothes
cyberpunk color palette
cyberpunk color scheme
cyberpunk colors
cyberpunk comic cover art
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cyberpunk)))
cyberpunk)))))
cyberpunk2077
cyberpunk”
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cycling!!
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dandelion seeds float
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danganronpa
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dark background”
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dark holography!!!
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dark warrior
dark washed tint
dark washed tint black
dark water
dark waters
dark wet london alley at night
dark wet road
dark winter
dark winter evening
dark witch
dark witch character
dark witch headdress
dark wizard
dark wood
dark woods
dark woods in the background
dark yellowish water
dark!
dark!! intricate
dark!!!
dark-art
dark-bringer
dark-esque
dark-hair
dark-matter
dark-skinned
dark-toned product photos
dark. no text
dark. studio lighting
darkart
darker
darker colors
darker intense
darker skin
darkest dungeon
darkest dungeon art style
darkest dungeon style
darkfantasy
darkintricate
darkly surreal
darkmode
darkness
darkness and flames
darkness aura
darkness aura red light
darkness background
darkness behind it
darkness dramatic
darkness ❄️ �️ taxidermetroid
darkness's background
darkness. dark
darknesss background
darkpowerful elegant
darkroom
darkseid
darkseid as mini figure
darksiders art style
darksiders halloween theme
darksiders style
darkslategray wall
darksouls
darksouls concept art
darksynth
darksynth character portrait
darkwave
darkwave goth aesthetic
darling
darling in the frank
darling wash off in the rain
darpa
darrell k sweet
darren aronofsky
darren bartley
darren quach
darren shaddick
dart board
darth biden
darth jar jar binks
darth maul
darth maul in gears of war
darth revan
darth sidious
darth vader
darth vader as a caterpillar
darth vader helmet
darth vader laughing
darth vader portrait
darth vader sitting at the table
darwyn cooke
dash cam
dash cam footage
dasha taran
dashcam footage
data
data bending
data center
data holograms
data mosh
data visualization
databending
datacentre
datamosh
datamoshed
datamoshing
datanft as a data avatar
datapipeline or river
datastream or river
date
date palm trees
date trees
dating app icon
datura
dau - al - set
daugerotype
daugerrotype
daughter
daughter of death
dave carson
dave chappelle
dave grohl
dave mckean and stina persson
dave mckean ink drips
dave rapoza
dave sim
daverapoza
davey baker
davi blight
david - lynch
david a
david a trampier
david and goliath
david attenborough
david bailey
david baldeon
david baldeon comic art
david bates
david beckham
david boreanaz as chris redfield
david bowie
david bowie as the joker
david bowie in super smash bros
david burnett
david choe
david cronenberg
david curtis
david febland
david finch
david fincher
david friedrich
david gandy
david gilmour
david hamilton
david hardy
david kassan
david klein
david kostic
david la chapelle
david lazar
david ligare masterpiece
david luong
david lynch
david lynch film
david lynch portrait
david lynch style
david mack
david marquez
david myers
david nakayama
david noren
david normal
david normal. photorealistic
david noton
david palladini
david production
david rios ferreira
david rubin
david rudnick
david schwimmer screaming
david shing
david spade
david spade as batman
david tennant as spawn
david tennant as the joker
david uzochukwu
david villegas
david wiesner
davide sorrenti
davinci
davinci sketch
davinci style
davy jones
davy jones with tentacles
dawn
dawn and dusk
dawn atmosphere
dawn background
dawn cgsociety
dawn light
dawn lighting
dawn mcteigue
dawn of a new world
dawn of the dead
dawn setting
day
day - glow face paint
day - glow facepaint
day - time
day after raining
day cityscape
day light
day lighting
day of all the dead
day of the dead
day of the tentacle
day of the tentacle style
day setting
day time
day-glo colors
day-light
dayanita singh
dayer diego artwork
dayglo
dayglo blue
dayglo pink
dayglo pink and blue
dayglo pink blue
daylight
daylight made in blender
daysies
daytime
daytime ethereal anime
daytoner
dayz
daz
daz 3 d
daz 3d
daz occlusion
daz studio
daz studio 3d
daz studio genesis iray
daz studio genesis iray female
daz studio genesis iray shaders
daz studio genesis iray ultra hd
daz. detailed
daz3d
daz3d genesis iray shaders
dazzle camouflage
dazzle camouflage!
dazzle camouflage!!
dazzling
dazzling dappled lighting
dazzling energy
dazzling gem in the hilt
dazzling lights
dbz
dc
dc art
dc comic
dc comics
dc comics art style
dc comics style
dc marvel fashion
dc style
dc universe
dc vs marvel fashion
dcim
dcs world
dcs world falcon bms
dcs world style
dd
ddlc
de - noise
de _ dust 2
de kooning
de tomaso
de-noise
dead
dead alive (1992)
dead alive 1992
dead and alive
dead bodies
dead bodies everywhere
dead bodies in mud
dead bodies in the background
dead bodies scattered in mud
dead body
dead but beautiful. poppies
dead cells
dead child
dead clown
dead eyes
dead fish
dead flowers
dead forest
dead forest background
dead fruits
dead inside
dead man's chest
dead mans chest
dead nature study
dead old
dead or alive
dead or alive 6
dead peoples
dead plants
dead plants and flowers
dead redemption
dead river
dead skin
dead soldiers
dead soldiers on the battlefield
dead souls
dead space
dead space artwork
dead tree
dead tree forest
dead trees
dead women
deadlands
deadly
deadly alien plants
deadly drone
deadpool
deadpool live action costume
deak ferrand
dean cornwall
dean cornwell style
dean winchester
deanna troi
death
death + robots series of netflix
death and corruption
death and dying
death and robots
death and robots two in the void
death dreaming about death
death god
death grips
death himself
death incarnate
death is split in two with smoke
death is swallowed up in victory
death knight
death metal
death metal album cover
death note
death note style
death note ， d & d
death note ， d d
death of all
death of the money lenders
death skull
death star
death stranding
death stranding art style
death stranding game
death tarot
death tarot card
death valley
death vally
deathburger
deathknight
deathly
deathly skin
deathpunk
deaths
deathscape
deathstar
deathwing
deayami kojima
debadged
debilitation
deblur
deblurring
deborah
debris
debris chips ruins
debris flying around
debris flying everywhere
debris on ground
debris on the floor
debris on the ground
debris spread
decadence
decadent
decadent spiral staircase
decadent throne room
decaing
decay
decay teeth
decay texture
decayed
decaying
decaying dappled sunlight
decaying face
decaying rich colors!
decent
decepticon armor plating
deception
deception and goth colors
deceptive
decipticon armor plating
decisive and stubborn portrait
decisive moment
deck
deck of many things
deckle edge
declan mckenna
declassified
deco
deco fashion
decoherence
decollage
decollage 4 k
decolletage
decollete
decomposing
decomposition
deconstructed
deconstructed waffle
decopunk
decora inspired
decora inspired illustrations
decorated
decorated civil war veteran
decorated ornaments
decorated polished wood
decorated with flowers
decorated with opera motifs
decorated with russian motifs
decorated with soviet motifs
decoration
decoration around the room
decorations
decorative
decorative art
decorative art deco border
decorative art nouveau border
decorative background
decorative border
decorative dark blue clothing
decorative design
decorative flowers
decorative frame
decorative golden elements
decorative leather armor
decorative lines
decorative ornaments
decorative panels
decorative roses
decrepit
dedined face
deep
deep - space imaging
deep 3 point perspective
deep aesthetic
deep aesthetic colors
deep and charming eyes
deep and dense coloration
deep aura
deep azure tones
deep black
deep black background
deep black robe
deep black roots
deep black skin
deep black sky
deep black tones
deep black tones contour
deep blacks
deep bleeding decaying colors
deep bleeding decaying colors!
deep blue
deep blue atmosphere
deep blue background
deep blue eyes
deep blue lighting
deep blue mood
deep blue night sky
deep blue ocean
deep blue ocean color
deep blue oceans
deep blue sea color
deep blue shadows
deep blue skin
deep blue sky
deep blue water
deep blue water color
deep blues
deep bokeh
deep bold colors
deep brown eyes
deep cave
deep chasm
deep clear pools of water
deep color
deep color scheme
deep colors
deep colors and dark shadows
deep colour
deep colour\'s
deep colours
deep colours.
deep colours. ”
deep complexity
deep complimentary colors
deep contrast
deep crevices of stone
deep crimson and dark shadows
deep cyberpunk mechanoid
deep dark
deep dark black
deep dark blue
deep dark color
deep dark forest
deep dark moody forest
deep dark purple waters
deep dark sea
deep dark universe
deep depth
deep depth field
deep depth of field
deep depth of focus
deep detailed
deep dimples
deep dish wheels
deep dream
deep earthy colours
deep emotional
deep emotional ambience
deep emotional impact
deep emotions
deep environment
deep eyes
deep fake
deep focus
deep focus d & d fantasy
deep focus d d fantasy
deep fog
deep forest
deep forest in the night
deep forest on background
deep fried
deep gaze
deep gaze to the side
deep golden sand desert
deep green
deep green eyes
deep halls
deep image
deep impasto
deep in a japanese bamboo forest
deep in the forest
deep in the ocean
deep in the woods
deep in thought
deep ink colors
deep inside the forest
deep jungle
deep jungle creatures
deep jungle from another world
deep jungle texture
deep learning
deep lighting
deep lines and shadows
deep look
deep love
deep lush vivid colors
deep mandelbulb landscape
deep meaning
deep moody colors
deep night
deep night sky
deep ocean
deep ocean sculpture
deep of field
deep overhangs. greeble. 8 k
deep overhangs. greeble. 8k
deep pastel colors
deep pastel colours
deep perspective
deep purple
deep purple and orange
deep purple hair
deep purple veil
deep pyro colors
deep red background
deep red eyes
deep red lips
deep red roots
deep reddish gold hair
deep rich colors
deep rich moody colors
deep rock galactic screenshot
deep sadness
deep sea
deep sea ambience
deep sea creature
deep sea creatures
deep sea cyberpunk
deep sea diver
deep sea fish
deep sea horror
deep sea landscape
deep sea monster
deep sea picture
deep sea themed
deep sense of horror atmosphere
deep sense of spirituality
deep shading
deep shadow
deep shadows
deep shadows and bokeh
deep shadows and colors
deep shadows hd
deep shafts
deep sinkhole
deep space
deep space 9
deep space background
deep space exploration
deep space exploration!!!
deep space hubble photograph
deep space in the background
deep space nine
deep space super structure
deep spectrum color
deep stillness and silence
deep tan skin
deep texture
deep tones
deep under water
deep underground
deep underwater
deep underwater scene
deep vibrant colors
deep view
deep vivid colors
deep water
deep woods
deep wrinkles!
deep!!!!!
deepdream
deepdream cosmic
deeper into the metaverse we go
deepfake
deeply detailed
deeply hyperdetailed
deeply ornate complexity
deeply texural
deepsea
deepspace exploration
deepspace exploration!!!
deer
deer ears
deer in sherwood forest
deer portrait
deer skull face
deers
deers and ravens
deers drinking water in the lake
deerstalker
deezen
def of field
default pose neutral expression
defeated
defective fluorescent lighting
defence
defending the vast looming city
defense attorney
defiant
defiant and beautiful
defiant look attitude
defibrillator
defined
defined cheek bones
defined cheekbones
defined edges
defined eyes
defined face
defined facial features
defined features
defined jawline
defined lines
defined muscles
defined upper body
definition
defocus
defocused background
defocused bars in the foreground
deforestation
deforested forest background
deformations
deformed
deformed face
deformed human body
defunct technology
defying god
defying gravity
degas
degradation
degradation filter
dehazed image
deity
deity leesha hannigan
deity of hydrangeas
deity)
deiv calviz
delacroix
delaroche
delectable
delete
delete duplicate content
delete duplicate contents
delete duplicated content
delete duplicating content
delft
delftware
delicacy
delicate
delicate and precise brushwork
delicate androgynous prince
delicate coral sea bottom
delicate detailing golden stroke
delicate details
delicate embellishments
delicate ex embellishments
delicate eyebrows
delicate face
delicate facial features
delicate features
delicate figure
delicate fingers
delicate fog
delicate garden on paper
delicate jaw
delicate pale pink lips
delicate patterned
delicate poses
delicate rain
delicate soft hazy lighting
delicated facial features
delicious
delicious food
delightful
delightful surroundings
delirious
delirium
deliver me cosmic sight
delivering mail
delivering packages for amazon
delivering parsel box
delorean
delorean background
delunay
deluxe
demented
dementia
demi moore
demi rose
demigod
demna gvasalia
demo scene
demobaza
demodog
demogorgon
demogorgon from stranger things
demolition
demon
demon anime girl
demon armor
demon berserker
demon black blue purple
demon boxing hero
demon boy
demon cat
demon days
demon eyes
demon girl
demon hero
demon horn
demon horns
demon knight of death
demon lord
demon male
demon necromancer
demon noble character design
demon rays
demon samurai
demon samurai mask
demon samurai warrior
demon slayer
demon slayer artstyle
demon slayer rui fanart
demon soul concept art
demon souls
demon tail
demon white horns
demon wings
demon woman
demon-fang gnoll
demoness
demonic
demonic  horror themed
demonic atmosphere
demonic creature
demonic dragon inspired armor
demonic energy
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demonic face
demonic magic ritual
demonic monster
demonic parasite
demonic photograph
demonic presence
demonic robes
demonic shrine
demonic sigils
demonic symbols
demonic undertones
demonic! compedium!
demoniccompedium
demonology
demons
demons of hell
demur
demura and alphonse mucha
demure
dendrites
dendritic
denim
denim blue audi a4 b6
denim hot-pants
denim jacket
denim jeans
denim short pants
denim shorts
denis forkas
denis sarazhin
denis velleneuve
denis villeneuve
denis villeneuve cinematography
denis villeneuve film look
denis villeneuve movie still
denizens
denmark
denning
dennis velleneuve
denoise
denoise deep depth of field
denoised
denoised photorealistic render
dense
dense architecture
dense atmosphere
dense brushstrokes
dense coniferous forest. spiders
dense fog
dense forest
dense ground fog
dense hypermaximalist metropolis
dense jungle
dense linework
dense lush forest at night
dense metropolis
dense rain
dense thickets on each side
dense trees
dense vegetation
dense volumetric fog
dense web of neurons firing
dense with greenery
densely packed buds of weed
densley overgrown with moss
dent wu
dentils
dentist
denys tsiperko
depair
depeche mode
deph of field
depicted as a 3 d render
depicted as a 3d render
depicted as a pixar character
depicted as a scifi scene
depicting a corgi made of fire
depicting a flower
deployed wings
depraved cybernetic demon
depressed
depressed dramatic bicep pose
depressed girl portrait
depressed mood
depressed sad expression
depressing
depressing and hopeless vibe
depressing atmosphere
depressing image
depression
depression and despair
depression atmosphere
depressive
depressive vibe
dept of field
depth
depth --uplight
depth blur
depth detail
depth detailed
depth details
depth field
depth haze
depth layering
depth map
depth of feild
depth of field
depth of field 1 0 0 mm
depth of field 100 mm
depth of field 20mm
depth of field 8k
depth of field : - 2
depth of field background
depth of field blur
depth of field insanely detailed
depth of field ”
depth of field!
depth of field”
depth of filed
depth of focus
depth of sharpness
depth of view
depth of view 8k
depth of vision
depth perception
depth perspective
depth sense of movement
depths
depths of field effect
der riese
deranged
derealisation
derealization
deregoue
derek ridgers
derek riggs
derelict
derelict architecture buildings
derelict house
derelict space ship
derevschikova
derg
dermal implants
deroo
derp
derpibooru
derpy
derriere
derse
des boutiques avec des neons
desaturated
desaturated and muted colors
desaturated blue
desaturated cold color palette
desaturated color
desaturated color theme
desaturated colors
desaturated colours
desaturated!
desaturated!!
descend into the deep
descending from the heavens
descent
descent into lunacy and madness
descent into madness
description
descriptions
descriptive
descriptive art
desenho
desert
desert alien planet
desert ambience
desert and blue sky
desert background
desert breathing armor
desert camouflage
desert circus
desert circus mystics
desert city
desert colors
desert composition
desert dunes
desert environment
desert everywhere
desert fighter ancient mage
desert flowers
desert game
desert highway
desert in the background
desert lands
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desert mesa
desert mirage
desert nomad
desert oasis
desert oasis background
desert of distortion
desert photography
desert planet
desert robe
desert scene
desert scenery
desert setting
desert sunrise in the background
desert temple
desert transition area
desert valley of bones
desert wasteland
desert white greenhouse
desert wind
desert with city in the skyline
desert!!!
desert!!!!!!!!!!!
deserted
deserted planet
deserted sand
deserted shinjuku junk
deserted shinjuku junk town
desertpunk
deserts and mountains
desi
desiccated
design
design art
design award
design award winner
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design concept art
design exposure anime john rei
design for magic the gathering
design milk
design on a white background
design on white background
design only
design process
design reference
design sheet
design studio
design tension
design thinking
design your own avatar
designed
designed for cozy aesthetics
designed for cozy aesthetics!
designed in blender
designer
designer art
designer clothes
designer fashion
designer furniture
designer pencil 3d sketch
designer pencil sketch
designer product
designer sunglasses
designers republic
desirable
desire
desjarlais
desk
desk fan
desks
desktop
desktop background
desktop computer
desktop screenshot
desktop wallpaper
desktopography
desna
desolate
desolate  long shot
desolate :: long shot
desolate arctic landscape
desolate desert landscape
desolate empty wasteland
desolate glacial landscape
desolate gloomy planet
desolate land
desolate landscape
desolate with zombies
desolate. digital illustration
desolated
desolated wasteland
desolation
despacito
despair
despera
desperate
desperate action
desperate pose
desperation
despicable
despicable me
despondent
desrt
dessert
desserts
destiny
destiny 2
destiny art
destiny concept art
destiny fanart
destitute
destroy lonely
destroyed
destroyed armor
destroyed body
destroyed buildings
destroyed cars
destroyed castle
destroyed church
destroyed city
destroyed city in the background
destroyed city on fire
destroyed clothes
destroyed flipped wrecked cars
destroyed forest
destroyed human structures
destroyed modern buildings
destroyed monastery
destroyed mountains
destroyed nature
destroyed planet
destroyed robot dogs remains
destroyed robots
destroyed ship
destroyed tanks
destroyed washington dc
destroyer of worlds
destroying a city
destroying a cityscape
destroying buildings
destroying city
destroying houses
destroying new york city
destroying the city
destroying the walls
destructible enviorments
destructible environments
destruction
destruction around her
destructive
detached sleeves
detail
detail acrylic palette knife
detail and care
detail content
detail face
detail on scene
detail render
detail shot
detail shots
detail structure
detail studio quality lighting
detail texture
detaild
detailed
detailed - face!!
detailed - i
detailed - wheels
detailed -4
detailed 1000k
detailed 2d illustration
detailed 3 d render
detailed 3d gothic oil painting
detailed 3d render
detailed 4 k
detailed 4 k drawing
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detailed 4 k photo
detailed 4k
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detailed 4k photograph
detailed 4k render
detailed 5 k details
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detailed 8 k
detailed 85 mm f / 1. 4
detailed 85mm f/1.4
detailed 8k
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detailed : cornea
detailed abstract
detailed academic bouguereau
detailed acrylic
detailed acrylic painting
detailed af
detailed airbrush art
detailed alloy wheels
detailed ambient lighting
detailed an empty jazz cafe
detailed anatomy
detailed and beautiful faces
detailed and bright
detailed and complex
detailed and complex environment
detailed and concise
detailed and creepy
detailed and fantasy
detailed and intricate
detailed and intricate image
detailed and oil painting
detailed and perfect face
detailed and realistic
detailed and realistic face
detailed and realistic faces
detailed and realistic hands
detailed and sharp
detailed and soft
detailed and symetric face
detailed and symmetric faces
detailed anime
detailed anime art
detailed anime artwork
detailed anime character art
detailed anime eyes with pupils
detailed anime face
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detailed annotated painting
detailed architecture
detailed archviz render
detailed armor
detailed armor with white scarf
detailed armour
detailed arms
detailed art
detailed art in color
detailed artwork
detailed astronaut
detailed atmospheric and gritty
detailed attractive face
detailed background
detailed backgrounds
detailed baroque oil painting
detailed beard
detailed beautfiul face
detailed beautiful animals
detailed beautiful face
detailed beautiful plants
detailed beautiful portrait
detailed beauty portrait
detailed big eyes
detailed body
detailed body and eyes
detailed body and face
detailed body shape
detailed body structure
detailed book illustration
detailed bronze armor
detailed brush strokes
detailed brushstrokes
detailed buildings
detailed bushido form smoke
detailed but rough
detailed cartoon
detailed carved ornaments
detailed character
detailed character art
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detailed characters
detailed charcoal drawing
detailed charcoal sketch
detailed chemical diagram
detailed cinematic lighting
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detailed cinematic render
detailed cinematic shot
detailed city
detailed city background
detailed classical architecture
detailed claws
detailed clay model
detailed close foot shot
detailed close up portrait
detailed close up shot
detailed closeup face
detailed cloth
detailed clothes
detailed clothes texture
detailed clothing
detailed clouds
detailed color portrait
detailed color scan
detailed color scan”
detailed colored textures
detailed colors
detailed coloured pencil
detailed concept art
detailed conceptual photography
detailed cosmic angelic robot
detailed cover artwork
detailed crash space ship
detailed creature
detailed crimson moon
detailed crow illustration
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detailed decorative flourishes
detailed deep black eyes
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detailed digital 2d fantasy art
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detailed dragon
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detailed dress and face
detailed droplets
detailed duck
detailed dynamic composition
detailed entangled fibres
detailed environment
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detailed expressive faces
detailed eyes
detailed eyes with pupils
detailed eyes!!
detailed face
detailed face )
detailed face and body
detailed face and eyes
detailed face background detail
detailed face details
detailed face face face face
detailed face features
detailed face features!
detailed face of a asian boy
detailed face of a asian girl
detailed face of a woman
detailed face of an android
detailed face of an arabic woman
detailed face portrait
detailed face with mask
detailed face with moustache
detailed face with red lips
detailed face!
detailed face!!
detailed faces
detailed faces looking up
detailed facial expression
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detailed factory
detailed fallout npc! high angle
detailed fanart
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detailed fantasy map
detailed fashion illustration
detailed feathers
detailed feathers and fur
detailed features
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detailed fences and stone walls
detailed fields nature
detailed filigree
detailed flawless face
detailed flowers
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detailed foot pov
detailed foot shot
detailed forest
detailed forest background
detailed full body
detailed full body concept
detailed full body concept art
detailed full body portrait
detailed fur
detailed futuristic architecture
detailed futuristic jewelry
detailed game art
detailed game art illustration
detailed geometry
detailed glad face
detailed glowing head
detailed glowing red implants
detailed gorgeous face
detailed gothic cloak
detailed gouache paintings
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dirk dzimirsky
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discworld
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displaying stock charts
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distributed consciousness
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dital painting
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dive bar with a karaoke machine
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dividing it into nine quarters
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dnd fantasy character
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dnd fantasy digital art
dnd fantasy magic
dnd fantsay art
dnd goliath character concept
dnd illustration
dnd in a dark forest
dnd last supper
dnd magazine cover
dnd map
dnd party
dnd portrait
dnd portrait of a tiefling
dnd render of a man
dnd setting!
dnd style
dnd triton
dnd)
dndbeyond
do
do hoang tuong artwork
do what we can
do you know de wae
do you want to know
doc marten boots
doc ock
doc savage
doc savage in the jungle
docked at harbor
docks
doctor
doctor doom
doctor house
doctor strange
doctor who
doctors mirror
doctors office
document
document photo
documentary
documentary footage
documentary lighting
documentary photo
documentary photograph
documentary photography
documentary still
documentary style
documentation
documents
dodecahedron
dodgers stadium concert in 1975
dodging bullets
doe
dof
dof 1. 3
dof 8 k
dof 8k
dof and bokeh
dof narrow
dof wide
dof:-1
doff
dofresh
dofus
dog
dog  jumps from mountain
dog  looks like elephant
dog - faced muscular goblin
dog as a god
dog as a greek god
dog ears
dog eating croissants in paris
dog head
dog in a space suite
dog jumps over hill
dog looks like elephant
dog man
dog playing the saxophone
dog sleeping
dog teeth
dog watching the car
dog wearing a crown
doge
doge meme
dogfighting a ufo with lasers
dogs
dogs playing poker
doing a backflip
doing a hot majestic pose
doing a kick
doing a kickflip over stairs
doing a majestic pose
doing a prayer
doing a sassy pose
doing a thumb up
doing a vision quest
doing an elegant pose
doing an elegant pose over you
doing martial arts
doing splits and stretching
doing the bateman stare
doing witchcraft
doja cat
doja cat as cat woman
dojo on a mountain
dokev
doki doki literature club
dolce & gabbana campaign
dolce gabbana campaign
doll
doll face
doll faces on a dress
doll house
doll in hand
doll phobia
dollar bank notes
dollar bill
dollar bills
dollar bills body horror
dollar bills floating
dollar sign
dollar sign pupils
dollars in pocket
dollfie dream
dollpunk
dolly parton
dolman
dolomites
dolomites in background
dolomites in the background
dolores abernathy
dolph lundgren
dolphin
dolphin snout under visor
dolphin swimming
dolphins
dolphins and swordfish
dolphins jumping
dolphins swimming
dome
dome of the rock
dome of wonders
domes
domespace
domestic
domestic caracal
dominance
dominant
dominant pose
dominant shades of black
dominant wihte and blue colours
dominating personality
dominating red color
dominatrix
dominatrix assassin robot
dominatrix robot
domineering
dominic toretto
dominik mayer
dominique ingres
domino mask
don
don bluth
don bluth animation
don bluth!!
don bluth!!!
don davis
don draper
don lawrence's
don lawrences
don lenzer
don punchatz
don quixote left the book
don ramon
don!!! bluth!!!
don!!!! bluth!!!!
don't be sad
don't trip
donald duck
donald duck as an astronaut
donald duck in real life
donald glover
donald glover as miles morales
donald trump
donald trump  s face
donald trump are best friends
donald trump as a cyborg
donald trump as a homeless man
donald trump as a jedi
donald trump as a jedi hero
donald trump as a knight
donald trump as a muppet
donald trump as an oompa loompa
donald trump as baron harkonnen
donald trump as captain america
donald trump as gollum
donald trump as jabba the hutt
donald trump as shrek
donald trump as superman
donald trump as thanos
donald trump as the hulk
donald trump as the joker
donald trump as the pope
donald trump as thor
donald trump at the beach
donald trump clown
donald trump crying
donald trump cyborg
donald trump face
donald trump fortnite skin
donald trump full body detailed
donald trump funko pop
donald trump hugging joe biden
donald trump in animal crossing
donald trump in attack on titan
donald trump in dark souls
donald trump in fortnite
donald trump in gta v
donald trump in jail
donald trump in prison
donald trump in skyrim
donald trump in super mario 64
donald trump is a space marine
donald trump kissing joe biden
donald trump mugshot
donald trump portrait
donald trump wearing a crown
donald trump ’ s face
donald trump's face
donald trump's head as modok
donald trumps face
donald trumps sexy face
donatello
donato
donato giancola and tom bagshaw
donbluth
done in the style of caravaggio
done in the style of matisse
donglu yu
dongson bronze artifacts
donkey
donkey ears
donkey kong
donkey kong country
donkey kong slips on a banana
donkey riding a playground swing
donned in an ebony cloak
donnie darko
donoto giancola
donut
doodad
doodle
doodle addicts
doodle art
doodle hand drawn
doodles
doom
doom and gloom
doom beautiful face
doom classic
doom demon
doom demon giger portrait
doom engine
doom eternal
doom eternal art style
doom eternal concept art
doom guy
doom guy resembling samus
doom hell theme
doom ii
doom inspired
doom metal
doom monster
doom of the gods
doom shotgun pov
doom slayer
doom slayer from doom eternal
doom video game
doomed
doomfist
doomfist from overwatch
doomguy
doomguy as a soviet soldier
doomsday
doomy
door
door gunner
door to lab
doors
doors of perception
doors that are cosmic portals
doors to various bedrooms
doors to various living quarters
doorway
doorways
doorways. zdzisław beksinski
dop
dope
doppler effect
dora maar and rene magritte
dora the explorer
dora the explorer as real girl
doraemon
doran
dore
dorian cleavanger
dorian cleavenger
doric
dormant
dormant in chains
dormant nature
dorne
dorohedoro
dors
doruk erdem
dos
dos game
dot
dot art
dot art on paper
dot gradient
dot painting
dot pupils
dota
dota 2
dota 2 concept art
dota 2 game screenshot
dota 2 hero
dota 2 screenshot
dota character
dota matte painting concept art
dota! matte painting concept art
dotado
dotamatte painting concept art
dotart
dots
dots abstract
dotting
double - exposure
double barrel shotgun
double barreled main gun
double bass
double chin
double denim
double exposure
double exposure effect
double exposure of dally life
double exposure of love
double exposure photography
double exposure portrait
double exposure tree bark
double exposures on 35mm film
double exposures on 35mm film!
double layer fold over hem
double long braids blue
double very long braids blue
double-exposure
doubt
doug bell
doug chiang
doug hill style
doug walker
doug wheeler
dough sculpture
dougherty patrick
douglas adams
douglas hoffman
douglas smith
doujin
dour
dove
dove cameron in a knight armor
dove in an ear canal
dover castle
doves
doves : : rococo
doves flying into the portal
down in the sewers of london
down left arm and back
down there
down-town
downhill landscape
downpour
downtown
downtown in the distance
downtown jacksonville florida
downtown mexico
downtown seattle at night
downtown solarpunk utopia
downturned hazel eyes
downward gaze
downward somber expression
dozens of galaxies
dozens of jeweled necklaces
dr 8 k
dr 8k
dr dre
dr seuss
dr seuss inspired
dr strange
dr strange and dr seuss
dr sues
dr suess
dr who
dr zeus
dra the dragon
drab
drab colors
drachenlord
draco
draconian
draconians
draconic
draconic design
draconic looking armor
draconic staff
dracos
dracula
dracula fangs! haunted house
dracula's castle
draft
drag
drag light bombs
drag queen
dragan bibin
dragging a pile of chains
dragon
dragon - inspired suit
dragon - like
dragon age
dragon age concept art
dragon age inquisition
dragon ancient full plate armor
dragon armor
dragon art
dragon ball
dragon ball artstyle
dragon ball concept art
dragon ball style
dragon ball super
dragon ball z
dragon ball z style
dragon blowing fire above
dragon body
dragon breathing fire
dragon centered
dragon claws
dragon crown
dragon cyborg
dragon design language
dragon eggs
dragon eye
dragon eyes
dragon face
dragon fangs
dragon flying
dragon flying in sky
dragon flying in the background
dragon flying in the sky
dragon fodder
dragon fruits
dragon girl
dragon head
dragon head!
dragon helmet
dragon in dragon lair
dragon in the background
dragon in the sky
dragon inspired armor
dragon inspired blue armor
dragon invasion of moscow
dragon kissing
dragon knight
dragon leg
dragon made out of molten lava
dragon maw
dragon mawshot
dragon mawshot art
dragon mouth
dragon paw
dragon paw art
dragon paws
dragon peacock. intricate
dragon pope
dragon portrait
dragon queen
dragon quest
dragon rider
dragon scale armor
dragon scale walls
dragon scales
dragon scales across hairline
dragon scales in hair
dragon shaped helmet
dragon skin
dragon skin background
dragon slayer
dragon snake with wings
dragon spits fire on a man
dragon staff
dragon tail
dragon tailed
dragon vore
dragon vore art
dragon wings
dragon with scars
dragon!
dragon! dragon!
dragon! dragon! dragon!
dragon! man
dragon's dogma
dragon's lair
dragon-inspired cloth robes
dragon-shaped human
dragonball
dragonball z
dragonborn
dragondragon
dragondragondragon
dragonflies
dragonfly
dragonfly-like
dragonfly-shaped
dragonlance illustration
dragonlike
dragonman
dragons
dragons flying around
dragons flying in the sky
dragons lair
dragon’s lair
dragwads
draincore
drainpipes
drak fantasy goblin
drake
drake in gta v
drake the rapper wearing a kurta
drake the rapper's face
drake the rappers face
drake's face
drakengard
drakenguard inspired
drakes face
drama
drama masks
dramatic
dramatic - lighting
dramatic 8 k uhd
dramatic action
dramatic action atmosphere
dramatic action photography
dramatic action pose
dramatic action scene
dramatic action shot
dramatic afternoon lighting
dramatic ambient light
dramatic ambient lighting
dramatic amtosphere
dramatic and atmospheric
dramatic and cinematic lights
dramatic and colorful lighting
dramatic and dynamic lighting
dramatic and emotional
dramatic and moody
dramatic angle
dramatic anime
dramatic architecture
dramatic art
dramatic artwork
dramatic atmosphere
dramatic aurora borealis
dramatic autumn landscape
dramatic background
dramatic backgroung
dramatic backlight
dramatic backlighting
dramatic backlit lighting
dramatic backlit window
dramatic beautiful lighting
dramatic black and white
dramatic black smoke everywhere
dramatic blue light
dramatic blue lighting
dramatic bored expression
dramatic bright lighting
dramatic brush painting
dramatic brush strokes
dramatic brushstrokes
dramatic buildings
dramatic camera angle
dramatic candlelight
dramatic cinematic
dramatic cinematic action shot
dramatic cinematic composition
dramatic cinematic detailed fur
dramatic cinematic light
dramatic cinematic lighting
dramatic cinematic perfect light
dramatic cinematic portrait
dramatic cinematic shot
dramatic cinematography
dramatic cinematography 3 5 mm
dramatic cinematography 35 mm
dramatic closeup composition
dramatic clouds
dramatic clouds and atmosphere
dramatic clouds cyan atmosphere
dramatic cloudy setting sun
dramatic cloudy sky
dramatic cold light
dramatic color
dramatic colored lighting
dramatic colorful lighting
dramatic colors
dramatic colors scheme
dramatic composition
dramatic concept art
dramatic contrast
dramatic contrast lighting
dramatic contrasting light
dramatic dark
dramatic dark atmosphere
dramatic dark eerie lighting
dramatic dark forest scenery
dramatic dark lighning
dramatic dark lighting
dramatic depth of field
dramatic details
dramatic diffused lighting
dramatic digital art
dramatic disaster
dramatic dreamlike lighting
dramatic duel of the fates
dramatic dynamic lighting
dramatic earth colors
dramatic elegant pose
dramatic entry
dramatic environment
dramatic epic cinematic lighting
dramatic everything
dramatic exploding nebulae
dramatic expressions
dramatic eyes
dramatic face
dramatic facial expression
dramatic fantasy art
dramatic film still
dramatic fire glow lighting
dramatic fire lighting
dramatic floating pose
dramatic framing
dramatic full moon lighting
dramatic glowing lighting
dramatic golden light
dramatic gradient lighting
dramatic green lighting
dramatic grey sky
dramatic hair color
dramatic highlights
dramatic illumination
dramatic illustration
dramatic ilumination
dramatic impactful colors
dramatic imposing pose
dramatic intense lighting
dramatic landscape
dramatic lens flares
dramatic lenses
dramatic lighing
dramatic light
dramatic light 8 0 mm camera
dramatic light 8 k
dramatic light 8k
dramatic light and shadow
dramatic light and shadows
dramatic light darkroom
dramatic light hdr
dramatic light on face
dramatic lightening
dramatic lightin
dramatic lighting
dramatic lighting !n-9
dramatic lighting - n 9
dramatic lighting 4k
dramatic lighting 8k resolution
dramatic lighting and colors
dramatic lighting fantasy
dramatic lighting from above
dramatic lighting from below
dramatic lighting from fire glow
dramatic lighting hd quality
dramatic lighting hyper detail
dramatic lighting lighting
dramatic lighting man
dramatic lighting on the face
dramatic lighting render
dramatic lighting sci fi
dramatic lighting shadows
dramatic lighting sharp focus
dramatic lighting with god rays
dramatic lighting!
dramatic lighting)))
dramatic lighting. concept art
dramatic lighting. realistic
dramatic lighting. stylized
dramatic lighting; 4k 8k
dramatic lighting”
dramatic lightning
dramatic lightning background
dramatic lightning digital art
dramatic lights
dramatic lights mystical scene
dramatic lights spiritual scene
dramatic ligthing
dramatic ligthting
dramatic low lighting
dramatic low-key lighting
dramatic low-key neon lighting
dramatic lowkey studio lighting
dramatic magic floating pose
dramatic masterpiece
dramatic matte painting
dramatic moment
dramatic mood
dramatic mood lighting
dramatic moody cold landscape
dramatic moody lighting
dramatic moody sunset lighting
dramatic moonlight
dramatic moonlight lighting
dramatic moonlit lighting
dramatic morning light
dramatic mountain background
dramatic mountains behind
dramatic mountains in background
dramatic movie still
dramatic mysterious lighting
dramatic nautical scene
dramatic nebula sky
dramatic neon lighting
dramatic news
dramatic night lighting
dramatic night sky
dramatic oil painting
dramatic painting
dramatic perspective
dramatic photo
dramatic photograph
dramatic photography
dramatic photorealistic
dramatic pink clouds
dramatic pink light
dramatic portrait
dramatic portraiture of namenlos
dramatic portraiture of uuen
dramatic pose
dramatic poses
dramatic posing
dramatic powerful nebula
dramatic powerful pose
dramatic powerful sky
dramatic press photo
dramatic product lighting
dramatic product photography
dramatic product shot
dramatic professional lighting
dramatic purple thunders
dramatic rain
dramatic reading book pose
dramatic reading spell book pose
dramatic red scary lighting
dramatic reddish light
dramatic rendering
dramatic representation
dramatic rim lighting
dramatic scene
dramatic scenery
dramatic sci-fi movie still
dramatic seductive pose
dramatic serious pose
dramatic shading
dramatic shadow
dramatic shadowing
dramatic shadows
dramatic sharp thorns
dramatic shot
dramatic simple shapes
dramatic situation
dramatic skies
dramatic sky
dramatic sky and landscape
dramatic sky in background
dramatic smile pose
dramatic smile pose intricate
dramatic smiling pose
dramatic smirk pose
dramatic smoke
dramatic smoke everywhere
dramatic smoking pose
dramatic soft light
dramatic soft lighting
dramatic soft shadow lighting
dramatic space battle
dramatic space sky
dramatic stadium lighting
dramatic stage lighting
dramatic standing
dramatic standing pose
dramatic starry night sky
dramatic storm clouds
dramatic storm sunset
dramatic stormy scene
dramatic stormy sky
dramatic studio light
dramatic studio lighting
dramatic sunrise
dramatic sunset
dramatic sunset and dramatic sky
dramatic sunset lighting
dramatic sunset nebula
dramatic swirling clouds
dramatic theater lighting
dramatic theming
dramatic thinking pose
dramatic three point lighting
dramatic view
dramatic volumetric light
dramatic volumetric lighting
dramatic volumetric lightning
dramatic warm color
dramatic warm morning light
dramatic white and blue lighting
dramatic wide angle
dramatic wide angle shot
dramatic wielding gun pose
dramatic wielding katana pose
dramatic wielding sword pose
dramatic zoom
dramatic ”
dramatic!! lighting
dramatic. fantasy
dramatically art
dramaticlighting
dramaticlly deep composition
dramatictic lighting
dramatic”
dramedy
drammatic light
drammatic lighting
dramtic lighting
dramtic lit
dramtically lit
draped drapes
draped in crystals
draped in fleshy green and pink
draped in flowing fabric
draped in gold
draped in pink and green robes
draped in purple
draped in purple and gold silk
draped in shiny gold and silver
draped in shiny golden oil
draped in shiny oil
draped in silk
draped in silky gold
draped in silky purple and gold
draped in transparent cloth
draped in velvet and flowers
draped with horrors and spines
draped with red hybiscus
draped with water and spines
drapery
drapes
drark
draw
draw with wacom tablet
drawcrowd
drawfolio
drawind style
drawing
drawing 4k
drawing an arrow from his quiver
drawing architecture
drawing faces
drawing for children
drawing gustave dore
drawing of a skull
drawing on a parchment
drawing on pencil
drawing pictures on a notebook
drawing sketches
drawing sketches on his notebook
drawing study
drawing style
drawings
drawn
drawn and painted
drawn image
drawn in a neo - noir style
drawn in a noir style
drawn in ballpoint pen
drawn in fine-tip pen
drawn in microsoft paint
drawn in the style of artgerm
drawn in the style of mark arian
drawn like the anime speed racer
drawn on paper
drawn on white parchment paper
drawn projects on paper
drawn with a single line
drawn with colored pencils
drawn with crayons
drawn with dots
drawn with photoshop
drdisrespect as a gundam
drdisrespect as napoleon
drdisrespect posing as napoleon
dread
dread +
dread + highly detailed
dread infernal
dread scary spaceship
dreadful
dreadjim
dreadlock black hair
dreadlock breed hair
dreadlock hair
dreadlocks
dreadnought
dreads
dream
dream - like
dream - like atmosphere
dream - like heavy atmosphere
dream a little dream of me
dream a portrait of a man
dream aesthetic
dream animal cute eyes
dream atmosphere
dream core
dream creature costumes
dream design
dream ethnology
dream heavenly cloudy sky
dream landscape
dream light
dream lighting
dream like
dream like atmosphere
dream like atmosphere 8k
dream magical
dream medium portrait top light
dream of better days
dream of the endless
dream on
dream pool
dream portal
dream recording
dream scenery art
dream sequence
dream sequence. the apprentice
dream theater
dream wave aesthetic
dream within a dream
dream world
dream-like
dream-like atmosphere
dream-like heavy atmosphere
dream-state
dreambotmothership
dreamcast
dreamcatcher
dreamcatchers
dreamcore
dreamcore aesthetic
dreamcore looking
dreaming
dreaming about a faraway place
dreaming bodies
dreaming face
dreaming illusion
dreaming of electric sheep
dreaming of kissing a girl
dreaming of outer space
dreamland
dreamland of chinese
dreamless night
dreamlike
dreamlike art
dreamlike atmosphere
dreamlike digital painting
dreamlike light incidence
dreamlike lighting
dreamlike stanley kubrick
dreamlike sunraise
dreamlike surrealism
dreamlike!
dreampool rooms
dreampunk
dreampunk romanticism artwork
dreams
dreams are like poetry
dreams invading mundane spaces
dreams of silver surfer
dreamscape
dreamscape architect
dreamscape in a jar
dreamscape maximized
dreamtime
dreamwalker
dreamwave
dreamworks
dreamworks and marvel
dreamworks animated bjork
dreamworks animation
dreamworks animation style
dreamworks style
dreamworld
dreamy
dreamy aesthetic
dreamy and detailed
dreamy and ethereal
dreamy and ethereal and dark
dreamy and romantic
dreamy art
dreamy atmosphere
dreamy atmosphere and drama
dreamy atmospheric
dreamy blurred lens
dreamy chinese town
dreamy clouds
dreamy colorful cyberpunk colors
dreamy colors
dreamy cyberpunk girl
dreamy ethereal vibe
dreamy eyes
dreamy feeling
dreamy floral background
dreamy gothic girl
dreamy green eyes
dreamy hazy
dreamy illustration
dreamy kodak color stock
dreamy landscape
dreamy light
dreamy light color palette
dreamy lighting
dreamy matte painting
dreamy mila kunis
dreamy mood
dreamy night
dreamy painting of coronavirus
dreamy psychedelic anime
dreamy puffy clouds
dreamy rock girl
dreamy scene
dreamy sequence
dreamy sky
dreamy soft
dreamy soft neon lights
dreamy style
dreamy vibe
dreamyart
dreary
dreary atmosphere
dreary lighting
dredd
dredged seabed
dren from splice
drenched body
drenched clothing
dress
dress and cloth
dress in the style of rococo
dress in voile
dress like taliban
dress made of bones
dress made of feathers
dress made of fire
dress made of green leaves
dress made of leaves
dress made of steel blades
dress made of water
dress made with circuit board
dress of leaves
dress shirt
dress shirt and tie
dressed
dressed a long white
dressed all in black
dressed anya taylor - joy
dressed as a ballerina
dressed as a judge's robes
dressed as a knight
dressed as a pastry chef
dressed as a queen
dressed as a scavenger
dressed as a western sheriff
dressed as a wizard
dressed as an oracle
dressed as emperor
dressed as schoolgirl
dressed as wonder woman
dressed beautiful gown
dressed black hoodie
dressed casually
dressed in 1970s menswear
dressed in a ((ragged))
dressed in a [ [ 1 2 th century
dressed in a beautiful
dressed in a beautiful red cloak
dressed in a beautiful white
dressed in a black cloak
dressed in a blue cloak
dressed in a flower dress
dressed in a frilly ((lace))
dressed in a frilly ((ragged))
dressed in a frilly lace
dressed in a frilly ragged
dressed in a gray
dressed in a green robe
dressed in a jodhpuri suit
dressed in a lacy
dressed in a medieval lacy
dressed in a pink dress
dressed in a ragged
dressed in a robe
dressed in a sari
dressed in a suit
dressed in a top and shorts
dressed in a white t shirt
dressed in a white t-shirt
dressed in a worn
dressed in an old white coat
dressed in armor
dressed in belts bikini
dressed in biker leather
dressed in bikini
dressed in black
dressed in black body armour
dressed in black lace
dressed in black leather
dressed in black velvet
dressed in blue
dressed in cloak
dressed in colorful silk
dressed in crustpunk clothing
dressed in dark garment
dressed in expensive clothes
dressed in laurel wreath
dressed in leather jacket
dressed in light armor
dressed in long fluent skirt
dressed in medieval clothes
dressed in orange inmate attire
dressed in ornate
dressed in plastic bags
dressed in punk clothing
dressed in purple
dressed in red paper bags
dressed in red velvet
dressed in roman armour
dressed in roman clothes
dressed in samurai armour
dressed in shako
dressed in short leather jacket
dressed in silk
dressed in spartan armour
dressed in stars and planets
dressed in tactical armor
dressed in velvet
dressed in white
dressed in white intricate lace
dressed in white robes
dressed like a cleric
dressed like in the 1940s
dressed like jesus christ
dressed like napoleon bonaparte
dressed senobith
dressed thobe
dressed well
dressed with expensive clothes
dressed with fluent clothes
dressed with long fluent clothes
dressing tassels gemstones
drew barrymore
drew hill
drew struzan illustration art
drew struzan inspiration
drew struzan style art
drew struzan tomasz alen kopera
drew struzan. epic
dribbble
dribbble 8k
dribbble illustration
dribbble. com
dribble popular
dribbling
dried acrylic paint
dried aquarium
dried blood
dried fern
dried flower
dried flowers
dried herbs
dried leaves
dried moss
dried palmtrees
dried petals
dried plants
dried vines
dries van noten
drifters
drifting
drifting around a corner
drifting in space
driftwood
driftwood sculpture
drink
drink milkshakes together
drink more coffee
drinking
drinking a beer
drinking a beer at train station
drinking a bottle of coca-cola
drinking a coffee
drinking a cup of coffee
drinking a glass of whiskey
drinking a martini
drinking a strawberry iced latte
drinking alcohol
drinking and smoking
drinking at the bar
drinking beer
drinking beer and laughing
drinking boba tea
drinking champagne
drinking cocktail
drinking coffee
drinking coffee at central perk
drinking cough syrup
drinking tea
drinking their hearts out
drinking whiskey
drinking wine
drinks
drinks bourbon
drip
drip painting
driping dry oil paint
dripped out
dripping
dripping bbq sauce
dripping black and grey paint
dripping black goo
dripping black iridescent liquid
dripping black paint
dripping blood
dripping blue natural iwakura
dripping candles
dripping colorful paint
dripping colors
dripping goo
dripping honey
dripping in neon paint
dripping light drops
dripping oil
dripping oil paint
dripping paint
dripping saliva
dripping skin
dripping stalagtites
dripping tar
dripping technique
dripping water
dripping wax
dripping wet
dripping with color
dripping with oil in the head
dripping with water
drippy
drippy oil painting
drips
drive in movie theater
drive out
driver
driveway
driving
driving a car
driving a hotrod
driving fast
driving in a little toy car
driving rain
driving through a 1 9 5 0 s town
driving through the city
drizzt do\'urden
drogon
droid
droids
drone
drone camera angle
drone camera lens orbs
drone footage
drone perspective
drone photo
drone photograpghy
drone photograph
drone photography
drone point of view
drone shot
drone speedways
drone view
drone view of a city
drone wide shot
drones
drones in the sky
drool
drooling
drooling ferrofluid
drooling ferrofluid. dslr
drooling goo
drooling teeth bared
drooping rabbity ears
drop of waters
drop shadow
drop shadows
droplets
droplets flow down the bottle
droplets on the walls
drops
drops are falling from above
drops around
drops of clean water
drops of water
drosera capensis
droste effect
drought
drow
drow ranger
drowned
drowned bandit lair
drowning
drugs
drugstore
druid
druid goddess
druid portrait
druid warrior
druidic runes
druillet
druillet colorful
drum & bass
drum bass
drum kit
drum pads
drum scanner
drum set
drumheller
drumkit
drummer
drums
drunk
drunk cowboy redhead long hair
drunk woman
drunkard
drunken boxing
drunken fist
drunken master
druzy geode
dry
dry archways
dry archways and spires
dry brush
dry brush background colors
dry brush texture
dry brush wash
dry brushing
dry desert
dry dirt
dry grass
dry ground
dry ice
dry landscape
dry river bed
dry skin
dry trees
dryad
dryad in the forest
ds
dslr
dslr +
dslr 1 5 mm
dslr 15 mm
dslr 15mm
dslr 24mm
dslr 3 5 mm
dslr 35 mm
dslr 35mm
dslr 5 0 mm
dslr 5 0 mm portrait photo
dslr 5 5 mm
dslr 50mm
dslr 55 mm
dslr 8 k
dslr 85mm
dslr 8k
dslr camera
dslr camera img_4016
dslr camera photo
dslr color
dslr depth of field
dslr fantasy
dslr film
dslr focus on the subjects
dslr hdr 8k
dslr high resolution
dslr hyper focused
dslr photo
dslr photo of a pretty teen girl
dslr photo of a vase on a table
dslr photograph
dslr photography
dslr photograpy
dsrl photo
dua lipa
dual magics
dual swords
dual tone
dual tone lighting
dual wielding axes
dual wielding large revolvers
dual wielding swords
dual wielding two magical swords
duality
duane hanson
duane loose
dubai
dubbel negative exposure
dubrovnik
dubstep
dubstep visualized
duck
duck shaped spaceship
duck shoes
duck sits at a table
duck themed spaceship
duckface
ducks
duel
duel between two cowboys
dueling
duff beer
dug stanat
dug stanat macabre art
dugeondraft
duke 3 d
duke nukem
duke nukem 3d
duke nukem 90s cover art
duke nukem art style
dukhara scavenger
dulac
dull
dull blue cloudy background
dull colors
dull colours
dull flaking paint
dull grey expressionism
dull palette
dull pastel colors
dull pink background
dull red
dull red flaking paint
dullahan
dulled contrast
dumb
dumbledore
dumplings on a plate
dun or grey
duncan fegredo
dunce
dune
dune (2021)
dune 1982 movie
dune city and temples of arrakis
dune movie
dune movie poster
dune style
dune the movie
dunes
dunes in the background
dungeon
dungeon and dragon
dungeon and dragon illustration
dungeon and dragons
dungeon and dragons art
dungeon background
dungeon's & dragons
dungeondraft
dungeons
dungeons & dragons
dungeons and dragon
dungeons and dragons
dungeons and dragons art
dungeons and dragons artwork
dungeons and dragons character
dungeons and dragons characters
dungeons and dragons fantasy art
dungeons and dragons portrait
dungeons and dragons style
dungeons dragons
dunking
dunkirk
dunwall city
dunwitch horror
duo tone
duochrome
duotone
duotone screen print
duotone!
durararara
durer
during a biohazard apocalypse
during a blood moon
during a hail storm
during a meteor storm
during a storm
during a sunset
during a thunderstorm
during an eclipse
during autumn
during dawn
during golden hour
during lunar eclipse
during night
during sandstorm
during snowfall
during spring
during sunrise
during sunset
during the day
during the night
durty colours
dusk
dusk atmosphere
dusk light
dusk lighting
dusk on a city street
dusk on jupiter
dusk setting
dusk sky
dusk sunset in background
dust
dust and blood
dust and blood in the air
dust and particles
dust and rats
dust and rock theme
dust and sand in the air
dust and scratches
dust and smoke
dust and steam
dust around
dust atmosphere
dust cloud
dust clouds
dust clouds  homoerotic
dust clouds and building debris
dust clouds | homoerotic
dust clouds. homoerotic
dust devils
dust floats in the air
dust in air
dust in the air
dust light
dust mask
dust mist
dust molecules
dust motes in air
dust particle
dust particles
dust particles in the air
dust partiles in the air
dust storm
dust swirling
duster
dustin lefevre
dustin panzino
dusting of snow
dustlight
dusty
dusty abandoned shinjuku
dusty air
dusty and smokey
dusty atmosphere
dusty background
dusty dirty floor
dusty environment
dusty floor
dusty ground
dusty library
dusty light
dusty lighting
dusty old ferrotype
dusty place
dusty rock in background
dusty rown bomber leather jacket
dusty space suit
dusty street
dusty unreal engine
dusty volumetric light
dusty volumetric lighting
dutch
dutch angle
dutch angle digital art
dutch angle from space view
dutch camera view
dutch golden age
dutch golden age painting
dutch houses along a river
dutch landscape
dutch masterpiece
dutch masters
dutch painting
dutch portrait from the 1600s
dutch still life from the 1600s
dutch still life of the 1600s
dutch style
dutch style painting
dutch tilt
dutch windmill gundam
dutiful return
duy beni serial
dvd
dvd cover
dvd package
dvd screencapture
dviant art
dwarf
dwarf cleric
dwarf scientist of 1 9 century
dwarf with white hair
dwarven
dwarven architecture
dwarven weapon
dwarven woman
dwarves
dwayne barlowe
dwayne johnson
dwayne johnson as batman
dwayne johnson as god of war
dwayne johnson as harry potter
dwayne johnson as hulk
dwayne johnson as kratos
dwayne johnson as samurai
dwayne johnson as spiderbatman
dwayne johnson as spiderman
dwayne johnson as superman
dwayne johnson as thanos
dwayne johnson as the joker
dwayne johnson as wonder woman
dwayne johnson in family guy
dwayne the rock johnson
dwell
dwight
dwight schrute
dxo photolab
dybanic
dye - transfer
dye contrast lighting
dye hair
dye-transfer
dyed hair
dying
dying earth
dylan cole
dylan kowalsk
dylan kowalski
dynamic
dynamic action
dynamic action pose
dynamic action poses
dynamic action shot
dynamic active running pose
dynamic and atmospheric lighting
dynamic and dominant
dynamic and dominating
dynamic angle
dynamic angled shot
dynamic angles
dynamic background
dynamic backlighting
dynamic beautiful lighting
dynamic blue sky
dynamic brush strokes
dynamic camera angle
dynamic character
dynamic character design
dynamic character posse
dynamic cinematic lighting
dynamic closeup
dynamic closeup composition
dynamic color
dynamic colorful background
dynamic colors
dynamic comparison
dynamic composition
dynamic composition and motion
dynamic contrast
dynamic curves
dynamic dance photography
dynamic dancing pose
dynamic dramatic ighting
dynamic dramatic lighting
dynamic dramatic shot
dynamic energic pose
dynamic extreme foreshortening
dynamic folds
dynamic hair movement
dynamic layout
dynamic light
dynamic lighting
dynamic lighting stars
dynamic lightning
dynamic lights
dynamic lights and shadows
dynamic lines
dynamic linework
dynamic low angle shot
dynamic moody lighting
dynamic motion
dynamic movement
dynamic movie still
dynamic moving pose
dynamic pearlescent teal light
dynamic perspective
dynamic perspective and angle
dynamic perspective pose
dynamic photography
dynamic portrait
dynamic portrait style
dynamic pose
dynamic pose and movement
dynamic pose and perspective
dynamic pose full body
dynamic pose perspective
dynamic pose realistic
dynamic poses
dynamic posing
dynamic poster
dynamic proportion
dynamic proportional
dynamic range
dynamic reflections
dynamic scene
dynamic shaders
dynamic shading
dynamic shadow
dynamic shadows
dynamic shot
dynamic skating
dynamic sky
dynamic splashes
dynamic stretching
dynamic view
dynamic volumetric lighting
dynamic wavy hair
dynamic wide angle lens
dynamic!!
dynamically shot
dynamics
dynamics lighting
dynasty
dynasty warriors
dysney
dyson sphere
dyson sphere in space
dyson sphere program
dyson sphere program pink planet
dystopia
dystopia city
dystopia core
dystopia style
dystopian
dystopian 1920s soviet mood
dystopian aesthetics
dystopian alien landscape
dystopian art
dystopian atmosphere
dystopian background
dystopian bad vibes
dystopian bounty hunter
dystopian brutalist atmosphere
dystopian celebration
dystopian city
dystopian city apartment
dystopian city skyline at night
dystopian cyberpunk
dystopian cyberpunk city
dystopian digital art
dystopian environment
dystopian feel
dystopian floor tile texture
dystopian future
dystopian gray forest background
dystopian grunge
dystopian landscape
dystopian lighting
dystopian mood
dystopian reichstag germany
dystopian retro 1920s vibe
dystopian sci - fi
dystopian sci-fi
dystopian sci-fi character
dystopian science fiction
dystopian scifi
dystopian scifi apocalypse
dystopian scifi gadgets
dystopian scifi gear
dystopian scifi outfit
dystopian setting
dystopian slums
dystopian society
dystopian style
dystopian surrealism
dystopian terror
dystopian toy
dystopian wasteland
dystopian world
dystopic
dystopin future
dystoptian
dzo
dzung phung dinh
e - girl
e-girl
e-sport style
e-sports logo vector
e. h. beatrice blue
e3
e4e620
e621
ea games
ea sport
ea star wars battlefront 2015
each faces precisely define
each having six wings
each land is a different color
each wearing correct era clothes
each wish resign ’ d
eagle
eagle beak
eagle coral
eagle eat snake
eagle feather
eagle head
eagle logo
eagle wings
eagles
eal
ealistic
ealistic shaded perfect face
ear
ear floof
earbuds
earbuds jewelry
earie setting
earing a shirt laughing
earings
earl
earl moran
earl nore
earle
early 1 9 0 0's
early 1 9 century
early 19 century
early 1900s
early 1900s newspaper
early 2 0 0 0 s
early 2 0 th century
early 2000 s
early 2000s
early 20s
early 90s cg
early black and white 8mm
early black and white photo
early cgi
early computer graphics
early cuyler
early dawn
early evening
early foggy morning
early in the morning
early medieval
early middle ages
early morning
early morning light
early morning light fog
early morning lighting
early morning lightning
early morning mood
early morning sun in the sky
early morning sunrise
early netherlandish painting
early night
early renaissance
early screen test
early spring
early xx century technology
earnest
earphones
earring
earring design
earrings
earrings.
earrings. highly detailed
ears
ears are listening
ears shine through the light
earth
earth and pastel colors
earth attuned
earth colors
earth covers lightly
earth eyes
earth globe on top
earth goddess mythology
earth in background
earth in foreground
earth in space
earth in the background
earth in the distance
earth in the foreground
earth in the sky
earth magic
earth on the window
earth outside
earth seen on the dark sky
earth tone color scheme
earth tone colors
earth tones
earth tones and blues
earth tones and soft color 1976
earth tones and soft color 1981
earth type pokemon
earth visible below
earthbound
earthquake
earthquake destruction
earthship
earthtone colors
earthtones
earthwave
earthy
earthy color scheme
earthy colors
earthy colours
earthy light pastel colours
earthy tones
easel
easports
east african man with curly hair
easter
eastern art style
eastern european
eastern european origin
eastern front
eastman 5 3 8 4 film
eastman 5384 film
eastman color
eastman exr 50d 5245/7245
eastman kodak
eastmancolor
easy
easy edges
easy go
easy to understand
easy to use
easygoing
eating
eating a burger
eating a cheese platter
eating a cheeseburger
eating a donut
eating a hamburger
eating a mushroom
eating a pizza
eating a pizza margherita
eating a rat
eating alien food
eating burgers
eating cakes
eating camera pov
eating cheese
eating chips and watching tv
eating garlic bread
eating ice - cream
eating ice cream
eating inside mcdonalds
eating mars bar candy
eating meat
eating noodles
eating outside
eating pizza
eating ramen
eating rotting fruit
eating spaghetti
eating spaghetti from a bowl
eating sushi
eats bambus
eats pasta all the time
ebay
ebay listing
ebay listing thumbnail
ebay photo
ebay product
ebay website
ebony
ebony art deco
ebony rococo
ebony skin
ebony wood bow
eboy
eccentric
ecchi
ecchi anime style
ecchi style
echo from overwatch
ecktochrome
eclectic
eclecticism
eclipse
eclipse of aurora on mars
eco
eco-friendly theme
ecology
ecommerce photograph
economic boom
economist
ecopunk
ecopunk rococo
ecosystem
ecovillage
ecru cloth
ecstasy
ecstasy and cry from far
ecstatic
ecstatic crowd
ecstatic expression
ecstatic face expression
ectoplasm
ed
ed asner starring as ben grimm
ed hopper
ed o'brien (guitar)
ed repka
ed sheeran
edd
edd cartier
eddie
eddie adams
eddie del rio
eddie h. hinestroza
eddotorial illustration
ede laszlo
edelgard fire emblem
edelgard from fire emblem
eden
eden at dawn
eden garden
edgar allan poe
edgar allan poe as the raven
edgar maxence
edgar maxence and ross tran
edgar rice burroughs
edgard maxence
edge
edge - to - edge print
edge detection
edge glow
edge highlights
edge light
edge lighting
edge of interstellar space
edge of nothingness love
edge of the universe
edge of the world
edge of tomorrow
edge to edge
edge vignette
edge-to-edge print
edges
edgy
edgy adventure sci fi
edible
edible crypto
edible flowers
edin durmisevic
edinburgh
edinburgh castle
edison bulb
edited
edited in photoshop
editor's pickup
editorial
editorial awarded
editorial awarded design
editorial fashion photography
editorial footage
editorial illustration
editorial illustration colorful
editorial image
editorial model
editorial of dior magazine
editorial photo
editorial photo from magazine
editorial photograph
editorial photography
editorial portrait
editorial story
editors choice
editors pickup
editor’s pickup
edm
edm fans
edmonia lewis
edmund blair and charlie bowater
edmund blair leighton
edo
edo era
edo era house in the background
edo japan
edo period
edo style
edon guraziu
edouard caplain
edouard groult
edouard leon cortes
edouard manet
edu souza
eduard pronin
education of the masses
educational
educational display case
educational supplies
edvard escher guay
edvard munch john berkey
edvige faini
edward
edward buba
edward burne jones
edward hooper
edward hopper
edward hopper and ilya kushinov
edward hopper and james gilleard
edward hopper and jenny saville
edward hopper and milo manara
edward hopper dramatic
edward hopper filonov
edward hopper painting
edward hopper style
edward hopper vibe
edward hopper. sharp focus
edward hughes
edward munch
edward norton
edward okun
edward robert
edward rucha
edward scissorhands
edward steichen
edward weston and gustave doré
edwardian
edwin church
edwin long
edwin longsden long
eeg nodes on scalp
eeire
eeire mood
eel nebula
eeri
eerie
eerie and atmospheric
eerie and grim art style
eerie and moody polaroid
eerie and unsettling
eerie art style
eerie atmosphere
eerie atmospheric
eerie atmospheric lighting
eerie back light
eerie color
eerie colors
eerie dark atmosphere
eerie david lynch cinematography
eerie fog
eerie glow
eerie glowing eyes
eerie highly detailed
eerie horror
eerie horror mood
eerie jungle
eerie light
eerie lighting
eerie looking
eerie luminosity
eerie mist
eerie mood
eerie moonlight
eerie moorlands behind her
eerie music
eerie nostalgic concept art
eerie person
eerie person silhouette
eerie room
eerie self - portrait
eerie shimmering surroundings
eerie sky
eerie spiritual grainy
eerie thriller aesthetic!!!!
eerie vibe
eerie vibes
eerie vibrancy
eerie ”
eerie!
eerie!!! atmosphere
eerieatmosphere
eeriewave
eerily beautiful
eero aarnio
eery
eery dead swamp setting
eery light
eevee
eeyrie
ef 8 5 mm f 1. 8 usm
ef 85 mm f 1. 8 usm
effect
effective altruism
effects
effervescent
efficient
egg
egg yolk
eggo waffle inside toaster slot
eggplant
eggs
eggs benedict cumberbatch
eggshell color
ego death
egon schiele and max ernst
egon schiele!
egor letov
egotistical
eguchi
egypt
egypt god
egypt makeup
egypt themed art
egyptian
egyptian art
egyptian atmosphere
egyptian cat goddess
egyptian clothes
egyptian clothing
egyptian cyborg armor
egyptian environment
egyptian god
egyptian gods
egyptian iconography
egyptian landscape
egyptian makeup
egyptian mummy
egyptian mummy king
egyptian mythology
egyptian princess
egyptian samurai
egyptian setting
egyptian style
egyptian sumerian features
egyptian symbolism
egyptian warrior
egyptology
ehime
eichiro oda
eichler home
eiffel tower
eiffel tower in istanbul
eiffel tower in the middle
eiffel tower is burning
eiffel tower left
eiffel tower photography
eight eight eight
eight-dimensional
eighteen-dimensional
eighties amateur photography
eighties eros
eighties look
eighties miami vice
eighties pinup style
eighties-pinup style
eiichiro oda style
eiko hosoe
eiko ishioka
einar jonsson
einar jonsson and bouguereau
einstein
eka's portal
ekas portal
ekasportal
ekaterina
ektachrome
ektachrome  1976
ektachrome ( 1 9 7 6 )
ektachrome color photograph
ektachrome film
ektachrome photograph
ektochrome
el anatsui
el bosco and dan mumford
el capitan
el chavo
el chavo del ocho
el dia los muertos
el dia los muertos!
el dorado
elaborate
elaborate braided hair
elaborate carved wood balconies
elaborate clothing
elaborate composition
elaborate costume
elaborate cult robes
elaborate detail
elaborate details
elaborate digital art
elaborate earrings
elaborate floral ornament
elaborate geometric ornament
elaborate gold jewelry
elaborate hair
elaborate hair worn up
elaborate hairstyles
elaborate illustration
elaborate ink illustration
elaborate inking lines
elaborate jewelry
elaborate latticed balconies
elaborate lights. mask on face
elaborate long black hairstyle
elaborate long hairstyle
elaborate matte painting
elaborate oled jewelry
elaborate ornate growth
elaborate ornate head piece
elaborate ornate jewellery
elaborate patterned makeup
elaborate polished
elaborate rube goldberg style
elaborate stage effects
elaborated depth of field
elaborately costumed
elaine
elaine benes
elated gaunt onion head
elbaite
elbow
elbow gloves
elbow pads
eldar samurai
elden ring
elden ring art
elden ring boss
elden ring capitol
elden ring cinematic lighting
elden ring class
elden ring concept art
elden ring inspired
elden ring monster
elden ring official
elden ring style
eldenring
elder
elder ring
elder scrolls
elder scrolls art
elder scrolls colourised
elder scrolls online
elder scrolls v
elder sign
elder thing
elderly
elderly greek goddess
elderly woman
elderscrolls
eldrazi
eldricht abominations
eldritch
eldritch abomination
eldritch abomination garfield
eldritch being
eldritch color scheme
eldritch eyes
eldritch god
eldritch goddess
eldritch horror
eldritch horror goddess
eldritch horrors
eldritch journalist
eldritch legislature
eldritch lovecraftian secretary
eldritch space
eldritchpunk
eleanor tomlinson
election poster
electric
electric arc
electric arcs
electric aura
electric aura with particles
electric blue
electric brainstorm
electric breeze
electric cables
electric cats that fly over ice
electric color
electric colors
electric colours
electric energy
electric eyes
electric guitar
electric guitars
electric hair
electric martina
electric motors
electric orange glowing lights
electric sheep
electric sky
electric sparks
electric storm
electric swirls
electric vines and swirls
electric wallpaper
electric woman
electrical
electrical appendages
electrical arcs
electrical aura
electrical cables
electrical case display
electrical details
electrical plant location
electrical signals
electrical wires
electrical wiring
electrical wiring!
electricity
electricity archs
electricity aura
electricity highly detailed
electricity lightning
electricity superpowers
electrified hair
electrifying
electrixbunny
electrode
electromagnetic field dmt lsd
electron flow
electron microscope
electron microscope image
electron microscope photography
electron microscope view
electron microscopy photography
electronic
electronic ads
electronic bikini
electronic billboards
electronic brain
electronic case display
electronic circuitry
electronic components
electronic hardcore music
electronic microscope 1 9 9 0
electronic music
electronic wires
electronics
electronics see through
electrons
electrostatic hum
elegance
elegant
elegant ancient greek dress
elegant and extremely ornamental
elegant and graceful
elegant and ornate
elegant and proud
elegant and refined
elegant and refined painting
elegant architecture
elegant armor
elegant art
elegant art nouveau style
elegant asymmetrical
elegant atmosphere
elegant baroque
elegant bikini
elegant biomechanical machinery
elegant bridges between towers
elegant cape
elegant cat
elegant cinematic fantasy art
elegant cinematic pose
elegant clean design
elegant clothes
elegant clothing
elegant color palette
elegant colorful
elegant composition
elegant confident pose
elegant coral sea bottom
elegant corset
elegant dark blue dress
elegant decollete
elegant design
elegant digital painting
elegant drawing
elegant dress
elegant evening gowns
elegant evening gowns!
elegant fabric
elegant face
elegant fantasy style braids
elegant fashion model
elegant feet
elegant floating pose
elegant flowers
elegant freckles
elegant furniture
elegant futuristic wardrobe
elegant girl
elegant girl in urban outfit
elegant glamor pose
elegant glamourous cosplay
elegant gleaming jewelry
elegant gold body
elegant gold skin
elegant hair
elegant hand
elegant harmony
elegant high art
elegant high quality
elegant highly detailed
elegant horror artwork
elegant interior
elegant intricate
elegant intricate diagrams
elegant japanese woman
elegant lady
elegant lady with alabaster skin
elegant legs
elegant light
elegant lighting
elegant look
elegant minimalism
elegant noir
elegant oil painting
elegant photorealistic
elegant photorealistic cinematic
elegant portrait
elegant pose
elegant posed
elegant profile pose
elegant profile posing
elegant proportions
elegant red dress
elegant regal posture
elegant render
elegant scene
elegant seducing pose
elegant seductive pose
elegant shot
elegant sleek smooth body
elegant slim beige shirt
elegant smiling pose
elegant smooth silver armor
elegant study
elegant style
elegant suit
elegant trend
elegant tropical prints
elegant up to the elbow
elegant victorian vampire
elegant walkways between towers
elegant wardrobe
elegant white dress
elegant woman
elegant yellow skin
elegant!
elegant!!
elegant.
elegant. highly detailed
elegantly
elegantly dressed
elegantly posing over you
elegence
elegent
elegrant
elemental
elemental guardian of life
elements
elements in a composition
elena masci
elephant
elephant - crab creature
elephant head
elephant in the room
elephant man
elephant shrew
elephant skull
elephantine armored knight
elephants
elestial crystals
elevated consciousness
elevated street art
elevation
elevation view
elevator
elevator doors look like a mouth
elevator to the moon
eleven from stranger things
eleven-dimensional
eleven/millie bobbie brown
eleventh doctor
elf
elf ears
elf forest background
elf girl
elf girl wearing an flower suit
elf knight
elf long weaving brown hair
elf princess
elf princess knight
elf queen
elf queen nissa genesis mage
elf warrior
elf with blue skin
elfic priestess
elfin beauty
elfpunk
elgant
elias chatzoudis
eliezer yudkowsky
elijah wood as harry potter
elimination of humanity
eliran kantor
elisabeth moss
elisha cuthbert as a d&d paladin
elisha cuthbert as a paladin
elisha cuthbert as a rpg warrior
elite
elite dangerous
elite scientist
elitedangerous 💙💜 ornate ︎
elites
elizabeth
elizabeth erickson
elizabeth from bioshock infinite
elizabeth jane gardner
elizabeth olsen
elizabeth olsen as black widow
elizabeth olsen as harley quinn
elizabeth olsen as scarlet witch
elizabeth saltzman
elizabeth taylor
elizabeth williams
elize theron
elk
elke
elle fanning
elle fanning as an android
elle fanning at a diner
elle fanning at night
elle fanning in a black coat
elle fanning)
elle magazine
ellen jewett
ellen ripley
ellen von unwerth
ellie (last of us)
ellie bamber
ellie bamber fairy
ellie from the last of us
ellie victoria gale
elliot alderson
elliot anderson
elliott erwitt photography
ello
ellora
elm tree
elmo
elmo as a giant monster
elmo in fortnite
elokitty
elon musk
elon musk as a cyborg
elon musk as a greek god
elon musk as a musketeer
elon musk as a skyrim character
elon musk as a soldier
elon musk as captain america
elon musk as iron man
elon musk as jabba the hutt
elon musk as joker
elon musk as punisher
elon musk as slimy mollusk
elon musk as the incredible hulk
elon musk as thor
elon musk crying
elon musk eating crayons
elon musk funko pop
elon musk in attack on titan
elon musk in fortnite
elon musk in gta v
elon musk in minecraft
elon musk on mars
elon musk portrait
elon musk smoking weed
elon musk working at mcdonalds
elon tusk
elongated arms
elongated figure
eloquent
eloy band
eloy morales
elpis
elric of melnibone
elrond's house
els
elsa
elsa from frozen
elsa frozen
elson peter
elspeth knight errant
elstree
elton john
elton john age 25
elton john in a fist fight
elton john lennon
elusive
elven
elven angel meditating in space
elven architecture
elven armor
elven character with smirk
elven city
elven male
elven palace of ghemathar
elven princess
elven spirit meditating in space
elven warrior princess
elves
elves house
elves sitting on the couch
elvgren
elvira in tattered clothes
elvis
elvis presley
elvish
elvish blonde male warrior
elysian fields
elysium
emaciated
emaciated black evening gown
emaciated shaved face
emad mostaque
email
emanating and flowing energy
emanating dimensional magic
emanating magic from her palms
emanating menacing aura
emanating white smoke
emanating with blue aura
emanuele dascanio
embarrassed
embarrassing
embedded in clear epoxy
embedded with gemstones
embellished sequined
embellisher
embellishment
ember
embers
embers adrift in the air
embers flying
embers to waist
emblem
emblem of wisdom
embodiment of chaos
embodiment of concept
embossed
embossed paint
embrace
embrace porcelain doll
embrace the superego
embraced
embracing
embroidered brocade
embroidered brocade robes
embroidered robes
embroidered shirt
embroidered uniform guard
embroidered velvet
embroidery
emerald
emerald artifacts
emerald color palette
emerald coloured eyes
emerald earrings
emerald eyes
emerald gold and beksinski
emerald green eyes
emerald herald
emerald jewelry
emerald tablet
emerald tiara and necklace
emerald yellow eyes
emeralds
emergence of the pizza rat
emergency
emergency countermeasures
emergency room
emerging from a lamp
emerging from blue fire
emerging from her lamp
emerging from the darkness
emerging from the mist
emerging from the water
emerging hands
emil ferriss
emil melmoth
emilia clarke
emilia clarke as a greek goddess
emily blunt
emily rajtkowski
emily ratajkowski
eminem
eminem as a m & m candy
eminem as an m & m
emir
emissive
emissive bssrdf
emissive light
emissive lighting
emissive lights
emissive material
emiting mystical light
emitting diodes
emitting evil red aura
emitting light ornaments
emitting psychic powers
emitting smoke and sparks
emitting spore clouds
emma
emma bot queen
emma bridgewater and paperchase
emma frost
emma roberts
emma stone
emma stone as a pinup girl
emma stone as catwoman
emma stone face
emma stone in beige coat
emma stone in stranger things
emma stone in victoria secret
emma stone poison ivy
emma stone poison ivy dc comics
emma stone portrait
emma stone with an owl
emma stone with blond hair
emma uber
emma watson
emma watson as a greek goddess
emma watson as a knight
emma watson as a mermaid
emma watson as a tall nba player
emma watson as an angel
emma watson as an avocado
emma watson as an avocado chair
emma watson as black canary
emma watson as black widow
emma watson as blond aphrodite
emma watson as captain america
emma watson as catwoman
emma watson as driada
emma watson as galadriel
emma watson as giant man
emma watson as harley quinn
emma watson as harry potter
emma watson as hermione granger
emma watson as hulk
emma watson as iron man
emma watson as jessica rabbit
emma watson as joan of arc
emma watson as kratos
emma watson as lara croft
emma watson as mona lisa
emma watson as poison ivy
emma watson as princess leia
emma watson as sea mermaid
emma watson as she-hulk
emma watson as spiderman
emma watson as supergirl
emma watson as the flash
emma watson as the joker
emma watson as the queen of ice
emma watson as the scarlet witch
emma watson for cosmopolitan
emma watson for victorian secret
emma watson in a hi vis vest
emma watson in bed
emma watson in game of thrones
emma watson in star trek
emma watson in star wars
emma watson in starcraft ii
emma watson in ted lasso poster
emma watson is harley quinn
emma watson modeling for gucci
emma watson portrait
emma watson sitting in chair
emma watson smiling
emma watson vietnam door gunner
emma watson wearing a swimsuit
emma watson wearing fancy fish
emma watson wearing fancy hat
emma watson with anxious
emma!! watson!!
emmanuel macron
emmanuel shiru
emmanuel shiu
emmawatson
emmy rossum
emo
emo anime girl
emo boy with cat ears and tail
emo girl and her cat
emo girl eating pancakes
emo makeup
emo style
emoji
emote
emoticon
emotion
emotion : madness
emotional
emotional and expressive
emotional concept art
emotional conflict
emotional expression
emotional eyes
emotional face
emotional face shot
emotional lighting
emotional masterpiece
emotional oil painting
emotional pain
emotional picture
emotional release
emotional sad painting
emotional surrealist art
emotional vibrant
emotional vision
emotionally evocative
emotionally evoking
emotionally numb
emotionally touching
emotionless
emotions
emotions closeup
emotions. fantasy
emotive
emotive faces
emotive portrait
empathic
empathy machines
emperor
emperor biden
emperor of the known universe
emperor secret society
emphasis on tall buildings
empire
empire silhouette
empire state building
empire strikes back
empress
empress of the wasteland
emptiness
emptiness and peace
empty
empty and uncany expression
empty background
empty bathhouse hidden in a cave
empty black void
empty bloody - black eyesockets
empty buildings
empty buildings with vegetation
empty city
empty desert
empty edges
empty eye sockets
empty eyes
empty floor
empty hands
empty liminal space
empty metropolitan street
empty office
empty pet bottles
empty remote wilderness
empty road
empty road in the middle
empty room
empty room with black walls
empty snow field
empty space
empty space background
empty stage
empty stools
empty street
empty streets
empty streetscapes
empty warehouse background
empty wheat field
empty white room
empyrean
empyrean city
emtpy space
emulador
emulating reality
emylie boivin
en debut de matinee
enamel
enb
encampment
encarpus
encaustic painting
encephalopod
enchanted
enchanted and magic forest
enchanted dreams
enchanted dreams. instagram
enchanted forest
enchanted forest tower
enchanted garden
enchanted magical fantasy forest
enchanted plants
enchanted with nature spirit
enchanting
enchanting and otherworldly
enchanting lighting
enchanting vibes
enchantress
encho enchev
enclosed in rock
encompass violet irises
encrusted with jewels
encyclopedia illustration
end of an era
end of day
end of days
end of evangelion
end of evangelion poster
end of everything
end of humanity
end of the day
end of the universe
end of the world
end of times
end of world
end times
endangered
endgame boss
endings
endless
endless black hair
endless books
endless collaboration with ai
endless cosmos in the background
endless empty office building
endless flowing ethereal drapery
endless forest of high buildings
endless grass
endless hallways
endless hallways and bookshelves
endless horizon
endless lake
endless loop
endless night
endless sea
endless sky
endless street
endless summer art
endless universe
endless war
endoekeleton exposure
endor forest
endoskeleton
endre penovác
enduring
endwalker cinematic
energetic
energetic atmosphere
energetic beings patrolling
energetic composition
energetic jazz piano portrait
energetic mood
energetic varia suit
energized
energized face
energy
energy aura
energy body
energy core
energy drink
energy flowing
energy flows of water and fire
energy mote
energy pulses
energy pulsing
energy rifle
energy shield
energy spheres
energy trails
energy vortex
energy waves
enes dirig
eng kilian
engagement ring ads
engaging
engalnd porcelain tea set
engine
engine room
engine room on a starship
engineer
engineered
engineering
engineering bay
engineering blueprints
engineering diagram
engineering drawing
engineers building a giant mecha
engines
england
english
english countryside
english heritage
english style
english text
english village
engraved
engraved blade
engraved highly detailed
engraved texture
engraved vector
engraving
engraving drawing
engraving from 1700s
engraving illustration
engravings
engulfed in flames
engulfed in swirling flames
enhance
enhance colors
enhance face
enhance light effect
enhance lighting
enhanced
enhanced 8k render
enhanced colors
enhanced eye detail
enhanced face
enhanced faces
enhanced hands
enhanced light effect
enhanced lighting
enhanced noise
enhanced photo
enhanced quality
enhanced quality polaroid photo
enhanced sharpness
enhancements
enhanched
eniac computer
enigma
enigmatic
enigmatic and ectoplasmic figure
enigmatic atmosphere
enigmatic beauty
enigmatic lighting
enigmatic natural beauty
enigmatic scene
enigmatic!!
enjoyable
enjoying a glass of wine
enjoying a stroll in the forest
enjoying coffee at a coffee shop
enjoying life to its fullest
enjoying life!!! elegant
enjoying lifeelegant
enjoying the wind
enki bilal style
enlightened
enlightening
enlightenment
enlightenment tripping on dmt
enlightenment. intricate
enlightment
enoch bolles
enormous
enormous chrome man
enormous hands
enormous in size
enormous scale
enormous trees
enormously detailed
enraged
enscape render
enso
ent
ent treant dryad
entangled
entangled foliage
entangled vibrating
enter night
enter the dragon 1973
enter the void
entering a quantum wormhole
entering the mind maze
enterprise workflow engine
entertaining
entertainment district
entheogen
enthusiastic crowd
enticing
entire body
entire character
entire character!
entire city in view
entire city visible
entire person visible
entire person visible!
entire person!
entirely black full page black
entities
entity
entourage
entrance
entrance to 1900's mine
entrance to a dark tunnel
entrance to abandoned mine
entrance to an ancient dungeon
entrance to ethereal realm
entrapped in ice
entropic behaviors
entropy
ents
entwined bodies
entwined hearts and spades
entwined in vines
entwined in vines and nature
enveloped in ghosts
envious atmosphere
enviroment
enviroment design
enviromental portrait
environment
environment and concept art
environment art
environment artist
environment concept
environment concept art
environment design
environment design illustration
environment fog
environment friendly
environment highly detailed
environment lighting
environment painting
environmental
environmental artwork
environmental concept art
environmental design
environmental key art
environmental light
environmental lighting
environmental portrait
environmental shot
environments
environments )
eon flux
eora
eos - 1 d
eos 5 ds r
eos 5d
eos 5ds r
eos r5
eos-1d
eowyn fencing with liam neeson
epcot
ephemeral
epic
epic 1986 cinematic still
epic 3 d abstract emo girl
epic 3 d abstract model
epic 3 d omolu
epic 3 d oshun
epic 3 d oxaguian
epic 3 d oxossi
epic 3 d yemaya
epic 35 mm lens shot
epic 3d abstract emo girl
epic 3d abstract model
epic 3d oshun
epic 3d oxaguian
epic 3d yemaya
epic 7 0 mm lens shot
epic 70 mm lens shot
epic 8 k
epic 8 k hd anime shot
epic 8k
epic 8k hd anime shot
epic action
epic action pose
epic action scene
epic action shot
epic adventure
epic album art cover
epic album cover
epic and cinematic view
epic and classy portrait
epic and stunning
epic angel wings
epic angle
epic angle and pose
epic angles
epic anime
epic anime fantasy
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epic arcane magic
epic architecture
epic armor
epic art
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epic art style
epic artwork
epic atmosphere
epic award winning
epic background
epic battle
epic battle scene
epic battle screen of hero
epic battlescene
epic beard
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epic beautiful landscape
epic beautiful space scifi
epic biblical depiction
epic blue sky
epic boss battle
epic boss fight
epic buildings in the center
epic camera
epic castle
epic castle with tall spires
epic character art
epic character composition
epic character portrait
epic cinematic
epic cinematic concept art
epic cinematic lighting
epic cinematic matte painting
epic cinematic quality
epic cinematic scene
epic cinematic shot
epic cinematic still
epic cinematic tone
epic cinematography
epic clouds
epic clouds and godlike lighting
epic clouds and lighting
epic cold blue lighting
epic comic book art
epic comic book style
epic compisition
epic composistion
epic composition
epic composition 35mm
epic composition 8 5 mm
epic composition 85 mm
epic concept art
epic concept art. bokeh
epic concept art. warhammer 40k
epic cosmos
epic cover
epic coves crashing waves plants
epic cyberpunk
epic dark fantasy
epic debates
epic design
epic detail
epic digital art
epic digital art illustration
epic digital concept art
epic digital painting
epic dimensional light
epic diptych
epic dragon
epic dramatic lighting
epic dreamlike fantasy landscape
epic elder scrolls art
epic elven warrior
epic environment
epic exquisite character art
epic fantasty card game art
epic fantasy
epic fantasy art
epic fantasy art portrait
epic fantasy art style
epic fantasy art style hd
epic fantasy artwork
epic fantasy battle
epic fantasy card game art
epic fantasy character art
epic fantasy concept art
epic fantasy cyberpunk
epic fantasy d & d hobbit rogue
epic fantasy digital art
epic fantasy digital art style
epic fantasy game art
epic fantasy illustration
epic fantasy landscape
epic fantasy novel cover art
epic fantasy painting
epic fantasy sci fi illustration
epic fantasy style
epic fantasy style art
epic fantasy weapon art
epic feels
epic fight
epic fight scene
epic fights
epic forests
epic framing
epic full color illustration
epic full shot
epic game
epic game portrait
epic games
epic grandiose
epic graphic novel wallpaper
epic graphics
epic grimdark
epic high dynamic lighting
epic high fantasy
epic highlights
epic horror
epic illumination
epic illustration
epic image
epic khajuraho
epic land formations
epic landscape
epic legendary
epic legends game icon
epic level of detail
epic light
epic light novel art cover
epic light novel cover art
epic light scene
epic lighting
epic lighting effects
epic lighting from above
epic lighting”
epic lightning
epic lights
epic lost in space
epic low shot
epic mage girl character
epic magic effects
epic masterpiece
epic masterpiece torment
epic matte painting
epic matte painting of an island
epic megastructure
epic mma fight
epic moment
epic mood
epic moody sky
epic mountains
epic mountains in the background
epic movie poster
epic multifigures composition
epic music album cover
epic mysterious surrealism
epic mythology
epic natural light
epic naval battle
epic nebula
epic ninja suit
epic oil painting
epic orbital spaceships battle
epic painting
epic paladin armor
epic pencil illustration
epic perspective
epic photo
epic photograph
epic photography
epic portrait
epic portrait illustration
epic portrait of menacing
epic pose
epic poses
epic poster
epic poster art
epic professional digital art
epic propaganda poster
epic proportions
epic psycho
epic quality
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epic realm
epic red - orange moonlight
epic red - orange sunlight
epic retrowave art
epic rivendell fantasy
epic road - trip in outer space
epic romance
epic rpg artwork
epic rpg portrait
epic samurai warrrior
epic sandstorm battle
epic scale
epic scale fisheye view
epic scale ultrawide angle
epic scene
epic scene from marvel movie
epic scene full of computers
epic scene of zeus
epic scenery
epic scenic shot
epic sci - fi character art
epic sci fi battle
epic sci-fi character art
epic science fiction horror
epic scifi character art
epic scifi fantasy art
epic scifi movie still
epic scope
epic shafts
epic shamanic dmt art
epic shot
epic shot cinematography
epic size
epic skies
epic sky
epic smooth illustration
epic space battle
epic splash cover art
epic stance
epic stark lighting
epic stock photo
epic stunning
epic stunning atmosphere
epic style
epic sunrise
epic sunset
epic surrealism
epic surrealism 8k oil painting
epic tale of the twin sisters
epic theater
epic titan winnie the pooh
epic ultrawide shot
epic urban bakground
epic urban battle
epic vfx shot
epic vibrant
epic video game art
epic view
epic viking king
epic vista
epic vista of old ruins
epic visuals
epic volumetric light
epic volumetric lighting
epic wallpaper
epic wide angle
epic wide shot
epic wide sky and horzon
epic wrestling combat
epic. 1 0 0 mm
epic. fantasy
epic. in style of greg rutkowski
epic. octane render
epic.fantasy
epically luminous image
epicanthal fold
epicillustration
epicurious
epiphany
episode
episode still
epk
epoxy resin
eps
equation heaven
equations
equestria
equestrian photography
equine
equine photo
equine photography
equirectangular
equirectangular projection
er drama
eradication
erak note
eraserhead
erebos’s titan
eren jaeger
eren yeager
ergodox
ergonomic
eric bana
eric carle
eric cartman
eric cartman in real life
eric heschong
eric hu
eric lacombe
eric parker
eric wareheim with a corgi
eric winter
eric zener aykut aydogdu
eric zener cam de leon
erik johansson
erik johansson style
erik jones
erika ikuta
erin
erin hunter
erin moriarty
erin moriarty as scarlet witch
ermine
ernest gary gygax face
ernest khalimov body
ernie chan
ernst fuchs and artgerm
ernst haeckel and artgerm
ernst haeckel and ernst fuchs
ernst haekel
ernst haekl
erol otus
eros
eros and thanatos
erosion
erosion algorithm landscape
erosion channels river
erosions
erotic
errolson hugh
error
erste boden
erte
erupting volcano
erupting volcano in distance
eruption
erwin olaf
erza scarlet as a real person
esa
esao andrews
esao andrews : : ornate
esao andrews and dave mckean
esao andrews and yoshitaka amano
escalators
escape from new york
escape from tarkov
escaping
escaping air bubbles
eschaton
escher
escher mc
escher painting of a lake
escher style
escher++
escherichia coli
escherichia coli party
ese
esher
eso armor
esoteric
esoteric art
esoteric clothing
esoteric equation heaven
esoteric symbolism
espeon with a sniper
espiritus
espn
espoo
esports
esports logo
espresso machine
esrb
esrb mature
essence
essence of street forms
establishing
establishing action shot
establishing atmospheric shot
establishing shot
esteban maroto
estefania villegas burgos
esthetic
esthetic photo
estrange calligraphy
esty
esuthio
et
et in arcadia ego
etc
etched breastplate
etched inscriptions
etched relief
etching
etching illustration
etching render
eterea
eternal
eternal beauty
eternal blessing
eternal city
eternal darkness
eternal eclipse
eternals
eternity
ethan klein
ethan van sciver
ether
etheral
ethereal
ethereal abstract
ethereal and dreamy
ethereal and dreamy theme
ethereal and mecha theme
ethereal and otherworldly
ethereal angelic being of light
ethereal anime
ethereal art deco
ethereal atmosphere
ethereal aura
ethereal aurora spirits
ethereal back light
ethereal background
ethereal beams
ethereal beauty
ethereal blue lighting
ethereal bohemian waxwing bird
ethereal bubbles
ethereal cardinal bird
ethereal curtain
ethereal details
ethereal dreamy light
ethereal eel
ethereal essence
ethereal fairytale
ethereal fantasy
ethereal flowerpunk
ethereal fox
ethereal ghostly atmosphere
ethereal glare of the sun
ethereal glow
ethereal gold and silver tones
ethereal hair
ethereal hologram center
ethereal landscape
ethereal light
ethereal lighting
ethereal lighting - h 640
ethereal lighting colors scheme
ethereal lights
ethereal macaw
ethereal mist
ethereal opalescent mist
ethereal rainbow bubbles
ethereal rainbow nimbus
ethereal rainbows
ethereal realm
ethereal skin
ethereal soft and fuzzy glow
ethereal starlit city at sunset
ethereal vaporous tan skin
ethereal volumetric light
ethereal white dripping tar
ethereal wings
ethereal world
ethereal!!!
ethereal!!! ultra realistic
ethereal!!!!!!!
ethereum
ethereum!!!! logo
etherial
etheric
ethernet cable
ethernet cables
ethics
ethiopian
ethiopian civil war
ethnic
ethnic fantasy
ethnic group
ethnic origin
ethnicity : japanese
ethnographic
ethnography
ethos of ego
etienne - louis boullee style
etienne hebinger
etsuko miura
etsy
etsy stickers
eu
eu flag
eucalyptus
eucalyptus forest background
eucalyptus trees
euclid class
eugene carriere
eugene de blaas
eugene gottsnake
eugene grasset
euphoria
euphoric
europa
europa universalis iv
europe
european
european buildings
european dragon
european face
european folklore
european four-legged dragon
european japanese buildings
european palette
european river
european union
european union flag
european woman photograph
eurostar
eva
eva elfie
eva green
eva green and gemma arterton
eva green as metamorpho
eva green is metamorpho
eva unit 0 1
eva unit-00 in the back
evan lee
evan rachel wood
evanescence
evangeline lilly
evangelion
evangelion anime
evangelion anime poster
evangelion beast mode
evangelion concept art
evangelion style eyes
evangelion third impact inspired
evangelionic illustration
evanna lynch
eve
eve online
eve online movie still
eve ventrue
eve venture
evelyn
even a little mouse
even lighting
even skin tone
even surface
evening
evening ambience
evening at dusk
evening atmosphere
evening formal robes
evening lanterns
evening light
evening lighting
evening lights
evening makeup
evening mood
evening news program
evening sky
evening starlight
evening storm
evening sun
evening sunlight
evening sunset
evening time
evening!! in the forest
evenly lit
evenly spaced
event
event horizon
event photography
eventually
ever after high
ever created
ever-present
everdimensional
evergreen
evergreen branches
evergreen forest
evergreen valley
everlasting
everlasting summer
everlight
evermotion
everyday plain object
everyone having fun
everything
everything enclosed in a circle
everything fits on the screen
everything in a beautiful light
everything in fire
everything in focus
everything is blurred
everything is burning
everything is carpet and 3d
everything is dead
everything is in balance
everything is made of candy
everything is made out of ice
everything is neat
everything is on fire
everything seems abandoned
everywhere
everywhere at the end of time
evidence
evil
evil a i
evil album cover
evil alchemist gnome
evil and good
evil artificial intelligence
evil atmosphere
evil aura
evil baptism
evil bugs bunny
evil color scheme
evil crazy laugh
evil cult
evil dark
evil dead
evil dead face
evil death
evil devious male
evil energy
evil expression
evil eye
evil eyes
evil face
evil faces
evil fairies
evil fire sorceress in the woods
evil fluid
evil glow
evil god
evil godess
evil grin
evil guitars
evil horror vibes
evil inky swirly ripples
evil insane smiling laugh
evil intent
evil joe biden
evil knight
evil lair
evil laugh
evil lighting
evil look
evil looking
evil lurks
evil magic
evil male sorcerer
evil mastermind
evil mood
evil nanobot
evil pose
evil posed
evil presence
evil princess
evil princess of the wasteland
evil pug
evil realm magic painting vibes
evil robot
evil rock concert
evil smile
evil smile and glowing eyes
evil smile showing fangs
evil smirk
evil sorcerer
evil sorceress
evil sorceress witch
evil spirits roam with lanterns
evil standing smiling pose
evil steampunk pyromancer woman
evil symbols
evil vibe
evil villain
evil villain grin
evil warp energy
evil wizard
evil woman
evil yoda
evil zombie
evil. vibrant colors. cute
evocation
evocative
evoker robes
evokes delight
evokes feelings of joy
evolution
evolution of man
ewa juszkiewicz
ewan mcgregor
ewoks
ex libris
ex machina
ex machina (2014)
exact anatomy
exact textures
exactly 5 fingers
exagerated accurate details
exaggerated
exaggerated accents
exaggerated body features
exaggerated detail
exaggerated detailed
exaggerated details
exaggerated expression
exaggerated facial features
exaggerated features
exaggerated lighting
exaggerated muscle physique
exaggerated muscles
exaggerated perspective
exaggerated physique
exaggerated proportions
exaggerated texture
exaggeratedly large physique
exalted
examining ruins
exar kun
exasperated
exasperated expression
excalibur
exceedingly insanely intricate
excel running on the computer
excellent
excellent 3d render
excellent composition
excellent contrast
excellent detail
excellent light and shadows
excellent lighting
excellent quality
excellent textures
excellent use of negative space
exceptional
exceptional beauty
exceptional lighting
exchange logo
excited
excited expression
excited facial expression
excited russians
excitement
exciting
exciting expression
exciting illustration
exclusive
excrement
executive industry banner
exhaust smoke
exhausted
exhausted face close up
exhibant
exhibant au naturel
exhibit
exhibited at the british museum
exhibited in the british museum
exhibition
exhibition catalog
exhibition catalogue
exhibition display
exhibition hall lighting
exhibition of paintings
exhilarated
exist
existence is pain
existential
existential dread
existential horror
exit light
exiting from a wardrobe
exiting store
exlibris
exobiology
exodus 1 4 : 2 1 & 2 2
exophilia
exoplanet
exoplanet landscape
exorcist
exoskeleton
exoskeleton armor
exoskeleton power armor
exoskeletton
exotic
exotic alien elon musk
exotic alien features
exotic alien flora
exotic artifact
exotic artifacts
exotic costumes
exotic endless horizon
exotic expression
exotic eyes
exotic fey features
exotic fish
exotic flora
exotic god features
exotic lily ears
exotic shorthair cat
exotic trees
exotic vegetation
exotic wonder
exotropia eyes
expanded surrealism
expansive
expansive cinematic view
expansive detailed layered city
expansive grand scale
expansive view
expectation
expectation of adventure
expensive
expensive camera
expensive design
expensive masterpiece
expensive outfit
expensive restaurant
expensive top quality product
expensive visual effects
expensive voluminous dress
experiment
experiment in laboratory
experiment in visual art
experimental
experimental analog photography
experimental art
experimental film
experimental lighting
experimental photography
experimental studio light
experimental supersoldier
experimental techniques
experimenting
experimenting in her science lab
experiments
expert
expert artist
expert composition
expert design
expert figure photography
expert high detail concept art
expert human proportions
expert light effects on figure
expert photography
expert shading
expired black and white film
expired burned film from 1930s
expired color film
expired film
expired film analog photography
expired film stock
explode and chaos
exploded belly
exploded parts assembly
exploded view
exploded view of human anatomy
exploded view!!
exploding
exploding background
exploding galaxy
exploding into dust
exploding nebula
exploding nebulae
exploding planet in background
exploding powder
exploding roses
exploitable image
exploited
exploited!!
exploration
explore
explorer
explorer sketch
explorers
explorers of the ruins at dusk
exploring
exploring abandoned city
exploring new friendly lands
explosion
explosion background
explosion drawing
explosion in background
explosion in the background
explosion of color
explosion of colorful flowers
explosion of colors
explosion of data fragments
explosion of flowers
explosion of light
explosion of neon lights
explosions
explosions all around
explosions and fire
explosions and lasers
explosions and purple lasers
explosions and smoke
explosions in background
explosions in the background
explosions in the sky
explosions of power
explosions!
explosive
explosive background
explosive bubbles
explosive colors
explosive energy
explosive entrance
explosive lightning spell
explosive storm
expo-sure 1/800 sec at f/8
exponential
exponential distributions
exposed b & w photography
exposed b w photography
exposed back
exposed bone
exposed bones
exposed gold wires
exposed inner gears
exposed inner structure
exposed mechanics
exposed metal bones
exposed midriff
exposed thighs
exposed thighs!!!
exposed toes
exposed torso
exposed waist
exposed wires
exposed wires and gears
exposed wiring and gears
exposing screw
exposure
exposure 1/200
exposure 1/40secs
exposure time : 1 / 5
exposure time : 1 / 8 0 0
exposure time : 1 / 800
exposure time: 1/160
exposure time: 1/5
exposure time: 1/60
exposure time: 1/800
exposé
expresive brush
expressing joy
expressing joy. by krenz cushart
expressing strong emotions
expression
expressionism movement
expressionist
expressionist colour
expressionist painting
expressionistic
expressionistic lighting
expressions
expressive
expressive abstractionism
expressive and dreadfull
expressive and intensive
expressive beautiful painting
expressive beautiful portrait
expressive brush strokes
expressive brushstrokes
expressive color palette
expressive colors
expressive digital art
expressive digital painting
expressive emotional piece
expressive emotions
expressive eyes
expressive eyes!! intricate
expressive eyes. symmetry
expressive face
expressive facial features
expressive feminine face
expressive happy smug expression
expressive impressionist style
expressive linework
expressive oil painting
expressive painting
expressive pose
expressive poses
expressive real eyes
expressive rustic oil painting
expressive surprised expression
expressive!!!!!
expressively
exquisit detail
exquisit details
exquisit facial detail
exquisite
exquisite and beautiful
exquisite and handsome wings
exquisite and smooth detail
exquisite architecture
exquisite aristocratic
exquisite art
exquisite black accessories
exquisite corpse
exquisite creature poster art
exquisite decoration
exquisite detail
exquisite detail huge details
exquisite detail perfect
exquisite details
exquisite digital art
exquisite digital illustration
exquisite facial detail
exquisite fanart
exquisite fine art
exquisite fine details
exquisite floral details
exquisite handle
exquisite helmet detail
exquisite imaginative poster art
exquisite light
exquisite lighting
exquisite lightning
exquisite line art
exquisite marble details
exquisite masterpiece
exquisite matte painting
exquisite painting
exquisite quality
exquisite rendering
exquisite warframe fanart
exquisitely designed throne room
exquisitely detailed
exquisitely intricate details
exquisitely ornate
extemely detailed
extemporaneous
extended art
extended clip
extended robotic arms
exterior
exterior botanical garden
exterior design
exterior of scifi temple
exterior photo
exterior photography
exterior shot
exterior view
exterior wide shot
exteriors
exterminate monsters
exterminatus
exterminatus on earth
external staircases
extinct species
extra - details
extra bacon lettuce and tomato
extra bacon lettuce and tomatoes
extra bones
extra cheese
extra close-up
extra crisp
extra crisp image
extra detail
extra detail more details
extra detailed
extra detailed body
extra details
extra fleshy hands
extra greasy
extra hands
extra high resolution
extra ketchup
extra large witch hat
extra limbs
extra long hair
extra onions and ketchup
extra pickles and onions
extra realistic details
extra sharp
extra short blue plaid skirt
extra teeth
extra terrestrial plants
extra wide
extra wide angle
extra-dark natural black hair
extra-light natural blonde hair
extradimensional
extraodinary masterpiece
extraodinary masterpiece!!!!!!
extraordinarily detailed woman
extraordinary
extraordinary colorful landscape
extraordinary phenomenon
extraterrestial
extraterrestrial
extraterrestrial paradise
extraterrestrial plants novel
extraterrestrials
extraterrestrials reach for him
extravagant
extravagant dress
extravagant feathered collar
extravagant matte painting
extreamly beautiful eyes
extreamly detailed
extreamly detailed data center
extreamly realistic
extreme
extreme aesthetic
extreme angle
extreme backlighting
extreme blood
extreme bokeh
extreme bokeh foliage
extreme chromatic aberration
extreme close - up
extreme close - up shot
extreme close shot
extreme close up
extreme close up face shot
extreme close up portrait
extreme close up shot
extreme close-up
extreme closeup
extreme closeup portrait
extreme closeup shot
extreme clutter
extreme cold
extreme contrast
extreme contrast and sharpness
extreme depth
extreme depths
extreme detail
extreme detail 4 k
extreme detail 4k
extreme detail 8 k photo quality
extreme detail photo quality
extreme detail resolution
extreme detail skin
extreme detailed
extreme detailed face
extreme detailed face and body
extreme detailing
extreme details
extreme details perfect face
extreme drama
extreme emotion
extreme face contortion
extreme facial detail
extreme far view
extreme fear
extreme fisheye
extreme fisheye lens
extreme fluffy clouds
extreme fog
extreme foreshortening
extreme gundam
extreme hand detail
extreme high detail
extreme high intricate details
extreme high quality
extreme hight detail
extreme hyper detail
extreme illustration
extreme intricate details
extreme intricate metal details
extreme lifelike
extreme light
extreme log shot
extreme long shot
extreme low angle
extreme low angle long shot
extreme low angle shot
extreme motion
extreme motion blur
extreme motion blur on people
extreme muscles
extreme narrow
extreme panoramic
extreme perspective
extreme photorealism
extreme pose
extreme quality
extreme quality masterpiece
extreme rage
extreme realism
extreme realism and detail
extreme realistic
extreme realistic detail
extreme render
extreme resolution
extreme shitty car mods
extreme sports
extreme sports photography
extreme terror
extreme three point perspective
extreme view angle
extreme weather
extreme wide angle
extreme wide shot
extremelly detailed
extremely
extremely atmospheric lighting
extremely beautiful
extremely beautiful and ethereal
extremely beautiful face
extremely beautiful lighting
extremely bizarre disturbing
extremely clean
extremely clean lines
extremely clear and coherent
extremely clear faces coherent
extremely close shot
extremely coherent
extremely colorful
extremely complex
extremely contrast
extremely creative
extremely cute
extremely cute anime girl face
extremely dark
extremely detailed
extremely detailed + 8k
extremely detailed 4k
extremely detailed 8 k
extremely detailed and coherent
extremely detailed and intricate
extremely detailed and lifelike
extremely detailed and realistic
extremely detailed and sharp
extremely detailed art
extremely detailed artgerm
extremely detailed beautiful
extremely detailed blue eyes
extremely detailed brush
extremely detailed concept art
extremely detailed d&d map
extremely detailed digital art
extremely detailed eyes
extremely detailed face
extremely detailed face!
extremely detailed faces
extremely detailed frontal angle
extremely detailed fur
extremely detailed furnitures
extremely detailed giantess shot
extremely detailed goddess shot
extremely detailed gorgeous face
extremely detailed linework
extremely detailed man
extremely detailed masterpiece
extremely detailed oil painting
extremely detailed oil portrait
extremely detailed painting
extremely detailed photo
extremely detailed photography
extremely detailed portrait
extremely detailed raccoon
extremely detailed rocky crag
extremely detailed sand
extremely detailed shading
extremely detailed water texture
extremely detailed woman
extremely detailed!
extremely detailed!!!
extremely detailed. 8k
extremely dramatic
extremely dramatic lighting
extremely dynamic person
extremely emotional
extremely epic
extremely even lighting
extremely fat
extremely fine details
extremely fine ink lineart
extremely fine inking lines
extremely gendered
extremely gloomy lighting
extremely good lighting
extremely good quality lighting
extremely grainy
extremely graphic
extremely handsome
extremely happy
extremely happy raccoons
extremely high aperture
extremely high budget
extremely high contrast
extremely high contrast!
extremely high definition
extremely high definition shot
extremely high detail
extremely high detail!!
extremely high detailed
extremely high detailed face
extremely high details
extremely high fidelity
extremely high level of detail
extremely high quality
extremely high quality artwork
extremely high quality scan
extremely high resolution
extremely high-quality
extremely highly detailed
extremely higly detailed
extremely hot
extremely human detailed
extremely hyper - detailed
extremely hyper detailed
extremely hyper-detailed
extremely hyperdetailed
extremely intricate
extremely intricate and detailed
extremely intricate details
extremely intricate high detail
extremely life like
extremely lifelike
extremely long forehead
extremely long hair
extremely long shot
extremely long thick blond hair
extremely luminous bright design
extremely moody
extremely moody blue lighting
extremely moody lighting
extremely moody purple lighting
extremely muscular
extremely opulent
extremely pale
extremely pale blond hair
extremely pale white skin
extremely photorealistic
extremely plump
extremely polished
extremely precise
extremely psychedelic experience
extremely realistic
extremely realistic and detailed
extremely realistic and real
extremely realistic face
extremely realistic faces
extremely realistic photo
extremely scary
extremely sharp detail
extremely sharp faces
extremely sharp focus
extremely sharp lines
extremely sharply detailed
extremely strong wind
extremely symmetrical
extremely symmetrical!!
extremely textured
extremely uncomfortable
extremely wide angle
extremely wide angle shot
extremely windy
extremely-detailed
extremly detailed
extremly detailed objects
extremly detailed oil painting
extremly high detail
extremly high detailed
extremly high quality
extremly realistic
extremy detailed
exuberant organic elegant forms
exudes terror
eyardt
eyballs in the walls
eye
eye - contact
eye - level medium - angle shot
eye - level medium shot
eye - level view
eye art
eye bags
eye ball windows
eye catching
eye catching composition
eye closeup
eye color green
eye contact
eye cyberpunk bionics
eye fire
eye fish lens
eye focus
eye glass
eye glasses
eye implants
eye in forehead
eye level
eye level shot
eye level view
eye of a woman
eye of horus
eye of providence
eye of sauron
eye of the beholder
eye of the storm
eye patch
eye patch on one eye
eye patch over left eye
eye patch over one eye
eye piercings
eye reflection
eye reflections
eye relections
eye scar
eye shadow
eye stalks
eye tattoo
eye white). full body realistic
eye white. full body realistic
eye-catching
eye-contact
eyeball
eyeball growing form tree branch
eyeballs
eyeballs bulging
eyeballs in the walls
eyeballs intricated
eyebrow scar
eyebrows
eyecandy
eyeglasses
eyelashes
eyeless
eyeless watcher
eyelevel perspective image
eyelevel!!! view!!! photography
eyelids
eyelids half closed
eyeliner
eyepatch
eyepatches
eyes
eyes = purple
eyes and lips
eyes are colorful spirals
eyes are glowing
eyes are glowing red lightbulbs
eyes are green lights
eyes are large
eyes are multicolored led screen
eyes are rainbow spirals
eyes are yellow
eyes closed
eyes closed or not visible
eyes everywhere
eyes glowing
eyes growing on creature
eyes half closed
eyes in different directions
eyes in forehead
eyes in the bark
eyes in the style of nendoroid
eyes in the trees
eyes light up
eyes like stars
eyes look dead
eyes looking at the camera
eyes made out of macaroni
eyes of corporaptor hominis
eyes open
eyes open in wonder
eyes opened
eyes projected onto visor
eyes realistic
eyes rolled back
eyes shone bright in the night
eyes still visible
eyes wide
eyes wide open
eyes wide opened
eyes with catchlight
eyes!
eyes). full body
eyes). full body realistic
eyes. dark colors
eyes. full body
eyes. full body realistic
eyeshadow
eyesight
eytan zana
eyvind
ez water
ezra stoller
ezreal (league of legends
ezreal league of legends
f
f - 9 1 / 1 2 5
f - stop
f - zero
f / 1
f / 1 1
f / 1 1 bokeh depth of field
f / 1 1. 0
f / 1 6
f / 1. 2
f / 1. 2 5
f / 1. 3
f / 1. 4
f / 1. 8
f / 1. 9 6. 8 1 mm iso 4 0
f / 2
f / 2 0
f / 2 2
f / 2 4
f / 2. 2
f / 2. 5
f / 2. 8
f / 2. 8 l canon ef is lens
f / 3
f / 3 2
f / 4
f / 8
f 0. 4
f 1
f 1 1
f 1 6
f 1 8
f 1 driver charles leclerc
f 1. 2
f 1. 3
f 1. 4
f 1. 7
f 1. 8
f 1. 8 8 5 mm zeiss lens
f 1. 8 depth of field
f 1.2
f 1.4
f 1.4 kodak portra
f 1.8
f 1/3
f 1/4
f 2
f 2 0
f 2 2
f 2 4
f 2. 0
f 2. 4
f 2. 8
f 2. 8 3 5 mm
f 2. 8 aperture
f 2. 8 depth of field
f 2.5
f 2.8
f 3 2
f 3 5
f 3 5 fighter
f 3. 4
f 3. 5
f 3.5
f 4
f 4. 0
f 5. 8
f 6 aperture
f 8
f 8 aperture
f 8 apeture
f zero
f-22
f-stop 2.8
f. 1 4
f/1.2
f/1.3
f/1.4
f/1.8
f/1.8 cinematic lens
f/11
f/11 aperture
f/15
f/16
f/2
f/2 8
f/2.5
f/2.8
f/2.8 aperture
f/2.8l canon ef is lens
f/22
f/3.2
f/3.5
f/4
f/4.5
f/5.6
f/8
f/8.0
f/9
f/stop
f1 cars blurred in background
f1.2
f1.4
f1.7
f1.8
f1.8 anamorphic
f1.8 bokeh
f11
f11 aperture
f11:10
f11:6
f12
f16
f18
f2
f2.2
f2.8
f2.8 35mm
f2.8 50mm
f22
f22 crisp focus
f3.2
f3.5
f32
f4
f4.0
f4.0 120mm
f4.5
f42
f4”
f5.6
f50
f8
f8 aperture
fa
fa 1 8 hornet replica
fa-18 hornet
faberge
fabian wagner cinematography
fable
fabric
fabric embroidery
fabric texture
fabrics
fabrics and textiles
fabrics textiles
fabry glenn
fabulous
fabulous illustrations
face
face - up
face accessories
face accuracy
face anatomy
face and body
face and body clearly visible
face and body features
face and eyes
face and hand
face and shoulders
face and shoulders focus
face and skin is dark red
face and upper - body focus
face and upper body focus
face anime portrait
face as of dwayne johnson
face center close-up
face centered
face centered portrait
face centred
face clearly visible
face close - up
face close up
face close-up
face closeup
face cluse - up
face covered
face covered in dirt
face covered in moths
face covered in mud
face covers half of the frame
face detail
face detailing
face details
face details sharp
face details!
face doodles
face down
face enhance
face enhanced
face enhancement
face face face
face features
face female portrait
face focus
face focus!
face focused
face forward
face forwards
face fully visible
face high detail
face icon
face icon stylized minimalist
face illuminated
face in detail
face in focus
face in focus 1 8 9 0's
face in focus 1 8 9 0's liminal
face in focus 1890s liminal
face in the clouds
face in the shadows
face in water
face in-frame
face is
face is brightly lit
face is highly detailed
face is the focus
face is visible
face is wrapped in a black scarf
face like ester exposito
face like gal gadot
face like grasshopper
face like marilyn monroe
face like monkey
face line drawing
face looking skyward
face macro shot
face made of notation
face made out of clouds
face made out of planet
face mask
face melting
face melting into the universe
face morph
face muscles
face neck shoulders
face obscured
face of a alien deity
face of a man
face of a pale alien cultist
face of an armored villian
face of an human
face of an lion
face of an ox
face of anxiety and longing
face of emma watson
face of fear
face of mad pulcinella
face on head shot
face only
face paint
face paint around eyes
face painting
face partially obscured
face photo
face picture
face piercings
face portrait
face portrait of a woman
face portrait of an elegant
face profile
face realistic
face retouched
face scars
face shot
face show no emotion
face showing
face shown
face sketch
face study
face submerged in colorful oils
face symmetrical
face symmetry
face tattoo
face tattoos
face to face
face to face staring
face turnaround
face turnaround 3 6 0
face turnaround 360
face up
face variations
face very realistic
face visible
face with artgram
face with beard
face with scars
face with skull mask
face!!!! close - up
face-on
face-on head shot
facebook
facebook photo
facebook post
facebook post 2012
facebook profile picture
faceless
faceless human figures
faceless people
faceless people dark
facemask
facemask made of flowers
facepaint
facepaint facepaint facepaint
facepalm
faces
faces and characters
faces covered in shadows
faces in focus
faces look at each other
faces of too much caffeine
faces only
faceted
facets
facial
facial accuracy
facial actuary
facial beauty
facial closeup
facial details
facial expression
facial expressions
facial feature symmetry
facial features
facial features )
facial features)
facial focus
facial hair
facial muscles
facial piercings
facial portrait
facial portrait. john oliver
facial precision
facial retouch
facial scar
facial structure
facial symmetry
facial tattoos
facial texture
facial tribal markings
facinating
facinating and imposing
facing and looking at the viewer
facing away
facing away from camera
facing away from the camera
facing camera
facing camera directly
facing each other
facing forward
facing front
facing left
facing off in a duel
facing right
facing sideways
facing the camera
facing the viewer
facing the woods
facing to audience
facing worlds map
factories
factories and nature
factorio
factory
factory background
factory floor
fade
faded
faded and dusty
faded and limited color palette
faded background
faded chipped paint
faded color
faded color film
faded color palette
faded colors
faded colours
faded drawing
faded effect
faded fedora
faded glow
faded hat
faded lsd colors
faded memories
faded out colors
faded outline
faded parchment
faded pink
faded red and yelow
faded red colors
faded worn
faded!
faded!! very soft lights
fadedvery soft lights
fadeev
fadeev 8 k
fadeev 8k
fadeev! 8 k
fadeev8 k
fades to the horizon
fading
fading away
fading into the distance
fading off into the distance
fading off to the horizon
fading rainbow light
fading to dust and leaves
fae
fae priestess
fae teenage girl
fae wilds sky
faeornran
faerie
faeries
faery palace
fai
failed cosmetic surgery
failure
faint atmospheric lighting
faint dust in the air
faint dynamic lighting
faint feeling of doom
faint glow
faint light
faint red lips
faint smile
faint smile dark lipstick
faint sunrise
faint volumetric god rays
fair
fair complexion
fair complexity
fair curly hair
fair olive skin
fair skin
fair skinned
faired skin
fairground rides
fairies
fairies and scissors
fairies have wings
fairly muscular
fairy
fairy aesthetics
fairy atmosphere
fairy cgsociety
fairy circle
fairy circles
fairy dancing
fairy dust
fairy dust in the air
fairy forest
fairy fruit. octane render
fairy garden
fairy kingdom forest
fairy lights
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fatal
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february
february)
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ferrari logo on it's chest
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ferris wheel
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ferrofluid armor
ferrofluid oceans
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ferrofluids in background
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fertile
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few clouds
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fey
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fibres trial on the floor
fiction
fictitious birds fly
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field journal
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fiend folio
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fiery skull contemplating life
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fifteen-dimensional
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fig leaves
fig.1
fight
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fill light. studio
filled with bioluminescence
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film still from a horror movie
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finalrender:0.8|devfiantart
find the hidden object
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fine confidently
fine contours of faces
fine cyborg lace
fine day
fine delicate structure
fine detail
fine detail anime
fine detail post processing
fine detailed
fine detailed face
fine detailed features
fine detailing
fine details
fine details 8k octane rendering
fine details portrait
fine details. anime
fine details. anime. skins
fine details. anime. tentacles
fine details. girls frontline
fine details. red
fine digital art
fine dining
fine drawing
fine dust
fine edges
fine face
fine face features
fine facial features
fine facial proportionate
fine features
fine fiberglass
fine filigree foliage lace
fine film grain
fine fix broken line
fine fix duplicate line
fine foliage lace
fine grain texture
fine illustration
fine image on the store website
fine inking lines
fine intricate
fine lace
fine line
fine line art
fine line detail
fine line work
fine lines
fine lines and graphite
fine linework
fine mandelbrot fractal lace
fine painting
fine patterns and detail
fine pen
fine pen work
fine point pen
fine pointy chin
fine portrait
fine professional photo
fine proportionate
fine sharp high detail
fine simple delicate structure
fine skin details
fine stippled lighting
fine structure texture
fine swirling lines
fine texture
fine texture detail
fine texture structure
fine textures
fine texutre
fine tuned
fine white shirt
fine wind
fine workmanship
fine!!! lines
fine-art
fine-art photography
fine-detailed
fine-face
fine-grained
fineart
fineartamerica
fined detail
fineline detail
finelines
finely detailed
finely detailed angelic face
finely detailed car
finely detailed eyes
finely detailed face features
finely detailed facial features
finely detailed feature
finely detailed features
finely detailed furniture
finely detailed illustration
finely detailed perfect art
finely detailed perfect face
finely illustrated
finely illustrated face
finely masked
finely painted
finely textured
finely tuned detail
finer details
finer details : 3
finesse
finger
finger blend shading
finger of god
finger painting
fingerless gloves
fingerprints on clay
fingers
finished concept art
finland
finn from adventure time
finn wolfhard
finnian in grungerock alien sky
finnian macmanus
finnish cartoon
finnish naturalism
finnstark
fins
fintan magee
fiona staples
fiona staples and kinu nishimura
fiona staples and makoto shinkai
fiora from league of legends
fir forest
fir trees
fire
fire & smoke
fire above head
fire and ashes
fire and brimstone
fire and explosion
fire and explosions
fire and flame
fire and flames
fire and flames and smoke
fire and flames mane
fire and ice
fire and lava
fire and smoke
fire and smoke columns
fire and water
fire background
fire beam
fire behind him
fire breathing dragon
fire breathing geese
fire breathing. bowser
fire burst
fire demon
fire dragon
fire dress
fire elemental
fire embers
fire emblem
fire emblem three houses
fire escapes
fire everywhere
fire explosion in the background
fire eyes
fire fight
fire flames
fire flaming dragon serpent
fire flaming serpent
fire flies
fire from mouth
fire from sky
fire from some windows
fire giant
fire goddess
fire golem creature
fire hair
fire in background
fire in eye
fire in eyes
fire in hand
fire in the background
fire in the sky
fire lava
fire light
fire lines
fire lion
fire lit
fire mage
fire magic
fire on the background
fire on the horizon
fire particles
fire particles in front
fire pit
fire place roaring
fire poi
fire powers
fire powers fire swirling
fire punch
fire raining
fire reak real life
fire red
fire reflection
fire smoke
fire smoke and explosions
fire sparks
fire spell
fire staff
fire stainglass
fire storm
fire tendrils
fire texture
fire theme
fire through eyes
fire tornado
fire type
fire warning label
fire!! full body
firearms
firearms photography
fireball
fireball hand
fireball lighting her face
fireballs
firebending
firebreathing
firebreathing dragon
fireerstorm
firefighter
firefighting gear
fireflies
fireflies and sparkling wisps
fireflies around
fireflies flying
fireflies flying around
fireflies flying in the forest
fireflies glow in the dark
fireflies glowing
fireflies in the air
fireflies!!
fireflies!!!!
firefly
firefly forest at night
firefly lights
firefly trails
fireflys
firelight
firelit
firemagic
fireman
fireman sam
firenado
fireplace
fireplace lighting
firerstorm
fires
fires burning everywhere
fires glow lonely
fires in background
fires!! hyperrealistic
fireshyperrealistic
firestorm
firewall
firewatch
firework
firework smoke trails
fireworks
fireworks in background
fireworks in the background
fireworks on the background
firey
firey environment
firing a gun
firing it into a building
firing laser
firing laser rifle
firing synapses
firm
first - person view
first 4 figures
first aid kit
first contact
first draft
first ever photograph
first human on earth
first light
first love
first person
first person gun
first person perspective
first person point of view
first person pov
first person screenshot
first person shooter
first person shooter game hud
first person view
first person view perspective
first person weapon
first photograph ever taken
first place
first playstation graphics
first world war
first-person
first-person pov
first-person view
fish
fish - eye lens
fish are glowing in the ocean
fish eye
fish eye effect
fish eye lens
fish eye lense
fish eye view
fish face
fish flocks
fish flying over head
fish hooks
fish in the background
fish lens
fish lense
fish made of pancake
fish man
fish market
fish market stalls
fish net leggings
fish net stockings
fish scales
fish seafood markets
fish shoal
fish skin
fish squid and jellyfish
fish swimming
fish swimming around
fish tail
fish tank
fish-eye
fish-eye lens
fish-eye lense
fishbones
fishcore
fisher price
fisher price guillotine
fisher price public pool
fisher price redwood forest
fisherman
fisherman's hat
fishermans hat
fishes
fishes floating in bedroom
fishes swimming
fisheye
fisheye 4
fisheye camera
fisheye effect
fisheye lens
fisheye lens photo
fisheye lens photography
fisheye lens!!!
fisheye lense
fisheye perspective
fisheye photo
fisheye portrait
fisheye view
fisheye!!!
fisheye!!! lens
fisheye!!!!! lens
fisheye!!!!! lens photography
fisheye!!!!! lens!!!!!
fisheye!!!!!! lens
fisheyelens
fishing
fishing boat
fishing boats
fishing pole
fishing town
fishing village
fishlike
fishmen
fishnet
fishnet clothes
fishnet corset with choker
fishnet stockings
fishnet tights
fishnets
fishnets and a long tartan skirt
fishnets pantyhose
fishnets stockings
fishpeople
fissures
fist fight
fist of the north star
fist training
fistfight
fistfighting
fistfighting demons
fisting
fisting monk
fists in the air
fit
fit body
fit build
fit curvy physique
fit dainty figure
fit girl
fit male demon with white horns
fit physique
fit pic
fit woman
fitness
fitness model
fitzgerald
five elegant fingers
five fingers
five fingers on the hand
five foot bat in the philippines
five nights at freddy's
five nights at freddys
five nights at freddy’s
five o clock shadow
five o' clock shadow
five planets
five points of articulation
five score years ago
five star oil on canvas
five star stories
five-dimensional
fivestarstory style
fix duplicate content!
fixed eyes
fixing a leaking sink
fjord
fjords
fjords in background
flabbergasted
flag
flag in hands
flag in hands up
flag in his right hand
flags
flags of argentina behind
flagstones
flailing
flamboyant
flamboyant clothes
flame
flame and fire
flame colors bright
flame conjuring
flame conjuring armored
flame everywhere
flame ferns
flame fractal
flame in the fur
flame shrubs
flame spell
flame stones are scattered
flame thrower
flame vortex
flames
flames alongside the road
flames and smoke in background
flames are burning behind obama
flames around body
flames everywhere
flames from the ground
flames in background
flames in the background
flames of fire at background
flames surround her
flamethrower
flaming
flaming background
flaming eyes
flaming grim reaper
flaming hair
flaming heart
flaming katana
flaming leaves
flaming mountain
flaming skull
flaming sword
flaming torches and pitchforks
flamingo
flamingoes
flamingos
flan
flanders
flannel
flannel flower
flapper
flare
flared japanese black tile roof
flares
flares anamorphic
flaring gills
flaring gills and baleen
flash
flash animation
flash at noon
flash color polaroid
flash enabled
flash explosions
flash fill
flash gordon
flash image
flash light
flash lighting
flash lights
flash on
flash on camera
flash photo
flash photograph
flash photography
flash photography 4 k hdr
flash photography at night
flash polaroid photo
flash sheet
flash studio
flash vs sonic
flashes
flashing
flashing concert lights
flashing eyes
flashing lights
flashing neon lights
flashlight
flashlight lighting
flashlight on
flashy
flashy flash elegant
flashy modern background
flashy red lights
flasks
flasks in hands
flat
flat - color
flat 2 d
flat 2 d design
flat 2 d vector art
flat 2d
flat 2d design
flat 2d vector art
flat anime style
flat anime style shading
flat art
flat background
flat bold color
flat brush
flat brush strokes
flat cel shaded
flat cel shading mucha
flat cell shading
flat chest
flat chested
flat color
flat color and line
flat coloring
flat colors
flat colors and strokes
flat colour
flat colour-block style
flat colours
flat curves
flat desert
flat design
flat drawing
flat earth
flat earth theory
flat face
flat graphic design
flat graphic style
flat grey
flat grey background
flat grey color
flat icon
flat illustration
flat image
flat ironed hair
flat lay
flat light
flat lighting
flat linework
flat matte art
flat matte painting
flat metal antenna
flat metal hinges
flat minimalistic
flat mucha
flat natural tones
flat neutral tone
flat paint
flat painting
flat pancake head
flat panels
flat pastel colors
flat perspective
flat roads
flat shading
flat shading mucha
flat shaped chrome relief
flat shaped stone relief
flat style
flat surface
flat surreal design
flat surreal psychedelic colors
flat synthwave art style
flat texture
flat tone
flat top haircut
flat triangle - shaped head
flat triangles
flat vector
flat vector art
flat vector art background
flat vector graphic
flat vibrant colors
flat wastelands
flat water color texture
flat!!
flatlay
flatlay book collection
flattened
flattering hair
flattering photo
flaunting his wealth
flawless
flawless beauty
flawless completion
flawless composition
flawless epidermis
flawless face
flawless features
flawless olive skin
flawless skin
flawless structure
flax
flcl
flcl + golgo 13 :10
flcl cover
fleeing merchants
fleeting
fleeting dream
fleks
flemish baroque
flemish painting
flesh
flesh + technology
flesh - eating
flesh - tone color palette
flesh and blood
flesh and bones
flesh and metal
flesh blob
flesh buildings
flesh eating worms
flesh highly baroque ornate
flesh ornate
flesh texture
flesh with technology
flesh!
fleshscape
fleshy
fleshy - cyborg - zombie
fleshy - surfaces
fleshy bones
fleshy botanical
fleshy creature above her mouth
fleshy figures
fleshy musculature
fleshy person with extra limbs
fleshy skeletal
fleshy skeletal body
fleurfurr
flex
flex box position
flexible character code
flexing
flexing large muscles
flexiseal
flickering
flickering air
flickering fluorecent lights
flickering fluorescent lights
flickering light
flickering lights
flickr
flickr explore
flickr explore 5 0 mm
flickr photography
flies
flight
flight squadron insignia
flight suit
flight suit and gloves
flikr
flim
flintlock fantasy capital city
flip flops
flipped out hair
flirtatious
flirtatious and intrigued
flirting
flirting expression
flirting smiling
flirty
flirty anime witch casting magic
flitting around in the sky
float
float under moon light at night
floathing underwater in a lake
floating
floating across the cosmic ocean
floating alone
floating among stars
floating and flying island
floating away
floating beside planets
floating bodies
floating bouquets
floating bubbles
floating buildings
floating candles
floating cars
floating chinese lampoons
floating city
floating city in the sky
floating city on clouds
floating crown
floating crystals
floating detailes
floating drowned
floating dust
floating dust particles
floating embers
floating ghost
floating goldfish
floating graphics
floating headsets
floating in a cosmic nebula
floating in a misty daze
floating in a nebula
floating in a powerful zen state
floating in air
floating in deep space
floating in empty space
floating in mid - air
floating in mist
floating in outer space
floating in perfume
floating in smoke
floating in space
floating in the air
floating in the cosmos nebula
floating in the desert night
floating in the ocean
floating in the sky
floating in the universe
floating in water
floating in zero gravity
floating into the sky
floating island
floating island in the sky
floating islands
floating kelp
floating koi fish
floating lampoons
floating lands in-clouds
floating lanterns
floating lights
floating liquid
floating long hair
floating magical rocks
floating metallic objects
floating molecules
floating monitors
floating mountains
floating objects
floating on space
floating on the ocean
floating over a city sidewalk
floating palace
floating particle effects
floating particles
floating pieces
floating planets
floating planets and moons
floating power cables
floating robes
floating rocks
floating runes
floating sigils
floating skyscrapers
floating spellbook
floating spheres and shapes
floating spiral sand
floating stones
floating symbols
floating symbols and crystals
floating through space
floating translucent graphics
floating trees
floating under water
floating vehicles
floating vr headsets
floating waterfalls
floating world
floating!!! in a nebula
floating. greenish blue
floatiung in front of a nebula
floats carnival
floats in space
floats underwater in the sea
flock of birds
floeers in hair
floggers
flood
flood lighting
flooded
flooded ancient tower
flooded city
flooded fishing village
flooded ground
flooded old wooden city street
flooded station
flooded swamp
flooded tower
flooding
floodlight
floodplains
floods
flooko
floor
floor - to - ceiling windows
floor b2
floor flooded
floor fog
floor grills
floor lamps
floor plan
floor plan view
floor texture
floor tiles
floor to ceiling window
floorplan
floors
floppy disk eyes
floppy ears
flora
flora and fauna
flora borsi
flora world
flora-lush-crater
floral
floral art nouveau dress
floral art novuea dress
floral background
floral bling
floral clothes
floral clothes ”
floral couture
floral crown
floral design
floral details
floral dream
floral embroidery
floral environment
floral explosion
floral flowers colorful
floral growth
floral headdress
floral headpiece
floral jungle treehouse
floral lacework
floral motives
floral ornaments
floral painted backdrop
floral pattern
floral patterned skin
floral renewal
floral splash painting
floral sunset
floral tattoos
floral wallpaper
floral!
floral! intricate
floralintricate
floralpunk
floralpunk elysian maiden
floreal
florence
florence harrison
florence nightingale
florence pugh
florentine school
florescent lighting
florida
florida man
floro details
flour dust
flour dust flying
flour dust spray
flourescent colors
flourescent spot lights
flourishing nature
flow
flow everywhere
flow shapes
flow visualization
flower
flower artworks society
flower background
flower blossoms
flower buds
flower butterfly vest
flower child
flower crown
flower decorations
flower dress
flower elements
flower explosion
flower face
flower field
flower fields
flower frame
flower garden
flower garden summer morning
flower goddess
flower head
flower in hair
flower in her hair
flower mask
flower meadow
flower motif
flower of life
flower petals
flower pop art
flower pots
flower power
flower power motifs
flower queen
flower sepals forming helmet
flower shaman
flower shop scene
flower storm portrait
flower tiara
flowerbeds
flowercore
flowerfield
flowering buds
flowering pineapples
flowering pineapples and orchids
flowering vines
flowerpunk
flowers
flowers and blossoms
flowers and butterflies
flowers and foliage
flowers and gold
flowers and plants
flowers and stems
flowers and trees
flowers and vines
flowers and waterfalls
flowers around
flowers background
flowers blooming
flowers covering eyes
flowers everywhere
flowers exploding and spraying
flowers garden
flowers grow from the body
flowers growing out of his body
flowers growing out of its head
flowers in a flower bed
flowers in background
flowers in foreground
flowers in hair
flowers in her dark hair
flowers inside of a marble
flowers on hair
flowers on heir cheeks
flowers rain everywhere
flowers sea everywhere
flowers sea rainning everywhere
flowers with intricate detail
flowers with very long petals
flowers!!!!
flowers. baroque elements
flowery
flowery cottage
flowery dress
flowery wallpaper
flowey
flowing
flowing aqua silk
flowing backlit hair
flowing black gown
flowing black hair
flowing blonde hair
flowing blood red colored silk
flowing blood-red colored silk
flowing blue hair
flowing blue skirt
flowing book pages
flowing brush strokes
flowing cape
flowing clear water creek bed
flowing cloth and smoke
flowing curves
flowing dress
flowing energy
flowing fabric
flowing forms
flowing fur
flowing ginger hair
flowing glowing hair
flowing golden scarf
flowing gown
flowing hair
flowing hair and long robes
flowing hills
flowing lava
flowing lava and ash piles
flowing lettering
flowing lines
flowing long hair
flowing magical flesh robe
flowing magical robe
flowing mane
flowing mane and tail
flowing massive hair
flowing material
flowing milk
flowing mucus
flowing neon-colored silk
flowing pink hair
flowing pink-colored silk
flowing purple hair
flowing realistic fabric
flowing red hair
flowing rhythms
flowing robe
flowing robes
flowing robes and leather armor
flowing sakura silk
flowing sakura-colored silk
flowing salmon-colored silk
flowing shimmering fabrics
flowing silk sheets
flowing silver hair
flowing teal-colored silk
flowing tendrils
flowing water
flowing white dress
flowing white hair
flowing white robes
flowing wires with leaves
flowing with dark power
flowstatepaint
flowy
flowy golden hair
flowy hair
flowy hair standing on a rock
flowy white grey hair
flowy yellow golden hair
fluent composition
fluff
fluffly
fluffly!!!
fluffy
fluffy bangs
fluffy body
fluffy cat
fluffy cat t - shirt design
fluffy chest
fluffy clouds
fluffy ears
fluffy ears and a long
fluffy ebay product
fluffy face
fluffy fluffy fur
fluffy fox ears
fluffy full of light
fluffy fur
fluffy green belly
fluffy hair
fluffy mane
fluffy neck
fluffy orange skin
fluffy pastel clouds
fluffy pink anime clouds
fluffy tail
fluffy white clouds
fluffy white fur
fluffy!!!
fluffy''
fluid
fluid acrylic
fluid acrylic pour art
fluid and dynamic forms
fluid bag
fluid coloured smoke
fluid dynamics
fluid electricity
fluid energy
fluid lines
fluid sim
fluid simulation
fluid simulation in houdini
fluid smoke art
fluidity
fluids
flume
flume cover art
fluo colors
fluo details
fluorescent
fluorescent ceiling lights
fluorescent colors
fluorescent colours
fluorescent lamp
fluorescent led
fluorescent light
fluorescent lighting
fluorescent lights
fluorescent lights from ceiling
fluorescent mushrooms
fluorescent pink face paint
fluorescent skin
fluorescent spots
flushed cheeks
flushed face
flux
flux. fantasy
flux. high fantasy
fly
flying
flying above a tropical forest
flying above the clouds
flying across the universe
flying aircrafts
flying airships
flying angels
flying anime esper
flying around the bird
flying arrows
flying bats
flying beetles
flying birds
flying birds in distance
flying birds in the distance
flying black marble balls
flying books
flying buttresses
flying car
flying cars
flying castle
flying citadel with towers
flying cloud castle
flying cow
flying creatures
flying debris
flying dragon
flying dragons
flying drones
flying dust particles
flying elephant
flying emergency vehicles
flying fairies
flying giant animals
flying hair
flying in formation
flying in sky
flying in space
flying in the air
flying in the air over city
flying in the clouds
flying in the sky
flying into the sky
flying island
flying island in the sky
flying islands
flying leaves on backround
flying machinery
flying machines
flying magic books
flying mud
flying mythical beasts
flying on the broom
flying over a city
flying over a red sea
flying over dark gotham
flying over the horizon
flying over the ocean
flying particles
flying pig
flying rituals
flying rocky island
flying saucer
flying saucer in the sky
flying saucers
flying scarlet phoenix
flying scifi vehicle
flying screw
flying ships
flying ships in the background
flying shot
flying soccer balls
flying souls
flying spaceships
flying spaceships in background
flying spaghetti monster
flying through sky
flying through space
flying through sunset
flying through the air
flying through the clouds
flying through the sky
flying through time
flying toasters in heaven
flying towards the camera
flying trees and park items
flying vehicles
flying whale
flying whales
fnaf
foam
foamy bubbles
foamy waves
focal depth
focal length
focal length : 1 8. 0 mm
focal length : 2 0 0. 0 mm
focal length : 200. 0 mm
focal length : 3 6 mm
focal length : 5 0 mm
focal length : 50 mm
focal length : 8 5. 0 mm
focal length : 85. 0 mm
focal length: 160.0 mm
focal length: 18.0 mm
focal length: 35mm
focal length: 46.0 mm
focal length: 50mm
focal length: 85.0 mm
focal point
focus
focus and ( gaussian blur )
focus close on detesting eyes
focus close on dreaming eyes
focus close on mischievous eyes
focus detailed
focus eyes
focus face
focus gate
focus her back
focus illustration
focus in the foreground
focus mode
focus on anti-g flight suit
focus on art nouveau suit
focus on card
focus on darth vader
focus on droplets
focus on face
focus on face and facial details
focus on facial - detail
focus on facial - detail!!!!!!
focus on facial detail
focus on facial details
focus on facial features
focus on fearful model
focus on full - body
focus on generate the face
focus on giant tortoise
focus on girl
focus on girl model
focus on head
focus on her face
focus on his foot
focus on iris
focus on map
focus on model
focus on pursed lips
focus on smile
focus on sneakers only
focus on the elephant
focus on the face
focus on the foreground
focus on the moon
focus on the musicians
focus on the object
focus on the sky
focus on torso
focus on two androids
focus render
focus shot
focus stacked
focus stacking
focus unfocus
focus unreal engine 5
focus-stacked
focused
focused amber eyes
focused expression
focused eyes
focused face
focused gaze
focused light
focused macro photography
focused on her neck
focused on neck
focused photo
focused shot
focused stare
focusing
fog
fog and dirt
fog and dust
fog and mist
fog and rain
fog and starry skies
fog and swans over the river
fog atmosphere
fog background
fog bionic vogue
fog dramatic
fog filled
fog fills the area
fog golden hour
fog in background
fog in the background
fog machine
fog mads berg
fog mist smoke
fog obscures the background
fog of war
fog on ground
fog on the ground
fog on the water
fog rolling in
fog storm
fog volume
fog volumes
fog!
fog!!!
fog. 3 d
fog. by greg rutkowski
foggy
foggy ambience
foggy and atmospheric
foggy and dark
foggy area
foggy at dawn
foggy atmosphere
foggy backgeound
foggy background
foggy dark
foggy dark graveyard
foggy day
foggy day outside
foggy dystopian world
foggy effect
foggy environment
foggy evening
foggy forest
foggy forest at night
foggy forrest backdrop
foggy heavy rain
foggy jungle
foggy jungle in the background
foggy landscape
foggy light from fires
foggy lights
foggy mist
foggy mood
foggy morning
foggy morning light
foggy neon night
foggy night
foggy night sky
foggy photo 8 k
foggy photo 8k
foggy rainy day
foggy room
foggy sky
foggy sunset
foggy swamp
foggy twilight lighting
foggy volumetric light morning
foggy volumetric lighting
foggy water
foggy weather
foggy weather atmosphere
foggy wheat field
foggy!
fogy
foil
foil effect
folded
folded arms
folded geometry
folding - time
folds
folds of belly flab
folds of fabric
foliage
foliage clothing
folk
folk horror
folk medicine
folklore
folkloric illustration
folklorico
follow shot
follows the golden ratio
fond memories
font
fontawesome style
fonte à la cire perdue
food
food advertisement
food and consumerism
food art
food blog
food blog photo
food commercial 4 k
food court
food court in a mall
food focus
food head
food network
food particles
food photo
food photograph
food photography
food photography 4 k
food photography gourmet
food photography”
food pov
food stall
food stalls
food styling
food stylist
food. craft and adventure
foodphoto
foodporn
fool
fool and pixel jeff
foolish
foot
foot path
foot wraps
footage
footage from space camera
footage of a theater stage
football
football armor
football hooligans
football mascot
football player
football players butkus
footbridges between houses
footprints
footprints in the sand
footsteps in the snow
for 1 9 7 0 s'sci - fi
for 1970 ssci - fi
for a beautiful
for a catalogue
for aaa game
for displaying recipes
for gq
for hire 3d artist
for honor charector concept art
for junior
for kids
for pokemon red and blue
for printing
for ps5
for the highschool yearbook
for vogue
forbearing
forbidden
forbidden - knowledge
forbidden beauty
forbidden city
forbidden creepy mood
forbidden information
forbidden knowledge
forbidden planet
forbidden planet 1 9 5 6
forbidden planet 1956
forbidden tree
forbidding
forbidding spirit
forbidding spirit!!
forboding
force
forced perspective
forced witness
forcefield
forcing him to flee
forcing you to buy skyrim
ford
ford f-150 raptor
ford fusion
ford model t
forearm tattoo
foreboding
foreboding and eerie lighting
foreboding and epic
foreboding atmosphere
foreboding background
foreboding room
foreboding sea
foreground
foreground background
foreground focus
foreground/background
forehead jewelry
forehead only
foreign landscape
forelight
foresee
foreseeing the future
foreshortened perspective
foreshortening
foreshortening photography
forest
forest and fear
forest and moon
forest and river
forest and waterfall
forest at night
forest backdrop
forest background
forest city streets behind her
forest clearing
forest clearing landscape
forest colors
forest details
forest drawing elegant
forest dryad
forest elf bard playing lute
forest environment
forest fae
forest fairy
forest fire
forest fires in the distance
forest floor
forest green
forest gump tomato body
forest hunter lady
forest in background
forest in the background
forest in the distance
forest in the morning light
forest landscape
forest light
forest made out of mushroom
forest of neckties
forest on background
forest on the horizont
forest outside
forest path
forest picnic
forest pink fog planet
forest plains of north yorkshire
forest plains of yorkshire
forest portal
forest punk
forest ray light
forest ritual
forest scene
forest scenery
forest setting
forest setting in iceland
forest setting with skyscrapers
forest soul
forest spirit
forest style studio shot
forest theme
forest themed
forest trail
forest trees
forest with flowers blue
forest with lake
forest with trees with faces
forest. white trees
forested
forested background
foresthour
forestpunk
forestpunk background
forestry
forests
forests. map-style skyrim
forge
forged
forges
forgetful
forgetfulness
forgiveness
forgotten
forgotten and lost in the forest
forgotten realms
forgotten valley
forhonorgame
fork
fork fork
fork fork fork
fork lightning
forked snake tongue sticking out
forks
forlorn
form
form and shape exploration
form exploration
formal
formal attire
formal black suit. detailed
formal business suit
formal gardens
formal portrait
formal portrait of don quixote
formal suit
formal wear
formalism
former
former gasometer in rome
formian pathfinder
forming a burning hand spell
forming a heart with their necks
formless brests
formline
forms
formula 1
formula 1 garage
formula one car
formulae
formulas
fornite
fornite game. octane render
forrest
forrest background
forrest in background
forrest in the background
forsaken crocodile god
forsaken spirits
forshortening
fortninte
fortnite
fortnite art style
fortnite character
fortnite skin
fortnite style
fortress
fortress gateway
fortress mega structure city
fortresses
fortunato depero painting style
fortune teller
forward angle
forward facing
forward facing angle
forward facing pose
forward lighting
forwards facing
forza
fossil
fossil of yoda
fossil ornaments
foster
foster and partners
fosters
foto
foto realista
foto realistic
fotografia
fotografia blanco y negro
fotorealism
fotorealistic
fotoscape
found doodled in a sketchbook
found film
found footage
found footage horror
found footage style
found footage video
found in a cave made of clay
found in space
found in the ruins of pompeii
found objects
found on a volcano
found on google street view
found papers
found polaroid photo
found schematic in a notebook
found scribbled in a notebook
found written in a notebook
foundation
foundry vtt
fountain
fountain in the middle
fountain of water
fountain of youth
fountain pen
fountains
fountains and arches
four
four arms
four faces in one creature
four fingers maximum
four hands
four horsemen of the apocalypse
four humanoid bears
four leaf clover
four legged
four legs
four seasons
four stories high
four wings
four years old
four-dimensional
fourteen-dimensional
fourth dimension
fourth of july
fourze
fov 9 0 degrees
fov 90 degrees
fox
fox animal
fox as a monkey
fox carving art
fox ears
fox ears illustration
fox flying through landscape
fox from league of legends chibi
fox in a lab coat
fox legs
fox mccloud
fox news
fox nobushi
fox nobushi holding a naginata
fox people
fox scientist
fox tail
fox wearing a tiara
foxes
foxes and deer
foxes playing in a meadow
foxgirl
foxish guy in a lab coat
foxy
fozen ice hard edges
fps
fps game
fps game concept
fps game concept art
fps shooter game
fps view
fpv
fra
fractal
fractal 3 d structure
fractal 3d structure
fractal algorightmic art
fractal amalgamation
fractal apophysis
fractal armor
fractal arrows
fractal art
fractal automata
fractal automaton
fractal bacground
fractal background
fractal baroque
fractal beard
fractal biomech
fractal blue leaves
fractal buildings
fractal burning halo
fractal ceramic armor
fractal chaos background
fractal cloud
fractal crystal
fractal crystals
fractal cyborg ninja background
fractal design
fractal designs
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fractal details diamond skin
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fractal fiberglass tendrils
fractal fire background
fractal flame
fractal flame. highly_detailded
fractal flowers on the skin
fractal forest
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fractal fungus
fractal gems
fractal geometry
fractal glass
fractal hair
fractal human silhouette
fractal insane
fractal ivory carved ruff
fractal lace
fractal landscape
fractal leaves
fractal lighting
fractal magic
fractal of scary dirac equations
fractal paisley inlay
fractal pattern
fractal pattern background
fractal patterns
fractal render
fractal sacred geometry
fractal shapes
fractal silk
fractal skin
fractal sky
fractal structure
fractal structures
fractal style
fractal system circuit
fractal tarot card style
fractal texture
fractal thunder dan mumford
fractal veins
fractal veins. cyborg
fractal veins. dragon cyborg
fractal vines
fractal wave interference
fractal waves
fractal world
fractal!
fractal!!!!!!!
fractalism
fractalization
fractalpunk
fractals
fractals background
fractals in the background
fractals swirling outward
fractals!! water
fracture
fractured
fractured reality
fragile
fragile girl
fragile girl holding an arrow
fragile looking
fragility
fragmented
fragmented typography
fragments
fragonard
fragrant plants
frail
frame
frame around pciture
frame around picture
frame from pixar movie
frame from prometheus movie
framed
framed 4 k
framed art
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frameless
frames
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framestorevfx
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france
francesca woodman style
francesco may wilson
francine van hove
francis - bacon
francis bacon
francis bacon and agnes cecile
francis bacon and cy twombly
francis bacon and jenny saville
francis bacon and rene magritte
francis bacon art
francis bacon artwork
francis bacon masterpiece
francis bacon painting
francis ford coppola
francis goya
francisco de goya
francisco de zurbaran
francisco goya painting
francisco goya style
francois - edouard picot
francois boucher
francois boucher oil painting
francois boucher style
francois dufrene
francois legault
francois schuiten
francoise nielly
frank bairstow
frank cho
frank dicksee
frank dillane
frank dillane as a satyr
frank dillane as puck
frank fanzzeta
frank franzetta
frank franzettta
frank franzzeta
frank franzzeta and sakimichan
frank frazeta
frank frazetta alphonso mucha
frank frazetta and glenn fabry
frank frazetta and jeff easley
frank frazetta art
frank frazetta daarken
frank frazetta manga style
frank frazetta painting
frank frazetta style
frank frazetta style painting
frank frazetta8k
frank frazette
frank frazzeta
frank frazzetta
frank frzetta
frank gehry
frank gehry architecture
frank hampson
frank hampson and mcbess
frank herbert
frank kelly freas
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frank miller creative design
frank miller style
frank moth
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frank ocean
frank quitely
frank r paul
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frank sedlacek
frank sinatra
frank stephenson
frank tenney johnson
frank weston and gustave baumann
frank zappa
frankenstein
frans hals style
frans lanting
fransico goya
frantic
frantic dancing pose
frantic excitement
frantic smile
frantisek kupka
franz frazetta
franz schubert look alike
frayed edges. light leaks
frazetta
frazetta and vallejo
freak show
freaking out
freakshow
freaky
freckle
freckled
freckled face
freckled pale skin
freckles
freckles on chicks
freckles on his face
freckles on the nose
freckles!!!
fred flintstone eating tamales
fred freeman
fred tomaselli
freddie mercury
freddie mercury smiling
freddy fazbear
freddy fazbear's pizzeria
freddy krueger
freddy krueger style
freddy mamani silvestre facade
frederic bennett
frederic church and vasily perov
frederic daoust
frederic edwin
frederic thomas cole
frederick bacon
frederick judd waugh
frederik heyman
free
free spaces
free to kill and to die
free-fire
freedom
freedom fighter
freedom from cptsd
freeform ferrofluids
freemason symbol
freemasons
freeway
freeze frame
freeze time
freezing
freezing blue skin
freida pinto
freman
french
french academic painting
french architecture
french bande dessinée
french bob
french bob hair
french braid hairstyle
french braids
french bulldog
french comic art
french comic style
french door window
french emperor
french facial features
french features
french flag
french fries
french fries as arms
french fries on the side
french fry pattern ambience
french garden
french girl
french impressionism
french kiss
french maid
french man from louisiana
french new wave
french nouveau
french noveau
french provincial furniture
french resistance
french revolution
french special ops
french village exterior
french village interior
frenetic
frenetic brushwork
frenetic craziness
frenetic oil painting
frenzy
frequencies
frequency
frequency indie album cover
fresco
fresco painting
fresh
fresh atmosphere
fresh bakeries in the background
fresh basil
fresh colors
fresh cool colors
fresh food market people
fresh from the printer
fresh fruit
fresh kill
fresh modern look
fresh modern style
fresh rainbow bodypainting
fresnel effect
freud lucian
freudian
freya
frictional games
frida
frida castelli
friday night funkin
fridge
fried chicken
friedly smile
friend
friendly
friendly art
friendly carrot cyborg
friendly eyes
friendly face
friendly guy and small creature
friendly humanoid cyber robot
friendly humanoid insect robot
friendly humanoid robot
friendly robot
friendly seductive smile
friendly smile
friends
friends 9 0 s sitcom
friends 9 0 s sitcom screenshot
friends 9 0 s tv show screenshot
friends 90 s sitcom
friends 90 s tv show screenshot
friends scene
friends sitcom
friends sitcom screenshot
friends tv show episode
friendship
fries
frightened
frightened and angry
frightened look
frightening
frightening and staring
frightening appearance
frightening surroundings
frightful
frill
frilled blooming collar
frills
frilly outfit
fringe
fritz lang
fritz lang and shinya tsukamoto
fritz wagner
fritz willis
frizzy hair
frock
frodo
frodo in star trek
frog
frog - elephant creature
frog head
frog perspective
frog playing on guitar
frog themed
froggy
froggy chair
frogs
frogzilla creature
from 1 0 0 0 feet in distance
from 1890
from 1977
from 1985
from 1986
from 2001
from 8 k matte
from a 2 0 1 9 sci fi 8 k movie
from a 2019 sci fi 8k movie
from a bird's eye view
from a birds eye view
from a distance
from a dream
from a huge red glass bong
from a marvel movie
from a movie scene
from a scene from twin peaks
from above
from afar
from akira  1988  style
from akira ( 1 9 8 8 ) style
from alice in wonderland
from aliens franchise
from arcane
from arknights
from attack on titan
from avengers
from avengers: endgame (2019)
from back
from behind
from below
from berserk
from better call saul
from blade runner
from blade runner 2049 (2017)
from blade runner(1982)
from blade runner1982
from bravely default ii
from china
from commedia dell'arte
from commedia dellarte
from cryptid academia
from disney pixar's up (2009)
from disney pixars up 2009
from doctor who series
from doom
from doom and halo
from doom and warhammer
from dungeons & dragons
from dungeons and dragons
from dungeons dragons
from east to west
from ego perspective
from egypt
from evangelion
from falcon bms
from family guy
from far away
from ff7
from final fantasy
from final fantasy vii
from final fantasy xiii
from frank herbert novels
from girls frontline
from guardians of the galaxy
from guild wars
from half-life
from half-life 2
from hearthstone
from her ankles to her neck
from horizon: zero down
from horror movies
from hotline miami
from inside a temple
from inside the giant palace
from italica
from jaws (1975)
from joker (2019)
from jurassic world (2015)
from jurassic world 2015
from kehinde wiley
from kenshin
from kingdom hearts
from kong skull island (2017)
from kong skull island 2017
from league of legends
from left
from legend of zelda
from lineage 2
from lorax movie
from lord of the rings
from louvre
from luxor
from magic the gathering
from marvel studios
from matrix ( 1 9 9 9 )
from matrix(1999)
from metal gear
from movie batman
from movie bladerunner
from naruto
from nature journal
from ncsoft
from netflix's arcane
from netflixs arcane
from of thrones
from one piece
from outer space
from overlord
from overwatch
from pacific rim
from pathfinder
from police academy 2 (1985)
from police academy 21985
from ramayan
from reading to playing games
from rick and morty
from sam and max
from scene from inception
from scene from twin peaks
from sengoku period
from side
from skyrim
from slumdog millionaire
from software game
from sonic
from star trek
from star trek 2021
from star wars
from star wars (1977)
from star wars legends
from starcraft
from street level
from the 7 0 s
from the 8 0 s
from the avengers (2012)
from the avengers 2012
from the azur lane videogame
from the back
from the black mage cemetery
from the black mage cemetery!!!
from the borderlands series
from the distance
from the duchy of lituania
from the elbow
from the front
from the game pathologic 2
from the grand budapest hotel
from the hit 9 0 s tv show
from the hit 90 s tv show
from the mandalorian (2019)
from the mandalorian 2019
from the movie mad max fury road
from the movie predator 1987
from the movie quest for fire
from the movie the thing
from the new john wick movie
from the office  2005
from the office ( 2 0 0 5 )
from the outside it looks folksy
from the phantom menace
from the roof
from the sandman netflix show
from the side
from the studio ghibli film
from the thing
from the tusk movie
from the waist up
from then on a basketball
from three kingdoms
from touhou
from toy story
from transformers
from twin peaks
from valve
from vermintide 2 video game
from vogue magazine
from waist up
from warcraft
from wheaton illinois
from wikipedia
from witcher (2021)
from world of warcraft
from wow
from xenoblade chronicles
from yowamushi pedal
from'animation types'
fromme seele
fromsoftware
fronds
front
front - facing perspective
front and back
front and back view
front and center
front and side elevation
front and side view
front and side views
front angle
front back view and side view
front camera
front closeup
front cover of a new video game
front elevation
front elevation view
front energy game card
front face
front face asymmetrical
front face symmetrical
front facing
front facing camera
front facing portrait of samurai
front facing shot
front facing the camera
front facing view
front facing!!
front facing!!!
front facing!!!!
front flash
front game card
front label
front left speaker
front light
front lighting
front lit
front mission
front of car angle
front of the treasure box
front on
front orthographic
front page
front page of art station
front perspective
front photo
front portrait
front portrait of a girl
front profile
front profile shot
front profile!!!!
front shot
front side
front side full
front side view
front side view full sheet
front side views full
front symetrical
front top side view
front trading card
front view
front view 1 9 9 0
front view 1990
front view 2 0 0 0
front view 2000
front view dramatic
front-view
frontal
frontal close up
frontal picture
frontal portrait
frontal portrait of a young
frontal pose
frontal realistic
frontal shot
frontal view
frontal view of a set of armor
frontlight
frontpage
frontshot
frontview
frost
frost clings to her skin
frost dragon
frost gem
frost giant
frost on the canvas
frostbite
frostbite 3 engine
frostbite 3 engine rendered
frostbite 3 rendered
frostbite engine
frosted
frosted glass
frosted texture
frostine engine
frosting on head and shoulders
frostpunk
frosty
frosty breath
frosty white eyes
frottage
frown
frown fashion model
frown!
frowning
frowning expression
froz
frozen
frozen and covered in ice
frozen cold stare
frozen ice statue
frozen ii klaus film
frozen ii movie still
frozen in motion
frozen in time
frozen klaus film
frozen lake
frozen like a statue
frozen river
frozen sea
frozen tear
frozen time
frozen time effect
frozen waterfall
frozen zombie man with a crown
fruggles
fruit
fruit and feathers
fruit and flowers
fruit basket
fruit bowl
fruit celebrity
fruit eyes
fruit machines
fruit monster
fruit trees
fruit world
fruitcore
fruits
fruits in a basket
frustrated
frustrated detailed
frustrated expression
frustrated face
frustration
fry
frying nails
fstop of 8
fubuki
fuchsia
fuchsia and blue
fuchsia skin
fuchsia skin below the armor
fuchsia skin beneath the armor
fuji
fuji 4 0 0 h
fuji 5 0 r 3 5 mm
fuji 50 r 35 mm
fuji 8 0 0
fuji 8 0 0 film
fuji 800
fuji 800 film
fuji choco
fuji choko
fuji film
fuji lut
fuji pro 400h
fuji provia
fuji provia film
fuji superia
fuji velvia
fuji x 1 0 0 f
fuji x 100 f
fujicolor photo
fujicolor sample
fujicolor superia
fujicolor superia 1 6 0 0 photo
fujicolor superia 1600 photo
fujicolor superia photo
fujicolor with flash
fujifilm
fujifilm 5 6 mm f 1. 2
fujifilm 56 mm f 1. 2
fujifilm quicksnap 400
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fujifilm velvia 5 0
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fujifilm x - h 2 s
fujifilm x - pro 2
fujifilm x - t 3 0
fujifilm x - t 4
fujifilm x-t3
fujifilm x100v
fujifilm”
fujin
fujita goro
fujita goro and jones
fujita goro!
fukaya yuichiro
ful dressed
fulcolor octane remder
fulcolor octane reminder
full
full - bodied portrait
full - body
full - body - front - shot
full - body and head view
full - body art
full - body artwork
full - body majestic angel
full - body portrait
full - body portrait of a ranger
full - body shot
full - body tattoos
full - color
full - face close - up portrait
full - frame
full - hd
full - length
full - length photo
full - length portrait
full - length view
full - shot
full - view
full 8k high quality resolution
full armor
full armor cloth
full art
full art illustration
full bangs
full beard
full big moon
full black dress
full blood moon
full bloody moon
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full bodied portrait
full body
full body 1 / 6 nihei tsutomu
full body 8k
full body action concept art
full body action pose
full body adoptable
full body and face
full body and face and head
full body and head
full body and head shot
full body and head view
full body angle
full body armor
full body armour
full body art
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full body black and red longcoat
full body camera shot
full body cgsociety
full body character
full body character concept
full body character concept art
full body character design
full body character drawing
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full body close-up shot
full body concept
full body concept art
full body cute young lady
full body detailed
full body details
full body devil man
full body devil woman
full body dnd character portrait
full body dragon concept
full body dramatic profile
full body drawing
full body dynamic pose
full body extreme closeup
full body female
full body focus
full body framing
full body frontal view
full body full height
full body grayscale drawing
full body green dress
full body greg rutkowski
full body hero
full body human legs
full body illustration
full body image
full body in camera
full body in frame
full body in shot
full body in view
full body length
full body length shot
full body made of white feathers
full body mascot
full body mech
full body mecha suit
full body model
full body painting
full body perfect
full body photgraph
full body photo
full body photo of steve
full body photogenic shot
full body photograph
full body photography
full body photoshoot
full body picture
full body picture of a male monk
full body pictures
full body plated armor
full body playboy set
full body portrait
full body portrait of a group
full body portrait of a short!
full body portrait of a zentaur
full body portrait of jean grey
full body portrait painting
full body portrait posing
full body portrait shot
full body portraits
full body pose
full body posing
full body potrait holding bottle
full body pov
full body profile
full body profile camera shot
full body profile pose
full body purple and white cloak
full body purple cloak
full body realistic portrait
full body red mech
full body render
full body robot with human mask
full body sarcastic pose
full body savage devilman
full body shoot
full body shot
full body shot 4k
full body shot close up
full body shot hyperdetailed
full body shot in bikini
full body shot of duke nukem 3d
full body shot!!
full body shots
full body single character
full body snow outside
full body sprite
full body sunny weather
full body tattoo
full body transmogrify
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full body visible
full body white dress
full body white purple cloak
full body wide shot
full body with costume
full body with dynamic pose
full body within frame
full body worn out damaged cape
full body worn out torn cape
full body wuxia
full body x-force outfit
full body xianxia
full body zenkai! asuka suit
full body!
full body! pretty face
full body! shot
full body!!
full body!! dynamic pose
full body!! maximalist details
full body!!!
full body!!!!!!
full body-n 9
full body:: snow outside::
full body:: sunny weather::
full body;
full body; front view
full bodydynamic pose
full bodypretty face
full bodyshot
full borg hyper detailed
full building
full car
full card design
full cast
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full character body
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full cheeks
full close-up portrait
full clothing
full color
full color american nightmare
full color catalog print
full color digital art
full color digital illustration
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full color manga cover
full color manga visual style
full color mixed media painting
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full cybernetic combat suit
full darkness
full daylight
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full details anatomy poster
full device
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full dress of lava showcase
full dress uniform
full dynamic colour
full entire body fun pose
full extremely slim body
full f/22
full face
full face and body portrait
full face close up portrait
full face epic portrait
full face frontal centered
full face frontal centred
full face occult silver mask
full face portrait
full face portrait composition
full face shot
full face shot of rimuru tempest
full face tattoo epic portrait
full face view
full faced
full faces
full figure
full figure centered in portrait
full figure portrait
full figure poster
full figured
full figured mother earth
full focus
full frame
full frame 3 5 mm
full frame 35 mm
full frame image
full frame mirrorless sensor
full frame photography
full frame shot
full front view
full frontal
full frontal lighting
full frontal portrait
full frontal shot
full glossy lips
full growth
full growth from the back
full hair
full hd
full hd render
full head
full head and shoulders
full head of hair
full head shot
full heart - shaped face
full height
full height sculpture
full height view
full helmet
full human hairy body
full ice hockey goalie gear
full image
full leather armor
full legs
full leight
full lenght
full lenght shot
full lenght view
full lenght view. white plastic
full length
full length and white stockings
full length character
full length photo
full length portait
full length portrait
full length shot
full length view
full lips
full makup
full mask
full metal alchemist
full metal jacket
full metal overlay
full mood
full moon
full moon background
full moon buried in sand
full moon in background
full moon in clouds
full moon in the background
full moon in the sky overhead
full moon lighting
full moon on the sky
full moon raining night
full moon with stars
full morning sun
full object in middle
full of alien military equipment
full of black goo
full of clear glass facades
full of color
full of colorful vegetation
full of colors
full of colors and rich detail
full of colour
full of colour 8-w 1024
full of colour w 1024
full of colours
full of detail
full of details
full of expressions
full of ferrofluid
full of flowers
full of glass facades
full of glass. cgsociety
full of golden layers
full of goo
full of grass
full of greenish liquid
full of high buildings
full of insect robots
full of joy
full of life
full of light
full of mirrors
full of nature
full of paintings of angels
full of people
full of reflections
full of sand and dust
full of sand and glitter
full of silver layers
full of skyscrappers
full of smoke and dust
full of steaming coffee
full of tar
full of things
full of trash
full of wonderful things
full of wonders
full of yellow flowers flowers
full og shrek
full page
full page black
full page grid sprite sheet
full page illustration
full page periodical image
full page scan
full page writings
full pallet image
full perfect
full perfect face
full photo
full piano
full picture
full plate
full plate armor
full plate armor with cloth
full platinum blond
full portrait
full portrait of electromancer
full portrait of elementalist
full portrait of magical knight
full pose
full poster
full pov
full product shot
full profile
full protrait
full red moon
full res
full resolution
full room view
full round face
full round face!
full samurai armor spiderman
full scene
full scene camera shot
full scene shot
full screen
full shot
full shot  fs
full shot ( fs )
full shot fashion photography
full shot photo
full shot photograph
full shot rpg
full size
full size persona
full skull shaped face cover
full slim body
full soldier clothing
full stature
full steel armour
full subject
full subject in frame
full subject in frame and view
full subject in shot
full subject in view
full subject shown in photo
full suit of gilded plate armor
full sun
full team
full traditional chinese armor
full trees
full uniform
full upper body
full view
full view blank background
full view of a car
full view of a sport car
full view of face and body
full view of seahorse
full view with focus on subject
full volume composition
full white beard
full width
full with people
full!! body! shot
full-body
full-body armor
full-body art
full-body character portrait
full-body photography
full-body portrait
full-body shot
full-body tattoos
full-body view
full-body-shot
full-bofy plan
full-color
full-cosplay
full-cosplay wario
full-face portrait
full-figure
full-frame of bernie sanders
full-hd
full-length
full-length portrait
full-length view
full-shot
full-size
full_body
full_body!!
fullbody
fullbody art
fullbody commission for
fullbody dynamic poses
fullbody of standing tom selleck
fullbody or portrait
fullbody painting
fullbody photo
fullbody portrait
fullbody rpg portrait
fullbody shot
fullbody shot turnaround
fullbody view
fullbody!! dynamic action pose
fullbodydynamic action pose
fullbodyshot
fullbodysuit
fulldome
fuller
fullest body
fullface
fullhd
fullmetal alchemist
fullmetal alchemist brotherhood
fullmoon
fulls of rats. pop art
fullshot
fully - clothed
fully - clothed!!!
fully armor
fully armored
fully armoured
fully black eyes no pupils
fully body
fully body photo
fully body portrait
fully body pose
fully body posing
fully body shot
fully built buildings
fully chocolate
fully clothed
fully clothed in armor
fully clothed in red robes
fully clothed. painting of sexy
fully colored
fully covered
fully covered in colorful paint
fully covered in drapes
fully decorated
fully detailed
fully detailed render
fully dressed
fully frontal view
fully functional
fully in frame
fully open sky
fully photorealistic
fully posable
fully red eyes
fully red eyes no pupils
fully rendered light to shadow
fully robotic!! cat
fully robotic!! catgirl
fully robotic!! girl
fully roboticcat
fully roboticgirl
fully space suited
fully symmetrical
fully tattooed body
fully visible
fume
fume fx
fumes
fuming effigy
fumo
fumo plush
fun
fun - w 704
fun pose
fun rave outfit
fun smile
fun vibrant watercolor
functional
functional and elegant look
funeral
funeral veil
funereal
fungal
fungal enchanter
fungal god
fungal growth
fungal pages
fungal polyps
fungi
fungus
fungus and plants
funimation
funk
funkadelic
funko
funko pop
funko pop of donald trump
funko pop! donald trump
funko pop”
funky
funky furniture
funky hair
funniest meme ever
funny
funny and silly
funny and weird
funny art
funny artwork
funny camera shot
funny cartoonish
funny cat
funny emoji
funny face
funny faces
funny hat
funny illustration
funny jumbled letters
funny meme
funny meme photo
funny photo
funny picture
funny professional photo
funny sculpture
funny sunglasses
funny weird illustration
funtime corporation branding
fur
fur and leather armor
fur armor
fur attire
fur cape
fur coat
fur covered legs
fur covering her chest
fur details
fur hdr
fur hood
fur jacket
fur lined cape
fur scarf
fur shoulder pads
fur simulation
fur texture
fur visible
fur with mud
fur-clad barbarian goliath
furaffinity
furaffinity award winning
furaffinity commission
furaffinity fursona
furaffinity!!!!
furio tedesch
furio tedeschi
furio tedesschi
furious
furious action scene
furious dark haired women
furious god zeus
furious gorgeous woman
furious lights
furless
furnished room
furnished with fairy furniture
furniture
furniture and decor
furniture around
furniture concept photo!!
furniture design
furniture design art deco
furniture design sheet
furniture magazine
furniture overturned
furniture photography
furr
furr covering her chest
furraffinity
furred
furries wearing tails
furry
furry anime
furry arms
furry art
furry art!!!
furry artist
furry badger
furry body
furry brown body
furry character
furry character portrait
furry chest
furry convention
furry creatures
furry digital art
furry face
furry fandom
furry fantasy art
furry female
furry ferret
furry fluffy iridescent dragon
furry friendly monster
furry furaffinity
furry fursona
furry legs
furry mawshot
furry mawshot art
furry paw
furry paw art
furry paw pov art
furry paws
furry paws furry
furry pelt
furry shot
furry style
furry tail
furry wolf
furs
fursona
fursona art
fursona commission
fursona furry art commission
fursona wearing stylish clothes
fursona!!!!
fursuit
fursuit!!!!
fury
fury art
fury road
fus rei
fuschia and vermillion and cyan
fuschia leds
fuschia skin
fused aircraft parts
fused into epoxide
fused mecha parts
fuselage
fusing
fusing in the middle
fusion
fusion reactor
futurama
future
future activist
future architecture
future art
future baroque
future city
future clothing
future coder
future coder looking on
future coder man looking on
future combat gear
future concept art
future design
future doctrine
future fashion
future funk
future high - tech
future inflatable jacket
future jeep concept suv
future locomotive style
future miramar
future perfect
future retro
future science fiction. game cg
future soldier clothing
future tech
future technology
future techwear
future warrior
future!!
futured
futureistic
futurepunk
futurepunk dungeon
futuresynth
futurisitc sunglasses
futurism & harlem renaissance
futurism aesthetic
futurism painting
futurism style
futurism!!!!!
futurisma
futurist
futurist style
futuristic
futuristic aesthetic
futuristic alien city
futuristic alternate timeline
futuristic ambiance
futuristic and ethereal
futuristic ar glasses
futuristic architectural art
futuristic architecture
futuristic architecture concept
futuristic armor
futuristic art
futuristic art style
futuristic assault rifle
futuristic astronaut
futuristic atmosphere
futuristic attire
futuristic avatars
futuristic background
futuristic balenciaga sneakers
futuristic ballroom. big eyes
futuristic base
futuristic battle tank
futuristic battlefield
futuristic blame
futuristic building
futuristic buildings
futuristic but colorful shading
futuristic car
futuristic car concept
futuristic cars
futuristic cars and mecha robots
futuristic casino
futuristic castle
futuristic chemistry lab
futuristic chrome city
futuristic chrome drones
futuristic chrome vehicle
futuristic church
futuristic city
futuristic city backdrop
futuristic city backgrond
futuristic city background
futuristic city in background
futuristic city scape
futuristic city street
futuristic cityscape
futuristic cloning facility
futuristic clothes
futuristic clothing
futuristic clothing and helmet
futuristic computer
futuristic concept art
futuristic concept car
futuristic concept design
futuristic cubism
futuristic cyber clothing
futuristic cyber subway station
futuristic cybernetic helmet
futuristic cyberpunk
futuristic cyberpunk city
futuristic cyberpunk scenario
futuristic cyberpunk tokyo night
futuristic decor
futuristic decoration
futuristic design
futuristic dieselpunk street
futuristic digital art
futuristic digital painting
futuristic dome
futuristic dramatic lighting
futuristic dystopian city
futuristic dystopian endless
futuristic dystrophia
futuristic electric guitar
futuristic engineering
futuristic environment
futuristic estructures in ruins
futuristic fantasy
futuristic fashion
futuristic fashion clothing
futuristic fashion show
futuristic favela
futuristic female beekeeper
futuristic fine lasers tracing
futuristic fine textures
futuristic france
futuristic furniture
futuristic gadgets
futuristic gas mask
futuristic glasses lenses
futuristic glossy latex suit
futuristic government chambers
futuristic gun
futuristic hairstyle
futuristic hall
futuristic hi-tech details
futuristic high tech
futuristic himba teenage girl
futuristic himba young woman
futuristic horror
futuristic house
futuristic in the desert
futuristic interior
futuristic kitbash
futuristic laboratory
futuristic landscape
futuristic lasers
futuristic lasers tracing
futuristic lighting
futuristic look
futuristic looking living room
futuristic low-poly battle tank
futuristic marrakech
futuristic marrakech morocco
futuristic mechanical
futuristic metropolis
futuristic motorcycle
futuristic nazi german city
futuristic neon
futuristic nft card game
futuristic noir
futuristic orbital station!!!!
futuristic outpost building
futuristic painting
futuristic palace
futuristic persian palace
futuristic phnom-penh cambodia
futuristic pistol
futuristic pizza hut at night
futuristic poster
futuristic precious metals
futuristic product car shot
futuristic product design
futuristic production
futuristic production facility
futuristic psychedelic hippy
futuristic robot
futuristic robot angel
futuristic robot body
futuristic robot devil
futuristic robot organisms
futuristic robotics
futuristic robots
futuristic room
futuristic room background
futuristic royalty
futuristic ruins
futuristic sao paulo
futuristic sci-fi
futuristic sci-fi fashion
futuristic science fiction
futuristic scientific laboratory
futuristic scifi
futuristic setting
futuristic shapes
futuristic skyscraper
futuristic sneakers
futuristic soldier
futuristic solid colors
futuristic space port
futuristic space ship interrior
futuristic space station
futuristic spaceship
futuristic spaceships
futuristic spacesuit
futuristic sport arena
futuristic spy
futuristic star wars vibe
futuristic starship
futuristic starship crew member
futuristic street
futuristic structures
futuristic style
futuristic style spiderman
futuristic sunglasses
futuristic suzuki
futuristic tank is on fire
futuristic taxi
futuristic tech wear
futuristic technology
futuristic techwear
futuristic tetsuo
futuristic tower
futuristic tractors
futuristic typography
futuristic underwater metropolis
futuristic utopia
futuristic utopian architecture
futuristic utopian city
futuristic utopian fantasy
futuristic utopian metropolis
futuristic valley
futuristic vehicle
futuristic vehicles
futuristic vr headset
futuristic weapon
futuristic weapon shotgun
futuristic wheelchair
futuristic white-space-bikini
futuristic woman portrait
futuristic world
futuristic year 2 0 0 0 text
futuristic year 4 0 0 0
futuristic year 4000
futuristic yellow lens
futuristic. game cg
futuritic architecture
futuritic high tech architecture
futurustic
fututistic
fuzzy
fuzzy details
fuzzy ghost
fuzzy orange puppet
fuzzy polaroid photograph
fvckrender
fxaa
fyling silk
fzd school of design
g - man ending
g 7 summit
g 7 summit press photos
g cgsociety
g liulian art style
g6
gabagool
gabe newell as a superman
gabriel hardman
gabriele dell'otto
gabriele dellotto
gadget
gadget hackwrench
gadgets
gadigal
gaia
gaia human and digital
gainax
gainax anime style
gains
gaint black moon
gakkin and ernst haeckel
gal gadot
gal gadot and tribbles
gal gadot as captain america
gal gadot as captain marvel
gal gadot as harley quinn
gal gadot as hell lord
gal gadot as supergirl
gal gadot as wonder woman
gal gadot china plate
gal gadot portrait
gal yosef
galactic
galactic background
galactic bonsai
galactic crusader
galactic dark colors
galactic deity
galactic dmt entity
galactic dreamcatchers
galactic energy
galactic entity
galactic landscape
galactic light colors
galactic megastructure
galactic nebula
galactic news
galactic sized goddess
galactic temple
galactic waterfalls
galactic yellow violet colors
galactus
galactus!!!!
galadriel
galadriel from lord of the rings
galadriel in lothlórien
galaxies
galaxies and nebulae
galaxies and star in the sky
galaxies and stars
galaxies and stars in background
galaxies and stars visible
galaxies in background
galaxies in the background
galaxies in the sky
galaxies swirling around
galaxies visible
galaxy
galaxy + baroque
galaxy and nebulas
galaxy background
galaxy center remotely visible
galaxy color scheme
galaxy colored
galaxy cosmic nebula
galaxy eyes
galaxy gas brushstrokes
galaxy in a bottle
galaxy in background
galaxy in eyes
galaxy in the background
galaxy in the eye 👁️
galaxy in the night sky
galaxy in the sky
galaxy inside
galaxy nebula
galaxy orbit system
galaxy raytracing
galaxy reflected helmet
galaxy reflected in helmet
galaxy simulation
galaxy sky
galaxy space hunter
galaxy space sci - fi
galaxy theme
galaxy theme colors
galaxy themed room
galaxy whale
gale-force winds
galileo
gallant
galleon
gallery
gallery display photograph
gallery lighting
gallery quality
gallery setting
galloping
galloping through the forest
gallows
galvalume metal roofing
gambles like no one
gambling
gambrel roof
gambrel roof building
game
game aesthetic
game and watch
game art
game art matte painting
game art!!
game asset
game asset occult
game asset of fighters
game asset of plant and tree
game asset sheet
game assets
game background
game board
game box art
game card frame
game case
game cg
game character
game character art
game character concept art
game character design
game characters
game characters head designs
game concept
game concept art
game concept art sprite sheet
game cover
game cover art
game design
game design asset pack
game design concept
game design concept art
game dev art
game dungeons and dragons
game engine
game engine lighting
game environment
game environment design
game footage
game graphics
game hud
game icon
game icon asset
game icon stylized
game icons
game illustration
game interface
game key art
game level
game level design
game logo
game maker
game map
game map matte painting
game of the year
game of throne art
game of thrones
game of thrones style
game overlay
game pack
game poster
game promotional poster
game ready
game render
game resources
game scene graph
game screen
game screens hot
game screenshot
game texture
game top down view
game ui
game ui asset design
gameboy
gameboy advanced
gamecube
gamedesign
gamedev
gameplay
gameplay footage
gameplay of horizon zero dawn
gameplay screenshot
gameplay screenshot with ui
gameplay still
gameplay video
gamer
gamer aesthetic
gamer screen on metallic desk
gamer themed
gamers keyboard
gamers magazine cover
games
games 3 d
games lag
games of thrones
games workshop
gameshow
gamin
gaming
gaming chair
gaming chair as a toilet
gaming computers
gaming headset
gaming monitors. artstation
gaming pc
gaming pc case
gaming room
gaming room in 2 0 4 0
gaming table
gaming toilet
gamma
gamora
ganapati
gandalf
gandalf (lotr
gandalf as a woman
gandalf as chewbacca
gandalf attacks ancient athens
gandalf attacks the white house
gandalf casts a lightningbolt
gandalf fighting darth vader
gandalf from lord of the rings
gandalf riding a border collie
gandalf the grey
gandalf the white
gandhi
ganesh
ganesha
gang
gang clothing
gang flags
gang house
gang members
gang saints wear yellow bandanas
gangly
gangly arms
gangrel
gangs
gangsta rap
gangsta robot
gangster
ganja
ganondorf
gantz
gape
gaping gills and baleen
gaping maw
gaping mouth
gapmoe
gapmoe grimdark
gapmoe yandere
gapmoe yandere grimdark
gaps and pauses
garage
garage kit
garavato
garbage
garbage on the ground. rain. fog
garbage pail kids
garbage pail kids style
garbage plastic
garbage wheel bin
garbed in a purple gown
garden
garden at home
garden background
garden behind the glasses
garden environment
garden flowers pattern
garden gnomes preparing for war
garden landscape
garden of eden
garden pond scene
garden road
garden setting
garden utopia
garden with flowers
garden with flowers background
garden with fruits on trees
gardena architecture
gardening
gardens
gardens and fountains
gardens with flower beds
gareth pugh aw 2 0 1 1
garfield
garfield cat face
garfield comic
garfield eating lasagna
garfield the cat
gargantuan
gargoyle
gargoyles
garis edelweiss
garish
garlic on background
garmash
garments
garner holt
garnet
garnet from steven universe
garnish
garnishment
garniture
garrulus glandarius
garrus vakarian
garrus vakarian from mass effect
garry's mod
garrys mod
garter belt
garters
gary
gary baseman
gary busey
gary chalk
gary houston
gary larson
gary montalbano
gary oldman as a pear
gary ruddell
gary tonge
gas
gas fire in cracks
gas lamps
gas lanterns
gas lighting
gas mask
gas mask in ukiyo-e style
gas masks
gas station
gas station in space
gas street lamps. country road
gaseous materials
gaslight
gaslight fantasy capital city
gasmask
gasoline engine
gaspar noe
gaspar noe and christopher doyle
gaspar peeter
gaston brussiere
gaston bussiere and artgerm
gaston bussiere. blond braid
gastronomica magazine
gastronomy magazine
gate
gate to hell
gatekeeper
gates of heaven
gates of hell
gateway
gateway to another dimension
gateway to another universe
gateway to futurisma
gatling gun
gatson bussiere
gaturro
gauche painting
gaudi sagrada familia
gaudi style”
gaudy
gaudy colors
gauged ears
gauges
gaughan
gauguin
gaunt
gaunt cheeks
gauntlets
gauss
gauss rifle
gaussian blur
gaussian noise
gauthier leblanc
gauze
gauze angel dress
gavel
gawr gura
gay
gay pride
gay rights
gaze
gaze down
gazebos
gazeta
gazing at the water
gazing dark brown eyes
gazing eyes
gazing off into the horizon
gear
gear aurora
gear halo
gear mecha
gearing up for battle
gears
gears and lights
gears of death
gears of war
gears of war cover art
gearwheels
gecko
gecko sitting inside a terrarium
geeen skin
geek
geenss archenti flores
geert wilders
geforce rtx 3090 on fire
gehenna
geiger
geiger art
geisha
geisha hairstyle
geisha make up
geisha makeup
geisha mask
geisha photo portrait
geisha prima ballerina
geisha tattoo
gel lighting neon glow
gel spiked blond hair
gel spiked hair
gelatin silver finish
gelatin silver process
gelatin silver process photo
gelatine silver process
gelatinous
gelatinous green goop
gelatinous symmetrical
gelatinous with a smile
gelatinous with teeth
gelbooru
gelbooru anime image
gelios lens
gem
gem - tones
gem stones
gem tones
gemini
gemini gold cloth
gemini star formation
gemma chan
gemma chan beautiful girl
gemma chan girl portrait
gemma chen
gemmy woud - binendijk
gemmy woud binnendijk
gems
gems and diamond for fruit
gems and gold
gemstone
gemstone forehead
gemstone necklace
gemstones
gemstones and gold
gemstones and treasures
gemstones for eyes
gen z
genderless
genderswap
gendo ikari smoking a joint
gene espy
gene kelly as the terminator
gene wolfe
general
general fantasy
general greivous
general grievous
general grievous from star wars
general human form
general studio lighting
general uniform
generate multiple random colors
generate realistic face
generative
generative adversarial network
generative art
generative art nebula
generative design
generative line art
generative technology
generic
generic furry style
generic kpop boy band
generic male npc face
generous
generous cleavage open jacket
genesis
genesis of aquarion
genetic
genetically engineered
genevieve gauckler
genevieve o'reilly
gengar
genghis khan
genie
genious composition
genius
genius composition
genius design
genius party
genji
genji from overwatch
genndy tartakovski
genndy tartakovsky
genos
genos from one punch man
genshin
genshin impact
genshin impact character
genshin impact style
gentelman
gentle
gentle ambient lighting
gentle an harmonic colors
gentle bokeh
gentle calm doting pose
gentle expression
gentle face
gentle femdom
gentle lighting
gentle mists
gentle round face
gentle shadowing
gentle smile
gentle smoke effect
gentle snow
gentle sparkling forest stream
gentleman
gentleman's club lounge
gentlemans club lounge
gentlemens dinner
gently caressing earth
genuine smile
genx
genzoman
genzoman and frank franzzeta
genzoman and gary gygax
geo
geocities
geode
geodesic
geodesic architecture
geodesic building
geodesic dome
geodesic domes
geof darrow art
geoff darrow
geoffroy thoorens
geological strata
geology
geomerty
geometric
geometric 3 d render
geometric 3 d shapes
geometric 3d render
geometric 3d shapes
geometric abstract
geometric abstract beauty
geometric abstraction
geometric art
geometric art deco
geometric backdrop; led
geometric but organic
geometric colorful
geometric curves
geometric design
geometric dripped ink background
geometric figures
geometric forms
geometric futuristic cityscape
geometric golden ratio details
geometric light rays
geometric liminal space
geometric lines
geometric lines in the sky
geometric ornament
geometric pattern
geometric patterns
geometric patterns ornaments
geometric polygons
geometric shape
geometric shapes
geometric shapes background
geometric style
geometric tattoos
geometric tesseract
geometric third eye triangle
geometric type
geometric wallpaper
geometric wolf
geometric wood carvings
geometrical
geometrical drawings
geometrical figures
geometrical forms
geometrical masterpiece
geometrical shapes
geometrical shapes and lines
geometrically correct
geometrically perfect
geometrically realistic
geometries
geometry
geometry and astrology
geometry dash
geomorphology
george
george andries roth
george ault painting style
george carlin
george clinton
george clooney
george costanza
george doutsiopoulos
george floyd
george goodwin kilburne
george h.w. bush
george h.w. bush destroys iraq
george harrison
george hull
george kamitani
george lopez
george miller
george orwell
george pal
george patsouras
george pemba
george soros
george soros full body shot
george strait
george w bush
george washington
georges desvallières
georges mathieu
georgeus
georgia anne muldrow
georgia o'keefe
georgia okeefe
georgian dress
georgian dress amazing fabric
georgic
georgy kurasov
gerald
gerald brom and andy warhol
gerald brom and vincent di fate
gerald brom style
gerald brom. rich colors
gerald whitcomb
geralt
geralt of rivia
geralt of rivia in dark souls
gerard brom
gerard kravchuk
gerard van honthorst
gerenuk
geri keary
gerit dou
german
german and soviet soldiers
german and soviet tanks firing
german chancellor
german experssionism
german expresionism
german expressionism
german expressionist woodcut
german forest
german renaissance architecture
german romanticism style
german shepherd
germanic
germany
germany. wide shot
gerome
gertrude
gestalt
gestation inside a watermelon
gestural
gestural lines
gesture
gesture draw
gesture drawings
gesture drawn
gesture dynamic
gestures
getter robo
getting groceries
getting his tacos and drink)
getting ready to fight
getty images
getty images proshot
gettyimages
geysers of steam
gfd logo
gfx association
ghailan
ghailan!
ghanaian movie poster
ghastly
ghetto
ghetto blaster
ghibli
ghibli and disney animation
ghibli and ilya kuvshinov
ghibli animated film
ghibli art style
ghibli artstyle
ghibli colorful
ghibli film
ghibli inspired
ghibli miyazaki movie poster
ghibli moebius
ghibli screenshot
ghibli spirited away vibe
ghibli studio
ghibli studio anime style
ghibli studio art
ghibli studio style
ghibli style
ghibli tom bagshaw
ghibli vibe
ghibli. octane render
ghost
ghost children
ghost faces
ghost giant above the horizon
ghost in the machine
ghost in the shell
ghost in the shell 2017
ghost in the shell art style
ghost in the shell bullet vfx
ghost in the shell color scheme
ghost in the shell geisha robot
ghost in the shell movie scene
ghost in the shell style
ghost mask
ghost neon
ghost of a young girl
ghost of kiev
ghost of the fire spirit
ghost of tsushima
ghost rider
ghost room
ghost shrimp
ghost sphere
ghost town
ghost-in-the-shell
ghostblade
ghostbusters
ghostbusters proton pack
ghostbusters trap
ghostface
ghostly
ghostly atmosphere
ghostly darkness
ghostly figure
ghostly figures
ghostly form
ghostly ghost
ghostly low light
ghostly mist
ghostly necromancer
ghostly particles
ghostly silhouettes
ghostly smoke
ghostly spirits
ghostly tango
ghostly teenager bedroom
ghostly white trees
ghostpunk
ghosts
ghosts in the background
ghosts night sky
ghosts theme
ghostshrimp
ghoul
ghouls
ghutra and egal
gi
gi joe
giacometti
gianna michaels
giant
giant afro
giant afro!
giant airships in the sky
giant alice in wonderland
giant and fat
giant angry rat
giant anime mecha
giant aquarium in natural cave
giant archways
giant athanor
giant axe
giant aztec space city
giant aztec spaceship
giant bananas natural disaster
giant bear human hybrid
giant broken robot
giant bugs
giant carnation flower as a head
giant carnation flower head
giant castle walls
giant cat monster
giant centipede
giant cherry trees
giant claws loom
giant clay statue
giant clock
giant clouds
giant coiling snake machine
giant columns
giant columns palace
giant corn flower head
giant crab
giant crater in distance
giant crowd
giant crypto vault
giant cthulhu
giant cumulonimbus cloud
giant dahlia flower crown head
giant dahlia flower head
giant daisy flower as a head
giant daisy flower as head
giant daisy flower head
giant daisy flower over head
giant daisy flower under head
giant daisy flowers head
giant dragon flying in the sky
giant dragon resting in a cave
giant dragon wings
giant earrings
giant eldritch horror
giant ethereal creature
giant explosion
giant eye magic spell
giant eyeball
giant eyeballs
giant eyes in the grass
giant fantasy creatures
giant ferret
giant flower head
giant flowers
giant fluorescent fungi
giant frankenstein
giant glass dome in space
giant gold head statue ruins
giant golden nuclear hornet
giant grave structures
giant greek columns
giant guns
giant hall
giant hammer
giant head statue ruins
giant headphones rocking out
giant horns
giant imposing mountain
giant imposing steampunk tower
giant insects
giant interlocked gears cogs
giant kaiju dragon monster
giant kaiju starfish monster
giant kicking foot
giant led screens
giant legs
giant lotus mandala
giant majestic archways
giant marble chess pieces
giant mech
giant mecha robot
giant mechanical rose
giant mechanisms
giant medieval tower concept art
giant moai statues
giant moon
giant mouth
giant mushroom
giant mushrooms
giant octopus
giant pig
giant pig grass
giant pink full moon
giant purple dahlia flower head
giant quadrupedal mining robot
giant raindorps
giant red carnation afro head
giant red flower afro
giant red led screens
giant river
giant road sign armor champion
giant robot
giant robot bones
giant robot foot
giant robot mech
giant robot skull
giant robots
giant rocks
giant rose flower as a head
giant rose flower head
giant scale
giant screens
giant sculpture
giant sentinel crashed on earth
giant sequoia
giant snake
giant snake castle
giant spaceship
giant speakers
giant spider
giant spider foreground
giant squids battling in the sky
giant star
giant stunning goddess shot
giant submarine
giant sun
giant sunflower as a face
giant super moon
giant sword
giant tardigrade
giant teeth
giant tentacles
giant terrestrial starfish!!!!!
giant thorns
giant threes
giant tomb structures
giant towering pillars
giant tree
giant trees
giant video screens
giant waves
giant whale tank with legs
giant white daisy flower as head
giant white daisy flower face
giant white daisy flower head
giant white daisy flower on head
giant white tree
giant wings
giant wooden club
giant yoda
giantess
giantess art
giantess shot
giants
gibli
giddy smirk
giesha demon
gif
gifts
gig
giga
giga chad
giga chad capaybara
giga chad crying
gigachad
gigachad in minecraft
gigachad jesus
gigachad meme
gigachad muscular
gigachad portrait
gigantic
gigantic castle
gigantic creature
gigantic cthulhu
gigantic forest trees
gigantic girl head
gigantic landing pad
gigantic landscape!
gigantic man head
gigantic monster
gigantic mountains
gigantic neon signs
gigantic oily tentacles and eyes
gigantic pillars
gigantic pillars and flowers
gigantic pink ringlets
gigantic pristine marble columns
gigantic robot head
gigantic scale
gigantic size
gigantic skyscrapers
gigantic sun
gigantic tight pink ringlets
gigantic titan winnie the pooh
gigantic tower
gigantic trees
gigantic woman head
gigapixel
gigapixel maximum upscale
gigapixel photo
giger )
giger and jacek yerka
giger art
giger background liminal void
giger h r
giger inspired
giger portrait of queen dragon
giger spider joker
giger style
giger textures
giger ’ s xenomorph
giger's biomechanical xenomorph
giger:1
gigers biomechanical xenomorph
giggling
gil elvgren 50mm
gil elvgren style
gilbert stuart style
gilbert williams
gilded
gilded black uniform
gilded details
gilded gold
gilded gold and diamonds
gilded gold halo behind head
gilded lotus princess
gilded marbled paper background
gilded outfit
gilded relief
gilded shiny armour
gilded swirls on monochrome
gilded with gold
gilded. floral
gildhardho
gilding
gilgamesh
gill elvgren
gilleard
gilleard james
gillian anderson
gilligan's island
gills
gills and scales
gilmore girls aesthetic
gilt
gilt metal
gilt silk fabric
gilt silk torn fabric
gilt-leaf winnower
gimli
gimli from lord of the rings
gina heyer
ginger
ginger bearded man with glasses
ginger cat
ginger cat in mid action
ginger hair
ginger hair and fur
ginger hair with freckles
ginger wavy hair
ginger. art nouveau
gingerbread candy village
gingerbread people
ginko showing a new mushi
ginormous
gintama
gintas galvanauskas
gintas galvanauskas. sharp focus
giorgetto giugiaro
giorgia meloni
giorgio
giorgio a. tsoukalos
giorgio de chirico and klimt
giorgio di chirico
giorgio griffa
giorgio vasari
giorno giovanna
giovanni baglione
giovanni paolo panini
giovanni paolo panini style epic
gipf project
giraffe
giraffe army
giraffe head and giraffe body
giraffe standing on the rooftop
giraffe weevil
giraffes
giratina
giraud
giraud!
giraud!!
girih
girl
girl and a goat
girl at a fashion show in hell
girl clouds
girl creates something great
girl cute-fine-face
girl dancing in a flower field
girl dancing on cliff
girl design lush horns
girl drinks monster energy
girl figure
girl floating in a flower field
girl frontal in a flower field
girl graceful
girl in a bed of flowers
girl in a dress
girl in a flower field
girl in a record store
girl in a suit
girl in flowers
girl in knight armor
girl in love
girl in mecha cyber armor
girl in raincoat
girl in space
girl in studio
girl in suit
girl in white dress dancing
girl is running
girl jumping in a flower field
girl looking at the ocean waves
girl looks at the space
girl making a phone call
girl next door innocent look
girl of the alps
girl on the beach
girl pinup
girl portrait
girl running
girl screamin yolo - aesthetic
girl silver hair
girl silver ponytail hair
girl sitting in a flower field
girl sitting on a rooftop
girl sketch
girl standing
girl standing in a flower field
girl standing in flower field
girl standing on cliff
girl standing on mountain
girl under lantern
girl venizian
girl venizian era
girl walking between dunes
girl walking in a canyon
girl walking in a flower field
girl walking in dark forest
girl walking in flower field
girl walking in forest
girl walking in wheat field
girl walking on cliff
girl walking on mountain
girl watching sunset
girl wearing headphones
girl wearing hoodie
girl wearing robotic suit
girl wearing round glasses
girl wearing uniform
girl wears a red dress
girl with a birdcage on her head
girl with a flower face
girl with a flower head
girl with a pearl earring
girl with a pearl earringl
girl with angel wings
girl with black hair
girl with blue hair
girl with brown hair
girl with cat ears
girl with cat ears at a rave
girl with dark brown hair
girl with feathers
girl with fox ears
girl with glasses
girl with long hair
girl with messy bun hairstyle
girl with pearl earring
girl with plaits
girl with short white hair
girl with super long hair
girl with the pearl earring
girl with warship parts
girl with white eyes
girl with white hair
girl's room
girlboss
girls
girls frontline
girls frontline cg
girls frontline style
girls frontline universe
girls resting
girls room
girls' last tour
girly
girly bedroom
gitital art
gits
gits anime
giulio rosati
giuseppe
giuseppe arcimboldo walter white
giuseppe dangelico pino
give me a higher love
givenchy
giving a speech
giving a thumbs up
giving a thumbs up to the camera
giving an interview
giving flasks to other gnomes
giving gifts to people
giving the middle finger
giving the thumbs up
giygas
glabrous
glacier
glacier coloring
glacier landscape
glacier national park
glacier photography
glaciers
glaciers and ice and snow
gladiator
gladiator armor
gladiator wearing space suit
glados
glados from portal 2
glagolitic glyph
glair
glaive
glam
glam hair
glam make-up
glam metal hair
glam photo
glam rockers drinking wine
glamor hairstyle
glamor pose
glamor profile pose
glamor shot
glamorous
glamorous aerith portrait
glamorous and sexy geisha
glamorous angewoman digimon
glamorous composition
glamorous fashion pose
glamorous hairstyle
glamorous jill valentine
glamorous pose
glamorous runway model
glamorous setting
glamorous tifa lockheart
glamour
glamour fashion pose
glamour lighting
glamour photography
glamour portrait
glamour pose
glamour shot
glamourous
glamourous cosplay
glamping
glare
glare face
glare on the water
glares
glaring at the camera
glasgow
glasgow in background
glass
glass - reflecting - stars
glass and gold and jade
glass and gold pipes
glass and lights
glass and metal : : peugot onyx
glass and steel
glass antikythera
glass arms
glass ball
glass ball at the waist
glass beads
glass beads clay amulets
glass bottle
glass bubble helmet
glass buildings
glass bulbs
glass ceilings
glass cover
glass cyborg
glass debris pieces
glass dome
glass domes
glass eyeballs
glass face
glass facial muscles
glass flame
glass floor
glass flowers
glass greenhouse
glass helmets
glass helmets and dove wings
glass houses
glass jar
glass labware
glass mosaic
glass negative
glass obelisk of the void
glass obelisks
glass obelisks!!
glass of milk
glass oled mecha visor
glass oled visor head
glass openings
glass orbs
glass paint
glass panes
glass pipes showing red
glass plate photography
glass railing
glass rain
glass reflections
glass reflections on top
glass refraction
glass room
glass sculpture
glass sculpture of a heart
glass shader
glass shards
glass shattering
glass skin
glass skull
glass space helmet
glass spaceship
glass sphere
glass spheres
glass spheres on a red cube
glass studio
glass tableware
glass texture
glass torso
glass vials
glass visor
glass wall
glass walls
glass window
glass windows
glass with rum
glass-cast heart
glasses
glasses frames
glasses glasses
glasses without frames
glasses |
glassware
glassy
glassy eyes
glassy fracture
glassy reflections
glaze
glaze oil painting
glazed
glazed ceramic
glazersout
glazing
gleaming
gleaming silver
gleaming silver and rich colors
gleaming skin
gleaming white
glen
glen orbik
glenn barr
glenn brown
glenn fabry and frank frazetta
glennray tutor and edward hopper
gliding
glimmering
glimmering whale
glimpse of red
glinting
glinting metal
glinting particles of ice
glints
glisten
glistening
glistening body
glistening clouds in background
glistening gold
glistening seafoam
glistening skin
glistering
glistering!!! oiled - skin!!!
glisteringoiled - skin
glitch
glitch art
glitch art aesthetic
glitch color palette
glitch creatures
glitch effect
glitch effect sunlight
glitch effects
glitch effects over the eyes
glitch eyes
glitch in the matrix
glitch shimmer
glitch! art
glitch!!
glitchart
glitchcore
glitchcore rebirth
glitchcore witchcore
glitched
glitched background
glitched fantasy painting
glitched pattern
glitches
glitches in reality
glitchpunk
glitchpunk girl
glitchwave
glitchy
gliter
glitter
glitter accents on figure
glitter background
glitter crystals
glitter dress
glitter gif
glitter makeup
glitter sticker
glitter storm
glitter tornados
glittering
glittering and soft
glittering hair
glittering ice
glittering light
glittering metal paint
glittering multiversal ornaments
glittering silver ornaments
glittering skin
glittering stars scattered about
glittering sun rays
glittery
glittery short black hair
glittery wedding
glitz pro
gloating
global awarded
global illuminaition
global illumination
global illumination hdri
global illumination lighting
global illumination!! intricate
global illumination. vfx
global illuminationintricate
global ilumination
global light
global lighting
global radiant light
global warming
globes
glocks
gloden armor
glomy
gloom
gloom and lights
gloom haven
gloomcore illustration
gloomhaven
gloomy
gloomy and depressed
gloomy and foggy atmosphere
gloomy atmosphere
gloomy background
gloomy cinematic lighting
gloomy colors
gloomy earthy colors
gloomy face
gloomy foggy atmosphere
gloomy forest
gloomy landscape
gloomy library
gloomy lighting
gloomy lighting!!!
gloomy lights in the sky
gloomy medieval background
gloomy misty atmosphere
gloomy mood
gloomy mood. greg rutkowski
gloomy moody clouds
gloomy skies
gloomy sky
gloomy style
gloomy weather
gloomy weather. high quality
gloomy weather. hyperrealistic
gloomy. by makoto shinkai
gloomy/wet atmosphere
gloopy
glorified image
glorious
glorious composition
glorious galactic collision
glorious light
glorious lighting
glorious long blond hair
glorious sunlight
glorious supernatural power
glory
glory be to the magic within
glory days
glory war
gloss
gloss finish
glossy
glossy and drippy
glossy design
glossy digital painting
glossy eyes
glossy finish
glossy flecks of iridescence
glossy from rain
glossy intricate design
glossy lips
glossy magazine cover
glossy magazine painting
glossy magazine photoshoot
glossy metallic human spine
glossy old advertising poster
glossy paint
glossy painting
glossy photo
glossy plastic
glossy reflections
glossy shiny reflective
glossy skin
glossy sphere
glossy surface
glossy white
glossy white armor
glossy white metal
glossy yellow
gloves
gloves and jewelry. motion
gloves on hands
glow
glow around helmet
glow effect
glow filter
glow galaxy background
glow in dark
glow in the dark
glow lighting
glow lights
glow of neon lights
glow sticks
glow up
glow wave
glowering judging eyes
glowforge template
glowin eye
glowin eyes
glowing
glowing (((white laser))) eyes
glowing - instruments
glowing - thin - wires
glowing accents
glowing aesthetic
glowing alien symbols
glowing amber
glowing and epic
glowing angelic being
glowing arcane eyes
glowing atari sign
glowing aura
glowing aura around her
glowing aztec tattoos
glowing background
glowing background lighting
glowing black aura
glowing black dark velvet
glowing blue
glowing blue butterflies
glowing blue by greg rutkowski
glowing blue eyes
glowing blue face
glowing blue interior components
glowing blue mushrooms
glowing blue mushrooms in mire
glowing blue veins
glowing bones
glowing butterflies
glowing buttons
glowing candles
glowing ceremonial markings
glowing chess knight
glowing chess pawn
glowing clouds
glowing colorful fog
glowing colors
glowing computer screens
glowing cracks
glowing crimson head
glowing crystal cave
glowing crystal on a rock
glowing crystals
glowing crystals on the ground
glowing cyan blue plasma
glowing cyber - eyes
glowing cybernetic augments
glowing dandelion seed storm
glowing dark aura
glowing dark red eyes
glowing delicate flower
glowing details
glowing details!
glowing digital runes
glowing draconic staff
glowing drapes
glowing dust
glowing edges
glowing effect
glowing electric aura
glowing ember eyes
glowing energy
glowing energy effects
glowing engines
glowing epicentre
glowing eye
glowing eyes
glowing eyes everywhere
glowing eyes in helmet
glowing eyes shiny
glowing eyes!! intricate
glowing face
glowing feathers
glowing fiery background
glowing fiery red eyes
glowing fingers
glowing fire
glowing fire background
glowing firebugs
glowing fireflies
glowing flowers
glowing flowing hair
glowing fog
glowing fog intricate
glowing forest
glowing fractals
glowing from inside
glowing from within
glowing fungus
glowing glittery dust in the air
glowing god rays
glowing gold embers
glowing golden aura
glowing golden eyes
glowing green
glowing green crystals
glowing green eyes
glowing green lights
glowing green neon eyes
glowing green rocks
glowing green soul blade
glowing grey eyes
glowing guy creature
glowing hair
glowing halo
glowing halo above his head
glowing hands
glowing head
glowing headlights
glowing heating coils
glowing highlights
glowing holy aura
glowing hot sun
glowing hue of teal
glowing imperial motifs
glowing in power
glowing in the dark
glowing inside
glowing interior components
glowing interior lighting
glowing internal light
glowing iridescent accents
glowing iris
glowing jar
glowing jellyfish
glowing lamps
glowing lanterns
glowing laser beam eyes
glowing lasers
glowing lava
glowing lava!!!
glowing leaves
glowing leds
glowing lens flare
glowing lens flare wraith girl
glowing light
glowing light and shadow
glowing light and shadows
glowing light atmosphere
glowing light orbs
glowing lightening
glowing lighting
glowing lights
glowing lights in armor
glowing lights intricate
glowing lights intricate elegant
glowing lights! digital painting
glowing lights!!
glowing lights!! highly detailed
glowing lights!! intricate
glowing lights!! sci - fi
glowing lights!! sci-fi
glowing lightsintricate
glowing lightssci - fi
glowing lightssci-fi
glowing line cracks in armor
glowing lines
glowing liquid
glowing luminescent invocations
glowing magenta face
glowing magenta laser eyes
glowing magic
glowing magic sigils
glowing magical eyes
glowing magical shiny skin
glowing magma sphere
glowing mechanical eye
glowing mechanical eye camera
glowing mint face
glowing moss
glowing mouth
glowing mri x-ray
glowing mushrooms
glowing nacreous clouds
glowing neon
glowing neon bow
glowing neon flowers
glowing neon lights
glowing neon skin
glowing neon vray
glowing oil
glowing oled visor
glowing ominous clouds
glowing orange eyes
glowing orange lasers
glowing orbs
glowing owl
glowing owls
glowing paper lanterns
glowing particles
glowing particulate
glowing peach face
glowing people
glowing pink eyes
glowing pink face
glowing pink laser eyes
glowing plants
glowing pools of lava
glowing porcelain skin
glowing potions
glowing powder
glowing pumpkins under a tree
glowing purple
glowing purple eyes
glowing purple led eyes
glowing quartz crystal skull
glowing rainbow face
glowing rainbow neon ink
glowing raytracing
glowing red
glowing red and gold hair
glowing red eyes
glowing red laser eyes
glowing red mushrooms
glowing red skull
glowing red veins
glowing reflections
glowing retinas
glowing rich colors
glowing robot motifs
glowing runes
glowing screen
glowing screens
glowing skin
glowing skin face
glowing snow
glowing spacesuit
glowing sphere
glowing spiral background
glowing spores flying
glowing sprites
glowing stained glass backdrop
glowing stars
glowing street signs
glowing swirling mist
glowing sword
glowing sword in hand
glowing tattoos
glowing temple in the distance
glowing thin wires
glowing thing wires
glowing third eye
glowing threads of drop
glowing tiny blue lines
glowing tubes
glowing veins
glowing veins of white
glowing violet laser beam eyes
glowing visor
glowing water
glowing water with caustics
glowing white accent lighting
glowing white eyes
glowing white face
glowing white laser eyes
glowing white lasers
glowing white mechanical eye
glowing white neon eyes
glowing white owl
glowing windows
glowing wires
glowing wires everywhere
glowing with colored light
glowing with magic
glowing with silver light
glowing yellow eyes
glowing yellow face
glowing-eyes-and-mouth
glows
glowsticks
glowwave
glowwave girl portrait
glowy
glowy gold eyes
glowy light
glowy pink eyes
glsl - shaders
glue dropping
glued
gluttony
glyphs
gm
gm screen
gmaster
gmaster lens
gmod
gmunk
gnarled
gnarled fingers
gnarled trees
gnarly
gnarly details
gnarly details soft light
gnarly trees
gnoll
gnome
gnome druid
gnome illusionist
gnomon
gnostic
gnosticism
go nagai
go pro
go pro footage
goalie carey price
goat
goat body
goat horns
goat horns on her head
goat horns on his head
goatee
goatee beard
goats
goauche painting
goblin
goblin art
goblin chainwalker
goblin female portrait
goblin funko pop
goblin girl dnd character
goblincore
goblinko
goblins
goblins and trolls
goblins attacking
goblins partying at a rave
god
god - rays
god bless america
god emperor
god emperor biden
god emperor bongbong marcos
god emperor of dune
god had dog chihuahua's head
god is a girl
god is a woman
god is dead
god king of ai art
god light
god light shafts
god lighting
god like
god looking at me
god of cats
god of death
god of dragons
god of dreams
god of moon
god of nature
god of the dead
god of the forest
god of the ocean
god of the sun
god of the underworld
god of thunder
god of time and space
god of war
god of war 2 0 1 8
god of war 2018
god of wealth
god of winter
god ray
god ray across her face
god ray through clouds
god rays
god rays in volumes of fog
god rays of light
god rays pass through the window
god rays through fog
god rays through fog. cinematic
god rays volume light
god rays!!!
god save the queen
god save the queen!!!
god shiva the destroyer
god style
god sun rays
god's rays
god's rays highly detailed
god's ways are mysterious
god-rays
god. dramatic gold blue lighting
god\'s creation
godbeams
goddamn! plus
goddess
goddess art
goddess checking her phone
goddess close-up portrait
goddess inanna
goddess jewelry
goddess of anger
goddess of autumn
goddess of beauty
goddess of death
goddess of death in a graveyard
goddess of dust
goddess of fire
goddess of galaxies
goddess of greek mythology
goddess of illusion
goddess of light
goddess of lightning
goddess of love
goddess of love and peace
goddess of mischief
goddess of nature
goddess of sleep
goddess of space and time
goddess of spring
goddess of summer
goddess of the forest
goddess of the hunt
goddess of the hunt and the moon
goddess of the moon
goddess of the ocean
goddess of the sea
goddess of the sun
goddess of the underworld
goddess of time
goddess of travel
goddess of war
goddess of winter
goddess of wisdom
goddess portrait
goddess queen
goddess shot
goddess. extremely high detail
godes
godes sculpture
godess
godesses
godfather
godhand
godlike
godlike and stoic
godlike design
godlike temperature
godly
godly and ethereal
godly figures
godly light
godmachine
godness
godray
godray lighting
godray on plants
godrays
godrays at sunset
godrays digital painting
godrays from the right
gods
gods and men
gods creation
gods eye view
gods of the deep
gods ray
gods rays
godsend
godsent
godward
godwin akpan
godzilla
godzilla portrait
godzilla tea party with barbie
godzilla vs mechagodzilla
goes hard
gofl course and swimming
gogeta
goggle eyes
goggles
goggles around his neck
goggles on forehead
gogo
gogo : :
gogo dancer
going forward to the sunset
going gray
going super saiyan
gojo satoru
goku
goku as an asian man
goku fighting jesus
goku from dragon ball
goku from dragon ball z
goku from dragonball z
goku fused with optimus prime
goku in fortnite
goku in real life
goku portrait
gold
gold accents
gold accessories
gold adornements
gold adornments
gold and black
gold and black blu
gold and black color scheme
gold and black makeup
gold and black metal
gold and blue
gold and cobalt tiles
gold and gems
gold and green
gold and indigo
gold and luxury materials
gold and pearl necklaces
gold and pearls
gold and purple
gold and red
gold and red accents
gold and red filigree
gold and red metal
gold and silver
gold and silver and brass
gold and silver armour suit
gold and silver highlights
gold and silver ink
gold and silver shapes
gold and silver tones
gold and steel intricate
gold and teal color scheme
gold and treasure
gold and white
gold and white cloak
gold and white eyes
gold and white robes
gold and yellow notched antlers
gold armor
gold armour
gold armour and crown
gold armour suit
gold background
gold bars
gold belt
gold black
gold black and aqua colors
gold black and rainbow colors
gold body
gold bodypaint
gold bra
gold bracelet
gold brocaded dark blue clothes
gold cables
gold chain
gold chain belt
gold chain necklace
gold chains
gold choker
gold cloth
gold clothes
gold coast australia
gold coins
gold collar
gold color scheme
gold colored eyes
gold colours
gold crown
gold crown and filaments
gold dappled light
gold dappled lighting
gold decoration
gold decorations
gold detailed collar
gold detailed collar!!!!!
gold detailed line work
gold details
gold dripping in spiral
gold dust
gold earring
gold elements
gold embroidery
gold encrustations
gold ethereal light
gold eyes
gold eyeshadow
gold filigree
gold flake
gold flaked flowers
gold flakes
gold floral headdress
gold flowers
gold foil
gold fractal details
gold framed
gold galleon ship
gold gates of heaven!!!!!!!!
gold gilded circle halo
gold glasses
gold glow
gold green and purple colors
gold green and purple colors”
gold green blue purple
gold green creature
gold hair
gold hazel eyes
gold heavy armor. dramatic
gold helmet
gold highlights
gold hoop earings
gold hour
gold hour light
gold inlay
gold jewellery
gold jewelry
gold jewerly
gold jewlery
gold layers
gold leaf
gold leaf art
gold leaf painting
gold leaf texture
gold leaves
gold light
gold line tattoos
gold linens
gold madhubani
gold mask
gold medal
gold metal
gold metals
gold mist
gold necklace
gold noble
gold obsidian armor
gold ornaments
gold ornate jewely
gold paint
gold paladin
gold pipelines
gold plated
gold raining in the background
gold ram horns
gold ratio
gold refractions off water
gold rings
gold sci - fi armour
gold shirt
gold silk
gold silver
gold skin
gold skulls
gold soccer shorts
gold sparks
gold speckles
gold striated swirling finish
gold suit
gold suit jacket in snake print
gold sword
gold teeth
gold theme
gold throne
gold tiara
gold tint
gold transparent bikini
gold trim
gold trimmings
gold waistcoat
gold watch
gold waterfalls
gold wings
gold wings on head
gold wires
gold-shot
golden
golden - ratio
golden 1 9 2 0 s
golden accents
golden accessories
golden age
golden age illustration
golden age illustrator
golden age seraph bunkers
golden and black dress
golden and blue hour
golden and copper armor
golden and copper shining armor
golden and silver colors
golden and silver jewerly
golden apple
golden arches
golden arches logo
golden armor
golden armor wearing
golden armor with diamonds
golden armour
golden art nouveau scythe blade
golden aura
golden autumn
golden background
golden background with flowers
golden bay new zealand
golden blue outfit
golden bodypaint
golden chain
golden chains
golden chalice
golden chinese text
golden circlet
golden cityscape
golden clouds
golden collar
golden color scheme
golden colors
golden colour
golden computers
golden corset
golden cracks
golden crown
golden curve composition
golden curve structure
golden dapple lighting
golden dappled dynamic lighting
golden dappled lighting
golden dawn
golden detailing
golden details
golden details!
golden dragon
golden dragonborn
golden dress
golden eal
golden earring
golden earrings
golden edges and fractals
golden elements
golden elements at borders
golden embers flying
golden engines
golden etched armor
golden etched breastplate
golden eyelashes
golden eyes
golden fabric background
golden face tattoos
golden feathers
golden filigree
golden filter
golden fire palette
golden fireflies lights
golden fish in water exoskeleton
golden flowers
golden frame
golden full body armor
golden gate
golden gate bridge
golden gates
golden glistening
golden gloves
golden glow
golden goddess
golden goddess athena
golden grasslands
golden hair
golden hair blowing the wind
golden halo
golden halo behind her head
golden heavenly lights
golden hour
golden hour 4k
golden hour 8 k
golden hour 8k
golden hour background
golden hour cinematic
golden hour closeup photo
golden hour dusk sky
golden hour firefly wisps
golden hour hues
golden hour in beijing
golden hour in boracay
golden hour in manhattan
golden hour in pismo california
golden hour in tokyo
golden hour intricate
golden hour lighing
golden hour light
golden hour lighting
golden hour look
golden hour photo
golden hour photograph
golden hour photography
golden hour scene
golden hour sun
golden hour sun rays
golden hour sunlight
golden hour sunset
golden hour sunset lighting
golden hour time
golden hours
golden hour”
golden hues
golden inlays
golden intricate details
golden jewelery
golden jewellery
golden jewelries
golden jewelry
golden jewelry filigree
golden jewels
golden key
golden lace pattern
golden leaves
golden leaves at frame border
golden light
golden light film grain
golden lighting
golden linings
golden machine parts
golden magical shoulder pads
golden mammoth in a ruined city
golden mask
golden mean
golden meteors
golden miniatures
golden mist
golden morning light
golden mountain in the desert
golden necklace
golden number
golden orbs
golden orbs and fireflies
golden organic structures
golden ornaments
golden pauldrons
golden pillars
golden pommel
golden pyramids
golden rainbow tubing
golden raito
golden ratio
golden ratio  details
golden ratio background
golden ratio composition
golden ratio concept
golden ratio details
golden ratio face
golden ratio illustration
golden ratio jewelry
golden ratio jewelry candy
golden ratio jewelry lights
golden ratio patterns
golden ratio!!
golden ratio!!!
golden ratio!!!!
golden ratio!!!!!
golden ration
golden ration composition
golden rays
golden rays of sunlight
golden retriever
golden ribbon
golden ribbons
golden robes
golden rule
golden sacred tree
golden shapes
golden shining eyes
golden silver elements
golden skin
golden skin tone
golden smooth material
golden snakes
golden spirals
golden steampunk
golden steampunk city atmosphere
golden straight hair
golden sun
golden sunlight
golden sunset
golden sword
golden taurus
golden tech robes
golden teeth
golden theme
golden thread
golden threads
golden throne
golden time
golden toilet
golden towers
golden treasures on the walls
golden trims
golden turquoise steampunk
golden twilight stained glass
golden watery eyes
golden windows
golden wings
golden wood carved in relief
golden yellow eyes
golden!!! palette
golden-hour
golden-ratio
golden-white robes
goldenhour
goldenpalette
goldenwolf
goldfish
goldilocks
goldsrc
golem
golen ratio
golf course
golf course in background
golf digest
golgari findbroker
goliath
gollum
gollum as a titan
golondrinas
gond painting
gondola
gondolas
gondor
gong
gong li
gongbi
gonzalez
gonzalo fuenmayor
gonzo
goo
good
good - looking
good against evil
good anatomy
good and evil
good art
good at cards
good bone structure
good boy giant mecha wolf hound
good cinematic lighting
good clear quality
good colors
good composition
good contrast
good day
good definition of cheekbones
good detail
good drop
good edge control
good face
good focus
good friends
good graphic
good hips and long legs
good housekeeping
good light
good lighted photo
good lighting
good lighting beautiful epic
good looking
good looking face
good morning
good music
good news on sunday
good night
good photography
good posture
good proportions
good quality
good quality lighting
good quality photography
good shading
good smile company
good smile company anime style
good smile company fantasy
good soup
good special effects
good times
good times photograph
good value control
good versus evil
good vibes
good vibrations
good vs evil
good young girl
goodness
goodnight
gooey
gooey skin
goofy
goofy cartoon
goofy smile
googie architecture
googie kitsch aesthetic
googie motifs
google
google arts and cultures
google design
google earth
google glass
google images
google images search result
google logo
google maps
google maps street view
google parti quality
google parti resolution
google point of view
google street view
googles
googly eyes
goonies
goop
goopy
goose
goose!!!!!
goosebumps
goosebumps book cover
goosebumps books
gooseman
gooses
gopher
gopnik in a black leather jacket
gopro
gopro camera
gopro footage
gopro lens
gopro photo
gopro shot
gopro shot of the hell
gordon freeman
gordon freeman as a woman
gordon murray
gordon onslow ford
gordon ramsay
gordon ramsey
gore
gorecore
gorge in the mountain
gorgeous
gorgeous 3d render
gorgeous 4 k
gorgeous and beautiful
gorgeous and huge head ornaments
gorgeous aristocrat robe
gorgeous art
gorgeous atmosphere
gorgeous attractive face
gorgeous background
gorgeous beautiful
gorgeous beautiful woman
gorgeous bikini model
gorgeous brush strokes
gorgeous buildings
gorgeous chinese model
gorgeous cinematic lighting
gorgeous clouds
gorgeous colors
gorgeous composition
gorgeous dark
gorgeous detailed face
gorgeous digital art
gorgeous digital painting
gorgeous elegant attractive
gorgeous eyes
gorgeous face
gorgeous face and figure
gorgeous face portrait
gorgeous faces
gorgeous features
gorgeous female
gorgeous female alison brie
gorgeous female audrey hepburn
gorgeous female jade tailor
gorgeous female paladin
gorgeous female samara weaving
gorgeous figure
gorgeous goddess of leo
gorgeous hair
gorgeous highly detailed face
gorgeous jungle ranger
gorgeous kacey rohl
gorgeous lady
gorgeous latina face
gorgeous lighting
gorgeous maid
gorgeous nebula is born
gorgeous painting
gorgeous portrait
gorgeous romantic sunset
gorgeous scenery
gorgeous soft lighting
gorgeous stella maeve magician
gorgeous sunlight and shadows
gorgeous symmetrical face
gorgeous view
gorgeous volumetric lighting
gorgeous werefox fursona
gorgeous woman
gorgeous young alison brie
gorgeous young korean woman
gorgeous young model
gorgeous!!!
gorget
gorgon
gorilla
gorillaz
gorillaz album cover
gorillaz and daft punk records
gorillaz art
gorillaz phase 1
gorillaz style
goro
goro and kunkle
goro from mortal kombat
goron brute
gorrilaz
gorthalax the insatiable
gory
gossamer wings
gostoso
got shut down
goth
goth aesthetic
goth asthetics
goth chic
goth clown girl
goth cybersuit
goth family
goth girl
goth girl aesthetic
goth makeup
goth ninja
goth people dancing
goth punk clothes
goth style
goth transformers
goth vibe
goth woman
gotham
gotham city
gotham city background
gotham city double exposure
gotham city style
gotham setting
gothcore
gothic
gothic - cyberpunk
gothic 2
gothic aesthetic
gothic and baroque
gothic and futuristic
gothic antique theme
gothic arch frame
gothic architecture
gothic armor
gothic art style
gothic atmosphere
gothic background
gothic baroque citadel
gothic building style
gothic castle
gothic castle in background
gothic castle in the background
gothic cathedral
gothic church background
gothic city streets behind her
gothic clothes
gothic clothing
gothic coat
gothic dress
gothic epic library
gothic epic library concept
gothic eyeliner
gothic face
gothic fantasy
gothic fantasy art
gothic fashion
gothic fog
gothic fog ambience
gothic girl
gothic girl dressed in black
gothic girl face
gothic girl smoking
gothic girl smoking a cigarette
gothic harts
gothic horror
gothic horror vibes
gothic influence
gothic interior
gothic jacket
gothic knight
gothic library
gothic lighting
gothic locomotive
gothic maiden
gothic maiden anime girl
gothic maiden of the dark
gothic maiden shaman
gothic makeup
gothic mansion
gothic mansion room
gothic manuscript with intricate
gothic outfit
gothic painting
gothic princess portrait
gothic quarter
gothic regal
gothic regal and tattered black
gothic revival
gothic rich deep colors
gothic romance
gothic ship on ocean
gothic stained glass style
gothic style
gothic tattoos
gothic wearing
gothic wearing hooded dark cloak
gothic wraith maiden
gothic!!
gothic-futurist architecture
gothique
gotic harts
goto fujita
gouache
gouache 3 d shading
gouache 3d shading
gouache and ink
gouache and wash paints
gouache and wash paints color
gouache illustration
gouache matte painting
gouache on canvas
gouache on paper
gouache paint
gouache painting
gouche
gouf
gouf evangelion
gouf mobile suit
gourd
gourds
gourmet
gourmet and crafts
gourmet cooking
gourmet michelin restaurant
gourmet restaurant
gourmet style
government archive
government archive photograph
goward
gown
goya
goya painting
goya the colossus painting
gpt
gpu
gpu fire
gpus go brrr
gq
gq cover : :
gq magazine
gq magazine cover
graafland
grabado
grabbing a rifle
grace
grace and blessing
grace jones
grace jones fashion
grace kelly
grace moretz
graceful
graceful and elegant
graceful arms
graceful beauty
graceful body structure
graceful curves
graceful face
graceful gaze
gracefully belly dancing pose
gracious
gracked
gradation
graded with davinci resolve
gradient
gradient and patterns wallpaper
gradient aperture
gradient background
gradient black green gold
gradient black to purple
gradient black to silver
gradient blue
gradient blue black
gradient brown to red
gradient brown to silver
gradient brown to white
gradient color
gradient colors
gradient cyan
gradient cyan to blue
gradient cyan to purple
gradient dark purple
gradient dark red
gradient darker to bottom
gradient filter
gradient from green to black
gradient from warm to cold tone
gradient green
gradient green black
gradient hair
gradient iridescence colors
gradient light blue
gradient light green
gradient light pink
gradient light purple
gradient light red
gradient light yellow
gradient maroon
gradient mixed with nebula sky
gradient orange
gradient pastel green
gradient pink
gradient purple
gradient red
gradient red to black
gradient red to yellow
gradient sapphire
gradient shading
gradient studio background
gradient white blue green
gradient white to gold
gradient white to red
gradient white to silver
gradient yellow
gradient yellow to red
gradients
gradients and soft light
grading
gradins
gradins view
gradius
graduation photo
graeme base
graffiti _ background  smoke
graffiti _ background ( smoke )
graffiti and posters on the wall
graffiti art style
graffiti background
graffiti buff
graffiti concrete
graffiti elements
graffiti in an abandoned bunker
graffiti in background
graffiti in the background
graffiti inspired
graffiti letters
graffiti monster
graffiti of a homer simpson
graffiti on a wall
graffiti on the side
graffiti on the wall
graffiti on the walls
graffiti on wall
graffiti on walls
graffiti paint
graffiti painting
graffiti street art
graffiti style
graffiti throws
graffiti wildstyle
graffiti writing
graffitis
grafit studio
grafitti
grafitti art
grafitti paint
grafity
graflex
graflex 4 x 5
graflex camera
graham humphreys
graham ingels
grain
grain 1 9 9 0
grain 1990
grain effect
grain gelios lens
grain kodak
grained risograph
grainy
grainy 1980 cinematography
grainy 1985 cinematography
grainy black and white footage
grainy black and white photo
grainy black-and-white photo
grainy damaged photo
grainy film
grainy film kodak
grainy film photo
grainy film photograph
grainy film photography
grainy film still
grainy filter
grainy footage
grainy image
grainy low quality
grainy low quality photograph
grainy monochrome photo
grainy movie still
grainy photo
grainy photo of an ugly man
grainy photo of an ugly woman
grainy photograph
grainy photorealistic
grainy picture
grainy polaroid
grainy poster art
grainy quality
grainy risograph matte painting
grainy tape
grainy texture
grainy texturized dusty
grainy tri-x pushed to 3200
grainy vintage
grainy. poorly rated
grain”
grammy award winning
granblue fantasy
grand
grand admiral thrawn
grand angel wings
grand battle
grand budapest hotel
grand canyon
grand canyon in background
grand canyon | golden hour
grand composition
grand finale
grand library
grand majestic mountains
grand master
grand piano
grand scale
grand theft auto
grand theft auto 5
grand theft auto cover art
grand theft auto poster
grand theft auto style
grand theft auto v
grand theft auto video game
grand!
grand-composition
grandeur
grandfailure
grandfatherly
grandiose
grandiose portrait
grandma
granite
granny weatherwax
grant us eyes
grant wood
granting him a noble title
granular detail
granville chandor
grape
grape hyacinth
grapes
graph design
graph signals
graphene
graphic
graphic 4 5
graphic art
graphic artist artgerm
graphic artist davi blight
graphic artist magali villeneuve
graphic artist peter mohrbacher
graphic artist steve argyle
graphic composition
graphic design
graphic design background
graphic design contest winner
graphic design poster
graphic design | meta design
graphic detail
graphic glitches
graphic illustration
graphic manga
graphic musicality
graphic novel
graphic novel art
graphic novel cover art
graphic novel style
graphic of enchanted terrarium
graphic print
graphic shapes
graphic style
graphic tees
graphic templates
graphical
graphical errors
graphics
graphics $ 9 9 call now
graphics card
graphics mods
graphic”
graphite
graphite drawing
graphite on paper
graphs
grasp of darkness
grasp of darkness!!!
grasping
grasping intricate filigree
grasping pseudopods
grass
grass - like
grass and flowers
grass and rocks
grass and water
grass and weeds
grass and weeds”
grass field
grass field surrounding the city
grass landscape
grass mountain landscape
grass mountain night landscape
grass spiral mountain landscape
grass surrounding it
grass texture
grass texture material
grass. kodak
grasshopper 3d
grassland
grasslands
grassy
grassy autumn park outdoor
grassy field
grassy fields
grassy hill
grassy hills
grassy knoll
grassy plains
grassy stones
grave
gravel
gravel and scree ground
gravels around
graves
graveside
gravestones
graveyard
graveyard background
graveyard landscape
graveyard landscape at night
graveyard tombstones
gravitas
gravitational lens
gravitational lenses
gravitational lensing
gravitational singularity
gravity
gravity failing
gravity falls
gravity falls style
gravity is strong
gravity mess
gravity rush
gravity rush inspired
gray
gray alien
gray and orange colours
gray anthropomorphic
gray background
gray beard
gray black white and red noir
gray canvas
gray clouds
gray color
gray concrete
gray dull background
gray eyes
gray fog
gray hair
gray hair and beard
gray haired
gray hoodie
gray men
gray mottled skin
gray scale
gray shirt
gray shorts and black socks
gray skies
gray skin
gray skin. grunge
gray sky
gray sky with wispy clouds
gray stone
gray stone wall
gray wasteland
graying hair
grayish
grayscale
grayscale 1 9 0 0 s picture
grayscale 1900 s picture
grayscale monochromatic
grayscale pantone
grayscale photo with red dress
grayscale photography
grayscale phtoto with red dress
grazing
greasy
greasy hair
great
great _ hairstyle
great a'tuin
great action
great architecture
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great atuin
great barrier reef
great body
great britain
great character design
great cinematic lighting
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great compostion
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great detail
great detail. 2 4 mm
great detail. 24 mm
great detailed face
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great digital art with details
great emperor of russian empire
great fantasy mood
great finesse hyper detailed
great high details
great horns
great king of stovokor
great level of detail
great leviathan
great leviathan turtle
great light
great light and shadows
great light and shadows”
great lighting
great likeness
great masterpiece
great migration
great mystical winged serpent
great old one
great photographers
great photography
great pinterest photo
great proportions
great pyramid of giza
great quality
great quality )
great red feather
great river
great sense for composition
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great soul
great space
great teacher onizuka
great textures and lighting
great use of line work and color
great view
great volumetric lighting
great wall
great wave
great wave of hokusai
great wave off kanagawa
great white shark
great wings
great wolf
great works of the masters
greatest art ever made
greatest elven warrior
greatly detailed
greatly illustrated
greatsword
greaves
greco roman statue
greco-roman art
greeble
greeble detail
greeble tech
greeble texture
greeble!!
greebles
greece
greed
greedy
greek
greek acropolis
greek amazon warrior
greek ameera al taweel
greek architecture
greek armor
greek art
greek dark myth
greek dark ritual myth
greek elements
greek ethnicity
greek fabric
greek fantasy
greek fantasy landscape
greek fantasy panorama
greek god
greek god in mecha style
greek goddess
greek goddess athena
greek godness portrait sculpture
greek gods
greek marble statue
greek mask
greek myth
greek myth digital painting
greek mythology
greek mythology characters
greek nose
greek pool
greek romanian
greek sculpture
greek setting
greek statue
greek style
greek temple
greek temple background
greek titan goddess themis
greek white marble
greek-esque columns and ruins
greeks
green
green 1967 ford mustang gt
green accent lighting
green alley
green alleys
green ambient light
green and black
green and black color scheme
green and black colors
green and black hair
green and blue
green and blue and warm theme
green and blue color scheme
green and blue colors
green and blue colour palette
green and blue palette
green and blue tones
green and brown clothes
green and brown color palette
green and brown tones
green and gold
green and gold palette
green and orange theme
green and pink
green and pink colour palette
green and pink fabric
green and purple
green and purple color scheme
green and purple hair
green and purple studio lighting
green and red
green and red plants
green and red radioactive swamp
green and red tones
green and warm theme
green and white
green and yellow
green and yellow colors
green and yellow tones
green apples
green architecture
green armor
green arms
green aura
green background
green backlight leaves
green big eyes
green bikini
green bioluminescent chrometype
green blessing
green blue red colors
green body
green body. red eyes
green bright red
green bulging eyes
green cape
green charts
green cloak
green clothes
green clothing
green color palette
green color scheme
green colored bomber jacket
green colored skin
green colored skin!!
green colored theme
green colors
green colours
green concert light
green corduroy pants
green crt monitors
green crystal
green cyrodiil plains
green digital world
green doom guy
green dragon
green dress
green dress with a black hood
green eays
green eggs and ham
green energy
green eye
green eyes
green eyes fine face pretty face
green eyes!!!
green eyes!!!!
green eyes. 2 4 mm
green eyes. high contrast lines
green face
green facemask
green feathers
green feathery hair
green field
green field with village ruins
green fields
green fields in the background
green fire
green flag
green flags
green floor
green flora forest
green flowing hair
green flush moss
green fog
green foliage
green forest
green fur
green gas spreading across land
green glow
green glowing cracks on face
green glowing eyes
green glowing runes
green glows
green goblin
green goblin portrait
green gold
green grass
green grasse trees and river
green hair
green halter top
green hat
green head
green highlights
green hill
green hill zone
green hills
green hills savanna tree
green hood
green house
green hue
green iris
green jewelry
green jungle
green jungle background
green lantern
green leaves
green legs
green letters
green light
green light coming from window
green light dust
green lighting
green lightning
green lights
green lightsaber
green lily pads
green lines
green litham veil
green magenta and gold
green magenta and gold ”
green magic
green man
green mane
green matrix code
green matrix light
green meadow
green meadows
green mist
green moss
green moss all over
green neon
green neon details
green neon signs
green ocean
green oozing pool pit
green orc female
green pastures stretch for miles
green pickup car
green plant
green plants
green power ranger
green pupills
green radioactive glow
green rain
green ratio
green robe
green robes
green rubber suit godzilla
green saliva
green scary lights
green scheme
green screen
green screen background
green sea
green sea urchin
green shades
green shirt
green skin
green skin with scales
green skin!
green skin. intricate
green skinned
green sky
green slime
green slime dripping
green slime everywhere
green smoggy sky
green smoke
green snakes
green snakes background
green spaces
green sparkles
green square
green stained glass
green steam rising from soup
green steampunk lasers
green suit and bowtie
green supernatural eyes
green swimsuit
green tea
green technology
green terrace
green theme
green tiara
green tint
green tonalities
green tone
green tones
green tones and golden charms
green trees
green tunic
green valley below
green vegetation
green vines
green visor
green wall
green wallpaper background
green walls
green water
green waters
green wavy hair
green whispy fog
green witch walking her garden
green world
green: 0.25
green: 0.5
greenery
greenery growing
greenhouse
greenhouse in the background
greenish blue tones
greenish colors
greenish expired film
greenish lighting
greenish skin
greenish tinge
greens
greens and blues
greens)
greeting card
greeting hand on head
greg
greg ]
greg beeple
greg broadmore
greg crewdson
greg gandy
greg hildebrandt and mark brooks
greg hildebrandt highly detailed
greg hildebrant
greg irons
greg kutkowski
greg manchess
greg olsen
greg rukowski
greg rutgowski
greg ruth
greg ruthkowski
greg ruthowski
greg rutknowski
greg rutkoswki
greg rutkovski
greg rutkovsky
greg rutkowki
greg rutkowksi
greg rutkowksi and artgerm
greg rutkowsi
greg rutkowsk
greg rutkowski
greg rutkowski - c 1 0
greg rutkowski - c 1 5
greg rutkowski - h 7 6 8
greg rutkowski 4 k
greg rutkowski 4k
greg rutkowski 8 k
greg rutkowski 8k
greg rutkowski ; whimsical
greg rutkowski and alex ross
greg rutkowski and anders zorn
greg rutkowski and artgerm
greg rutkowski and carvaggio
greg rutkowski and craig mullins
greg rutkowski and da vinci
greg rutkowski and dave gibbons
greg rutkowski and edgar maxence
greg rutkowski and francis bacon
greg rutkowski and h r giger
greg rutkowski and ilya repin
greg rutkowski and ivan shishkin
greg rutkowski and j.dickenson
greg rutkowski and jakub rebelka
greg rutkowski and james gurney
greg rutkowski and krenz cushart
greg rutkowski and krenzcushart
greg rutkowski and larry elmore
greg rutkowski and luis royo
greg rutkowski and makoto shinka
greg rutkowski and mario testino
greg rutkowski and moebius
greg rutkowski and rob ross
greg rutkowski and ross tran
greg rutkowski and studio ghibli
greg rutkowski and wayne barlowe
greg rutkowski and wlum
greg rutkowski art
greg rutkowski artistry works
greg rutkowski carne_griffiths
greg rutkowski concept art
greg rutkowski detailed
greg rutkowski futurism
greg rutkowski highly detailed
greg rutkowski makoto shinkai
greg rutkowski octane render
greg rutkowski painting
greg rutkowski ruan jia
greg rutkowski studio ghibli
greg rutkowski style
greg rutkowski uhd 8k
greg rutkowski winter
greg rutkowski wlop
greg rutkowski ”
greg rutkowski. cinematic
greg rutkowski. octane render
greg rutkowski. sharp focus
greg rutkowsky
greg rutkuwsky
greg rutkwoski
greg rutkwowski
greg rutowski
greg rutwoski
greg simkins
greg tocchini
gregg araki
gregoire and manon
gregory
gregory euclide
gregory manchess ”
greig fraser
greig fraser cinematography
grek rutkowski
gremlin
gremlins movie
grenade
grenade explosion
grenade in hand
gret ritkowski dramatic
greta garbo
greta thunberg
greta thunberg as gollum
greta thunberg smiling
grey
grey - scale
grey alien
grey aliens
grey and blue theme
grey and dark theme
grey and gold color palette
grey and silver
grey armor
grey backdrop
grey background
grey beard
grey cloth
grey clothes
grey clouds
grey cloudy skies
grey cobble stones
grey color scheme
grey colored eyes
grey colours
grey contacts
grey ears
grey eyes
grey facial flesh
grey forest background
grey forest in the background
grey fur
grey hair
grey hair and a cowboy hat
grey hairs
grey hoodie
grey hoodie opened
grey jacket
grey matter
grey matter and neurons
grey metal body
grey mist
grey mouse ears
grey mustache
grey orange
grey pants and black dress shoes
grey porcelain sculpture
grey robe
grey robes
grey rubber undersuit
grey scale
grey shift
grey skies
grey skies rain
grey skies with two rainbows
grey skin
grey skinned
grey skirt
grey sky
grey snake scale skin
grey striped walls
grey suit
grey sweater
grey tarnished longcoat
grey tarnished robes
grey tones
grey trimmed beard
grey turtleneck coat
grey vegetables
grey warehouse background
grey-eyed
greybeard
greyish blue eyes
greyish tones
greys
greys anatomy book
greyscale
greyscale 1 9 9 0
greyscale 1990
greyscale grain 1 9 6 0
greyscale grain 1960
gric
grid
grid and web
grid arrangement
grid layout
grid montage of eyes
grid montage of shapes
grid of eye shapes
grid of styles
gridless
gridless dnd map
grief
grief and despair
grieving
grieving. intricate
griffin
griffin-like style
griffith
griffith from berserk
grill
grilled artichoke
grilled chicken
grills
grim
grim - lighting
grim - wolf
grim and gloomy lighting
grim atmosphere
grim color palette
grim colors
grim crushing atmosphere
grim dark
grim dark orientalism
grim dawn
grim expression
grim facial expression
grim fantasy
grim fashion model looking up
grim gwent card
grim lighting
grim reaper
grim reaper except a rat
grim vibrance orientalism
grim yet sparkling atmosphere
grim-hatter
grimace
grimacing
grimdark
grimdark art
grimdark atmosphere
grimdark chaos fortress
grimdark dramatic lighting
grimdark fantasy
grimdark fantasy fortress
grimdark horror
grimdark horror style
grimdark matte fantasy painting
grimdark paladin
grimdark realism
grimdark steampunk fantasy
grimdark vibes
grimdark vibrant palette
grime
grime and grunge
grimes
grimes - book 1 album cover
grimgar
grimm
grimoire
grimoire page
grimshaw
grimy
grimy streets backdrop
grimy walls
grin
grind
grindcore
grindhouse
grinning
grinning evily
grinning lasciviously
grinning sinisterly
gripping
grisaille
grit
gritty
gritty and noisy
gritty atmosphere
gritty background
gritty coloring
gritty distopian
gritty feeling
gritty film noir
gritty image
gritty portrait
gritty realism
gritty realistic smoke
gritty tech
gritty texture
gritty textured
gritty world
gritty!!!
grizzled
grizzled beard
grizzly
grizzly bear riding a horse
grog strongjaw
grogu
groom
groomed beard
groomed eyebrows
groomed facial hair
groot
groot costume
groovival aesthetic
groovy
groovy hairband
groovy lighting
groovy vibe
gross
gross alien
grossmünster
grotesk font
grotesque
grotesque hell
grotesque joker
grotesque large alien
grotto
grotty
ground
ground - level medium shot
ground - level view
ground angle
ground angle hd 8 k
ground angle uhd 8 k
ground beef
ground breaking
ground broken
ground camera
ground covered in maggots
ground covered in mist
ground covered with snow
ground detailed
ground explosion
ground fog
ground haze
ground level
ground level camera view
ground level shot
ground level view
ground level view of soviet town
ground meat
ground mist
ground mist swirling vortexes
ground perspective
ground perspective; detailed
ground red meat
ground up angle
ground very detailed
ground view
ground view shot
ground-breaking
ground-level view
groundbreaking
groundbreaking art
group
group intelligence
group mass composition
group of people
group of people in a dark room
group of people in an elevator
group of seven
group photo
group portrait
group portraits
group sit at table
groups of happy humans
groups of happy people
groups of people
growing
growing off a tree
growing out of a giant rose
growling
grown together
grown up
grows up to the sky
growth
growth of a couple
growth on head
growths
grteg rutkowski
gru from despicable me
gruesome
gruffness
grumpy
grumpy  old
grumpy [ old ]
grumpy cat
grumpy cat smiling ear to ear
grumpy gorilla
grunge
grunge aesthetic
grunge aesthetic!!!
grunge aesthetic!!! (
grunge art
grunge cyberpunk
grunge fashion
grunge rock
grunge style
grunge wall
grunged up
grungy
grungy dystopia
grungy gothic
grungy nightmare
grungy steel
grungy woman
grungy woman with rainbow hair
grungy; colorful
grungy; oil on canvas
gryffindor
gryphon
gsociety
gt40
gt40 and countach
gta
gta 4 loading screen
gta 4 loading screen artwork
gta 5
gta 5 artwork
gta 5 comics
gta 5 cover
gta 5 cover art
gta 5 cover art style
gta 5 cover style
gta 5 loading screen
gta 5 loading screen poster
gta 5 screenshot
gta 5 skin tone
gta 6
gta 6 gameplay
gta 6 leak
gta 6 style
gta : dubai
gta art
gta art style
gta artstyle
gta character
gta chinatown
gta chinatown art style
gta chinatown pop art style
gta chinatown wars art style
gta chinatowon art style
gta cover
gta cover art
gta in moscow
gta iv art style
gta loading screen
gta loading screen art
gta san andreas
gta san andreas cover art
gta style
gta v
gta v art
gta v art cover
gta v artwork
gta v character
gta v cover
gta v cover art
gta v cover!!
gta v gameplay screenshot
gta v loading screen
gta v loading screen art
gta v poster
gta v poster style
gta v screenshot
gta v street style
gta v style
gta vi
gta vice city art style
gta vice city cover art
gta vice city remastered
gta vice city style
gta vice city style art
gta5
gta5 style
gtav
gthl.art
gtr xu1
gtx 3090
guache
guan yu
guanajuato
guangjian
guangjian huang
guantanamo bay prison
guanyin
guanyin of the southern seas
guapo
guard
guardian of the holy lake
guardian project
guardians of the galaxy
guardians of the galaxy style
guardians of the galaxy-style 4k
guardrail
guardrails
guards
guards intricate
gucci
gucci bread
gucci catwalk
gucci clothes
gucci glasses
gucci goddess
gucci poster
guccimaze
guerilla heroico
guerilla soldier
guernica
guerrillero heroico
guggimon
gui
gui guimaraes
guide
guido
guido crepax
guido reni style
guild commission
guild wars 2
guildwar artwork
guillaume tholly
guillem h. pongiluppi
guillermo del toro :
guilloche
guillotine
guillotine rgb
guilt
guilty crown
guilty gear
guilty gear art direction
guilty gear art style
guilty gear strive graphics
guilty gear strive splash art
guinea pig looking up at the sky
guitar
guitar concept art
guitar in the shape of a deer
guitar shape build
guitar solo
guitarist
guitarists
guitars
gujian
gulf
gulf of naples
gulf war photography
gulliver ’ s travels
gulliver's travels
gullivers travels
gulper eel
gum rubber outsole
gum tissue
gumball machine
gumball watterson
gumdrop bunnies
gumdrops
gummy bear
gummy bears
gummy candies
gummy worms
gumroad
gun
gun art reference
gun at bottom of screen
gun auction
gun fight
gun metal grey
gun scraps
gunbreaker
gunbroker
gundam
gundam armor
gundam box art
gundam head
gundam is windmill shaped
gundam robot
gundam style
gundam wing style armor
gunfire
gungnir
gungrave
gunma prefecture
gunmetal grey
gunner
gunpla
gunplay
gunpowder smoke
guns
guns blazing
gunship
gunshots
gunshots fired
gunshu
gunslinger
gunslingers
gunsmithing
gunther von hagens
gunwoo kim and j.dickenson
guren lagan
gurenn lagann
gurney
gurney with ambient lighting
guro art
gurren lagan
gurren lagann
gus fring
gushy gills and baleen
gushy gills and blush
gustav
gustav klimpt
gustav klimt 4 k
gustav klimt 4k
gustav klimt 8k
gustav klimt and mel ramos
gustav klimt painting
gustav klimt style
gustav klimt yoh yoshinari
gustav moreau
gustave
gustave bauman
gustave dore and greg rutkowski
gustave dore and junji ito
gustave dore and marco mazzoni
gustave dore background
gustave dore engraving
gustave dore lithography
gustave dore style
gustave dore' background
gustave dore\' background
gustave doré style
gustave klimt
gustave loiseau
gustavo dore
gustavo fring
guts
guts and teeth
guts berserk
guts from berserk
guwahati
guweiz
guweiz and makoto shinkai
guweiz masterpiece
guweiz on artstation pixiv
guweiz on pixiv artstation
guy
guy billout
guy bourdin
guy fieri
guy fieri in star wars
guy fieri in stranger things
guy using the purple fancy suit
guy with horse head
guybrush threepwood
guys
guyver
guyver dark hero
guyver style
gvc design
gwanghwamun
gwar
gwelf
gwen stacy
gwent
gwyn
gym
gymasium
gynoid body
gynoid cyborg body
gyo fujikawa
gyokugan
gypsophila
gypsy
gyro zeppeli
gyroscope
gérard depardieu as wario
h 1 0 2 4
h 1 0 8 0
h 1024
h 1088
h 576
h 6 4 0
h 640
h 7 0 4
h 7 6 8
h 768
h 8 0 0
h 8 0 0 c 1 0. 0
h 800
h 9 6 0
h 960
h e giger
h r
h r geiger
h r giger
h r giger and alphonse mucha
h r giger style
h- 1024
h. giger
h. hydrochaeri
h. hydrochaeris
h. kyoht luterman
h. p lovecraft
h. p. lovecraft
h. r geiger
h. r giger
h. r. giger
h. r. giger and beksinski
h. r. giger and tom bagshaw
h. r. giger hieroglyphs
h. r. giger style
h. u. d
h.p. lovecraft as cthulhu
h.r giger
h.r. geiger
h.r. giger and jeffrey smith
h.r. giger city
h.r. giger design of iron man
h.r. giger style
h.r.giger
h0
h3h3
haafingar hold
habitat 6 7
habitat 67
habitat 67 background
habl telescope
habs jersey
habs logo
habs mascot youppi pokemon shiny
hacker
hackerman
hacking
hacking effects
hacking into the mainframe
hacknaut cyberpunk
hackrobat
hackrobat!!!
had
hades
hades supergiant
hadouken
hadron antimatter vacuum reactor
hadron collider
hadron collider technology
haeckel
haeckel and alasdair gray
haggard
hagrid
hagrid in cute anime style
hahaha
hahaha!'
hahahaha
haibane renmei
haida
haida gwaii
hail satan
hailee steinfeld
hailstorm
hair
hair : long brown
hair are cable wires
hair are curled wired cables
hair are wired cables
hair becoming autumn red leaves
hair blackbangs hair
hair blocking eyes
hair blowing
hair blowing in the wind
hair blowing the wind
hair blue two long braids
hair bow
hair bun
hair color
hair coloring
hair covering eyes
hair detailing
hair died to a hazelnut brown
hair down
hair down to floor
hair down to waist
hair dyed to a hazelnut brown
hair fanned around
hair floating covering chest
hair floating in the wind
hair flowing
hair flowing down
hair fluid
hair fluttering in the wind
hair gel combed backwards
hair in a ponytail
hair in a ponytail. shirt
hair in pigtails
hair in slick low ponytail
hair in the wind
hair in wind
hair is floating
hair is the focus
hair jewellery
hair light
hair like dark clouds
hair like fire
hair looks like a helmet
hair loopies
hair loops
hair made of fire
hair made of shimmering ghosts
hair made of trees
hair monster
hair ornaments
hair over face
hair over her eyes
hair ponytail
hair strands
hair styled in a bun
hair styles
hair texture
hair tied and braided loosely
hair tied back
hair tied in a bun
hair tied in a cute way
hair tied in a ponytail
hair tied up in a bun
hair tied up in a pony tail
hair visible
hair waving in the wind
hair whitebangs hair
hair worn up
hairband
hairdryer
hairless
hairs
hairs fluttering on the wing
hairs fly in the wind
hairspray
hairstyle
hairstyle red mohawk
hairworks
hairy
hairy arms
hairy bodies
hairy body
hairy chest
hairy chest and hairy body
hairy legs
hairy orange body
hairy orange skin
hairy shoulders
hairy torso
hajime isayama
hajime katoki
hajime no ipo box fight
hajime no ippo manga inspired
hajime sorayama aaron horkey
hajime sorayama designed girl
hajime sorayama style
hajime soryama
hajime yatate
hakama kimono
hakusensha
halation
halations
halberd
half & half hair
half & half hair dye
half - body
half - body composition
half - body shot
half - closed eyes
half - demon horn
half - electric striking woman
half - electric woman
half - finished robot
half - frame square glasses
half - length head portrait
half - length photo
half - length portrait
half - life
half - life 2
half - life engine
half - life style
half - occupied with people
half - orc
half - turn
half a smile on her soul
half african
half and half
half and half dye
half and half hair dye
half android
half asian
half bear
half blue
half blueprint
half body
half body cropping
half body photo
half body portrait
half body portrait of juliana
half body shot
half bunny
half cat
half cat - half mouse
half crocodile
half cut
half cyborg
half dog
half done
half dragon
half elephant
half elf woman
half face
half face in the water
half figure front
half figure shot
half fish
half goat
half grizzly bear
half half hair
half helmet
half horse
half horse - half mouse
half human
half human face
half human half alien
half human half robot
half human half spider
half humanoid
half image
half in shadow
half invoker half megumin
half length
half length shot
half life
half life 2
half life 2 on a nintendo 64
half life 3
half life 3 ( 2 0 2 6 )
half life 32026
half life logo on chest
half lion
half machine
half male and half female
half man
half man half angus bull
half man half asian black bear
half man half asian black bull
half man half bear half pig
half man half wolf
half moon
half octopus
half octopus half machine
half onesided smile
half orc half elf woman
half pig
half pikachu
half portrait
half rear lighting
half red
half robot
half robot and half woman
half robot half bear
half shark alligator half man
half shaved haircut
half skull face
half skull half face
half smile
half submerged
half submerged in heavy sand
half submerged in water
half textured half wireframe
half tiger
half tone
half turned around
half turtle
half updo hairstyle
half wolf
half wolf half shark
half woman
half woman half skeleton
half wooden pinocchio
half-body
half-body portrait
half-body shot
half-elf time wizard
half-frame square glasses
half-length portrait
half-life
half-life 2
half-life 2: episode 2 (pc)
half-life 3
half-man
half-plate armor
half-tone-line-stacking
half-turned lady in evening gown
half-woman half-butterfly
halfbody headshot
halfbody portrait
halfing
halfmoon in space
halfrear
halfrear lighting
halftone
halftone dots
halftone effect
halftone pattern
halftone print
halftone texture
halftones
halfway through
hall of mirrors
halle berry
halloween
halloween art style
halloween atmosphere
halloween celebration
halloween decorations
halloween film
halloween ghost under a sheet
halloween night
halloween scene
halloween theme
halloween wallpaper with ghosts
halls
halls of space
hallucination
hallucinatory
hallucinatory art
hallucinogen
hallucinogenic
halluzinogenic
hallway
hallway landscape
hallways
halo
halo / nimbus
halo 3
halo above head
halo ark
halo array
halo effect
halo halo halo halo 8k
halo infinite
halo infinite!!
halo master chief
halo of fire
halo of light
halo over her head
halo reach
halo warthog
halo. octane render
halogen
halos
halter neck
halter top
halter-top
haluzinogetic
halyomorpha halys
ham
hamar
hamburger
hamburger mix jellyfish
hamburger monster
hamburgers
hamburgers and soda
hamburglar
hamlet
hamlets
hammer
hammer and sickle
hammer and sickles
hammer weapon
hammer's slammers
hammers
hammershoi
hammershøi
hamsa hand
hamster
hamsters
hamsters holding rifles
han solo
hana alisa omer
hana yata
hanafuda
hanayamata
hanbok
hanbok apron
hand
hand - drawn
hand - drawn 2 d art
hand - drawn 2d art
hand - drawn animation
hand - drawn digital art
hand - painted
hand - tinted
hand built ceramics
hand carved
hand carved brown resin
hand controlling
hand draw
hand drawing
hand drawn
hand drawn animation
hand drawn cartoon
hand drawn illustration
hand drawn svg
hand drawn type
hand gesture
hand gestures
hand glazed pottery shards
hand grenades
hand holding a knife
hand holding cap brim
hand holding cigarette
hand holding medieval sword
hand holdings
hand instead of a face
hand made
hand model
hand of fear
hand on cheek
hand on her chin
hand on hip
hand on hips
hand on his cheek
hand on table
hand on the doorknob
hand over mouth
hand painted
hand painted cartoon art style
hand painted style
hand painted textures
hand painted textures on model
hand photography
hand to hand combat
hand to hand combat with machete
hand transplanted to head
hand with five fingers
hand wraps
hand-drawn
hand-painted
handbag
handcrafted
handcrafted paper background
handcuffed
handcuffs
handcuffs king size canopy bed
handdrawn
handgun
handguns
handheld
handlebar mustache
handling laboratory equipment
handling riffle on chest
handmade
handmade pottery
handpaint texture
handpainted
hands
hands anatomy
hands behind back
hands behind her body pose!
hands behind her pose
hands behind her pose!
hands crossed
hands down
hands hidden
hands hidden under the bouquet
hands holding big glowing orbs
hands in air
hands in her hair
hands in her hair. side-view
hands in pockets
hands in the air
hands not visible
hands of men
hands on counter
hands on face
hands on hips
hands pressed together in bow
hands raised
hands raised in the air
hands reaching for her
hands retouched
hands shielding face
hands straight down
hands that are fox - paws
hands touching light drops
hands up
hands which exchange seeds
hands with five fingers
handsome
handsome and attractive
handsome and elegant
handsome anime eyes
handsome anime pose
handsome attractive face
handsome chad chin
handsome detailed face
handsome donald trump
handsome drow
handsome eyes
handsome face
handsome face and beautiful face
handsome frankenstein
handsome girl
handsome guy in demon slayer art
handsome hip hop young black man
handsome japanese demon boy
handsome joe biden
handsome male
handsome male vampire
handsome man
handsome prince
handsome prince of persia
handsome saitama
handsome saitama. intricate
handsome squidward
handsome squidward as male
handsome squidward portrait
handsome stunning realistic
handsome symmetrical faces
handsome weasel fursona portrait
handsome young man
handsome young man face
handwriting title on the left
handwritten
handwritten equation heaven
hanfu
hangar
hanging
hanging beef carcasses
hanging bridge
hanging bridge!!
hanging cables
hanging from a hot air balloon
hanging from a tree
hanging from the ceiling
hanging from white web
hanging gardens
hanging in the louvre
hanging lanterns
hanging out with orbs
hanging plants
hanging rope
hanging scroll
hanging scroll on wall
hanging silk scroll
hanging tiffany chandeliers
hanging trees
hanging upside down
hanging upside down from a tree
hanging veins
hanging vines
hangzhou
hank hill
hank hill as the god of propane
hank hill from king of the hill
hank hill wearing a white toga
hanna barbera
hanna barbera style
hanna montana
hanna moon
hannah af klint
hannah dodd
hannah hoch
hannah yata
hannah yata charlie immer
hannibal
hannibal lecter
hannibal lector
hannover
hans
hans bellmer and nadav kander
hans bellmer and wlop
hans belmer
hans dahl
hans giger
hans holbein
hans ruedi giger
hans thoma
hans zatka
hans zatska
hans zatzka
hans zimmer soundtrack
hansa yellow
hansel and gretel
hanuman!! head building
hanumanhead building
hanzo from overwatch
haphazard
haphazardly executed
haphazardly layered scenes
happily smiling at the camera
happily tired
happiness
happiness!
happy
happy accidents
happy ambience
happy and beautiful
happy and disarmed
happy and smiling
happy and spirited expression
happy apearance
happy appearance
happy atmosphere
happy big chungus
happy birthday
happy birthday candles
happy brave magical cuteness
happy cat
happy chef
happy clouds behind
happy color
happy colors
happy colors dariusz zawadzki
happy colors. rob gonsalves
happy colours
happy couple
happy cozy feelings
happy dachshund catching a ball
happy dog
happy expression
happy expressions
happy eyes
happy face
happy faces
happy facial expression
happy family
happy fashion model
happy fashion model face
happy feeling
happy female alien
happy finnish lapphund dog
happy friend
happy girl
happy grin
happy intricate
happy italian beach scene
happy kid
happy lighting
happy look
happy meal toy
happy mood
happy people
happy robot
happy smile
happy smiley
happy smiling
happy smiling human eye
happy smiling human eyes
happy sunny day
happy toes
happy tones
happy tree friends
happy trees
happy trippy mood
happy vibes
happy wise. he has bouncy belly
happy with his mouth open
happy!!!
harajuku
harajuku fashion
harajuku hair
harajuku street fashion
harbor
harbour
harbour in background
hard
hard angles
hard atmosphere
hard backlight
hard black shadows
hard boiled
hard breathing
hard brush
hard brush painting
hard brush strokes
hard clay
hard contrast
hard cover book
hard dramatic lighting
hard edges
hard edges concept art
hard edges! graffiti
hard edgesgraffiti
hard flash
hard focus
hard hat
hard key light
hard light
hard light and long shadows
hard light digital painting
hard lighting
hard lighting!
hard line
hard lines
hard morning light
hard outline
hard paint
hard predatory look
hard rain
hard rain falling
hard rain pouring straight down
hard rubber chest
hard sci fi
hard sci-fi
hard science fiction
hard shadows
hard surface
hard surface 3 d
hard surface character pinterest
hard surface concept art
hard surface model
hard surface modeling
hard surface modelling
hard surfaces
hard surfaces modelling
hardcore
hardcore techno
hardmesh
hardmesh post
hardops
hardsurface
hardsurface armour
hardsurface modelling
hardturm
hardware
hardwood floor
hardwood floor boards
hardwood floors
hardy fowler
harem
haris nukem
harlem
harley davidson
harley davidson motorbike
harley queen
harley quinn
harley quinn film still
harley quinn standing
harmonic colors
harmonious
harmonious colors
harmonized
harmony
harmony black room
harmony blue jacket
harmony color
harmony of
harmony of butterfly
harmony of desert
harmony of nature
harmony of neon glowing coral
harmony of swamp
harmony of swirly clouds
harmony of the spheres
harmony of the universe
harness
harnesses
harnesses and garters
harold mccauley
harold newton
harp
harper's bazaar
harper's bazaar art
harpers bazaar
harpers bazaar art
harpy
harpy eagle framed photo
harpy woman
harriet tubman skateboarding
harrison ford
harrison ford from indiana jone
harrowing
harrowing apocalyptic atmosphere
harry
harry clarke artwork
harry potter
harry potter at a tardis console
harry potter film
harry potter in a maid’s uniform
harry potter in cyberpunk
harry potter movie screenshot
harry potter portrait
harry potter riding a broomstick
harry potter series setting
harry potter smoking weed
harry potter style
harry potter!!
harry styles
harry volk clip art style
harry wingfield
harry's revenge
harsh
harsh bloom lighting
harsh contrast lighting
harsh contrasts
harsh fairy tale
harsh flash
harsh flash photo
harsh flash photo at night
harsh flash photography
harsh good looking face
harsh light
harsh light and shadow
harsh lighting
harsh neon lighting
harsh neon lights
harsh overhead sunlight
harsh shadows
harsh shadows and reflections
harsh sun light
harsh sunlight
hartper's bazaar
hartpers bazaar
haruhi suzumiya
haruhiko mikimoto
harumi
haruno sakura
harvest
harvest fall vibrance
harvest fall vibrancy
has a laurel wreath
has a very realistic look to it
has black wings
has blood
has cables
has cyberpunk style
has fire powers
has gold
has horns: a sharp
has long blonde hair
has two adorable blue eyes
hasan piker
hasanabi
hasbro
hasbulla
hasbulla magomedov
hashima island
hashing
hashtags
hasselblad
hasselblad 5 0 mm f 1. 8 lens
hasselblad 500c
hasselblad camera
hasselblad dslr
hasselblad film bokeh
hasselblad medium format
hasselblad medium format camera
hasselblad nikon
hasselblad photo
hasselblad photograph
hasselblad photography
hasselblad quality
hasselblad x 1 d
hasselblad x 1 d - 5 0 c
hasselblad x 1 d - 50 c
hasselblad x 1 d sharp focus
hasselblad x1d-50c
hasselblade shot
hasselblade wide shot
hasselblatt
hasselbrad photography
hassleblad
hastur
hastur the king in yellow
hasui kwase
hat
hat and hoodie
hat covering eyes
hatched ear
hatched pointed ears
hatching
hate
hatred
hats
hatsune miku
hatsune miku cosplay
hatsune miku face
hatsune miku in warhammer 40k
hatsune miku portrait
hatsune miku short hair
haughty facial expression
haunted
haunted and sad expression
haunted background
haunted expression
haunted eyes
haunted forest
haunted gothic hotel
haunted gravestones
haunted green swirling souls
haunted house
haunted house interior
haunted house themed
haunted kodachrome
haunted liminal abandoned room
haunted mansion
haunted painting
haunted sad expression
haunted spaceship hallway
haunted woods
haunting
haunting and spooky
haunting appearance
haunting atmosphere
haunting beautiful brush strokes
haunting beautiful young woman
haunting eyes
haunting face
haunting!
hauntingly beautiful
hauntingly beautiful art
hauntingly beautiful zombie
hauntingly surreal
hauntology
haute couture
haute couture fashion
haute couture fashion shoot
haute couture! fashion!
haute couturefashion
hauting
haval f 7
havanese dog
have a call to action
have a sense of design
having a cool party
having a good time
having a great time
having a picnic
having a snack
having a time of our un-life
having an awkward dinner date
having fun
having fun in the sun
having fun. vibrant
havoc
havrylo pustoviyt
hawaii
hawaii beach
hawaiian shirt
hawk
hawk wings
hawken
hawkeye
hawkins
hay
hayao
hayao miyazak
hayao miyazaki style
hayao miyazaki's animation style
hayao miyazaki\'s movies
hayao miyazki
hayao myiazaki
hayden christensen
haymaker impact
hazard stripes
haze
haze over the shoulder shot
hazel amber eyes
hazel eyes
hazel green eyes
hazel iris eyes
hazer and light in dark
hazler eyes
hazmat
hazmat suits
hazy
hazy and dreary
hazy and misty
hazy atmosphere
hazy fog
hazy light rays
hazy memory
hazy morning foggy
hazy sun and mystical
hazy sunset with dramatic clouds
hazy water
hbo
hbo adaptation
hbo showtime boxing
hcl
hd
hd - n 9
hd - photo
hd 16k
hd 3 d
hd 35mm photography
hd 3d mixed media collage
hd 4 k
hd 4 k 8 k
hd 4 k high detailed
hd 4 k photo
hd 4d
hd 4k
hd 4k digital art
hd 4k game screenshot
hd 4k photo
hd 4k ultra
hd 8 k
hd 8 k film photography
hd 8k
hd 8k image high detail
hd aerial photography
hd anime
hd anime cityscape
hd anime wallaper
hd anime wallpaper
hd art
hd artwork
hd award-winning photo
hd blender render
hd concept art
hd criterion
hd detailed texture
hd digital art
hd digital painting
hd digital photography
hd digital render
hd display
hd dslr 8k+
hd elegant
hd fantasy art
hd food photography
hd footage
hd fur
hd illustration
hd image
hd macro photograph
hd macro photographs
hd masterpiece
hd mixed media
hd mixed media 3d collage
hd mixed media collage
hd mod
hd movie photo
hd nature photography
hd octane render
hd phone wallpaper
hd photo
hd photograph
hd photography
hd photography 4k
hd photography street
hd photorealism
hd photorealistic
hd photorealistic image
hd picture
hd professional photography
hd quality
hd realistic
hd remaster
hd remastered
hd render
hd render 4k
hd rendering
hd resolution
hd rtx
hd scan
hd screenshot
hd shot
hd star wars photo
hd texture
hd textures
hd vector art
hd vfx
hd vfx - 9
hd vibrant
hd wallpaper
hd —h 1024
hd''
hdd
hdpe
hdr
hdr 4 k
hdr 4k
hdr 8 k
hdr 8 k dop dof
hdr 8k
hdr ambient background
hdr cinematic lighting
hdr color
hdr colors
hdr detail
hdr digital painting
hdr dramatic cinematic lighting
hdr fabric textures
hdr human detailed
hdr lighting
hdr on
hdr photo
hdr photograph
hdr photography
hdr photorealism
hdr raytracing
hdr refractions
hdr render
hdr render in unreal engine 5
hdr shot
hdr smooth
hdr!
hdri
hdri lighting
hdrp
hdrp render
hdt
hd”
he - man
he - man!!!!!
he also wears a grey beanie
he can ’ t believe his eyes
he dreams of all the battles won
he got a big french musctache
he has a 5 o'clock shadow
he has a beard and graying hair
he has a big egg
he has a devastated expression
he has a glow coming from him
he has a pistol
he has a pistol!!
he has a red hat
he has a treasure with him
he has an elongated head shape
he has big knives on his belt
he has boots
he has dark grey hairs
he has eyes of fire
he has goat man legs
he has headphones
he has no nose
he has short curly brown hair
he has yellow wolf eyes
he holds her while she sleeps
he is 3 5 years old
he is a long boi ”
he is a mad old man
he is a rockstar
he is a white male from vermont
he is about 2 0 years old
he is about 2 5 years old
he is about 20 years old
he is about 20 years old | short
he is about 3 0 years old
he is about 30 years old
he is about 4 0 years old
he is about 40 years old
he is about 5 0 years old
he is about 50 years old
he is about 6 0 years old
he is about 60 years old
he is about 7 0 years old
he is about 8 0 years old
he is all alone
he is an artist
he is angry
he is at camp
he is at college
he is carrying a black briefcase
he is casting a lighting spell
he is covered with barnacles
he is dancing
he is floundering
he is from virginia
he is greeting you warmly
he is happy
he is holding a large book
he is holding a smartphone
he is in a alchemist lab
he is in a mosque
he is in shock
he is sad
he is screaming
he is smiling
he is smoking a cigarette
he is the goodness in manking
he is traversing a shadowy city
he is very happy
he is very relaxed
he is wearing a black
he is wearing a black t-shirt
he is wearing a black trenchcoat
he is wearing a brown sweater
he is wearing a hat
he is wearing a suit
he is wearing a top hat
he is wearing a trenchcoat
he is! about 3 0 years old
he looks like a human minion
he looks like tye sheridan
he looks very sophisticated
he man
he wears a big coat
he wears a blue shirt
he wears an eyepatch
he wears dark visors
he's a hitman in peacoat
he's on an old sailing boat
he's very menacing and evil
he-man
he-man rides the battle cat
he-man with a dark manner
he-man!!!!!
head
head - and - shoulders shot
head - on centralized
head and body
head and bodyshot
head and bust only
head and chest only
head and full body view
head and shoulder
head and shoulder portrait
head and shoulder shot
head and shoulders
head and shoulders 8 0 mm camera
head and shoulders in frame
head and shoulders masterpiece
head and shoulders photography
head and shoulders portrait
head and shoulders shot
head and shoulders view
head and torso
head and torso only
head and upper body
head and upper body in frame
head and upper body only
head and upper body portrait
head and waist potrait
head bent back in laughter
head bowed slightly
head centered portrait
head details
head down
head exploding
head focus
head fully visible
head in a jar
head in focus
head in frame
head in hands
head in profile
head is an egg
head is an onion
head is centered
head is up
head looking up
head macro
head made of carnations flower
head made of giant daisies
head neck shoulders
head of a bear
head of a lion
head of an eagle
head of broccoli
head of green hedgehog
head of old man
head on
head only
head piece
head portrait
head scarf
head shoot
head shot
head slightly tilted
head straight down
head tilted down
head tilted downward
head to toe
head to waist
head torso legs feet
head turned
head turned to the side
head-and-shoulders shot
head-on centralized
head-to-shoulder
head-to-toe
headbang till your brain bleeds
headbanging
headcrab
headdress
heade
heade martin johnson
header
header text”
header with logo
headless
headlight washer
headlights
headlights are on
headlights shine with neon light
headlights turned on
headphone stand
headphones
headphones dj rave
headphones on
headphones on head
headphones on his head
headpiece
headpiece headpiece headpiece
headpiecehigh quality
headroom
heads
heads are all over the ground
heads of wooden of bird face
headsculpt
headset
headshot
headshot and bodyshot
headshot half figure
headshot of young female furry
headshot photo
headshot photograph
headshot photography
headshot photoshoot
headshot portrait
headshot profile picture
headspace
healer
healing
healing energy
healing glowing lights
healing pods
healing through motion
healing tubes
health
health bar hud
health potion
health spa and meditation center
health supporter
healthcare
healthcare worker
healthy
hear no evil
hearst castle
heart
heart - shaped face
heart effects
heart emblem on chest
heart eyes
heart kidney lungs
heart made of flowers
heart of darkness
heart of the internet
heart operation
heart rate
heart shaped face
heart-shaped face
heartbreak
heartbreaking
heartbroken
hearthstone
hearthstone art
hearthstone art style
hearthstone artwork
hearthstone card
hearthstone card art
hearthstone card artwork
hearthstone card game
hearthstone card game artwork
hearthstone card game artwork.
hearthstone card game artwork. ”
hearthstone coloring style
hearthstone colour style
hearthstone concept art
hearthstone inspired
hearthstone official splash art
hearthstone splash art
hearthstone style
hearthstone style art
hearthstone weapon art
hearts
hearts of iron portrait
hearts of iron portrait style
hearts symbol
heartstone
heartstone original art style
heartwarming
hearty breakfast
heat
heat death of the universe
heat haze
heat ripples
heat shimmering
heat wave
heat waves
heated coils
heath clifford
heather theurer
heatwave
heaven
heaven and hell
heaven background
heaven gate
heaven in the top
heaven lighting
heaven on earth
heaven paradise
heaven pink
heaven planet in background
heaven vs hell
heaven!!!!!!!!
heavenly
heavenly atmosphere
heavenly bright light
heavenly clouds
heavenly color scheme
heavenly colors
heavenly dramatic lighting
heavenly glow
heavenly landscape
heavenly light
heavenly lighting
heavenly lights
heavenly marble
heavenly rapture
heavenly symbols
heavenlydaemonic
heavens
heavens gate
heavey metal magazine cover
heavily armed
heavily armored
heavily armoured
heavily detailed
heavily detailed and intricate
heavily downvoted
heavily equipped
heavily exaggerated proportions
heavily gothic ornamental
heavily ornamental
heavily stylized
heavily upvoted
heavy
heavy - lidded eyes
heavy armor
heavy atmosphere
heavy atmospheric
heavy battle
heavy birch forest
heavy black obsidian armor
heavy blue led lights
heavy blues rock
heavy body modification
heavy bokeh
heavy bokeh and blur
heavy boots
heavy brow
heavy brown metallic armor
heavy brushstrokes
heavy cavalry
heavy chestplate armor
heavy cloak
heavy clouds
heavy color compression filter
heavy colour compression
heavy compression filter
heavy conduits
heavy contour lines
heavy contrast
heavy cubism
heavy depth of field
heavy detail
heavy detailed
heavy exaggerated proportions
heavy eyebrows
heavy eyeliner
heavy eyes to the side
heavy fantasy armor
heavy film grain
heavy filmgrain
heavy fire
heavy fire on the background
heavy fog
heavy fog machine
heavy forest
heavy forest outside
heavy gesture style closeup
heavy gold armour
heavy gold jewellery
heavy gold obsidian armor
heavy golden armour
heavy grain
heavy grain high quality
heavy grain-s 150
heavy grainy picture
heavy green
heavy gun
heavy impasto
heavy impasto technique
heavy in detail
heavy ink
heavy ink!!!!!!! mike mignola
heavy inkmike mignola
heavy jpeg artifact
heavy jpeg artifact blurry
heavy knight helmet
heavy lens flare
heavy lensflare
heavy line work
heavy lineart
heavy lines
heavy linework
heavy looking
heavy machinegun on top
heavy machinery
heavy make up
heavy makeup
heavy mascara
heavy metal
heavy metal 1981
heavy metal album cover
heavy metal armor
heavy metal art style
heavy metal artwork
heavy metal band promo
heavy metal comic cover art
heavy metal concert
heavy metal magazine
heavy metal magazine cover
heavy metal rave shoes
heavy metal style
heavy metal the movie
heavy metal tshirt design
heavy metal!
heavy mist
heavy mood
heavy motion blur
heavy newsprint raster
heavy outlines
heavy paint
heavy pigment
heavy plate armor
heavy rain
heavy rain and lightning
heavy rain and mist
heavy rain approaching
heavy rain from thick clouds
heavy rainfall
heavy raining
heavy rainning at tokyo night
heavy rainy
heavy rock
heavy rock band promo photo
heavy rock concert
heavy shading
heavy shadows
heavy smoke
heavy snow fall
heavy snow storm
heavy stacks of pearl necklaces
heavy storm
heavy traffic
heavy two tone shading
heavy under paint
heavy vignette
heavy vignette!
heavy weapons fire
heavy weapons guy
heavy white and golden armor
heavy winter aesthetics
heavy-duty boots
heavy-gauge filigree
hebrew
hecate
hecate goddess
hectic
hedge
hedgehog
hedgehog babies
hedgehog cosmonaut portrait
hedgehog magus
hedgehog wearing a top hat
hedgehog with glasses
hedgehog with pipe
hedgemaze
hedges
hedi slimane
hedonic treadmill
hedonism
heels
heels on her feet
hegre
heian
heidi
height 1 7 8
height detailed body elements
height detailed hd realistic 8 k
height map
height realistic details
heilung
heironymus bosch
heise jinyao
heise-lian yan fang
heisenberg
helen mcrory
helen the bean queen
helene fischer in downton abbey
helene frankenthaler
helga pataki's teeth
helga patakis teeth
helghast
helianthus flowers
helicopter
helicopter drones
helicopter footage over city
helicopter view
helicopters
helicopters and tremendous birds
helicopters firing
helio oiticica
helios 44-2
helipad
hell
hell and heaven
hell background
hell fire
hell gate
hell hurricance
hell in the background
hell in the bottom
hell is other people
hell on earth
hell scape
hell scheme
hell storm
hell with people suffering
hellbards
hellboy
hellcore
hellenistic sculptures
hellfire
hellfire background
hellhounds
helliquary
hellish
hellish background
hellish doom hallway
hellish landscape
hellish scene
hellish setting
hellish!!!
hellmut newton
hello
hello darkness my old friend
hello kitty
hello world
hellraiser
hellscape
helltaker
helluva boss
helm
helmet
helmet instead of a head
helmet is off
helmet made of glass
helmet of a forgotten deity
helmet off
helmet on
helmet on face
helmet removed
helmet view
helmet visor smashed
helmet with led lights
helmets
helmond
helmut lang
help
help me
helpful
helpless
helsinki
helvetica
heman style
hemera
hemlocks
hemp
hen of the woods mushrooms
heng z
henri - edmond
henri berthaut
henri cartier - bresson
henri cartier bresson
henri de toulouse - lautrec
henri gillet
henri moore
henri moore giant
henri selick
henrik fisker
henrique alvim corrêa
henry ascensio
henry asencio
henry cartier bresson
henry cavill
henry cavill as a greek god
henry cavill as a space marine
henry cavill as a warrior
henry cavill as arthas menethil
henry cavill as batman
henry cavill as geralt of rivia
henry cavill as james bond
henry cavill as wolverine
henry cavill is a greek god
henry cavill!!!
henry darger
henry de toulouse lautrec
henry dorsett case
henry dreyfuss
henry justice
henry meynell rheam
henry moore
henry taylor
henry viii
heonhwa choe
hephaestus
her back is to us
her belly button is exposed
her belly is fat and round
her black hair is a long curly
her body made of flames
her expression is solemn
her eyes are closed
her face flushing and sweat
her face framed with curls
her face hurts
her face in discs
her face is a mauve flower
her face is coated in a white
her face is coated in a whitish
her face is covered with mud
her face is in shadow
her face looks like an orchid
her face muddy and sweat
her foot sticking out
her footwear is red high heels
her friend
her friend the cunning coyote
her gaze is downcast
her hair blowing in the wind
her hair flowing down
her hair is in a pony tail
her hair is long and straight
her hair is made out of fire
her hair is natural disheveled
her hair is on fire
her hair is silver
her hair is the milky way
her hair is tied above her head
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hipster hair fringe
hires
hires textures
hiro
hiroaki samura
hiroaki tsutsumi style
hirohiko araki art
hirohiko araki artwork
hirohiko araki style
hiromasa ogura
hironaka
hiroo isono
hiroshi sugimoto
hiroya oku
hiroya oku painterly
hiroyuki imaishi
hiroyuki kato
hiroyuki takahashi color scheme
hiroyuki-mitsume takahashi
hirstute and muscled
his arms and legs are hanging
his arms are behind his back
his arms spread
his arms spread. ready to fly
his back is turned
his body buried in the sand
his cape is the american flag
his clothes are torn and dirty
his eyes are bleeding intense
his eyes are closed
his eyes are red and glowing
his eyes glowing red
his eyes glowing yellow
his eyes look wise
his frank blue eyes
his friends are angry
his hair is messy and unkempt
his hair is messy and wild
his hair moves with the wind
his hands buried in his face
his head covered in jewels
his head covered in roots
his head is a pear
his head is a skull
his last moment
his legs spread apart
his mind contemplating eternity
his nose is a black beak
his one yes glow red
his palms are sweaty
his smile threw shadows
his trunk is a huge tentacle
his trunk is a long tentacle
his whole head fits in the frame
hisashi eguchi
hisasi
hisoka
hisoka from hunter × hunter
hispanic
histological
historic
historic artworks society
historic moment
historic photo
historic photograph
historic portrait
historical
historical archive
historical armor
historical artistic depiction
historical artworks
historical baroque dress
historical baroque dress dark
historical documentary
historical event
historical fiction
historical footage
historical hero
historical image
historical painting
historical photo
historical photograph
historical photography
historical photos circa 1 9 4 7
historical picture
historical record
historical reenactment
historical setting
historically accurate
history
history channel
history drama
history painting
histroical
hitchcock
hitler
hitler playing basketball
hitman
hito steyerl
hitoshi ashinano
hive
hive city
hives
hivis
hiyao miyazaki
hl 2
ho scale
hoang lap
hoang long ly
hobbit
hobbit hole
hobbit monastery on hawaii
hobbithead
hobbiton
hobbiton at night
hobbits
hockey arena game illustration
hockey mask
hockey team logo
hodie mihi cras tibi
hoffman bicycle trip
hog rider
hogwarts
hogwarts 2 0 7 7
hogwarts castle
hogwarts great hall
hogwarts gryffindor common room
hogwarts legacy
hogwarts library
hogwarts setting
hogwarts stairwell
hogwarts style
hokusai style
hold
hold mechanical bow and arrow
hold spear
hold sword in the forest
hold up smartphone
holding
holding a bagpipe
holding a baguette
holding a ball
holding a balloon
holding a baseball bat
holding a baseball bat!!
holding a battery
holding a beer
holding a beer!!
holding a bell
holding a big camera
holding a big sword
holding a big two - handed staff
holding a birthday cake
holding a black katana
holding a blaster
holding a blue lightsaber
holding a boba milky oolong tea
holding a bomb
holding a book
holding a bottle
holding a bottle of arak
holding a bottle of beer
holding a bow
holding a bow and arrow
holding a boxer puppy
holding a briefcase
holding a broadsword
holding a bucket of kfc
holding a burning wood piece
holding a cactus
holding a caduceus staff
holding a camera
holding a can of beer
holding a candle
holding a candle holder
holding a cane
holding a cat
holding a chainsaw
holding a cigar
holding a cigarette
holding a clipboard
holding a colossal sword
holding a crossbow
holding a crowbar
holding a crystal ball
holding a drink
holding a dslr camera
holding a electric guitar
holding a fantasy sword
holding a flaming sword
holding a flower
holding a football
holding a futuristic gun
holding a galaxy
holding a giant book
holding a giant flail
holding a giant strawberry
holding a giant sword
holding a giant weapon
holding a glass of red wine
holding a glass of wine
holding a glock
holding a glowing orb
holding a goat head staff
holding a gold bag
holding a gold! cane!
holding a golden bell
holding a guitar
holding a gun
holding a hammer
holding a holy symbol
holding a huge gavel
holding a huge shield
holding a jack - o - lantern
holding a katana
holding a kitchen knife
holding a knife
holding a lantern
holding a large glass of beer
holding a large pizza
holding a large pizza!!!
holding a laser gun
holding a leather purse
holding a light saber
holding a lightsaber
holding a lightsabre. splash art
holding a long sword
holding a long sword  divine
holding a long sword | divine
holding a longbow
holding a longsword
holding a lute
holding a magic needle
holding a magic staff
holding a magic wand
holding a martini
holding a microphone
holding a milkor mgl
holding a miniature earth
holding a mp7
holding a mug of beer
holding a nintendo switch
holding a paintbrush
holding a paintbrush in his hand
holding a pair of fans. unreal 5
holding a pentagram shield
holding a pistol
holding a planet
holding a pocket watch
holding a poisonous axe
holding a postcard from chicago
holding a pudica pose
holding a pug for a picture
holding a rabbit
holding a rainbow tiger gem
holding a ray gun
holding a ray-gun
holding a record
holding a red banner
holding a red lightsaber
holding a red orchid
holding a red rose
holding a revolver
holding a rifle
holding a rocket
holding a rocket launcher
holding a rose
holding a rose in a hand
holding a sacrificial dagger
holding a samurai sword
holding a scepter
holding a scythe
holding a shield
holding a shield and gladius
holding a shield and hammer
holding a shining orb of data
holding a shot glass of whiskey
holding a shotgun
holding a silver electric guitar
holding a skull
holding a skull staff
holding a small vape
holding a smoking ray-gun
holding a snake
holding a sniper rifle
holding a snowboard
holding a spear
holding a spectacular broadsword
holding a squid
holding a stack of books
holding a staff
holding a stuff
holding a sword
holding a sword & shield
holding a sword and a chisel
holding a sword and shield
holding a sword on her shoulder
holding a sword shield
holding a syringe
holding a syringe!!
holding a tangerine
holding a tankard of ale
holding a tattered magical book
holding a teddy bear
holding a thick staff
holding a tin can
holding a tiny galaxy
holding a torch
holding a tower shield
holding a trident
holding a two - handed sword
holding a umbrella
holding a very advance phone
holding a vinyl record
holding a wand
holding a war hammer
holding a weapon
holding a whip
holding a white duck
holding a white flag
holding a white fluffy kitten
holding a wood piece
holding a wooden staff
holding a wrench
holding a yellow toothbrush
holding a 🛡 and an 🪓
holding ace card
holding an ace card
holding an activated lightsaber
holding an apple
holding an avocado in his hand
holding an axe
holding an electric guitar
holding an epee
holding an epée
holding an fish in his hand
holding an umbrella
holding ar-15
holding arms on holsters
holding axe
holding bat
holding beer
holding beer bottles
holding books
holding bow and arrow
holding broadsword
holding brush
holding cigar
holding close
holding controller
holding court
holding dagger
holding daggers
holding daisy
holding each other
holding each other hands
holding easter eggs
holding ego weapons
holding electric guitar
holding electric guitars
holding electricity
holding electricity and birds
holding enormous mace
holding enormous sword
holding fire and electricity
holding flamethrower
holding flask in hand
holding flowers
holding futuristic crossbow
holding gift
holding glass of wine
holding glowing laptop computer
holding gold
holding gold watch
holding golden chains
holding green fire
holding grimoire
holding guitars
holding gun
holding gun down
holding hand
holding handgun
holding hands
holding hands in the moonlight
holding helmet
holding her yorkshire terrier
holding his hands up to his face
holding his trident
holding hk pistol in hand
holding holy symbol
holding hot sauce
holding in his hand
holding in the hands easter eggs
holding intimately
holding it out to the camera
holding jagged scimitar
holding katana
holding khopesh
holding khopesh and shield
holding knife
holding laser swords
holding lightsaber
holding lightsaber 4 k
holding lightsaber 4k
holding magic flowers
holding magical fiery battle-axe
holding magical kitchen knives
holding maracas
holding meatloaf
holding mesh bag with bagels
holding microphone
holding miniature earth in hands
holding mjolnir
holding nevermore
holding nikon camera
holding notebook
holding onto a galaxy
holding origami qilin
holding paintbrushes
holding paws
holding pdw
holding pencil
holding pistol
holding pizza
holding polaroid camera
holding red umbrella
holding revolver pistols
holding rifle
holding sabre
holding scale
holding scale and holding sword
holding sci-fi rifle
holding scimitar made of bone
holding shield and sword
holding shotgun
holding shotgun down
holding spear
holding spell book
holding sword
holding swords
holding syringe
holding the earth
holding the elder wand
holding the larry o'brien trophy
holding the larry obrien trophy
holding the moon upon a stick
holding the pint of ale
holding toasted brioche bun
holding trident
holding two swords
holding umbrella
holding up a large shield
holding up a night lamp
holding up a pizza
holding walking stick
holding wand
holding wands
holding weapon and shield
holding wine bottle
holding wood saw
holds a black sword
holds a small knife in hand
holds a smart phone in one hand
holds a sword
holds playing cards
holes
holes in a religious man
holes in the lower jaw
holga
holga 120n
holga hasselblad
holi festival of rich color
holiday
holiday resort
holiday season
holiday vibe
holistic medicine advertisement
hollis dunlap
hollister ranch
hollosy simon and da vinci
hollow
hollow cheeks
hollow earth
hollow earth infographic
hollow eyes
hollow knight
hollow knight concept art
hollow knight screenshot
hollow knight style
hollow souls
holly bruce
holly herndon origami statue
hollywood
hollywood cinematic
hollywood concept
hollywood movie
hollywood movie poster
hollywood movie still
hollywood promotional image
hollywood scene
hollywood standard
hollywood style
hollósy simon
holman hunt
holo
holo if a wolf girl
holo is a wolf girl
holo sticker
holodeck
hologram
hologram center
hologram colors
hologram hovering around her
hologram sci - fi hood
holograms
holograph
holographic
holographic accents
holographic artifacts
holographic blade!
holographic case display
holographic colors
holographic creatures
holographic design
holographic display lenses
holographic effect
holographic interface
holographic krypton ion
holographic material
holographic memories
holographic neon highlights
holographic optical illusions
holographic plastic
holographic projection
holographic rainbow
holographic runes
holographic space
holographic suit
holographic texture
holographic tones
holographic undertones
holographic!!!
holographics
holography space
hololive
holosomnia
holster
holstering sword
holy
holy and beautiful
holy and sacred
holy armor
holy aura
holy ceremony
holy city  illustration
holy city | illustration
holy cross
holy crusader
holy crusader medieval
holy crusader medieval knight
holy cyborg necromancer girl
holy dice in the clouds
holy energy
holy fire spell art
holy flame crown spell
holy flame spell
holy geometry
holy glow
holy grail
holy halo
holy iconography
holy imagery
holy inquisition
holy light
holy light halo
holy light rays
holy lights
holy machine
holy magic
holy man looking at ground
holy medieval crusader
holy paladin
holy place
holy rays
holy rays of spiritual light
holy roman empire
holy sacred light rays
holy spirit
holy sword in his hands
holy themed
holywood actress
holywood quality
holywood scene
home
home album pocket camera photo
home alone
home and garden
home display
home interior
home office
home office interior
home page screenshot
home photography portrait
home setting
home shopping network
home video
home video footage
home wicca scene
homelander
homelander from the boys
homeless
homeloaf
homely atmosphere
homemade
homer simpson
homer simpson as a real person
homer simpson as mona lisa
homer simpson in gears of war
homer simpson in gta v
homer simpson in minecraft
homer simpson in real life
homer simpson in stranger things
homer simpson lookalike
homer simpson style
homes and garden magazine
homes and gardens
homestar runner
homestuck
homeworld
homeworld skies
homeworld style
homicide
homicide in the snow
homoerotic
homoerotic!
homoerotic!!
homoeroticism
honda civic
honda nsx
honda rc 2 1 3
honda rc 213
hone finished
hone onna skeleton geisha
honest
honey
honey - colored eyes
honey and bee hive
honey birdette
honey dripping
honey dripping from ceiling
honey ripples
honey wind
honeycomb
honeycomb background
honeycomb halls
honeycomb structure
honeysuckle
hong june hyung
hong kong
hong kong 1 9 5 4
hong kong buildings
hong lei
hong soonsang
hongbsws
honor
honorable
honored
hood
hood and cape
hood and shadows covering face
hood covers his eyes
hooded
hooded blue cloak
hooded cloak
hooded cloaked sith lord
hooded cloaks
hooded cowl
hooded figure
hooded figure surreal
hooded figures
hooded fur coat
hooded skull
hoodie
hoody
hoody woman explorer
hoofs
hoog detail
hook as ring
hooked - up
hooked nose
hooked nose and square jaw
hoop earrings
hop cone juice
hope
hopeful
hopeful and bright
hopeful expression
hopeless
hopeless emotions
hopeless grey
hopelessness
hopi kachina
hopper
hopper and gilleard
horde of zombies
horde3d
horizon
horizon centered
horizon forbidden west
horizon forbideen west
horizon line focus
horizon of an erupting volcano
horizon view
horizon zero dawn
horizon zero dawn 8 k
horizon zero dawn 8k
horizon zero dawn aesthetic
horizon zero dawn machine
horizont
horizontal
horizontal orientation
horizontal symmetry
horizontal view
horizontally leaping
horizontally leaping!!!
horizontally symmetrical
horn
horned
horned beetle
horned god
horned helmet
horned ram goddess
horned skull mask
hornet from hollow knight
horns
horns and red eyes
horns on head
horns on its head
horns under his cheek
horns with indicator lights
horns!
horns. dark colors
horor
horrible
horrible rotting flesh
horrific
horrific background
horrific beautiful vibe
horrific cinematic lighting
horrific digital art
horrific forest
horrific sentient meatloaf
horrific surreal nightmare
horrific yet beautiful vibe
horrified
horrified expression
horrified look in his eyes
horrifying
horrifying :4
horrifying creature
horrifying fear
horrifying grin. horror
horrifying monster
horror
horror - core
horror aesthetic
horror animatronic
horror art
horror artwork
horror atmosphere
horror cgi 4 k
horror cgi 4k
horror concept art
horror core
horror details
horror dramatic moment
horror element
horror elements
horror environment
horror face
horror fairy tale
horror fantasy
horror fantasy art
horror fiction
horror film
horror film poster
horror film still
horror footage
horror game
horror game graphics
horror grotesque
horror illustration
horror image
horror lighting
horror macabre face
horror manga
horror monster masterpiece
horror mood
horror movie
horror movie aesthetic
horror movie characters
horror movie cinematic
horror movie cinematography
horror movie lighting
horror movie lightning
horror movie poster
horror movie poster art
horror movie poster style
horror movie scene
horror movie slasher
horror movie still
horror movies
horror painting
horror photo
horror photography
horror poster
horror poster 9 0 s
horror poster 90 s
horror reality
horror rotten teeth
horror saw teeth
horror scary terror
horror sccene
horror scene
horror sci fi
horror sci-fi
horror science fiction
horror setting
horror smile
horror spooky
horror style
horror surreal art
horror surrealism
horror symmetrical face
horror terrifying
horror theme
horror themed
horror tones
horror vacui
horror vibe
horror vibes
horror video game
horror wallpaper aesthetic
horror ”
horror!! highly detailed
horrorcore
horrorifiying
horroristic shadows
horrors filmed on camera
horror”
horse
horse close - up
horse head animal merge
horse in background
horse in costume astronaut
horse is running
horse is up on it's hindlegs
horse is up on its hind legs
horse is up on its hindlegs
horse laying down
horse legs and human body
horse on top
horse race
horse racing
horse racing concept art
horse racing sketch
horse rider
horse sitting on astronaut back
horse warrior
horse whiskers
horsemen of the apocalypse
horses
horses in run
horses racing
horst p. horst
hortorium
horzon
hosada
hoses
hoses:10
hoshino yukinobu
hospital
hospital background
hospital bound
hospital in background
hospital interior
hospital lighting
hospital room
hospital ward
hospital!
host of the nightmare
hostages
hostile
hostile aura
hot
hot air balloon
hot air balloons
hot air refraction
hot and dry
hot and humid
hot and sunny
hot and sunny highly-detailed
hot bank
hot cocoa drink
hot coffee
hot colors
hot daddy
hot day
hot dog
hot fire giantess
hot fire goddess
hot food
hot glue
hot hot hot
hot neon green ornaments
hot pants
hot petite
hot petite teen girl
hot pink
hot pink and black
hot pink and gold color scheme
hot pink halter top
hot reptile humanoid woman
hot springs hidden cave
hot summer day
hot summer sun
hot summertime
hot summertime hippie
hot sun
hot sun from above
hot temperature
hot topic
hot topic anime convention
hot toys
hot tub
hot weather
hot wheels
hot with shining sun
hot-pants
hotdogs
hotei is on the table
hotel
hotel room
hotline miami
hotoverse
hotrods driving down a street
hotspring hidden in a cave
hottoys
hotwheels
hou china
houdini
houdini & photoshop
houdini 3 d
houdini 3 d render
houdini 3d
houdini algorhitmic pattern
houdini algorithm generative art
houdini fluid simulation
houdini particles
houdini photoshop
houdini render
houdini rendering
houdini sidefx
houdini sim
houdini simulation
houdini vfx
houdini-hard-surface
hour glass
hourglass
hourglass figure
hourglass intricate detail
hourglass shaped eye irises
hourglass slim body
hourglass slim figure
hourglass volumetric lighting
hourglass ⌛️
hourglass! slim figure
hourglassslim figure
house
house and home
house background
house bolton
house illustration
house in forest
house in the wood
house interior
house kitchen on a sunny day
house on a hill
house on background
house on fire
house plants
house telvanni
house windows
house's and shops and buildings
housefire
houseplant
houseplants
houses
houses and buildings
houses and roads
houses and shops and buildings
houses in the shape of mushrooms
houses of parliament
houses on fire
houses on stilts
houses with faces
housing architecture
houzz
hoverboard
hoverboards
hovering
hovering above a lake in yukon
hovering drone
hovering in the air
hovering indecision
how a river
how pre-raphaelites
how pretty
how river
how to
how to draw
how to draw anime
how to train your dragon
how-to
howard
howard brodie
howard brown
howard pak
howard v brown
however
howl's moving castle
howl\'s moving castle at night
howling
howling at the moon
howls moving castle
howl’s moving castle
howrah bridge
hoyte van hoytema
hozier
hp lovecraft
hp lovecraft style
hp mp stamina bars
hq
hq 4k phone wallpaper
hq 4k wallpaper
hq 8k cinematic
hq 8k scan
hq artwork
hq lighting
hq print
hq render
hq scan
hq textures
hq very detailed
hq ”
hr
hr geiger
hr geiger design
hr giger
hr giger ) ( ( stained glass
hr giger and vincent di fate
hr giger and zdzisław beksiński
hr giger artwork
hr giger muscles
hr giger style
hr giger. 8 k
hr giger. 8k
hr ginger
hrd
hrushevka on background
hsiao-ron
hsl
hsv
hua cheng
huang yuxing and aya takano
hubbard sundblom
hubble
hubble deep field background
hubble image
hubble photo
hubble photo background
hubble photograph
hubble photography
hubble space telescope
hubble telescope
hubble telescope images
huble
hublot
hud
hud face
hud included
hudson river school painting
hudson river school style
hue
huell babineaux
hues of subtle grey
hug
huge
huge adorable eyes
huge ancient trees
huge and megalithic
huge anime eyes
huge armies
huge battle
huge belt
huge black circle
huge black glowing sun
huge black round hole
huge blond moustache
huge blossoms
huge bubbles
huge bulbous pitch black eyes
huge bull emerging from the sand
huge central tree
huge chasm
huge chin
huge city
huge clouds
huge computer screens
huge crab
huge creature
huge crowd
huge crowd on drugs
huge cup of coffee
huge curly pink hair
huge details
huge distance
huge downslanted eyes
huge dramatic brush strokes
huge ducati panigrale motorbike
huge earrings
huge earrings and queer make up
huge explosion
huge explosions
huge explosions everywhere
huge eyes
huge factories
huge feathery wings
huge ficus macrophylla
huge fire
huge flame fantasy plant
huge flower
huge flowers
huge futuristic building
huge futuristic temple city
huge gargantuan black sun
huge gargantuan scale
huge gate
huge giant old ruins
huge glass structure
huge glistening muscles
huge greek columns
huge greenhouse
huge head tiny body
huge highly detailed wings
huge holograms
huge horns
huge incredibly immense trees
huge jet rocket engine backpack
huge juicy lips
huge ladybug mothership
huge ladybug motherships
huge level structure
huge lips
huge machine cranes
huge machine robot
huge magic circles on the hand
huge mansion
huge mechanical clocks
huge modern downtown city
huge muscles
huge muscles bodybuilder toddler
huge mustache
huge nuclear mushroom cloud
huge obsidian structures
huge office
huge old ruins
huge oversized sword
huge pauldrons
huge pink hair
huge prismatic glowing sun
huge red moon
huge rose flower face
huge rose flower head
huge scale
huge scene
huge scythe
huge shoulder pauldrons
huge smashed mansion
huge smile
huge space battle
huge speakers
huge spikey teeth
huge spines
huge statues
huge storm
huge stylized pauldrons
huge success
huge support buttresses
huge surprised eyes
huge suspended wooden bridge
huge sword
huge symmetric wings
huge teeth
huge telescope on mauna kea
huge tentacles
huge towering magical university
huge tree trunks
huge trees
huge tusks
huge universe
huge veins
huge viewscreen at front
huge waterfall
huge waterfalls
huge waves
huge weapon
huge white teeth
huge wings
huge wings growing out of arms
huge!!!
huge-eyed
hugging
hugging and cradling
hugging each other
hugging her knees
hugging his knees
huggy wuggy
hugh definition
hugh detailed
hugh grant
hugh grant man vs wild
hugh jackman portrait
hugh kretschmer
hugh laurie
hugh quality
hugh ward
hughly detailed
huginn and muninn flying above
hugo boss prize
hugo pratt
hugs
huhd
huhd - 8 k
huhd - 8k
huifeng huang
huke
huleeb
hulk
hulk body type
hulk costume
hulk hogan
hulk like physique
hulking
hulkish
hull
hull is a opera house
hulton archives
humachine humanized experiment
human
human - like
human - shaped
human : - 2
human :: sorceress
human actress
human anatomy
human anatomy features
human and animal skulls
human and dragon fusion
human arms
human babies
human back legs and sneakers
human bodies
human bodies intertwined
human body
human body and head
human body breaking away
human body parts
human body plan
human body with dragon features
human brain
human cat hybrid
human character
human chimera
human circulatory system
human computer
human design
human dressed as a bull
human evolution
human eye
human eyes
human eyes!
human face
human face realistic
human face with bright red yes
human faces
human facial features
human farm
human features
human female
human fighter
human figure
human figures
human fingers
human flesh
human form
human futuristic city
human game protagonist designs
human glowing
human goku
human hand
human hands
human head
human head skin
human head with blonde hair
human heads everywhere
human heart
human jaw
human lamb hybrid
human leg photography
human legs
human like
human like a cyborg
human machine interface
human male
human male character art
human male demon
human need death angel
human onion hybrid
human perspective
human photo
human physique study
human portrait
human prisoners
human proportion
human realistic face
human sacrifice
human shape
human silhouette
human silhouettes
human skeleton
human skin
human skin texture
human skull
human skulls on ground
human soldiers
human soul
human spine
human staring blankly ahead
human structure
human structure bee concept art
human structure concept art
human subjugation
human torch
human torso
human torso and head
human torso on a horse body
human torture
human warrior
human with one robot eye
human-animal hybrid
human-like
human-skin pelt
humanisation
humanity
humanity like cosmic structure
humanity's cosmic future
humanity's last sacrifice
humanitys cosmic future
humanization
humanized
humanoid
humanoid animal
humanoid body
humanoid car head
humanoid character
humanoid cheetah
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i am groot
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i-d magazine
i_5589.jpeg
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in a nest
in a nightclub
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in a painting of a cat
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in a park
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in an art gallery]
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in clothes! intricate
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in crimson red
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in cyberpunk 2 0 7 7 cp 2 0 7 7
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in oil”
in one frame
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in pilote
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in style of andy warhol
in style of anna dittmann
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in style of atey ghailan
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in style of beeple
in style of beksinski
in style of billelis
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in style of cyril rolando
in style of cytus and deemo
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in style of kyoto animation
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in style of leonardo da vinci
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in style of lord of the rings
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in style of makoto shinkai
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in style of marcel duchamp
in style of mark arian
in style of marvel
in style of marvel and dc
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in style of midjourney
in style of mike savad”
in style of minecraft
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in style of monkeybone
in style of nadine ijewere
in style of nan goldin
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in style of niko pirosmani
in style of norman foster
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in style of paul gauguin
in style of peter mohrbacher
in style of petra collins
in style of photogrammetry cloud
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in style of pixar
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in style of primal apes
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in style of ren hang
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in style of robert mapplethorpe
in style of ruan jia
in style of salvador dali
in style of sandro botticelli
in style of simplified realism
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in style of stanislav vovchuk
in style of stanley donwood
in style of steve henderson
in style of studio ghibli
in style of syd mead
in style of terry richardson
in style of thawan duchanee
in style of tim burton
in style of tim walker
in style of tom bagshaw
in style of tyler mitchell
in style of ultra high detail
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in style of valentin serov
in style of van gogh
in style of wlop
in style of wold of warcraft
in style of yoji shinkawa
in style of zaha hadid
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in style of zdzisław beksinski
in style of zdzisław beksiński
in style of zootopia
in style the demon seated
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in sun glasses
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in the 4 0 th millenia
in the 40 th millenia
in the air
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in the anime film ergo proxy
in the anime ghost in the shell
in the anime series ergo proxy
in the apocalyptic future
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in the art style of 8 0 s anime
in the art style of bowater
in the art style of dan mumford
in the art style of filip hodas
in the art style of mohrbacher
in the art style of quetzecoatl
in the art style of ukiyo - e
in the astral plane ) ) )
in the australian desert
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in the background a thunderstorm
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in the beach
in the bedroom at a sleepover
in the bottom there a lot of fog
in the burning soil desert
in the center
in the center midground
in the center of the frame
in the center of the image
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in the clouds
in the cockpit of a fighter jet
in the colors hot pink and cyan
in the colosseum
in the cosmos
in the countryside
in the cover of new york times
in the cover of time magazine
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in the cyberpunk countryside
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in the dark elevator
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in the deep
in the deep forest
in the desert
in the desert beside the gulf
in the design of eliot kohek
in the distance
in the distance is a rocky hill
in the distant future
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in the flow of time
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in the forest
in the forest at night
in the future
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in the glamour style
in the golden hour
in the grass
in the gryffindor common room
in the hall of area 55
in the high grass
in the high school gym
in the hillside
in the iceland calm water
in the iconic style of moebius
in the john wick movie
in the jungle
in the jungle. bloom
in the justice league
in the kitchen
in the last of us
in the macys parade
in the magic room
in the magical forest
in the matrix
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in the middle of a lake
in the middle of a small colony
in the middle of a snow storm
in the middle of an arena
in the middle of an empty room
in the middle of dark forest
in the middle of new york
in the middle of nowhere
in the middle of round ruins
in the middle of the battlefield
in the middle of the city
in the middle of the day
in the middle of the desert
in the middle of the night
in the middle of the ocean!!!!!
in the middle of the street
in the middle of the woods
in the midst of high mountains
in the mood for love
in the moon
in the moonlight
in the morning
in the morning light
in the morning mist
in the mountains
in the movie 2001 a spaceodyssey
in the movie arrival
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in the movie blade runner 2049
in the movie bladerunner
in the movie dune
in the movie hard target (1993)
in the movie hard target 1993
in the movie jurassic park
in the movie kill bill
in the movie lifeforce (1985)
in the movie lifeforce (1985) 8k
in the movie lifeforce 19858k
in the movie transformers
in the multiverse
in the museum
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in the new action-movie sonic
in the new matrix movie
in the night
in the ocean
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in the original box
in the oval office
in the painting style of renoir
in the park
in the pub
in the rain
in the rain in the early evening
in the redwood forest
in the role of captain kirk
in the ruins of london
in the sea
in the shadows
in the shape an audio waveform
in the shape of a cinnamon roll
in the shape of a ent
in the shape of a rat
in the show westworld
in the silver hour
in the skin of fortnite
in the sky
in the snow
in the snow mountains
in the space
in the spotlight
in the starry night
in the steppe
in the stlye of moebius
in the street
in the streets of tokyo
in the studio ghibli anime film
in the style artgerm
in the style bev dolittle
in the style dan mumford artwork
in the style dora maar
in the style if greg rutkowski
in the style jordan grimmer
in the style of akira
in the style of an oil painting
in the style of blade runner
in the style of clint cearley
in the style of h r giger
in the style of hans thoma
in the style of john baldessari
in the style of ross tran
in the style of sachin teng
in the style of sifu 🔥 😎 🕹️ 👀 :2
in the style of tom bagshaw
in the style saul bass
in the style star trek 8 k
in the style star trek 8k
in the style wes anderson
in the styles of cameron look
in the styles of greg rutkowski
in the styles of joan miro
in the styles of thomas eakins
in the styles of tom coletti
in the sun
in the sunset
in the swiss alps
in the technique of old masters
in the thick fog
in the tropical wood
in the underworld
in the universe
in the universe.highly realistic
in the void
in the void of space
in the walking dead
in the water
in the white clouds fairyland
in the white house
in the winter
in the wood
in the woods
in the woods at night
in the world of adventure time
in the world of andrew wyeth
in the yard
in the year 3 0 0 0
in the year 3000
in the zoo exhibit
in their noble mansion
in theme of bladerunner movie
in thick full bronze armour
in thick layers of rhythms
in this church interior
in this ominous scene
in this painting
in thomas ruff style
in time square
in times square
in tokio
in tokyo akihabara
in tokyo at night
in tokyo shinjuku
in town
in travis charest style
in trenches
in triangular formation
in tricorn hat
in tshirt
in twin peaks
in uniform
in unreal 3 d engine
in unreal 3d engine
in unreal engine
in valhalla
in victorian aristocrat
in volcano
in volumetric lighting
in volumetric soft glowing mist
in warehouse
in water
in water up to her shoulders
in water! john everett millais
in water! up to shoulders
in waterup to shoulders
in wedding dresses
in white clothes
in white clouds fairyland
in white futuristic armor
in white lettering
in white room
in white turtleneck shirt
in winter
in wonderland
in woods
in world of warcraft
in year 3000
in yellow bikini
in yoji shinkawa's art style
in yoji shinkawas art style
in zootopia city
in-frame
in-game
in-game 3d model
in-game cimematic
in-game cinematic
in-game footage
in-game screenshot
inafune design
inanna
inari shrine
inauguration
inc
inca
inca style
inca themed smooth
incandescent
incandescent lighting
incense
incense smoke fills the air
inception
inception concept
including a long tail
including a matador & a bull
including a matador a bull
incoherent
incommensurate
incompetent
incomplete
incomprehensible
incomprehensible and frightening
incomprehensible detail
incomprehensible scale
incomprehensible size
inconceivable
incongruous
incredible
incredible anime movie scene
incredible art
incredible colors
incredible creative screenshot
incredible depth
incredible detail
incredible detailed
incredible details
incredible detalization
incredible digital art
incredible fineline
incredible hd detail
incredible hulk
incredible isometric screenshot
incredible light
incredible lighting
incredible masterpiece
incredible miyazaki
incredible movie poster
incredible painting
incredible perspective
incredible quality
incredible reflections
incredible ruan jia movie poster
incredible screenshot
incredible sharp detail
incredible sharp details
incredible vfx
incredible wide screenshot
incredibly beautiful
incredibly cute
incredibly detailed
incredibly detailed art
incredibly detailed atlantic cod
incredibly detailed face
incredibly detailed faces
incredibly detailed oil painting
incredibly ethereal
incredibly high detailed
incredibly intricate
incredibly lifelike attributes
incredibly realistic
incredibly sharp focus
incredibly skinny
incredibly strong and tall
incredulous
incrinate
incrinate content details
incubator medpods
independence
indescribable
indi creates
indi sulta
india
india ink
india third eye tika
india tika third eye
indian
indian art
indian empress
indian flag
indian forest
indian girl with brown skin
indian god
indian goddess
indian goddess of wealth
indian ink
indian master
indian patterns
indian script
indian style
indian super model
indian temple
indian warrior
indiana jones
indiana jones on monkey island
indie
indie concept art
indie film
indie game
indie game concept art
indie games
indie video game horror
indiecraft aesthetic
indieground
indigenous
indigenous art
indigenous man
indignant
indigo
indigo and red iron oxide
indigo and venetian red
indigo background
indigo blooming flowers garden
indigo filter
indigo magician
indigo occult pattern
indigo rainbow
indigo renderer
indigo! and venetian red!
indistinct
indistinct man with his hand up
indistinguishably unique
individual
indonesia
indonesia national geographic
indoor
indoor casino
indoor dim light
indoor lab scene
indoor light
indoor lighting
indoor liminal space
indoor picture
indoor pool
indoor scene
indoor setting
indoor shot
indoor smooth light
indoor soft lighting
indoors
indore
indsutrial steampunk
industrial
industrial aesthetic
industrial apparent
industrial architecture
industrial art style
industrial background
industrial buildings
industrial colours
industrial complex
industrial design
industrial design blueprint
industrial design concept
industrial environment
industrial factory
industrial fires and smog
industrial futuristic ice mine
industrial giant skyscrapers
industrial gotham city
industrial humanity spaceship
industrial light & magic
industrial light and magic
industrial light magic
industrial lighting
industrial machinery
industrial mechanical robot dogs
industrial party
industrial photography
industrial pipes
industrial plant environment
industrial revolution
industrial robot
industrial robotic cats
industrial robots
industrial rusty pipes
industrial saliva
industrial saliva ooze
industrial sci - fi
industrial sci fi
industrial sci-fi
industrial scifi
industrial setting
industrial space
industrial steam
industrial surrounding
industrialization
industrialpunk papier - colle
industries
industry
inevitability
inevitable doom
infantry
infantry girl
infantry girls
infared photography
infected mushroom
infernal
infernal art in good quality
infernal enigma
infernal nymph
infernal nymph!!!
infernal relics
inferno
infestation
infested with pitch green
infini - d - render
infinite
infinite angel wings
infinite angelic wings
infinite celestial library
infinite consciousness
infinite corridor
infinite crystal ascent
infinite dawn
infinite fractal mandala tunnel
infinite fractal tesseract
infinite fractal waves
infinite hallway
infinite hallways
infinite in extent
infinite intricacy
infinite library
infinite mirrors
infinite possibilities
infinite power
infinite psychedelic waves
infinite quantum portal
infinite quantum waves
infinite recursion
infinite reflections
infinite regress
infinite regression
infinite sky
infinite space clock background
infinite wings
infinitely long corridors
infinity
infinity blade armor
infinity blade concept art
infinity concentric pool
infinity gauntlet
infinity glyph
infinity glyph waves
infinity hieroglyph waves
infinity mirror
infinity pool
infinity pool mirrors
infinity stones
infinity symbol
infinity symbol like a cat
infinity time loop
inflatable
inflatable bouncer castle
inflatable future shoes
inflatable landscape with forest
inflateble shapes
influencer
influencers
infographic
infographic and marginalia
infographic of active volcanoes
infographic of space travel
infographic style
infographic with illustrations
infographics
infographics. logo
infographics. logo. blue
infography
information
information visualization
informative graphs and diagrams
informative guide
informative texts
infp girl
infp young woman
infra - red
infrared
infrared ambient light
infrared camera
infrared camera view from bomber
infrared color scheme
infrared film
infrared hair
infrared photography
infrastructure
infused
infused with a dream
infused with aurora borealis
infused with lightning
infused with zerg hydralisk
ingame
ingame image
ingame screenshot
ingame shot
inglaze
ingmar bergman
ingredients on the table
ingres
inhabited initials
inhabited on many levels
inhaling the souls of humanity
inherent vice
inhuman
inio asano
initial d
initial d anime
initial d anime 1 0 8 0 p
initial d anime 1080 p
initial d anime screenshot
injured
ink
ink and ballpoint
ink and charcoal illustration
ink and colour on silk
ink and colours on silk
ink and muted colours on silk
ink and paper
ink and pen
ink and pencil
ink and screentone
ink and wash
ink and watercolor
ink and watercolor illustration
ink and watercolor wash
ink and watercolors
ink art
ink artwork
ink block painting
ink blot
ink brush
ink dots
ink draw
ink drawing
ink drips
ink flourishes
ink illustration
ink lines
ink manga drawing
ink on canvas
ink on paper
ink on parchment
ink on post it note
ink on skin
ink outline
ink outlines
ink paint
ink painting
ink painting ) ) ) )
ink painting on paper
ink pen
ink pen sketch
ink sketch
ink splash
ink splashes
ink splatter
ink splatters
ink stains
ink style
ink under drawing
ink under paint
ink wash
ink wash painting
inka harpy girl
inkarnate
inkblots of color
inked
inked and colored
inked digital
inked drawing
inked lines
inking
inking etching screen print
inking on skin
inks
inkscape
inktober
inky blackness
inky illustration
inlaid with gold
inlaid with gold rococo
inlets
innate studio
inner ear
inner glow
inner illumination
inner peace
inner ring
inner ward of a medieval castle
innocence
innocent
innocent face
innocent look
innocent look. rich vivid colors
innocent mood
innocent smile
innovation
innovative
innovative avant - garde art
innovative avant-garde art
innovative product concepts
innsmouth
innsmouth ocean - dwellers
inputs
inquisition
inquisitive
inquisitive look
inquisitive. detailed expression
inricate
inrincate
insainly detailed
insandely detailed
insane
insane action pose
insane complexity
insane composition
insane crazy laugh
insane detail
insane detailed
insane details
insane engine
insane eyes
insane face
insane intricate
insane lightning
insane nightmare
insane quality
insane smile
insane special effects
insaneley detailed
insanely
insanely  artwork
insanely 3d
insanely beautiful
insanely compelling
insanely complex
insanely complex details
insanely cute
insanely detail
insanely detailed
insanely detailed and intricate
insanely detailed art
insanely detailed c 10.0
insanely detailed face
insanely detailed intricate
insanely detailed linework
insanely detailed matte painting
insanely detailed octane render
insanely detailed rendering
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insanely fine detail
insanely highly detailed artwork
insanely inflated hips
insanely integrate
insanely integrate details
insanely intricate
insanely intricate and detailed
insanely intricate details
insanely mystical
insanely nice hair style
insanely precise
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insanely realistic
insanely super detailed
insanity
insanity :7
insanity accurate features
insanity girl summon her death
insanity in his eyes
insanity of the mind
insanley detailed
insanly detailed
inscribed runes
inscribed with occult symbols
insect
insect antennae. jan van eyck
insect trainer girl
insect wings
insectile forearms folded
insectoid
insects
insects and birds
inset xray cross-section
insibidi symbols
inside
inside a bar
inside a barn
inside a beer glass
inside a brutalist space ship
inside a casino
inside a castle
inside a castle courtyard
inside a castle library
inside a cave
inside a cavernous stomach
inside a child's bedroom
inside a circus tent
inside a cluttered art studio
inside a cluttered bedroom
inside a cluttered kitchen
inside a cozy apartment
inside a crowded space station
inside a crypt
inside a dark room
inside a decayed hospital room
inside a decayed operating room
inside a decayed surgical room
inside a decaying hospital room
inside a dome
inside a dungeon
inside a farm barn
inside a frame on a tiled wall
inside a french cafe
inside a futuristic army base
inside a gang hideout
inside a gas station
inside a glass box
inside a glass jar
inside a globe
inside a gorge
inside a grand
inside a grand messy studio
inside a grand ornate room
inside a grand studio
inside a haunted destroyed house
inside a haunted house
inside a humongous cave
inside a library
inside a magical item shop
inside a marble
inside a massive dark cave
inside a mechanical cat's head
inside a medieval hobbit home
inside a messy room
inside a modern apartment
inside a museum
inside a palace
inside a rpg game
inside a ruined abbey
inside a science facility
inside a shed
inside a snow globe
inside a space ship
inside a space station
inside a spaceship
inside a supermarket
inside a tall vetical room
inside a tavern
inside a tomb
inside a warehouse
inside a waterfall
inside abandoned space ship
inside airplane
inside an alien jungle
inside an arabian market bazaar
inside an dystopian
inside an empty ussr submarine
inside an enormous vast cave
inside an epic
inside an epic gothic castle
inside an gothic dystopian
inside an old apartment
inside an old magical shop
inside an old magical sweet shop
inside an old weapon shop
inside an ornate hospital room
inside an otherworldly planet
inside an underwater train
inside ancient greek ruins
inside area 51
inside berghain
inside building
inside cathedral
inside covens den
inside dark oil
inside elementary school
inside frame
inside future fighter
inside futuristic hangar
inside glass orb
inside gothic doll manor bedroom
inside haunted house
inside head cobwebs
inside her surreal vr castle
inside her temple
inside house in village
inside in a glass box
inside iron and machines
inside its box
inside jimmy hendrix
inside large window of ship
inside mc escher architecture
inside mirror
inside of a 1970s science lab
inside of a bedroom
inside of a black hole
inside of a cabin
inside of a car
inside of a cave
inside of a dark cave
inside of a metro train
inside of a tokyo garage
inside of an auto dealership
inside of an expansive cave
inside old cabin
inside on a rainy day
inside out and outside in
inside page of comic book
inside primitive hut
inside stylized border
inside the building
inside the curl of a wave
inside the flower
inside the picture is infinity
inside the roman colliseum
inside the ruined gothic church
inside the sepulchre
inside the tardis
inside the tomb of jesus
inside the train
inside the tunnel
inside the wishing well cavern
inside view
inside water
inside white room
insidious
insightful
insignia
insipiring
insisted on cutting in line
insmouth
insomnia
inspect in inventory image
inspiration
inspirational
inspirational and powerful
inspirational architecture
inspirational arthouse
inspirational artwork
inspirational quote
inspirational tone
inspire
inspire and overcome
inspired
inspired art deco
inspired blizzard games
inspired gacha club game
inspired in balthus
inspired in donato giancola
inspired in hirohiko araki
inspired in kris from deltarrune
inspired in super bomberman
inspired lord of the rings
inspired moebius
inspired tom bagshaw
inspired victorian sci - fi
inspired western comic
inspiring
inspiring art
inspiring birds eye vista view
inspiring digital art
inspiring digital painting
inspiring gothic architecture
inspo
instagram art
instagram digital
instagram filter
instagram filters
instagram hd
instagram highlights
instagram influencer
instagram model
instagram photo
instagram photo amazing view
instagram photo shoot
instagram picture
instagram post
instagram post 4k
instagram selfie
instagram story
installation
installation art
installation view
instant photograph of the sky
instant photography
instax
instense god rays in the sky
instruction
instructions
instructions to build it
instrument
instrument of life
instruments
insurmountable
intact humanoid servo
intaglio
intaglio style
intangible
intarsia
intecrate details
integral painting
integrated circuit
integrated design
integrated in the mountains
integrated synthetic android
integrating with technology
integrating with technology!!
integration
intellectual cat
intellectual hedgehog
intelligence
intelligent
intelligent arrangement
intelligent design
intelligent eyes
intelligent eyes. centered
intelligent eyes. symmetrical
intelligent face
intelligent man
intense
intense albino
intense atmosphere
intense atmospheric
intense battle
intense black eyes
intense black line art
intense blue eyes
intense bounced light
intense clouds
intense color
intense colors
intense colours
intense combat
intense contrast
intense demonic look in the eyes
intense desire
intense detail
intense details
intense dramatic hdr
intense emotion
intense emotional power
intense emotions
intense expression
intense eyes
intense face
intense facial expression
intense fantasy
intense fighting
intense flames
intense flirting
intense gaze
intense heavy street battle
intense knowledge
intense lens flare
intense light beams
intense lighting
intense line art
intense line work
intense look
intense look in the eyes
intense moment
intense painting
intense scene
intense sci-fi details
intense screaming expression
intense shading
intense shadows
intense smoke
intense smoldering
intense soul penetrating eyes
intense stare
intense subsurface scattering
intense sunlight
intense watercolor
intense watery glowing red eyes
intense white hair
intensely beautiful image
intensely focused
intensly emotional
inter - dimensional
inter dimensional
inter dimensional clockwork
inter dimensional villa
inter-dimensional mirrors
interacte smooth flowing lines
interacting and posing
interconnected
interconnected human lifeforms
interconnections
intercrossed
intercrossed animal
intercrossed humans
interdimensional
interdimensional lightning
interest face
interesting
interesting angle
interesting background
interesting camera angle
interesting character design
interesting color scheme
interesting color use
interesting composition
interesting details
interesting geometry
interesting lights
interesting perspective
interesting shapes & form
interesting shapes form
interesting shot
interesting skin coloring
interesting textures
interface
interference
intergalactic
interior
interior abandoned hospital
interior architecture view
interior background
interior background art
interior decoration
interior design
interior design architecture
interior design background
interior design details
interior design living room
interior design photography
interior gameplay screenshot
interior light
interior lighting
interior living room
interior of a bathroom
interior of a hobbit hole
interior of a huge robot factory
interior of a library
interior of a living room
interior of a loft
interior of a marble dome
interior of a mountain hut
interior of a small
interior of a small room
interior of a star destroyer
interior of a tent
interior of a victorian house
interior of an alien spaceship
interior of buckingham palace
interior of room
interior of staten island ferry
interior of the hall in area 55
interior of the old cathedral
interior photo
interior photograph
interior photography
interior room
interior scene
interior shot
interior view
interior white wall
interior wood
interior! design
interiordesign
interiors
interlacing paths
interlocked
intermediate art
internal conflict
internal contours
internal environment
internal organs
internals revealed
international photography awards
international space station
internet
internet art
internet meme
interplanetary cathedral
interpretation
interrupting the big game
intersection
interstellar
interstellar black hole
interstellar civilisation
interstellar film ( 2 0 1 4 )
interstellar galaxy
interstellar hyper realism
interstellar infinity portal
interstellar movie
interstellar night
interstellar space setting
interstellar space suit
interstellar stormy bright sky
interstellar vortex through time
intertwined
intertwined a dissolving
intertwined full body view
interventricular foramen
interview
inticate
inticrate
intimacy
intimate
intimate dark moody
intimate holding close
intimate lighting
intimately holding close
intimidating
intimidating appearance
intimidating clouds
intimidating enforcer
intimidating expression
intimidating floating sand
intimidating full body armor
intimidating glare
intimidating lighting
intimidating man
intimidating pose
intimidating stance
intimidating stare
intimidating woman
intircate
into bright light
into darkest cosmos
into glory ride
into the mirrorverse
into the spider verse
into the spiderverse
into the unknown
into the void
intoxicatingly blurry
intracate brushwork
intracate detail
intravenous drip
intrecate details
intrecate light
intricant
intricant details
intricat
intricate
intricate  8k
intricate 3 d illustration
intricate 3 d sculpture
intricate 3d sculpture
intricate 8 k detail
intricate 8 k render
intricate 8k detail
intricate 8k render
intricate a whole fantasy leaf
intricate abstract
intricate abstract upper body
intricate abstract. cyberpunk
intricate abstract. gucci style
intricate accuracy
intricate accurate details
intricate african jewellery
intricate alien botanicals
intricate alien technology
intricate and
intricate and beautiful
intricate and beautiful details
intricate and complex
intricate and decorated
intricate and detailed
intricate and detailed lace set
intricate and elegant
intricate and epic composition
intricate and epic concept art
intricate and finely detailed
intricate and highly detailed
intricate and intense oil paint
intricate and ornate
intricate and very beautiful
intricate and wet oil paint
intricate anime girl k-on kyoani
intricate arcane wiccan designs
intricate architecture
intricate armor
intricate armor details
intricate armour costumes
intricate art
intricate art deco leaf designs
intricate art deco pasta designs
intricate art deco patterns
intricate art nouveau
intricate art nouveau frame
intricate art photography
intricate artwork
intricate artwork masterpiece
intricate artwork. neon eyes
intricate artwork. octane render
intricate assasin armor
intricate assasin mecha armor
intricate automatism
intricate background
intricate baroque armour
intricate baroque style
intricate beautiful close up
intricate beautiful faces
intricate beautiful painting
intricate beauty
intricate biopunk patterns
intricate black obsidian
intricate body
intricate border
intricate border designs
intricate braided hair
intricate branches
intricate broken space helmet
intricate bronze armour
intricate brush stroke detail
intricate brush strokes
intricate carved wood
intricate carving
intricate carvings
intricate castle interior
intricate charcoal illustration
intricate chrome chains
intricate chrome headdress
intricate cinematic light
intricate classic art
intricate clothes
intricate clothing
intricate clown costume
intricate clown makeup
intricate cobblestone
intricate colorful
intricate colorful masterpiece
intricate colors
intricate complex background
intricate complexity
intricate complicity
intricate composition
intricate concept art
intricate concept art painting
intricate copper details
intricate costume design
intricate crown
intricate crystal jelly ornate
intricate cyberpunk armor
intricate cyberpunk city
intricate cyberpunk make - up
intricate cyborg
intricate cyborg armor
intricate dark background
intricate design
intricate design pop art
intricate designs
intricate detail
intricate detail and composition
intricate detail and quality
intricate detail drawing
intricate detail realism hdr
intricate detail. unreal engine
intricate detail?
intricate detailed
intricate detailed 4 k
intricate detailed 4k
intricate detailed 8 k
intricate detailed 8k
intricate detailed anatomy
intricate detailed armour
intricate detailed background
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intricate detailed digital art
intricate detailed dress
intricate detailed environment
intricate detailed face
intricate detailed garden
intricate detailed illustration
intricate detailed oil painting
intricate detailed painting
intricate detailed photography
intricate detailed racing car
intricate detailed roof
intricate detailed tiara
intricate detailing
intricate details
intricate details and ornaments
intricate details and textures
intricate details illustration
intricate details in environment
intricate details in the frames
intricate details painting
intricate details photograph
intricate details!!!
intricate details!!!!
intricate details. front on
intricate devilish designs
intricate digital art
intricate digital artwork
intricate digital painting
intricate dotart
intricate dramatic lighting
intricate drawing
intricate dress
intricate electronics
intricate elegant
intricate elements
intricate embroidery
intricate engraving
intricate environment
intricate environment - n 9
intricate environments
intricate etched armor
intricate eye detail focus
intricate eyes
intricate face
intricate faces
intricate facial detail
intricate facial details
intricate facial features
intricate facial skin details
intricate fantasy
intricate fantasy dress
intricate fantasy painting
intricate fantasy robes
intricate fantasy spear
intricate fashion clothing
intricate features
intricate filagree
intricate filigree
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intricate fine ornaments
intricate fire designs
intricate flower designs
intricate flower tiara
intricate flowing paint
intricate fluid details
intricate foreground
intricate fractal
intricate fractal abstraction
intricate fractal armor
intricate fractal details
intricate fractals
intricate from baldurs gate
intricate futurism
intricate futuristic jewelry
intricate futuristic led jewelry
intricate galactic designs
intricate galaxy inlay
intricate gears and lenses
intricate gears details
intricate geisha kimono
intricate geometry
intricate glow accents
intricate glowing mecha armor
intricate gold crown
intricate gold elements
intricate gold headdress
intricate gold jewlery
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intricate golden armor
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intricate gothic bones and meat
intricate gown
intricate graffiti
intricate greg rutkowski
intricate ground stone
intricate hair
intricate hair details
intricate hairstyle
intricate hakama
intricate headpiece
intricate heterochromatic sad
intricate heterochromia sad
intricate high detail
intricate high details
intricate high tech helmet
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intricate highly detailed 8 k
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intricate insanely detailed
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intricate iridescent opal armor
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intricate jewelry
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intricate latex set
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intricate led jewellery
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intricate light
intricate lighting
intricate lights
intricate line
intricate line art
intricate line drawing
intricate line drawings
intricate line work
intricate lineart
intricate lines
intricate linework
intricate lining
intricate machine in space
intricate machinery
intricate macro closeup
intricate makeup
intricate mandelbulb fractal
intricate manga drawing
intricate mask
intricate masterpiece
intricate mathematical shape
intricate matte painting
intricate mecha armor
intricate mechanical body
intricate mechanical details
intricate mechanics
intricate mechanisms
intricate medieval armour
intricate metal
intricate metal armor
intricate metal armour
intricate mine
intricate mural
intricate mycelial lace
intricate neon circuit pattern
intricate octane
intricate oil
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intricate oil painting
intricate oil painting artwork
intricate oil painting detail
intricate oil pastel glow
intricate oil sweeps
intricate oils
intricate organic painting
intricate ornament
intricate ornament halo
intricate ornamental details
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intricate ornamentation
intricate ornamented tiara
intricate ornaments
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intricate ornate anime cgi style
intricate ornate armor
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intricate outfit
intricate overlay flames imagery
intricate painting
intricate paisley filigree
intricate parts
intricate pasta waves
intricate pattern
intricate patterns
intricate patterns 4k
intricate patterns on face
intricate penwork
intricate photo
intricate planetary gears
intricate plants
intricate plate armor
intricate portrait
intricate portrait design
intricate psychedelic patterns
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intricate realistic fantasy
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intricate render
intricate ritualistic tattoos
intricate robes
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intricate roots
intricate scenery
intricate scrollwork
intricate sculpture
intricate set
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intricate silk clothing
intricate skeletal decorations
intricate skin pattern texture
intricate sky
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intricate spirals
intricate stained glass
intricate stained glass triptych
intricate steampunk imagery
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intricate stone carvings
intricate story
intricate suit
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intricate tapestry
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intricate texture
intricate textures
intricate thin details in gold
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intricate ultra fine detail
intricate upper body
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intricate white and gold armor
intricate white and gold neon
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intricate wiccan scene detailing
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intricate wires
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intricate zdzislaw beksinski
intricate ”
intricate! organic
intricate!!
intricate!!!
intricate!!!!! professional
intricate. detailed
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intricately carved
intricately carved antique bone
intricately defined
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intricately detailed background
intricately detailed buildings
intricately detailed clothing
intricately detailed engravings
intricately detailed markings
intricately detailed scales
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intricately deteailed
intricately hyperdetailed
intricately proportional
intricateorganic
intricate”
intriciate detail
intrigante
intriguing
intriguing details
intriguing eyes
intriguing helmet
intriguing lighting
intriguing outfit
intriguing volume flutter
intriicate
intrincate
intrincate clothing
intrincate detail
intrincate details
intrincate detalied
intrincate ornaments
intrincated
intrinsic
intrinsic design
intrinsic detail
intrinsic details
intro to uncut gems
introduction factory photo
introspective
introspective meandering
introvert
introverted
intruder
intrusive thoughts
intubation equipment
intuition
intuitive
inuit
inuit heritage
inuk
inuyasha
invader zim
invading army background
invading kashyyyk
invasion
invasion of the tripmachines
invasion time on planet earth
inventing a time machine
inventory item
inverse color
inverse dark glowing power aura
inverted
inverted color scheme
inverted colors
inverted fisheye lens!!!
inverted neon rainbow drip paint
inverted rainbow drip paint
inverted triangle body type
investigation
invicible
invincible
invisible
invisible man
invisible woman
invisiblity
invitation card
inviting
inviting posture
invoke fear and dread
ion iliescu
ios
ios app icon
ios emoji
ios icon
iowa
ioyful vibe and lighting
ipad pro
iper realistic
iperdetailed
iphone
iphone 1 3
iphone 1 3 pro max
iphone 12
iphone 12 camera
iphone 13
iphone 15 background
iphone 4 k
iphone 4k
iphone background
iphone camera
iphone capture
iphone detailed photo
iphone in hand
iphone photo
iphone picture
iphone screenshot
iphone selfie
iphone video
iphone wallpaper
ipseity
iq 4
ir
ir 660 nm
iralki nadar
iram of the pillars
iran
iranian
iraq nadar
iray
iray shaders
ireland
irelia
irelia from league of legends
iren horrors
iridescence
iridescence colors
iridescence reflecting
iridescence water elemental
iridescent
iridescent # imaginativerealism
iridescent accents
iridescent accents. vibrant
iridescent aesthetic
iridescent and gold and crystal
iridescent and golden
iridescent and opalescent
iridescent background
iridescent beetles
iridescent clouds
iridescent color
iridescent color palette
iridescent colors
iridescent colours
iridescent crystals
iridescent cybernetic processor
iridescent deep colors
iridescent details
iridescent digital art
iridescent ethereal veils
iridescent eyes
iridescent fractal
iridescent fractal headdress
iridescent fractal whirls
iridescent glass
iridescent glistening smoke
iridescent glowing chips
iridescent hair
iridescent highlights
iridescent image lighting
iridescent lighting
iridescent membranes
iridescent metals
iridescent moth wings
iridescent neon
iridescent palette
iridescent radiance
iridescent reflections
iridescent scales
iridescent scales on her body
iridescent sci-fi kimono
iridescent shimmering pools
iridescent shiny sheer lingerie
iridescent skin
iridescent sky
iridescent smoke
iridescent smoke behind
iridescent soapy bubbles
iridescent specular highlights
iridescent technology and weapon
iridescent texture
iridescent titanium
iridescent tubes
iridescent venetian blown glass
iridescent visor
iridescent wings
iridescent wiring
iridescent. fantasy
iridiscense
iridiscent
iridiscent accents
iridiscent fabric
iridiscent gradient
iridiscent light
iridiscent rim light
irina french
irina french art
irina nordsol kuzmina
iris
iris compiet
iris human's eye photo
iris humans eye photo
iris van herpen
iris van herpen baroque dress
iris van herpen rankin
iris van herpen!!!
irisdicense
irises
irish
irish forest
irish genes
irish mountains background
irish youtuber
irobot
iroc
iron
iron age : leonardo da vinci
iron and asphalt
iron arc gate door texture
iron armor
iron cladding
iron claws
iron crown
iron frame
iron gate
iron gate door texture
iron giant
iron giant at sunset
iron helm
iron helm maximalist
iron helmet
iron maiden
iron maiden album cover
iron man
iron mask and helmet
iron railing
iron smelting pits
iron smelting pits space marines
iron throne
iron throne from game of thrones
ironborn
ironic
ironic surrealism
ironman
ironpunk
irori fireplace
irradiated humans
irrdescent glow
irredecent
irregular
irregularly shaped mosaic tiles
irresistable
irresistible
irreverent characters
irridescent
irridescent ghostly
irrigation
irritated expression
irving penn
irwin penn
is ((drinking a cup of tea))
is a stunning
is at dawn and bluish
is being drawn into a blackhole
is drinking a cup of tea
is essentially arbitrary
is evil gremlin
is holding a tiny planet earth
is looking at a bird
is looking at the camera
is playing a lute
is relaxing on a beach
is tall
is this loss?
is totally sad and cries
is wearing a swimsuit
isaac asimov
isaac asimov and marc simonetti
isaac asimov ”
isaac newton
isaac zuren
isabela moner
isabella
isekai
isekai manga panel
ishikei
ishirō honda
ishowspeed
ishtar gate
islam
islamic
islamic architecture
islamic art
islamic calligraphy
islamic interior design
islamic revolution
island
island background
island floating in the sky
island in a blue sea
island in the background
island landscape
island with cave
islandpunk
islands
islands on horizon
islands!!!!!
ismail
ismail inceoglu and ruan jia
iso
iso 1 0 0
iso 1 0 0 wide view
iso 1 2 0 0
iso 1 6 0 0
iso 100
iso 1000
iso 1200
iso 125
iso 1250
iso 160
iso 1600
iso 2 0 0
iso 200
iso 300
iso 4 0 0
iso 400
iso 50
iso 500
iso 64
iso 8 0 0
iso 800
iso : 2 0 0
iso : 200
iso-250
iso100
iso1200
iso400
iso640
iso: 1600
iso: 200
iso: 400
isolate
isolate translucent
isolated
isolated background
isolated on white
isolated on white background
isolated on whites
isolated space station in space
isolated white background
isolation
isolde
isomeric view
isometric
isometric 2 d game art
isometric 3 d
isometric 3d
isometric 3d fantasy
isometric 3d fantasy cute dog
isometric 3d fantasy cute house
isometric 3d fantasy island
isometric 3d fantasy turtle
isometric 3d render
isometric 8k
isometric aerial view
isometric angle
isometric angles
isometric art
isometric camera
isometric dead space game
isometric design
isometric drawing
isometric futuristic building
isometric futuristic game
isometric game
isometric game art
isometric game asset
isometric house
isometric illustration
isometric illustration fun
isometric invironment
isometric island in the sky
isometric map
isometric orthographic
isometric palace
isometric perspective
isometric perspective view
isometric pikachu figure
isometric pixel art
isometric pixelart
isometric projection
isometric staircase
isometric style
isometric tokyo city
isometric top down left view
isometric view
isometric view from behind
isometric view of a wizard tower
isometric view!!!!
isometric view. game rpg
isometric viewpoint
isometric views
isometric voxel
isometric voxel art
isometry
isono
isopod
israel
issey miyake
istanbul
istock
istockphoto
istvan sandorfi
istván sándorfi
it
it doesn't hurt me ( ye - yeah
it follows :7
it glows from the outside
it goes in forever
it has a lake in the distance
it has a piercing gaze
it has a red and black paint
it has lemon skin texture
it has six thrusters in the back
it idn't greasy
it is a place of worship
it is afternoon
it is emitting a bright
it is flying through space
it is glowing
it is night
it is night and raining
it is night time
it is photorealistic
it is raining
it is raining heavily
it is raining outside the window
it is sunset
it is the captain of a crew
it is very detailed
it is very huge
it is visibly angry at the tv
it is wednesday my dudes
it kills you instantly
it specialist
it was full of bones
it was the best of times
it was the worst of times
it would take place in space
it's a deep night
it's all in your head
it's californication
it's dark
it's flying between a storm
it's getting dark
it's is relaxing on a beach
it's late at night
it's morbin time
it's night
it's night time
it's raining
it's raining outside
it's running between a storm
it's the dead of night
it's very huge
it's wearing a cute little hat
it's you and me
it\'s name is greeny
it\'s raining
itachi
itachi uchiha
italian
italian beach scene
italian flag
italian futurism
italian futurism style
italian garb the future
italian horror
italian looking emma
italian masterpiece
italian masterpieces
italian mediterranean city
italian pizza
italian poster art
italian renaissance
italian renaissance architecture
italian renaissance painting
italian renaissance workshop
italian style
italo calvino
italy
itatchi uchiha
item
item art
items
items and gadget
iterations = 5 0 0 0
iterior
ito
ito junji art
ito ogure
its a deep dream
its bowl overflowing with plants
its dark
its detailed intricate
its flying between a storm
its hour come around at last
its hour come round at last
its is relaxing on a beach
its name is greeny
its night
its night time
its raining
its the dead of night
its you and me
itsuko azuma
itō jakuchu
iu
iu lee ji-eun as a super villain
iv drip
iv pole
ivan
ivan aivakovsky
ivan aivazovski
ivan bolivian
ivan chermayeff
ivan laliashvili
ivan plusch artwork
ivan seal
ivan shishk
ivan shishkin and greg rutkowski
ivan shiskin
ive discovered life
ivory
ivory and black marble
ivory and copper
ivory and ebony
ivory carved mantle
ivory carved ruff
ivory carving
ivory make up
ivory make up on the eyes
ivory pale skin
ivory rococo
ivory skin
ivory towers
ivy
ivy vine leaf and flower top
ivy vines
ivy's
iwagumi aquascape
iwakura
iwakura lain
ixions wheel
izombie
izuku midoriya
j - lo
j c leyendecker
j c leyendecker 8 k
j m w turner
j scott campbell
j. c leyendecker
j. c. 8 k
j. c. 8k
j. c. leyendecker 8 k
j. c. leyendecker 8k
j. c. leyendecker. black hair
j. c. leyendecker. d&d
j. c. penny wish book 1 9 8 2
j. dickenson
j. h. williams iii
j. j. abrams
j. lesaffre
j. scott campbell
j. todd anderson
j. w. godward
j.c leyendecker
j.c. leyendecker wlop
j.dickenson
j.dickenson and rembrandt
j.j. abrams
j.r.r. tolkien
ja miyazaki
ja mong
jabba el hutt
jabba the hutt
jabberwocky
jacek yerka and vladimir kush
jacen solo
jack
jack - o'- lantern
jack and the bean stalk
jack baker from resident evil 7
jack black
jack black holding a hamburger
jack burton drinking beer
jack nicholson
jack nicholson as napoleon
jack nicholson is the bottle
jack nicholson plays terminator
jack russel dog
jack russel terrier
jack russel terrier surprised
jack skellington
jack sparrow
jack the ripper
jack white
jack white of the white stripes
jack-o-lanterns
jackal
jackal skull
jacket
jacket over bare torso
jackie chan
jackie chan doing lsd
jackie tsai style
jacksepticeye
jacksepticeye as a muppet
jackson pollock painting
jackstraws
jacky tsai style
jacob & co
jacob co
jacob riis
jacqueline e
jacques - laurent agasse
jacques - louis david
jacques - yves cousteau
jacques louis david
jacques louis david style
jacques villegle
jade
jade colored eyes
jade green
jade sculpture
jade tiara and necklace
jaded
jaeyeon nam
jagged
jagged blocks of stone
jagged edges
jagged metal landscape
jagged rocks
jagged spiraling shapes
jagged sword
jaguar
jaguar e - type
jaguar e - type car
jaguar head tattoodesign
jahbu art
jaidenanimations
jail
jaime jasso
jaime lannister
jaina proudmoore
jaina solo
jair bolsonaro
jajaboonords flipjimtots
jakarta
jake gyllenhaal
jake parker
jake the dog
jakob eirich
jakob rozalski
jakub gazmercik
jakub kasper
jakub rebelka
jama jurabaev
jamaica
jamaican
jamaican colors
jamaican vibe
jamal campbell
jamel shabazz
jamel shabbaz
james
james bama
james baxter and mark henn
james bond
james c
james cameron
james cameron avatar style
james chadderton
james clyne
james collinson
james daly
james dean
james e reynolds
james edmiston
james gene
james gillard
james gilleard and james jean
james gilleard artwork
james gleeson
james guilleard
james gunn
james gurney and andreas rocha
james gurney and greg rutkowski
james gurney brom
james gurney cinematic lighting
james gurney painting style
james gurney weta studio
james jean & alphonse mucha
james jean aesthetic
james jean alphonse mucha
james jean and clyde caldwell
james jean and fenghua zhong
james jean and jenny saville
james jean and peter mohrbacher
james jean and petra cortright
james jean and rolf armstrong
james jean and victo ngai
james jean and wlop
james jean and yoji shinkawa
james jean andrei riabovitchev
james jean art
james jean artwork
james jean color palette
james jean marc
james jean marc simonetti
james jean soft light 4 k
james jean soft light 4k
james jean style
james jean ”
james jean!
james jean!!!
james lebron
james mccarthy
james mcdermott
james nares
james o barr
james piack
james r. eads
james stokoe
james terrell art
james turrel
james turrell
james turrell building
james wan
james web telescope
james webb
james webb space telescope
james webb telescope
james webb telescope images
james webb telescope photo
james yang
james zapata
jamey jones
jamie campbell bower
jamie chung
jamie coreth
jamie hewlet
jamie hewlett and ayami kojima
jamie hewlett art
jamie lannister
jamie lee curtis
jamie mckelvie comic art
jamie reid
jamming to music
jan artkowski
jan brueghel
jan davidsz de heem
jan davidszoon de heem
jan matejko and j. dickenson
jan matejko and j.dickenson
jan saudek
jan svankmayer
jan svankmeyer
jan urschel
jan vermeer
jana brike art
janapese
jane austen
jane clarke
jane newland
jane rosenberg
janice sung
janis rozentals
january
january 20th
january and february
janusz jurek
janusz kaminski
japan
japan 1980s
japan animation
japan at night
japan deeper travel exploration
japan grungerock from colors
japan harvest
japan lush forest
japan mountains
japan nature
japan poster
japan rural travel
japan shonan enoshima
japan sightseeing
japan soldier in world war 2
japan studio game
japan tokyo skytree
japan travel aesthetic
japan travel and adventure
japan travel and tourism
japan travel scrapbook
japan watercolour
japan. volumetric lighting
japandi
japaneese style
japanese
japanese 1 9 8 0's album cover
japanese action figure
japanese akihabara cafe
japanese animation
japanese animation style
japanese anime
japanese anime artist drawn
japanese anime style
japanese architecture
japanese art
japanese art art
japanese art on behance
japanese art style
japanese bathhouse
japanese calligraphy
japanese cartoon style
japanese cgi
japanese city
japanese city at night
japanese city pop color palette
japanese city street fashion
japanese clothes
japanese collection product
japanese comic book
japanese countryside
japanese crane bird in center
japanese cyborg
japanese dc live-action movie
japanese design
japanese dog
japanese downtown
japanese drift car
japanese engineering
japanese facial features
japanese fantasy
japanese flower arrangements
japanese fusion cuisine
japanese garden
japanese geisha
japanese girl school uniform
japanese glass house
japanese god
japanese goddess
japanese gothic
japanese graphic design
japanese heritage
japanese high school
japanese horror
japanese horror movie footage
japanese house
japanese illustration
japanese illustrator
japanese influences
japanese ink drawing from 1850
japanese inspiration
japanese inspired poster
japanese kanji
japanese kanji everywhere
japanese kawaii style
japanese kimono
japanese landscape
japanese light novel cover
japanese lightning goddess
japanese live-action movie
japanese magazine collage
japanese magazine covers
japanese maid cafe
japanese manga
japanese manga style
japanese maples
japanese mascot
japanese mecha
japanese model
japanese neighborhood
japanese neon signs
japanese onsen
japanese painting
japanese pop surrealism
japanese popsurrealism
japanese pottery
japanese print art
japanese related with flowers
japanese robot geisha
japanese romantacism
japanese rural town
japanese samurai
japanese scenery in edo period
japanese school uniform
japanese sci - fi books art
japanese shibari with flowers
japanese shoujo manga
japanese shrine
japanese shrine waterfall
japanese street
japanese street fashion
japanese streetwear
japanese style
japanese style painting
japanese temple
japanese temples
japanese text
japanese text kanji
japanese torii in a moutain
japanese town
japanese tv show
japanese typography
japanese vase
japanese vfx
japanese vhs cover art
japanese villa syd mead
japanese village
japanese warrior
japanese watercolor
japanese woman
japanese wood cut
japanese woodblock
japanese woodblock print
japanese woodblock print of r2d2
japanese woodblock print style
japanesse farmer
japanic style
japense village in background
japonisme
japonisme 3 d 8 k ultra detailed
jaquet droz
jar jar binks
jar jar binks as a sith lord
jar of honey
jar on a shelf
jaroslaw jasnikowski
jars
jasmine
jason
jason chan and gilles beloeil
jason engle
jason fabok
jason fabok. greg staples
jason harkness
jason manley
jason momoa
jason momoa as assyrian
jason momoa as conan
jason scheier
jason statham
jason statham as batman
jason statham skiing
jason voorhees
jaunty
javanese mythology
javascript enabled
jaw and tongue
jaw dropping
jaw dropping beauty
jaw-dropping beauty
jaws
jay bauman
jay leno with gray skin
jay naylor
jaya su berg
jayison devadas
jayison devadas style
jaylen brown
jayne mansfield in a swimsuit
jayson tatum
jayson tatum as che guevara
jayson tatum as guerilla heroica
jayson tatum as mother mary
jazz
jazz age
jazz album cover
jazz quintet
jazza
jazza and rossdraws
jazzy
jc leyendecker
jc leyendecker and sachin teng
jc leyendecker!! phil hale!
jc park
jcb
jdm
jealous face
jealousy
jean
jean - baptiste belin
jean - baptiste monge
jean - honore fragonard
jean - leon gerome
jean - michel basquiat
jean and multicolor shoes
jean baptiste monge
jean christian biville
jean delville and mark brooks
jean delville fertile
jean deville
jean dupas
jean francois millet
jean girard
jean giraud 8 k
jean giraud 8k
jean giraud and waterhouse
jean giraud portrait
jean gireaud
jean grey
jean henri gaston giraud
jean jacket
jean james
jean jules linden
jean leon gerome
jean luc picard
jean michel basquiat
jean pants
jean paul fiction
jean paul gaultier
jean pierre ugarte
jean shorts
jean-honore fragonard
jean-joseph benjamin-constant
jean-leon gerome
jean-sebastien rossbach
jean-victor bertin
jeans
jeans and black boots
jeans and boots
jeans and knee high black boots
jeans and t shirt
jeans pants
jeddah city street
jedi
jedi fallen order teaser
jedi knight
jedi master
jedi robes
jedi with light saber
jeehyung lee wlop
jeep
jeep in background
jeep wrangler
jeff
jeff - hall
jeff bezos
jeff bridges
jeff easley and ross tran
jeff easley and scott m fischer
jeff easley cinematic
jeff easley dramatic light
jeff goldblum
jeff lyons
jeff smith
jeff the killer
jeffery catherine jones
jeffery epstein
jeffery smith
jeffrey catherine jones pose
jeffrey epstein
jeffrey jones
jeffrey jones dynamic lighting
jeffrey smith and h.r. giger
jeffrey smith and rlon wang
jeffrey smith and yves klein
jeffrey smith art
jeffrey wright
jehan georges vibert
jello
jelly
jelly - like texture
jelly - like texture. photograph
jelly fish
jelly fish dancing
jelly fishes
jelly glow
jellybeans
jellycat
jellyfish
jellyfish element
jellyfish elements
jellyfish fractal
jellyfish gelatin
jellyfish god
jellyfish headdress
jellyfish pheonix
jellyfish phoenix
jellyfish phoenix dragon
jellyfish phoenix head
jellyfish priestess
jellyfish shrine maiden
jellyfish shrine maiden 8k
jellyfish temple
jellyfish wearing glasses
jellyfishes
jellymeat
jemal shabazz
jen atkin
jen bartel
jen yoon
jen zee
jenga tower
jenna barton
jenna coleman
jenna coleman as the doctor
jenni pasanen
jennifer aniston
jennifer connelly
jennifer connelly as a stunning
jennifer lawrence
jennifer lopez
jennifer wuestling
jenny savile
jenny saville and nicola samori
jenny seville
jeongseok lee
jereme momoa as tarzan
jeremiah
jeremy
jeremy bennett
jeremy cheung
jeremy clarkson
jeremy cowart
jeremy elbertson
jeremy enecio
jeremy lipkin
jeremy lipkin and rob rey
jeremy lipking
jeremy lipking full length shot
jeremy mann
jeremy mann and alphonse mucha
jeremy mann and claude monet
jeremy mann art
jeremy mann painting
jeremy pitkin
jeremy renner
jerez
jerma
jerma 9 8 5
jerma 985
jerma in a liminal space
jerma985
jerma985 as the joker
jerma985 in fortnite
jerome powell
jerome powell punching a bear
jerry
jerry garcia
jerry seinfeld
jerusalem
jesper esjing
jesse mccree
jesse pinkman
jesse pinkman as spider-man
jesse pinkman in gta v
jessica
jessica alba
jessica alba woman
jessica chastain
jessica nigri
jessica nigri face!!
jessica rabbit
jessica rossier and brian froud
jessica rossier color scheme
jessica rossier fantasy art
jester
jester hat
jester shoes
jester themed
jesus
jesus alonso iglesias
jesus christ
jesus christ fighting lucifer
jesus christ going super saiyan
jesus christ in mass effect
jesus christ is the doom slayer
jesus christ on the cross
jesus christ smoking a blunt
jesus face
jesus holding a cute cat
jesus hugging a woman
jesus kissing a maria maddalena
jesus of nazareth
jesus on cross
jesus on the cross
jesus riding a dragon
jesus walking on water
jesus wasted at a party
jeszika le vye
jet - black skin
jet black hair
jet black haired cyberpunk girl
jet black leggins
jet black tuffe coat
jet black tuffle coat
jet engines
jet fighter
jet fighter background
jet fighters
jet parts
jet set radio
jet set radio artwork
jet set radio future
jet turbine
jet wings on the back
jetfire
jetfire!!!!!
jetpack
jetpacks
jets
jetsons
jetstream sam
jetstream sam from metal gear
jett from valorant
jewel
jewel case
jewel crown
jewel embellishment
jewel fishes
jewel tones
jeweled
jeweled choker
jeweled costume
jeweled crown
jeweled headdress
jeweled ornament over forehead
jeweled ornate leather armour
jeweled technology
jeweled veil
jewelled
jewellery
jewelry
jewelry design
jewelry display
jewelry engraved in scarab
jewelry iridescent
jewelry lighting
jewelry made of bones
jewelry pearls
jewelry photography
jewels
jewish young man with glasses
jewlery
jfk
jfk assassination
ji-min
jia
jia ruan
jia zhangke
jiang sword
jigsaw
jill stingray
jim carrey
jim carrey as jack sparrow
jim carrey as terminator
jim carrey as the joker
jim carrey caricature realism
jim carrey in gears of war
jim carry
jim cheung
jim cornette
jim cramer
jim henson
jim henson creature shop
jim henson creature workshop
jim jarmusch
jim kay
jim morrison
jim warren
jim warren and rob gonsalves
jim woodring
jimi hendrix
jimi hendrix full body
jimi hendrix full body shot
jimi hendrix playing guitar
jimi hendrix style poster
jimin
jimin\'s grecian nose
jimin\'s plump lips
jimin\'s right eyelid swollen
jimins grecian nose
jimins plump lips
jimmy mcgill in court
jimmy nelsson
jimmy neutron
jimmy page
jin - roh
jin kagetsu
jin kim
jin shan
jin shan and ross tran
jingna zhang
jinsung lim
jinx
jinx expression
jinx face
jinx from arcane
jinx from league of legends
jinyiwei
jinyoung shin
jinyoung shin aesthetic
jinyoung shin art
jiro matsumoto and yusuke murata
jisoo from blackpink
jisoo of blackpink
jisu choe
jiyun chae
jj abrams
jjba
jk
jk uniform
jm animation
jmw turner
joan cornella
joan miro and moebius
joan of arc
joanna gaines
joao ruas
joaquin phoenix
joaquin sorolla
joaquin sorolla ) )
jock
jodie bateman
jodie foster
jodie whittaker
jodorowky's dune movie
jodorowkys dune movie
jodorowsky's dune
jodorowsky's dune movie
jodorowsky's robocop
jodorowskys dune movie
jodorwoski's dune
jodorwoskis dune
jody highroller
joe
joe alves
joe bidden
joe biden
joe biden action figure
joe biden as a hooded arch mage
joe biden as a jojo character
joe biden as a sith lord
joe biden as a transgender woman
joe biden as an anime villain
joe biden as batman
joe biden as captain america
joe biden as superman
joe biden as thanos
joe biden as the joker
joe biden as the joker in batman
joe biden casts a long shadow
joe biden cosplaying bioshock
joe biden crying
joe biden dressed as a dinosaur
joe biden eating ice cream
joe biden flying with jet pack
joe biden full body portrait
joe biden full body shot
joe biden funko pop
joe biden in fortnite
joe biden in gta v
joe biden in hell
joe biden in minecraft
joe biden in skyrim
joe biden in team fortress 2
joe biden in toy story
joe biden in valorant
joe biden is obi - wan kenobi
joe biden kissing donald trump
joe biden looking sinister
joe biden portrait
joe biden riding a dragon
joe biden riding a skateboard
joe biden screaming
joe biden sharkman
joe biden with glowing eyes
joe biden with glowing red eyes
joe biden with laser eyes
joe boom
joe botardo
joe chiodo
joe gb fenton
joe hisaishi
joe keery
joe kubert
joe madureira
joe pera
joe rogan
joe taslim
joe webb
joel - peter witkin
joel fletcher
joel meyerowitz
joel peter witkin
joel sternfeld
joel torres
joel-peter witkin
joep hommerson
joey king
joey l
johan liebert
johan liebert mixed with alucard
johan liebert mixed with dante
johann heinrich füssli
johann tischbein
johanna martine
johanna rupprecht
johanna rupprecht style
johannen voss
johannes vemeer
johannes vormeer
johfra
john
john alvin
john baer
john banovich
john bauer and bouguereau
john berkey and norman rockwell
john berkey white plastic panels
john berkley
john berry
john blance
john buscema
john candy
john caple
john carmack
john carpenter body horror
john carpenter's the thing
john carpenters the thing
john carter of mars
john cena
john cho
john coulthart
john currin
john eaves
john egbert
john frederick lewis
john goodman as donald trump
john harris. concept art
john henry dearle
john j. park
john jude palencar
john kenn mortensen
john krasinski
john kricfalusi
john lasseter
john lasseter portrait
john lennon
john lennon as a stag beetle
john liberto
john longstaff
john martin landscape
john mcafee as ironman
john mcneill whistler
john oates face
john oliver
john oliver and adam driver
john oliver in front
john oliver in the rain
john park
john park and greg rutkowski
john paul ii
john pawson
john picacio and brom
john redcorn
john romita junior
john romita senior
john sargent
john schoenher
john schoenherr
john signer sargent
john singer sargant
john singer sargent style
john snow
john stephens
john tenniel
john wallin liberto
john waterhouse
john waters
john waters movie
john watkiss
john wayne
john wick
john willaim godward
john william
john william waterhouse style
john williams waterhouse
johnatan wayshak
johnny
johnny bravo
johnny cash
johnny craig
johnny depp
johnny depp as captain america
johnny depp as the joker
johnny depp as tony stark
johnny depp portrait
johnny silverhand
johnson heade
johnson ting
joints
jojo
jojo anime style
jojo bizarre adventure
jojo cover art
jojo style
jojo's bizarre adventure
jojo's bizarre adventures
jojo's bizzare adventure
jojo's bizzare adventure!!
jojo's bizzare adventure!!!!
jojo\'s bizarre adventure
jojos bizarre adventure
jojos bizzare adventure
joke
joker
joker as naruto
joker looks like naruto
joker makeup
joker smile
joker wearing vader's armor
joker wearing vader's armor suit
joker wearing vaders armor suit
jokowi
jollibee city
jolly
jon berkey
jon kuo
jon lothian
jon macnair
jon mccoy
jon mcnaughton
jon sibal. volumetric light
jon snow
jon tron as loki in the avengers
jonah hill
jonas roscinas
jonathan dufresne
jonathan ivy
jonathan winterhart
jonathan yeo painting
jonathan zawada
jonny duddle and jim kay
jonny greenwood
jonny greenwood (lead guitar)
jonny wan
jontron
jony ive
jony ives
joon ahn
joongwon charles jeong
joongwon jeong
joop geesink
joost swarte
jordan
jordan grimmer and james jean
jordan grimmer and natasha tan
jordan lamarre - wan
jordan matter photography
jordan peele's face
jordan peeles face
jordan peterson
jordan peterson as a lobster
jordu schell
jorge lacera
jorge luis borges. eldritch
jormungand anime
jormungandr
josan gonzales
josan gonzales and dan mumford
josan gonzales!!!
josan gonzalez and tyler edlin
jose ladronn
jose miguel roman frances
josef sudek
joseph and joseph
joseph farquharson
joseph joestar
joseph leyendecker
joseph mallord william turner
joseph maria olbrich
joseph michael linsner
joseph moncada
joseph stalin
joseph todorovitch
joseph todorovitch ”
josephine wall and pino daeni
josh black
josh brolin
josh grover
joshua cotter
joshua middleton
joshua middleton and artgerm
joshua middleton art
joshua middleton artwork
joshua middleton comic art
joshua middleton comic cover art
josip broz tito
jossi of blackpink
jotaro
jotaro kujo
jotaro vs dio
journalism
journalism photo
journalist photo
journalistic photo
journalistic photograph
journalistic photography
journey
journey thatgamecompany
journey to the west
journey to the west. pop art
jovana rikalo
jovial
joy
joy ang
joy gaze
joyce ballantyne
joyce ballantyne style
joyful
joyful 4k
joyful look
joyful people in the house
joyful smirk
joyous
joyous trumpets
joyous wide memorable
joytoy
jpeg artefacts
jpeg artefacts on canvas
jpeg artifact
jpeg artifacting
jpeg artifacts
jpg artifacts
jpl
jpop clothing
jr
jr ewing from dallas
jr sc maglev
jrpg
jrpg aztec street fashion
jrpg character
jrpg character art
jrpg fashion
jrr tolkien
jsc
jsrf
juan caloto
juan diaz canales
juan gimenez
juan jose serrano
juan miro
juan sanchez cotan
juanjo guarnid
juanjo guarnido
juanmao
jubilant blissful atmosphere
judge
judge dredd
judge judy
judge's gavel
judgement day
judgment day
judy boyle intricate
judy chicago
judy garland
judy hopps
judy hopps from zootopia
jugendstil
jugendstil background
jugendstil drawings
jugendstill
juggernaut
juice
juicy
juicy color
juicy legs
juicy meat
jujutsu kaisen
jukebox
jules
jules bastien - lepage
jules bastien le - page
jules bastien lepage
jules julien
jules verne
julia fuentes
julia garner
julia gorokhova
julia hetta
julia hill
julia roberts
julia sarda
julian calle
julian falat
julian ope
julian opie
julie dillon
julie heffernan
julio lacerda
julius caesar
july
july 2 0 1 1
july 2011
jumbled together
jump
jump comics
jump kick
jump pose
jump scare
jumping
jumping at the viewer
jumping flying and eating frogs
jumping for joy
jumping leaping heroic attack
jumping spider
jumping spider mixed with owl
jumping towards viewer
jumpscare
jumpsuit
jumpsuits
jung gi kim
jung jaehyun
jungian symbols of winter
jungkook
jungle
jungle around him
jungle as the background
jungle background
jungle background with ruins
jungle camo
jungle clearing
jungle environment
jungle fruit
jungle gown
jungle grunge
jungle in background
jungle landscape
jungle nature
jungle scene
jungle setting
jungle vines
jungle vines and fireflies
junglecore
junglepunk
jungles
jungles in the background
jungles of vietnam beautiful
junichi oda
junji ito 4 k
junji ito 4k
junji ito and greg rutkowski
junji ito and horiyoshi iii
junji ito artwork
junji ito style
junji ito undertones
junji ito's uzumaki
junji murakami
junk
junk everywhere
junk on the ground
junk town
junk tv
junk yard
junker queen from overwatch
junki ito
junko enoshima
junko enoshima from danganronpa
junkrat from overwatch
junktown
junkyard
juno promotional image
junya watanabe
jupiter
jupiter moon mars
jurassic
jurassic image
jurassic park
jurassic world
jurgen klopp
jurgen klopp laughing
jurgens
juri han from street fighter
juri misaki
jury
just
just a cute little thing
just after rain
just art for dark metal music
just beautiful
just eyes
just mind-blowing
just one head
just one rainbow 8 k
just take a pinch of psychedelic
just the face
justice
justice league
justify
justify content center
justify contents center
justin bieber
justin gerard and greg rutkowski
justin hartley as superman
justin kohn
justin roiland
justin sun
justin todd
justina blakeney
juxtapos
juxtaposition dystopian
juxtapoz
juxtapoz aesthetic
juxtapoz magazine
jwst
jörmungandr
k
k high definition
k-on
k-on!
k-pop
k9
k_euler_ancestral
kabuki makeup
kabuki mask
kabuto
kacper niepokolczycki
kaethe butcher
kafka
kafkaesque
kagamine rin
kaguya ōtsutsuki
kahikatea
kai carpenter
kai fine art
kai vermehr
kai'sa
kaigetsudo ando
kaiju
kaiju battle
kaiju cephalopod
kaiju starfish
kaiju towering above new york
kaiju-eiga
kailasa temple
kailee mandel
kaitlyn michelle siragusa
kaizen arachimary
kajo baldisimo and craig mullins
kakar cheung
kakashi
kakashi hatake
kakejiku
kaki body suit
kakyoin
kaladesh
kaladesh concept art. mechanical
kaladin stormblessed
kaleidoscope
kaleidoscope of machine guns
kaleidoscopic
kaleidoscopic colors
kalevala
kali
kalighat
kalighat flowers
kalighat hanuman!! head building
kalighat hanumanhead building
kalighat highly detailed
kama russian electrocar
kamakura period
kamakura scenery
kamelot album cover
kamen anev
kamen rider
kamen rider action pose
kamen rider character
kamen rider ghost
kami
kamwei fong
kan liu
kanagawa prefecture
kanamemo
kancolle
kandinski
kandinsky style
kandinsky touches
kane from command & conquer
kaneki ken
kangaroo
kangaroos
kanji
kanji insignia and numbering
kanji markings
kanji tattoos and decals
kanliu666
kano tan'yu
kano tanyu
kano)
kansas town at midnight
kantai collection arcade
kantai collection style
kantoku
kanye
kanye west
kanye west album cover
kanye west as a jedi
kanye west as emperor napoleon
kanye west as the joker
kanye west donda album cover
kanye west funko pop
kanye west in gta v
kanye west made out of congee
kanye west torso
kaoru mori
kara walker
kara walker james jean
karachi skyline background
karate
karate kick
karate outfit
karate pose
karch kiraly
kardur\'s vicious return
kardur’s vicious return
karel thole
karen carr
karen gillan
karen gillan batgirl
karen vikke
karim rashid
karl blossfeldt
karl heilmayer
karl marx
karl marx funko pop
karl marx pondering his orb
karl pilkington
karl spitzweg
karl spitzweg. sharp focus
karl spitzweg. unreal engine
karl urban
karl urban as a dragonslayer
karl urban as wolverine
karla ortiz
karma
karma sutra
karo
karol bak and peter mohrbacher
karol bak major buster keaton
karol bak of emma watson nun
karol bak uhd
karolina cummings
karst landscape
karst landscape ; wide shot
karst pillars forest
karthus from league of legends
kasai
kashin
kastuhiro otomo
kasumi arimura style 3/4
kat dennings
katamari damacy
katana
katana in hand
katana scabbard
katana zero
katana zero video game character
katanas strapped to her back
katara from avatar
katarina
katarina from league of legends
katayama bokuyo
kate beckinsale
kate beckinsale weared as thief
kate bishop
kate moss
kate oleska and jim kay
kate upton
katekyo hitman reborn
katelynn mini cute style
katey truhn
kathe kollwitz
katherine lam
katherine mcnamara inspired
katheryn winnick
kathy zyduck
katinka reinke
katniss everdeen
katniss everdeen rock climbing
katsuhika hokusai
katsuhiro
katsuhiro - otomo
katsuhiro and inio asano
katsuhiro otomo
katsuhiro otomo and junji ito
katsuhiro otomo style
katsuhiro tomo
katsuhuro otomo
katsuhuro otomo highly detailed
katsura masakazu
katsushika
katsushika hokusai style
katy perry
katy perry fixing a leaking sink
katy perry in a red dress
katy perry wearing old clothes
kauai
kauai springtime
kavisky
kawaai
kawacy
kawai
kawaii
kawaii aesthetic
kawaii anime manga style
kawaii cat
kawaii chibi
kawaii colors
kawaii cute dog
kawaii cutest sticker ever
kawaii decora rainbowcore
kawaii hair style
kawaii hairstyle
kawaii hq render
kawaii japanese style
kawaii manga style
kawaii playful pose of a dancer
kawaii rainy gloomy
kawaii realistic portrait
kawaii shirt and jeans
kawaii style
kawaii swat team
kawaii vibe
kawaii yoda
kawaii yoda icon
kawanabe kyosai
kawasaki
kaworu nagisa
kaws
kay neilsen
kay nielsen and wadim kashin
kaya scodelario
kayne west
kazakh
kazakh empress
kazue kato
kazuki takahashi
kazuki tanahashi
kazuma kaneko
kazuo oga
kazuo umezu artwork
kazuya takahashi
kda
kda and sam yang
kdp
keanu reaves riding a unicorn
keanu reeves
keanu reeves as geralt of rivia
keanu reeves as iron man
keanu reeves as wolverine
keanu reevez in disco elysium
kebab
keepers of time and space
keeping you sane
keetongu bionicle
kefka ff6
kehinde wiley
keira knightley
keisuke goto
keita morimoto
keith haring style
keith harring
keith thompson
kek
kelogsloops and greg rutkowski
kelp
kelp and moss all over
kelp forest
kemetic
kemetic symbolism
kemono
kemonomimi
ken
ken barthelmey
ken currie
ken doll
ken ishikawa
ken kaneki
ken sugimori art
ken wong
kendall jenner
kendrick lamar
keng lye
kenket
kenku
kenne gregoire
kenner style action figure
kenneth blom
kenneth copeland
kenneth willardt
kenny mcbride
kenny schar
kenny scharf
kenny wong x pop mart
kenopsia
kent monkman
kentaro
kentaro miura
kentaro miura art
kentaro miura art style
kentaro miura manga art style
kentaro miura manga style
kentaro miura style
kentaro miura!
kenton nelson
kenwood
keqing from genshin impact
kerala motifs
kerala village
kerberos
kerberos panzer
kerberos realm
kerem beyit
kerlee
kerli koiv
kerli koiv as anime girl
kermit
kermit dressed as the undertaker
kermit driving a car
kermit the frog
kermit the frog as darth vader
kermit the frog as thanos
kerouac magazine
kessler art
ketamine dreams
ketchup
kete butcher
keter class
kettle
kevin bacon made out of bacon
kevin garnett
kevin hart
kevin hou
kevin mitnick as a bank robber
kevin o'leary
kevin rudd
kevin tighe with randy mantooth
kevlar
kewpie
kewpie mayonnaise
key anime art
key anime visual
key anime visuals
key art
key art anime sharp focus
key character poster
key frame
key hole on blue ball
key is on the center
key is on the center of image
key light
key lighting
key still
key visual
key visual of attack on titan
keyboard
keyboardist
keyboards
keyframe
keyframe illustration
keygen
keyhole
keyleth
keylight
keys
keyshot
keyshot lighting
keyshot product render
keyshot product render pinterest
keyshot render
kezie demessance
kezrek
kfc
kfc chicken
khabib
khajuraho
khakis
khamenei
khedival opera house
khokhloma plate of borscht
khomeini
khorinis
khorne
khreschatyk
khrushchyovkas
khyzyl saleem
kia soul
kick flip
kickboxer fighter
kickboxing
kickflip
kicking
kicking a florida mansion
kicking up dirt
kid
kid a
kid and mad scientist walking
kid drawing
kid lit
kid named finger
kid playing with slime monster
kid's drawing
kidmo
kidmo!!!
kidney
kidneys
kids
kids book illustration
kids drawing
kids fantasy drawing
kids movie
kids place
kids playing
kids playing at the beach
kids talking to fruit people
kids toy
kids toys
kiera knightley
kiera knightly
kiera knightly in repose
kiernan shipka
kieth thomsen
kiki
kiki picasso style
kiki smith
kiki's delivery service
kikis delivery service
kiko mizuhara
kilart
kilian eng and jake parker
kilian eng and thomas kinkade
kilian eng vibrant colors
kilian eng vibrant colours
kilian eng. mœbius
kilin eng
kill bill
kill la kill
kill la kill illustration
killed in war
killer
killer - girl
killer boots
killer queen
killer whale
killian eng
killian eng beksinski
killing demons
killing the great ones
killstar
killua zoldyck
killua zoldyck black hair
killua zoldyck made of jewlery
killua zoldyck portrait
killy from blame
killy from blame!
killzone
kim doyoung
kim hyun joo
kim jong - il
kim jong - un
kim jong un
kim jong-il
kim jong-un
kim jung gi
kim jung gi style
kim jung giu
kim jung-gi
kim kardashian
kim kardashian as a cop
kim kardashian as harley quinn
kim possible
kim wexler
kim wexler and saul goodman
kimagure orange road
kimberly asstyn
kimetsu no yaiba
kimi no na wa
kimi takemura
kimi vera
kimitake yoshioka
kimono
kimono robe detailed toggles
kimura
kind
kind appearence
kind cyborg girl with flowers
kind expression
kind eyes
kind face
kind features
kind smile
kind!! appearence
kindchenschema
kindness
kindred spirits
kinemacolor
kinetic
king
king arthur
king arthur's court
king arthurs court
king crimson
king ghidorah
king in yellow
king kong
king of capitalism
king of entropy
king of fighters style
king of hell
king of kings
king of pirates
king of the desert
king of the flies
king of the hill
king of the jungle
king of the sea
king of time reaper
king pepe with rainbow wig
king richard the lionheart
king's quest
kingdom
kingdom hearts
kingdom hearts 3
kingdom hearts styled gameplay
kingdom hearts worlds
kingdom of elves
kingdoms of ether
kingpin
kings quest
kings row in the background
kingslanding
kinkade
kinkade. award winning
kinmoza
kinmoza!
kino
kino's journey
kino's journey (2003)
kintsugi
kintsugi!!
kintsukuroi
kinu nishimura
kirbi fagan
kirchner
kirigami
kirill leonov
kirisame marisa
kirisame marisa from touhou
kirito
kirokaze
kirokaze and paul robertson
kirsi salonen
kirsty mitchell
kiseijuu
kiss
kiss mouth to mouth
kisses are wordless spells
kissing
kissing each other
kissing smile
kissing the joker
kissing together
kissing together cutely
kit
kit bash
kit bashing
kitana from mortal kombat
kitava
kitava insatiable hunger
kitbash
kitbash 3 d
kitbash 3 d texture vibrant
kitbash 3d texture vibrant
kitbashing
kitbashing component
kitboga
kitch
kitchen
kitchen background
kitchen counter
kitchen table
kitchenette and conferenceroom
kites
kitsch
kitsch fashion
kitsune
kitsune holding torch
kitsune inspired armor
kitsune mask
kitsune mask on head
kitsune three - tailed fox
kitt
kitten
kitten playing with ball of yarn
kitten puppy teddy mix
kitten sandwish
kittens
kittens in outer space
kitty
kitty-bird hybrid
kiwi
kiwi fruit
kiyoko suzuki
klaus film
klaus movie poster
klaus movie twins poster
klaus schwab
klaus schwab eating bugs
klee
klee. mathematical paradise
klein bottle
klimt and nixeu
klingon
klipspringer
knee
knee high socks
knee pads
knee-high boots
kneehigh boots
kneeling
kneeling and looking up
kneeling at the shiny floor
kneeling before the pope
kneeling in prayer
kneeling!!
knees
knees tucked in
knees tucked in  rain falls
knees tucked in | rain falls
knees upturned
knees weak
knick - knacks
knick knacks
knickknacks
knife
knife - like teeth
knight
knight and princess
knight armor
knight armored in red
knight chess
knight drinks beer
knight helmet
knight in armor
knight in armor dance popping
knight of cups
knight on horseback
knight rider
knight rider kitt
knight wearing helmet
knight wearing plate armor
knights
knights armor
knights in battle
knights of zodiac girl
knights templar in a space suit
knitted hat
knitted mesh material
knives
knobbly knees
knobs
knockout punch
knolling
knotted trees
knowing expression
knowledge
knowledge of anatomy
known as voryn dagoth in life
knyazev konstantin
ko-fi
koala
koala playing a guitar
kobalt blue
kobe bryant
kobold
koda kazuma
kodachromatic
kodachrome
kodachrome  1976
kodachrome ( 1 9 7 6 )
kodachrome 6 4
kodachrome 6 4 color slide film
kodachrome 64
kodachrome 64 color slide film
kodachrome 8 k
kodachrome camera
kodachrome colors
kodachrome colour
kodachrome film
kodachrome k135
kodachrome photo
kodachrome photograph
kodackchrome
kodacolor
kodak
kodak 110
kodak 2 5 0
kodak 2383
kodak 2383 film
kodak 2383 vision color
kodak 35mm
kodak 4 0 0
kodak 400
kodak 5219
kodak 5247 stock
kodak brownie camera
kodak camera
kodak color
kodak color film
kodak color film stock
kodak color stock
kodak d-76
kodak ektachrome
kodak ektachrome 1 2 0
kodak ektachrome 10
kodak ektachrome 100
kodak ektachrome 120
kodak ektachrome e 1 0 0
kodak ektachrome e 100
kodak ektachrome e100
kodak ektar
kodak ektar 1 0 0
kodak ektar 100
kodak ektar 100 film
kodak ektochrome film
kodak film
kodak film look
kodak film photo
kodak film photography
kodak film stock
kodak fling 200
kodak gold
kodak gold 2 0 0
kodak gold 200
kodak gold 200 film
kodak gold 3 5 mm film
kodak gold 4 0 0
kodak gold film
kodak hie infrared film
kodak kodachrome 400
kodak kodachrome film
kodak photo
kodak photograph
kodak porta
kodak portra
kodak portra 1 6 0
kodak portra 160
kodak portra 160 professional
kodak portra 4 0 0
kodak portra 400
kodak portra 400 film
kodak portra 8 0 0
kodak portra 800
kodak portra 800 film
kodak portra film
kodak portra film 800
kodak professional lenses
kodak promax 4 0 0
kodak stock
kodak tri - x 3 5 mm
kodak tri - x 35 mm
kodak tri-x 400
kodak tri-x pan film
kodak ultra f9
kodak vision 2 0 0 t
kodak vision 200 t
kodak vision 3 500t
kodak vision 3500t
kodak vision 5 0 0 t
kodak vision3 500t
kodakchrome
kodakchrome : : 8 k
kodakchrome : : 8k
kohei horikoshi
koi
koi colors
koi fish
koi fish floating in space
koi fishes
koi pond
koji morimoto
koji morimoto shinjuku
kojima
koju morimoto
komatsuzaki retro-futurism
kombi
komorebi
konachan
konami
konami concept art
konami hyperboy
konami mecha
kong: skull island (2017)
kong: skull island 2017
kongming lanterns in the sky
konica minolta
konosuba
konosuba anime style
konpeki no kantai
konstantin porubov
konstantin razumov
koons
koopa
kopera
korean
korean art nouveau anime
korean artist
korean audrey hepburn
korean audrey hepburn automata
korean countryside
korean face features
korean film noir
korean girl
korean hanbok
korean idol
korean kpop star
korean mmo
korean mmorpg
korean muscle boy 2 1 years old
korean muscle boy 21 years old
korean mythology
korean supper
korean symmetrical face
korean traditional palace
korean typography
korean war
korean woman
korean women's fashion model
korean womens fashion model
korean writing
korg from thor as a soldier
korra from the legend of korra
kotegawa yui
kothophed
koto no ha no niwa
kousuke oono
kowloon
kowloon cyberpunk
kowloon cyberpunk cityscape
kowloon walled city
kowloon walled city style
koyaanisqatsi
koyoharu gotouge
kpop
kpop amino
kpop star
kpop style colors
kraken
krakens
kramer
krampus
kramskoi
kramskoi 4 k
kratos
kraut rock soundtrack
kremlin
kremlin towers are destroyed
krenz
krenz c
krenz cushar
krenz cushart and artem demura
krenz cushart and artgerm
krenz cushart and asher duran
krenz cushart and wenjun lin
krenzcushart
kreuzberg
kris kuksi
kristanna loken
kristen bell
kristen bell as a greek goddess
kristen bell as a mermaid
kristen stewart
krita
krull
krypton
krypton ion
krystal
krystal from star fox
krysten ritter
krzysztof kononowicz
krzysztof porchowski jr
kuala lumpur
kuang hong
kubo and the two strings
kubric stare
kubrick
kubrick stare
kubrik
kumadori makeup
kumamoto
kundalini energy
kung fu
kung fu panda
kung-fu
kunoichi
kuntilanak
kuntilanak on bayan tree
kuntilanak on tree
kupka
kurdish lawyer
kurdish samurai
kurdish soldier
kurdistan
kurisu makise
kurisu makise steins gate anime
kurt cobain
kurt russell
kusanagi
kutsuya terada
kuvshinov
kuvshinov ilya
kuzma petrov - vodkin
kvothe from name of the wind
kwak ji young
kyle hotz
kyle mclaughlin
kylie jenner
kylie jenner as catwoman
kylie minogue
kylie minogue as barbarella
kylo ren
kyoani
kyoani studio
kyoto
kyoto animation
kyoto animation anime key visual
kyoto animation key
kyoto animation key visual
kyoto animation productions
kyoto animation still
kyoto animation studio
kyoto inspired
kyoto japan setting
kyoto studio
kyrie irving as che guevara
kyuyong eom and boris vallejo
kyza
kyza saleem
kārlis padegs
l - system
l critical role
l vampire
l · lawliet
l7m
la bouche couverte de sang
la catrina
la llorona
la nouvelle vague
lab
lab background
lab coat
lab coat and tee shirt
lab covered in mold and moss
lab in the background
labcoat
labcoats
label
label design
labeled
labeled diagram
labeled diagrams
labelled
labels
labile temper
labirynth
laboratory
laboratory background
laboratory in the background
labrador
labradorite
lace
lace dress
lace underwear
lace veil
lace web
laced
laced dress
laced lingerie
laces
laces and ribbons
lacey
lacey accessories
lachlan bailey
lacivious pose
lack of ground
lack of sunlight
lacking in three-dimensionality
lacquer on canvas
lacquered
lacquered armor
lacquered glass
lacquered oak reception desk
lacquerware
lacrosse player
lada
lada car
ladder
ladders
ladies
ladislas starevich
ladislaus eugen petrovit
lady
lady davis
lady diana
lady dimitrescu
lady gaga
lady gaga and judy garland
lady gaga artpop act ii
lady gaga artpop act ii album
lady gaga as evita
lady gaga as harley queen
lady gaga as harley quinn
lady gaga as president
lady gaga harley queen
lady gaga is harley quinn
lady gaga president of argentina
lady gaga with her venus hair
lady in black coat and pantyhose
lady in red armor
lady kima
lady of elche
lady of the entropy
lady palutena
lady using yellow dress
lady with glowing flowers dress
ladybug
ladybug as a monster
ladybug hobbits
ladybug robots
ladybugs
laetitia casta
lag in the game
lago di sorapis
lagoon
lagus the thieving bunny
laica chrose
laid - back
laid back features
laika
laika films
lain
lain iwakura
lain serial experiments
lake
lake background
lake baikal in the background
lake blue
lake filed with molten gold
lake foreground
lake house
lake in foreground
lake in the background
lake in the distance
lake in the forest
lake kawaguchi
lake reflection
lake scene
lake setting
lake view
lakes
lakes and waterfalls
lakeside
lakeside mountains
lalafell
lalisa manobal
lalisa manoban of blackpink
lama
lama with dreadlocks
lamb and goat fused as one
lamb wearing a sweater
lambda
lambent lighting
lamborghini
lamborghini aventador photoshoot
lamborghini countach
lamellar armor
lament
lament configuration
lamented
lamia
laminal space
lamp
lamp  armchair
lamp  fish tank
lamp ( ( ( armchair ) ) ) )
lamp ( ( ( fish tank ) ) ) )
lamp ( ( ( gym ) ) ) )
lamp ( ( ( mirror ) ) ) )
lamp posts
lamplight
lampposts
lamps
lamps and flowers
lamps on ground
lan mcque
lana del rey
lana del rey and zoë kravitz
lance
lance reddick
land
land mines
land of advanced races
land of ruins
land rover defender
land rover defender 110 (1985)
land rover defender 1101985
landcape
landed spaceship in background
landfill
landing gear
landing in epic space battle
landing lights
landing on the moon
landscape
landscape - scenery
landscape 35mm veduta photo
landscape 4k
landscape architecture photo
landscape art
landscape background
landscape composition
landscape from a car window
landscape illustration
landscape imagery
landscape in background
landscape is lush
landscape mode
landscape of a future city
landscape of africa
landscape of an alien world
landscape of apocalypse city
landscape of flat wastelands
landscape of geometric shapes
landscape of heaven
landscape oil painting
landscape painting
landscape perspective
landscape photo
landscape photo-imagery
landscape photo-reality
landscape photograph
landscape photography
landscape photography 4k
landscape scenery
landscape shot
landscape view
landscape vista
landscape vista photography
landscape wallpaper
landscape wide shot
landscape with red mountains
landscapes
landscapes: the joy of life
landslide road
landslides
lane brown
langford's basilisk
langoliers
language
language learning logo
lankhmar
lanky
lanscape
lantern
lantern candle
lantern fly
lantern light
lantern light besides
lanterns
lanterns on the porch
laos
lapel
lapis lazuli
lapis lazuli gradients
lapland
laptop
laptops
laputa
laquer and steel
lara croft
lara croft as spiderwoman
lara croft eating durian
lara croft medieval
lara croft relaxing
lara stone
larapi
large
large alien eyes
large and in charge
large and tall
large anime eyes
large antennae
large aquiline nose
large aquiline nose!!
large array
large arrays
large arrays data holograms
large black eyes
large black eyes!!!
large black kettle on hearth
large black smile
large blue diamonds
large blue engines
large blue eyes
large blue laser eyes
large body
large bokeh elements
large bosum
large brain in a vat
large breasts
large breasts size
large brush
large brush strokes
large brushstrokes
large bubble archaeologies
large bushy eyebrows
large bust
large caliber sniper fire
large cape
large chain
large chest
large city
large clouds
large clouds visible
large colorful images
large comfy bed
large commercial led screens
large computer monitor
large cornicione
large couch
large cracks
large creatures in distance
large creepy eyes
large crowd
large crowd of androids
large crowds of peasants
large crown
large crown helmet
large cute anime eyes
large cyberarrays data holograms
large dark eyes
large dark gradients
large depth of field
large detailed moon
large diagonal brush strokes
large diamonds
large dieselpunk flying airships
large diffused glowing aura
large diffused light
large diorama
large downslanted eyes
large draped cloth
large ears
large eddies
large electrical gold sparks
large entirely - black eyes
large entirely-black eyes
large environment
large exotic flowers
large explosions
large eyebrows
large eyes
large eyes and flowing long hair
large eyes and menacing smile
large eyes with visible pupils
large fangs
large film grain
large fish eyes
large fists
large flames
large flower head
large forehead
large format
large format camera
large format film
large format film camera
large format photograph
large format picture
large friendly eyes
large front teeth
large full lips
large full mouth
large futuristic residence
large gaping mouth
large gate
large geometric shapes
large glass ceilings
large glass windows
large glasses
large globe
large glowing eyes
large glowing moon
large green dragon
large green eyes
large green glass windows
large grey eyes
large group of crabs and worms
large hadron collider
large hall
large happy eyes
large head
large highlights
large hips
large horned tail
large horns
large individual rose petals
large intricate details
large jars on shelves
large jungle flowers
large landscape with village
large leaves
large led lights
large led screens
large lightning storm
large lips
large long pointy teeth
large magical trees
large medium and small elements
large messy hair style
large metal jaw
large modern residence
large moon
large moon in the sky
large mosquito wings
large motifs
large mountain
large mountains in back
large mouth
large mouth with teeth
large muscles
large nike logo
large nose
large noses
large obsidian crystals
large octoberfest invite card
large opaque blossoms
large opaque visor
large open room
large open windows
large overhangs
large painting
large pants
large pastel
large patches of plain colours
large path
large patterns
large piercing eyes
large pillars
large pipes
large planet in the background
large planets in the background
large plants in the background
large polygons
large pore fungi embroidered
large pores
large portrait
large potted plant
large props
large purple eyes
large red eyes
large red eyes!!!
large robot
large rocks
large rocks with thick moss
large rocky mountain
large room
large rose flower head
large rose petals
large round eyes
large round window
large scale
large scale battle
large scale photo
large scale scene
large screen
large screens
large sensor dslr photo
large shell
large shot
large shoulders
large sky
large smile
large stained glass windows
large staircase
large star crystals
large stone statues of heroes
large stones
large strokes
large sun in sky
large sweet jars on shelves
large sword
large tail
large tall
large teeth
large temples
large terrarium
large text
large thighs
large tongue
large translucent art statues
large tree
large tree casting shadow
large trees
large triangular shapes
large twin sunset
large vertical blank spaces
large view
large viewscreens
large vines
large waves
large waves crashing over it
large waves hitting the cliff
large white border
large white clouds
large white wings
large window
large windows
large windows to forest at night
large windows to french town
large wings
large wings on back
large wingspan
large xenomorph approaching
large yellow eyes
large!!!!!! eyeglasses!!!
large)}]
large-format photography
large-scale
largeeyeglasses
largely biomechanical
larger than earth
larger than galaxies
larger than planets
larger than the planet
largest haunches ever
lariennechan
larry david
larry david eating a sandwich
larry david playing poker
larry elmore : 0. 5
larry macdougall
larvae
larynx
las pozas
las vegas
las vegas at night
las vegas strip
lasagna
lascaux
lascivious pose
laser
laser atomic microscope
laser beam
laser beam ; outdoor
laser beam eyes
laser beams
laser blasts
laser cannon
laser cut
laser cut textures
laser eye
laser eyes
laser fire
laser gun
laser guns
laser light
laser light *
laser light show
laser lighting
laser lights
laser lights background
laser point between the eyes
laser rifle
laser rifles
laser sharp
laser show
laser sights on weapons
laser sword
laser turret
laser weapons
laser wip
laserpunk
laserpunk fullbodysuit
lasers
lasers all over the scene
lasers and explosions
lasers and neon circuits
lasers firing
lasers for lights
lasers in mid flight
lasers shooting out of eyes
lashes
lasso
lasso tool
last day on earth
last exile
last exile anime
last jedi
last light
last light on mountain top
last man standing
last of us
last photo
last photo ever taken
last selfie ever taken
last selfie on earth
last stand
last supper
last supper composition
last supper realistic robot
laszlo
lat lighting
late - 4 0 s aged
late - 40 s aged
late 1 9 6 0's
late 1 9 th century
late 19 th century
late 2000s
late 2000’s
late 80's
late 80s
late 90s
late afternoon
late afternoon light
late afternoon lighting
late afternoon sun
late antiquity
late at night
late autumn
late baroque painting
late evening
late gothic religious paintings
late italian renaissance
late meiji period
late morning
late night
late night melancholic photo
late night raining
late summer evening
latent space
later confirm hyperrealism
lateral view
latex
latex domme
latex dress
latex flesh and facial muscles
latex outfits
latex shiny
latex skin
latex suit and raincoat
lathe
latin
latin writing
latina
latina skin
latinas
latino
latino american
latino features
latitude and longitude
latte art
lattice
laugh
laugh lines
laughing
laughing alien face
laughing and joking
laughing and yelling
laughing bear musician
laughing emoji
laughing groom
laughing hysterically
laughing man
laughing maniacally
laughing menacingly
laughing out loud
laughing your head off
laughingstock
laughs
laughter
laughter and screaming face
launch of a rocket
launch test
launch tracking missiles
launching a straight ball
launching to space
laundry hanging
laura letinsky and steve mccurry
laura palmer
laura sava
laura watson
laura zalenga
laure lipton
laurel wreath on his head
laurels of glory
laurent durieux
laurie
laurie greasely
laurie greasley
laurie greasley and james jean
laurie greasly
laurie lipton and michael whelan
lauterbrunnen valley
lava
lava and fire goddess
lava and smoke
lava erupting
lava field
lava flowing
lava flows
lava in the background
lava lamp
lava lamps
lava reflections
lava river
lava rivers
lava rock
lava streams
lava texture
lava waterfalls
lava!!!
lavander and yellow color scheme
lavender
lavender and pink eyes
lavender blush
lavender eyes
lavender fields in full bloom
lavender flowers
lavender hair
lavender plants
lavish
lavish rococo baroque setting
lavishly decorated
lavs flowing through the land
law - alligned
law aligned
law contrasts
law of thirds
lawn
lawns
lawrence alma - tadema
lawrence alma tadema
lawrence of arabia
lawther sit at table playing dnd
lawyer
lawyer clothing
lawyer suit
layed on a red velvet fabric
layer upon layer
layered
layered composition
layered detailed
layered fog
layered impasto
layered paper
layered paper art
layered paper style
layered skirts
layered stratocumulus clouds
layered texture
layers
layers of architecture
layers of colorful reflections
layers of strata
layers on layers on layers
laying back on a pillow
laying back on the bed
laying down
laying down in the grass
laying down with wrists together
laying in bed
laying on a beach
laying on a bed
laying on beach
laying on her back
laying on her back on a bed
laying on roses
laying on sand
laying on the ground
laying on their back
laying teddy bear
laying under a tree on a farm
layout
layout design
layout frame
layout of map
lazer blast
lazers
lazertag
lazy
lazy eye
lazy eyes
lblsck hair
lcd screen
ldpe
le corbeusier
le corbusier
lead - covered spire
lead designer greg rutkowski
lead guitar
leader
leader of fascist hungary
leaders
leading a battle
leading lines
leading spartans into battle
leading to a beautiful
leading to the sky
leaf
leaf green
leaf hair
leafs
leafs falling
leafy sea dragon
league of legend
league of legend illustration
league of legends
league of legends arcane
league of legends art
league of legends art style
league of legends champion
league of legends character
league of legends character art
league of legends concept art
league of legends inspired
league of legends inventory item
league of legends splash art
league of legends splash screen
league of legends splashart
league of legends splashscreen
league of legends style
league of legends style art
league of legends wallpapers
leak
leaked
leaked footage
leaked from nasa
leaked image
leaked photo
leaking
leaking oil
leaking pistons
leaks
leaky make - up
lean
lean and muscular
lean and toned
lean body
lean but muscular
lean face
lean man with light tan skin
lean sleek styling
leaning against the wall
leaning against the window
leaning on door
leaning on the wall
leaning towards watercolor
leap of faith
leaping
leaping from babaob tree
leaping into the air
leaping towards viewer
leaping with arms up
learning
leather
leather and suede
leather apron
leather armor
leather armored
leather armour
leather belt
leather body suit
leather bomber jacket
leather boots
leather boots and cape
leather bunny costume bodysuit
leather clothes
leather clothes under the armor
leather clothing
leather clothing and boots
leather coat
leather couch
leather couches
leather cuffs around wrists
leather duffle coat
leather fur jacket
leather gloves
leather hunting attire
leather interior
leather jacket
leather jacket and denim jeans
leather jacket and denim vest
leather jackets
leather jerkin
leather jewelry
leather padding
leather pants
leather pants  natural lighting
leather pants with a belt
leather pants | natural lighting
leather pilots uniform
leather pouch
leather robes
leather scifi armor
leather shoes
leather shoulder armor
leather sofa
leather stockings
leather straps
leather thigh-high boots
leather top
leather trench coat
leather tunic
leatherface
leathery
leave
leaves
leaves and magic
leaves and simple cloth
leaves and vines
leaves as dollars!! glow
leaves as dollarsglow
leaves falling
leaves foliage and stems
leaves in foreground
leaves in the air
leaves on branches
leaves trap
leaves twigs wood
leaving a room
leaving for battle
lebanon kirsten dunst
leblanc
lebron james
lebron james as che guevara
lebron james in gta v cover art
lebron james solemn at tea party
lecherous pose
lecouffe deharme
lecture halls and gambling dens
led
led - lit armor
led color
led detailed spacesuit
led details
led display
led displays
led effects
led floodlights
led gamers keyboard
led gaming
led glowing purple eyes
led indicator
led lamps
led light
led light accents
led light strips
led lighting
led lighting effects
led lights
led lights around the place
led purple eyes
led screens
led zeppelin
ledmund leighton
leds
leds horizon zero dawn machine
leds visor helmet
ledspace
lee bermejo
lee bermejo and darwyn cooke
lee brown coye
lee conklin
lee griggs
lee griggs and jason chan
lee ji - eun
lee ji-eun
lee kuan yew
lee madgwick & liam wong
lee madgwick & yuto yamada
lee madgwick and hubert robert
lee madgwick and zack snyder
lee madgwick liam wong
lee madgwick yuto yamada
lee mccall
lee miller
leeloo
leeloo outfit
leering
leesha hannigan
left
left - hand drive
left 4 dead
left 4 dead 2
left align
left align content
left behind
left ear
left eye red stripe
left eye stripe
left hand
left hand propping up the head
left profile
left right front back
left right symmetry
left trad
leftlight
leftover meat pie
leg
leg and hip shot
leg and thigh shot
leg high
leg shot
legacy
legacy of kain soul reaver
legend
legend of korra
legend of korra setting
legend of the cryptids
legend of zelda
legendary
legendary armor
legendary cinematic
legendary dragon
legendary epic shot
legendary god holding spear
legendary item
legendary masterpiece
legendary space ship
legendary sword of technology
legendary warrior
legendery
legends
legends of runeterra
legends of tomorrow
leggings
leggins
legible text
lego
lego avatar
lego batman movie color script
lego character
lego city
lego mini figures
lego minionic thor
lego mixels
lego movie still
lego movie style
lego set
lego style
legolas
legolas as an elf ranger
legolas from lord of the rings
legos
legoshi from beastars
legs
legs and arms
legs crossed
legs intertwined
legs replaced with human legs
legs spread
legs stretched!!! intricate
legs taking your pov
legs visible
lei jin
lei min
leica
leica 1 0 0 mm f 0. 8
leica 100 mm f 0. 8
leica 35mm
leica 5 0 mm f 1. 8 lens
leica 50 mm f 1. 8 lens
leica 8 5 mm
leica 8 5 mm 1. 2 f
leica 8k still from an a24 film
leica iii
leica lens high quality
leica m - a
leica m 6
leica m 9
leica m6
leica m6 film camera
leica m9
leica noctilux
leica s
leica s photograph
leica sl 2
leica sl 2 3 0 mm
leica sl 2 5 0 mm
leica sl 2 5 0 mm b & w
leica sl 2 technicolor
leica sl 230mm
leica sl 250 mm
leica sl2 30mm
leica sl2 35mm black and white
leica sl2 50mm
leica sl2 50mm hd
leica sl230mm
leica sl250mm
leica summicron-t 23 mm f/2
leica summilux 5 0 mm f / 1. 4
leiji matsumoto
leisure activities
lemon
lemon demon
lemon slices float on the water
lemon wearing sunglasses
lemonade
lemonlight
lemons
lemons on the ground
lena oxton
leng jun
lengthwise
leni robredo
lenin
lens
lens 5 0 mm
lens 50 mm
lens blur
lens distortion
lens dust
lens effect
lens flair
lens flare
lens flare glow
lens flare lighting
lens flare photo real
lens flare snow storm
lens flare. occult photorealism
lens flare. ominous
lens flares
lens flares. cinematic
lens glare
lens interoperability
lens orbs
lens zooming
lensculture portrait awards
lense flare
lense flares
lenses
lensflare
lensflares
leon battista alberti
leon francois comerre
leon tukker
leonard cohen
leonard da vinci style
leonard nimoy
leonardo
leonardo calamati
leonardo da vinci
leonardo da vinci anatomy study
leonardo da vinci and raphael
leonardo da vinci painting
leonardo da vinci sketch
leonardo da vinci style
leonardo da vinci)
leonardo davinci
leonardo davinci detail
leonardo dicaprio
leonardo dicaprio meme
leonardo dicaprio's face
leonardo divinci
leonardo fioravanti
leonetto cappiello
leonid
leonid afremov and tim white
leonora karrington
leotard
leotard and leg warmers
lepra
lerapi
les catacombes
les edwards poster art
lesbian
lesbian art
lesbian embrace
lesbian kiss
lesbians
leslie david and lisa frank
leslie zhang
less detailing
less tentacles
let there be light
let's be friends
let's get dangerous
let's play
lethal preservation
lets be friends
lets get dangerous
lets get weird
letter a
letter s
letterbox
letterboxing
lettering
lettering clean
letters
lettuce
level 1 plate armour
level design
level structure
levels
levers
levi ackerman
leviathan
leviathan cross
leviathan cthulhu squid
levitan
levitating
levitating above the ground
levitating agricultural sphere
levitating citadel
levitating sand
levitating twin nuns
levitation
lewandowski
lewd
lewis carrol
lewis jones
lexi belle
leyedecker
leyendecker
leyendecker style
lgbt
lgbt art
lgbt flag
lgbtq
li bingbing
li zixin
liam
liam brazier
liam brazier and nielly
liam neeson
liam neeson as burl gage
liam neeson versus godzilla
liana
liana wrap two fantasy creatures
lianas
liang mark
liang xing
lianna
liberation
libertas
liberty curls
liberty leading the people
liberty statue sitdown pose
libra
libra symbol
librarian
library
library background
library books
library in the style of baroque
library interior
library interior background
library nerd glasses
library of babel
library of congress
library of forever
library of ruina concept art
lich
lich demon mage class
lich king
lich vecna (d&d)
lich vecna d&d
lichen
lichen macro
lichens
lichtenberg figure
licking
licking out
licking tongue
licorice allsort filling
lie detector test
lie on a golden stone
lie on white clouds fairyland
liege
liepke
lies
life
life after death
life after the plague
life and death
life and death mixing together
life cycle
life drawing
life is not what it used to be
life is strange
life like
life like face
life like plants
life magazine
life magazine photo
life magazine reportage photo
life meaning
life photography
life simulator game screenshot
life size
life story
life-like
life-size
life. america 2 0 9 8
life. america 2098
lifeless
lifelike
lifelike attributes
lifelike incredible hair
lifelike reproduction in 8k
lifelike texture
lifelike. sci - fi
lifestyle
liflike
lifted
lifting weights
lighning storm
light
light - brown wall
light - hearted
light above palace
light academia aesthetic
light and clouds
light and dark
light and darkness separated
light and medium values
light and shadow
light and shadow effects
light and shadows
light armor
light armour
light art
light at the end of the tunnel
light atmosphere
light background
light beam
light beams
light beams night
light beams with dust
light beard
light behind
light beige pillows
light being
light bends to him
light bends to it
light bleed
light blond hair
light blood spatter
light bloom
light bloom sunlight
light blue
light blue and white tones
light blue background
light blue clear sky
light blue colors
light blue dress portrait
light blue eyes
light blue filter
light blue hair
light blue mist
light blue pastel background
light blue piercing eyes
light blue robes
light blue skin
light blue sky
light blue sky with clouds
light blue water
light blues
light blur
light blush
light boho carpet
light borwn hair
light bown iris
light box
light brazen
light breaks through the roofs
light bronze brown hair
light brown
light brown background
light brown coat
light brown colors
light brown eyes
light brown fur
light brown hair
light brown hair blue eyes
light brown long wavy hair
light brown messy hair
light brown neat hair
light brown piercing eyes
light brown skin
light brown skin!
light brown trenchcoat
light bulb
light bulbs
light casting onto the ground
light caustics
light caustics effect
light charcoal drawing
light cinematic
light circles
light clothing
light cloud
light clouds
light color
light color palette
light colors
light colours
light coming from above
light coming from crt monitor
light coming from the entrance
light coming from the right side
light coming from the windows
light coming from windows
light coming in from the left
light coming off of the armor
light coming through
light coming through the window
light coming through window
light cone
light contrast
light cream and white colors
light cute freckles
light cyan
light dark
light dark long hair. apathetic
light diffraction
light diffusion
light disperions
light dispersion
light displacement
light dress
light dust
light effect
light effect. feminine
light effect. hyper detailed
light effect. modern fantasy
light effects
light emitting from fingertips
light entering through a blind
light eyes
light falling on face
light film grain
light fixtures
light fog
light fogged
light frame
light freckles
light from above
light from bottom
light from right
light from top
light from top right
light from window
light glare
light glow
light glowy yellow eyes
light gold armor
light gold hair
light gradient
light gray eyes
light gray long hair
light green
light green and deep blue mood
light green dress
light green eyes
light green mist
light green tone beautiful face
light greens and whites
light grey
light grey backdrop
light grey background
light grey blue and golden
light grey crown
light grey fur
light grey hair
light grey hoodie opened
light grey mist
light grey-blue eyes
light greyscale
light ground fog
light hair
light half opened dress
light halos
light haze
light holographic tones
light house
light illumination at sunset
light inside it
light inside the hut
light instead of hands
light intriguing piercing eyes
light iridescent color
light iridescent hair color
light is mine to travel
light kingdom backdrop
light language
light leak
light leaks
light leather armor
light light atmosphere
light lighting side view
light lines
light looping
light make up
light makeup
light makeup focus
light micrograph
light mist
light mode
light morning fog
light night
light novel cover art
light of god
light of hell
light of infinity
light of sin
light of sorrow
light of the gods
light on top
light orange mist
light orange values
light over boy
light paint
light painting
light particles
light particules
light pink
light pink background
light pink bikini
light pink clouds
light pink hair
light pink hair with pink flames
light pink lipstick
light pink mist
light pink tonalities
light platinum long blond hair
light prisms
light purple
light purple mist
light rain
light ray
light ray from a blurry window
light rays
light rays and shadows
light rays beaming through
light rays from above
light rays from the surface
light rays illuminate dust
light rays illuminating dust
light rays shining through
light rays. refraction
light red and deep blue mood
light red and deep orange mood
light red and orange mood
light red hair
light reflecting off windows
light reflection
light reflections
light reflexes
light refracting
light refracting through prisms
light refraction
light refractions
light room
light sabers
light scatter
light sensor
light separated from dark
light shadows
light shaft
light shafts
light shimmering
light shining
light shining through
light show
light simulation
light skin
light skin tone
light skinned african young girl
light smile
light smiling
light snow
light snowfall
light sorcerer
light source from the left
light source on left
light sparkles
light spatter of blood
light speed
light streaks
light streaks in the sky
light streams
light stubble
light stubble beard
light stubble with red shirt
light study
light tan
light through glass
light through the mist
light through windows
light toned
light traces
light tracing
light trail
light trails
light transport
light transport simulation
light water
light watercolour
light watercolour painting
light white background
light wind
light wood
light yagami
light yellow
light yellow hair
light-blue
light-blue eyes
light-blue steel-plate
light-brown skin
light-red lips
lightbeam eyes
lightbeams
lightbeams shining through
lightblue acrylic paintdrip tar
lightbox
lightbright
lightbulb
lightening
lightening tree
lighter brown hair
lightgreen
lighthearted
lighthearted celebration
lighthouse
lighthouse in the desert
lighting
lighting 8k
lighting and thunder
lighting background
lighting bolts
lighting chiaroscuro
lighting color scheme
lighting eyes
lighting from spotlights
lighting her with a rim light
lighting on concrete
lighting path traced
lighting refraction
lighting study
lighting. fantasy
lightly dirty face
lightly dressed
lightly texural
lightning
lightning and rain
lightning arc plasma
lightning around
lightning background
lightning behind
lightning beings
lightning bolt
lightning bolt scar on forehead
lightning bolts
lightning bursts
lightning clouds
lightning creating life
lightning dragons attack
lightning effects
lightning electricity coil
lightning eyes
lightning fantasy magic
lightning flashes
lightning from above
lightning halo
lightning helmet
lightning in a bottle
lightning in background
lightning in the background
lightning in the distance
lightning in the sky
lightning mage spell icon
lightning master
lightning sparks
lightning storm
lightning storm background
lightning storm hitting the car
lightning storm in background
lightning storms
lightning strike
lightning strikes
lightning symbol
lightning!!
lightning!!!
lightnings
lightpainting
lightpainting luminescent
lightpainting motion blur
lightrays
lightroom
lightroom hyperdetailed
lightroom preset
lights
lights and shadows
lights and smoke
lights are on down the street
lights are on in the windows
lights beam
lights beaming out of eyes
lights caustic
lights in distance
lights in the dark
lights in the night
lights inside
lights off
lights on
lights on ceiling
lights through fog
lights with bloom
lightsaber
lightsaber duel
lightsaber katana
lightsaber wallpaper 4 k
lightsaber wallpaper 4k
lightsabers
lightshafts
lightshafts and foggy atmosphere
lightshow
lightsome space
lightspeed
lightwave 3d
lightweight
lightweight armor
lightweight armour
lightweight but imposing
lightweight leather armour
lightyear ( film
ligjt trail
ligne claire
ligtt sword
like
like a catalog photograph
like a crown
like a cyberpunk workshop
like a professional model
like a scene from blade runner
like a scifi action movie
like a young god
like alice in wonderland
like andy lau
like anime
like artgerm
like bebop
like gorilla
like in museum
like ironman
like jiufen
like kyoto
like lady mechanika
like liangchao wei
like magic the gathering
like matrix
like peter tarka
like rolf armstrong style
like small pieces
like tears in rain time to die
lil peep
lil uzi vert
lil wayne
lilac
lilac bushes
lilac hair
lilac sunrays
lilacs
liliana vess
lilies
lilies and daffodils
lilith
lillies
lilly collins
lilo and stitch
lily
lily allen
lily collins
lily flower
lily flowers
lily flowers. 8 k
lily flowers. 8k
lily frank
lily pad
lily pads
lily petals
lim chuan shin
limb mutations
limbo
limbo game
limbs
limbs made from vines
lime
lime and gold hue
lime and violet
lime green
limestone
liminal
liminal  diffusion
liminal ( diffusion
liminal aesthetic
liminal bookshop
liminal creepy
liminal diffusion
liminal eerie midnight backlit
liminal highly detailed
liminal space
liminal space aesthetic
liminal space hallway
liminal space in outer space
liminal space in outer space!!!
liminal space photography
liminal space retro
liminal space style
liminal spaces
liminal spaces and environments
liminalspaces
limit
limited color palette
limited colors
limited colour palette
limited concentration
limited edition
limited edition print
limited palette
limited time offer
limmy
limp bizkit performing in heaven
lin hsiang
lina
linda wilder
linden trees
lindsay adler
lindsey look
line
line - art
line art
line art - n 9
line art colouring page
line art illustration
line art portrait
line art!!
line brush
line dancing at a party
line drawing
line drawing tattoos
line drawn
line sketch
line sketch!!
line sleek
line vector art
line work
line work concept art
line-drawing
lineage 2 revolution style
linear gamma
linear illustration
lineart behance hd
lined in cotton
lined paper
lined up horizontally
lined with neon tube lights
lineless
linen
linen canvas
lines
lines and movement
lines and movement and motion
lines of energy
lines of lights
linework
lingerie
lingerie!!
lingeries beauty
link
link from the legend of zelda
link from zelda
link from zelda using computer
link in a cave
link the movie
linkedin
linkin park
linn olofsdotter
lino print
linocut
linocut art
linocut art print
linocut print
linocut print of fantasy forest
linocut wood block print
linsey levendall
linus from linustechtips
linus sebastian
linus tech tips
linux
linux mint
lion
lion body
lion fish
lion head
lion icon
lion in a meadow with hornbeam
lion mane
lion resting in the shade
lion warrior
lion with crown
lion's gate
lion's mane
lionardo davinchi
lionel messi
lionel messi as a muppet
lionel messi portrait
lioness
lions
lions mane
liosh
lip scar
lipgloss
lipkin
lips
lips of megan fox
lips on cigarette
lips slightly parted
lips wide parted
lipstick
liquid
liquid acrylic painting
liquid cat
liquid clouds
liquid cooled
liquid cooled desktop
liquid cooling
liquid fire
liquid glass
liquid glowing aura
liquid glue spots
liquid gold
liquid golden and black fluid
liquid hands and feet spinning
liquid headdress
liquid high glossy
liquid interface
liquid light
liquid magic
liquid marble fluid painting
liquid metal
liquid oilpaint
liquid painting
liquid physics
liquid polished metal
liquid refraction
liquid sculpture
liquid shadows engulf
liquid simulation
liquid simulation background
liquid smoke
liquid smoke twisting
liquid that has a viscous
liquid translucent amber
liquid wax
liquids
liquified
lisa
lisa - frank
lisa ann
lisa brawn
lisa frank & sho murase
lisa frank and stephen gammell
lisa frank sho murase
lisa frank style
lisa kristine
lisa parker
lisa simpson wearing braces
lisbon
lisbon city at night
list
listening to godly music
listening to music
listening to music at 2 am
listing image
lit candles
lit from above
lit from behind
lit from below
lit from below with red lighting
lit from bottom
lit from the side
lit in a dawn light
lit up
lit up in a dark room
lit windows
lit with candles
lit. 'honored ka'bah')
lit. 'the cube'
lit. honored kabah
lit. the cube
lita cabellut
lithe
lithe woman
lithograph
lithograph engraving
lithograph print
lithographic print
lithographics
lithography
lithoraph engraving
litrpg novel cover
litter
littered
littered with garbage
little
little angry girl with blue hair
little bo peep
little boy
little boy wearing nun outfit
little brother
little cottage
little detail
little elf girl
little elf tomboy
little fog
little girl
little girl meets robot
little girl with blue hair
little girl with magical powers
little kid
little light
little mermaid
little mermaid magical kingdom
little nightmares
little people
little people!!!
little planet
little red riding hood
little remaining
little shy smile
little smile
little windows
little witch academia
littlefoot
liv tyler
live
live - action
live 1 9 8 6
live 3hr festival videoclip
live action
live action children's tv show
live action childrens tv show
live action film
live action movie
live at lollapalooza
live broadcast
live colors
live concert
live concert lighting
live concert of beyonce
live concert photography
live footage
live in concert
live on stage
live performance
live-action
live-action adaptation
live-action archival footage
live-action john waters film
live2d
live2d virtual youtuber model
lived in
lively
lively atmospheric
lively colors
lively irregular edges
lively perspective
liverpool football club
livia prima
livid colors
living
living flora
living food adorable pancake
living in the database!!!
living nature
living planet
living room
living room background
living room interior
living room interior design
living room vibe
living room wall background
living room with split levels
living spore microorganisms
living the good life
living tree
liz truss
lizard
lizard head
lizard king / queen forgiveing
lizard legs
lizard on ground
lizard person
lizard pose
lizard skin
lizard tail
lizard tongue
lizardfolk
lizardlike tongue
lizardman
lizardman art
lizardman thief
lizardman warrior concept
lizards
lizzo
llama
llama all the way
llama angel of sunrise
llama anthro portrait
llama head
llama portrait
llama with dreadlocks
llustration
lo - fi
lo - fi colors
lo fi
lo fi colors
lo-fi
lo-fi art
lo-fi illustration style
lo-fi retro videogame
loading screen
loading screen. 8k resolution
loadscreen
loadscreen”
loaves
loba andrade from apex legends
lobelia
lobo
lobotomy of a beautiful woman
lobster
lobster themed armour
local bar
local close up
local conspirologist
local foods
local gym
local heroes
local illumination
local people chasing to attack
located in a castle
located in a swamp at sunrise
located in a wizard's shop
located in hajibektash complex
location  chicago  alley
location  favela _ wall
location ( favela )
location ( favela _ wall )
location [ chicago ( alley ) ]
location in a apartment
location is inside the tardis
location of a dark old house
loch ness monster
lock
lock and load
lockbox
locker room
lockers
lockheed concept art
locklegion
lockpicks and pile of gold
locomotive
locusts and flies
lodoss
lofi
lofi aesthetic
lofi album art
lofi art
lofi artstyle
lofi bioshock steampunk portrait
lofi colors
lofi feel
lofi girl
lofi girl aesthetic
lofi hip hop
lofi portrait
lofi portrait at a window
lofi steampunk bioshock portrait
lofi technology
lofi vibe
lofi vibes
loftis
lofty heavens
log cabin
log cabin beneath the alps
log homes
log houses built on hills
logan
logan cure
logan's run
logical anatomy
logo
logo art
logo concept design
logo design
logo for a social network
logo for lunch delivery
logo for research lab
logo graphic design
logo has
logo has”
logo in abstract style
logo of a cat field clinic
logo style
logo vector art
logo without text
logopond
logos
logotype
logotype design
logo”
loin cloth
loincloth
lois greenfield
lois van baarle and bouguereau
lois van baarle and rossdraws
lois van rossdraws
loish
loish and clamp style
loish and goro fujita
loish and ross tran
loish and wlop
loish art style
loish van baarle
loish |
lokah samastah sukhino bhavantu
loki
loki horns
loki portrait
loki's pet project
lokis pet project
lol
lol style
lola bunny fanart
lola dupre
loli
loli in dress
lolish
lolita
lolita fashion
lolita style
lollipop
lollipops
lolth
lomo
lomo effect
lomo photograph
lomo saltado
lomography
lomography effect
lomography lady grey
lomography photo
lomography photo effect
london
london architecture
london at night
london big ben at background
london bus
london cemetery
london eye
london fashion week
london gang member
london south bank
london streets in background
london underground tube station
lone female
lone girl waiting for the train
lone industrial!!! spaceship!!
lone industrialspaceship
lone person in the distance
lone petite female goddess
lone ranger
lone silhouette in the distance
lone survivor
lone wolf
loneliness
lonely
lonely and sad
lonely astronaut
lonely atmosphere
lonely family
lonely human walking
lonely landscape
lonely rider
lonely scenery yet peaceful!!
lonely tree
lonely vibe
lonely!! stop light glowing
loner
lonesome
long
long - haired chihuahua
long - haired siberian cat
long - shot
long - tailed tuxedo coat
long afro hair
long and orange mustache
long antennae
long arm
long arms
long ashy hair
long ashy hair | gentle lighting
long auburn hair
long azure blue hair
long bangs
long beach background
long beard
long beautiful flowing kimono
long black beard
long black braided hair
long black braids
long black crown
long black curly hair
long black hair
long black hair in a ponytail
long black hair with bangs
long black jacket
long black ponytail
long black shiny hair
long black straight hair
long blond braided hair
long blond drill curls
long blond hair
long blonde flaming hair
long blonde hair
long blonde hair and big eyes
long blonde hair and blue eyes
long blonde hair and large eyes
long blonde or red hair
long blue cape
long blue dress
long blue hair
long blue-cape
long bob cut
long bob hair
long boi
long boney limbs
long bow and arrows
long braided ash blonde hair
long braided black hair
long braided blond hair
long braided blue hair
long braided curly blonde hair
long braided curly brown hair
long braided curly hair
long braided curly silver hair
long braided green hair
long braided hair
long braided hair on top of head
long braided hair pulled back
long braided orange red hair
long braided purple hair
long braided silver hair
long braided teal hair
long braided viking beard
long braided white hair
long braids
long bronze brown hair
long brown beard
long brown hair
long brown puffy curly hair
long brown wavy hair
long brunette hair
long brush strokes
long bubblegum hair
long cape
long cast shadows
long chin
long cinematic shot
long claws
long cloak
long cloths red like silk
long coat
long coffee brown hair
long cornrows
long coyote like ears
long crimson hair
long crooked nose
long curl hair
long curl red hair
long curly black hair
long curly blond hair
long curly blonde hair
long curly brown hair
long curly fur
long curly green hair
long curly hair
long curly hair intricate
long curly light blond hair
long curvy black hair
long curvy hair
long cyan hair
long dark braided hair
long dark curly hair
long dark hair
long dark hair with bangs
long dark hairs
long dark hallway
long dark purple hair
long dark red hair
long dark shadows
long dark tattered umbra
long detailed ornate scythe
long distance
long distance photo
long distance shooting
long distance shot
long dreadlocks
long dress
long dress female
long dress with apron
long earrings
long ears
long elegant tail
long elegant tail behind
long elf ears
long exposure
long exposure 8 k
long exposure 8k
long exposure ; sharp focus
long exposure outside the city
long exposure photo
long exposure photograph
long exposure photography
long exposure shot
long exposure time
long eyelashes
long eyeslashes
long face
long fan blown dark reddish hair
long fangs
long fine flowing hair
long fine hair
long fingernails
long fingers
long fire hair
long flat hair
long flowing black hair
long flowing blue hair
long flowing braided hair
long flowing brown hair
long flowing cape and cowl
long flowing dark hair
long flowing fabric
long flowing fins
long flowing hair
long flowing hair underwater
long flowing intricate dress
long flowing medusa hair
long flowing pink hair
long flowing red hair
long flowing white hair
long flowing white robe
long flowy hair
long fluffy blond curly hair
long fluffy curly blond hair
long fluffy fur
long fluffy hair
long focal
long focal length
long free black straight hair
long front end
long full body shot
long fur
long ginger hair
long ginger hair windy
long glowing colourful hair
long glowing ethereal hair
long glowing hair
long glowing red hair
long goatee beard
long golden hair
long gown
long grass
long grass in the foreground
long green hair
long grey beard
long grey hair
long hair
long hair and red shirt
long hair blowing in the wind
long hair blue centred
long hair centred
long hair girl
long hair shawl
long hair straight down
long hair windy
long hair with a ponytail
long hair with bangs
long hair with full bangs
long hair with pastel colors
long hair ， full color
long hair!!!!
long haircut
long haired humanoid fursona
long hairs
long hairstyle
long hall way
long hallway
long highway
long hook nose
long horns
long lashes
long legs
long length slick white hair
long lens
long light bronze brown hair
long light pink hair
long light platinum blonde hair
long light purple hair
long light red hair
long limbs
long line-up of suspects
long long flowing hair
long loose red hair
long lost technology
long luxurious blond hair
long luxurious flowing gown
long luxurious gown
long luxurious intricate gown
long luxurious light blond hair
long magenta haire
long mane
long messy curly hair
long messy hair
long metal spikes
long metallic hair
long muzzle
long nails
long neck
long necks
long night cap
long nose
long open black mouth
long orange hair
long orange hair floating on air
long orange sweatshirt
long paint brush strokes
long perspective
long petals
long pigtail
long pink hair
long platinum hair
long pointy ears
long pointy pink nose
long ponytail
long purple hair
long rabbit ears
long range
long raven hair
long red cape
long red hair
long red hair white streak hair
long road
long shadow
long shadows
long shaggy blonde hair
long shaggy hair
long shaggy red hair
long shaggy-haired rocker
long sharp teeth
long shiny bronze brown hair
long shiny hair
long shirt
long shot
long shot angle
long shot dramatic lighting
long shot from back
long shot from the back
long shot jorogumo
long shot kodak portra 4 0 0
long shot kodak portra 400
long shot photography
long shot view
long shot wide shot full shot
long shutter speed
long silver hair
long silver hair purple tips
long silver hair with a flower
long skirt
long sleek tail behind
long sleeve
long sleeves
long snout
long spider paws
long spikes
long spiky fluffy smooth hair
long spiky hair
long spiky pink hair
long straight bangs
long straight beautiful hair
long straight black hair
long straight blonde hair
long straight golden hair
long straight green black hair
long straight hair
long straight red hair
long straight silver hair
long strawberry - blond hair
long street
long swept back blond hair
long swirly dark hair
long sword
long sword in her hand
long table
long tail
long tail with horns
long tails
long thick grass
long thick shiny black beak
long thick shiny gold beak
long thin black hair
long thin legs
long tie
long tongue
long torn red cape
long trippy hair
long trunk holding a wand
long tunic
long twirling moustache
long unkempt beard
long view
long vines
long violet and green trees
long violet hair
long wavy auburn hair
long wavy black hair
long wavy blond hair
long wavy blonde hair
long wavy brown hair
long wavy brunette hair
long wavy dark hair
long wavy fur
long wavy hair
long wavy orange hair
long wavy purple hair
long wavy red hair
long wavy white hair
long weasel
long weaving brown hair
long white beard
long white cape
long white cloak
long white dress
long white hair
long white hair and bangs
long white hair and beard
long white hair and white beard
long white hair!!!
long white mane
long wild black curly hair
long wild spiky hair
long windblown black hair
long winding stairs going down
long windy hair style
long wings
long wispy tentacles
long yellow hair
long-arms
long-range shots
long-shot
longbow
longbow arrow
longbows
longcoat
longer blond hair
longer eyebrows
longer hair
longhouse
longing
longque chen
longshot
longspear
longsword
look
look at all that detail
look at all that detail!
look at me
look at that detail
look at that detail!
look at the details
look at the front horizontally
look down a cellar staircase
look into the distance
look like model
look like someone is dancing
look me inside of my eyes
look of desire
look of hate
look twice
look what you have done
lookalike
lookbook
looked at big window
looking across the shoulder
looking angry
looking around a corner
looking at bloody fist
looking at camera
looking at camera!!!
looking at each other mindlessly
looking at his phone
looking at it rewards the soul
looking at monitor
looking at porthole window
looking at spaceships at dock
looking at sunset
looking at the camera
looking at the ceiling
looking at the city
looking at the full moon
looking at the ground
looking at the moon
looking at the mountains
looking at the ocean
looking at the sky
looking at the stars
looking at the treasure box
looking at the viewer
looking at us from a porthole
looking at viewer
looking at you
looking away
looking away from camera
looking away from the camera
looking away from viewer
looking back at the camera
looking backwards
looking confident
looking confused
looking content
looking cute
looking defiantly at the camera
looking directly at the camera
looking directly at the viewer
looking distracted
looking distracted and awkward
looking down
looking down a cliff
looking down at a massive crater
looking down at the forest floor
looking down at the valley
looking down at you
looking down from above
looking down on the camera
looking down on the view
looking down road
looking down street
looking downwards
looking exhausted
looking far
looking for clues
looking forward
looking from behind
looking from shoulder
looking from side
looking from side and bottom
looking from side and bottom!
looking from side!
looking from slightly below
looking frontal view
looking furious
looking happy
looking heckin cool and stylish
looking her shoulder
looking heroic
looking hot
looking in a mirror
looking in front
looking in mirror
looking in mirror at older self
looking in the mirror
looking in the window
looking intensely at the camera
looking into a mirror
looking into a mysterious cave
looking into camera
looking into the camera
looking into the distance
looking into the flames
looking into the horizon
looking left
looking like a bird
looking like annie leonhart
looking majestic in forest
looking melancholy
looking menacing
looking nostalgic
looking off into the distance
looking off into the sunset
looking off to the side
looking old
looking onto the horizon
looking out
looking out a window
looking out at a pink ocean
looking out at a red ocean
looking out at a sunset
looking out at the ocean
looking out into space
looking out into the cosmos
looking out of the window
looking out open window
looking out over the sea
looking out the window
looking out window
looking outside
looking over city
looking over west virginia
looking partly to the left
looking rabid
looking regal and classic
looking sad
looking scared
looking seductive
looking serious
looking sideway
looking sideways
looking smart
looking smug
looking straight
looking straight ahead
looking straight forward
looking straight into camera
looking straight into the camera
looking straight to camera
looking straight to the camera
looking surprised
looking the camera
looking this way
looking threatening
looking through a portal
looking through a window frame
looking through frosted glass
looking tired
looking to camera
looking to his left
looking to his side
looking to stars
looking to the camera
looking to the right
looking to the side
looking to the side off camera
looking to the sky
looking towards camera
looking towards the camera
looking towards the horizon
looking up
looking up at camera
looking up at the camera
looking up at the stars
looking up into the sky
looking up onto the sky
looking up perspective
looking up to the sky
looking up. poppies
looking upward
looking upwards
lookout tower
looks a bit similar to amy adams
looks a blend of grimes
looks at the big explosion
looks at the camera
looks at the nuclear explosion
looks different
looks directly at camera
looks like a mix of grimes
looks like a tree silhouette
looks like audrey hepburn
looks like britney spears
looks like christina ricci
looks like domhnall gleeson
looks like ebru şahin
looks like fabiula nascimento
looks like jerma985
looks like laura barriales
looks like varicose veins
looks like young liv tyler
looks photorealistic
looks realistic
looks sad and solemn
looks smart
loom
looming
looming creature with a long
looming drone
looming head
looming over a city
looming over a horde of gold
looming over ant pov
looming over earth
looming over earth in space
looming over the earth
looming over you
looming surreal sky
looming trees
loomis
looney tunes
loong
loony toons
loony toons style
loony tunes style
loop lighting
looper artifacts
looping
loose
loose - fitting blouses
loose braided hair
loose brush strokes
loose coat collar sailor uniform
loose fantasy clothing
loose gestural painterly
loose linework
loose messy hair
loose pages
loose papers
loose pencil sketch
loose watercolor drawing
loose white braid
loose wires
loosely cropped
loosely cross hatched
loosely detailed
loosely drawn
loot
loot box
loot of books
loots of clouds
lop eared
loputyn and matcha
lorax movie
lord
lord from hell on the throne
lord loss
lord of beasts
lord of change
lord of cinder
lord of daggers
lord of regeneration
lord of the hambone
lord of the jungle
lord of the ring
lord of the ring art
lord of the rings
lord of the rings aesthetic
lord of the rings artifacts
lord of the rings calendar
lord of the rings concept art
lord of the rings map
lord of the rings movie
lord of the rings movie poster
lord of the rings style
lord shiva
lorde
loreal
lorem ipsum dolor sit amet
lorena avarez
lorenzo ghiberti
lorenzo lanfranconi
lorenzo vitturi
loreta lux
lori earley
lorica segmentata
lorica segmentum
los angeles
los angeles 2 0 1 5
los angeles at night
los angeles ca
los angelos
los carpinteros
loser
losing control
losing control over one's life
losing control over ones life
losing feathers
loss
loss and despair
loss comic
loss in despair
loss of control
loss of molecular cohesion
lossless
lossless quality
lost
lost boys
lost city
lost city of atlantis
lost civilizations
lost edges
lost footage
lost grimoire
lost highway
lost in a cave
lost in a dreamy fairy landscape
lost in a forest
lost in a lucid dream
lost in code
lost in despair
lost in static
lost in the forest
lost in the immensity of space
lost in the milky void
lost in the moment
lost in the void
lost in thought
lost look
lost photo
lost photograph
lost place photo
lost runes
lost series
lost souls
lost souls searching for nothing
lost temple
lost world
lost world pyramid
lostfish
lostus flowers
lot of detail
lot of details
lot of exotic vegetation
lot of foam
lot of trees
lot of vegetation
lothlorien
lothlorien at night
lotr
lotr )
lotr fanart
lotr of style
lotr style
lotr)
lots blue colours
lots de details
lots of 3 d cyborg elements
lots of beakers
lots of blood
lots of books
lots of bridges
lots of bubbles
lots of building
lots of buildings
lots of cables and neon signs
lots of cables and wiring
lots of candles
lots of colors
lots of cotton plants
lots of cymbals
lots of decoration and furniture
lots of detail
lots of details
lots of diamonds unearthed
lots of dices everywere
lots of dreadlocks on the head
lots of elements
lots of embers
lots of fire
lots of flames
lots of flowers
lots of flying cars
lots of foliage
lots of fur
lots of furniture
lots of glass details
lots of hanging cables and wires
lots of jars and boxes of herbs
lots of leaves
lots of light
lots of lights
lots of little daisies
lots of macaroni! over a desk
lots of makeup
lots of mozzarella balls
lots of mud puddles and craters
lots of negative space
lots of negative space”
lots of oak and olive trees
lots of ocean
lots of particles
lots of pastel colour
lots of people
lots of pictures
lots of plants
lots of plants and flowers
lots of raindrops
lots of reflective surfaces
lots of roman arches
lots of sakura flowers
lots of shops
lots of signs
lots of signs and shops
lots of smoke
lots of snakes
lots of space
lots of stars
lots of steam and sparks
lots of sunlight
lots of swirling
lots of trees
lots of vegetation
lots of weapon
lots of weird alien plants
lots of white cotton
lots of windows
lots of wires
lots of wrinkles
lotus
lotus floral crown girl
lotus flower
lotus flowers
lotus flowers on the water
lotus mandala
lotus petals
lotus pond
lotus pose
lotuses
lotz karoly
lou
lou romano color scheme
loud
loud colors
loud music
louis dupre
louis icart
louis kahn
louis royo
louis sullivan
louis vuitton
louis vuitton suit
louis william wain
louis william wain watercolor
louis xiv
louise dalh - wolfe
louise zhang
louisiana
louisiana swamps
loundraw
lounge
lounge background
lounge furniture
lounge room
lounging on expensive sofa
lourmarin
louvre
louvre catalogue photography
louvre collection
louvre contest winner
louvre museum
lovable and furry
love
love and belonging
love and death
love and mercy
love art
love at first sight
love craft
love craftian
love death + robots
love death and robots
love death robot
love hate love
love in motion
love interest
love is begin of all
love is infinity
love is the most relevant theme
love moive
love of wisdom
love os begin of all
love peace and unity
love scene
love story
love theme
loveable guy
lovecraft art
lovecraft country
lovecraft eldritch horror
lovecraft horror
lovecraft illustration
lovecraft mythos
lovecraft style
lovecraftian
lovecraftian atmosphere
lovecraftian background
lovecraftian cosmic fear
lovecraftian creature
lovecraftian eldritch horror
lovecraftian hellscape
lovecraftian horror
lovecraftian horror!
lovecraftian horror!!
lovecraftian horrors
lovecraftian inspiration
lovecraftian landscape
lovecraftian monster
lovecraftian nightmare
lovecraftian sea creature
lovecraftian style
lovecratftian horror
lovecratian
lovely
lovely and cute
lovely and symmetrical face
lovely bohemian princess
lovely bokeh
lovely brigitte from overwatch
lovely colors
lovely couple
lovely dark autumn princess
lovely delicate face
lovely detailed faces
lovely eyes
lovely green eyes
lovely kiss
lovely languid princess
lovely pastel colors
lovely queen
lovely smile
lovely valley
lovely woman
lovercraft
lovern kindzierski
lovers
lovers eat
lovers melting into bed
loverslab
loving
loving amber eyes
loving athmosphere
loving embrace
loving eyes
loving stare
lovingly looking at camera
low
low - angle
low - angle go - pro view
low - angle shot
low - angle shot from behind
low - key light
low - key lighting
low - key studio lighting
low - level view
low - light photograph
low - light photography
low - lighting
low - poly
low - poly 3 d model
low - poly 3d model
low - poly aliased
low - poly hands
low - relief stone sculpture
low angel
low angle
low angle 8k hd nature photo
low angle camera
low angle camera view
low angle dimetric composition
low angle dimetric rendering
low angle facing sky
low angle fish eye lens
low angle fisheye view
low angle looking up
low angle mist
low angle perspective
low angle photo
low angle photograph
low angle photography
low angle shot
low angle uhd 8 k
low angle uhd 8k
low angle view
low angle wide shot
low angle!!!!
low aperature
low aperture
low aperture street level
low brightness
low brow
low budget
low camera
low camera angle
low camera angle at water level
low camera position
low ceiling
low cinematic lighting
low clouds after rain
low colour
low contrast
low cut dress
low cut top
low dark light
low definition
low density
low depth field
low depth field effect
low depth of field
low detail
low detailed
low detailed. digital painting
low details
low details and clean lines
low dof
low dutch angle
low effort graffiti
low energy
low exposure
low fantasy
low fat levels
low fi
low fidelity
low field of view
low focal point
low fog
low fog layer
low gamma
low global light
low grain film
low horizon
low iso
low key
low key color palette
low key dramatic lighting
low key light
low key lighting
low key lights
low key oil painting
low level
low level view
low light
low light cinematic
low light museum
low light photography
low light room
low lighting
low lightning
low lights
low mood
low noise
low perspective
low point of view
low poly
low poly 3 d
low poly 3 d render
low poly 3d model
low poly 3d render
low poly art
low poly character
low poly graphics
low poly model
low poly outlines
low poly render
low poly style
low poly vector illustration
low polygon
low polygon effect
low polygons illustration
low pov
low pressure system
low profile
low purple flame
low quality
low quality 3d model
low quality footage
low quality grainy
low quality instant camera photo
low quality photo
low quality photograph
low quality print
low quality sharpened graphics
low quality video
low res
low resolution
low resolution sync
low saturated
low saturated colors
low saturated red and blue light
low saturation
low saturation colors
low shot
low shot angle
low shot camera
low shot camera angle
low shutter speed
low spacial lighting : :
low sun
low sunset
low tones
low tons colors
low ultrawide shot
low vantage point
low view
low visibility
low visibility creepy
low volumetric light
low wide angle
low wide angle shot
low-angle
low-angle shot
low-contrast
low-emission-neon
low-key
low-key light
low-key lighting
low-key neon lighting
low-key neon lightning
low-light
low-light photograph
low-poly
low-poly digital art
low-poly elements
low-poly elements!!!
lowbrow
lowbrow art
lowbrow art style
lowbrow pop art style
lowbrow pop surrealism
lowbrow surrealism
lowbrow surrealistic
lower and upper levels
lower back
lower back of a beautiful
lower back of a beautiful woman
lower body
lower half of his body is snake
lower quality
lower saxony
lowkey lighting
lowpoly
lowpoly landscape
lowres
lowrider crash test
lowrider style
lowriders
lowshot
loyal
lozhkin
lpoty
lsd
lsd dream emulator
lsd dream emulator psx
lsd face
lsd feathers
lsd ripples
lsd tattoo design
lsd trip
lsd visuals
lsd water
lsd waves
lu ji
lubezki
luca
lucario
lucario from pokemon
lucas arts adventure
lucas cranach
lucas films
lucas graziano
lucasarts
lucasfilm
lucasfilm - c 1 0
lucasfilm jesper ejsing
lucha libre
lucian freud and drew struzan
lucid
lucid and intricate
lucid dream
lucid dream series
lucid dreaming
lucien freud
lucien lévy-dhurmer
lucien rudaux
lucifer
lucifer the star
lucina from fire emblem
lucio as a woman
lucio from overwatch
lucky luke
lucky star
lucy hale
lucy hale and maisie williams
lucy in the sky with diamonds
lucy liu kill bill
lucy liu portrait
lucy the god of time
ludek pesek
ludovic plouffe
ludwig hohlwein
luffy
luffy (one piece
luffy as a marble statue
luffy dressed as naruto
luffy from one piece
luffy gear 5
luggage
lugubrious
luigi
luigi loir
luigi's nose
luigis nose
luis melo
luis nieves sr
luis ricardo falero
luis rollo
luis royo and alex ebel
luis royo and greg rutkowski
luisiana
luiz escanuela
luka mivsek
luke evans
luke skywalker
lulu chen
lumberjack
lumberjack flannel
lumen
lumen global illumination
lumen reflections
lumen technology
lumens
lumi
luminal
luminance
luminar ai
lumine
luminecent eyes fine details
luminescence
luminescence，highly detailed
luminescent
luminescent blue eyes
luminescent colors
luminescent concept art
luminescent eyes
luminescent fabrics
luminescent matte painting
luminescent oil painting
luminescent plants
luminescent wisps
luminism
luminism woods
luminist
luminist and baroque style
luminist polar landscape
luminist style
luminol light
luminosity
luminous
luminous black hole portal
luminous body
luminous capes
luminous cockpit
luminous colors
luminous color’s
luminous design
luminous eyes
luminous fire halo
luminous flowers
luminous grassy background
luminous halo
luminous lighting
luminous muted colors
luminous nebula
luminous red eyes
luminous scene
luminous scifi engine
luminous skies
luminous sky
luminous sky heaven background
luminous sparkling crystals
luminous stellar sky
luminous veins
luminous water elemental
lumiol
lumion
lumion 8 k uhd
lumion render
lumion rendering
lumnious
lumnoius colorful
lump of native gold
lumpy skin
lunapunk
lunar
lunar backdrop
lunar busy street
lunar color palette
lunar cycles
lunar eclipse
lunar goddess
lunar horizon
lunar landscape
lunar mythos
lunar mythos future perfect
lunar soil
lunar themed attire
lunar time
lunar walk
lunatic asylum
lunch time on jupiter
lunch time on uranus
lunchmeat
lung dragon
lunging at camera :4
lungs
lupita nyong'o
lurid
lurid colors
lurking
lurking horror
lurking in the darkness
lurking in the shadows
luscious
luscious ) in the last of us
luscious brushstrokes
luscious lips
luscious patty with sesame seeds
luscious red lips
luscious with sesame seeds
lush
lush alien landscape
lush and colorful eclipse
lush and green
lush black hair
lush botany
lush brooklyn urban landscaping
lush chic garden
lush countryside
lush environment
lush evergreen forest
lush exotic vegetation
lush fairy forest
lush farm lands
lush fertile fecund
lush field
lush flora
lush flowery outdoors
lush foliage
lush foliage cyberpunk
lush forest
lush forest foliage
lush forest in background
lush forest in valley below
lush forest landscape
lush forests
lush garden
lush garden in the background
lush garden leaves and flowers
lush garden spaceship
lush garden surroundings
lush gardens hanging
lush gnarly plants
lush grass
lush green
lush green cactus
lush green deep forest
lush green forest
lush green meadow
lush greenery
lush greens
lush japanese landscape
lush jungle
lush landscape
lush landscaping
lush lawn
lush lighting
lush mossy canyon
lush nature
lush oasis
lush paradise
lush pastoral woodland scene
lush plant and magical details
lush plant growth
lush plants
lush plants and bonsai trees
lush plants and flowers
lush plants and lanterns
lush plants flowers
lush rain forest
lush rainforest
lush sakura
lush sakura trees
lush scenery
lush scenic landscape
lush surroundings
lush trees
lush trees and flowers
lush tress made of city lights
lush trunda vegetation
lush unkempt black hair
lush valley
lush vegetation
lush vegetation and ferns
lush vegetation in the center
lush vegetation with ferns
lush verdant plants
lush vista
lush wildlife
lush winter forest landscape
lushious
lustful vegetation
lustrous
lustrous minerals
lut
lute
luthien
luts
lux
lux 3 5 fle
lux 35 fle
lux from league of legends
luxanna crownguard
luxcore render
luxcorerender
luxigon
luxor
luxuriant
luxurious
luxurious environment
luxurious indoor setting
luxurious neckless
luxurious onsens
luxurious ornate golden jewelry
luxurious suit
luxurious theme
luxurious wedding
luxurious wooden coffee table
luxurious wooden cottage
luxury
luxury advertisement
luxury and elite
luxury architecture
luxury bespoke kitchen design
luxury brand
luxury castle
luxury condo interior
luxury dress
luxury equipment
luxury fashion
luxury fashion illustration
luxury furniture
luxury hd render
luxury item showcase
luxury journal cover
luxury lifestyle
luxury materials
lv
lv jewelry
lviv
lviv historic centre
lya pilnev
lycra costume
lydia
lying
lying a throne in a fantasy land
lying at the bed
lying down
lying dynamic pose
lying in bed
lying in bed!!!! blanket
lying on a bed of daisies
lying on a fuzzy blanket
lying on a mandala
lying on an abstract
lying on an empty
lying on back
lying on bones
lying on field
lying on lily pad
lying on the grass
lying on the ground
lying on the woods path
lying pose on stones
lying scattered across an empty
lynchian
lynchian atmosphere
lynchian!!!! ominious
lynda carter wonder woman
lynn skordal
lynn varley
lynx
lysergic
lysergic acid diethylamide
lászló szabados
lê long
m
m & m candy dispenser
m & m candy dispenser!!!
m & m figure
m & m mascot
m & m plush
m & m plush!!!
m 1 abrams tank
m 1 tank fury road
m c escher
m m candy dispenser
m m figure
m m mascot
m m plush
m. c. esher
m. c. esther
m. k. kaluta
m. w. kaluta
m.c escher
m.w kaluta
m.zuiko 75mm
m4 sopmod ii girls frontline
maarten schröder and tom bagshaw
mac
mac and cheese
mac miller
macabre
macabre art
macabre poster art
macabre spectacle
macabre w- 768
macaque inside alien base
macaron
mace and shield
macguire is a tall
machenic
machete
machiavellian
machiavellian puppetmaster
machin3
machina
machinarium
machine
machine - punk
machine - punk!
machine city
machine elf
machine elves
machine face
machine garden
machine gun
machine gun fire
machine guns
machine learning
machine noir dieselpunk grimcore
machine noir grimcore
machine parts
machine parts embedded into face
machine planet
machine revolution
machine vs demons
machine. delusions
machine.delusions
machineparts
machinery
machinery and wires
machines
machines and futurist robots
machines and wires everywhere
machines cocktail music
machinic desire
macho
macho man randy savage
macho pose
machu picchu
macintosh plus
mack sztaba
mackenzie foy
macoto takahashi
macpaint
macrame
macro
macro 20mm
macro 8 mm
macro 8mm photo
macro art
macro bokeh ”
macro camera lens
macro camera shot
macro close up
macro detail
macro details
macro face shot
macro focus
macro furry
macro giantess
macro head face
macro head shot
macro image
macro image!!!!!
macro lens
macro lens product photo
macro lense
macro perspective
macro photo
macro photo of a human eye
macro photograph
macro photography
macro photography 25mm
macro photography 8k
macro shot
macro shot head
macro shot lens flare
macro up view
macro up view metallic
macro view
macro!!!!!!
macron with afro hair
macrophoto
macrophotography
macros shot
macroshot
macross
macross delta
macross delta splash art
macross franchise
macross frontier
macross frontier splash art
macuahuitl
mad
mad cuttlefish
mad dog jones
mad dog on a chain
mad elf princess
mad grin
mad hatter
mad john martin
mad magazine
mad magazine aesthetic
mad magazine cover
mad magazine illustration
mad man screaming
mad max
mad max battlemech
mad max beyond thunderdome 1985
mad max dust storm
mad max fury road
mad max fury road film still
mad max fury road style
mad max inspired
mad max jacket
mad max style
mad men
mad monster party film
mad old bald zombie
mad painting
mad scientist
mad scientist working
mad trash bags
madagascar
madara uchiha
maddening forbidden knowledge
maddy style
made entirely from gradients
made for an art gallery
made from mechanical parts
made from million point clouds
made from old stone
made from paper
made in 1 9 9 0
made in 1800's
made in 1800s
made in 1990
made in 1999
made in 2019
made in adobe illustrator
made in blender
made in bronze
made in gold
made in maya
made in maya and photoshop
made in paint tool sai2
made in photoshop
made in rpg maker
made in tones of white and grey
made in unreal engine
made in unreal engine 4
made in unreal engine 5
made in zbrush
made of alcohol ink on parchment
made of all of the above
made of all white ceramic tiles
made of baked beans
made of bamboo
made of beads and yarn
made of bees
made of blood
made of bonsai
made of boulder opal
made of bronze
made of brushed steel
made of cactus spines
made of candy
made of candy and lollypops
made of cardboard
made of carrara marble
made of cement
made of cement and concrete
made of cheese
made of clay
made of colorful dried flowers
made of concrete
made of cotton candy
made of crystal
made of crystalized synapse
made of crystals
made of diamond
made of dots
made of dried flowers
made of drink
made of fabric
made of feathers
made of felt and cloth and beads
made of ferrofluid
made of flesh and muscles
made of flowers
made of flowers and berries
made of flowers and fruit
made of flowers and leaves
made of food
made of fruit and flowers
made of glass
made of glazed
made of glowing oil
made of glowing wax
made of glowing wax and ceramic
made of gold
made of high tech materials
made of holographic texture
made of honey
made of ice
made of insects
made of intricate lace skeleton
made of intricate metal and wood
made of lab tissue
made of lasers
made of lava
made of leaf skeleton
made of leaf skeletons
made of leaves
made of lego
made of liquid
made of liquid metal
made of liquid metal and marble
made of liquid purple metal
made of lollypops
made of marble
made of metal
made of mist
made of money
made of multicolored crystals
made of mushrooms
made of nanomaterials
made of neon light
made of notation
made of oil and water
made of paper
made of paperclips
made of plastic
made of polished broze
made of rubber
made of silk paper
made of smooth black goo
made of spaghetti
made of steel
made of stone
made of stone and concrete
made of swiss cheese wheels
made of tar
made of trash
made of tree and fantasy valley
made of vines
made of water
made of wax and metal
made of wax and oil
made of wax and water
made of wildflowers
made of wire
made of wood
made of wood!!!!!
made of woods
made of wool
made of wrought iron
made out of clay
made out of clear plastic
made out of glass
made out of legos
made out of plastic
made out of rain
made out of shiny silver
made out of shiny white metal
made out of sweets
made out of wool
made purely out of water
made up of many bits of metal
made with anime painter studio
made with illustrator
made with millions of stroke
made with photoshop
made with unreal engine
made with zbrush
madeline from celeste
madenning knowlege
madgwick
madhouse
madhouse anime studios
madhouse inc
madhouse studio
madhouse studios
madhubani
madison beer
madison beer as leeloo
madison beer girl portrait
madison square garden
madly grinning
madmax
madness
madness combat
madness creature
madoka kaname
madoka magica
madonna
madonna genderbend elegant
madonna portrait
madotsuki
madrid
madrid. extreme long shot
mads berg
mads mikkelsen
mads mikkelsen as a vampire
mads mikkelsen smoking cigarette
maelstrom
mafia
mafia background hyper detailed
maga
magali
magali villeneuve
magali villeneuve and monet
magali villeneuve'
magalie villeneuve
magazine
magazine 1990's
magazine 1990s
magazine ad
magazine advertisement
magazine art cover illustration
magazine article
magazine centerfold
magazine collage
magazine collage art
magazine collage style
magazine cover
magazine cover art
magazine design
magazine illustration
magazine illustrations
magazine pages
magazine photo
magazine photo from 2007
magazine photo shoot
magazine photograph
magazine photography
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magazine sales
magazine scan
magazine scraps
magazine shoot
magazine spread
magazines
magda torres gurza
magdalena andersson
magdalena radziej
mage
mage duel
mage fighting a ghoul
mage robe based on a toucan
mage smoking pipe
mage the ascension
mage tower far away
magenta
magenta and blue
magenta and crimson and cyan
magenta and gray
magenta colours
magenta lighting. fantasy
magenta shirt
magenta trees
mages
magestic
maggie cheung
maggots
magic
magic : the gathering
magic : the gathering art
magic aesthetic
magic amulet
magic anatomy
magic and fantasy
magic and lightning
magic and steam - punk inspired
magic armor
magic art
magic art flowing from hands
magic artifacs and magic weapons
magic artifacts
magic atmosphere
magic atmospheric
magic aura
magic background
magic barrage
magic belt
magic blue
magic blue fire
magic book
magic broom
magic bubble barrier
magic card
magic circle
magic colors
magic conduits
magic crystal bridge colorfully
magic crystal ring
magic details
magic doorway
magic effects
magic energy
magic experiment
magic eye
magic eye style poster
magic eyes
magic fairy forest
magic fairyland
magic fantasy
magic fantasy highly detailed
magic flames
magic fog
magic forest
magic frozen ice phoenix egg
magic glowing blur
magic heart
magic hour
magic hour lighting
magic hour photography
magic in the air
magic item
magic jungle
magic kingdom
magic lab background
magic laboratory setting
magic leather armor
magic library
magic light
magic lighting
magic lighting overlays
magic lightning ring
magic lights
magic list
magic long hair
magic mist
magic mushroom
magic mushrooms
magic music
magic mystery world
magic overlays
magic parade float
magic particles
magic particles in the air
magic particles; magic swirls
magic portal
magic portal in the sky
magic portal to another world
magic potions
magic powder
magic realism matte painting
magic realism painting
magic ring with a diamond
magic rune
magic runes
magic school
magic school uniform
magic shop
magic smoke
magic smoke everywhere
magic special effects
magic spell
magic spell icon
magic spells
magic spells and weapons
magic staff
magic star
magic stone
magic stone portal in the forest
magic storm
magic storm and thunder clouds
magic surrealism
magic sword
magic symbols
magic the gatheing
magic the gathering
magic the gathering art
magic the gathering art style
magic the gathering artstyle
magic the gathering artwork
magic the gathering card
magic the gathering card art
magic the gathering cards
magic the gathering concept art
magic the gathering sorceress
magic the gathering style
magic tree
magic uniform
magic uniform university
magic vibes
magic wand
magic wooden staff
magic world
magic world. colorful
magic: the gathering
magical
magical aesthetics
magical and alchemical weapons
magical and mystical
magical area
magical artifact
magical atmosphere
magical atmosphere + masterpiece
magical aura
magical aura of insanity
magical background
magical battlefield background
magical battlefield backround
magical beach
magical being
magical botanic
magical brutalism
magical buildings
magical castle
magical castle school on a hill
magical cauldron
magical cave
magical city
magical clearing
magical colored theme
magical colorful flowers
magical colors and atmosphere
magical colours and atmosphere
magical composition
magical concept art
magical creatures
magical crystals
magical details
magical digital
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magical dress
magical dust
magical effect
magical elements
magical elven geometry
magical energy
magical environment
magical eyes
magical fairy background
magical fairy floating in space
magical fantasy
magical fantasy 2 d concept art
magical fantasy forest
magical fauvism
magical feeling
magical feeling vibes
magical fishing rod weapon
magical flowers
magical fog
magical forest
magical forest background
magical forest backround
magical forest in the background
magical forest with fireflies
magical garden plant creatures
magical girl
magical girl anime mahou shojo
magical glow
magical glowing eyes
magical glowing sphere in midair
magical glowing time portal
magical island
magical items
magical jungle
magical landscape
magical leafs falling
magical library bokeh
magical light
magical lighting
magical lights
magical longbow
magical mood
magical moon shines overhead
magical mystical
magical notes
magical ocean
magical particles
magical plants
magical portal
magical portal gateway
magical portal opened
magical potions
magical power
magical powers
magical realism and dark fantasy
magical realism bizarre art
magical realism painting
magical realism style
magical realism symbolist
magical realist
magical runes flying
magical scene
magical school
magical school student uniform
magical shiny skin
magical sigils
magical soviet town
magical sparkling colored dust
magical sparkling lake
magical sparks
magical storm fog
magical stormy reflections
magical summer
magical sword
magical symbols
magical tree
magical village
magical weird
magical white fog
magical witchcraft
magical world
magically dense
magically glowing
magically shaped surrealism
magicavoxel
magicavoxel cinematic lighting
magician
magician dog performing on stage
magick
magick symbols
magicpunk
magicrealism
magitech
magitech!
magitek
magma
magma and lava
magma cascades
magma pool
magnesium
magnetic
magnetic waves
magneto
magnification
magnificence
magnificent
magnificent background
magnificent design
magnificent lighting
magnificent oval face
magnificent super wide angle
magnificiant landscape
magnificient clouds
magnified
magnifying glass
magnolia
magnolia big leaves and stems
magnolia goliath head ornaments
magnolia leaves and stems
magnolia stems
magnolias
magnum award winning photograph
magnum opus
magnum photos
magnum photos 4k
magnus carlsen
magpie
magritte
magritte painting
maher morcos
mahindra thar
mahira khan as a d&d wizard
mahira khan as a mage
mahmud barzanji
mahogany eyes
mahogany wood
mai anh tran
mai shiranui
mai yoneyama
maia sandu hyperrealistic
maid
maid costume
maid dress
maid outfit
maiden
maiden and fool and crone
maiden with copper hair
maika monroe batgirl
main character
main character center focus
main colour - black
maine coon
maintain aspect ratio
maintenance
maintenance area
maintenance photo
maisie williams
maison ikkoku
maison laffite
majestic
majestic action
majestic and futuristic
majestic beautiful world
majestic big dove wings
majestic clouds
majestic dunes
majestic forest grove
majestic horses
majestic interstellar portal
majestic japanese dragon
majestic landscape
majestic light
majestic lighting
majestic masterpiece
majestic matte painting
majestic mountains
majestic nature scenery
majestic occult gates
majestic painting
majestic pose
majestic saint woman
majestic snowy mountains
majestic spires
majestic sweeping action
majestic symmetrical eagle wings
majestic view
majestic wings
majestic!!! beautiful!!!
majestical lighting
majesticbeautiful
majestic”
majesty
majesty in noble clothes
major arcana
major arcana cards
major arcana clothes
major arcana mason sparkles sky
major arcana palace occult
major arcana sky
majora mask
majora's mask
majoras mask
make - up
make all elements sharp
make it spicey
make up
make-up
makes the sea area glowing water
makeshift house
makeshift houses
makeshift weapons
makeup
makeup. unreal engine
making a deal with the devil
making a potion
making eye contact
making love
making of
making out
making the best smug smile
makise kurisu
makoto
makoto kano
makoto kobayashi
makoto oil painting
makoto shinka
makoto shinkai
makoto shinkai  apex legends
makoto shinkai ( apex legends )
makoto shinkai and (cain kuga)
makoto shinkai and artgerm
makoto shinkai and bioware
makoto shinkai and tom bagshaw
makoto shinkai art style
makoto shinkai cyril rolando
makoto shinkai movie
makoto shinkai style
makoto shinkai!
makoto shinkai!!
makoto shinkai. digital render
makoto shinkai. high detail
makoto shinkai. octane render
makoto shinkai. —h 2160
makoto sinkai
makoto yukimura
malachite
malayalis attacking
malaysia jungle
malaysia with a paddy field
malaysian
malcolm hart
malcolm liepke
malcom liepke
maldives in background
male
male aeromorph
male anatomy
male and female
male android
male anime character
male anime style
male art
male artificer
male barbarian
male bard
male blonde elf ranger
male body
male calisthenics
male centaur centaur chimera
male character
male character design
male cyborg
male cyborg military commander
male djinn man demon hybrid
male drow
male elf ranger
male emaciated
male face
male face and bust
male full body organic armor
male god svarog portrait
male half-elf
male hero
male holosmith
male jester
male medusa
male model
male necromancer
male paladin
male physician
male plate armored chest
male polar explorer
male portrait
male robotic anthro dolphin
male robotic anthro dragon
male robotic anthro orca
male rogue
male soldier in the forest
male teenager
male ulzzang
male vampire
male vampire of clan banu haqim
male warrior
male warrior silver armor
male with halo
male wizard
male wood elf rogue
male!!
male!!!!!!!
maler collier
maler collieri
males and females breakdancing
malevolent
malevolent entity
malevolent expression
malevolent shrine
malevolent smile
malfunction
malibu canyon
malice
malice mizer
malicious
malika favre
mall
mall background
mall goth
mallard (anas platyrhynchos)
malls
mallsoft
malnourished
malt
mama i need wife
mami wata
mamimi samejima
mamiya
mamiya 7 4 3
mamiya rb 6 7
mamiya rb 67
mammals
mammatus clouds
mammoth
mamoru hosoda
mamoru nagano
mamoru oshii
mamoru ushii
mamou - mani
man
man and machine
man and woman
man and woman in love
man and woman walking together
man bear pig
man carving himself out of stone
man dressed as a chicken
man drinking beer
man engine
man esthete with disgust face
man face
man finds happiness
man from uncle
man grabbing a womans waist
man holding a balloon
man holding spear
man in a jester costume
man in adidas tracksuit
man in black
man in black suit
man in dark blue full body suit
man in futurist 6 0 ´ s lab
man in futurist 60 ´ s lab
man in his 30s
man in his 40s
man in horse costume
man in silver space suit
man in white t - shirt
man is carrying a rifle
man is terrified
man is with black skin
man looks out of a train window
man male demon
man mutating to zombie
man playing guitar
man portrait made out of ice
man proposing his girlfriend
man ray - h 1200
man ray 1 9 9 0
man reading newspaper in a bar
man riding a giant mouse
man screaming
man sitting facing away
man smokes a cigar
man standing
man standing in defensive pose
man steal computers
man turning into pony
man vs wild
man walking
man walking through city
man wearing a closed cowl
man with a blue heart
man with a crown
man with a gun
man with a head of barn owl
man with beard
man with glasses
man with scythe
man with the soul of a dragon
man with thin lines on the face
man?
mana
mana art
mana blast
mana flowing around it
mana in the air
mana shooting from his hands
manabu ikeda
manara
manbearpig
manchester
manchester city
mandables
mandala
mandala art
mandala ornament
mandala white bones
mandalas
mandalorian
mandalorian armor
mandalorian deadpool
mandel eyes
mandelblub fractal
mandelbot fractal
mandelbot fractal anatomical
mandelbrot
mandelbrot flowers and trees
mandelbrot fractal
mandelbrot fractal skin
mandelbrot fractals
mandelbrot pattern
mandelbrot set
mandelbulb
mandelbulb 3 d
mandelbulb 3 d buildings
mandelbulb 3d
mandelbulb 3d buildings
mandelbulb 3d fractal
mandelbulb architecture
mandelbulb flowers and trees
mandelbulb fractal
mandelbulb textures
mandelbulb. black and white
mandelbulber fractal
mandelbulber vector art
mandelbulber3d
mandkaws
mandy jurgens 8 k 1 5 0 mpx
mandy jurgens art
mandy jurgens golden ratio
mane
manet
manga
manga 1 9 9 0
manga 1980
manga 1990
manga and anime
manga and anime 1 9 8 0
manga and anime 1 9 9 0
manga and anime 1 9 9 9
manga and anime 1980
manga and anime 1990
manga and anime 1990 high detail
manga and anime 1999
manga and anime 2 0 0 0
manga and anime 2000
manga and anime 2010
manga anime
manga art
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manga book anime style
manga character
manga comic
manga comic book cover
manga cover
manga cover art
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manga drawing
manga front cover
manga illustration
manga page
manga painting
manga panel
manga panels
manga pen
manga scans
manga style
manga style of kentaro miura
manga style only
manga styling
manga tones
manga!
manga!!!
manga”
mangeta smoke red light
mangled
mango
mangosteen
mangrove swamp
mangrove trees
mangy
manhattan
manhatten on top of skyscrapers
manhwa
mania
maniac
maniac look
maniacal
manic
manic pixie dream girl
manically laughing
manicured
manicured garden of eden
manicured solarpunk greenery
manierism
manifestation
manila
manimalist
manipulation
manipulative
manliness
manliness given form
manly
manly design
manly face
manly monster tough guy
manmade
manmohan singh blue turban
manmohan singh face
mann
mannequin
mannequins
manowar album
manowar album cover
mansik yang
mansion
mansions of madness
manta ray
manta ray made of pancake
mantegna
manticore
mantid features
mantis
mantis and swordfishes
mantis eyes
mantis head god temple
mantis head monster god temple
mantra rendering
manual
manuel sanjulian
manuel sanjulianblue
manufactured in the 1920s
manufacturing
manuka
manuscript
many angles
many arms
many beings walking about
many belts
many birds and roses fly around
many buildings
many cccp flags
many color scheme
many colors
many colors in the background
many columns
many computer screens
many copies of them
many crowns!! upon his head
many crownsupon his head
many cryogenic pods
many cute fluffy caracals
many details
many doors
many doorways
many electronics
many exotic cars
many exotic high end features
many exquisite detail
many eyes
many eyes in space
many eyes on head
many fingers
many fireflies
many floating spheres
many floors
many flowers
many giant eye balls
many glowing lights
many goats
many golden layers
many hands
many heads
many hearts
many holes
many interstellar plants
many intricate details
many islands
many large flying monster eyes
many large green windows
many large windows
many leaves
many legs
many light sources
many lightning
many lights
many mech flowers
many mechflowers
many monitors
many mouths
many npcs on the streets
many origami orchid flowers
many origami roses
many overgrown scrap cars
many parts
many partygoers
many peonies
many people
many people walking about
many people worshipping
many pieces
many pipes
many plants
many plants and infinite pool
many rainbow bubbles
many reflections
many rusty joints
many sakura origami roses
many scars
many scars. cinematic lighting
many scrap cars
many screens
many shadows
many shards
many skyscrapers
many small and colorful stones
many small details
many small stones
many smooth curves
many speakers
many stars
many stars in sky
many stars in the night sky
many stone statues
many suckered tentacles. hybrid
many swords
many teeth
many tentacles
many terrible eyes in space
many thick dark knotted branches
many treasures
many trees
many trees and plants
many variations of thom yorke
many wild animals
many windows
many wires
many wires attached to her
many years gone
many zippers
mao
mao hamaguchi
mao zedong
maori
maori art
maori ornament
map
map cartography
map key
map of a dungeon in waterdeep
map of europe
map of fantacy world
map patreon
mapbox
maple story
maple story gun girl
maple story indiana jones
maple syrup
maple syrup & hot fudge
maple syrup fluid
maple syrup highlights
maple syrup sea
maple tree
maple trees along street
maple trees with fall foliage
maplestory
maplestory mouse
mapo tofu
mapo tofu cartoon
mappa
mappa studio
mappa studio artstyle
mapplethorpe
mar - a - lago fbi raid lego set
mar planet
mar-a-lago
mar-a-lago fbi raid lego set
mara demon
mara jade
mara jade and luke skywalker
mara jade skywalker
marat zakirov
marathon
marathon race
marauders map
marbella
marbella landscape
marble
marble and gold
marble and hint gold
marble and wood and glass
marble background
marble bust
marble carving
marble cloth
marble columns
marble columns in background
marble countertops
marble floor
marble floors
marble hole
marble material
marble reflexes
marble room
marble sculpture
marble sculptures
marble skin
marble slabs
marble statue
marble statues
marble stone
marble table
marble texture
marble walls
marble white columns
marble white complexion
marble white statue of a dragon
marble!! (eos 5ds r
marbled
marbled columns
marbled swirls
marbled veins
marbles
marbling
marbling effect
marc
marc _ simonetti
marc adamus
marc brunet
marc davis
marc davis and glen keane
marc davis artwork
marc newsom
marc newson
marc riboud
marc silvestri
marc simonette
marc simonetti and jeffrey smith
marc simonetti and sakimicha
marc simonetti. intricate
marcel marcel and metzinger
marceline from adventure time
march
marcin rubinkowski
marco bucci
marco lense
marco plouffe
marco the phoenix
marcus whinney
mardin old town castle
marduk
marfan syndrome
margaret keane style
margaret thatcher
margaret watts hughes
margay
marge
marge simpson
margiela campaign
marginalia
margins
margot robbie
margot robbie as a fairy
margot robbie as a greek goddess
margot robbie as captain america
margot robbie as cat woman
margot robbie as catwoman
margot robbie as harley quinn
margot robbie as princess leia
margot robbie as tank girl
margot robbie as wonder woman
margot robbie face
margot robbie in gta v
margot robbie on the beach
margot robbie portrait
maria borges
maria callas
maria fortuny
maria of metropolis
maria panfilova
maria sybilla merian
mariana trench
marianne collins
mariano rajoy
marie - gabrielle capet style
marie antoinette
marie curie
marie spartali stillman
mariel hemmingway
marigold
marigold background
marigold celestial vibe
marigold flowers
marihuana
marijuana
marijuana  wet
marijuana ) wet
marijuana asymmetrical
marijuana buds
marijuana greenery
marijuana leaf
marijuana leaves
marijuana leaves ) wet
marijuana leaves in background
marijuana organic painting
marijuana photography
marijuana plants
marijuana smoke
marijuana themed
marijuana trees
marijuana!
mariko mori
marilyn church h
marilyn manson
marilyn monroe
marilyn monroe portrait
marin kitagawa
marin kitagawa fanart
marina abramovic
marina federovna
marine
marine animal
marine armor
marine microbiology
mario
mario and luigi
mario as a real person
mario as a soldier
mario as a world war i soldier
mario as the witcher
mario bros
mario bros game screen
mario cart
mario clouds
mario dabbing
mario feng
mario hat
mario in real life
mario kart
mario martinez
mario riding a giraffe
mario robinson. oil on canvas
mario sprite sheet walk cycle
mario testino
mario theme
mario world
marisa kirisame
marischa becker
maritime
maritime pine
mariusz lewandowski
marjaryasana and bitilasana
marjorie cameron
mark
mark arian 0. 2 5
mark armstron
mark brookes
mark brooks and brad kunkle
mark brooks detailed
mark brooks frank frazetta
mark brown
mark e smith
mark edward fischbach
mark hamill
mark hammil portrait
mark kostabi
mark lee
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mark miner
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mark riddick
mark rothko
mark ryden
mark ryden and lisa frank
mark ryden highly detailed
mark ryden in the style of
mark ryden style
mark schultz
mark simonetti
mark twain
mark twain as huckleberry finn
mark van haitsma
mark verhaagen
mark waid
mark zuckenberg
mark zuckerberg
mark zuckerberg as a human
mark zuckerberg as a lizard
mark zuckerberg as a robot
mark zuckerberg as a zucchini
mark zuckerberg as catwoman
mark zuckerberg in gta v
mark zuckerberg plays terminator
markarth
marked muscles
marker
marker art
markers
marker”
market
market in ancient rome
market in japan
market place
market setting
market square
market stalls
market street
marketing
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marketing illustration
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marketplace
markets
marketsquare
marking lines
markings on her face
markings on his face
markings on robot
markiplier
markiplier with a knife
markus gunnar
markus reugels
markus vogt
marlene dumas
marlon brando as baron harkonnen
marlon brando as the godfather
marmalade
marmoset
marmoset render
marmoset toolbag
marmoset toolbag render
marmoset toolbag rendered
maroon
maroon accents
maroon and blue accents
maroon and white
maroon doc marten boots
maroon hat
maroon metallic accents
maroon mist
maroon red
marquee
marquis cut
marrakech
marriage
mars
mars aerial photography
mars as background
mars attacks
mars base
mars black
mars candy bars
mars city
mars colony
mars energy
mars in distance
mars invasion 2 0 3 3 - 2 0 4 2
mars landscape
mars mission
mars one mission
mars ravelo
mars ravelo and greg rutkowski
mars rover in background
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marsden
marsh
marsh vegetation
marshal mathers
marshes
marshmallow
marshmallow graham cracker
marshmallows
marsterpiece
marta syrko
marterpiece
martha greta kempton
martha jungwirth sketch
martha rosler
marthe jonkers
martial
martial art pose
martial artist dryad
martial arts
martial arts tournament
martian
martian architecture
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martin ansin
martin ansin artwork portrait
martin emond
martin johnson
martin johnson heade matrix
martin luther king
martin mottet
martin parr
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martin sandiego
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martine
martinière
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marton gyula kiss ( kimagu )
marty mcfly
martyrdom
marvano
marvel
marvel 8 0 s style
marvel animation
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marvel movie footage
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mary anning
mary delany
mary devinat
mary elizabeth winstead
mary ellen mark
mary in a sanctuary
mary jane
mary jane ansell
mary louise brooks
mary louise brooks 2 0 years old
mary louise brooks 2 5 years old
mary louise brooks 20 years old
mary louise brooks 25 years old
mary louise brooks is half robot
mary poppins
mary stevenson cassatt
maryport
marzanna
masahiro ito
masai
masakazu katsura
masami kurumada
masami suda
masamune
masamune shiro
masanori warugai
masashi ando
masashi kishimoto
masayoshi suto and artgerm
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maschinen krieger
mascot
mascot illustration
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masculine and handsome
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masculine face
masculine features
masculine figure
masculine jawline
masculine jawline!
masculine muscular figure
masculine pose
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masculinity
mash potatoes
mash up
masha krasnova
mashed potatoes
mashed potatoes and gravy
mashup
mask
mask inside mask
mask off
masked
masked doctors
masked face
masked female violinists
masked heroes
masked person in corner
masking
masks
masks on wires
mason
masonic
masonic art
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masonic symbols
masqua
masquerade mask
mass effect
mass effect artifacts
mass effect concept art
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mass housing
mass-hallucination fever-dream
massacre
massai warrior
masses of people in the distance
massive
massive 7 0 s hi fi system
massive angel wings
massive arch
massive architecture
massive battery
massive battlefront
massive boots
massive cannon mounted on back
massive clouds
massive construction machines
massive cosmos scale
massive crowd
massive decorated doors
massive destruction
massive energy storm
massive galaxy
massive glowing neon axe
massive green dragon!!!
massive legs towering over you
massive motion blur
massive mountains
massive muscles
massive river
massive robot
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massive scale cosmic being
massive spaceships
massive steampunk hands
massive structures
massive tank
massive teeth
massive tree
massive trees
massive trees with warm windows
massive vertical grand prix race
massive waves
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massys
master
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master chief
master chief from halo
master chief in cyberpunk city
master color woodblock print
master drawing
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master of ice
master of puppets
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master oil painting
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masterful composition!!!
masterful detailed watercolor
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masterpiece 8k
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masterpiece album cover
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masterpiece zdzislaw beksinski
masterpiece ”
masterpiece!
masterpiece! portrait of arwen
masterpiece!!
masterpiece!!!
masterpiece!!!!
masterpiece'
masterpiece. intricate artwork
masterpiece. rendered in blender
masterpieces
masterpieceunderwater scene
masterpiece”
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mastodon
mastodonic
masuimi max
mat black metal
mat collishaw
mat drawing paper
match point
matchach
matching colors
mate
mate colors
mate painting
mate painting mix rendering
matej retro jan
matej ‘retro’ jan
mateo dineen
materia
material
material art
material brass & copper gold
material design
material is!!! plum!!!
material is!!! watermelon!!!
material iswatermelon
material pack
material study
materials white stone
maternal
maternal photography 4 k
maternity feeling
mateus 9 5
mateus 95
math
math art
math equations
math equations in the background
math inspired
mathematical
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mathematics
mathematics and geometry
mathematics unifying science
mather brown
mathew brady photo
maths
mati klarwein and moebius
matisse
matisse caravaggio
matlab
matoko shinkai
matriarchy
matrioshka brain
matrix
matrix )
matrix code
matrix film color
matrix film pinterest color
matrix lut
matrix movie color grading
matrix style
matrix symbols in the background
matrix text
matrix theme
matrix)
matt berry
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matt colors outdoor
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matt murdock vs saul goodman
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matt painting
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matt rhodes
matt white color armor
matt white ice color armor
matt wisniewski
matt wisniewski style
matte
matte accents
matte art
matte background
matte background. unreal engine
matte black
matte black background
matte black pants
matte black paper
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matte color
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matte painting movie poster
matte painting of human mind
matte painting of steam machines
matte painting portrait shot
matte painting scenery
matte painting unreal engine
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matte painting ”
matte painting”
matte paiting
matte paitning
matte photo
matte photo-realistic
matte pink armor
matte print
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matte white background
matte white paint
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matted brown fur
mattel
mattell
matteo salvini
mattepainting
matter
matter art
matter painting
mattey
matthew benedict
matthew mcconaughey
matthew perry graphic novel
matthew perry laughing
matthew stewart
matthew williams
mature
mature color scheme
mature colors
mature content
mature facial features
mature male
maui
maurice sapiro
maurits cornelis
maurits cornelis escher
maurits cornelius escher
maurizio cattelan
maus in forest
mausoleum
mausoleum ruins
mausoleum tall ruins
mauve and cinnabar and cyan
mauve and cyan
mauve background
maven
maverick studio
maw art
mawshot
max accurate
max bedulenko
max carlier
max dennison
max details
max ernst and ernst haeckel
max fleischer
max graphics settings
max hay
max headroom
max masnyy
max payne
max payne (pc) (2001)
max payne pc 2001
max prentis
max quality
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max rive
max verstappen
maxar
maxfield parrish!!!!
maxillipeds
maxim cover
maxim cover : :
maxim magazine
maxim magazine cover
maxim shirkov
maxim sukharev
maxim verehin stained glass
maximalism digital art
maximalism. stunning
maximalist
maximalist art nouveau
maximalist details
maximalist fashion
maximalist fashion dress
maximalist magazine collage art
maximalist maximalism vaporwave
maximalist sculpted design
maximalist vaporwave
maximalsm
maximized
maximum detail
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maximum natural texture
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maximus jacobs
maxis
maxon cinema 4d
maxon one
maxwell boas
maxwell render
maxxis
may
may 1 0
may 6 8
may 68
may gibbs
may)
maya
maya 3 d
maya 8 k
maya 8k
maya ali
maya ali as a cyber sorceress
maya ali as a d&d sorcerer
maya ali as a lightning mage
maya ali as a mage
maya ali as a sorcerer
maya ali as a storm sorcerer
maya ali as a wind mage
maya ali as a wind sorcerer
maya ali as a wind sorceress
maya ali as d&d mage
maya ali as d&d sorcerer
maya ali mage
maya ali sorcerer
maya ali sorceress
maya ali wind sorcerer
maya bee
maya bloch artwork
maya deren
maya engine
maya fey from ace attorney
maya render
maya renderer
maya takamura
maya temples
mayan
mayan god
mayan jaguar warrior
mayan priestess
mayan style
mayan temple in the jungle
maybe small waves
maybelline
mayeb
mayer re-l
mayfield parish
mayhem
mayo
mayonnaise
mayuri shiina
mayuri shiina from steins gate
mazarineee
maze
maze like
maze of streets
maze-like
mazinger
mazoku
mazzoni marco
mc donalds
mc escher and ronny khalil
mc escher architecture
mc escher art
mc escher illustration
mc escher painting
mc escher style
mc escher style architecture
mc escher tesselation
mc escher tessellation
mc escher!!
mc ride
mcbess
mcbess illustration
mcbess poster
mccree from overwatch
mcdonald
mcdonalds
mcdonalds interior background
mcdonalds restaurant
mcdonalds wrapper on table
mcfarlane
mcgill
mclaren
mclaren f1
mcquarrie
mcu
mcu effects
mcu photograph
mcu style
mdae from matchsticks shiva
mdma
me
me and you
mead
meadow
meadow background
meadow flowers
meadow in the background
meadow in the forest
meadow with flowers
meadows
meadows on hills
meaining of physical reality
mean
mean expression
mean eyes
mean face
mean look
mean smirk
meaning of life
meaning of physical reality
meaningful
meanwhile in a parallel universe
measurements
meat
meat and blood stained glass
meat and lichens
meat factory
meat machine
meat texture
meat veins
meat with veins
meatloaf
meats on the ground
meaty
mecca
mech
mech body
mech concept art
mech machines firing bullets
mech robot
mech robot futuristic
mech shaped like a manatee
mech suit
mech warrior
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mecha armor
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mecha floor
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mechagodzilla
mechanic
mechanic punk outfit
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mechanical - parts
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mechanical accents!
mechanical angel
mechanical arm
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mechanical bird
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mechanical clock
mechanical cute bird
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mechanical design
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mechanical frog
mechanical gear
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mechanical heart in center
mechanical hydraulics
mechanical implants
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mechanical keyboard
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mechanical vehicles
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mechanized police infantry
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mechanized transport
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mechanized witch girl
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mechanoluminescence
mechwarrior
med bay
medal
medallion
medallions
medeival fantasy town
media art
media facde
media photo
medibang
medic
medical
medical anatomy
medical background
medical book
medical complex
medical depiction
medical diagram
medical dissection
medical doctor
medical drawing
medical drawing with annotations
medical equipment
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medical lab
medical labels
medical laboratory
medical lighting
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medical mask
medical mecha canine
medical muscle anatomy
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medical reference
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medical stitches vaporwave
medical supplies
medicine
medieval
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medieval alchemist in the dark
medieval arabia
medieval architecture
medieval armor
medieval armour
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medieval battle
medieval bestiary
medieval book illustration
medieval castle
medieval castle background
medieval castle on background
medieval citadel
medieval cities
medieval city
medieval city background
medieval city map
medieval clothes
medieval clothing
medieval coastal village
medieval coin texture
medieval concept art
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medieval d&d mood
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medieval demons
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medieval dress yellow ochre
medieval dress. witch
medieval era
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medieval fantasy battle
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medieval french landscape
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medieval gates
medieval globe
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medieval holy crusader knight
medieval house
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medieval japan
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medieval knight power armour
medieval leather armour
medieval manuscript
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medieval map of small town
medieval old king
medieval painting
medieval peasants
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medieval poster
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medieval robes
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medieval sword
medieval sword on stone
medieval tapestry
medieval tavern
medieval time
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medieval tumbledown houses
medieval village
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medieval warrior
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medieval zombie peasant
medieval!!
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medievil spear
meditating
meditating in lotus position
meditating pose
meditation
meditation pose
meditations on moloch
meditative
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mediterranean
mediterranean architecture
mediterranean beach background
mediterranean city
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mediterranean fisher village
mediterranean island scenery
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medium
medium  graffiti
medium - format print
medium - long curly brown hair
medium - shot
medium - shot 1 6 mm film
medium - shot 16 mm film
medium 5 0 mm lens
medium 7 0 mm lens
medium [ graffiti
medium [ graffiti ]
medium and large design elements
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medium armor
medium black hair
medium blonde
medium blue
medium breed
medium brown hair
medium close - up
medium close - up  mcu
medium close - up ( mcu )
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medium close up portrait
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medium hair
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medium length black hair
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medium length photography
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medium long brown hair
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medium long wavy ginger hair
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medium portrait of a goblin
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medium shot photo 8k ultrahd
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medium shot. by hayao miyazaki
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medium sized tattoo sketch
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medium yellow blond hair
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medium: black pencil
medium: colored pencil
mediumslateblue flowers
medival
mednyanszky laszlo
medusa
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medusa portrait painting
meet the actor behind the scenes
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meeting room
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mega structure city
mega-beautiful pencil shadowing
mega-detailed
megabuildings
megacity
megacity background
megacity streets seen from above
megadeth
megafauna
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megalithic buildings
megaliths
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megalodon
megalophoba
megalophobia
megalopolis
megaman
megaman as pikachu
megan fox
megan fox as a mcdonalds cashier
megan fox as beautiful mountains
megan fox as lara croft
megan fox colorful portrait
megan fox made out of mayonnaise
megan fox portrait
megan fox witch queen
megan fox with heavy eye makeup
megapixel
megara
megascan
megascan textures
megascans
megascans texture
megastructure
megastructure background
megastructure background)
megastructure in the background
megastructure theme engine
megastructures
megatall building
megumin
megumin from konosuba
megumu okada
mehndi patterns
mei from overwatch
mei-ling zhou
mekka
melancholic
melancholic art
melancholic atmosphere
melancholic expression
melancholic face
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melancholic!!
melancholic!! liminal space
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melanie martinez
melbourne
melinda matyas
mellotron
mellow
mellow and soft
mellow sky blue lighting
melted
melted cheddar
melted cheese
melted cyborg
melted paint
melted wax
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melting 4d cubes
melting and dripping. eerie
melting cheese
melting clouds
melting face
melting glass
melting ice cube
melting in coral pattern
melting into jolteon
melting into lilligant
melting into rivers
melting into vulpix
melting paint
melting paint drips
melting pixels
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melting spaceships
melting spaceships!
melting sun
melting volumetric smoke and fog
melting wax
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member of the endless
membrane
membrane pregnancy sac
membrane wings
membranes
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meme culture
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meme of the year
meme template
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memes
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memorable scene
memories
memory
memory trapped in eternal time
memphis
memphis abstract minimal art
memphis design
memphis group
memphis rap
men
men and women
men fall for him
men in black
men in tuxedos
men look up at the sky
men's fashion
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menacing orc
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menacing statues
menacing!
menacing!!!
menacing. unreal 5
menacingly
mendelbrot fractal
menger sponge
menhirs
meni chatzipanagiotou
mental
mental alchemy
mental health
mental illness
mental pandemonium
mental ray
meow
mephitic vapors
mercator projection
mercedes
mercedes and volkswagen
mercedez benz
mercenary
merchant collector
merchant stands
merchant street
merchant tents
merchants
merciless
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mercury dress
mercy ( overwatch )
mercy from overwatch
mercy from overwatch game (2016)
meredit frampton style
meredith schomburg
meret elisabeth oppenheim
merfolk riding seahorses
merge
merged
merged character
merged machima
merging crocodile head
merging with tree in a forest
merlin
mermaid
mermaid body
mermaid cyborg with a laser whip
mermaid in distance
mermaid tail
mermaids
mermaids and fish
mermaids in distance
merriam daniel
merry
merry england
mert alas and marcus piggott
mert and marcus
mesa
mesa plateau
mesh
mesh fabrics
mesh headdress
mesh roots
mesh roots. closeup
mesh shirt
mesh structure
mesh wire
mesmerising
mesmerized
mesmerizing
mesmerizing blue eyes
mesmerizing fractal hypercubes
mesomorph
mesopotamic
mess
message
messages
messi
messi as a viking
messi as cyborg
messi dunking on ronaldo
messy
messy bangs
messy beard
messy bed
messy bedroom
messy black / red hair
messy black bob hair
messy black hair
messy blond hair
messy blonde hair
messy blue hair
messy bob
messy brown hair
messy brown short hair
messy brush strokes
messy bun
messy clothes
messy cords
messy curly hair
messy curly pastel hair
messy eater
messy hair
messy hair bedhead
messy hairstyle
messy lines
messy long black hair
messy manbun
messy maximalist interior
messy ponytail
messy room
messy short brown hair
messy short hair
messy shoulder-length dark hair
messy spiked red hair
messy square vibrant red hair
messy wavy white hair
meszoly geza
met
met collection
met gala
meta
meta design
metaballs
metabaron
metahuman
metahumans
metal
metal album cover
metal album cover art
metal and glowing eyes
metal armor
metal art
metal award winning
metal bars
metal bikini
metal body
metal border
metal brain
metal brain!!
metal cat ears and glowing eyes
metal chain and black cape
metal chrome
metal cladding wall
metal claws
metal dragon tail
metal ears
metal ears purple eyes
metal eye piece
metal floor
metal font
metal framed portal
metal garments
metal gauntlet
metal gear
metal gear mech
metal gear movie still
metal gear rising
metal gear rising revengeance
metal gear solid
metal gear solid anime cyberpunk
metal gear solid art style
metal gear solid concept art
metal gear solid inspired
metal gear solid revengeance
metal gear solid style
metal gear style
metal gears
metal halo
metal handles
metal hard surfaces
metal hurlant
metal jaw
metal joints
metal key for the doors
metal kitchen utensils
metal lace
metal lid
metal mask
metal neck rings
metal orchid flower
metal panels
metal parts
metal plate photograph
metal readymade
metal robots
metal rust and plaster materials
metal scales
metal scapes
metal sculpture
metal shaded
metal shoulder pauldrons
metal shutter
metal skin
metal skin with some scratches
metal skull with red eyes
metal slug concept art
metal speculars
metal surfaces
metal swords
metal tail
metal teeth
metal texture
metal towers and sewers
metal wings
metal with chipped paint texture
metal with graffiti on the side
metal works
metalhead
metalic
metalic green
metalic parts
metalic reflection
metalic reflections
metallic
metallic architecture
metallic armor
metallic arrows
metallic asteroid
metallic brass accessories
metallic bridge
metallic bronze skin
metallic buttons
metallic colors
metallic cyan bodysuit
metallic flecked paint
metallic flecks
metallic galactic
metallic green armor
metallic light
metallic mask around the mouth
metallic nebula
metallic neoprene woman
metallic paint
metallic patterns
metallic polished surfaces
metallic red
metallic reflections
metallic reflective
metallic reflective surfaces
metallic scales
metallic scepter
metallic sculpture
metallic shield
metallic shiny skin. intricate
metallic shutter
metallic skeleton
metallic skin
metallic surface
metallic surfaces
metallic texture
metallic torso
metallica
metalomania
metalwork
metamorphose
metamorphosis
metamorphosis complex 3d render
metaphor
metaphorical
metaphysical
metaphysical foggy environment
metart
metatron
metaverse
metaverse concept art
metaverses
meteor
meteor impact behind a dinosaur
meteor shower
meteorite
meteorites
meteors
meteors are falling from the sky
meteors falling
meth head
meth lab
meticulous
meticulous brush strokes
meticulous composition
meticulous detail
meticulous details
metmuseum
metro
metro 2 0 3 3
metroid
metroid dread
metroid prime
metroidvania
metropolis
metropolis background
metropolis filmic gotham city
metropolitan
metropolitan museum
metropolitan museum collection
metropolitan museum of art
metropolitan museum of fine art
metropolitan museum photo
metzinger
mew
mewtwo
mexican
mexican desert
mexican folk art
mexican folklore
mexican muralism ) ravi supa
mexican mustache
mexican standoff
mexican vaquero
mexican warrior
mexico
mexico city
meze audio
mezzanine
mezzotint
mf doom
mf doom mask
mf doom reptile eyes
mf doom with reptile eyes
mgm studios
mgs
mia khalifa
mia kischner
miami
miami beach
miami heat colors
miami synthwave
miami vice
miami. illustration
michael
michael alford
michael ancel
michael angelo
michael angelo inspired
michael bair
michael bay
michael bay movie
michael bay style
michael bosanko
michael carson
michael cheval (unreal engine
michael dante dimartino
michael fassbender
michael garmash and
michael garmash and rob rey
michael hoppen
michael hussar
michael hutter
michael jackson
michael jackson as spiderman
michael jackson moon dance
michael jackson portrait
michael jordan
michael kaluta
michael kaluta and jia ruan
michael kirkbride
michael kors
michael kutsche
michael mann
michael margetts
michael maurino
michael mcdonald
michael myers
michael myers mask
michael okuda
michael page
michael pangrazio
michael parkes
michael scott
michael shapcott
michael sowa
michael vicente
michael wadleigh
michael weisheim beresin
michael welan
michael wellen
michael whelan and gustave done
michael whelan and gustave dore
michael whelan and karol bak
michael whelan and pixar
michael whelan and tomer hanuka
michael whelan art
michael_jackson
michaelangelo style
michal
michal lisowski
michal mraz
micheal whelan
michel - ange
michel - ange 8 k
michel ocelot
michelangelo 1550s
michelangelo da caravaggio
michelangelo painting
michelangelo sculpture
michelangelo style
michelin 5 star
michelin man family portrait
michelin restaurant
michelin star
michelin star food
michelin star photography
michelin star restaurant
michelin starred restaurant
michellin star
michigan
michilin star
mick jagger
mickael lelièvre
mickey mouse
mickey mouse ears
mickey mouse head
mickey mouse is thor
micro
micro - hurricane
micro - organisms
micro art
micro detail
micro detail 4k
micro detailed
micro detailing
micro details
micro expressions
micro lens
micro machines
micro macro auto focus
micro macro autofocus
micro pov
micro-details
microbiology
microbus
microchip
microchip leaves
microchips
microdetails
micron
micron pen
micron pen drawing
micronauts
microorganism
microorganisms
microphone
microphone silluette
microphones
micropohone
microscope
microscopic
microscopic cat
microscopic detail
microscopic photo
microscopic picture
microscopic tardigrade
microscopic tardigrades
microscopic view
microscopy
microsoft
microsoft paint art
microsoft windows
microsoft windows logo
mid - 3 0 s
mid - 3 0 s aged
mid - action
mid - century modern
mid - century sofa
mid - length hair
mid - shot
mid 1 9 th century
mid 19 th century
mid 2 0's female
mid 20s female
mid 9 0 s
mid action
mid action swing
mid air
mid air shot
mid body
mid body portrait
mid body shot
mid century
mid century art
mid century modern cartoon style
mid closeup
mid day
mid day lighting
mid distance
mid fall
mid length portrait photograph
mid long hair
mid morning lighting
mid night
mid portrait
mid range shot
mid riff
mid shot
mid shot photo
mid shot portrait
mid thirties
mid tone
mid view
mid view from below her feet
mid-20s
mid-30s
mid-century modern
mid-century modern furniture
mid-day
mid-shot
mid-shot of a hunky
mid-shot portrait of a beautiful
mid-transformation
mid-twenties
mid-view
midafternoon
midair
midcentury modern
midday
midday light
midday lighting
midday photograph
midday sun
midday sunlight
middle - age
middle - earth
middle age
middle aged
middle aged balding superhero
middle aged man
middle ages
middle body shot
middle centered
middle close up
middle close up composition
middle close up shot
middle distance
middle earth
middle earth landscape
middle east
middle eastern
middle eastern details
middle eastern skin
middle eastern style vendors
middle finger
middle focus
middle length hair
middle of space
middle of the day
middle of the night
middle of the ocean
middle of the page
middle shot
middle shot waist up
middle-aged witch
middle-earth
midgar
midi skirt
midium shot
midjourney
midjourney style
midjourney!
midlands
midnight
midnight blue
midnight city lights
midnight color palette
midnight colors
midnight fog - mist
midnight fog - mist!
midnight hour
midnight hour on jupiter
midnight mist lights
midnight mist streetlights
midnight moonlight
midnight theme
midnight zone
midnight-blue
midriff
midshot
midshot single subject
midsommar
midsommar - t
midsommar color theme
midsommar style
midsummer
midwest countryside
midwest town
mies van der rohe
miffy
might cinematic lighting
might delete later
mighty
mighty nein
mighty plump female sorceress
mighty princess of the wasteland
mignogna
mignola
migrant mother
miguel angel
miguel iglesias
mihail spil-haufter
mihaly munkacsy
mihoyo
mihoyo art style
mika kurai demon
mika pikazo
mikado
mikasa ackerman
mike
mike deodato jr
mike ehrmantraut
mike franchina
mike judge
mike judge art style
mike kime
mike mignogna
mike mignola style
mike nash
mike pence
mike ploog
mike stoklasa
mike tomlin as doctor who
mike trout
mike tyson
mike tyson with a toad
mike wazowski
mikhail vrubel and wadim kashin
mikko
mikko lagerstedt
miko
miku
mikudayo
mila jovovich as spiderwoman
mila kunis
mila kunis as catwoman
mila kunis as daenerys targaryen
mila kunis wearing black choker
milan fashion week backstage
milan jozing
milan schere
mild
mild breeze wind
mild colors
mild colours
mild depth of field
mild expressionism
mild impressionism
mildly scarred on his face
miles davis
miles df
miles johnstone
miles morales
miles morales!!!
miley cyrus
milf
militarism
militaristic
militaristic!!!
military
military art
military base
military boots
military buildings
military camp in the background
military carrier rig
military chris foss
military clothing
military composure
military design
military drone
military equipment
military flags
military gear
military girl
military helmet
military industrial complex
military insignia
military outfit
military outpost
military pants
military parade
military photography
military pilot clothing
military police
military robot
military soldier behavior
military storage crate
military tank fury road
military uniform
military vehicles
military vest
military weaponry
military-grade
milk
milk - bath effect
milk and mocha style
milk bar magazine
milk bath photography
milk cubes
milk dripping
milk duds
milk puddles
milkboys
milkman
milkshake
milky way
milky way environment
milky way galaxy
milky way nebula
milky white skin
milkyway
milkyway light
mill
milla jovovich
millais
millaise and greg rutkowski
millenium falcon
millennia ago
millennial vibes
millennium falcon
million of likes
millionaire technocrat
mills
milo manara
milo manara - h 1 2 0 0
milo manara style
miloš zeman
milt gross
miltary
mime
mimic
mimic. random position content
mimikyu
mimmo rottela
mina petrovic
minaba hideo
minagho
minarets
minas tirith
minas tirith in the background
mind
mind - bending
mind - bending reality
mind - blowing
mind bending
mind blow
mind blowing
mind blown
mind character
mind control
mind exploration
mind flayer
mind flayers
mind wandering
mind-bending
mind-bending digital art
mind-bending geometry
mind-blowing detail
mind-blowing details
mind-boggling cosmic geometry
mind-breaking
mindblowing
mindflayer
mindscape
mine cart
minecraft
minecraft build
minecraft cake
minecraft creeper
minecraft creeper in real life
minecraft gameplay
minecraft in real life
minecraft landscape
minecraft lords
minecraft mods
minecraft screenshot
minecraft skin
minecraft steve
minecraft style
minecraft villager
minecraft watchtower builds
miner kilbourne kellogg
mineral
mineral and gold jewelry
mineral collections
mineral grains
minerals
miners
mines
mines of moria
ming dynasty
mingchen shen
mini
mini amphitheatre
mini cooper
mini cooper s
mini dv camera found footage
mini figure
mini jeans skirt
mini lake
mini magic city
mini model
mini planets
mini skirt
mini-skirt
mini. abstract illustration
miniature
miniature action figure
miniature animal
miniature bear
miniature cafe diorama
miniature city
miniature cosmos
miniature faking
miniature forest
miniature fox
miniature frog
miniature human brain
miniature kitten
miniature model
miniature of a sports car
miniature origami figurine
miniature painting techniques
miniature photography
miniature photography closeup
miniature pig
miniature porcelain model
miniature porcelain portrait
miniature product photo
miniature resine figure
miniature tachikoma
miniature world
miniaturecore
miniatures
minifigure
minigun
minimal
minimal art
minimal art style
minimal artifacting
minimal artifacts
minimal background
minimal black skull warpaint
minimal bodycon feminine costume
minimal canon 5 0 mm
minimal clothing
minimal composition
minimal contraption
minimal design
minimal design armor style
minimal features
minimal geometric
minimal kitchen
minimal modern
minimal modern pixel sorting
minimal movie poster
minimal movie posters
minimal outlines
minimal painting
minimal palette
minimal pink palette
minimal shading
minimal sleek design armor style
minimal structure
minimal style
minimal. sharp focus
minimalism
minimalissimo
minimalist
minimalist ) ) ) ) )
minimalist abstract art
minimalist and clean
minimalist architecture
minimalist art
minimalist art style
minimalist background
minimalist cartoon style
minimalist cinematic lighting
minimalist composition
minimalist design
minimalist desk
minimalist environment
minimalist furniture
minimalist home office
minimalist illustration
minimalist ink drawing of a city
minimalist interior design
minimalist lighting
minimalist line art
minimalist line drawing
minimalist lines
minimalist logo vector art
minimalist logo without text
minimalist movie poster
minimalist painting
minimalist photo
minimalist photorealist
minimalist poster art
minimalist rule of thirds
minimalist sticker
minimalist structure
minimalist style
minimalist stylized cover art
minimalist svg
minimalist vector art
minimalist wallpaper
minimalistic
minimalistic aesthetics
minimalistic and beautiful
minimalistic architecture
minimalistic art
minimalistic background
minimalistic composition
minimalistic design
minimalistic drawing
minimalistic house in the wood
minimalistic illustration
minimalistic logo
minimalistic painting
minimalistic style
minimalistic!! simple
minimalisticsimple
mining
mining outpost
mining scrap metal
minion
minion as a darksouls boss
minion as lovecraft's monster
minion giving a thumbs up
minion in space
minion iron man
minion with many many many eyes
minions
minions background
minions digimon
minions screaming
miniskirt
minn
minna sundberg
minneapolis
minneapolis as background
minnie mouse
minor blur
minority report the movie
minoru nomata
minotaur
minotaur from path of exile
minotaur in ancient armor
minotaur warrior
minotaur warrior with axe
mint
mint condition
mint green eyes
mint higlights
mint leaves
minute details
minutely detailed
miracle
miraculous
miraculous cloudy backdrop
miraculous ladybug
mirage
mirai nikki
miranda cosgrove
miranda kerr
miranda meeks
miranda otto
miranda otto as eowyn
mircea suciu
mirka andolfo
miro
miro petrov
mirror
mirror and glass surfaces
mirror background
mirror dripping droplet
mirror dripping droplet!
mirror eye implants
mirror hallways
mirror lake
mirror like water
mirror reflection
mirror reflections
mirror selfie
mirror shades
mirror texture
mirror world
mirror's edge
mirror's edge in russia
mirrored
mirroring
mirrorless camera
mirrors
mirrors edge
mirrors edge art style
mirrors everywhere
misa amane
misa amane #
misa amane *
misato katsuragi
miscellaneous objects
mischief managed
mischievous
mischievous expression
mischievous grin
mischievous look
mischievous!!
mischievous!!!
miserable
misery and despair
misha gordin
mismatched
miss aniela
miss fortune
miss fortune league of legends
miss universe
missile explosions
missiles
missiles explosions in the sky
missing jaw
missing panels
missing poster
missing teeth
mission arts environment
mission impossible
missle turrets
mist
mist amidst lightning
mist and fog
mist and rain
mist and smoke
mist art of illusion
mist below buildings
mist filters
mist floats in the air
mist from waterfall
mist in valley
mist low over ground
mist rising from head
mist vapor
mistborn
misterious
mistic
mistic ambient
mistic atmosphere
misting
mistress
mists
misty
misty alleyway
misty alleyways
misty and eerie
misty and foggy
misty and raining
misty and wet
misty athmosphere
misty atmosphere
misty atmospherics
misty background
misty castle
misty clouds
misty environment
misty fog
misty forest
misty forest scene
misty from pokemon
misty garden
misty ghost town
misty ground
misty japanese bamboo forest
misty mood
misty morning
misty mountains
misty neon lights
misty night
misty ominous atmosphere
misty space
misty swamp
misty weather
misty wisps
misty wood
misty woods
mit technology review
mitch foust
mitch mcconnell as a turtle
mitchell mohrhauser
mithology
mitose
mitre
mitsumayo
mittens
miura
miura kentaro
miura kentaro style
mix
mix between tribal and hi-tech
mix of aesthetics
mix of biden and obama
mix of ethnicities and genders
mix of styles
mix of venom and cthulhu
mix styles of tsutomu nihei
mix with rivendell architecture
mixed
mixed - media
mixed animal
mixed art
mixed art styles
mixed development
mixed leather armor
mixed liquid acrylic
mixed materials
mixed media
mixed media 3 d collage
mixed media 3d collage
mixed media collage
mixed media illustration
mixed media on toned paper
mixed media painterly details
mixed media painting
mixed media photography
mixed media style illustration
mixed media torn paper collage
mixed media with claymorphism
mixed medias
mixed plate and leather armor
mixed race
mixed race woman
mixed styles
mixed techniques
mixed with snake
mixed-race woman
mixer rendering
mixing
mixing drinks
mixing solarpunk
mixmedia
mixture animal
mixture between an! owl and wolf
mixture turkish and russian
miyagawa choshun
miyamoto abduzeedo
miyamoto musashi
miyazaki film
miyazaki hayao
miyazaki movie
miyazaki movie scene
miyazaki style
miyazaki's animated film
miyazakis animated film
mizutsune
mjolnir
mjolnir armor from halo infinite
mk ninja
mkbhd
mkbhd as iron man
mlp
mlp fanart
mm
mma
mma southpaw stance
mmmmm
mmo
mmorpg
mmorpg fantasy
mmorpg gameplay
mo salah
moab
moai
moai seedling
moai statue giving a ted talk
moana
moat
mob psycho 100
mobeus
mobie still
mobile
mobile app
mobile game
mobile game art
mobile game asset
mobile game background
mobile game style
mobile gimball camera
mobile learning app prototype
mobile legends
mobile still frame. 4k uhd
mobile suit
mobile suit gundam
mobile wallpaper
mobius
mobius strip shaped planet
mobster
mocap
mocha swirl color scheme
mochiduki key
moco
mod
mod green bob wig
modded
model
model agency
model design
model face
model is wearing techtical vest
model kit
model miniature
model of atom
model painting
model photograph
model photography
model pose
model posing
model sheet
model shoot
model standing pose
model trees
model wears a puffer jacket
model with attractive body
model エリサヘス s from acquamodels
modeled
modeled in 3 d
modeled in poser
modeled lighting
modeling
modeling for dulce and gabanna
modeling photograph kerli koiv
modeling photography
modeling shoot
modeling studio
modelled in zbrush
modelling
modelling clay
models
modelsociety
moderate atmospheric lighting
moderate colors
moderate saturation
moderately detailed
modern
modern - art - vector
modern adaptation
modern aesthetic
modern and minimalist
modern anime
modern anime style
modern architecture
modern architecture design
modern art
modern art deco
modern art museum
modern art noveau
modern art style
modern bathroom
modern buildings
modern calligraphy art
modern car design
modern cartoon tv show
modern casual clothing
modern chicago streets
modern city
modern city background
modern city scape
modern city street
modern clean white armor
modern cloth
modern clothes
modern clothing
modern color
modern color palette
modern colors
modern composition
modern concept art
modern crystal martini glass
modern cubism
modern cyberpunk
modern cyberpunk anime
modern dance aesthetic
modern day
modern design
modern desktop wallpaper
modern details
modern digital art
modern earthy neutral earthy
modern edgy graphic design
modern fantasy
modern fashion
modern fashion outfit
modern fine art
modern gallery furniture
modern glass building
modern graphic design
modern haircut
modern helmet
modern high sharpness photo
modern house
modern house in the wood
modern house made of tree
modern illustration
modern indoors
modern interior design
modern japanese living room
modern lighting
modern living room
modern logo
modern look
modern lush condo as shopfront
modern maximalist fashion dress
modern maximalist suit
modern mecha anime
modern military gear
modern minimal design
modern minimalist
modern minimalist f 2 0
modern minimalist f 2 0 clean
modern minimalist f 20
modern minimalist f 20 clean
modern neighborhood
modern new york
modern nocturnal background
modern occultist
modern office
modern painting
modern pastel colours
modern photo
modern photograph
modern photography
modern portrait
modern realism
modern roman empire
modern rustic
modern rustic”
modern sci-fi anime
modern scientific documents
modern sculpture
modern setting
modern simplified vector art
modern space suit
modern street
modern street art
modern studio light soft colour
modern style
modern stylish glamour tutu
modern supernatural horror
modern supernatural urban horror
modern technology
modern technology building
modern times
modern tokyo
modern tones
modern very sharp photo
modern war photography
modern warfare
modernism art
modernist
modernist art style
modernist buildings
modernist composition
modernist design
modernist headphones
modernist interior
modernistic
modern”
modest
modest flowing gown
modest light armor
modest tone
modest!
modestly clothed
modestly dressed
modified
modigliani
modular
modular detailed
modular game dev art
modular graphene
modular item
modular origami
modular synth
modular synthesizer helmet
modulated line
moe
moebious
moebius
moebius + artgerm
moebius + loish
moebius + loish + wlop
moebius aesthetic
moebius and alphonse mucha
moebius and android jones
moebius and dan hillier
moebius and edward hopper
moebius and giger
moebius and kilian eng
moebius and makoto shinkai
moebius and mike mignola
moebius and mohrbacher
moebius and tsutomu nihei
moebius art
moebius artwork
moebius comic
moebius comic style
moebius illustration art
moebius moebius
moebius starwatcher
moebius starwatcher comic
moebius style
moebius!
moebius!!!
moebius. rich colors
mogao grottoes
mogul
mogul khan
mohamed chahin
mohamed chahin style
mohamed reda
mohamed reda and hr giger
mohawk
mohawk haircut
mohawk hairstyle
mohawks
moholy - nagy
moholy nagy
mohrbacher
moira from overwatch
moire
moist
moist brown carpet
moist dirty carpet
moist foggy
moist mossy white stones
moisture
mojang
mojave desert
mokoto shinkai
mold
moldy
moldy walls
mole on cheek
molecular
molecular gastronomy
molecules
moleksandra shchaslyva
molly from neuromancer
molly from the novel neuromancer
molly millions
molly weasley
moloch
molotow premium color palette
molten
molten glass
molten metal house
molten plastic
mom
mom's spaghetti
mom's spaghetti he's nervous
moma
moma museum
moment
moment cinebloom filter
moment frozen in time
momento mori
momma and papa
momoshiki ōtsutsuki
mona lisa
mona lisa as a goth girl
mona lisa as a real person
mona lisa painting
mona lisa style
mona lisa taking a selfie
mona lisa with a mustache
monaco
monalisa
monarch butterflies
monarchy
monastery
moncler jacket
monet and da vinchi art style
monet painted
monet painting
monet style
monet. stunning lighting
money
money falling from the sky
money sign pupils
monge jean-baptiste monge
mongezi ncaphayi
mongol
mongolia
mongolian invasion of iraq
monia merlo
monica bellucci
monica langlois
monika
monitor
monitors
monk
monk clothes
monk meditate
monk's robe
monkey
monkey d luffy
monkey d. luffy
monkey dressed as a scientist
monkey island
monkey king
monkey limbs
monkey navy seals
monkey punch
monkey reading a book
monkeys
monks
monks robe
monks!!!!!!!!! fire
mono
mono eye window
mono eyed
mono printing
mono-yellow
monochromatic
monochromatic airbrush painting
monochromatic background
monochromatic blue
monochromatic color scheme
monochromatic colour palette
monochromatic green
monochromatic ink
monochromatic photo
monochromatic red
monochromatic teal
monochrome
monochrome 3 d model
monochrome 3d model
monochrome and red color bleed
monochrome artwork
monochrome artwork!!
monochrome background
monochrome black and white
monochrome bw
monochrome color
monochrome color palate
monochrome contrast bw
monochrome drawing
monochrome film
monochrome hdr
monochrome lomography
monochrome!!!!!
monochrome:-2
monocle
monocolor
monocolor mosaics
monocular
monogon
monogram
monokini
monokubo
monolith
monolithic
monolithic granite spikes
monolithic retro futuristic
monolithic temple
monoliths
mononoke
monorail
monorail station
monotone
monserrat gudiol
monsoon
monsoon on tropical island
monster
monster anatomy
monster art
monster ashore
monster character design
monster concept art
monster design
monster destroying pyongyang
monster doré
monster energy
monster energy drink
monster girl
monster graveyard chair
monster high
monster high makeup
monster hunter
monster hunter monster
monster hunter the movie
monster hunter world
monster hunters
monster manual
monster manual entry
monster manula
monster movie
monster slimy
monster statues
monster teeth
monster teeth covered in red
monster theme
monster truck
monster truck rally
monstera
monstera deliciosa
monstergirl
monsters
monsters in the background
monsters inc
monsters inc pixar
monstrocity animal
monstrosity
monstrous
monstrous animal statues
monstrous ant
monstrous creatures
monstrous mask
monstrous skull
montage
montage of grid shapes
montana
monthly
montreal
montreal habs canadiens figurine
monument
monument valley
monument valley landscape
monumental
monumental achievement
monumental giant palace
monumental mountains
monumental structures
monumentally art composition
monuments
mooc
mood
mood light
mood lighting
mood painting
mood scary
moodboard
moody
moody : : wes anderson
moody :: studio ghibli
moody aesthetic
moody ambiance
moody and atmospheric
moody atmosphere
moody atmospheric
moody atmospheric lighting
moody atmospherics
moody beautiful colors
moody blue lighting
moody cinematic atmosphere
moody cinematic lighting
moody cinematography
moody cloudy sky
moody color scheme
moody colors
moody dark
moody dark colors
moody details
moody dim faint lighting
moody dim lighting
moody dynamic lighting
moody emotional cinematic
moody environment
moody evening light
moody feel
moody gold planet
moody grindhouse
moody hazy lighting
moody high exposure
moody iconic scene
moody light
moody lighting
moody lightning
moody lights
moody lights!! intricate
moody lightsintricate
moody mining planet
moody misty fantasy art
moody morning light
moody muted colors
moody neutral hipster tones
moody night lighting
moody night time scene
moody painting
moody photography
moody purple glow light
moody scene
moody setting
moody shadows
moody sky
moody sky at the back
moody strange cinematography
moody sunset
moody sunset and dramatic sky
moody sunset background
moody sunset in background
moody tones
moody vibe
moody vibrant colors
moody volumetric lighting
moody::alejandro jodorowsky
moog
moog hd
moog modular synthesizer
moolight
moomin
moomins
moon
moon and candle
moon and stars
moon and stars in night sky
moon background
moon backlight
moon base
moon beams
moon beams dramatic light
moon bear samurai
moon behind
moon behind him
moon bull samurai
moon crash into earth
moon craters
moon glow
moon goddess
moon hitting earth
moon in background
moon in background!
moon in his crown
moon in sky
moon in the background
moon in the night sky
moon in the sky
moon knight
moon landing
moon landscape
moon light
moon light fish eye illustrator
moon light in the top background
moon lighting
moon ligth
moon mission
moon rays
moon reflecting on the water
moon rising
moon ryas
moon shine
moon shining
moon shining golden light
moon surface
moon surface background
moonbeams
moonbow
moonglow
mooniq priem
moonless night
moonlight
moonlight background
moonlight god rays
moonlight grey
moonlight in the darkness
moonlight lighting
moonlight night
moonlight rays
moonlight shafts
moonlight shining on wildflowers
moonlight showing injuries
moonlight snow
moonlight snowing
moonlight through the trees
moonlight through trees
moonlit
moonlit backdrop
moonlit clouds background
moonlit forest
moonlit forest environment
moonlit kerala village
moonlit lighting
moonlit night
moonlit night dreamy atmosphere
moonlit night sky
moonlit nightscape
moonlit ocean
moonlit parking lot
moonlit purple sky
moonlit sky
moonlit starry sky environment
moonray render
moonrays
moons
moons in background
moonscape
moonshine
moonshine cybin
moonstone
moonwalker photo
moor
moored
moorish architecture
moorland
moose
mopar
moped
morality
morandi
morandi color palette
morandi color scheme
morandi colour scheme
morbid
morbid art
morbid colors
morbid fantasy
morbid photography
morbidly obese
morbius
mordancage
mordor
mordor as a bustling city
more
more and more composision
more and more cyberpunk
more and more flowers
more and more scars
more coherent
more dark purple color scheme
more darkness on bottom
more darkness on the bottom
more details
more intense
more reflection
more relief effects
more scars
more scars and fire
more tan face
more textures
more thunderstorm
morearea
morena baccarin
morgan freeman
morgan freeman as gordon freeman
morgan weistling
morgana
morgana from league of legends
morgoth
morhbacher
moringa juice
moringa oleifera leaves
mork borg
morning
morning atmosphere
morning coffee
morning dawn
morning detail
morning dramatic cinematic light
morning fog
morning glory flowers
morning glow
morning golden hour
morning hard light
morning haze
morning hour
morning hour on jupiter
morning light
morning light showing injuries
morning lighting
morning mist
morning mood
morning shot
morning sun
morning sun - rays
morning sunlight
morning sunrise
morning time
morning time on jupiter
morning. hyperrealism
morningstar
moroccan
moroccan city
moroccan mosque
moroccan new york city street
moroccan queens ny
moroccan tea set
moroccan tile archways
morocco
morose
morph
morph dna
morpheus
morphing
morphing dog head
morphing skulls
morphing wings king vulture head
morphosis
morrigan
morrigan aensland
morrocan lamp
morrowind
morrowind armor
mort drucker
mort drucker illustration
mort kunstler
mortal engines
mortal kombat
mortal kombat 11
mortal shell
mortality
mortar
mortar and pestle
mortar heads
morticia addams
mortified at its existence
morty from rick and morty
mosaic
mosaic floor
mosaic stone floor
mosaic style
moscow
moscow kremlin
moscow kremlin is on fire
moscow metro
moses
mosh pit
moshrooms on ground
moskvich
mosque
mosque interior
mosque synagogue interior
moss
moss and ferns
moss and flowers
moss and mud
moss and vegetation
moss ball
moss covered
moss growing on their clothes
moss highly detailed
moss in the shape of a face
moss landscape
moss on the walls
moss patches
moss plants
moss terrarium
mossy
mossy buildings
mossy ground
mossy head
mossy overgrowth
mossy rock
mossy rocks
mossy ruins
mossy stone
mossy trunk
most beautiful woman on earth
most blues
most detailed
most dope
most epic landscape
most memorable scene
most perfect desert on the world
most popular
most respected design
most strongest pose
most wanted
most winning awards
mostly black
mostly cloudy sky
mostly greyscale
mostly wood
motel
motes of light
moth
moth inspired dress
moth orchids
moth wings
mother
mother earth
mother nature
mother of all decks
mother of pearl iridescent
mother of pearl jewelry
mother of witchers
mother sarah artist
mother theresa
motherboard
motherboard circuitry
motherly
mothership
mothership in the sky
mothman
moths
motif
motion
motion and action and tragedy
motion blur
motion blur bokeh
motion blur lights
motion blur of people walking
motion blured movement
motion blurred background
motion capture system
motion design
motion graphic
motion graphics
motion lines
motion photo
motion shapes color design
motion still
motionb blur
motionblur
motionless. award winning
motivation
motivational
motivational poster
moto gp ads in 1 9 9 0 s
motocross bike
motocross rider on dirt jump
motoko kusanagi
motor sport photography
motorbike
motorbiker
motorbikers race in hell
motorcycle
motorcycle concept art
motorcycle helmet
motorcycles
motorsports photography
mottled coloring
mottling coloring
motu
mouldy juice
moulin rouge
moulin rouge!
moulting
mount
mount doom
mount fuji
mount fuji background
mount fuji in the background
mount fuji on the background
mount olympus
mount rushmore
mountain
mountain background
mountain battles
mountain behind meadow
mountain bike helmet
mountain forest in background
mountain fortress city
mountain fuji on the background
mountain in background
mountain in the background
mountain label
mountain lake
mountain lake in sierra nevada
mountain lakes
mountain landscape
mountain pass
mountain plants
mountain range
mountain ranges
mountain scape. film still
mountain scene
mountain scene in a llama face
mountain snow
mountain sunrise
mountain valley
mountain valley to factory
mountain valley to fortress
mountain water
mountaineous background
mountainous
mountainous area
mountainous area. rare flora
mountainous background
mountainous jungle setting
mountainous landscape
mountainous setting
mountainous terrain
mountains
mountains and a huge old city
mountains and colorful sunset
mountains and colorful sunset!!
mountains and lakes
mountains and ocean
mountains and oceans
mountains and rivers
mountains and sunset
mountains and sunset!!
mountains at background
mountains background
mountains in a background
mountains in background
mountains in distance
mountains in fog background
mountains in the background
mountains in the distance
mountains made out of icebergs
mountains of ice cream
mountains of madness
mountains on background
mountains on the background
mountains river trees
mountains surrounding
mountainscape
mountainside
mournful
mourning
mourning family
mouse
mouse body
mouse ears
mouse face
mouse guard
mouse guard sits on a stump
mouse head
mouse holding a drum
mouse in clothes
mouse nose
mouse photo
mouse with drum
mousefolk
moustache
moutain
moutain in background
moutains
mouth
mouth agape
mouth closed
mouth half open
mouth in mouth
mouth in the bark
mouth is simple and pleasant
mouth of hell
mouth open
mouth open in a terrifying roar
mouth shut
mouth slightly open
mouth wide open
mouth wired shut
mouths
mouthwatering
move still
movement
movement effects
movie
movie accurate
movie action still frame
movie announcement
movie art
movie artwork
movie atmosphere
movie cgi
movie character
movie cinematography
movie clip
movie clockwerk orange
movie composition
movie concept
movie concept art
movie cover
movie environment design
movie film still
movie filmstill
movie firearms
movie footage
movie frame
movie frame still
movie grain
movie illustration
movie imax shot
movie inspired
movie leaks
movie level realism
movie lighting
movie lightning
movie lights
movie monster
movie pacific rim
movie photo
movie picture
movie poster
movie poster 1993 anime
movie poster 7 0's
movie poster 70s
movie poster art
movie poster character
movie poster for timecop academy
movie poster from 1960s
movie poster of earthbound 2
movie poster of street fighter
movie poster of the punic wars
movie poster painting
movie poster promo
movie poster style
movie poster with no text
movie posters
movie posters!
movie premiere poster
movie promo
movie promotional art
movie promotional image
movie quality
movie quality battle
movie scene
movie scene close up
movie scene portrait closeup
movie screen shot
movie screencap
movie screenshot
movie screenshot from star wars
movie set
movie setup
movie set”
movie shot
movie sonic
movie star
movie still
movie still 8 k
movie still 8k
movie still frame
movie still from blade runner
movie still from bladerunner
movie still from game of thrones
movie still from star wars
movie still from the matrix
movie still of a alien cyborg
movie still of a cool cyborg
movie still of a cyborg
movie still of a snarling
movie still of a tired
movie still of a villain cyborg
movie still of aztec cyborg
movie still of cyborg
movie still of emma watson
movie still of james bond
movie still of robot evangelion
movie still of robot goku
movie still of the alien girl
movie still promotion
movie stills
movie stills photography
movie style
movie texture
movie the lord of the rings
movie trailer
movie-cover-art headshot
movie-quality
movies
movies with guns
moviestill
moving
moving castle
moving forward
moving heads light beams
moving poetry
mowhawk
mowing of the hay
mozzarella
mozzarella everywhere
mp5
mp5s
mp7
mpc
mr bean
mr bean as captain america
mr bean as spiderman
mr bean as thanos
mr bean face
mr bean in the matrix
mr beast
mr clean
mr krabs
mr robert is drinking fresh tea
mr sandman
mr universe
mr. bean
mr. bean (rowan atkinson)
mr. bean depicted as a muppet
mr. bean rowan atkinson
mr. grinch
mr. house
mr. krabs
mr. nimbus character design
mr. roboto
mrbeast
mri
mrs bean
mrs doubtfire as a witch
mrs mills
mrt
ms dos
ms paint
ms paint drawing
mscow
mspaint
msxotto
mt elbrus at night
mt. fuji
mta subway entrance
mtg
mtg art
mtg art style
mtg card
mtg card trading
mtg d & d
mtg d d
mtg style
mtv
mu pan
much detail
much detailed
much wow
mucha
mucha : :
mucha and artem demura
mucha art nouveau
mucha bouguereau
mucha klimt
mucha klimt and tom bagshaw
mucha m. c. escher
mucha style
mucha style 4k
mucha tiffany kilian eng
mucha vibe
mucha |
mucha. art nouveau. gloomhaven
muchain
muck
mud
mud and brick houses
mud and rusty pipes
mud on face
muddy
muddy colors
muddy embankment
muddy fur
muddy ground
muddy road
muddy village square
mudoken
muffet from undertale
muffled colours
mug shot
mugler
mugshot
mugshot of darth vader
mugshot!!!
mugshot!!!!!
mugshots
muhammad ali
muira
mukbang
mulan
mulato
mule
mulholland drive
mullet
mullet haircut
mullet long haircut
multi - coloured
multi - layer
multi - level
multi chromatic
multi color smoke
multi colored
multi colors
multi colour
multi layered huge architectures
multi layered thick brush marks
multi pastel colors
multi toned
multi-dimensional
multi-dimensional latent space
multi-layered artworks
multi-part
multicam
multicam (camo)
multicam camo
multicam uniform
multicolor
multicolor color scheme
multicolor glints
multicolored
multicolored crocheted cats
multicolored digital art
multicolored faces
multicolored glints
multicolored hair
multicolored tshirt art
multicolored vector art
multicolored weed leaves
multicolors. beksinski
multicoloured
multidimensional
multiexposure
multilayer
multilayered outfit
multilayered paint
multiphase flow
multiplayer set-piece
multiplayer set-piece :9
multiplayer set-piece ambush
multiple
multiple angles
multiple arms
multiple bass drums
multiple brush strokes
multiple colors
multiple desks
multiple details
multiple dimensions
multiple earrings
multiple exposure
multiple eyes
multiple faces
multiple flames
multiple floating swords
multiple golden necklaces
multiple guitars
multiple illusory arms
multiple layers
multiple levels
multiple lights
multiple limbs
multiple moons
multiple moons glowing
multiple perspectives
multiple point perspective
multiple poses
multiple purple halos
multiple small black eyes
multiple stars visible
multiple stories
multiple suns
multiple views
multiple waterfalls
multiple wide angles
multiscopy
multistory building
multiversal hair
multiverse
multiverse portal
multiverse!!!!!!
mumbai in the future
mummified in bandages
mummy
mummy portrait
munching pizza
mundane
mundy
munich
munkácsi and hollósy simon
munkácsy
muppet
muppet punk
muppet show
muppets
muppets in mad max
muppets movie
mural
mural art
muralism
murals
murano glass sculpture
murata
murata range
murata yasushi nirasawa style
murder
murder scene
murderous
murderous carnival freak
murdoc niccals
murky
murky dusty deep
murky lighting
murky water
murloc tinyfin
muscle
muscle body with battle scars
muscle cars
muscle definition
muscle extremely detailed
muscle striation visible
muscle tissue
musclebound and hulking
muscled
muscled humanoid balrog demon
muscled mila kunis
muscles
muscular
muscular and exhausted woman
muscular and terrifying
muscular arms
muscular bald man
muscular bernie sanders
muscular bodies
muscular body
muscular body tattooed
muscular body type
muscular build
muscular bull headed man
muscular character
muscular characters
muscular chest
muscular eldritch clockwork
muscular female druid
muscular female gnome engineer
muscular figure
muscular gigachad benediction
muscular girl
muscular joe biden
muscular legs
muscular magician man
muscular male
muscular male body
muscular male hero
muscular male undead cyborg
muscular man
muscular masculine figure
muscular men
muscular men entwined together
muscular neck
muscular sweat lara croft
muscular system reference
muscular thighs
muscular torso
muscular ultraviolent woman
muscular upper body
muscular warrior women
muscular werewolf
muscular!
muscular! crossfit
muscular! cyberpunk
muscular! fantasy
muscular! green
muscular! white
muscular!!
muscular!! sci-fi
muscular!!!
muscularcyberpunk
muscularfantasy
musculargreen
muscularsci-fi
musculated
musculature
musculine
musculous
muse
musee d'orsay 8 k
musee dorsay 8k
museum
museum archival photo
museum archive
museum archive photo
museum art
museum artifact
museum background
museum catalog
museum catalog photograph
museum catalog photography
museum collection
museum curator
museum diffuse lighting
museum display
museum exhibit
museum exposition
museum ink painting
museum item
museum light
museum lighting
museum masterpiece
museum of art
museum of modern art
museum painting
museum photo
museum photograph
museum photography
museum photoshot
museum picture
museum piece
museum quality
museum quality painting
museum quality photo
museum quality work
museum sculpture
museum setting
museum work
mushoku tensei
mushroom
mushroom cap
mushroom city
mushroom cloud
mushroom cloud in the background
mushroom cloud on horizon
mushroom forest
mushroom forest arch
mushroom fractals
mushroom house
mushroom houses
mushroom hut in background
mushroom kingdom
mushroom structures
mushroom trees
mushroom umbrella
mushrooms
mushrooms and peyote at the base
mushrooms and plants
mushrooms everywhere
mushrooms grow
mushrooms grow from the body
mushrooms milky way
mushrooms on the ground
mushrooms style and macro style
music
music album art
music album cover
music being played
music festival
music in the air
music instrument in hand
music instruments
music is life
music notes
music phd
music poster
music records
music show
music theme
music video
musical
musical instrument
musical instruments
musical keyboard
musical notes
musical organ
musicality
musician
musicians
musicians playing instruments
musk ( 2 0 2 4 )
musk's mars migration program
muskular
muslim
muspelheim
mustache
mustache and soul patch
mustafar
mustang
mustard
mustard and ketchup
musty
musée d'orsay catalogue
musée dorsay catalogue
mutahar laughing
mutant
mutants
mutants creatures swarming
mutants roaming in the evening
mutated
mutated flowers
mutated wildlife
mutation
mute
mute colors
mute dramatic colours
muted
muted and dull colors
muted and pastel shades
muted arm colors
muted background
muted blue and red tones
muted brown
muted brown yellow and blacks
muted browns
muted but vibrant colors
muted cold colors
muted color
muted color (blues
muted color blues
muted color palette
muted color scheme
muted color tones
muted color. minimalist
muted colored bodysuit
muted colorful smoke
muted colors
muted colors with minimalism
muted colors!
muted colors!!!
muted colors. ue 5
muted colors. ue5
muted colour
muted colour palette
muted coloures
muted colours
muted colours 8 k
muted colours 8k
muted complementary colors
muted cyberpunk style
muted dark colors
muted deep neon color
muted dramtic color
muted fall colors
muted flat surreal design
muted green
muted intense colors
muted lighting
muted lights
muted multi-color lapis
muted neon colors
muted neon smoke
muted palette
muted palette mostly white
muted pastel colors
muted pastel tones
muted pastels
muted rainbow tubing
muted realistic colors
muted stage effects
muted tonal colors
muted tones
muted vaporwave unreal ombre
muted water color
muted water reflections
muted watercolor. minimalist
mutiversal tsunami
mutsumi akasaki
mutt
mutton chops
muttonchops
mutud colors
muy definido
muy dotado
muzinabu
muzzle flash
mx2
my blood is boiling
my computer icon
my dress up darling anime
my dress-up darling
my favorite friend
my heart is human
my hero academia
my home
my little everything
my little pony
my mom is going to be so mad
my neigbor totoro
my neighbor totoro
my pov
my rendition
my sleep paralysis demon
my son! the jaws that bite
my sonthe jaws that bite
my soul drifted away
my soul drifted away '
my true identity
myanmar
myazaki
mycelium
mycena acicula
mycologist
mycology
mylene farmer
myllypuro water tower
myminifactory
myportfolio
myriad
myrtle
myself
myserious man
myst
myst island
myst on the air
mysterio
mysterious
mysterious - eerie - ominous
mysterious ambient lighting
mysterious and lucid
mysterious and scary forest
mysterious atmosphere
mysterious atmospheric lighting
mysterious black slime
mysterious breath
mysterious canyon streams
mysterious coffee shop girl
mysterious crazy world
mysterious creature
mysterious creepy
mysterious dense forest
mysterious eerie portrait
mysterious exterior
mysterious feeling
mysterious figure
mysterious fog
mysterious forest
mysterious gaze
mysterious girl
mysterious glamour
mysterious glow
mysterious highly detailed
mysterious inner glow
mysterious island
mysterious jungle painting
mysterious laboratory
mysterious light
mysterious lighting
mysterious man
mysterious mist
mysterious mood
mysterious paranoia
mysterious portrait
mysterious portrait of a woman
mysterious temple setting
mysterious vibes
mysterious woman
mysterious x
mysterious ， stream
mysterious!
mystery
mystery and detective themed
mystery code
mystery horror
mystery pokemon
mystic
mystic alchemical occult art
mystic art
mystic athmosphere
mystic atmosphere
mystic colors
mystic dark cave
mystic eye
mystic eyes
mystic haze
mystic hermit
mystic hue
mystic hue clouds
mystic hues
mystic illustration
mystic light
mystic ninja
mystic sheperd
mystic style
mystic unity
mystic winter landscape
mystical
mystical and mysterious
mystical and new age symbolism
mystical anubis valkyrie
mystical art
mystical atlantean valkyrie
mystical atmosphere
mystical baroque
mystical birds
mystical blue fog
mystical cathedral windows
mystical color scheme
mystical colors
mystical cosmic lighting
mystical cosmic messenger
mystical energy
mystical energy in the air
mystical eyes
mystical fantasy
mystical fantasy landscape
mystical feeling
mystical figure
mystical fog
mystical forest
mystical forest background
mystical forest lagoon
mystical insects
mystical kew gardens
mystical lake
mystical lake view vista
mystical light
mystical lighting
mystical lights
mystical night
mystical oil on linen
mystical orange fog
mystical post apocalyptic cyborg
mystical purity
mystical scene
mystical sci-fi concept art
mystical setting
mystical shadows
mystical swirls
mystical third eye
mystical valkyrie
mystical”
mysticism
mystifying
mystique
myth
myth of narcissus
mythic
mythic island
mythical
mythical cosmic shrine
mythical creature
mythical creatures
mythical floral hills
mythical gigantic space cavern
mythical shrine
mythical whimsical creatures
mythological
mythological map
mythological painting
mythology
mythology artwork
mythos of id
mythpunk
möbius
möbius and æon flux
múseca illil
n - 4
n - 6
n - 9
n -4
n -9
n 2
n 4
n 6
n 6 4
n 6 4 graphics
n 64
n 64 graphics
n 7
n 7 armor
n 9
n eddy take a trip to tokyo
n- 4
n- 9
n-4
n-9
n4
n64
n64 graphics
n7
n9
naboo
nacho man randy savage
nachos
nachosaurus
nacre
nacre colors
nacre painting
nacreous lights
nadar
nadav kander
nadezhda tikhomirova
nadir lighting
nag
naga
naga-hakash
naga-tirr
nagas
nagasaki
nagash editorial
nagatoro
nagel
nagito komaeda
nagito komaeda from danganronpa
nail art
nail polish
nails
naive
naivistic art
nakolki
nam june paik
namco
name is tripmachine
name of the character is chad
nami
nami from one piece
nami one piece
nanae kawahara
nancy grossman
nancy pelosi
nanite
nano
nano hex armor hull
nano parts
nano technology
nanocarbon-vest
nanocarbon-vest greaves
nanogirl
nanogirlv 2
nanomachines
nanomaterials
nanopunk
nanotech demonic monster horror
nanotechnology
nanquan
naoki ikushima
naomi campbell
naoto fukasawa
naoto hattori
naotto hattori
naoya tanaka
napa
naples
napoleon
napoleon crossing the alps
napoleon dynamite
napoleonic
napoleonic wars
naranbaatar ganbold
narasimha
narcissist
narcissistic
narendra modi
narnia
narrative nature
narrative realism
narrow
narrow and winding cozy streets
narrow angle
narrow blue grey eyes
narrow depth of field
narrow eyes
narrow face
narrow focus
narrow footpath
narrow hallway
narrow lips
narrow nose
narrow passage
narrow shot
narrow street
narrow streets
narrow tired blue grey eyes
narrow waist
narrow wings behind
narumi kakinouchi
naruto
naruto artstyle
naruto in gta v
naruto uzumaki
narval
nas
nasa
nasa and roscosmos
nasa archival photo
nasa archives
nasa canadarm
nasa eva suit
nasa footage
nasa image
nasa images
nasa photo
nasa photograph
nasa photography
nasa photos
nasa picture
nasa quality
nasa space photography
nasa space tourism
nasa true color 8k image
nasa true color photograph
nasal strip
nascar
nascar race
nasreddine dinet
nasty
nasus
nat geo
nat geo award winner
natali portman
natalia dyer
natalie dormer
natalie from epic battle fantasy
natalie portman
natalie portman as a goddess
natalie portman as catwoman
natalie portman in star trek
natalie shau
natalie shau tom bagshaw
natasha tan maciej kuciara
nate berkus
nate fakes
natgeo
nathalie emmanuel
nathan fielder
nathan fielder and groucho marx
nathan fillion
nathan for you
nathan fowkes
nation geographic style
national - geographic
national archives
national art school mfa
national gallery
national geograph
national geographic
national geographic channel
national geographic cover
national geographic footage
national geographic magazine
national geographic photo
national geographic photo award
national geographic photo shoot
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national geographic photographs
national geographic photography
national geographic photos
national geographic photoshoot
national geographic photo”
national geographic quality
national geographic style”
national geographic ”
national geographical
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national geograpic
national geohraphic
national portrait gallery
nationalgalleryofart
nationalist
native american
native american art
native american folk art
native american warrior
native art
native rich jewelry
natsume yuujinchou
natsumi mukai artwrok
natural
natural and organic and flowing
natural atmosphere
natural background
natural beard
natural beauty
natural beauty expressive pose
natural blonde gold like hair
natural bones and skin
natural botanical gardens
natural brown hair
natural candle lighting
natural cave wall
natural color palette
natural color scheme
natural color skin
natural colors
natural colours
natural complexion
natural contour aesthetics!!
natural disaster
natural dull colors
natural dull colours
natural dynamic range color
natural earth tone colors
natural environment
natural eyes
natural fragile pose
natural geographic
natural geographic photography
natural grassy background
natural grizzled skin
natural hair
natural hands and arms
natural history
natural indoor lighting
natural landscape
natural landscape background
natural landscape beauty
natural lighing
natural light
natural light beam
natural light canon eos c 3 0 0
natural light canon eos c 300
natural light in room
natural light outside
natural light window
natural lighting
natural lighting. 8 k
natural lightning
natural lights
natural lips
natural look
natural make-up
natural makeup
natural materials
natural materials  high detail
natural materials :: high detail
natural media
natural mini gardens
natural morning light
natural mustache
natural muted tones
natural overcast lighting
natural physique
natural point rose
natural point rose'
natural pose
natural prison light
natural realistic render
natural reflections
natural sensuality
natural shaders
natural short hair
natural skin
natural skin tone
natural skin tone 4k textures
natural skin tones
natural soft light
natural soft pale skin
natural soft rim light
natural starlight
natural stone road
natural straight black eyebrows
natural straight eyebrows
natural study
natural sunlight
natural textures
natural tones
natural tpose
natural volumetric light
natural volumetric lighting
natural weight
natural window lighting
natural wood top
naturalist
naturalist illustration
naturalistic
naturalistic technique
nature
nature aesthetics
nature and clouds in background
nature and floral aesthetics
nature art
nature background
nature colors
nature documentary
nature documentry footage
nature druid
nature druid dreaming
nature elements
nature environment
nature futurism
nature goddess
nature grotesque dark
nature growing around the city
nature illustration tattoo
nature in all its beauty
nature journal
nature journal cover
nature landscape
nature lighting
nature macro
nature magazine
nature magic
nature meets architecture
nature outside
nature overgrowth
nature painting
nature photo
nature photograph
nature photography
nature photography 4k
nature returning to the city
nature scenery
nature show
nature study
nature taking back
nature taking over
nature trees
nature unity
nature utopia
nature wallpaper
naturecore
naturel
naturist
naudline pierre
naughty
naughty expression
naughty smile
nausicaa
nausicaa ghibli
nautical maps
nautical maps grafitti
nautical siren
nautilus
nautilus brain
naval background
naval battle
naval battles
naval landscape
naval warfare
navel
naver fanpop
navid negahban
navigation command center
navigator glasses
navy
navy blue
navy blue armor
navy blue carpet
navy blue shorts
navy flags
navy hair
navy leotard costume
navy shirt
navy-blue
nazare (portugal)
nazare portugal
nazca design
nazgul
nazgul from lord of the rings
nazgul mandelbulb
nazi germany
nazi propaganda
nazism
naïve art
nba
nba 2 k 2 1
nba finals
nba logo
nba style bulldog mascot
nbc
nc wyeth
nc wyeth painting
nd 4
nd 6
nd4
ne zha from smite
neal adam
neal adams  centered
neal adams  dark
neal adams  portrait
neal adams  sinister
neal adams | centered
neal adams | dark
neal adams | portrait
neal adams | sinister
neal asher sci - fi
neal hanson
neanderthal people
neanderthal people eating sushi
near a galaxy
near a japanese shrine
near a jetty
near a lake
near a river
near a small lake
near a stone gate
near a window
near a window window
near crystal temple in atlantis
near farm
near forest
near future
near future 2 0 3 0
near his barrel home
near kitchen stove
near lake baikal
near pond
near the beach
near the black cauldron
near the sea
near the seashore
near the window
nearest neighbor
nearly napping
neat
neat and tidy
neat baerd
neat beard
neat braided hair
neat drawing
neat hair
neat hair with bangs
neat intricate braided hair
neat nails
neat white beard and hair
neatly coming out of her head
nebula
nebula aura surrounding subject
nebula background
nebula clouds
nebula explosion
nebula highly detailed
nebula in background
nebula in the background
nebula in the sky
nebula in universe
nebula reflections
nebula size
nebula sized
nebula sky
nebula space background
nebula sunset
nebula waterfalls
nebula with the shape of a skull
nebulae
nebulae background
nebulae colors
nebulae!!
nebulae. volumetric lighting
nebulas
nebulas in background
nebulas swirls
nebulizer equipment
nebulous
nebulous bouquets
neck
neck chains
neck rings
neck shackle
neck tattoos
neck up
neck visible
neck wrinkles
neck zoomed in
neck zoomed in from lips down
neckbeard
necklace
necklace on display
neckline
necktie
neco arc
necro
necromancer
necromancer sitting at table
necromancer sorceress
necromancy
necromorph
necromunda
necronom v
necronomicon
necronomicon style
necropolis
necrosis
ned flanders
ned kelly
need for speed
need for speed : carbon
needle felting
needle felting art
needles
nefarious
nefarious smirk
neferpitou
nefertiti
negao
negative
negative and positive shapes
negative energy
negative mood
negative no not mona lisa pose
negative self-talk
negative space
negative space is mandatory
neighborhood
neighborhood outside window
neighborhood themed
neil blevin
neil blevins and jordan grimmer
neil blomkamp film landscape
neil breen face
neil degrasse tyson
neil gaiman
neil leifer
neil richards
neil ross
neil young
neil young design
neill blomkamp
neko
nekomimi
nekro
nekro borja
nekro iii
nekro petros afshar
nekro xiii
nendoroid
nendoroid 3 d
nendoroid eyes
nendroid
neo
neo - andean architecture
neo - classical
neo - classical composition
neo - classical style
neo - dada
neo - expressionism
neo - fauvism
neo - figurative
neo - gothic
neo - gothic architecture
neo - gypsy caravan
neo - impressionism
neo - impressionist
neo - impressionist surrealism
neo - noir
neo - noir setting
neo - noir style
neo - noire
neo - primitivism
neo - renaissance
neo - romanticism
neo - tokyo
neo brutalism
neo brutralism
neo classical architecture
neo culture technology
neo expressionism
neo expressionism art
neo from matrix
neo from the matrix
neo geo
neo goth
neo gothic
neo kyiv
neo noir
neo noir style
neo noire
neo norilsk
neo rauch and nadav kander
neo rococo expressionist
neo soul
neo surrealism
neo tokyo
neo tokyo background
neo vaporwave
neo-andean architecture
neo-classical
neo-classical composition
neo-classical painting
neo-classicism
neo-cyberpunk
neo-expressionist
neo-expressionist style
neo-expressionistic
neo-gothic
neo-gothic concept
neo-noir
neo-pagan
neo-rococo expressionist style
neo-tokyo
neoartcore
neoartcore and charlie bowater
neobrutalist architecture
neobrutalistic
neochrome colors
neoclassic
neoclassic oil painting
neoclassic painting
neoclassical
neoclassical architecture
neoclassical art
neoclassical composition
neoclassical painting
neoclassical paintings
neoclassical police station
neoclassical portrait
neoclassical style
neoclassical tower with dome
neoclassicism style
neoclassicist painting
neodada
neoexpressionism
neoexpressionism ”
neoexpressionism”
neoexpressionist
neofuture
neofuturistic
neofuturistic highly detailed
neogothic
neogothic art
neoimpressionist
neolithic standing stones
neon
neon accent lights
neon accents
neon advertisements
neon aesthetic
neon ambiance
neon ambience
neon ambient lighting
neon ancient ruins
neon and dark
neon armor
neon art
neon art style
neon aztec
neon background
neon background lighting
neon backlighting
neon backlit
neon bar lights
neon basement
neon billboards
neon bioluminescence
neon blacklight color scheme
neon blue
neon blue and yellow lights
neon blue color
neon blue glass forehead
neon blue highlights
neon circles
neon city
neon city domes
neon city in the background
neon cityscape background
neon color
neon color bioluminescence
neon color bleed
neon color details
neon color mixed media painterly
neon color palette
neon color scheme
neon colored
neon colored dress
neon colored haze
neon colored suit
neon coloring
neon colors
neon colors vibrant colors
neon colours
neon cross
neon cybernetic implants
neon cyberpunk
neon cyberpunk colors
neon cyberpunk highlights
neon cyberpunk style
neon cyberpunk vibrant colors
neon dark lighting
neon digital art
neon edges on bottom of body
neon electronic
neon electronic signs
neon eyeballs
neon eyes
neon face paint
neon face tattoo
neon floral pattern
neon flowers
neon fluorescent
neon fluorescent colors
neon fog
neon genesis
neon genesis evangelion
neon genesis evangelion fanart
neon genesis evangelion style
neon geometric inks
neon geometry
neon glitch
neon glow
neon glow concept art
neon glow soft bokeh
neon glowing
neon glowing eyes
neon glowing lines
neon glowing spells
neon glowing wood
neon glowy edges
neon gradient
neon graffiti
neon greek
neon green
neon green lava streets
neon hair
neon hand sports bracelet
neon heart reactor
neon highlights
neon holograms
neon hooves
neon inc
neon ink
neon jacuzzi
neon jellyfish
neon jellyfish headdress
neon jungle
neon lamp
neon landscape
neon lens flare
neon lenses
neon lenses for eyes
neon letters tripmachine
neon ligh
neon light
neon light and fantasy
neon light edge
neon light language
neon light rooftop
neon light showing injuries
neon lighting
neon lighting and spotlights
neon lighting medium full shot
neon lightning
neon lights
neon lights above shops
neon lights all around
neon lights and adds
neon lights everywhere
neon lights in background
neon lights in the background
neon lights in the city below
neon lights lots of trees
neon lights outside
neon ligths
neon lines
neon lit
neon madhubani
neon megacity in the background
neon motion
neon mushrooms
neon music notes
neon night
neon noir
neon noire
neon operator
neon operator margot robbie
neon orange
neon outline
neon outlines
neon paint drip
neon palette
neon particles
neon pastel
neon pastel color palette
neon pastel colors
neon pillars
neon pink
neon pink and black color scheme
neon purple
neon purple light
neon radioactive swamp
neon rain
neon rainbow drip paint
neon rainbow quartz
neon rainy cyberpunk setting
neon reflections
neon reflections in the puddles
neon rim light
neon road
neon roman
neon samurai
neon scales and cyborg tech
neon shops
neon sign
neon signs
neon signs in background
neon signs in the distance
neon smoke
neon sparkles everywhere
neon standup bar
neon static
neon steampunk
neon street
neon streetlights
neon style
neon sunglasses
neon sunglasses!
neon synth wave
neon tattoo
neon tint
neon tokyo
neon tones
neon tube jewelry
neon version of style jim burns
neon vibe
neon vibes
neon virtual networks
neon visor
neon volumetric lights
neon wings
neon wires
neon wiring
neon yellow glowing mushrooms
neon yellow madhubani
neon yellow stars
neon! retrowave
neon!! light
neon!!!!!! atmosphere
neon-noir background
neon-yellow-holographic wings
neonlights
neonpunk
neonretrowave
neons
neonwave
neoplasticism art
neoprene
neorenaissance
neosurrealism
neosurrealism. digital art
neosvr
neosvr!!!
neotenous
neotokyo
neotokyo source
neotrad
neotraditional modern
neotraditional modern minimalist
neotraditional tattoos
neotribal
nepal
nepali architecture buildings
nephilim
neptune
nerd
nerd emoji
nerds
nerdy
nerdy appearance
nerdy black girl super hero
nerdy glasses
nerdy man character portrait
nerdy music teacher with phd
nerf or nothing
neri oxman
nerve cells
nerve system
nerves
nerves and muscles
nervous
nervous and terrified
nervous system
nes
ness from earthbound
nest
nest is made of sticks
nest of vipers
nesting glass doors
nestled in a forest
nestor canavarro hyperrealist
net of being
netfilx !n-9
netfilx n-9
netflix
netflix animation
netflix logo
netflix neon logo concept art
netflix series
netflix trese
nether
netherlands
netrunner
nets
nets and boats
nettie wakefield
netting
network
networking
neural
neural acrylic paint
neural machine
neural network
neural networks
neural pointillism
neuro web
neurochip
neurograph
neurological marvel
neuromancer
neuromancy
neuromorphic chip
neuron
neuron dendritic monster
neurons
neurons and synapses
neurons firing
neuroscience
neutral
neutral background
neutral color
neutral color neo - fauvism
neutral colors
neutral colours
neutral dull colors
neutral expression
neutral expressions
neutral face
neutral flat lighting
neutral focused gaze
neutral light
neutral lighting
neutral milk hotel
neutral pose
neutral sky
neutral tones
neutron
neutron star
never before seen
never ending story
never erased
never gonna give you up
never gonna let you down
never one - to - one
never seen before
neverending - story
neverending story
neverwinter nights
neville page
new
new - york skyline in winter
new adidas logo design
new adventure
new age
new age album cover
new ai race
new album cover
new art nouveau
new balance pop up store
new baroque
new boards of canada album cover
new cats movie
new character
new classic
new contemporary
new contemporary art
new costume concept design
new design
new emoji of biting your lip
new england architecture
new england ipa
new gods
new guinea mud man
new hampshire
new hampshire mountain
new horror movie advertising
new jersey
new mexican desert background
new mexico
new mexico desert
new mexico with a yellow filter
new modern
new musical instruments
new orleans
new pokemon
new polaroid
new realism
new release
new scifi movie
new song
new species
new stranger things season
new tokyo
new vegas style
new vehicle
new wave
new wave of british heavy metal
new years eve
new york
new york alleyway
new york back street
new york backdrop
new york background
new york buildings
new york city
new york city as backdrop
new york city at night
new york city background
new york city in the year 2100
new york city skyline
new york harbour
new york in the future
new york in the seventies
new york skyline
new york streets
new york times
new york zoo in the background
new yorker cartoon
new yorker illustration
new yorker magazine cover
new zealand
new zealand landscape
new zeeland
newgrounds
newly hatched dragon
news
news archive
news broadcast
news coverage
news feed
news footage
news photo
news photograph
news photography
news report
news reporter anthony conn
news segment
newsboy cap
newspaper
newspaper article
newspaper clipping
newspaper clippings
newspaper collage
newspaper comic strip
newspaper illustration
newspaper photo
newspaper photograph
newspaper photography
newspaper picture
newspaper style
newspaper. stock certificate
newstyle
newsweek
newton's cradle
newts
next gen
next gen game
next gen graphics
next gen rendering
next generation graphics
next to a big window
next to a cup
next to a farm house and a barn
next to a plant
next to a pool
next to a red barn
next to a river
next to a small river
next to a tree
next to a tropical pool
next to a waterfall
next to a window
next to an rv
next to farm fields and trees
next to it is a toy ray gun
next to sliced strawberries
next to some alien plants
next to the dinosaur are cavemen
next to the reflecting ocean
next to the sea
next to window
next-gen graphics
nexters
ney matogrosso with malice
neymar
neymar in fortnite
neymar in gta v
neymar jr
nezha
nezha : birth of the demon child
nezuko
nezuko-chan
nfl
nft
nft art
nft portrait
nft style
nfts
ngai victo
ngai victo and dougherty patrick
nge
nge dark fantasy
nhl
nhl logo
niagara falls
nice
nice afternoon lighting
nice art
nice background bokeh
nice clouds
nice color scheme
nice colors
nice colour scheme
nice composition
nice composition and photography
nice deep colours
nice face
nice legs
nice lighting
nice slight overcast weather
nice spring afternoon lighting
nice studio lighting
nice style culture
nice sunset
nice view
nice weather
nicely detailed
nichijou
nicholas
nicholas cage
nicholas cage as monkey d luffy
nicholas cage as superman
nicholas ii of russia
nicholas kalmakoff
nicholas roerich!!
nicholas weltyk
nick alm
nick cave pondering his orb
nick drnaso
nick knight
nick land
nick park
nick silva
nick silva and ilya kuvshinov
nick wilde
nick wilde from zootopia
nickelodeon
nicki minaj
nicki minaj curvy
nico delort
nico robin
nico tanigawa
nico wearing a white dress
nicodemus
nicodemus yang-mattisson
nicol bolas
nicola salvior
nicola samuri
nicolas bouvier
nicolas bouvier sparth
nicolas cage
nicolas cage as an android
nicolas cage as dracula
nicolas cage as superman
nicolas cage mossy statue
nicolas cage's face on an egg
nicolas cages face on an egg
nicolas delort
nicole aniston
nicole de boer
nicole kidman
nicovideo
niea 7
niel davis
niele toroni
nielly
niels otto møller
nier
nier 2 b
nier : automata inspired
nier autoamata
nier automata
nier automata concept art
nier automata concept artsyle
nier automata spaceship style
nier inspired
nier:automata
nier:automata inspired
niflheim
nigel thornberry
night
night - time
night - time city background
night ambience
night and bright sun
night atmosphere
night background
night black sky background
night blade runner
night cam footage
night camera
night city
night city background
night city in the background
night city on the background
night cityscape
night clouds
night club
night clubs and neons
night color
night colors
night covered in stars
night cyberpunk city
night dramatic lighting
night fill lighting
night forest
night forest background
night fury
night in a dark forest
night in the city
night in the forest
night landscape
night landscape background
night life
night life buildings
night light
night lighting
night lights
night mood
night on a summer miami beach
night on nar shaddaa
night outdoors
night outside
night photo
night photography
night realism
night scene
night scenery
night sea
night sea storm
night setting
night shot
night skies
night sky
night sky background
night sky full of flowers
night sky full of stars
night sky photography
night sky reflected in the water
night sky with clouds and stars
night sky with dazzling stars
night sky with many meteorites
night sky with moon
night sky with stars
night sky; 8k
night starry sky full of cats
night stars
night street
night time
night time australian outback
night time city background
night time dark with neon colors
night time footage
night time lighting
night time low light
night time moonlight
night time photograph
night time photography
night time raid
night time render
night time scene
night time with starry sky
night tokyo metropoly
night under the starry sky
night view
night vision
night vision goggles
night vision very blurry
night with moon and candle
night!
night-time
night. by greg rutkowski
nightcafe
nightcap
nightclub
nightclub background
nightclub dancing inspired
nightcore
nightfall
nightfall. quiet
nightgown
nighthawks
nightime
nightime long exposure
nightime village background
nightlife
nightlight
nightlight study
nightmare
nightmare - like dream
nightmare anomalies
nightmare before christmas
nightmare creature
nightmare creatures
nightmare digital art
nightmare fuel
nightmare fuel!!!
nightmare in the park
nightmare inducing
nightmare landscape
nightmare portrait
nightmare render
nightmare scene
nightmare vision
nightmare world
nightmares
nightmarish
nightmarish illustration
nights
nightscape
nightshot
nightsky
nighttime
nighttime foreground
nighttime in gotham city
nighttime moonlit
nighttime nature landscape
nighttime photography
nighttime scene
nighttime!
nighttime!!
nighttime!!!!!!
nightvision
nigredo
nigt
nigth
nigth moon ligth
nihilism
nihilistic
nihonga
nika maisuradze
nike
nike air max
nike alpha huarache 7 elite
nike cycling suit
nike logo
nike shirt
nikkon
nikkor
nikkor 20mm f1.8 lens
niko oneshot riding a roomba
nikola jokic as the joker
nikola tesla
nikolai karelin
nikolai tesla in his lab
nikolay
nikolay georgiev
nikolay kopeykin
nikon
nikon 1 0 - 2 4 mm
nikon 10 - 24 mm
nikon 10mm
nikon 200mm
nikon 3 5 mm
nikon 3 5 mm photography
nikon 35 mm
nikon 35 mm photography
nikon 35mm
nikon 5 0 mm
nikon 50 mm
nikon 50mm
nikon 50mm camera
nikon 50mm f/1.8g
nikon 85mm f/5.6
nikon camera
nikon camera 8 k
nikon camera 8k
nikon d 4 x
nikon d 6
nikon d 7 2 0 0
nikon d 7 5 0
nikon d 7 8 0
nikon d 7200
nikon d 750
nikon d 780
nikon d 8 0 7
nikon d 8 1 0
nikon d 8 5 0
nikon d 8 5 0 camera
nikon d 810
nikon d 850
nikon d3
nikon d6
nikon d750
nikon d810
nikon d850
nikon lens
nikon ltm lens 3 5 mm lens
nikon ltm lens 35 mm lens
nikon macro 40mm
nikon mirrorless
nikon photography
nikon shot
nikon z 9
nikon z7
nilah
nile river environment
nilian animation studio
nimble
nimbus
nina agdal
nina dobrev
nina masic
nina tryggvadottir
ninchaku
nine separated hd
nine tails
nine-dimensional
ninetales
nineteen eighties
nineteen seventies
nineteen-dimensional
nineteenth century
nineteenth century london
nineties nostalgia
nineties photography
ninja
ninja cat
ninja jesus
ninja outfit
ninja scroll anime style
ninja scrolls
ninja turtle eating pizza
ninja turtles
ninja warrior
ninja with katana of water wave
ninjala
ninjas
nintendo
nintendo 6 4
nintendo 6 4 release day
nintendo 6 4 screenshot
nintendo 64
nintendo 64 screenshot
nintendo clouds
nintendo ds
nintendo ds video game
nintendo game
nintendo game art
nintendo official media
nintendo style
nintendo switch
nintendo64
nintendocore
nipple
nipsey hussle
nirvana
nisachar art
nishihara isao
nishimiya shouko
nissan gtr r 3 4
nissan gtr r 34
nite - owl
nitid and detailed background
nitid horizon
nitro colors
nitro paint
nitrogen-rich soil
nivanh chanthara
nixeu
nixeu and greg rutkowski
nixeu and sakimichan
nixie tube
nixie tubes
nixri
nizou yamamoto
nmm
no
no - pupils
no - text no - logo
no aditional arms
no aditional characters
no aditional legs
no aditional legs!
no animals
no arms
no army
no artefacts
no artifacts
no artifacts!!!!!
no background
no background and shadows
no bangs
no beard
no bloom
no blur
no blur dof bokeh
no body
no border
no borders
no bricks
no buildings
no cars
no cars. sharp focus
no character
no characters
no clothes
no clouds
no collage
no color
no colors
no crop
no cropping
no curves
no cutoff
no dark portents
no decaying lines
no depth of field
no detail
no details
no distorsion
no distortion
no distortion on subject faces
no distortions
no dof
no double figure
no double head
no duplicate
no duplicate content
no duplicate image
no ears
no epaulettes
no escape
no extra arms
no extra arms!
no extra character
no extra characters
no extra ears
no extra hands
no extra limbs
no eyebrow
no eyebrows
no eyes
no face
no face mask
no faces
no faces visible
no facial
no facial features
no facial hair
no fear
no fears
no filter
no fine details
no flag patch
no flash
no focus
no fog
no foliage
no frame
no frames
no fur
no furniture
no future
no future!!!!
no glasses
no glow
no gold
no gold colours
no gradient
no gradients
no grain
no grass
no grass or trees
no gravity
no green
no greenery
no grid lines
no ground
no ground visible
no hair
no hair completely bald
no hands
no hands visible
no hard shadow
no hat
no hd
no head
no headgear
no helmet
no helmet!!!!
no helmets
no hood | | realistic shaded
no hoodie
no horns
no horses
no human
no humans
no imperfections
no irises
no jagged lines
no jersey
no land
no legs
no less
no letters
no life
no light
no light penetrates to room
no light reflecting off paint
no lighting
no lights
no lights in bedroom
no lipstick
no logo
no logo!!!
no long neck
no makeup
no makeup wavy hair
no man
no man sky
no man sky concept art
no man's land
no man's sky
no man's sky landscape
no man's sky righteous auditor
no mans land
no mans sky
no mans sky spaceship
no mask
no medals
no more
no more heroes concept art
no more than three characters
no mountains
no mouth
no mustache
no noise
no nose
no nudity
no numbers
no one around
no outline
no pants
no path
no people
no people 4k
no person
no planets
no plants
no power
no rainbow
no red colour
no reins
no repeat
no repetition
no restaurant
no rider
no shade
no shading
no shadow
no shadows
no shirt
no shirt under the vest
no shoes
no signature
no skin
no skin shown
no sky
no sky scrapers
no smoke
no speech bubbles
no spot over right eye
no spot right eye
no stipe
no stubble
no sun
no surroundings
no tail
no talking
no tattoos
no teeth
no text
no text!
no textures
no tiling
no trees
no trees around
no trees or grass
no trees ， hyper detailed
no two bodies
no two heads
no two rooms are identical. 4k
no type
no ui
no water
no watermark
no watermark signature
no watermarks
no windows
no wings
no women
no words
no words 4 k
no yellow color
no yellow color in eyes
noah bradley concept art
noah bradley. sharp focus
noah's ark
noahs ark
nobel prize
nobility
noble
noble elf male in black robes
nobles
nobodies
nobody
nobody here
nobody knows the future
nobody living there
nobutaka ike
nobuyoshi araki
noctilucent
noctilux 50mm
nocturnal
nocturnal palette
nocturnal spiritual scene
nocturne
nocturne from league of legends
nodachi
noelle stevenson
nofilter
noh theatre mask
noir
noir animation
noir art house
noir arthouse
noir detective
noir detective and a fedora
noir effect
noir film
noir film still
noir film world
noir film world character design
noir movie scene
noir photorealism
noir style
noire
noire moody scene
noire photo
noise
noise and grain
noise effect
noise effects filter
noise film
noise film photo
noise filtered
noise grain film
noise rock album cover
noise to volume
noisy
noisy and glitched
noisy film grain effect
noisy filter
nomad
nomad masterpiece
nomadic
nomadic urbanism
nomads
non - euclidean
non - euclidean space
non - euclidian
non - fiction
non - human
non binary future world
non binary model
non blurry
non fiction
non human
non-binary
non-centered shot
non-euclidean
non-euclidean geometry
non-illuminated backdrop
non-pleated section
non-symmetrical fractals
non-traditional avant-garde
nonagon infinity
nonbinary model
nonduality
nongraphic
noodles
noon
noot noot
nootropic stimulant
nord
nordic
nordic crown
nordic folk
nordic forest colors
nordic landscape
nordic mythology
nordic noire
nordic pastel colors
nordic summer
nordic wedding ring
norihiro yagi
norilsk
noriyoshi
noriyoshi ohrai and hans zatzka
noriyoshi ohrai masterpiece
norm rockwell
norm rockwell in africa
normal clothes
normal distributions
normal face
normal hands
normal legs
normal map
normal nose
normal people with costumes
normal physique
normal place with weird feeling
normal proportions
normal rockwell
norman ackroyd
norman foster
norman mingo painting
norman rockwel
norman rockwell
norman rockwell akihiko yoshida
norman rockwell and james jean
norman rockwell artwork style
norman rockwell j
norman rockwell oil painting
norman rockwell painting
norman rockwell ross tran
norman rockwell style
norman rockwell. detailed
normandy
norrlandsskog
norse
norse god
norse goddess
norse inspired
norse mythology
norse warrior
north
north adult female warrior
north female warrior
north hemisphere
north island brown kiwi
north korea
north korea propaganda style
north korean slasher
north melbourne street
north pole
northen lights background
northern finland
northern france
northern lights
northern lights background
northern lights in space
northern renaissance
northern renaissance art
northern renaissance style
northern star at night
norway
norway fjord
norway fjords
norway mountains
norwegian
norwegian landscape
norwegian man
nose
nose made of wood
nose of angelina jolie
nose of red skull
nose piercing
nose ring
noseless
nosey neighbors
nosferatu
nosferatu princess
nostalgia
nostalgia critic
nostalgia for a fairytale
nostalgia guy and small creature
nostalgia of a fairytale
nostalgiacore
nostalgic
nostalgic 8k
nostalgic and euphoric
nostalgic and melancholic 4 k
nostalgic atmosphere
nostalgic feeling
nostalgic heart-warming
nostalgic high saturation
nostalgic lighting
nostalgic melancholic artwork
nostalgic melancholy
nostalgic memory
nostalgic photography
nostalgic vibes
nostalgy
nostlagia
nostromo
not a lot of grass
not a messenger from above
not a single tear
not blurry
not clear
not cropped
not enough details
not face
not going anywhere. oil painting
not intelligent
not isometric
not japanese
not often
not ominous at all
not realistic
not ruins
not scarry
not smiling
not train tracks
not wearing many clothes
not yet day
not yet the beginning
notan
notan art
notan sun in the background
notation
note detailed
notebook
notes
nothing
nothingness
nothofagus
noticeable tear on the cheek
notices
notorious b i g
notorious big
nouveau
nouveau art
nouveau painting
nouveau painting masterpiece
nouveau realisme decollage
nouvelle vague
nouvelle vague style
nova
novel color scheme
novel cover
novel cover art for a book
novelance
november
novi stars
now
now at mcdonalds
now i am become death
now i have become death
now i work for the bbc
noxious
noxious poison diaper
npc
npc talking
npc with a saint's halo
npc with a saint\'s halo
npc with a saints halo
npr
nsfw
ntricate oil painting
nubian
nubile body
nubulae
nubulae hubble
nuclear
nuclear aftermath
nuclear apocalypse
nuclear attack
nuclear background
nuclear blast imminent
nuclear blast moon eclipse
nuclear bomb
nuclear bomb blast
nuclear bomb explosion
nuclear cloud
nuclear energy future
nuclear explosion
nuclear explosion background
nuclear explosion from space
nuclear explosion on the horizon
nuclear explosion!!!
nuclear explosions paint sky
nuclear fallout
nuclear fashion
nuclear fungus
nuclear fusion
nuclear mushroom
nuclear mushroom cloud
nuclear mushroom smoke
nuclear power
nuclear power plant
nuclear powered
nuclear reactor
nuclear sunset
nuclear war
nuclear waste
nuclear wasteland
nuclear winter
nudibranch
nug pic
nugget
nugget and sausage on plate
nuka cola
nuke
nuke dropping
null
numb
number 31 on jersey
number 31!!!!!
numenera
numerical
numerology
numerous dimly glowing eyes
numerous fires
numerous limbs
nun
nun fashion model
nun fashion model looking up
nun outfit
nuremberg
nuremberg chronicle
nurgle
nuri iyem
nurse
nurse costume
nurse girl
nurse scrubs
nurse uniform
nurse's leather suit
nursery poster
nurses leather suit
nursing
nursing home
nut
nute gunray
nuttavut baiphowongse
nvidia
nvidia ai
nvidia and behance
nvidia geforce
nvidia graphics
nvidia octane
nvidia omniverse
nvidia optix
nvidia promotional image
nvidia ray tracing
nvidia raytracing
nvidia raytracing demo
nvidia raytracing demo)
nvidia raytracing demo))
nvidia raytracing demo))))
nvidia rtx reflections
ny
ny style grafitti
nyan cat
nyarlathotep
nyaruko-san
nyc
nychos
nychos and arcimboldo
nychos art aesthetic
nylon
nylon fashion
nylon tights
nymph
nymph in the water
nymphaea
nypd
o - yoroi
o pattern
o you the second of the first
o'neill cylinder
o'neill cylinder colony
oak
oak acorns
oak leaf beard
oak leaves
oak leaves!!
oak parquet
oak tree
oak tree ent
oak trees
oak trees and dry grass
oaks
oasis
oasis in the desert
oasis infront
obama
obama as a samurai
obama as batman
obama as the joker
obama in fortnite
obama in skyrim
obama kissing donald trump
obama prism
obama riding an unicorn
obamna
obelisk
obelisks
obese
obese )
obey
obfuscate light
obi strip
obi wan kenobi
obi-wan kenobi
object
object art
object centered
object concept art
object content
object design
object details
object features
object oriented ontology
object spotlight
objects
objects levitating
objects that float
objects well lit
oblivion
oblivion armor
obnoxious
obscure
obscured
obscured face
obscured hooded person walking
obscured underexposed view
observation deck
observed from afar in the fog
obsession
obsidian
obsidian accents
obsidian globe
obsidian hooves
obsidian metal
obsidian pomegranade
obsidian skin
obsidian slime
obsidian towers in the distance
obsidians skin
obsolete worlds
obunga
obverse and reverse
obviously drunk
oc
oc commission
oc rendered
ocarina of time
ocarina of time movie
ocatane
ocatane render
ocatne render
occasional small rubble
occlusion
occlusion pass
occlusion shadow
occult
occult abiogenesis
occult aesthetic
occult aesthetics alchemy
occult alchemy
occult art
occult cyberpunk
occult details
occult diagram
occult dream
occult inspired
occult jewelry
occult propaganda
occult ritual
occult robes
occult signs
occult stuff
occult symbolism
occult symbols
occult symbols and diamonds
occult symbols hidden
occult symbols present
occultist
ocd
ocean
ocean and rock landscape
ocean background
ocean background setting
ocean caustics
ocean cliff side
ocean cliff view
ocean dept
ocean eyes
ocean floor
ocean giant creature bloop
ocean in distance
ocean in the background
ocean in the distance
ocean of canvas catching fire
ocean on alien planet titan
ocean on planet titan
ocean pattern
ocean pattern and night sky
ocean shoreline on the horizon
ocean simulation
ocean specular
ocean spray
ocean sprites
ocean storm
ocean swells
ocean to the horizon
ocean view
ocean wave
ocean waves
oceanic naval battles
oceans
oceanside
ocher
ocher and turquoise colors
ocher details
ochre
ochre ancient palette
oct
octa 8k
octa core
octae render
octagon render 8k
octain
octain render
octal
octan
octan render
octance render
octane
octane  cinematic
octane - render
octane 1 2 8 k
octane 128k
octane 2. 0 render
octane 3 d
octane 3 d render
octane 3d
octane 3d render
octane 4 k render
octane 4k
octane 4k render
octane 8
octane 8 k
octane 8 k render
octane 8k
octane 8k render
octane art
octane cgsociety
octane cinema 4 d render
octane cinema 4d render
octane engine
octane engine render
octane engine render 8k hdr
octane fender
octane hd 4k
octane high quality render
octane highly detailed
octane highly detailed cinematic
octane highly render
octane lighting
octane photoreal 3 d render
octane photoreal 3d render
octane processing
octane rander
octane ray tracing
octane realphoto
octane redner
octane redshift
octane remder
octane rend
octane rende
octane render
octane render + unreal render
octane render - 8 k
octane render - 8k
octane render - n 6
octane render - n 9
octane render - t
octane render -ar 16:9 h 1024
octane render / source
octane render 1 2 8 k
octane render 1 2 8 k resolution
octane render 128k
octane render 128k resolution
octane render 16k
octane render 3d
octane render 4 k
octane render 4k
octane render 4k photoreal
octane render 8 k
octane render 8 k hd
octane render 8 k hdr
octane render 8 k uhd
octane render 8k
octane render 8k hd
octane render 8k hdr
octane render 8k uhd
octane render : :
octane render : : 3
octane render ]
octane render ] ”
octane render a lonely rainbow
octane render aesethic
octane render aesthetic
octane render and unreal engine
octane render blender 8 k
octane render cinematic
octane render detailed
octane render dynamic lighting
octane render fantasy style
octane render godrays
octane render h 1024
octane render hd 4 k
octane render hd 4k
octane render hdr
octane render hyperdetailed
octane render hyperrealistic
octane render in maya
octane render painting
octane render photorealistic
octane render pinterest
octane render quality
octane render sci - fi
octane render sharp focus
octane render uhd
octane render uhd 4k
octane render unreal engine 8 k
octane render w 1024
octane render ”
octane render!!!
octane render**
octane render. 8 k
octane render. 8k
octane render. by makoto shinkai
octane render. fog
octane render. realistic
octane render.:1
octane rendered
octane renderer
octane renderer. cgsociety
octane renderer”
octane rendering
octane render”
octane rendring
octane renter
octane sharp cinematic
octane tender
octane trending on cgsociety
octane ue 5
octane ue5
octane unreal engine 5
octane unreal render
octane--8k
octane. trending on artstation
octane]
octanerender
octanerender 3 d
octante render
octave
octave render
octave rendey
october
octoberfest
octoberfest poster
octopath traveler
octopath traveller style
octopath voyager
octopus
octopus cat
octopus face
octopus goddess
octopus silhouette at depth
octopus tentacles
octopus wearing a spacesuit
octopus wrestling with a sphere
octorok
octrane render
octsne render
ocult
oculus quest 2
oculus rift
oda non
odd
odd eye
odd lighting
odd pipes
oddities
oddities everywhere
oddly familiar
oddly shimmering
odeith
odeo
odilon
odilon redon. drexler
odin
odin's stone arena background
odins stone arena background
odissey
odontophobia
odst
oesophagus
of
of a 1 7 th century
of a 15th century
of a 17 th century
of a 17th century
of a beautiful
of a beautiful female knight
of a beautiful female warframe
of a beautiful goddess
of a beautiful saryn warframe
of a family leaving a spaceship
of a family standing in a park
of a fox wearing a santa hat
of a full body
of a futuristic selk'nam
of a futuristic selknam
of a futuristic woman selk'nam
of a futuristic woman selknam
of a gang of circus clowns
of a lightsaber hilt
of a lovely
of a muscular
of a old 13th century
of a old 15th century
of a old 16th century
of a old 17th century
of a old 18th century
of a ramlethal valentine
of a river
of a schoolgirl posing
of a shirtless
of a small village with a lake
of a sniper girl in war
of a ufo propulsion system
of a woman
of a young woman
of a youthful japanese beauty
of a youthful japanese girl
of alien artifacts
of an beautiful angel girl
of an beautiful girl
of an cyber gods
of an elden ring elf
of an enigmatic mysterious man
of an evil elf
of an evil tree wizard
of an old man
of anthro leopard warlock
of augean stables
of bamboo
of beautiful angel
of course
of elven queen galadriel
of emma watson
of ethereal fantasy
of harley quinn
of indian princess
of letting go
of life the beginning
of lucifer
of mad men on madison avenue
of of aura hd
of peppa pig
of portrait of justin beber
of romanticism a center image
of space travel
of spiked gears of war skulls
of taiwanese girl with tattoos
of the cat in the hat
of the game portal
of the muscular
of totoro
of virtua figther
of walter white
off - putting
off - road
off - white
off - white collection
off - white style
off camera flash
off center
off putting
off the shoulder shirt
off white
off-camera flash
off-roading
off-white
off-white plated armor
off-world
offering a plate of food
offering the viewer a pill
offhand
office
office background
office building
office ceiling panels
office clothes
office cubicle background
office cubicles
office furniture
office interior
office room interior
office/thrift store/social hall
officer
officers uniform
offices
official
official anime artwork
official anime key media
official anime key visual
official anime media
official anime still
official art
official artbook
official artwork
official artwork hdr
official character art
official character illustration
official concept art
official courthouse
official dior editorial
official documentation
official fan art
official fanart
official fanart behance hd
official government photo
official illustration
official jil sander editorial
official judge dredd fanart
official marvel media
official media
official modern animation
official modern anime
official music video
official overwatch game art
official photo
official photo portrait
official photos
official portrait
official poster
official poster artwork
official prada editorial
official print
official product image
official product photo
official render
official screenshot
official simpsons movie artwork
official splash art
official store photo
official studio anime still
official studio ghibli still
official trailer
official valentino editorial
official versace editorial
official vogue editorial
official vuitton editorial
official white house portrait
offputting
offset
offset and takeoff
offset lithography
offset lithography print
offset photography
offset printing technique
offshore winds
offwhite
oft painting
often described as flame-like
og
oganic rippling spirals
ogre
ogre chef in an apron
ogre-ish jon taffer!!! in a bar
ogre-ish jon tafferin a bar
oh
oh lawd he coming
oh no
oh the places you'll go
oh the places youll go
oh yeah
ohararyu
ohio
ohrai noriyoshi
oil
oil - painting
oil and acrylic on canvas
oil and alien on canvas
oil and canvas
oil art
oil canvas
oil canvas of lucifer
oil canvas painting
oil colors
oil dereks on horizon
oil digital painting
oil glaze
oil glazing
oil in canvas
oil in canvas style
oil inks
oil like flowing
oil lines
oil on aged tin
oil on board
oil on canevas
oil on canva
oil on canvas
oil on canvas (1921)
oil on canvas (1921)”
oil on canvas 1921
oil on canvas 4k
oil on canvas 8 k
oil on canvas 8k
oil on canvas big brushstrokes
oil on canvas detailed
oil on canvas high angle view
oil on canvas intricate
oil on canvas on geometric rock
oil on canvas painting
oil on canvas sharp
oil on canvas. cinematic
oil on canvas; nuclear fallout
oil on canvas”
oil on linen
oil on oak wood
oil on panel
oil on the canvas
oil on water
oil on wood
oil paining
oil paint
oil paint and spray paint
oil paint concept art
oil paint impasto relief
oil paint impasto reliefs
oil paint on canvas
oil paint style
oil painted
oil painting
oil painting 4 k
oil painting 4k
oil painting a bigger splash
oil painting award winning
oil painting brush
oil painting dnd
oil painting from the 1980s
oil painting h 768
oil painting hue
oil painting hyper real
oil painting in a modern style
oil painting marks
oil painting masterpiece
oil painting of an overpopulated
oil painting of breakfast
oil painting of cat
oil painting of cat witch
oil painting of dragon
oil painting of dragon forest
oil painting of princess vulvine
oil painting of realistic woman
oil painting of whiskey bottle
oil painting on canvas
oil painting on matte canvas
oil painting photo real
oil painting portrait
oil painting rebrandt
oil painting style
oil painting with brushstrokes
oil painting without frame
oil painting ”
oil painting!!!
oil painting. hd
oil paintings
oil painting”
oil paints
oil paiting
oil panting
oil panting on canvas
oil pastel
oil pastel gestural lines
oil pastels
oil pastels and gold
oil portrait
oil rig
oil slick
oil slick colors
oil slick hair
oil slick in the water
oil slick multicolored hair
oil slick nebula
oil spill
oil spills
oil to waist
oil-on-canvas
oil-painting
oild painting
oiled
oiled hardwood floors
oiled skin
oilfield scene
oill painting
oilpaint
oilpainting
oilpunk
oils
oils on canvas
oily
oily high relief
oily puddles
oily sheen
oily skin
oily substances
oizys
okabe rintarou
okami
okata kazuto
okay
okay one fear
okinawa churaumi aquarium
okinawa japan
okita sougo
oklahoma
oktane
oktane render
okuda
okuda sam miguel
olafur eliasson
olan mills studio
oland
olbivion
olchas logan cure liang xing
old
old - school dark fantasy art
old 3d graphics
old abandoned building
old abandoned car sinking
old abandoned house
old abbey in the background
old american midwest
old and wrinkled
old and young
old apartment
old artbook
old asian village
old black and white photo
old bolt action rifles
old book
old book chained to the wrist
old books and dip pen
old bounty hanter
old brick walls
old bridge
old building
old buildings
old cabin
old camera
old car
old castle
old cemetery
old cgi 3 d rendered bryce 3 d
old cgi 3d rendered bryce 3 d
old chairs
old charismatic mechanic
old chines painting
old cinema
old city
old clothing
old cmputers on the sidewalk
old color photo
old color photograph
old comics in city
old computer
old computer monitor
old computers
old copper pipes
old couch
old cowboy in the arctic
old cyborg merchant
old damagaed polaroid photo
old dark forest
old dhaka
old distorted camcorder video
old english
old english garb
old experimentation cabinet
old fashion
old fashioned
old film
old film overlay
old film photo
old footage
old furniture
old furnitures
old gigachad with grey beard
old god
old gods
old gothic crypt
old gray hair
old greek goddess
old growth forest
old hollywood
old hollywood themed
old home decor
old house
old humanoid ents
old internet art
old japanese art
old japanese street market
old jeddah city alley
old jeddah city street
old jedi master
old john lennon
old kitchen backdrop
old kitchen backdrop angled view
old lady
old lady cyborg merchant
old lady screaming and laughing
old library
old lumber mill remains
old magazine cover without text
old male
old male archmage
old male warlock
old man
old man doing hard work
old man doing with mask
old man in tokyo at night
old man portrait
old manuscript
old manuscripts and scrolls
old master
old master painting
old masters
old masters light composition
old mexican magician closes eyes
old moist carpet
old movie
old movie scene
old mystic ruins
old oil painting
old painting
old paints
old paper
old parchment
old pawn shop
old photo
old photo camera
old photo from the 1930s
old photo of a creepy landscape
old photo scattered
old photo style
old photo width 768
old photobook
old photograph
old photography
old picture
old pirate ship
old polaroid
old polaroid photo
old polaroid photography
old retro museum exhibition
old retro pulp
old retro pulp noir comic cover
old roman style
old ruins
old ruins tower
old rusty pickup - truck
old rusty pickup-truck
old scars
old school
old school fpr
old school runescape
old scientific document
old scientific documents
old script
old scroll
old scuba
old sepia photography
old shops
old signs
old sketch
old skin
old stone bridge over the creek
old stone steps
old stone wall
old style
old style photo
old tape
old testament
old testament angel
old timey
old tintype
old town
old town mardin
old tree
old trees
old tv
old tv and radio hardware
old village
old village in the distance
old vintage paper
old vintage photo
old vintage vhs
old war poster
old weathered paper
old west
old west town
old wetplate daguerreotype
old wizard robe
old woman
old wood
old wood floors
old wooden ship
old wool suit
old world
old yearbook photo
old-fashioned tarot card
older brother vibes
older male
older sister vibes
older woman
oldman with mustach
oldwest
ole thomas style
oled
oled 8k
oled lights in corners
oled visor for eyes
oled visor over eyes
oleg bulakh
oleg korolev
oleg zherebin
oleo
oleo painting
olga buzova
olga kurylenko
olga zakharova
oli on painting
olive
olive garden
olive green
olive green and venetian red
olive green slacks
olive oil
olive skin
olive skin color
olive skinned
olive thigh skin
olive tree
olive trees
oliver cook
oliver vernon
olives
olivia
olivia culpo
olivia culpo as milady de winter
olivia de bernardinis
olivia kemp
olivia newton-john
olivia pope
olivia wilde
olivier ledroit
olmsted
olya bossak
olympian god
olympic diving springoard
olympics
olympics ceremony
olympics event 1930's
olympics event 1930s
olympics footage
olympus
olympus platform
omaha beach
oman
omar ortiz
omar shanti himalaya tibet
ombre
ombre fishnet gown
ombre purple and pink hairstyle
ombre velvet gown
omega
omen from valorant
omg
omina tachibana
ominious
ominous
ominous and eerie forest
ominous and intense
ominous apocalyptic background
ominous assassin
ominous atmosphere
ominous background
ominous beautiful mood
ominous clouds
ominous creature hiding detailed
ominous dark background
ominous depths
ominous dramatic wide angle
ominous evening
ominous figure in the background
ominous foggy environment
ominous gothic aesthetic
ominous lighting
ominous mist
ominous mood
ominous neon lighting
ominous night mist
ominous nighttime storm
ominous photo
ominous red cumulonimbus clouds
ominous skies
ominous sky
ominous space battle background
ominous tone
ominous vibe
ominous vibes
ominous yet melancholic
ominous! landscape of north bend
ommatidia
omni magazine
omni man vs invincible
omnidimensional
omnious
omnious atmosphere
omnious background
omnious intricate
omnious sky
omnious style
omnipotent
omnipresent ai god
omniscient
omniverous layered teeth
omoide emanon
omori
on
on 16k
on a advanced lab
on a alien planet
on a balcony
on a baroque throne
on a battle field
on a battlefield
on a beach
on a birdge
on a black background
on a black wall
on a boat
on a boat on a lake
on a botanical herbarium paper
on a branch
on a bridge
on a bright day
on a candle holder
on a canva
on a checkered floor
on a clear magnificent night sky
on a clear night sky
on a clearing
on a cliff
on a cloudy day
on a coffee table
on a couch
on a crowded space station
on a dark background
on a dark desert highway
on a dark rock background
on a dark swampy bsttlefield
on a dark winter's day
on a desert planet
on a desert road
on a desk
on a desolate plain
on a far planet
on a farm
on a flat color black background
on a floating rock island
on a football field
on a futuristic shopping mall
on a galaxy looking background
on a gray background
on a great neoclassical square
on a green hill
on a green hill between trees
on a green lawn
on a gundam
on a hill
on a horse
on a hot australian day
on a jungle forest
on a jungle forest train track
on a kayak in a forest
on a lake
on a landing pad
on a large marble wall
on a lush fertile alien planet
on a mannequin. high quality
on a mannequin. high resolution
on a mannequin. studio lighting
on a marble pedestal
on a mini world
on a misty forest
on a mosaic marble floor dais
on a mountain
on a notebook page
on a pale background
on a parking lot
on a pedestal
on a pirate ship
on a pirate ship background
on a planet of lush foliage
on a planet of maelstrom
on a plate
on a plate in a busy diner
on a poststamp
on a racetrack
on a rainy day
on a red background
on a reflective gold plate
on a remote desert planet
on a remote planet
on a riverbank
on a road
on a rock
on a rooftop
on a rough wooden dungeon table
on a sidewalk of vancouver
on a soccer field
on a space station
on a spaceship
on a stage
on a stained glass window
on a street
on a street at night
on a street race track
on a sumptuous tablecloth
on a sumptuous velvet tablecloth
on a sunny beach
on a sunny day
on a super yacht
on a table
on a throne of crystals
on a throne of gemstones
on a tree
on a velvet table cloth
on a velvet tablecloth
on a village
on a wall
on a wet london street
on a white background
on a white table
on a wooden desk
on a wooden plate
on a wooden table
on a wooden tray
on a yacht at sea
on a yellow canva
on a yellow paper
on a young beautiful woman neck
on aircraft carrier
on alien planet
on all fours
on amino
on an alien grassland
on an alien planet
on an empty moonlit hill
on an empty stage from above
on an epic intricate oroboros
on an epic oroboros
on an icy throne
on an indian street
on an island
on an operating table
on another planet
on artstastion
on background red lake on fire
on beach
on beyond zebra
on black background
on black paper
on blue fire
on canvas
on center
on cg society
on clear background
on clouds
on cosmic cloudscape
on dark paper
on deep black velvet
on deep forest peak
on desk
on display
on display in a fossil museum
on display in a museum
on display in the louvre
on display ”
on display”
on dune
on edge
on exoplanet
on fire
on flickr in 2 0 0 3
on flickr in 2007
on forest jungle path
on forest path
on future tokyo night rooftop
on grey background
on grey paper sketch ink style
on ground
on hasselblaad
on her knees
on her throne
on high-quality paper
on his hind legs
on his left hand
on his right hand
on interstellar space
on island
on its own
on jungle night !!!
on kitchen table
on knees
on liberty island
on loan from louvre
on location
on madison avenue
on mahogany desk
on mars
on medium grade paper
on moon
on my bed
on ocean
on old paper
on old parchment
on old parchment paper
on paper
on parchment
on path to enlightenment
on pentax 67
on pinterest
on planet jupiter
on ps5
on realistic hud
on rock concert stage
on rooftop
on rooftop tokyo night
on set
on ship
on sidewalk
on simple background
on space harbor
on space station
on stage
on stilts
on textured base; store website
on textured disc base
on the african plains
on the altar
on the art contemporary art
on the battlefield
on the beach
on the beach at noonday
on the beach at sunset
on the beach during sunset
on the bed
on the bow
on the calm lake
on the calm lake surface
on the coast
on the concrete ground
on the cover of a magazine
on the deck of a sailing ship
on the deck of a ship
on the desert
on the field
on the frozen danube
on the ground
on the moon
on the mountain
on the nostromo
on the ocean
on the ocean water
on the orange surface of mars
on the path to enlightenment
on the qt
on the runway
on the sand
on the sea
on the side of the road
on the sidewalk
on the stage
on the street
on the streets
on the surface of an asteroid
on the surface of mars
on the surface of the moon
on the surface of the ocean
on the tennis coat
on the top of a hill
on the top of a mountain
on the vast wheat fields
on the walls
on the white background
on tokyo cyberpunk night rooftop
on top of a hill
on top of a mountain
on top of it
on trees
on tv
on vellum
on white
on white background
on white paper
on wild west
on wood
on wooden table
on yellow paper
once
once upon a midnight dreary
once upon a time
oncept art
one
one - eyed man
one - eyed monster
one - line drawing
one a demon - like creature
one a demon-like creature
one angel
one apple and one onion
one arm of the robot body
one big beak
one big inkblot on the paper
one black
one blimp in the distance
one blonde
one blonde and one brunette
one blue ) and huge bat ears
one broadsword
one car
one caveman is cowering in fear
one ceiling fan
one character
one contrasting small feature
one day
one disassembled
one dog
one eye
one eye closed
one eye is read
one eye red
one eye wink
one eyebrow
one face
one famous person
one fist raised high in triumph
one foot raised off the ground
one galaxy
one giant oak
one girl
one girl has white hair
one green
one green eye and one blue eye
one head
one head only
one head portrait
one hit wonderland
one holds apple in hand
one house
one ice cube
one is a brunette
one is a redhead
one is blond
one is red
one large room
one legged amputee
one light
one line
one line art
one line artwork print
one line drawing
one man
one man human
one man is blond
one man vampire
one million broken glass shards
one million lave rivers
one model
one motorbike in center of frame
one mouth
one nose
one object
one object content
one of his eyes is scratched
one of the weavers of destiny
one on each side
one onion ring
one panel
one panel comic
one person
one person in frame
one person only
one piece
one piece artstyle
one piece ship sailing
one piece style
one pixel brush
one point light
one point lighting
one point lightning
one point perspective
one punch man
one punch man manga
one purple eye
one rainy day
one raised eyebrow
one red tomato slice
one reptilian eye
one ring to rule them all
one sandwich with fried tofu
one side haircut
one single continues line
one single gas lamp
one single head
one single lamp
one single man in a hazmat suit
one single mask
one single tribe member
one small boat
one subject
one thing
one thousand years of longing
one tomato slice
one tree
one woman
one yellow and one blue eye
one-eyed
one-line drawing
one-point perspective
oneill cylinder
oni
oni horns
oni mask
onimous
onion
onion man
onion man portrait
onions
oniric
online
online casino logo
onlookers
only
only a few bangs of hair
only a mouth with long
only art
only black and white
only dark colours
only eyeball
only face
only five fingers
only head
only head and chest
only head and shoulders
only hope
only one character
only one face
only one head
only one head single portrait
only one person in frame
only one robot kid on the ground
only red colour
only snow i the background
only snow in the background
only the head
only two hands
only visited during dreams
only with blue
only with red
onmyoji
onmyoji detailed art
onmyoji portrait
onscreen info and labels
onsen
onsens
onstudio ghibli
onyx
onyx bracelets
ooak
ooh
oompa loompa virus
oona chaplin
oops
ootd
ooze
oozing
oozing bile )
oozing black goo
oozing blood
oozing lava
oozing pustules
oozing slime
oozium
op art brain
op art with big bold patterns
opal
opal crystals
opal diamond
opal flesh
opal petals
opal statues
opal statues adorned in jewels
opalescence
opalescent
opalescent mist
opalescent night background
opalescent palace
opaque
opaque glass
opaque visor
opart
open
open back dress
open bank vault
open belly
open book page
open books
open ceiling
open door
open eye freedom
open eyes
open field
open for commissions
open hand
open happy mouth
open jacket
open landscape
open magic book glowing
open metal maw
open mouth
open mouth and big teeth
open neck collar
open negative space
open ocean
open office
open palm
open peat fire in center of room
open plains
open plan
open portal to another dimension
open road
open robotic eyes
open room
open shiny floor
open shirt
open skies
open sky
open space
open street maps
open synthetic maw
open top
open v chest clothes
open vault
open wall architecture
open wide eyes
open window
open window at night
open window ib background
open windows
open wings
open world
opened dress
opened mouth
opengl
opening
opening a shining portal
opening door
opening scene
opening shot
opening third eye
opera
operating on burgers
operating room
operation
operation highjump
ophanim has bird wings
ophelia
oppai
oppai cyberpunk
oppai proportions
opportunistic expression
opposite of urban sprawl
opposite the lift-shaft
oppression
oppressive
oppressive atmosphere
oppressive lighting
oprah okra winfrey sentient veg
optane render
optic ripple
optical art
optical caustic lighting
optical caustics
optical fiber
optical flare
optical illusion art
optical illusion woodblock print
optical illusions
optical lens flare fx
optical lenses
optical mineralogy
optics
optimism
optimist future
optimistic
optimistic colors
optimistic future
optimistic matte painting
optimistic smile
optimization 4k 8k ultra hd
optimus prime
optimus sun orientation
optix
opulence
opulent
opulent pearl necklace
opus francigenum
opus tesellatum
or black
or perhaps a fairy tale
oranate and brooding
orange
orange - haired anime boy
orange / pink sky
orange and black
orange and black tones
orange and blue
orange and blue color scheme
orange and blue colors
orange and blue sky
orange and blue tones
orange and brown leaves for hair
orange and cyan lighting
orange and cyan paint decals
orange and green power
orange and orange slices
orange and purple color scheme
orange and purple electricity
orange and red lighting
orange and red sky
orange and teal
orange and teal color
orange and turquoise
orange and turquoise and purple
orange and turquoise ans purple
orange and white
orange and white color scheme
orange and yellow costume
orange backgorund
orange background
orange balaclava
orange blooming flowers garden
orange body
orange braided hair
orange candle flames
orange cat
orange clouds
orange color
orange color scheme
orange color theme
orange color tone
orange colors
orange dawn
orange demons
orange details
orange electricity
orange extremely coherent
orange eyes
orange fire/blue ice duality!
orange flowers
orange flowing hair
orange fluffy belly
orange fluffy spines
orange fog
orange fur
orange fuzzy muppet
orange gas giant
orange gi
orange glow
orange glowing hair
orange gradient
orange grass
orange grey white
orange hair
orange hair bow
orange halo
orange halo around her head
orange halter top
orange head
orange highlights
orange hoodie
orange hue
orange jacket
orange jumpsuit
orange lamp
orange light
orange lighting
orange lights
orange line
orange lit sky
orange metal ears
orange minerals
orange mist
orange neon
orange neon backlighting
orange neon stripes
orange palette
orange pastel colors
orange planet
orange plants
orange pupils
orange purple and gold ”
orange racing stripes
orange ray
orange red black white
orange reflective puffy coat
orange ribbons
orange robe
orange rocks
orange roof
orange safety labels
orange safety vest
orange shoulder pads
orange skies
orange skin
orange skin and long fiery hair
orange skin. intricate
orange sky
orange slices
orange spike aura in motion
orange subsurface scattering
orange sun set
orange sunglasses
orange sunset
orange teal lighting
orange theme
orange tint
orange to blue gradient
orange tone
orange tones
orange yellow
orange yellow ethereal
orange: 0.5
oranges
oranges floating in the water
orangutan
orantalist
orazio gentileschi style
orb
orb of agamento
orbit of earth
orbit soviet city
orbital
orbital graphical lines
orbital rings
orbital station
orbiting a gas giant
orbiting a gas giant planet
orbiting a strange planet
orbiting earth
orbiting space ships
orbs
orc
orc fused with vines
orc looking into the camera
orc male readies his rifle
orc merged with vines
orc on vines
orc themed
orc village
orc warrior
orchestra
orchid
orchid made of mother of pearl
orchid stems
orchids
orcs
orcs lord of the rings
order
order now
ordered
orderly
ordinary
oregon
oregon trail
orelsan
organ harvesting
organic
organic 8k artistic photography
organic acrylic flow art
organic and intricate
organic and robotic
organic architecture
organic armor
organic biomass
organic buildings
organic ceramic fractal forms
organic ceramic white
organic color
organic crystals
organic cyborg
organic design
organic detail
organic dress
organic flesh
organic flowing background
organic form
organic form ”
organic forms
organic fractal structures
organic growth
organic headpiece
organic horror
organic ink
organic isometric design
organic lines
organic liquid metal
organic liquid textures
organic matte painting
organic matter
organic mechanical shapes
organic mushroom lichen moss
organic ornament
organic ornaments
organic painting
organic polycarbon
organic rippling spirals
organic seductive geisha
organic shape
organic shapes
organic steel
organic structure
organic swirling igneous rock
organic texture
organic transistors
organic volumetric lights
organics
organism
organized
organized composition
organized composition!
organs
orgnic headpiece
ori
ori and the blind
ori and the blind forest
ori and the will of the wisps
ori toor
orianna
oriental
oriental art nouveau
oriental carpets
oriental face
oriental fantasy
oriental scene
oriental tattoos
oriental wallpaper
orientalism
orientalism painting
orientalisme
orientalist
orientalist painting
origami
origami crane drawings
origami studio 3 design
origin 100i
origin jumpworks
original
original and creative landscape
original artwork
original chibi bunny girl
original modern artwork
original post impressionist art
original simpsons cartoon style
original sin
original style
orisa from overwatch
orisha
ork
orkish sword
orkney islands
orlando bloom as legolas
ornament
ornament crown
ornamental
ornamental aesthetics
ornamental aesthetics intricate
ornamental armor
ornamental arrows
ornamental bone carvings
ornamental bow
ornamental edges
ornamental gothic - cyberpunk
ornamental halo
ornamental rober
ornamentation
ornamented
ornamented armor
ornaments
ornat concept art illustration
ornate
ornate and detailed
ornate and elegant
ornate and flowing
ornate and hyper detailed
ornate and intricate
ornate and intricate backdrop
ornate and intricate jewelry
ornate architecture
ornate armor
ornate armor covered in thorns
ornate armour
ornate art
ornate art nouveau detail
ornate attire
ornate back tuxedo
ornate backdrop
ornate background
ornate bikini armor
ornate black tuxedo
ornate border
ornate border frame
ornate borders
ornate borders + concept art
ornate carved architecture
ornate city ruins
ornate cloak
ornate clothing
ornate colored gems
ornate colored gems and crystals
ornate complexity
ornate cosplay
ornate crown
ornate crystal crown hood
ornate cyberpunk armor
ornate cyberpunk interior
ornate cyberpunk robes
ornate dark green clothing
ornate dark red opulent clothing
ornate dashing red garments
ornate declotage
ornate decoration
ornate decorative background
ornate designs on desk
ornate detail
ornate detailed background
ornate detailed hair
ornate details
ornate dining hall
ornate dramatic bat wing helmet
ornate dress
ornate egg
ornate exoskeleton
ornate filigree armor
ornate floral
ornate flower design
ornate flowers
ornate flowing robe
ornate flowing robes
ornate flowing silvered robes
ornate frame
ornate french architecture
ornate frilly dress
ornate frilly regal shirt
ornate furniture
ornate galactic gold
ornate gem in pommel
ornate gems
ornate geometric patterns
ornate giger background
ornate gilded cosmic machine
ornate gilded details
ornate gilded medieval icon
ornate gold
ornate gold border
ornate gold crown
ornate golden background
ornate goth dress
ornate gothic armor
ornate gothic baroque spikes
ornate gown
ornate hair
ornate headdress
ornate headpiece
ornate heavy plate armour
ornate hospital room
ornate intricate
ornate intricate crown
ornate iron armour
ornate jeweled crown
ornate jewelry
ornate jewels
ornate korean polearm behind her
ornate long blond hair
ornate long flowing blonde hair
ornate mask
ornate mask and fabrics
ornate medieval religious icon
ornate metal gold headpiece
ornate metallic helmet
ornate painting
ornate palace made of green
ornate pattern
ornate patterned people
ornate patterns
ornate piracy
ornate poncho
ornate portrait
ornate retreat
ornate robes
ornate royal gown
ornate royal robes
ornate sea background
ornate silver armour
ornate spikes
ornate spiral stairs
ornate steel armour
ornate supreme demon overlord
ornate tentacles growing around
ornate tiled architecture
ornate turban
ornate turkic palace background
ornate white and gold armour
ornate with diamonds
ornate with fiery explosions
ornate with gold trimmings
ornate with white diamonds
ornate wood
ornated
ornately detailed
ornately dressed
ornithopter
orphism style
orrery
orson welles
orthochromatic look filter
orthodox
orthodox christianity
orthodox cyberpunk
orthodox icon
orthodox icon neo-gothic
orthodox icons
orthodox mosaic
orthodox saint
orthodox saint christopher
orthodox symbolism diesel punk
orthodoxy
orthogonal
orthogonal perspective
orthographic
orthographic 3d rendering
orthographic front view
orthographic perspective
orthographic projection
orthographic view
orthographic views
orthoview
ortografico
ortographic
orton effect
orton effect intricate
orwellian
orwellian dystopia
orwellian style
oryantalist
osaka skyline background
osama bin laden funko pop
osborne macharia
oscar
oscar award winning
oscar de la renta
oscar niemeyer
oscar nominated cinematography
oscar the grouch
oscar winner
oscar winning
oscar winning animation
oscar winning graphics
oscilloscope
osiris
osl
osr
ossdraws
ossuary
ostrich feathers
otaku
otaku gangasta
otcean render
other bedrooms can be seen
other dimensions
other mother
other smaller buildings
other stuff
other women dancing behind
other worldly
other worldly colors
other worlds
otherland
otherwordly
otherwordly concept
otherwordly futuristic city
otherworldly
otherworldly fantasy
otherworldly technology
otherworldly visuals
otherworldy
otjize
otl archer
otomo
otomo manga
otorhinology
otoy octane
ots shot
otter
otto dix and ryohei hase
otto mueller
otto preminger
otto schmidt
ottoman empire
ottoman empire era
ottoman miniature style
ottoman sultan
ottoman sultanate
ottoman sultanate space marines
otzi
ouch
ouchh and and innate studio
oud
our art corner
our desperation
our planet
ouroboros
out - of - focus background
out in the dark
out of body
out of body experience
out of business
out of focus
out of focus background
out of place
out of this world
out of time and space
out or focus
out worldly colours
out-of-focus background
outback
outdoor
outdoor art installation
outdoor campfire pit
outdoor design
outdoor fairgrounds
outdoor fine art photography
outdoor fine photography
outdoor lighting
outdoor magazine
outdoor photo
outdoor rave
outdoor scene
outdoor staircase
outdoors
outdoors at night
outdoors business portrait
outdoors european cityscape
outdoors lighting
outdoors mesa setting
outdoors ruined cityscape
outdoors setting
outdoors tropical cityscape
outer glow
outer space
outer space environment
outer space is visible
outer space nebula background
outer wilds
outer wilds screenshot
outer worlds
outerspace
outfit
outfit : jeans and white vest
outfit design
outfit designs
outfit photo
outfit photograph
outfit-of-the-day
outfit: cop
outher worldly colours
outlast
outlast 2
outlast game
outlaw
outline
outline art
outline drawing
outline glow
outline glow lens flare
outline sketch
outlined
outlined art
outlined hand drawn
outlined silhouettes
outlined!!!
outlines
outlive streetwear collection
outputs
outrageously fluffy
outram
outrun
outrun art style
outrun color palette
outrun style
outrun style and colours
outside
outside a saloon
outside a storm rages
outside alone smoking weed
outside enclosure
outside in a farm
outside in parking lot
outside in snow snowing
outside in space
outside intricate
outside large window of ship
outside lighting
outside of time and space
outside on the ground
outside the school of magic
outside the simulation
outside the window a storm rages
outside the'school of magic '
outside theschool of magic
outside view
outside winter landscape
outsider
outsider art
outsider art style
outsider art!!!
outstanding
outstanding detail
outstanding epic
outstretched wings
outter space
outter worlds
outward slanted eyes
outworldly colours
oval eyes
oval face
oval face shape
oval office
oval shape face
oval shaped face
oven
over - detailed
over - the - shoulder - shot
over a calanque
over a chalk cliff
over a cliff
over a dish and over a table
over detailed art
over exposure
over grown botanical garden
over his shoulder
over it's head
over looking saturn
over proportion
over saturated colors
over the garden wall
over the head of a sea wolf
over the hills
over the horizon
over the ocean
over the shoulder
over the shoulder closeup
over the shoulder perspective
over the shoulder point of view
over the shoulder shot
over the shoulder view
over the tree tops
over vivid dark wood table
over water
over-detailed
over-detailed art
over-knee boots
over-shoulder shot
over-the-shoulder shot
over-the-shoulder-shot
overall architectural design
overalls
overalls and a white beard
overbearing
overblown
overcast
overcast bokeh
overcast bokeh - c 5
overcast bokeh - c 8
overcast color scheme
overcast dawn
overcast day
overcast dusk
overcast flat midday sunlight
overcast gray skies
overcast lake
overcast lighting
overcast mood
overcast skies
overcast sky
overcast weather
overcast! cinematic focus
overcast!!!
overcast!!! cinematic focus
overcastcinematic focus
overclocking
overcomplicated
overcrowded
overdetailed
overdetailed art
overdetailed art. dslr
overdetailed digital art
overdetalized
overdimensional
overdone
overdose
overecast
overexposed
overexposed flash
overexposed photograph
overexposed sunlight
overexposure
overflowing
overflowing energy
overflowing feast buffet table
overflowing with baked beans
overflowing with diapers
overglaze
overgrown
overgrown background
overgrown cave
overgrown city
overgrown environment
overgrown foliage
overgrown forest
overgrown garden
overgrown garden environment
overgrown grass
overgrown greenery
overgrown in a thick forest
overgrown ivy plants
overgrown jungle
overgrown jungle environment
overgrown jungle ruins
overgrown lush plants
overgrown mall
overgrown place
overgrown plants
overgrown ruins
overgrown spamp
overgrown stone cave
overgrown swamp
overgrown trees
overgrown vegetation
overgrown vines
overgrown with aquatic plants
overgrown with colorful coral
overgrown with exotic fungus
overgrown with flowers
overgrown with funghi
overgrown with huge ferns
overgrown with huge rare fungus
overgrown with lush plants
overgrown with lush vines
overgrown with moss
overgrown with orchids
overgrown with plants
overgrown with puffy orchids
overgrown with shiny blobs
overgrown with thick orchids
overgrown with trees
overgrown with vegetation
overgrown with vines
overgrown with weeds
overgrowth
overgrowth of grass
overgrowth. by makoto shinkai
overhanging branches
overhead
overhead angle
overhead birdseye view
overhead canopy
overhead ground floor
overhead light
overhead lighting
overhead photography
overhead shot
overhead sun
overhead view
overhead view of a table
overhead wires
overheard camera view of kitchen
overjoyed
overknee socks
overlaid
overlaid sacred geometry
overlaid with aizome patterns
overlaid with arabic text
overlaid with chinese adverts
overlaid with chinese text
overlaid with cyrillic words
overlap perspective
overlapping
overlapping layers
overlay
overload
overlooking
overlooking a dark street
overlooking a desolate wasteland
overlooking a modern city
overlooking a valley
overlooking a valley with trees
overlooking a vast serene forest
overlooking martian landscape
overlooking river aufidius italy
overlooking sf from twin peaks
overlooking the beach
overlooking the ocean
overlord
overlord billie eilish
overlord season 4
overlord!!!
overlords
overly detailed textures
overpass
overpopulated
overpowered
overpowering
oversaturated
oversaturated lens flair
overshadowing kim jong-il
oversharpened
oversized
oversized brown mustache
oversized emerald eyes
oversized enginee
oversized hoodie
oversized pauldrons
oversized shuriken
oversized_hindquarters
overturned chalice
overturned ornate chalice
overview
overwatch
overwatch art team
overwatch building
overwatch character
overwatch character concept art
overwatch design
overwatch fanart
overwatch game
overwatch inspired
overwatch skin
overwatch splash art
overwatch style
overwatch tracer
overwatch tracer character
overwatch tracer in a bikini
overweight
overweight!! teenage boy
overwhelming
overwhelming depth and detail
overwhelming energy
owen gent
owen klatte
owl
owl crown
owl feathers
owl helmet
owl mask
owl princess with crown
owl studio
owl studio and j.dickenson
owl vector polygon geometric
owl wearing black biker jacket
owl wizard
owlbear
owls
owlship
owo
owsley
ox
oxygen mask
oxygen tank
oyama kojima
oyasumi punpun
oysters
oz
oz series
ozabu
ozymandias
ozzy osbourne as batman
p
p cattaneo
p. a. works
p. craig russell
p. j. n
p.a.works
p90
pa works
pablo amaringo
pablo carpio and jordan grimmer
pablo dominguez
pablo hurtado de mendoza
pablo olivera
pablo perdomo
pablo picasso and mark brooks
pablo picasso painting
pablo picasso. graffiti art
pacal votan
pacific northwest
pacific northwest coast
pacific rim
pacific rim jaeger
pacific-rim-mech in background
pacing
pacing in server room
pack
package cover
packaging
packaging award
packaging design
packaging of the world
packshot
pacman
padded shoulders jacket
paddle and ball
paddle of water
paddy boehm
paddy fields and river flowing
padlocks
padmasana
padme amidala
pagan
pagan goddess
pagan occultism
pagans
page
page from initial d manga
page scan from book
pages
pagoda
pagoda with a lot of wind chimes
pagodas on hills
paid art assets
paid artwork
pain
pain and torment
pain from naruto
pained expression
painful
painful vibe
painfully adorable
paining
painitng
painstaking detail
paint
paint brush strokes
paint drip
paint dripping
paint drips
paint drips liquid wax
paint drops
paint flecks
paint is falling off
paint knife
paint on black velvet canvas
paint on canvas
paint peeling off the walls
paint pour
paint pour smoke
paint smeared with palette knife
paint smears
paint spill
paint splash
paint splashes
paint splashes and splatter
paint splatter
paint splatters
paint strokes
paint style
paint swirl aesthetic
paint swirls
paint swirls and phyllotaxis
paint texture
paint tool sai
paint tool sai!! blue
paint tool saiblue
paint tubes
paint-on-glass painting
paintball world cup
paintbrush
paintbrush and palettes
paintchips
painted
painted action figure
painted as a game concept art
painted black
painted by andreas rocha
painted character portrait
painted digital art
painted face
painted illustration poster
painted in 1530
painted in acrylic
painted in anime painter studio
painted in bright water colors
painted in high resolution
painted in oil colours
painted in the style arcane
painted in zbrush
painted landscape
painted look
painted marble sculptures
painted metal
painted metal and glass
painted nails
painted on a brick wall
painted on a giant wall
painted on silk
painted overalls
painted pale yellow and green
painted portrait
painted portrait of mordekaiser
painted portrait of rugged odin
painted portrait of rugged zeus
painted texture
painted texture maps
painted todd lockwood
painted tower of the moon
painted wargaming miniature
painted warhammer 40k miniature
painted warhammer miniature
painted with a palette knife
painted with a thick brush
painted with a thin brush
painted with colour on white
painted with oil
painted with pastels
painterly
painterly brushwork
painterly concept art
painterly digital art
painterly feeling
painterly humanoid mecha
painterly illustration
painterly portrait
painterly style
painterly texture
painters
painting
painting a canvas
painting a self portrait
painting alphonse mucha
painting art
painting art work
painting big bees flying around
painting by android jones
painting by dan mumford
painting by hieronymus bosch
painting by john howe
painting by simon stalenhag
painting come to life
painting for a wall
painting hr giger tent in a room
painting illustration
painting in luminist style
painting in the museum
painting in the style of ed mell
painting in the style of renoir
painting ivan shishkin
painting of
painting of a cat
painting of a dreamscape
painting of a forest
painting of a hummingbird
painting of a knight
painting of a man
painting of a room
painting of a sand landscape
painting of a woman
painting of an undercover cup
painting of angela merkel
painting of ariana grande
painting of beautiful
painting of c - 3 p 0
painting of cute dog
painting of donald trump
painting of flood waters
painting of goliath
painting of landscape
painting of landscape of faroe
painting of lavender field
painting of mehrangarh fort
painting of mona lisa
painting of one health potion
painting of ornate space ship
painting of samarkand
painting of splashing water
painting of the beatles
painting of white human figures
painting of wild hunt in the sky
painting oil
painting oil on canvas
painting on a badge
painting on a badge!!!!
painting on a canvas
painting on black canvas
painting on canvas
painting on grey scale face
painting on silk
painting on the ceiling
painting style
painting vladimir volegov
paintings
paints
paints mixing
painttool sai
painttoolsai
pair of keycards on table
pair of keys
paisible night lighting
paisley
paisley wallpaper
paiting
palace
palace background
palace dance
palace floating in heaven
palace floating in the sky
palace of the chalice
palace of versailles
palace on top of the hill
palace ， a girl in hanfu
paladin
paladin armor
paladin golden armor
paladin!
palast der republik in berlin
palatial scene
pale
pale - skinned
pale - white skin
pale and coloured kimono
pale and sickly
pale as marble
pale as the first snow of winter
pale beautiful skin
pale beige sky
pale black armor
pale blue
pale blue armor
pale blue backlight
pale blue eyes
pale blue eyes!
pale blue faces
pale blue fog
pale blue glowing cybernetic eye
pale blue outfit
pale blue skin
pale bluish skin
pale colors
pale complexion
pale cyan and grey fabric
pale face
pale fair skin
pale fair skin!!
pale glowing skin
pale goth beauty
pale gradients design
pale gray skin
pale green background
pale green backlit glow
pale green glow
pale green halter top
pale greens and whites
pale grey skin
pale hair
pale head
pale ivory skin
pale light
pale milky white porcelain skin
pale muted pastel moody colors
pale orange colors
pale pastel colours
pale pink and gold kimono
pale pink bikini
pale pink grass
pale pointed ears
pale porcelain white skin
pale red
pale round face
pale skin
pale skin and dark eyes
pale skin and purple eyes
pale skin curly blond hair
pale skin!
pale sky
pale smooth
pale snow white skin
pale sober colors 9 0 %
pale sober colors 90 %
pale thin lips
pale tone
pale white detailed reptile skin
pale white face
pale white skin
pale woman
pale woman covered with blood
pale yellow sky
pale yellow wallpaper
pale yellow walls
pale young ghost girl
pale!
pale-skinned
pale-skinned persian girl
paleo art
paleoart
paleolithic cave art
paleolithic cave painting
paleolithic painting
paleozoic atlantis
paler. millions of glass-walled
palermo city street
palette
palette is black violet gray red
palette knife
palette knife and brush strokes
palette knife background
palette knife painting
palette knife texture
palette knife textures
palette knife! and brush strokes
palette of primaries
palettes
palladian
palladium
palladium veins
pallet
pallid skin
palm
palm body
palm leaves on the beach
palm lines
palm pattern visible
palm skin
palm springs
palm tree
palm trees
palm trees in the background
palm trees james gurney
palm trees on the beach
palm trees outside the windows
palme d'or winner
palme dor winner
palms
palms and miami buildings
palmtrees
palpatine
pals have a birthday party
palutena
palutena smash
pam
pam from the office
pamela anderson
pamukkale
pan and plates
pan futurism
pan ren wei
panaormic
panasonic
panasonic 35mm
panavia tornado
panavia tornado replica
panavision
panavision millennium dxl 2
panavision millennium xl 2
panavision panaflex
panavision panaflex x
panavision panaflex x iii
panavision psr r-200
panavision psr-200 35mm
panavision x iii
pancake flat head
pancake short large head
pancakes
panda
panda panda panda
pandemic
pandemonium
pandora
pandora jungle
pandora style
panel
panel cuts
panel of black
paneled walls
panels
panic
panicking
panini
panning shot
panorama
panorama distant view
panorama of crooked ancient city
panorama shot
panorama view
panorama view of the sky
panoramic
panoramic anamorphic
panoramic centered view of girl
panoramic photography
panoramic shot
panoramic view
panoramic view of girl
panoramic widescreen view
panspermia
pantera ((countach))
pantheon
panther
panties
panting
pantone
pantone color
pantone marker portrait
pants
pantyhose
pantyhose tights
panzer
paolo eleuteri serpieri
paolo roversi
paolo veronese style
papa emeritus
papa john eating 50 pizzas a day
papa legba
paparazzi
paparazzi photo
paparazzi photograph
paparazzi photography
paparazzi shot
paper
paper airplane
paper art
paper background
paper border
paper chrysanthemums
paper collage
paper collage art
paper craft
paper craft low poly
paper crumpled texture
paper cup
paper cut art
paper cut out
paper cut out collage artwork
paper cutout
paper cutouts of plain colors
paper decoration
paper drawing
paper grain
paper marbling
paper modeling art
paper origami
paper origami flowers
paper quilling
paper sketch
paper texture
paper texture 1 9 5 6
paper texture. 1968
paperback
paperback cover
papercraft
papercut
papers
papers and tomes
papers on table
papier - mache
papier colle
paprika
papyrus
parachutes
parade
parade floats
parade setting
paradigm shift
paradise
paradise city
paradise garden massage
paradise in the background
paradisiac
paradox
paradoxal
parallax
parallax photography
parallax poster composition
parallax »
parallel
parallel content
parallel universe
parallelism
parametric
parametric architecture
parametric design
parametric flow
parametric solid works
parametric structure
paranoia
paranoia everywhere
paranoiascape ps1
paranoid
paranoid android
paranoid critical method
paranoid vibe
paranormal
paranormal activity
paranormal flashlight
paranormal monster
parapets
pararel
parasol
parasols
parce sepulto
parchment
parchment paper
pareidolia
parental advisory
parents watching
parhelion atmospheric halos
parietal art style
paris
paris 1982
paris 2010
paris background
paris city
paris eiffel tower
paris fashion week
paris hotel style
parisian buildings
parisian street at night
park
park background
park in background
park ji-min
park jimin
park landscape
park on a bright sunny day
park shin hye as a super villain
parked cars
parked spaceships
parking in the street
parking lot
parkour
parks
parks and gardens
parks and lakes
parks and monuments
parks and public space
parliament
parlor
parody
parody movie bloopers
parody work
parrot
parrot on head
parrots
part dean cornwell style
part fox
part leyendecker style
part of the screen
part petra cortright
part robot and part black human
parted
parted hair
parted light brown hair
parted lips
parthenon
partial anatomy
partial robotic body
partial symmetry features
partially bald
partially biomedical design
partially bloody crystallized
partially covered with dust
partially cupping her hands
partially cybernetic
partially glazed
partially gold
partially human
partially male hairy torso
partially masked
partially operational
partially spacey crystallized
partially submerged
particle
particle effects
particle lighting
particle physics
particle reflections
particle sim
particle simulation
particle waves
particles
particles and dust in the air
particles are flying
particles disintegration
particles explosion
particles floating
particles light
particles simulation
particulate
parties
partly cloudy
partly cloudy day
partly cloudy sky
partly sunken! in the lake!
partly sunny
partly underwater
parts
party
party at midnight
party atmosphere
party balloons
party hats
party in front
party in jungles
party lights
party people in background
partylights
paru itagaki
pascal blanche
pascal blanche and edward hopper
passages
passed out
passengers
passion
passion flower
passion fruits
passionate
passionate pose
passive house
passport
passport photo
past
pasta
pastel
pastel art
pastel artwork
pastel background
pastel blue
pastel blue eyes
pastel blues and pinks
pastel bright colors
pastel clothing
pastel color
pastel color gradients
pastel color palette
pastel color pallete
pastel color scheme
pastel color theme
pastel color tones
pastel colored
pastel colored sunrise
pastel colorful mold
pastel coloring
pastel colors
pastel colors only
pastel colors scheme
pastel colour palette
pastel colourful 3 d
pastel colours
pastel colours overlap
pastel cute slime
pastel deep colours
pastel drawing
pastel dull colors
pastel faded effect
pastel faded grey rainbow
pastel flower petals flying
pastel flowery background
pastel fur
pastel glaze
pastel goth
pastel goth aesthetic
pastel gradients
pastel green
pastel hair
pastel hues
pastel light pink very long hair
pastel lighting
pastel makeup
pastel matte colors
pastel neon
pastel neon colors
pastel oil inks
pastel orange sunset
pastel overflow
pastel painting
pastel palette
pastel palette silhouette
pastel pink
pastel pink concrete
pastel pink neon
pastel pink robes
pastel pink skin tone
pastel purple background
pastel rainbow
pastel roses
pastel shades
pastel simple art
pastel sky
pastel soft colors
pastel style
pastel style painting
pastel sunset
pastel synthwave
pastel texture
pastel tone
pastel tones
pastel vibe
pastel vivid triad colors
pastel'
pastelcolours
pastell colours
pastelle
pastelle colors
pastels
pastels colors
pastels colours
pastelwave
pastoral
pastoral backyard setting
pastoral environment
pastoral fantastic reality
pastose
pastries
pastry
pastry lizard
pastures
pat mcgrath
pat steir
pat steir and hilma af klint
patagonian
patch design
patch logo
patch logo design
patches of fur
patches of green fields
patches of moss
patches of red grass
patches of sky
patches of yellow sky
patchwork
patchwork doll
patchwork-streak style
patchy cactus
patchy flowers
patent
patent drawing
patent illustration
patent leather
patent registry
patent style
patented in 2039
path
path based rendering
path based unbiased rendering
path into lush forest
path of exile
path through a dense forest
path traced
path tracing
path tracing render
pathecolor
pathes
pathetic
pathetic robot
pathfinder
pathfinder nendoroid
pathology
pathology sample test tubes
pathos
paths
pathtracing
pathway
pathways
patina
patio
patiphan sottiwilai
patiphan sottiwilaiphong
patreon
patreon content
patricia piccinini
patrick
patrick bateman
patrick bateman)
patrick demarchelier
patrick faulwetter
patrick jones
patrick nagel
patrick nagel!
patrick nagle
patrick nagle!!
patrick nagle!!!
patrick o'keeffe
patrick okeeffe
patrick star
patrick stewart
patrick westwood style
patriot
patriotic
patriotic nihilism
patriotic!
patriotism
patron saint of 🛸🌈👩🏾
patryk hardziej
pattern
pattern art
pattern with optical illusion
patterned
patterned background
patterned clothing
patterned scarf
patterned tilework
patterned visionary art
patterns
patterns and textures
patterns facing to audience
paua shell
paul atreides
paul atreides as emperor of dune
paul barson
paul bonner
paul brason
paul carrick
paul chadeisson
paul dainton
paul davey
paul delaroche
paul giamatti
paul giamatti is donald trump
paul heaston
paul kratter
paul kwon
paul laffoley
paul lehr and beeple
paul lehr and m. c. escher
paul lung
paul mccartney
paul outerbridge masterpiece
paul pepera
paul pepera pablo roldan
paul pope
paul rand
paul robertson
paul rudd
paul thomas anderson
paul walker
paul-chadeisson
paulie shore
pauline baynes
pauline hanson as a clown
pauline hanson as the joker
paved
paved roads
pavel shvedov
pavel tchelitchew
pavements
pavilion
paw art
paw pads
paw patrol
paw pov
paw shot
pawel rebisz
pawg
paws
paws firm to the ground
paws on head
paws on steering wheel
paws on wheel
payday 2
payne's grey
payne's grey and venetian red
paynes grey
paynes grey and venetian red
pbr
pbr material
pbr materials
pbr render
pbr texture
pbr textures
pc
pc boxart
pc game
pc game with ui
pc gaming
pc gpu fov settings
pc screen image
pc screenshot
pc wallpaper
pch
peace
peace and love
peace and quiet
peace atmopshere
peace sign
peaceful
peaceful ambience
peaceful and graceful
peaceful and quiet
peaceful and serene
peaceful animals
peaceful atmosphere
peaceful beautiful waterfall
peaceful cloud
peaceful clouds
peaceful day
peaceful elven forest
peaceful environment
peaceful evening harbor
peaceful expression
peaceful face
peaceful landscape
peaceful looking animals
peaceful lushious forest
peaceful mood
peaceful puffy clouds
peaceful scene
peaceful suburban scene
peaceful wooden mansion
peacefull
peacefully drinking river water
peach
peach and goma style
peach embellishment
peaches
peacock
peacock colors
peacock feather wrap the head
peacock feathers
peacock in the desert
peacock. intricate
peak
peak experience ”
peaked wooden roofs
peaks
peaky blinders
peaky blinders (2018)
peaky blinders gang
peanuts
pear
pear for a head
pearl
pearl choker
pearl earring
pearl necklace
pearl silverplate
pearlescent
pearlescent hues
pearlescent skin
pearlescent white
pearlized
pearls
pearls and chains
pearls and gold chains
pearls and oyesters
pearls and shells
pearls of sweat
pearly flagstones
pearly sky
pears
peasant boy and girl first kiss
peasant dress
peasant wedding at midnight
peasent
pebbles
peder balke
peder severin kroyer
pedestal
pedestrians
pediophobia
pedro correa
pee wee herman
peeled lemons
peeled oranges
peeling face skin
peeling paint
peep the horror
peepo the frog!!!
peering over from his heavy
pegasus
peggy hill
peinture d'une fée sanguinaire
peinture à l'huile
peinture à lhuile
peking opera
pekka halonen
pelisse
pelosi
pelt
pelt coats
pelton
pelzebug
pembrokeshire
pen
pen & ink drawing
pen - and - ink illustration
pen and ink
pen and ink doodles
pen and ink drawing
pen and ink drawings
pen and ink illustration
pen and ink monochrome
pen and ink on paper
pen and ink painting
pen and ink style
pen and ink work sharp focus
pen and ink work. sharp focus
pen and paper
pen and watercolor
pen draw
pen drawing
pen ink drawing
pen lines
pen on paper
pen on white paper
pen painting
pen sketch
pen-and-ink illustration
pencil
pencil and charcoal
pencil and ink
pencil and ink drawing
pencil and ink manga
pencil and ink manga drawing
pencil and paper
pencil and vine charcoal drawing
pencil and watercolor
pencil and watercolour
pencil art
pencil art on paper
pencil doodle
pencil draw
pencil drawing
pencil drawing illustration
pencil drawing of mkbhd
pencil drawing. el greco
pencil illustration
pencil lines
pencil marks
pencil marks hd
pencil miniskirt
pencil on paper
pencil painting
pencil sketch
pencil skirt
pencil stroke
pencil style
pencile and ink
pencils
pendant
pendants
pendleton ward
penelope cruz
penguin
penguin surfing on a bong
penguins
penguinz0
pengzhen zhang
penned illustrations
penned in cyan ink
penned with black on white
penned with thin colors on white
pennywise
pennywise style
pennywise theme
penrose stairs
pensive
pensive and hopeful expression
pensive expression
pensive lonely
pensive wizard examining eggs
pentacle
pentagon
pentagram
pentagrams
pentatomidae
pentax
pentax 67
pentax k 1 0 0 0
pentax k 1000
pentax k-1000
pentax k1000
penthouse
penumbral shadowcreature
penup
peolple run on the street
peonies
peony
peony flower
peony flowers
people
people and creatures walking
people angling at the edge
people are eating
people are panicking
people are wearing masks
people at night
people at the table
people at work
people crying
people dancing
people dancing in background
people didn't notice
people drink cocktails
people drinking beer
people drowning
people enjoying the show
people facing fire circle
people falling off a playground
people fighting
people flee
people fleeing
people in beach
people in the streets
people inside eating meals
people look into the frame
people looking at a house
people on a picnic
people on the ground
people on the streets
people out of focus
people outside eating meals
people outside walk
people panic in the foreground
people panicking
people resting on the grass
people running
people running away
people running in fear
people screaming
people shopping
people sitting at tables
people swimming
people waiting in bus stop
people walking
people walking around
people walking down a street
people walking in street
people walking in the distance
people walking into the horizon
people walking on street
people watching
people watching around
people with mohawks
people with umbrellas
people's perspective
people's silhouettes close up
peoples
peoples perspective
peoples silhouettes close up
pepe
pepe frog
pepe moreno
pepe the frog
pepe the frog snowboarding
pepe the frogs at war
pepe the miner
peppa pig
peppa pig robbing a bank
pepper
pepper no. 3 5
peppermint motif
pepperoni
pepsi
per un pugno di dollari
perception of value
perceptual decoupling
perched in a tree
perched on a mossy branch
perched on a rock
perched on a skyscraper
perched on intricate throne
percy jackson
perdita weeks!
peredvishniki
perez fabian
perfecly detailed
perfect
perfect 4 k
perfect anatomy
perfect and proportional body
perfect android girl
perfect android girl family
perfect angle
perfect animal
perfect anime
perfect anime cyborg woman
perfect anime face
perfect art
perfect artwork
perfect beautiful
perfect blue
perfect blue eyes
perfect body
perfect body and face
perfect body face and hands
perfect body shape
perfect bright studio lighting
perfect central composition
perfect cinematic light
perfect circle
perfect coherence
perfect coherent composition
perfect colorful eyeshadows
perfect coloring
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perfect composition and lighting
perfect composition artem demura
perfect composition body
perfect crisp light
perfect crisp moonlight
perfect crisp sunlight
perfect cut
perfect cyborg female
perfect dark
perfect design
perfect detail
perfect detailed eyes
perfect detailed face
perfect details
perfect digital art
perfect drow
perfect dynamic body form
perfect dynamic environment
perfect dynamic form
perfect dynamic pose
perfect dynamic position
perfect dynamic posture
perfect expression
perfect eye
perfect eyes
perfect eyes!!
perfect face
perfect face )
perfect face anatomy
perfect face and anatomy
perfect face and body
perfect face and boy
perfect face and eyes
perfect face details
perfect face model
perfect face proportions
perfect face shape
perfect face symmetry
perfect face template
perfect face!!
perfect face!! intricate
perfect face!!!
perfect faceintricate
perfect faces
perfect facial detail
perfect facial features
perfect facial proportions
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perfect facial symmetry
perfect factions
perfect fces
perfect features
perfect female body
perfect female body silhouette
perfect film clarity
perfect focus
perfect framing
perfect future
perfect geometry
perfect green fairway
perfect guns
perfect hand
perfect hand anatomy
perfect hands
perfect handsome face
perfect high resolution
perfect human female specimen
perfect hyperdetailed face
perfect intricate
perfect kerning
perfect landscape
perfect letters
perfect light
perfect lighting
perfect lighting hd
perfect lighting in a snow storm
perfect lighting pixel sorting
perfect lightning
perfect likeness
perfect lineart
perfect lines
perfect lips
perfect location lighting
perfect man
perfect master piece
perfect maximalistic composition
perfect military composure
perfect moment
perfect movie shot
perfect muscle structure
perfect natural skin tones
perfect nose
perfect pen and ink line art
perfect photo
perfect photorealistic eyes
perfect pixel art
perfect portrait composition
perfect proportion
perfect proportions
perfect proportions define face
perfect proportions defined face
perfect proportions fine - face
perfect proportions fine face
perfect quality
perfect readability
perfect rembrandt lighting
perfect render
perfect scene
perfect shading
perfect shadow
perfect shadows
perfect shape
perfect shot
perfect silky straight hair
perfect skin
perfect skin tones
perfect smile
perfect smile and tooth
perfect smile vogue
perfect spring day with
perfect straight lines
perfect structure
perfect studio light
perfect studio lighting
perfect symmetric coherent face
perfect symmetric face
perfect symmetrica body shape
perfect symmetrical
perfect symmetrical body
perfect symmetrical eye
perfect symmetrical eyes
perfect symmetrical face
perfect symmetrical face!
perfect symmetrical image
perfect symmetry
perfect teeth
perfect topology
perfect vibrant green eyes
perfect weather
perfect white haired girl
perfect woman
perfect world
perfect!!!
perfect-full-shot
perfection
perfection!
perfectionism
perfectly calm waters
perfectly centered
perfectly centered portrait
perfectly clear face
perfectly defined features
perfectly detailed
perfectly detailed eyes
perfectly detailed linework
perfectly drawn hands
perfectly face
perfectly lit face
perfectly lit. movie still
perfectly poised
perfectly proportioned
perfectly proportioned face
perfectly realistic yet surreal
perfectly shaded
perfectly shaded body
perfectly shaded face
perfectly symmetric
perfectly symmetrical
perfectly symmetrical alien face
perfectly symmetrical face
perfectly tileable
perforated metal
performance
performance art
performing
performing a music video
performing on stage
performing to dead soldiers
perfume
perfume bottle
perhaps
periodic table
perky woman made of petals
perlin noise
permaculture
permanent marker
permed hair
perpendicular to the camera
perpetual motion machine
perplexing
perry rhodan
perry the platypus
perseides meteor shower
persephone
persephone as goddess of death
persephone in spring
persian
persian carpet
persian carpets
persian cat
persian design
persian folklore illustration
persian folkore artstyle
persian folkore illustration
persian folktale art style
persian folktale artstyle
persian princess
persian queen
persian rug
persian rugs
persian style architecture
persian warrior
person
person centered composition
person in foreground
person made of tree
person made out of glass
person wearing gas mask
persona
persona 4
persona 5
persona 5 art style
persona 5 art style wlop
persona 5 artwork
persona 5 inspired
persona 5 phantom thief style
persona 5 style
persona art style
personal
personal computer horror
personal room background
personification
personification of greed
personification of marijuana
personified
personify
perspective
perspective 3 d octane render
perspective 3d octane render
perspective artwork
perspective correction
perspective depth
perspective from below
perspective perception
perspective piece
perspective shot
perspective shot from the sky
perspective sky
perspective view
perspective wide angle view
perspicious detail
peru
peruvian
peruvian boy looking
peruvian looking
pespective
pessimism
pestilence
pestle
pet
pet animal
pet bird
pet rat on shoulder
petal pink gradient scheme
petals
petals falling
petals falling everywhere
pete davidson
pete davidson walking a kangaroo
pete mc nally
pete mohrbacher
pete morbacher
peter
peter andrew
peter andrew jones
peter capaldi
peter chan
peter chung
peter chung + moebius + loish
peter cushing as the 10th doctor
peter deligdisch
peter dinklage
peter driben
peter dunham
peter dutton as a potato
peter eisenman
peter falk as columbo
peter gric and dan mumford
peter griffin
peter griffin as a real person
peter griffin body type
peter griffin face
peter griffin hairstyle
peter griffin in real life
peter guthrie
peter henket
peter hurley
peter ilsted
peter jackson
peter jones
peter kemp
peter konig
peter lloyd
peter lundqvist
peter marlow photography
peter mohbacher
peter mohrbacher
peter mohrbacher and artgerm
peter mohrbacher and dan mumford
peter mohrbacher and takayuk
peter mohrbacher artstyle
peter mohrbacher c 2 0
peter mohrbacher c 20
peter mohrbacher digital art
peter mohrbacher donato giancola
peter mohrbacher highly detailed
peter mohrbacher style
peter mohrbacher''
peter mohrbacher. unreal engine
peter morbacher
peter morbacher style
peter mordenbacher
peter morhbacher
peter mullen
peter murbacher
peter palombi
peter pan
peter parker
peter parker as spiderman
peter paul rebens
peter saville
peter sculthorpe
peter singhof
peter tarka
peter temple
peter the goat
peter the great
peter the great of russia
peter xiao
peterdraws
petite
petite body
petite girl
petite pear slim figure
petra collins
petra collins and mc. escher
petra cortright
petri dish art. animal eyes
petri rahkola
petrol
petrol aesthetic
petrol energy
petroleum vitamin
petros
petros afshar speedart
petros and leonid
pets
petscop
petspective room layout
petting a cat
petting zoo
petzval lens
petzval lens. featured on flickr
petzval lens. out of focus
peugeot prestige
pewdiepie
pewdiepie selfie at a bridge
pews
peyote cactus desert
peyote colors
pfp
phalanster
phalanx of ashigaru mice
phallic
phantasmagoric
phantasmagorical
phantasy
phantasy star online
phantom
phantom crash
phantom dust gameplay screenshot
phantom ghosts in the background
phantom grip
phantom list
pharah
pharah from overwatch
pharaoh
pharaoh clothes
pharmacy
pharoah
phase 2
phasmophobia
phation
pheasant guard sits on a stump
pheasant holding a sword
phenomenal
phenomenal photography
phenomenally aesthetic
phi
phil hale
phil hale and kent williams
phil hale artwork
phil jimenez
phil noto
phil noto and james jean
phil noto and rebecca guay
phil noto comic art
phil noto comicbook cover art
phil spencer
philip coles
philip k d
philip selway (drums)
philip taaffe
philippe caza
philippe dru
philippe starck
philippines
philosopher
philosopher alvin plantinga
philosophical
philosophical physics
philosophical splashes of colors
philosophy
philosopy
phlegm
phobia
phobos
phoebe tonkin
phoenix
phoenix dragon
phoenix flames
phoenix head
phoenix head. burning wax
phoenix in fire
phoenix jellyfish
phoenix rising
phoenix rising from the ashes
phoenix warrior
phoenix wright
phoenix-inspired
phograph
phography
phone
phone background
phone camera
phone in hand
phone photo
phone quality
phone recording
phone wallpaper
phone wallpaper hd
phone wallpaper. intricate
phone wearing a cowboy hat
phone!! held up to visor
phoneheld up to visor
phong shaded
phong yintion j - jiang geping
phonk album cover
phonk music background
phosphorescent
phot
photgraph
photgraphy
photo
photo  far
photo  far _ shot
photo ( far _ shot )
photo - bash
photo - editing
photo - manipulation
photo - real
photo - realism
photo - realistic
photo - realistic )
photo - realistic nikon 3 5 mm
photo - realistic nikon 35 mm
photo - realistic wallpaper
photo - realostic
photo - shot
photo 1 5 mm
photo 1 9 9 0 s
photo 1960
photo 1998
photo 2 0 1 0
photo 2010
photo 3 5 mm leica
photo 3 d
photo 35 mm leica
photo 35mm
photo 3d
photo 4 k
photo 4k
photo 50mm
photo 8 k
photo 85mm
photo 8k
photo [ far ]
photo art
photo bash
photo blurring
photo booth
photo collage
photo courtesy museum of art
photo documentary style
photo documetalism
photo epic of the year
photo finish
photo for a magazine
photo for a store
photo for magazine
photo for vogue
photo from 1940s
photo from 2022
photo from a promo shoot
photo from a spectator
photo from above
photo from behind
photo from behind of a cowboy
photo from space
photo from the 1850s of a farmer
photo from the 7 0 s
photo from the 70 s
photo from the 70s
photo from the 80s
photo from the back
photo from the dig site
photo from the olympic games
photo from the side
photo from vogue magazine
photo high definition
photo hyper realistic
photo illustration
photo in 4 k
photo in color
photo in style of paola kudacki
photo in style of tyler mitchell
photo journalism
photo kodak 35mm colored
photo kodak lens
photo manipulation
photo microscope
photo mid shot
photo model
photo mosaic
photo of
photo of a 50-year-old white man
photo of a beautiful
photo of a beautiful window
photo of a beautiful woman
photo of a big theaterstage
photo of a black woman
photo of a camp fire underwater
photo of a cat
photo of a classroom
photo of a crazy wasp
photo of a dyson sphere
photo of a ghostly pirate
photo of a hamster
photo of a hamsters on a date
photo of a hand jewellery model
photo of a huge theaterstage
photo of a man
photo of a mechanical butterfly
photo of a model
photo of a painting
photo of a rose
photo of a victorian dollhouse
photo of a woman
photo of asuna from sao
photo of barack obama
photo of boris johnson running
photo of breeze kaze
photo of crocodile
photo of cthulhu
photo of demon gollum
photo of donald trump
photo of džesika devic
photo of earth from space
photo of emma watson
photo of emma watson in swimsuit
photo of futuristic cityscape
photo of genghis khan
photo of ghost of anubis
photo of green river
photo of happy
photo of head
photo of margot robbie
photo of mark zuckerberg
photo of monstrous tornado
photo of penelope cruz
photo of poor condition
photo of putin
photo of real life pokemons
photo of scp-173
photo of sheryl sandberg
photo of shiprock
photo of slim girl
photo of slim girl model
photo of sophia loren
photo of the beauty gal gadot
photo of the cinema screen
photo of the girl
photo of the middle of the ocean
photo of the statue of liberty
photo of the year
photo of the year 2 0 2 2
photo of the year 2022
photo of war
photo of wolf
photo of young woman
photo of zurich
photo on iphone
photo photo
photo photograph
photo pinterest
photo portrait
photo print
photo product
photo quality
photo real
photo real ultra high detail
photo realisitc
photo realism
photo realist
photo realistic
photo realistic 4k
photo realistic image
photo realistic octane render
photo realistic painting
photo realistic portrait
photo realistic render
photo realistic style
photo realistic symmetrical
photo realistic”
photo reality
photo render
photo session
photo shoot
photo shot
photo shot my martha cooper
photo still
photo still of
photo still of behind view
photo still of interior
photo still of posterior view
photo studio
photo studio background
photo studio composition
photo studio quality
photo style
photo style of shawn paul tan
photo taken at night
photo taken from a boat
photo taken from above
photo taken from behind
photo taken from far
photo taken from far away
photo taken from the ground
photo taken in 1 9 3 0
photo taken in 1930
photo taken in 1989
photo taken in 2 0 2 0
photo taken in 2018
photo taken in 2020
photo taken of an epic intricate
photo taken on a nikon
photo taken on an old box camera
photo taken on fujifilm superia
photo taken with an iphone
photo taken with canon 5d
photo taken with ektachrome
photo taken with fujifilm superia
photo taken with nikon d 7 5 0
photo taken with nikon d 750
photo taken with nikon d750
photo taken with provia
photo taken with provia 1967
photo taken with sony a7r
photo taken with sony a7r camera
photo-bash
photo-real
photo-realism
photo-realistic
photo-realistic face
photo-realistic low lighting
photo-realistic maximum detail
photo-realistic unreal engine
photo-shopped
photobash
photobashing
photochrom print
photocollage
photocopied
photocopy
photodirector 3 6 5
photodirector 365
photogenic
photogenic details on armor
photografy
photogrammetry
photograpgy
photograph
photograph 3 5 mm
photograph 35 mm
photograph 4 k
photograph 4k
photograph captured in a forest
photograph captured in the woods
photograph credit: ap
photograph from above
photograph magazine
photograph of 3d ios room
photograph of a red kite bird
photograph of a sleeve tattoo
photograph of a techwear woman
photograph of april
photograph of christopher walken
photograph of drake the rapper
photograph of enchanted garden
photograph of san francisco
photograph of taylor swift
photograph of the city street
photograph of the year
photograph of three ravers
photograph portrait
photograph quality
photograph realism
photograph taken in 1989
photograph taken in 2 0 2 0
photograph taken in 2020
photograph ”
photographed
photographed for reuters
photographed from behind
photographed from the back
photographed in film
photographed on colour film
photographed on damaged film
photographed on ektachrome film
photographed on expired film
photographer
photographer art wolfe
photographic
photographic color scheme blur
photographic filter
photographic hyperrealism
photographic isometric cathedral
photographic journalism
photographic portrait
photographic print
photographic quality
photographic realism
photographic reality
photographic render
photographic still
photographic stills
photographic style of avedon
photographic. imposingly tall
photographs
photography
photography )
photography 4 k
photography 4k
photography 50mm f1.4
photography alexey gurylev
photography alexey kurylev
photography at night
photography award
photography award )
photography award photo
photography award winning
photography award)
photography awardm
photography canon
photography cinestill
photography fashion edition
photography from vogue magazine
photography hight quality
photography hyperrealism
photography of albert watson
photography of bill brauer
photography of charline von heyl
photography of enchanted forest
photography of gregory crewdson
photography of kurzgesagt
photography of todd hido
photography photojournalism
photography photorealism
photography photorealistic
photography portrait
photography portrait 4 k
photography shot
photography shot at blue hour
photography studio
photography style
photography ultrafine detail
photography: journalism
photography]
photography”
photograph”
photograpphy
photogravure
photogtaphy
photohoot
photoillustration
photojournalism
photojournalism detailed
photojournalism photography
photolab
photolithography
photomanipulation
photomontage
photomorph
photomorph artwork
photons
photoralistic rendering
photorea
photoreailstic
photoreaistic
photoreal
photoreal details
photoreal elegant
photoreal epic composition
photoreal octane render
photoreal octane rendering
photoreal render
photorealiatic
photorealiscic face
photorealisitc
photorealism
photorealism 4 k
photorealism 4k
photorealism 8k
photorealism art
photorealism oil painting
photorealism pastel
photorealism style
photorealism ”
photorealism!!
photorealism!!!
photorealism. trending on flickr
photorealist
photorealist style
photorealistc
photorealiste
photorealisti
photorealistic
photorealistic - h 6 4 0
photorealistic - h 640
photorealistic -20
photorealistic 3 d
photorealistic 3 d octane render
photorealistic 3 d render
photorealistic 35mm
photorealistic 35mm'
photorealistic 3d art
photorealistic 3d artwork
photorealistic 3d octane render
photorealistic 3d render
photorealistic 4 k
photorealistic 4k
photorealistic 8 k
photorealistic 8k
photorealistic anamorphic lens
photorealistic and intricate
photorealistic anime
photorealistic anime girl render
photorealistic art
photorealistic art style
photorealistic artstyle
photorealistic beautiful face
photorealistic blade runner
photorealistic brush strokes
photorealistic camera shot
photorealistic cgi
photorealistic cinematic
photorealistic cinematic render
photorealistic colors
photorealistic concept art
photorealistic content
photorealistic dark concept art
photorealistic detail
photorealistic detailed
photorealistic detailed picture
photorealistic details
photorealistic details content
photorealistic digital art
photorealistic digital arts
photorealistic digital painting
photorealistic disney
photorealistic drawing
photorealistic eyes
photorealistic eyes render
photorealistic face
photorealistic faces
photorealistic facial features
photorealistic fan art
photorealistic features
photorealistic full body
photorealistic graphics
photorealistic hd
photorealistic high detail
photorealistic highly detailed
photorealistic homer simpson
photorealistic horror
photorealistic human goku
photorealistic hyperdetail
photorealistic illustration
photorealistic image
photorealistic imagery
photorealistic landscape
photorealistic lighting
photorealistic logan movie still
photorealistic magazine picture
photorealistic masterpiece
photorealistic matte painting
photorealistic movie still
photorealistic music album cover
photorealistic octane 3d render
photorealistic octane render
photorealistic octane render 8 k
photorealistic octane render 8k
photorealistic oil on canvas
photorealistic oil painting
photorealistic painting
photorealistic perfect body
photorealistic photo
photorealistic photograph
photorealistic photography
photorealistic picture
photorealistic portrait
photorealistic portrait of bjork
photorealistic print of exotic
photorealistic quality
photorealistic raytracing
photorealistic render
photorealistic render in octane
photorealistic rendering
photorealistic resolution
photorealistic room
photorealistic screenshot
photorealistic shading
photorealistic shot
photorealistic skin texture
photorealistic skin tone
photorealistic special effects
photorealistic streetscape
photorealistic style
photorealistic texture
photorealistic ”
photorealistic!
photorealistic!!
photorealistic!!!
photorealistic!!!!
photorealistic!!!!!
photorealistic!!!!!!! art style
photorealistic-n 9
photorealistic. realistic
photorealisticart style
photorealistick
photorealistic”
photorealistoc
photorealitic
photoreallrstic
photorealsitic
photoreal”
photorelism
photorelistic
photorender
photorrealistic
photorrealistic concept art
photos
photos of family on wall
photoscan
photoscanned
photoshoot
photoshoot for skincare brand
photoshoot for vogue magazine
photoshoot portrait
photoshoot poses
photoshooting
photoshop
photoshop art
photoshop brush
photoshop collage
photoshop render
photoshop speedpaint
photoshop water art
photoshoped
photoshoppainting
photoshopped
photoshot
photostock
photosynthesis
photosynthetic
photoweb
photo”
photrealistic
phragmites
phthalo blue
phtorealistic
phtotrealistic
phuoc quan
physical
physical : tinyest midriff ever
physical based render
physical based rendering
physical correct light
physical correct light shadows
physical mental perfection
physical octane render
physical painting
physical particles
physicality-based render
physically - based render
physically accurate
physically based render
physically based rendering
physics
physics defying
physics splashes of colors
pi - slices
pi-slices and kidmograph
pianist
piano
piano guitar music notes key
piano in the background
pianos
pic
picard on a starboard
picasso
picasso and dali
picasso and moebius
picasso masterpiece
picasso style
pichacu cooks a squid
pick up behind you
pick wu
pickaxe
picking apples from a tree
picking flowers
picking up a can beans
picking up a flower
pickle
pickle rick
pickle rick from rick and morty
pickles
picnic
pics that go hard
picsart
pictogram
pictographs
picton blue
pictoplasma
pictorial
pictorial logo
pictorial mark
pictorialism
picture
picture book
picture frames
picture from behind
picture generation
picture inside covens den
picture of a female biker
picture of a loft in morning
picture of a male biker
picture of a male cleric
picture of an adult male warrior
picture of an interior loft
picture of female paladin
picture of the year
picture of the year award
picture portrait
picture taken from the ground
picture taken in 1 9 3 0
picture taken in 1930
picture taken in zoo
picture through the screen
pictured from the shoulders up
pictures in sequence
pictures of family on wall
picturesque
pidgey
pie eyes
pieces
pieces of a ripped flag
pieces of a ripped flag!!!
pieces of land
pierce brosnan
pierce brosnan as james bond
pierced
pierced navel
piercing
piercing blue eyes
piercing bright blue eyes
piercing brown eyes
piercing clear eyes
piercing detailed realistic eyes
piercing eye
piercing eyes
piercing gaze
piercing glare in the eyes
piercing glowing eyes
piercing green eyes
piercing grey eyes
piercing stare
piercing turquoise blue eyes
piercings
piercings and a mohawk
piercings collar
piercings resembling plasma jets
pierre - auguste renoir
pierre pellegrini and ash thorp
pierre puvis de chavannesa
pieter brueghel
pieter rudolph kleijn
pig
pig in a tuxedo
pig nose
pig pig pig
pigeon
piggy
piglet
pigment
pigment textures
pigs
pigtail
pigtail braids
pigtails
pigtails hair
pigtails hairstyle
pikachu
pikachu as a human
pikachu as jesus
pikachu eating a hamburger
pikachu in a forest
pikachu in space
pikachu on acid
pikachu tarot card
pikes
pilar
pilar gogar
pile of bodies
pile of skulls under his feet
piled around
piles of bodies
piles of bones
piles of books
piles of gold coins
piles of modular synth cables
piles of money
piles of paperwork
piles of trash
piles of trash and junk
pilgrim village setting
pilgrimage
pillar
pillars
pillars and arches
pillars of creation
pillars of ice background
pillars of marble
pillars on ceiling
pillhead
pillows
pills
pills and medicine
pilot
pilot girl
pilot outfit
piloting a small space shuttle
piltover
pimples
pimps
pin - up
pin - up girl
pin on anime
pin up
pin up girl
pin up style poster
pin-up
pin-up poster girl
pinball machine parts
pincushion lens effect
pine
pine color scheme
pine forest
pine forests
pine marten
pine tree
pine treehouse
pine trees
pine trees in the background
pine wood
pineapple
pineapples
pinecone
pines symbol in the corners
pines symbols
pingu
pingu sitting behind a computer
pinguin
pinhead from hellraiser
pinhole
pinhole analogue photo quality
pinhole camera
pinhole camera effect
pinhole effect
pinhole photo
pinhole photo : dream
pinhole photo quality
pinhole photography
pininfarina
pink
pink 7 inch high heels
pink accents
pink and black
pink and blue
pink and blue and green mist
pink and blue colors
pink and blue colour
pink and blue gradients
pink and blue hair
pink and blue lighting
pink and blue mohawk hairstyle
pink and blue neon
pink and blue palette
pink and gold
pink and gold color palette
pink and gold color scheme
pink and green
pink and green colour palette
pink and grey clouds
pink and grey muted colors
pink and orange
pink and orange colors
pink and orange neon lights
pink and pink details
pink and purple
pink and red color scheme
pink and red color style
pink and red colors
pink and teal
pink and teal and orange
pink and teal color palette
pink and triadic color scheme
pink and yellow
pink angry bubble
pink arches
pink asparagus
pink axolotl in a bucket
pink background
pink bees
pink body
pink body harness
pink bonsai tree
pink clothes
pink cloud background
pink cloud bokeh
pink clouds
pink clouds background
pink clouds in the sky
pink cloudy background
pink color palette
pink color scheme
pink colors
pink concrete
pink cowboy hat
pink crystal mohawk
pink diamonds
pink door
pink dress
pink eyes
pink face
pink flower
pink flowers
pink floyd
pink floyd album cover
pink fluffy fur
pink fog background
pink forest
pink fox
pink frosted donut
pink fur
pink girl
pink glasses
pink glitter mermaid gown
pink golden hour
pink gradient background
pink grass
pink hair
pink hair bow
pink hair covered with hairpins
pink halter top
pink headband
pink headphones
pink hearts in the background
pink hue
pink hues
pink iconic character
pink jellyfish everywhere
pink lamborghini aventador
pink landscape
pink light
pink lighting
pink lightning
pink lips
pink lipstick
pink lotus queen
pink magic
pink marble building
pink mini-skirt
pink mist
pink mohawk
pink moon
pink neon lights
pink nose
pink orange flowers
pink pastel
pink petals
pink petals fly
pink power
pink reflections
pink romantic tutu
pink rosa
pink rose
pink scheme
pink shadows
pink shirt
pink shoes
pink short hair
pink skies
pink skin
pink skirt
pink sky
pink slime everywhere
pink smoke
pink storm clouds
pink straight hair
pink studio lighting
pink sun
pink sunlight
pink sunset
pink sunset hue
pink tigers
pink tight mini-skirt
pink tinged heavenly clouds
pink tones
pink tree beside a large lake
pink trees
pink tutu
pink twintail hair and cyan eyes
pink vibe
pink violet light
pink volumetric studio lighting
pink water in a large bath
pink waterfalls
pink white and green
pink white turquoise
pink wispy hair
pink yellow and blue neon signs
pink yellow flowers
pink zen style
pinkie pie
pinkie pie as a sith lord
pinkie pie equine
pinks
pinky pie my little pony
pinnable
pinnacle studio
pinned down
pinned joints
pino daeni
pino daeni and dan mumford
pinocchio
pinocchio nose
pinpoint sharp focus
pins
pins of light
pinstripe suit
pinterest 4 k
pinterest and shutterstock
pinterest anime
pinterest filter
pinterest hair picture
pinterest keyshot product render
pinterest posing
pinterest render
pinterest style
pinterest tattoos
pinterest trending
pinterest wallpaper
pintrerest
pintrest
pinup
pinup art
pinup body
pinup girl
pinup model
pinup of tracer from overwatch
pinup pose
pinup style
pinup. lord of daggers
pioneering aesthetic
piotr jablonski
pipboy art
pipe
pipe brain
pipe jungle
pipe organ
pipe organ album art
pipe smoke
pipelines
pipes
pipes and valves
piping
piranesi style
piranesi's chamber
pirate
pirate captain
pirate clothes
pirate clothing
pirate flag in his arms
pirate logo
pirate neon ship
pirate portrait
pirate queen
pirate robot
pirate setting
pirate ship
pirate ship in background
pirate ship in space
pirate ships
pirate ships at war at night
pirate themed
pirate weapons
pirate woman
pirates
pirates flag
pirates in a tavern
pirates of the caribbean
pirates of the caribbean (2006)
pirates of the caribbean 2006
pirates of the carribean
pirates treasure map
piroca
pisces
pissed off
pistol
pistol and bazooka
pistol in holster
pistol on his hand
pistols
pistons
pistons and bolts
pit droid
pitbull
pitch bending
pitch black
pitch black background
pitch black room
pitch black skin
pitch black sky
pitch dark room
pitch darkness around the post
pitch perfect
pitchblack skin
pitchblack sky
pitchburn devils
pitchburn devils!
pitchfork
pith helmet
pits
pitt
pittsburg
pittsburgh
pittura metafisica
pivix
pixar
pixar  and disney animation
pixar (2018)
pixar 2018
pixar 3 d animation style
pixar 3d animation style
pixar 3d render
pixar 8 k photo
pixar 8k photo
pixar and da vinci
pixar and disney
pixar and disney 3d style
pixar and disney animation
pixar and disney style
pixar and ilya kuvshinov
pixar and industrial
pixar animation
pixar animation studio
pixar animation style
pixar animation，hyper detailed
pixar art
pixar art 3d
pixar art style
pixar artstyle
pixar cars in mad max fury road
pixar cars movie style
pixar cartoon
pixar cartoon style
pixar cgi
pixar character
pixar character design
pixar color palette
pixar concept art
pixar coronavirus movie
pixar cute
pixar cute character design
pixar digital movies
pixar disney
pixar doll deep focus
pixar film
pixar film setting
pixar highly detailed
pixar movie
pixar movie panorama
pixar movie poster
pixar movie scene
pixar movie screenshot
pixar movie still
pixar movie style
pixar octane render
pixar portrait
pixar portrait 8 k photo
pixar portrait 8k photo
pixar render
pixar rendering
pixar renderman
pixar renderman render
pixar splash art
pixar still
pixar studio
pixar studios
pixar style
pixar weta portrait
pixar zootopia. 3 d rendering
pixar. octane render
pixar”
pixel
pixel art 8 - bit
pixel art animation
pixel art dinosaur
pixel art isometric drawing
pixel art sprite
pixel art style
pixel artwork
pixel art”
pixel degradation
pixel game
pixel graphics
pixel mosaic
pixel perfect
pixel perfect photograph
pixel rain
pixel sort
pixel sorted
pixel sorting
pixel stretching
pixel style
pixel town
pixel-art
pixelactivist
pixelart
pixelate
pixelated
pixelated art
pixelated rain
pixeled stretching
pixellated
pixelperfect
pixels
pixels huh
pixelsort
pixelsorting
pixelsquid
pixer
pixie
pixie character
pixie cut
pixie cut with shaved side hair
pixie dust magic
pixie hair
pixie haircut
pixie haircut wlop
pixie undercut black hair
pixie undercut hairstyle
pixie undercut short black hair
pixilated
pixiv
pixiv 3dcg
pixiv daily ranking
pixiv frontpage
pixiv style
pixiv trending
pixivs
pixivs and
pixivs and junji ito
pixologic top row
pixta. jp
pixta.jp
pixv
pixvy
pizza
pizza advert
pizza box
pizza cosmos
pizza in foreground
pizza is everywhere
pizza on a table
pizza pie
pizza skyscrapers
pizza the hut
pizza universe
pizza!
pièce de résistance
pj crook
placards
place
place of battle
place setting
placed in a bloody hall
placed in a large living room
placed in a living room
placed in a lush forest
placid
plague
plague and fever. full body
plague doctor
plague doctor mask
plague mask
plaid shirt
plaid skirt
plaid tights
plain
plain background
plain black background
plain purple background
plain red background
plain stretching into distance
plain studio background
plain uniform sky
plain uniform sky at the back
plain view
plain walls somber white eyes
plain walls |light hearted
plain walls |somber white eyes
plain white background
plain white tshirt
plains
plan
plan drawing
plane
plane illustration
plane light
planes
planescape torment
planeswalker
planet
planet and stars
planet arrakis
planet earth
planet earth background
planet earth exploding
planet earth from space on fire
planet earth in foreground
planet earth in the background
planet earth in the foreground
planet hoth
planet in space
planet in space over the horizon
planet in the sky
planet landscape
planet mars
planet mars in the background
planet of the apes
planet of the cats
planet overgrown
planet saturn
planet surface
planet terror planet
planet uranus
planet with rings
planetary annihilation titans
planetary city
planetary gears
planetary landscape
planetes
planets
planets align
planets and galaxies
planets and stars
planets behind
planets colliding
planets crashing
planets in orbit
planets in the background
planets in the skies
planets in the sky
planets on the background
planets orbit
planner stickers
planning
plans
plant
plant armour
plant forest in glass tubes
plant monster
plant patterns
plant photography
plant predator
plant roots
plant sap
plant specimens
plant spirit
planted charge
planters
plants
plants allover
plants and flowers
plants and grass
plants and jungle
plants and patio
plants and trees
plants environment
plants everywhere
plants growing
plants growing on it. gouache
plants in a glass vase
plants in beakers
plants in glass vase
plants in glasses
plants in scientific glassware
plants inside cave
plants on balconies
plants on pots and on the walls
plasma
plasma charges
plasma display
plasma globe
plasma gun
plasma neon internal glow
plasma rays
plaster
plastic
plastic action figure
plastic and fabric
plastic army men
plastic barbie doll
plastic bottles
plastic ceramic material
plastic chair
plastic doll
plastic sea wrapped
plastic sheen
plastic skin
plastic texture
plastic toy
plastic waste
plastic wrap
plasticine
plasticine models
plasticized spiral flames
plataform 2 d game
plataform 2d game
plataform game
plate
plate armor
plate armored chest
plate armour
plate of borscht
plated
plated arm
plated armor
plated armor intricate
plates
plates of fruit
platform
platform 9 3 / 4
platform boots
platform game
platformer
platforms
plating
platinum
platinum attire
platinum blond
platinum blonde
platinum blonde hair
platinum blonde long hair
platinum cracked
platinum hair
platinum jewellery
platinum printing
platinum skin
platon
platonic forms
platonic ideal star wars
platonic solids
platypus
play
play - doh
play in dnd
play of light
play-doh
playable trailer
playbill
playbill of prima ballerina
playboi carti
playboi carti and lil uzi vert
playboi carti portrait
playboy
playboy bunny
playboy centerfold
playboy cover
playboy style
players handbook
playful
playful and cheerful
playful composition
playful composition canon
playful creativity
playful peasant man
playful pose
playful pose of a dancer
playful smile
playful smirk
playful updo
playful vibe
playful!!!
playground
playing
playing a gibson les paul guitar
playing a guitar
playing a korg ms-20 synthesizer
playing a mandolin
playing at swiming pool
playing at the beach
playing basketball
playing board games
playing card back
playing card suit hearts
playing cards
playing chess
playing computer games
playing dnd
playing drums
playing electric guitar
playing games
playing guitar
playing guitar onstage
playing guitars
playing harp in magical forest
playing in waterfalls
playing piano
playing poker
playing poker in a saloon
playing soccer
playing soccer at the beach
playing soccer on the beach
playing techno house music
playing tennis
playing the guitar
playing video games
playing with a fox
playing with a small firetruck
playing with fire
playing with foxes
playing with the water
playmates toys
playrix
playrix games
plays music
plays skateboards
plays videogames
playseat evolution
playstation
playstation 1
playstation 1 era
playstation 1 era graphics
playstation 1 game
playstation 1 graphics
playstation 2
playstation 2 gameplay still
playstation 2 graphics
playstation 2 horror game
playstation 2 screenshot
playstation 3
playstation 4
playstation 5
playstation 5 graphics
playstation 5 screenshot
playstation 6
playstion 5 screen
plaza
pleading face
pleasant
pleasant cozy atmosphere
pleasant face
pleasant lighting
please
please do your best
pleasent composition
pleasing
pleasing aesthetics
pleasing face
pleasing palette
pleasing tone colours
pleasure
pleated miniskirt
pleated skirt
pleinairism
plenty mozzarella
plesiosaur
plethora of colors ; mini dress
plexus
plotter
plotting
plows
plum blossom
plum color scheme
plumbing jungle
plumbus
plume made of fractals
plume made of geometry
plume made of seaweed
plume of seaweed
plumeria
plumes of smoke and ash
plumes of smoke in background
plump
plump lips
plurality
plus - x
plus size
plus size woman
plus-sized
plush
plush doll
plush furnishings
plush leather pad
plush leather pads
plush lips
plush mascot
plush toy
plushie
plushie photography
plushy
pluto
plutocracy
plutus su
png
pocahontas
pochi iida
pocket monster companion
pocket watch
pockets
pocketwatch
pod
pod racer
podium
pods
poe's angel
poes angel
poetic
poetic and symbolic
poetic sequence in mr. clean
poetical
poetry
pog
poggers
pogrom
poignant
point break
point cloud
point finger with ring on it
point of no return
point of view
point of view : up
point of view of visor wearer
point perspective
point-and-click adventure game
pointe pose
pointe poses
pointed arches
pointed black witch hat
pointed chin
pointed ears
pointed face
pointed face and grey eyes
pointed hoods
pointed nose
pointed teeth and several eyes
pointilism
pointillisme
pointillist style
pointing
pointing a pistol
pointing a pistol at us
pointing at the camera
pointing his bow
pointing index finger
pointing to heaven
points
pointy bunny ears
pointy conical hat
pointy ears
pointy hat
pointy jester shoes
pointy mask
pointy nose
pointy shell
pointy witch hat
pointè pose
pointé pose;open mouth
pointé pose;pursed lips
poised
poised beautiful body
poison
poison dripping
poison ivy
poison ivy from batman
poison teeth
poisonous
pokeball
pokedstudios
pokemon
pokemon anime style
pokemon art style
pokemon cap
pokemon card
pokemon card of duke nukem
pokemon card of duke nukem!
pokemon cards
pokemon fighting at world war 2
pokemon in the wild
pokemon inspired
pokemon military drill
pokemon style
pokemon sword and sheild
pokemon trading card game
pokemon trainer
pokemon trainer outfit
poker
poker card style
poker face
pokimane
pokémon
pokémon logo
polaeized light
poland
poland flag
polar
polar bear
polar bear in the far background
polar express
polariod
polarized lens
polarized sports sunglasses
polarizer
polarizer filter
polarizer filter : 1 0
polarizer filter : 10
polarizing filter
polaroid
polaroid 6 0 0
polaroid 6 0 0 film
polaroid 600
polaroid artistic photo
polaroid black and white picture
polaroid camera
polaroid clear
polaroid collage
polaroid color photo
polaroid color photograph
polaroid colour faded
polaroid effect
polaroid fashion photography
polaroid film
polaroid image
polaroid kodak portra
polaroid octane render
polaroid of a dream
polaroid photo
polaroid photo of trailerpark
polaroid photo style
polaroid photo with flash
polaroid photograph
polaroid photograph!
polaroid photography
polaroid picture
polaroid shot
polaroid vintage color photo
polaroide photo
pole dancing
polearm
polearm glaive
police
police box
police calling for back up
police cap
police car lights
police cars
police drones
police lights
police lights shine on her face
police man
police man!!
police officer
police officer hit
police officers
police officers under heavy fire
police scene
police shootout
police shot
police sirens in smoke
police sketch
police state
police station
police tape
police tesla
police uniform
policeman
policeman closeup
polinesian style
polish
polish food
polish hyper - casual
polish mansion kitchen
polish movie poster
polish poster art
polished
polished : :
polished and fancy
polished concrete
polished look
polished maple
polished metal
polished steel armor
polished white marble
polished with visible wood grain
politica
political art
political cartoon
political cartoon style
political meeting in the woods
political propaganda
politicians
politics
polka dot
polka dot tables
polkadots
pollen
pollock photorealistic
polluted
pollution
polovstian dances and chorus
polsat
poltergeist
polychaeta
polychromatic
polychromatic - colors
polychromatic color palette
polychromatic spectrum
polycount
polygon
polygon art
polygonal
polygonal art
polygonal fragments
polygonal iron steel walls
polygonal wooden walls
polygons
polygroups
polyhedral
polyhedron
polymer clay
polymer clay earrings
polymesh
polynesian god
polynesian style
polyphonic bio - communications
polyphonic communication
polyphonic ecstacy
pomegranade
pomegranate
pomegranates
pomeranian
pomeranian mix
pompadour
pompeian
pompeii
pompous
pompous expression
poncho
pond
pond landscape
pond scene
pond with frogs and lilypads
pondering
ponds
ponds of water
ponte 2 5 de abril
pontifex
pontormo
pony
pony facing away
pony tail
ponyo hayao mitazaki
ponytail
ponytail and beard
ponytail black hair
ponytail hair
ponytails
poofy
poofy manes of fur
poofy red wig
pooka
pool
pool caustics
pool party
pool tubes
poolrooms
pools
pools and streams
pools of blood
pools of water
poolside
poop
poor
poor buildings
poor fluorescent lighting
poor lighting
poor quality
poorly drawn
poorly lit
pop
pop - art
pop - art style
pop - surrealism
pop 3 d 8 k ultra detailed
pop 3d 8k ultra detailed
pop and vibrant colors
pop art
pop art cartoon style
pop art look
pop art masterpiece
pop art painting
pop art patterns
pop art poster
pop art stability
pop art style
pop art surrealism
pop colors
pop culture
pop figure of mom with long
pop japonisme 3 d ultra detailed
pop makeup style
pop motifs
pop punk art style
pop realism
pop surrealism art style
pop surrealism lowbrow art style
pop surrealist
pop up parade figure
pop-art
popart
popcorn as morpheus
popcorn machine
pope
pope surrenders
popeye
poppies
popping color
popping colors
poppy
pops of color
pops of triadic colors
popstar comeback single
populace
popular
popular interior design style
popular isekai anime
popular korean makeup
popular on art station
popular on behance
popular on etsy
popular on instagram
popular on pinterest
popular on pixiv
popular on sketchfab
popular south korean makeup
popularity
populated with aliens and people
porcelain
porcelain cyborg
porcelain cyborg armor
porcelain face
porcelain forcefield
porcelain highlighted skin
porcelain holly herndon statue
porcelain japanese mannequins
porcelain looking skin
porcelain organic
porcelain organic tissue
porcelain pale skin
porcelain sculpture
porcelain skin
porcelain skin tone
porcelain skin ”
porcelain skin. studio ghibli
porcelain white face
porcelain white skin
porche
porches
pores
pork
pork meat
pork pie hat
porky pig
porous skin
porsche
porsche 356
porsche 9 1 1
porsche 911
porsche 911 in the future engine
porsche gt 3 in hawaii city
porsche made out of lego
porsche rsr
port
port city
port scene background
portable generator
portait
portait image
portait of haifa wehbe
portait photo
portait photo profile picture
portal
portal 2
portal 2 game
portal 2 screenshot
portal 3
portal game
portal game 9 4 4 9 9 valve
portal game 9 9 9 9 9 valve
portal game 99999 valve
portal game valve
portal gun
portal in space
portal into anotheer dimension
portal made of roses
portal opening
portal to alternative reality
portal to another dimension
portal to another universe
portal to another world
portal to hell
portal to outer space
portal to the ethereal realm
portal!! game valve style
portal. zaha hadid
portals
portapotty
portcullis
portfolio
portfolio illustration
portfolio quality
portholes
portland oregon
portlet photo
portra
portra 160
portra 4 0 0
portra 400
portra 8 0 0
portra 8 0 0 ”
portra 800
portra 800 street photography
portraiit
portrait
portrait 4 / 3
portrait 6 0 - year - old woman
portrait 60 - year - old woman
portrait 8 k
portrait 8k
portrait a 1 5 - year - old boy
portrait a 15 - year - old boy
portrait a woman like reol
portrait androgynous girl
portrait anime girl
portrait anime kaya scodelario
portrait anime space cadet girl
portrait anya taylor-joy
portrait armored astronaut girl
portrait art
portrait art of rick sanchez
portrait batman ancient biblical
portrait beautiful sci - fi girl
portrait bender from futurama
portrait berserker barbarian
portrait big dark dog
portrait big dark punk
portrait black and white
portrait bust
portrait bust of old woman
portrait bust of woman
portrait bust of young woman
portrait c 1 2. 0
portrait c 12.0
portrait casting long shadows
portrait centric composition
portrait cersei lannister sit
portrait character design
portrait close - up
portrait close up
portrait close up of guy
portrait closeup
portrait color glamour
portrait composition
portrait cute-fine-face
portrait danny devito as gandalf
portrait de shanks le roux
portrait death clown
portrait demi rose
portrait demon half human
portrait digital art
portrait dnd
portrait drawing
portrait emily ratajkowski
portrait en buste
portrait ezio auditore as female
portrait face
portrait facial head
portrait fashion model
portrait featured on unsplash
portrait female elf wizard
portrait fennec fox animal
portrait from hell
portrait front face reference
portrait full body
portrait futuristic solider girl
portrait gapmoe yandere grimdark
portrait girl
portrait gracious saint
portrait happy colors
portrait hd
portrait illustration
portrait image
portrait imagery
portrait imagery!!!!!
portrait in center
portrait in the style of craola
portrait isometric drawing
portrait jisoo blackpink
portrait kevin mckidd
portrait knight female
portrait knights of zodiac girl
portrait lighting
portrait made of paint
portrait masterpiece
portrait mode
portrait mode photo
portrait n - 9
portrait ninja gaiden girl
portrait no. 1
portrait of ((charlize theron))
portrait of ((mischievous))
portrait of (charlize theron)
portrait of 1 5 - year - old boy
portrait of 14 years old boy
portrait of 15 - year - old boy
portrait of \'they live\' movie
portrait of a 40 years old women
portrait of a alien insect
portrait of a astronaut skeletor
portrait of a ballerina
portrait of a barbarian female
portrait of a barbarian woman
portrait of a beautiful
portrait of a beautiful angel
portrait of a beautiful female
portrait of a beautiful geisha
portrait of a beautiful girl
portrait of a beautiful goddess
portrait of a beautiful model
portrait of a beautiful person
portrait of a beautiful woman
portrait of a big
portrait of a blonde woman
portrait of a bloodborne hunter
portrait of a bugs bunny
portrait of a cat
portrait of a cat as a pirate
portrait of a cosmic entity
portrait of a cosmic god
portrait of a cosmic goddess
portrait of a cowboy
portrait of a crying ai artist
portrait of a cute girl
portrait of a cute monster
portrait of a cute woman
portrait of a cyber skeleton
portrait of a cyberpunk cyborg
portrait of a cyberpunk machine
portrait of a cyberpunk man
portrait of a cyberpunk samurai
portrait of a cyborg
portrait of a cyborg queen
portrait of a dachshund
portrait of a dark fantasy nymph
portrait of a dark goddess
portrait of a dark witch
portrait of a demon
portrait of a detective
portrait of a digital shaman
portrait of a dnd character
portrait of a dog
portrait of a dragon
portrait of a dreamer
portrait of a dryad
portrait of a dwarf warrior
portrait of a european woman
portrait of a fairy
portrait of a female android
portrait of a female anime hero
portrait of a female art student
portrait of a female demon
portrait of a female druid
portrait of a female elf warlock
portrait of a female mage
portrait of a female model
portrait of a female necromancer
portrait of a female pathologist
portrait of a female ranger
portrait of a female soldier
portrait of a forest mage
portrait of a frog
portrait of a futuristic robot
portrait of a geisha
portrait of a geometric owl
portrait of a girl
portrait of a gnoll
portrait of a gnome called eldon
portrait of a goblin
portrait of a god of death
portrait of a goddess
portrait of a goth catgirl
portrait of a grey alien
portrait of a handsome
portrait of a happy quokka
portrait of a heavenly god
portrait of a hipster skeletor
portrait of a holy necromancer
portrait of a humanoid alien
portrait of a hyena girl
portrait of a japanese girl
portrait of a japanese teen
portrait of a joker
portrait of a king
portrait of a kitten
portrait of a knight
portrait of a lady vampire
portrait of a lion
portrait of a lisa frank
portrait of a llama
portrait of a mad general
portrait of a mage
portrait of a male hydromancer
portrait of a male punk
portrait of a male warrior
portrait of a man
portrait of a man in a suit
portrait of a man in profile
portrait of a mark zuckerberg
portrait of a mech
portrait of a mechanical girl
portrait of a medieval nobleman
portrait of a medieval old king
portrait of a melancholic lady
portrait of a melancholic woman
portrait of a mid 1 9 th century
portrait of a mid 19 th century
portrait of a mind flayer
portrait of a minotaur
portrait of a muscular
portrait of a muscular man
portrait of a museum art curator
portrait of a mystical giant eye
portrait of a napoleonic admiral
portrait of a navy seal soldier
portrait of a necromancer
portrait of a norse moon goddess
portrait of a nurse on a stool
portrait of a old
portrait of a old woman
portrait of a ominous girl
portrait of a patchwork boy
portrait of a pink gang
portrait of a polar bear
portrait of a priestess
portrait of a princess
portrait of a pug as the pope
portrait of a queen
portrait of a rat mad scientist
portrait of a red haired girl
portrait of a red haired woman
portrait of a red sorcerer
portrait of a robot
portrait of a robot shaman
portrait of a rugged ranger
portrait of a rugged warrior
portrait of a rugged young man
portrait of a sacred serpent
portrait of a samurai
portrait of a samurai warrior
portrait of a sci - fi woman
portrait of a scorpion goddess
portrait of a sharp eyed
portrait of a slender elven man
portrait of a slightly rusty
portrait of a small
portrait of a small character
portrait of a smiling
portrait of a smoking woman
portrait of a soldier
portrait of a sorceress
portrait of a space alien
portrait of a space cyborg
portrait of a squid wizard
portrait of a starving ai artist
portrait of a steampunk catgirl
portrait of a steampunk ice lady
portrait of a stormtrooper
portrait of a sugar skull
portrait of a sunken ship
portrait of a teen robot
portrait of a us navy seal
portrait of a vampire
portrait of a victorian duke
portrait of a victorian era duc
portrait of a victorian era duke
portrait of a vigilante
portrait of a viking
portrait of a warrior
portrait of a werewolf
portrait of a white cat
portrait of a witch
portrait of a wizard
portrait of a wolf
portrait of a wolf head
portrait of a woman
portrait of a woman sleeping
portrait of a woman underwater
portrait of a woman warrior
portrait of a woman's face
portrait of a yari ashigaru
portrait of a young
portrait of a young elf wizard
portrait of a young empress
portrait of a young girl
portrait of a young italian male
portrait of a young man
portrait of a young pirate
portrait of a young pocahontas
portrait of a young teenage girl
portrait of a young witch
portrait of a young witch girl
portrait of a young woman
portrait of a young women
portrait of a zentaur
portrait of a zombie
portrait of abraham lincoln
portrait of achilles
portrait of adam jensen
portrait of ahri
portrait of ahsoka tano
portrait of alan turing
portrait of alan watts
portrait of albert einstein
portrait of albino mystic
portrait of alexander abdulov
portrait of alexandra daddario
portrait of alexandre dumas
portrait of alice in wonderland
portrait of an absurdly graceful
portrait of an absurdly ugly
portrait of an adventurer
portrait of an ai
portrait of an ai astronaut
portrait of an alcoholic
portrait of an alien
portrait of an android
portrait of an anime girl
portrait of an anthro fox
portrait of an astronaut
portrait of an elf
portrait of an elf queen
portrait of an elven warrior
portrait of an insectoid
portrait of an octopus goddess
portrait of an old
portrait of an old man
portrait of an orc
portrait of an orc warrior
portrait of an ork
portrait of ana de armas
portrait of angela merkel
portrait of angelina jolie
portrait of anime girl
portrait of anime woman
portrait of annasophia robb
portrait of anthropomorphic bear
portrait of anubis
portrait of anya taylor-joy
portrait of apex legends
portrait of archie andrews
portrait of ariana grande
portrait of aristotle
portrait of artemis
portrait of arya stark
portrait of astronaut
portrait of asuka langley soryu
portrait of audrey hepburn
portrait of avatar korra
portrait of bald
portrait of barack obama
portrait of barbara palvin
portrait of barbie doll
portrait of bart simpson
portrait of batman
portrait of beautiful
portrait of beautiful girl
portrait of beautiful samus aran
portrait of beautiful vampire
portrait of beautiful woman
portrait of beautiful young man
portrait of bedouin d&d
portrait of ben stiller
portrait of bender from futurama
portrait of benedict cumberbatch
portrait of benjamin netanyahu
portrait of bernie sanders
portrait of bertrand russell
portrait of beyonce
portrait of billie eilish
portrait of black labrador
portrait of black mermaid
portrait of blade runner rachael
portrait of bob odenkirk
portrait of bob ross
portrait of bojack horseman
portrait of brad pitt
portrait of bruce lee
portrait of captain america
portrait of captain jack sparrow
portrait of cate blanchett
portrait of celtic goddess diana
portrait of charlize theron
portrait of cheech and chong
portrait of chester bennington
portrait of chimpanzee in space
portrait of chris evans
portrait of christy ren
portrait of chuck clayton
portrait of chun - li
portrait of chun li
portrait of circe
portrait of circus artist
portrait of cleopatra
portrait of cloud strife
portrait of combat dancer
portrait of computer & circuits
portrait of computer circuits
portrait of conan the barbarian
portrait of crazy post malone
portrait of cristiano ronaldo
portrait of cthulhu
portrait of cute anime girl
portrait of cute girl
portrait of cute goth girl
portrait of cute mallard duck
portrait of cute pilot girl
portrait of cute soldier girl
portrait of cyborg
portrait of cyrano de bergerac
portrait of daemons
portrait of daft punk
portrait of dangerous
portrait of danny devito
portrait of danny gonzalez
portrait of danny trejo
portrait of darth vader
portrait of david bowie
portrait of deadpool
portrait of demi rose
portrait of demigod hercules
portrait of demon girl
portrait of depressed teen
portrait of destiny from sandman
portrait of doc savage
portrait of dog
portrait of dolly parton
portrait of don knotts
portrait of donald trump
portrait of dragoon
portrait of drizzt do'urden
portrait of dwayne johnson
portrait of edward and bella
portrait of einstein
portrait of elle fanning
portrait of elon musk
portrait of elsa from frozen
portrait of elsa of arendelle
portrait of elvis presley
portrait of emily blunt as queen
portrait of emma frost
portrait of emma stone
portrait of emma watson
portrait of emperor norton
portrait of emperor of mankind
portrait of emperor palpatine
portrait of eren yeager
portrait of ernest khalimov
portrait of eva green
portrait of evil girl
portrait of face
portrait of fairy
portrait of fairy princess
portrait of fairy woman
portrait of family of three
portrait of female
portrait of female android
portrait of female humanoid
portrait of female korean idol
portrait of female paladin
portrait of female rogue
portrait of female sorcerer
portrait of figther jet evading
portrait of fin wildcloak
portrait of florence pugh
portrait of forest gog
portrait of freddy fazbear
portrait of gal gadot
portrait of gandalf
portrait of garfield
portrait of geisha
portrait of george washington
portrait of geralt of rivia
portrait of gigachad
portrait of girl
portrait of girl in flower field
portrait of god
portrait of goku
portrait of gordon ramsay
portrait of greek god ares
portrait of grim reaper
portrait of guts from berserk
portrait of hades
portrait of hank hill
portrait of happy a young woman
portrait of harley quinn
portrait of harry potter
portrait of hatsune miku
portrait of he - man
portrait of head and body
portrait of helen of troy
portrait of henry cavill
portrait of hide the pain harold
portrait of high school girl
portrait of hildegard von bingen
portrait of hisoka hunter hunter
portrait of hollow knight
portrait of homelander
portrait of homer simpson
portrait of hugh laurie
portrait of immense
portrait of iron man
portrait of japanese gyaru
portrait of jean grey
portrait of jeff bezos
portrait of jerma985
portrait of jesus christ
portrait of jim morrison
portrait of jinx from arcane
portrait of joe biden
portrait of john cena
portrait of john f kennedy
portrait of john wick
portrait of johnny depp
portrait of joker
portrait of jordan peterson
portrait of jossi of blackpink
portrait of jughead jones
portrait of julia roberts
portrait of junkrat
portrait of kanye west
portrait of karl pilkington
portrait of kate winslet
portrait of katy perry
portrait of keanu reeves
portrait of kermit the frog
portrait of kim kardashian
portrait of kim petras
portrait of kim wexler
portrait of knight
portrait of kobe bryant
portrait of kpop idol
portrait of kratos
portrait of lady gaga
portrait of lady maria
portrait of lady mechanika
portrait of lain iwakura
portrait of lara croft
portrait of lebron james
portrait of leonardo dicaprio
portrait of linus torvalds
portrait of lolita
portrait of lucario
portrait of lucha libre dj
portrait of luffy from one piece
portrait of lumastina celerraria
portrait of machine man
portrait of maci holloway
portrait of mad lady scientist
portrait of magical blond prince
portrait of magical girl
portrait of magical lolita girl
portrait of magical young girl
portrait of male humanoid
portrait of man
portrait of man's terrified face
portrait of margot robbie
portrait of marilyn monroe
portrait of marine le pen
portrait of mario
portrait of mark zuckerberg
portrait of max caulfield
portrait of mayuri shiina
portrait of medieval old king
portrait of medusa
portrait of megan fox
portrait of megan fox as a queen
portrait of megan fox as demon
portrait of megara
portrait of mei from overwatch
portrait of merlin
portrait of mermaid
portrait of mermaid queen
portrait of mermaid warrior
portrait of metallic face
portrait of mf doom
portrait of mia farrow
portrait of michael jackson
portrait of mickey mouse
portrait of mischievous
portrait of modern darna
portrait of monk
portrait of morana
portrait of morgan freeman
portrait of morning coffee
portrait of mournful
portrait of mr bean
portrait of mulan
portrait of muscular
portrait of mélenchon
portrait of natalie portman
portrait of nicholas cage
portrait of nick offerman
portrait of nicole aniston
portrait of nicole kidman
portrait of nikola tesla
portrait of ninja slayer
portrait of nordic girl
portrait of normal teenage girl
portrait of obama
portrait of ororo munroe
portrait of orson welles
portrait of paris
portrait of paulette goddard
portrait of pee-wee herman
portrait of pele
portrait of penelope cruz
portrait of pepe the frog
portrait of pocahontas
portrait of portrait
portrait of princess
portrait of princess diana
portrait of princess leia
portrait of princess merida
portrait of princess peach
portrait of princess zelda
portrait of priyanka chopra
portrait of professor sinister
portrait of putin
portrait of putin in space
portrait of queen
portrait of queen elizabeth ii
portrait of queen of dreams
portrait of queen of light
portrait of quetzalcoatl
portrait of radical lolita girl
portrait of raven
portrait of real life totoro
portrait of retarded wolf
portrait of rick and morty
portrait of rick sanchez
portrait of ricky gervais
portrait of royal princess
portrait of rugged adult female
portrait of rugged male ranger
portrait of rugged zeus
portrait of rung
portrait of rutger hauer
portrait of ryan gosling
portrait of sadako of the ring
portrait of saint of the pit
portrait of saitama
portrait of sam hyde
portrait of samuel l. jackson
portrait of sanna marin
portrait of saul goodman
portrait of sauron
portrait of scarlett johansson
portrait of sherlock holmes
portrait of sherlyn chopra
portrait of shrek
portrait of sigmund freud
portrait of skeletor
portrait of small
portrait of snoop dogg
portrait of snow white
portrait of sofia vergara
portrait of soldier girl
portrait of sonic the hedgehog
portrait of space marine
portrait of spiderman
portrait of steve jobs
portrait of strange
portrait of super mario
portrait of tall
portrait of taylor swift
portrait of teen girl
portrait of teenage aphrodite
portrait of teenage emma stone
portrait of teenage girl
portrait of teenage medusa
portrait of terminator
portrait of thancred
portrait of the actress
portrait of the angel of death
portrait of the angelina jolie
portrait of the death angel
portrait of the exiled god
portrait of the god of death
portrait of the holy father
portrait of the hulk
portrait of the joker
portrait of the man
portrait of the mona lisa
portrait of the necromancer
portrait of the old necromancer
portrait of tifa lockhart
portrait of timothee chalamet
portrait of tintin
portrait of tom cruise
portrait of tom hanks
portrait of tom holland
portrait of trump
portrait of two girls kissing
portrait of two people
portrait of ultra realistic
portrait of vanessa morgan
portrait of very beautiful elf
portrait of very beautifull girl
portrait of virgin mary
portrait of vladimir putin
portrait of voldemort
portrait of walter white
portrait of wario from warioware
portrait of white teenage girl
portrait of wild
portrait of willow smith
portrait of wolverine
portrait of woman
portrait of woman's face
portrait of women
portrait of women embracing
portrait of wonder woman
portrait of xenomorph
portrait of yoda
portrait of young man
portrait of young woman
portrait of zelda
portrait of zendaya
portrait of zeraora
portrait of zeus
portrait oil painting
portrait painting
portrait painting of a princess
portrait painting of he - man
portrait painting of skeletor
portrait photo
portrait photo of a backdrop
portrait photo of an old man
portrait photo of emma watson
portrait photo of taylor swift
portrait photo of walter white
portrait photograph
portrait photography
portrait photography 4 k
portrait photography 4k
portrait photoreal
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portrait pixel art drawing
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portrait rugged girl
portrait sabrina lloyd
portrait sarah michelle gellar
portrait saruman
portrait shot
portrait shot 8 k
portrait shot 8k
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portrait skull clown
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portrait soft low light
portrait sophie mudd
portrait still detailed
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portrait tilda swinton
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portrait willow smith
portrait winning photograph
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portrait zeus
portrait!!
portrait!!!
portrait!!! mononoke-hime style
portrait!!!!
portrait!!!!!!
portrait. 8 k high definition
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portraits
portraits of a woman enraged
portraiture
portrait”
portre of a psyschonaut demon
portugal
posable pvc
pose
pose 1 of 1 6
pose 4 of 1 6
pose model
pose reference
pose study
pose(arms up + happy)
posed
posed in profile
poseidon
poser
poses
poses for the camera
posessed
posh
posing
posing and flexing
posing as a jojo character
posing as a queen
posing au naturel
posing elegantly
posing elegantly over the camera
posing elegantly over you
posing for a fight
posing for a fight intricate
posing for a picture
posing for art photography
posing for battle
posing for camera
posing for playboy photoshoot
posing for poster photography
posing for the camera
posing heroically
posing in a bedroom
posing in an urban street
posing in bed
posing in bra
posing in disguise among humans
posing in dramatic lighting
posing in front of bombed city
posing in leotard and tiara
posing in neo - tokyo futuristic
posing in waikiki
posing like a falling model
posing like a knight
posing like a statue
posing like a superhero
posing on a beach with the ocean
posing on the beach
posing on wheat field
posing ready for a fight
posing robotically
posing solo
posing together in bra
posing with a sword
posing with crossed arms
posing!!
positing on rock super hero pose
positive
positive and fun vibes
positive atmosphere
positive energy
positive mood
positive vibes
positivism
posse features
possessed
possibly an angel
possibly extra limbs
post - apocalypse
post - apocalyptic
post - apocalyptic car blueprint
post - apocalyptic city
post - apocalyptic city streets
post - apocalyptic cowboy
post - apocalyptic cowgirl
post - apocalyptic epcot center
post - apocalyptic magic kingdom
post - apocalyptic scavenger
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post - minimalism
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post - processing
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post - punk album cover
post - punk new age album cover
post - soviet courtyard
post - war scenery
post apocalypse
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post apocalyptic ancient ruins
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post apocalyptic wasteland
post apocalyptic world
post apocalyptic. makoto shinkai
post appocalyptic
post capitalism
post graduate
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post grunge
post grunge concept art
post grunge portrait
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post impressionism
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post nuclear fallout
post process
post process in photoshop
post processed
post processed 4k
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poster art
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poster for short film blind
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postprocessed
postprocessed)
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posuka demizu
pot
pot leaf
pot plants
pot-bellied
potato
potato skin
potatoes
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potion
potion belt
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potions
potrait
potrait of a female face
pots and pans
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pots with plants
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potted plants
pottery
pouches
pouncing
pounds of weed
pour cell painting
pour paint
pour painting art
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pouring techniques
pout
pouting
pouty
pouty face
pouty lips
pouty look :: octane render
pov
pov camera looking into the maw
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pov furry art
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powder
powdered sugar
powdered wig
power
power armor
power aura
power auras
power bi dashboard
power bright neon energy
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power implants
power lines
power metal album cover
power plants
power plants with smoke
power pose
power ranger
power rangers
power rangers film
power runes
power stance
power washed texture
powerful
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powerful and huge
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powerful explosions
powerful fantasy epic legends
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powerful male scarecrow
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powerfull
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powering up. hyperdetailed
powerlines
powerpuff girls style
powers
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pp
pr shoot
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practice
practising her sword staces
practising sword stances
prada
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prague
prague in the background
prairie
prairie in background
prairie landscaping
praise jesse pinkman
praise the blood moon
praise the omnissaiah
praise the sun
praised
praising the sun
prana
pranckevicius
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prayer flags
prayer hands
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praying at the sun
praying mantis
praying meditating
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praying with tobacco
pre - historic
pre - historic cave man family
pre - raphaelite
pre - raphaelite beautiful
pre - raphaelite brotherhood
pre - raphaelite painting
pre - raphaelite style
pre - raphaelites
pre - raphaelites style
pre - raphaelitism
pre - ww 1 submarine
pre raphaelite
pre raphaelite art style
pre renaissance art
pre-raphaelite
pre-raphaelite oil painting
pre-raphaelite painting
pre-raphaelite paintings
pre-raphaelite style
pre-raphaelite. art noveau
pre-raphaelites
pre-rendered
preaching in a fantasy city
precious
precious elements
precious gems
precious metal
precious metals
precious moments
precious stones
precious valuable elements
precise
precise anatomy
precise and intricate linework
precise architectural rendering
precise brush strokes
precise correct anatomy
precise faces
precise lines
precise linework
precise machinery
precise stippling
precise texture
precise textures
precise vector trace
precise! vector trace
precisely drawn
precisevector trace
precision
precision detail
precisionist
precisionist style
predator
predator 2
predator alien
predator eyes
predator film
predator movie
predatory praying mantis
predawn
prefect body
prefect symmetrical face
prefecture streets
pregnancy
pregnant
pregnant belly
pregnant female cyborg
prehistoric
prehistoric cave art
prehistoric cave painting
prehistoric fantasy
prehistoric hell pigs
prehistoric planet
prehistory
prelude to the esoteric
premiere
premium
premium bathroom design
premium cybernetics
premium prints available
premium quality
preparing for battle
preparing to fight
preppy style
preraphaelite
preraphaelite style
prerendered graphics
prerendered isometric graphics
presentation
presenting a large pizza
presenting magic gem
presenting pizza
presenting wares
presents
preserved historical
preserved museum piece
president
president donald trump
president joe biden
president of belorussia
president of brazil
president waluigi
presidental elections candidates
presidential
presidential cross
presidential fusion
presidential portrait
press
press conference
press paparazzi photograph
press photo
press photograph
press photography
press photos
press release
press shot
pressed flowers
pressed penny art
preston blair
pretentious
prettt
pretty
pretty anime character design
pretty anime face
pretty anime girl
pretty aquiline nose
pretty blueeyes
pretty clothing
pretty clothing!
pretty clouds
pretty color
pretty colors
pretty eva green vampire
pretty eyes
pretty face
pretty face features
pretty face sharp chine
pretty face with arms and legs
pretty face!!
pretty female amy adams
pretty female cleric
pretty female eva green vampire
pretty female phoebe tonkin
pretty flowers
pretty girl
pretty girl standing in the rain
pretty girl with blue hair
pretty highlights and specular
pretty lights
pretty long hair
pretty make up
pretty margot robbie vampire
pretty oval face
pretty pretty face
pretty samurai with afro
pretty smile
pretty sorceress
pretty space stars
pretty symmetrical face
pretty white dress
pretty woman
prey
prey movie ( 2 0 2 2 )
prey pov
price of persia movie
pride
pride and prejudice
pride flag in background
pride month
pride parade
prideful
prideful look
priest
priestess
priestess in a conical hat
priestess of the damned
prima ballerina
prima ballerina in rose garden
primal
primary color scheme
primary colors
primary colors are white
primary colours
prime
prime lens
prime lense
prime lense)
primeval duality
primeval fantasy
primeval jungle
primitive
primitive atmosphere
primordial
primordial waters
prince
prince crown of black gears
prince crown of pink gears
prince crown of red gears
prince in pastel!!!!
prince of egypt
prince vegeta
princes jasmin
princess
princess 'kida' kidagakash
princess ariel
princess at a royal banquet
princess diana
princess girl
princess in foreground
princess intergalactica
princess jasmine
princess kida kidagakash
princess leia
princess mononoke
princess mononoke color scheme
princess mononoke inspired
princess of amethyst
princess of cannabis
princess of darkness
princess of light
princess of the wasteland
princess peach
princess peach)
princess portrait
princess zelda
principal photography
principal set photography
print
print advertising
print magazine
print magazine quality
print on magazine
print ready
print!
printed circuit board
printed on a cream linen t-shirt
printed on paper
printed page
printerest
printmaking
prints
pripyat
prism
prism details
prism highlights
prism lights
prism shadows
prism undertones
prismacolor
prismacolor pencils
prismatic
prismatic colors
prismatic cumulus clouds
prismatic halo
prismatic highlights
prisms
prisms refracting light
prison
prison background
prison bars
prison cell
prison city
prison complex
prison jumpsuit
prison scene
prisoner
pristine
pristine and clean
pristine and clean design
pristine clean design
pristine concept art
pristine global illumination
pristine marble trunk
pristine quality wallpaper
pristine rippling oceanic waves
pristine skin
pristine water
pritty
pritzker architecture prize
private academy entrance
private collection
private moment
private school
prize winning
prize winning color photo
prize winning photo
prizewinning
pro - vida
pro photography
probably in her 30s
procedural
procedural code
procedural light
procedural rendering
procedural textures
procedurally generated
process
processing
processor
processualization
procrastination
procreate
procreate 2 0 2 2
procreate illustration
product
product - view
product advertisement
product advertising
product concept
product design
product design concept
product design render
product design shot
product display
product display photograph
product image
product introduction photo
product introduction photos
product label
product lighting
product lighting. 4 k
product photo
product photo studio lighting
product photograph
product photography
product photography 4 k
product picture
product placement
product render
product shoot
product shot
product showcase
product studio lighting
product view
production animation cel
production art
production i g
production i.g
production ig
production ig studios
production photo
production quality cinema model
production ready
production still
production volume rendering
productphoto
products photo from gun magazine
products shot
profesional photo
profesional photography
professional
professional 3d render
professional 8 k
professional advertisement
professional advertising
professional art
professional art photography
professional artist
professional artwork
professional award winning photo
professional background
professional body paint
professional branding
professional camera
professional capture
professional cgi
professional character design
professional character designer
professional closeup photo
professional color photograph
professional color photography
professional comercial vibe
professional composition
professional concept art
professional cooperate
professional corporate portrait
professional cosplay
professional design
professional detailed photo
professional digital art
professional digital edit
professional digital painting
professional digital photo
professional drawing
professional dslr photo
professional food photo
professional food photography
professional foot photography
professional foto
professional fruit photography
professional furry drawing
professional grade
professional graflex photograph
professional graphic design
professional gunsmithing
professional headshot
professional high quality scan
professional illustration
professional image
professional iphone photo
professional kodak lenses
professional light
professional lightimg
professional lighting
professional logo
professional logo design
professional makeup
professional master piece
professional model
professional model photography
professional modeling
professional nature photography
professional oil painting
professional online branding
professional paint job
professional painting
professional photo
professional photo shoot
professional photo-n 3
professional photograph
professional photographer
professional photography
professional photography 1 6 k
professional photography 16 k
professional photography canon
professional photoshoot
professional photoshooting
professional photoshop artwork
professional picture
professional portfolio
professional portrait
professional portrait drawing
professional portrait hd
professional portrait photo
professional portrait photograph
professional product photo
professional product photography
professional product shot
professional profile photo
professional profile picture
professional quality
professional render
professional result
professional retouch
professional shading
professional shoot
professional shooting
professional shot
professional sketch
professional sports style
professional studio
professional studio light
professional studio lightening
professional studio lighting
professional studio photo
professional studio photograph
professional studio photography
professional studio shot
professional unique master piece
professional unique masterpiece
professional vector graphic
professional wedding photography
professional woodcarving
professional work
professionally
professionally assembled
professionally color graded
professionally designed
professionally detailed
professionally done
professionally made
professionally post - processed
professionally post-processed
professionally retouched
professionally taken
professor calculus
professor clothes
proffesional
proffesional illustration
profile
profile close-up view
profile face
profile image
profile of anime girl
profile perspective
profile photo
profile photography
profile pic
profile picture
profile picture 1024px
profile portrait
profile portrait of a woman
profile pose
profile posing
profile putin
profile shot
profile shot of rimuru tempest
profile view
profile view perspective
profound
profound and vast
profound epiphany
profuse
programmable black goo
programmer
programming
progressive rock album cover
project zomboid screenshot
projection design installation
projection mapping
projections
projections of
proko
prolific
promethean alien engineer
prometheus
prometheus (2012)
prometheus 2012
prometheus alien engineering
prometheus engineer
prometheus engineering
prominent big eyes
prominent cheekbones
prominent jaw and visible fangs
prominent jawline
prominent keel-mounted railgun
prominent rosy cheek bones
promised land
promo
promo art
promo image
promo material
promo photo
promo photograph
promo poster
promo shoot
promo shot
promo still
promotion artwork
promotional
promotional art
promotional artwork
promotional image
promotional images
promotional material
promotional media
promotional movie poster
promotional movie posterart
promotional movie still
promotional photo
promotional photograph
promotional photography
promotional photoshoot
promotional picture
promotional portrait
promotional poster
promotional poster art
promotional render
promotional shot
promotional still
promotional still wide angle
prompt
prompt young woman
prompt: fragile looking figure
pronounced cheekbones
pronounced contours
pronounced eyes
pronounced facial contouring
proof
proof of concept
proof photograph
prop
prop design
prop rocks
propaganda
propaganda art
propaganda logo
propaganda metacentrist
propaganda poster
propaganda style
propaganda-print
propane tanks
proper anatomy
proper human male proportions
proper human proportions
proper proportions
proper shading
propganda poster
prophecy
prophet graphic novel
prophetic art
proporcional
proportion
proportional
proportional body
proportional face
proportional faces
proportional human body
proportional image
proportional object content
proportionally accurate
proportionally enormous arms
proportionate
proportionate body parts
proportionate face
proportionate facial details
proportions
proportions off
proportions on a circle
props
props containing trees
prosaic
prosperity
prostheses
prosthetic
prosthetic arm
prosthetic eyeballs
prosthetic leg
prosthetic limbs
prosthetic makeup
prosthetics
prostitutes
protagonist
protagonist in foreground
protal
protect
protection
protective
protest
protest movement
protesters holding placards
proteus vulgaris
protgraphy
proto - metal band promo
proto-metal
proto-metal concert
protogen
protons
protophyta
protoss
protoss temple!!!
protoss!!
protoss!!!!
prototype
prototype car
protozoa
protrait
protruding bones
protruding chin
proud
proud emperor
proud look
proud looking
proud looking away
proud onion hybrid thanos
proud serious expression
proud smile
proud smirk
proudly holding a salmon
proudly walking down the street
provenance
provia
provocative
provocative indian
prowling through the forest
prussia
prussian blue
prussian blue and azo yellow
prussian blue and raw sienna
prussian blue and venetian red
ps
ps 1
ps 1 graphics
ps 1 hagrid
ps 2
ps 2 game box keyart
ps 2 graphics
ps 2 screenshot
ps 3
ps 3 graphics
ps 3 screenshot
ps 3 video game
ps 4
ps 4 gameplay
ps 4 in - game cinematic
ps 4 screenshot
ps 5
ps 5 gameplay
ps 5 screenshot
ps1
ps1 game
ps1 game landscape
ps1 graphics
ps2
ps2 game
ps2 graphics
ps3 game
ps4
ps5
ps5 cinematic screen capture
ps5 game
ps5 render quality
pscychodelic
psd spritesheet
pseudo - medieval fantasy
psilocybin
psion
psionic
psp
psx
psx game graphics
psx graphics
psx screenshot
psychadelic
psychadellic colors
psychdelic lights
psychdelic multicolored
psychedel tint
psychedelia
psychedelic
psychedelic acid trip
psychedelic aesthetic
psychedelic and glittering
psychedelic architecture
psychedelic art nouveau
psychedelic art style
psychedelic artwork
psychedelic atmosphere
psychedelic background
psychedelic black light
psychedelic black light style
psychedelic clouds
psychedelic color scheme
psychedelic color theme
psychedelic colorization
psychedelic colors
psychedelic colouring
psychedelic colours
psychedelic concert poster
psychedelic cosmic horror
psychedelic digital art
psychedelic dmt
psychedelic dripping colors
psychedelic droplets of water
psychedelic dust
psychedelic effects
psychedelic experience
psychedelic fern
psychedelic fever dream
psychedelic floral planets
psychedelic flowers and trees
psychedelic flowing hair
psychedelic forest
psychedelic fractal art
psychedelic fractal pattern
psychedelic fractals
psychedelic frog
psychedelic glitch art
psychedelic goddess
psychedelic hair
psychedelic hallucinations
psychedelic highlights
psychedelic hip hop
psychedelic hip-hop
psychedelic illustration
psychedelic interconnections
psychedelic interpretation
psychedelic landscape
psychedelic laughing demon
psychedelic light show
psychedelic lighting
psychedelic lights and fog
psychedelic lsd
psychedelic lsd manga
psychedelic lush pine forest
psychedelic manga
psychedelic mushrooms
psychedelic mushrooms dream
psychedelic organic cyborg
psychedelic organic shaman
psychedelic overtones
psychedelic painting
psychedelic patterns
psychedelic photoluminescent
psychedelic shaman
psychedelic ski resort
psychedelic sky
psychedelic smoke background
psychedelic style
psychedelic surreal art
psychedelic swimsuit
psychedelic therapy
psychedelic triangular skeleton
psychedelic trip
psychedelic trippy visionary art
psychedelic vegetation
psychedelic vibrant colors
psychedelic visuals
psychedelic waves
psychedelic!
psychedelic!!
psychedelic!!!!
psychedelic!!!!!!!!!!!!!!!!!!!!!
psychedelics
psychedellic
psychedellic fractals
psychic
psychic girl
psychic mind flayer
psychic powers
psychic tv concert
psycho
psycho magick fuck it insane
psycho pass
psycho stupid fuck it insane
psychodelic
psychodelic colors
psychokinetic girl
psychologic
psychological
psychological horror
psychological horror movie still
psychology
psychology :: concept art
psychonaut universe
psychopath
psychopath face
psychosis
psychoslayer
psychotic
psychotropic psychedelic
psylocke
psytrance
psytrance and giger
psytrance artwork
pteranadon styling
pterodactyl
pterodactyl mecha
pterosaurs flying
ptsd
pub
pub sign
public art
public bus
public condemned
public shame
public space
public works mural
publicity cosplay
publicity photo
published art
published concept art
puce and vermillion
puddle of milk
puddle of water
puddle of water on the floor
puddles
puddles of turquoise water
puddles of water
puddles of water on the ground
pudenda
pudge from dota 2
pudgy
pudica gesture bouguereau style
pudica pose
pudica pose bouguereau style
pudica pose gesture
pueblo architecture
pueblo dense architecture
puerto rican super hero
puerto rico
puff sleeves
puffballs
puffer jacket
puffs of colored smoke
puffs of smoke
puffs of thick black smoke
puffy
puffy clouds
puffy clouds in background
puffy cloudscape
puffy cloudy sky
puffy cute clouds
puffy lips
puffy peaceful clouds
puffy sticker
puffy white clouds
pug
pug-faced
puka shell necklace
puke
puking blood
pulittzer winner
pulitzer
pulitzer award
pulitzer award winner
pulitzer prize
pulitzer prize for photography
pulitzer prize winning
pulitzer prize winning photo
pulitzer winning
pulitzer-prized photo
pulled
pulled apart
pulled into the spiral vortex
pulled pork sandwich
pulling strings
pulling the move'derp banshee '
pulling the move'the banshee '
pulling the movederp banshee
pulling the movethe banshee
pulling weeds out frantically
pullitzer
pullitzer winning
pulp
pulp art
pulp character portrait
pulp comic style
pulp femme fatale
pulp fiction
pulp fiction movie
pulp fiction style
pulp illustration
pulp magazine
pulp magazines
pulp magazines cover art
pulp noir
pulp sci fi
pulp sci-fi
pulp science fiction
pulp scifi
pulp scifi book cover art
pulp scifi illustration
pulp style
pulp style poster
pulsar
pulsating
pulse
pulse projections
pulse rifle
pulse rifles
pulsing energy
puma
pumpkin
pumpkin farm background
pumpkin head
pumpkin patch
pumpkins
punchable expression
punchable face
punching
punching in a bag
punished
punisher
punk
punk album cover
punk art
punk attitude
punk concert
punk fanzine 1 9 6 7
punk fanzine cut outs
punk girl
punk grim reaper
punk hairstyle
punk inspired
punk little girl
punk outfit
punk outfit. cute
punk party
punk portrait made out of paint
punk rock
punk rock album cover art style
punk rock clothes
punk rock poster
punk rock with mohawks
punk rocker
punk style
punk woman
punk xerox
punk!!!
punk-rocker
punkdrone
punks
punks not dead
punks not dead!
punks not dead!!!!
punpun onodera
pupil
pupils visible
puppet
puppet master
puppet on a string
puppets
puppies
puppy
puppy as a dj
pur champagne damery
pure
pure aura
pure b & w
pure b w
pure b&w
pure background color
pure black
pure blue sky
pure color background
pure evil
pure face
pure gold pillars
pure grey fur
pure image without duplication
pure joy
pure skin
pure white
pure white background
pure white hazy overcast sky
pure white overcast sky
purgatory
purity
purple
purple - tinted
purple accent lighting
purple accents
purple aethetic
purple alien
purple ambient light
purple ancient antler deity
purple and black
purple and black clothes
purple and black color palate
purple and black color scheme
purple and blue
purple and blue and green colors
purple and blue color scheme
purple and blue colored
purple and blue colour palette
purple and blue leather
purple and blue neon
purple and blue neons
purple and cyan lighting
purple and gold color scheme
purple and green
purple and green colors
purple and green fire
purple and pink
purple and pink and blue neons
purple and pink hair
purple and pink leather garments
purple and red
purple and red color bleed
purple and red colors
purple and red flowers
purple and scarlet colours
purple and white thich cloak
purple and yellow
purple and yellow lighting
purple armor
purple aura
purple background
purple beautiful sky
purple bioluminescence
purple blue color scheme
purple body
purple bridges with leggins
purple cat
purple checkerboard
purple checkerboard background
purple cloak
purple cloth
purple clothes
purple color
purple color lighting
purple color palette
purple color pallete
purple color scheme
purple color-theme
purple colored eyes
purple colored eyes!!!!
purple colors
purple colour scheme
purple crimson color scheme
purple crystal glass inlays
purple crystal inlays
purple crystal jewelry
purple crystals
purple cyberpunk city
purple dim light
purple drank
purple drapery
purple dress
purple energy
purple eye
purple eye color
purple eyes
purple eyes and white dress
purple eyes with red eye markers
purple eyes!!!
purple feathers
purple filter
purple fire
purple fire around magic arena
purple fire powers fire swirling
purple flesh
purple flower trees
purple flowers
purple flowing hair
purple fog
purple foliage
purple fur
purple future city
purple glow
purple glowing core in armor
purple glowing eyes
purple glowing inscription
purple gradient
purple greek robe
purple green color scheme
purple hair
purple hair portrait of woman
purple halter top
purple haze
purple head
purple highlights
purple hue
purple hues
purple jacket
purple laser lighting
purple leather dungeon bedroom
purple leather garments
purple leather jacket
purple leather wings
purple light
purple lighted street
purple lighting
purple lightning
purple lights
purple lipstick
purple liquid
purple liquid in cup glowing
purple long hair
purple magic
purple makeup
purple metal ears
purple mist
purple mullet
purple nebula
purple neon
purple neon colours
purple neon light
purple omnious sky
purple orange colors
purple outfit
purple overall shorts
purple palette
purple rain
purple raincoat
purple ribbons
purple robe and veil
purple robes
purple roofs
purple sand
purple scene lighting
purple shattered paint
purple shattered paint!
purple shiny bob haircut
purple skin
purple skin color
purple sky
purple smoke
purple sparkles
purple streaks in hair
purple sun
purple sunset
purple themed
purple tie
purple tint
purple tones
purple top
purple tornado
purple transparent sci - fi hood
purple trees
purple tubes
purple tunic
purple vest
purple volumetric lighting
purple water
purple watery eyes
purple. ambient lightning
purple. smooth shank
purples
purplish space in background
purpose is pump
purpose unknown
pursed lips
pus
pus - filled boils
push-up underwire
push-up underwire. intricate
pushead art
pustules
put on 1 0 0 pounds of muscle
put on 100 pounds of muscle
put on a mannequin
putin
putin as james bond 0 0 7
putin crying
putin face
putin inhaling from copium tank
putin is bald caveman
putin portrait
putin riding a bear
putin with beard
putrid
putti
puttin
putting makeup on
putting on lipgloss
puyallup berteronian
puzzle
puzzle art
puzzle-like room
puzzling
puṣkaracūḍa
pv
pvc
pvc armor
pvc figurine
pvc posable figure
pvc poseable
pygmy hippopotamus
pylons
pyongyang
pyongyang city
pyramid
pyramid background
pyramid head
pyramid head from silent hill
pyramid hoodvisor
pyramid in background
pyramid portal
pyramid ruins at sundown
pyramid scheme
pyramid surrounded with greenery
pyramid visor
pyramids
pyramids in background
pyramids in the background
pyramids in the desert
pyramids of egypt
pyramids of giza
pyramids style
pyranees
pyre
pyroclastic flow
pyrography
pyromallis
pyromallis nekro rene margitte
pyromallis rene maritte
pyromancer
pyrotechnics
pyrrhic victory
pyrrol scarlet
pythagorean theorem
python
q
q hayashida
q posket
qapla
qi baishi style
qi sheng luo
qiangshu
qichao wang
qifeng lin
qing dynasty
qing dynasty painting
qirex
qiu fang
qiyana
qled
qliphoth
quack medicine
quackery
quadrichromic
quadruped
quadrupedal
quaint
quaint village
quak
quake
quake  1996  pc game
quake ( 1 9 9 6 ) pc game
quake engine
quake strogg
qualia
quality
quality astral projection render
quality cinema model
quality digital art
quality draughtmanship
quality illustration
quality lighting
quality rendering
quantum
quantum break
quantum computer
quantum computing
quantum deep magic
quantum dynamics
quantum entanglement
quantum fractals
quantum leap
quantum mechanics
quantum paradox
quantum particles
quantum physics
quantum processor
quantum sensor resonator
quantum superposition
quantum technology
quantum tracerwave
quantum tracerwave!
quantum waves
quantum wavetracer
quantum wavetracing
quarter view
quartz
quartz crystal
quasar
quaternary
quebec
quechua
quechua!
quechua!!
queen
queen and ruler of the universe
queen chess piece
queen chess piece photo
queen chess piece photo portrait
queen crown
queen crown on top of her head
queen elizabeth
queen elizabeth ii
queen in a glass prison
queen jezebel
queen margherita of savoy
queen of atlantis
queen of blades
queen of bones
queen of darkness
queen of death
queen of dragons
queen of england
queen of flowers
queen of hearts
queen of heaven
queen of hell
queen of ice and storm
queen of longing
queen of nature
queen of snakes
queen of the dead
queen of the fairies
queen of the forest
queen of the galaxy
queen of the ghosts
queen of the jungle
queen of the night
queen of the sea mu yanling
queen of the underworld
queen of war
queen of warrior
queen of winter
queen victoria
queen's collector
queens collector
queens gambit
queer
queer woman
quentin mabille
quentin tarantino
quentin tarantino action shot
quentin tarantino movie posters
quest marker
question marks
quetzal
quetzal and star-nosed mole
quetzalcoatl
quick assembly
quick sketch
quick witted
quicksilver
quiet
quiet and serene atmosphere
quiet beauty
quiet disdain
quiet forest night scene
quiet from metal gear solid
quiet from metal gear solid v
quiet intensity
quiet street
quiet tokyo alley at night
quilt
quinacridone magenta
quinn
quint
quintessa
quirky
quirky 1 9 th century
quirky 19 th century
quirky king of faes ( with long
quirky shops
quivering lips
quixel
quixel megascan
quixel megascans
quixel mixer
quixel textures
quokka
quotev
quy ho
qvc
qwek dom
r - mk
r / art
r / battlemaps
r / paintedminis
r / vexilology
r /art
r 2 d 2
r 2d 2
r crumb
r&b
r-number
r. crumb
r. giger
r/art
r/aww
r/morrowind
r/skywind
r2d2
r6
raal
rabbids at a metal concert
rabbit
rabbit ears
rabbit face only
rabbit groot as marble statue
rabbit robot
rabbit shaped helmet
rabbit warrior
rabbit_bunny
rabbits
rabbt_character
rabid
rabies
raccoon
raccoon on roof
race
race car
race footage
race style
race track background
racecar
racers
rachel amber
rachel birkett
rachel mcadams
rachel wall
rachel walpole
rachel walpole art
rachel weisz
rachel weisz portrait
racing
rack
rack focus
racking focus
racks
racoon
racoon dog
racoon holding a laser gun
racy
rad
raden saleh
radial
radial color dispersion
radial light
radial symmetry
radially symmetrical
radiant
radiant alien
radiant atmosphere
radiant aura
radiant backlighting
radiant colors
radiant cut
radiant flares
radiant glory
radiant halo
radiant halo of light
radiant light
radiant lighting
radiant morning light
radiant nebula colors
radiant owl
radiant quality
radiant skin
radiant slime trail
radiant smile. ultra wide shot
radiant soft light
radiant vibrant colors
radiant volumetric lighting
radiating
radiating a glowing aura
radiating atomic neon corals
radiating dark energy aura
radiating golden light
radiating power
radiating rebirth energy
radiating with power
radiation
radiation glow
radiation mutated
radiator
radical
radio
radio box
radio equipment
radio goggles
radio signals
radio telescope
radioactive
radioactive horror painting
radioactive particles
radioactive spike field
radioactive swamp
radioactive wasteland
radiohead
radiohead album art cover
radiohead album cover
radiohead logo
radiohead singer thom yorke
radiolaria
radios
radiosity
radiosity rendering
radoslav svrzikapa
rads
raf grassetti
raf grassetti neville page
raf simons
raf simons fashion couture
rafael
rafael albuquerque
rafael albuquerque art
rafael albuquerque comic art
rafael grassetti
rafaelle monti and beksinski
rafał olbinsk and salvador dali
rafeal albuquerque
rafeal albuquerque comic art
raffael
raffaelle monti
raft gameplay
ragdoll cat
rage
rage against the machine
rage against the washing machine
raggae art
ragged
ragged clothes
raging
raging bugbear
raging sea foggy
raging storm
ragnarok
ragnarok online
rags
rahul gandhi face
rahul gandhi hairstyle
raiden from metal gear rising
raiden metal gear
raider
raiders of the lost ark
rail tracks
rail tracks lead from the mine
railgun
railing
railing along the canal
railings
railroad
rails
railway tracks going through it
railways
raily season
rain
rain aesthetic
rain and haze
rain and light fog
rain and lightning
rain and mud
rain and smoke
rain and smoke and lightning
rain and thick strands of mucus
rain droplets frozen in time
rain drops
rain drops on face
rain falling
rain falls
rain is falling
rain like a dream
rain lit
rain of blood
rain on screen
rain puddles
rain red color bleed
rain sensor
rain splashing
rain storm
rain stormy fog
rain water reflections in ground
rain! storm
rain!!!!
rain. hyper detailed
rainbow
rainbow accents
rainbow aura crystals
rainbow aura quartz
rainbow aurora
rainbow background
rainbow bg
rainbow bubbles
rainbow caustic lighting
rainbow clothes
rainbow clouds
rainbow color scheme
rainbow colored
rainbow colored clouds
rainbow colored dust mask
rainbow colored hair
rainbow colors
rainbow coloured rockets
rainbow colours
rainbow corals
rainbow dash
rainbow diffraction
rainbow drip paint
rainbow eyes
rainbow fireflies
rainbow fur
rainbow geometric face
rainbow gouache
rainbow gradient bloom
rainbow gradient reflection
rainbow hair
rainbow hair overlay
rainbow in the sky
rainbow iridescent accents
rainbow lighting
rainbow line - art
rainbow liquids
rainbow melting color scheme
rainbow mycelium
rainbow neon strips
rainbow overlay! beautifully lit
rainbow overlaybeautifully lit
rainbow reflection
rainbow reflections
rainbow river waterfall
rainbow road
rainbow sheen
rainbow sheep like cotton candy
rainbow shift
rainbow smoke
rainbow splash of ink
rainbow stripe backdrop
rainbow stripe background
rainbow trail
rainbow tubing
rainbow wings
rainbowcore
rainbows
rainbows in the background
rainbowshift
raindrop
raindrops
rainfall
rainfall and mud
rainforest
rainforest background
rainforest mountains
raining
raining ash
raining at night
raining award winning photo
raining blood
raining dollars
raining fire
raining glass shards
raining outside
raining outside the cafe
raining outside the window
raining portrait
raining rainbow
raining!
raining! nighttime
raining!!
raining!!!
rainingnighttime
rainn wilson
rainny
rainstorm
rainy
rainy afternoon
rainy and foggy
rainy and gloomy atmosphere
rainy and wet atmosphere
rainy atmosphere
rainy background
rainy city at night
rainy cyberpunk city
rainy day
rainy day in minecraft
rainy day outside
rainy day. game render
rainy day. nintendo64
rainy environment
rainy evening
rainy mood
rainy night
rainy night city street
rainy outside
rainy scene
rainy storm
rainy stormy night
rainy street
rainy streets in the background
rainy urban streets
rainy weather
rainy wet
rainy window
rainy; 90's photograph
rainy; 90s photograph
raised a bloody american flag
raised eyebrow
raised eyebrows
raised fist
raised hand
raised hands
raising an arm
raising between the buildings
raj koothrappali as saul goodman
raking light
raku
rally car
rally driving photo
ralph bakshi
ralph goings
ralph horley
ralph horsley vivid color
ralph macquarrie
ralph mcquarie
ralph mcquarrie concept art
ralph mcquarrie. centered image
ralph steadman
ralph steadman style
ram antlers
ram horns
ram sheep robot
ram skull
ram skull headpiece
ram skulls
ramatic cinematic lighting
rambling
rambo
ramen
ramil sunga
rammed earth courtyard
rammstein
rampaging
ramps
random
random and dramatic scene
random artist
random artists
random background scene
random characters
random cinematic view
random circular platforms
random colors
random content
random content position
random cows
random detail
random english words
random forest landscape
random metallic colors
random object movement
random object position
random positions floating
random scheme color
random style
random things
random volumetric lighting
random weather
randomly lustrous colored hair
randomly lustrous dyed hair
randomly segmented rooms
randy bishop
randy travis
range murata
range murata and artgerm
range murata jeremy lipking
range murata yoshitaka amano
ranger
ranjit ghosh
ranker
rankin
raoul hausmann
rap
rap album cover
rap album cover art
rap battle
rap bling
rap metal
rap scene
raper artist
raphael high renaissance
raphael hopper
raphael lecoste
raphael personnaz
raphael style
raphaelite
raphaelite and alphonse mucha
raphaelite and mucha
raphaelites
raphaël
rapidograph
rapids
rapier
rapper
rapper bling jewelry
rapper jewelry
rapping
rapping into microphone
rapping on stage at festival
raptor
raptor jesus
raptors
rapture
rapunzel
raqib shaw
rare
rare bird in the jungle
rare classified photo
rare ltd
rare mineral rare raw metal
rare photograph
rasing of consciousness
raspberries
raspberry
raspberry banana color
rasputin
rasputin as grubhub character
rasta
rastafarian
rasterized
rat
rat fink style
rat kind mecha
rat king
rat man
rat with crown
ratatouille style
ratchet & clank
ratchet and clank
ratchet clank
ratchet straps
rated e10
rated r
rated t for teen
rateo
ratfink
rating: general
rating:g
ratio
ratio 16:9
rational painting
rats
rats!!
ratz
rave
rave art
rave background
rave girl
rave inspired
rave makeup
rave otufit
rave outfit
rave party
rave poster
raven
raven angel wings
raven bird
raven black
raven black hair
raven hair
raven mask
raven monster
raven winged female vampire
raven wings
ravenholm
ravenous
ravens
ravens stormy sky of foreboding
raver
raver girl
ravine
ravishing
ravnica
raw
raw dual pixel
raw egg yolks
raw emotion
raw file
raw malachite
raw pork
raw sienna
ray
ray - traced
ray - traced lighting
ray - tracing
ray - tracing radiosity
ray casting
ray cinematic
ray fawkes
ray gods
ray harryhausen
ray lewis
ray lewis yelling
ray lighting from top of frame
ray of golden sunlight
ray of light
ray of light through smoke
ray of light through white hair
ray of lights
ray of sunlight
ray swanland
ray trace
ray trace 4 k
ray trace 4k
ray trace 8k
ray traced
ray traced lighting
ray traced reflections
ray traced sun light
ray tracing
ray tracing 8 k
ray tracing 8k
ray tracing ambient occlusion
ray tracing global illumination
ray tracing lighting
ray tracing on
ray tracing on epic settings
ray tracing reflection
ray tracing reflections
ray tracing x
ray tracing. fractal crystal
ray tracing. luminous
ray tracing. sharp focus
ray william johnson
ray-traced
ray-tracing
raya and the last dragon
raycasting
raygun gothic
raygun gothic style
rayleigh scattering
rayman
rayman legends
rayman legends boss
rayman raving rabbids
raymarching
raymond
rayonism light effects and bokeh
rayonnant style
raypunk
rays
rays of blue moonlight
rays of god
rays of god shining from above
rays of golden red sunlight
rays of golden sunlight
rays of life
rays of light
rays of moonlight
rays of shimmering light
rays of sun
rays of sunlight
rays of sunlights
rays of sunshine
rays of the sun
rays of volumetric light
raytacing
raytarced
raytrace
raytrace concept art
raytraced
raytraced 3d set design
raytraced blade
raytraced highlights
raytraced lighting
raytraced lights and shadows
raytraced realistic
raytraced reflections
raytraced render
raytracing
raytracing : :
raytracing effects
raytracing on
raytracing reflections
raytracing shadows
raytracting
raytrayced
razaras
raziel irl
razor sharp teeth
razorgirl
razzle dazzle
rb 6 s
rb6s
rb6s)
rbc
rdr 2 mod
rdr2
re engine
re engine render
re life
re-raphaelite fairies
reach
reaching
reaching for the sky
reaching out to each other
reaching towards the heavens
reaction diffusion
reaction diffusion linocut
reaction-diffusion pattern
reactor
reactor circuits
reactor core
read
read a directory book
readability
readable
readable diagram
readable font
readhead
reading
reading a book
reading a book next to a lion
reading a newspaper
reading an evil demonic book
reading engineering book
reading for a party
reading glasses
reading in library
reading new book
reading nook
reading the book about love
reading the newspaper
reading under a tree
ready
ready - made
ready for a meeting
ready for battle
ready for combat
ready player one
ready to eat
ready to fight
ready to fly
ready to model
ready to strike
ready-made
readymade
reagents
real
real aircrafts references
real anime
real atmosphere
real bats
real bongos
real camera
real caustics
real clown makeup
real colors
real congas
real detailed face
real details
real dlsr photography
real engine
real engine 5
real engine 5 cinematic
real engine render
real estate photography
real event
real eyes that are detailed
real face
real faces
real feather
real flesh texture
real footage
real fur
real fur and real skin
real guitars hanging on the wall
real hair
real hands
real heart
real heart!
real hellscape in background
real human
real human eyes
real human face
real human face with skin
real human faces
real human feet
real human female hand
real image
real life
real life big mom
real life characters
real life charmander
real life colors
real life homer simpson
real life image
real life peter griffin
real life photo
real life photo of a syrian man
real life photograph
real life photography
real life picture
real life pikachu
real life pokemon
real life pokemons
real life portrait
real life size
real life skin
real life totoro
real lighting
real masterpiece
real pearls
real photo
real photograph
real photograph on the street
real photography
real photoshoot queen of oceans
real pianos
real picture
real picture taken in zoo
real polaroid photo
real polaroid picture
real porcelain texture effect
real rotten flesh
real rotten flesh and blood
real saxophones
real scale
real shot
real skin
real sousaphones
real steel
real steel  2011
real steel ( 2 0 1 1 )
real sunset
real teeth
real texture effect
real trending on instagram image
real ungine
real vintage photo
real wings
real world
real-life
real-life accurate
real-life brook
real-life homer simpson
real-life photograph
real-life skin
real-life tom and jerry
realflow
realiatic
realisitc
realisitc photo
realisitic
realism
realism  beeple
realism 8k
realism : 9 5 %
realism art
realism art style
realism artstyle
realism drawing
realism in style of fornite game
realism old painting
realism painting
realism style
realism tattoo
realism tattoo design
realism tattoo drawing
realism tattoo sketch
realism | beeple
realisms
realism”
realist
realist aetherpunk
realist painting
realist proportions
realista
realistc
realistc octane render
realistic
realistic - anime
realistic - n 9
realistic 3 d render
realistic 3 d style
realistic 3d
realistic 3d model
realistic 3d render
realistic 4 k
realistic 4k
realistic 4k photo
realistic 8 k
realistic 8k
realistic 8k bernini sculpture
realistic 8k hd
realistic 8k photography
realistic 8k render
realistic afternoon lighting
realistic aircraft
realistic aircrafts
realistic alien
realistic anatomical proportions
realistic anatomy
realistic and coherent
realistic and defined face
realistic and detailed
realistic and detailed eyes
realistic and detailed face
realistic and ultra detailed
realistic anime
realistic anime 3 d style
realistic anime 3d style
realistic anime art style
realistic anime artstyle
realistic anime cat
realistic anime face
realistic anime style at pixiv
realistic apocalyptic war scene
realistic architecture
realistic armor
realistic art
realistic art deco
realistic art style
realistic artstyle
realistic artwork
realistic background
realistic beautiful
realistic beautiful big eyes
realistic beautiful face
realistic bikini
realistic black and white
realistic body
realistic body features and face
realistic body proportions
realistic body shape
realistic body structure
realistic brown fur
realistic building
realistic cars
realistic cartoon
realistic cgi
realistic cgi render
realistic character anatomy
realistic character concept
realistic character concept art
realistic cinema 4 d render
realistic cinematic
realistic cinematic lighting
realistic cinematic shot
realistic cinematic style
realistic cloth puppet
realistic clothing
realistic clouds
realistic clown makeup
realistic color
realistic colorful photography
realistic colors
realistic composition
realistic concept
realistic concept art
realistic content
realistic cosplay
realistic creature concept
realistic crisp textures
realistic cute girl painting
realistic depiction
realistic depth
realistic detail
realistic detailed
realistic detailed background
realistic detailed face
realistic detailed face portrait
realistic detailed masterpiece
realistic detailed octane render
realistic detailed painting
realistic detailed photography
realistic detailed revolver
realistic details
realistic digital art
realistic digital art 4 k
realistic digital art 4k
realistic digital drawing
realistic digital illustration
realistic digital painting
realistic dirt
realistic drawing
realistic elements
realistic engine
realistic epic shaders
realistic establishing shot
realistic explosion
realistic extremely detailed
realistic eyes
realistic eyes looking at camera
realistic fabric
realistic face
realistic face and anatomy
realistic face and body hybrid
realistic face and details
realistic face details
realistic face moody lighting
realistic faces
realistic faces and anatomy
realistic faces and details
realistic faces and expressions
realistic facial expression
realistic facial features
realistic facial features ”
realistic fantasy
realistic fantasy artwork
realistic fantasy illustration
realistic fantasy painting
realistic fantasy photography
realistic fantasy render
realistic features
realistic female faces
realistic female portrait
realistic fingers
realistic fire sharp focus
realistic flavor
realistic flesh
realistic flesh texture
realistic flowers oil painting
realistic footage
realistic fortnite
realistic fur
realistic garden
realistic geometry
realistic glass sculpture
realistic gold
realistic graphics
realistic graphite
realistic grass
realistic gun
realistic gun design
realistic guns
realistic hair
realistic hand
realistic hands
realistic hd photo
realistic highly detailed
realistic horror
realistic horrors
realistic human
realistic human anatomy
realistic human eyes
realistic human face
realistic human feet
realistic human movement
realistic hyper detailed
realistic illustration
realistic illustrative painting
realistic image
realistic information
realistic intricate concept art
realistic intricate detail
realistic jelly splashes
realistic lance
realistic landscape
realistic lifelike dragon
realistic light
realistic light and shadow
realistic lighting
realistic lightning
realistic line drawing
realistic manga
realistic masterpiece
realistic materials
realistic matte painting
realistic maya
realistic mechanical details
realistic mega structure
realistic metal
realistic metal reflections
realistic military carrier
realistic military equipment
realistic military gear
realistic moon in the background
realistic mouth
realistic movie still
realistic nature
realistic ocean
realistic octane 3d
realistic octane render
realistic oil on linen
realistic oil paint
realistic oil painting
realistic old photo
realistic old photograph
realistic paint
realistic paint job
realistic painting
realistic painting effect
realistic painting of a complex
realistic painting style
realistic paleoart
realistic parallel content
realistic pencil drawing
realistic people
realistic peoples
realistic perfect face
realistic person
realistic photo
realistic photo from nasa
realistic photo of a town
realistic photo of delicious pho
realistic photo shoot
realistic photo studio photoshop
realistic photograph
realistic photography
realistic photography paleoart
realistic photo”
realistic physical rendering
realistic picture
realistic pictures
realistic polaroid picture
realistic portrait
realistic portrait full body
realistic portrait photo
realistic portrait photography
realistic professional photo
realistic proportions
realistic proportions sfw
realistic proportions!!
realistic rackets
realistic refine flavor
realistic reflection
realistic reflections
realistic refraction
realistic renaissance portrait
realistic render
realistic rendering
realistic rendering for stool
realistic restored face
realistic robotic machinery
realistic rock figurine
realistic scale
realistic scene
realistic schoolgirl
realistic sci - fi
realistic scifi
realistic self portrait
realistic sensual gravure idol
realistic shaded
realistic shaded lighting
realistic shaded lighting poster
realistic shaded perfect anatomy
realistic shaded perfect body
realistic shaded perfect face
realistic shaded robotic parts
realistic shaders
realistic shading
realistic shading and lighting
realistic shading photorealism
realistic shadows
realistic shapes
realistic sharp details
realistic shot
realistic sketch
realistic skin
realistic skin color
realistic skin shader
realistic skin texture
realistic sky
realistic smoke
realistic soft lighting
realistic soldiers
realistic space
realistic spanish woman painting
realistic studio lighting
realistic studio photo
realistic studio portrait
realistic style
realistic symmetrical eyes
realistic tattoo
realistic texture
realistic textured
realistic textured magnetosphere
realistic textured skin
realistic textures
realistic textures from photos
realistic unreal engine 3 d game
realistic unreal engine 3d game
realistic unreal engine 5
realistic vfx simulation
realistic volumetric lighting
realistic warship design
realistic water
realistic water sharp focus
realistic water splashes
realistic watercolour
realistic weapon
realistic weapons
realistic wide angle photo
realistic windows
realistic wood swamp
realistic young anime girl
realistic young gravure idol
realistic | detailed face
realistic »
realistic ”
realistic!
realistic! 8k
realistic!!
realistic!!!
realistic''
realistic-n 9
realistic. 4 k
realistic. 8 k
realistic. beautiful lighting
realistic. cheng yi
realistic. dark atmosphere
realistic. detailed feathers
realistic. detailed fur
realistic. intricate
realistic. octane render
realistically
realistically detailed shading
realistically proportioned body
realistically proportioned face
realistically proportioned head
realistically rendered clothing
realistically rendered eyes
realistically rendered face
realistically shaded
realistictic
realistic”
realististic
realistoc
realitic
reality
reality bending
reality infused with dreams
reality tv
reality warping
realityengine
reallife
reallism
reallistic
reallusion character creator
really
really beautiful forest
really beautiful nature
really clear
really close - up shot
really cool pose
really detailed
really funny
really good looking face!!
really good vibes
really large bosum
really large bust
really long
really realistic
really scary
really short dark gray hairs
really short hair
really trying to ride it
realm
realm of life
realm of the ovarian machine
realms
realosm
realsitic
realstic lighting
reaper
reaper from overwatch
reaper miniatures
reaper minis
reaper of night!!!!
rear facing
rear lighting
rear shot
rear side portrait of a muscular
rear view
rear-shot
reasonable fantasy
reasonable lighting
reassuring
reaver drone
rebecca oborn
rebecca romijn as dorothy
rebecca sugar
rebel
rebelliousness
rebirth
rebirth rituals
rebirth symbolism
reboot
rebuild of evangelion 1 0 8 0 p
rebuild of evangelion 1080 p
receding hairline
recessed
recipe
recital
reckless cohort
reclaimed lumber
reclections
reclining
reclining on bed
recognizable
recolored
recommended
reconstruction
record jacket
recovering from pain
recreation
rectangle
rectangle white porcelain table
rectangles
rectangular
rectangular face
rectangular piece of art
rectangular pool
rectilinear
rectilinear vaporwave
recursion
recursive
recursive fractal
recursive fractals
recursive portrait
recursive!!!!
recusion beeple
recycled
red
red & black fur
red + black + dark blue + beige
red - black
red - cheeks
red - cheeks!!
red - eyed
red - iris
red - toned mist
red - yellow - blue building
red accents
red adornements
red adornments
red afro
red afro dreadlocks on fire
red airsoft electric pistol
red and black
red and black and white
red and black cape and hoodie
red and black color palette
red and black color scheme
red and black colors
red and black colour scheme
red and black costume
red and black costume!!!
red and black details
red and black flags
red and black flags waving
red and black robotic parts
red and black suit
red and black tones
red and blue
red and blue back light
red and blue black light
red and blue color scheme
red and blue color theme
red and blue eyes
red and blue garments
red and blue lighting
red and blue neon
red and blue reflections
red and brown color scheme
red and cinematic lighting
red and cyan
red and cyan ink
red and cyan theme
red and gold
red and gold cloth
red and gold ornate dress
red and gold sumptuous garb
red and golden color details
red and green
red and green color scheme
red and green hour
red and green lighting
red and green palette
red and green tones
red and grey only
red and magenta flowers
red and obsidian neon
red and orange color scheme
red and orange colored
red and orange glow
red and purple
red and purple coloring
red and purple nebula
red and purple palette
red and teal and yellow
red and teal color scheme
red and white
red and white and black colors
red and white color scheme
red and white color theme
red and white colors
red and white flowers
red and white lighting
red and white marble panels
red and white neon
red and white stripes
red and yellow
red and yellow color scheme
red and yellow light
red and yellow scheme
red apple
red apples
red armor
red ascot and a field cap
red atmosphere
red aura
red background
red background photorealistic
red backwards cap
red baggy eyes
red ballpoint pen
red bandana
red banners
red barn in distance
red beard
red beryl
red bikini
red biomechanical
red biomechanical details
red biomechanical dress
red bird
red birthmark
red black and gold color scheme
red black and white
red black fur
red black white colors
red black white golden colors
red blood
red blooming flowers
red blue
red blue and gold color scheme
red blue color scheme
red blue purple black fade
red blue theme
red blush
red body suit
red boots
red bow in hair
red bra
red braided hair
red bricks
red brocade and blue gemstones
red brown and blue color scheme
red brown and grey color scheme
red brown and white color scheme
red building
red bull
red camera
red cap
red cap with a capital m
red cape
red car
red carpet
red carpet photo
red carpet photography
red carpeted floor
red castle in background
red caviar instead of sand
red cheeks
red cinema camera
red cloak
red cloth
red cloth around his shoulders
red cloth background
red clothes
red cloud
red cloud light
red clouds
red clown nose
red coat
red color
red color bleed
red color palette
red color scheme
red color theme
red colored
red colors
red colour palette
red contact lenses
red contacts
red crimson crimson hair
red crimson hair
red cross
red cumulonimbus clouds
red curled hair
red curly hair
red curtain
red curtains
red dead
red dead redemption
red dead redemption 2
red dead redemption art style
red dead redemption2
red demon armor
red demon cyberpunk symbols
red desert
red desert mars
red details
red dish
red door blonde
red dot
red dragon
red dress
red dress and hat
red dress and magic witch hat
red dust
red dusty soil
red dyed hair
red eerie sky
red elegant shirt
red emerald
red eye
red eyed
red eyes
red eyes and a slim face
red eyes glowing
red eyes highly detailed
red eyes wide open
red fabric
red fabric coat
red face
red faced
red facial stripe
red feathered wings
red feathered wings for arms
red fiery watery eyes
red filter
red fire eyes
red flag
red flags
red flags holiday
red flames
red flames in background
red flesh strings
red floral dress
red flower
red flowers
red flowers of different types
red fluid
red fluid on walls of the church
red fog
red fog on the ground
red forest
red forman
red forman as ironman
red fox
red fur
red gems scattered like dust
red giant
red gloves
red glow
red glow eye
red glow in sky
red glowing eyes
red glowing hair
red glowing skin
red glowing streams of lava
red glowing veins
red gold and black outfit
red gold black
red gold color palette
red gold hair
red grass
red green
red green black teal
red green color palette
red green white black
red green yellow color scheme
red grid
red gundam head
red hair
red hair and attractive features
red hair and freckles
red hair girl
red hairband
red haired girl
red haired goddess
red haired teen boy
red haired young woman
red hat
red haze
red head
red headband
red headphones
red hearts
red heavy armor
red high heel pumps
red high heels
red highlights
red hood cosplay
red hooded mage
red hoodie
red hoods
red horizon
red horns
red hot
red hot soldering iron
red hue
red hues
red illuminating fog
red ink
red ink sharp focus
red interior
red intricate long braids
red iris
red irises
red irises and a slim face
red iron oxide
red jacket
red jewelry
red jumpsuit
red kimono
red kimono with flower patterns
red lace
red lake
red lanterns
red laser
red laser scanner
red lasers
red lava rivers
red leather armor
red leather corset
red leather interior
red leather jacket
red leather short coat
red leaves
red leaves on the ground
red led eyes
red led lights
red leds
red left eye
red left eye stripe
red lens flare
red light
red light bulbs
red light from some windows
red light saber
red lighting
red lighting on their faces
red lightning
red lightning!!
red lights
red lightsaber
red lips
red lipstick
red lipstick on face
red liquid
red liquid dripping from above
red long hair
red long wavy hair
red lunar eclipse
red magic
red magic surrounds her
red meat shreds
red mech
red mech arms and red mech legs
red mechanical body
red mesh in the facede
red mini cooper s
red mist
red monochrome
red mood in background
red moon
red moon over stormy ocean
red mouth
red narrow lava rivers
red nebula
red necktie
red neon
red neon details
red neon eyes
red neon light
red neon lights
red neon lights inside it
red neon roses
red nose
red ocher
red ogre riding a horse
red on black
red only
red orange and yellow leaves
red orange blue beige
red orange lighting
red oval turban
red paint
red paint detail
red paint strip across eyes
red painted metal
red palette
red panda
red panda on a propaganda poster
red peaks in the background
red pennants
red phoenix tattoo on back
red planetoid exploding
red poppies
red power
red puppils
red purple gradient
red rain
red realistic 3 d render
red realistic 3d render
red reflections
red reflective lens
red ribbon
red riding hood
red rim light
red rising planet
red river
red robe
red robes
red ronald mcdonald hair
red ronald mcdonald hairstyle
red ronald mcdonald messy hair
red roofs
red room
red rose
red rose in hair
red roses
red roses at the top
red round nose
red salamander
red sand
red sand beach
red sandstone natural sculptures
red scales
red scales on his back
red scarf
red sea
red selective coloring
red shell. dirt track
red shift
red shift render
red shirt
red shirt brown pants
red shoes
red short dress
red short hair
red silk flowing fabric
red silk scarf
red skies
red skin
red skinned
red skintight leather armor
red skull
red sky
red sky background
red sky blue
red smoke
red smoke coming from lamp
red smoke follows his movement
red son
red spike aura in motion
red sport clothing
red spotlight
red sprites in the atmosphere
red square
red square moscow
red stormy sky
red stripe
red suit
red sun
red sun in the background
red sun over paradise
red sunglasses
red sunglasses and a guitar
red sunset
red sweatband
red sweater and gray pants
red swirls
red t-shirt
red tail lights
red tailcoat
red tank top and wide blue pants
red tank-top
red tears
red theme
red tie
red tint
red tones
red torn fabric
red trees
red trusses
red turquoise accents
red umbrella
red uniform
red veins
red velvet
red velvet furniture
red vest
red waist-long hair
red wall
red wallpaper background
red wallpaper design
red warning lights
red water
red weapon 8 k s 3 5
red weapon 8k s 35
red webs
red webs and fungus
red white and black
red white and black color scheme
red white and black colors
red white and gold color scheme
red white background
red white black colors
red wig
red wine
red wings
red wires wrap around
red woods canopy
red woods canopy love
red writing
red yellow
red yellow black
red yellow blue
red yellow flag
red!! sand
red+yellow colours
red-eyed
red-eyes
red-fabric
red-purple gradient map
red-yellow colors
redahair and attractive features
reddish
reddish - brown
reddish beard
reddish exterior lighting
reddish gold hair
reddish hair
reddish lava highlights
reddish lighting
reddit meme
reddit moderator
reddit post
reddit vexillology
redering
redgold hair
redhead
redhead female cyberpunk
redhead girl
redhead queen in heavy red armor
redhead woman
redlettermedia
redneck
redneck country
redneck engineering
rednered with raytracing
redpink sunset
reds
reds)
redscale photography
redsfhit render
redshift
redshift houdini
redshift render
redshift renderer
redshift vibes
redshift. micro details
redsinski
reduce character duplication
reduce duplicate content
reduce duplicate image
reduce duplication
reduce duplication interference
reduce saturation
reduced colors
reduced contrast
reduced minimal illustration
reduced visibility
redundancy
redwall
redwood background
redwood forest
redwood forest themed armor
redwood sequoia trees
redwood trees
redwoods
reebok
reed on riverbank
reed richards
reeds
reef
reefs
reese witherspoon
reese witherspoon face
refer to late timothy leary
reference
reference art
reference images ( front
reference model sheet
reference sheet
reference sheet white background
refik anadol
refined
refined editorial photograph
refined face
refined face and muzzle
refined features
refined nose
refined spontaneity
reflect
reflect 8 k uhd
reflect 8k uhd
reflect photograph
reflected chrome
reflected in giant mirror
reflected light
reflecting
reflecting an erupting volcano
reflecting flower
reflecting light
reflecting light in a nightclub
reflecting mount fuji
reflecting pool
reflection
reflection and refraction
reflection echo
reflection in the water
reflection in water
reflection lumen mapping
reflection map
reflection of iphone in visor
reflection of led lights
reflection of phone in visor
reflection of the moon
reflection on helmet
reflection on the oil
reflection on the water
reflection on water
reflection puddles
reflections
reflections and refraction
reflections and refractions
reflections in copper
reflections on a glass table
reflections on the river
reflections on the water
reflections on wet streets
reflections ray
reflections smooth
reflections specular highlights
reflections. by makoto shinkai
reflections. shady
reflective
reflective aviator sunglasses
reflective chessboard
reflective chitin
reflective detailed textures
reflective eyes
reflective faces
reflective floor
reflective glass
reflective global illumination
reflective gradient
reflective ground
reflective lavender ocean water
reflective lens
reflective light
reflective lighting
reflective material
reflective metal
reflective metallic
reflective objects
reflective orchid flower
reflective pool
reflective puffer jacket
reflective puffy coat
reflective scales
reflective skin
reflective suit
reflective sunglasses
reflective surface
reflective surfaces
reflective vest
reflective visor
reflective water
reflective water koi pond
reflective windows
reflects
reflexes
reflexions
refracted
refracted color lines
refracted color sparkles
refracted light
refracted lighting
refracted line and sparkles
refracted lines and sparkles
refracted moon on the ocean
refracted moon sparkles
refracted sparkles
refracted sunset
refracted sunset lighting
refracting
refracting light
refraction
refraction and reflection
refraction caustics
refraction lights
refractions
refractions on lens
refractive
refractive adornments
refractive and reflective
refractive crystal
refractive crystal jewelry
refractive light
refractive optics
refreshing
refreshing colour
refrigerated storage facility
refrigerator
refugees
reg rutkowski
regal
regal aesthetic
regal and menacing visage
regal and proud robust woman
regal clothing
regal dark blue clothes
regal fast food joint
regal pose
regale
regency
regency-era
regeneration
reggae
reginald marsh
register
regret
regrets
regular build
regular sized
rei ayanami
rei hino as a princess
rei hiroe
rei hiroe black lagoon manga
rei kawakubo artwork
reimagined by industrial light and magic
reimu hakurei
reina rocin
reinassance
reinassance painting
reinassance style
reincarnated as a slime
reincarnation
reindeer made out of shadows
reinhardt from overwatch
reisen udongein inaba
reivaille
rejected concepts
relatable
relax
relaxed
relaxed colors
relaxed dwarf with white hair
relaxed expression
relaxed eyebrows
relaxed octopus on moon
relaxed pose
relaxed poses
relaxed posture
relaxed style
relaxed. blue background
relaxed. gold background
relaxing
relaxing after a hard day
relaxing and smiling at camera
relaxing at the beach
relaxing atmosphere
relaxing calm vibes
relaxing concept art
relaxing environment
relaxing mood
relaxing on a couch
relaxing on a modern couch
relaxing on a yacht at sea
relaxing on the beach
relaxing on the beach at sunset
relaxing on the couch
releasing pain
relentlessly detailed
relic
relief
relief concept
relief engraving
relief sculpture
religion
religious
religious art
religious awe
religious composition
religious iconography
religious imagery
religious masterpiece portrait
religious painting
religious robes
religious sculpture
religión
reliquary
relish
relistic
reluvy5213
rem koolhaas
rem rezero
remains
remake
remarkable
remarkable geisha make up
remarkable joker make up
remaster
remastered
remastered chromatic aberration
rembrandt and gigadō ashiyuki
rembrandt art style
rembrandt exhibition
rembrandt light
rembrandt lighting
rembrandt lighting 35mm
rembrandt lighting scheme
rembrandt lightning
rembrandt oil
rembrandt oil painting
rembrandt painting
rembrandt style
rembrandt style painting
rembrandt!
rembranlt
rembrant
remedios varo uranga
remedios varo y salvador dali
remembering his life
remembrance
remilia scarlet from touhou
reminded me of the grim reaper
reminiscent of blade runner
remnants of old spaceship
remote control disco backpack
remote icelandic village
remove
remove duplicate content
remove duplicate content!!!!
removed watermarks
ren and stimpy
ren and stimpy style
ren heng
ren xun and maruyama ōkyo
rena nounen style 3/4
renaissance
renaissance ambiance
renaissance art
renaissance art style
renaissance artwork
renaissance autumnal
renaissance color palette
renaissance colorful dress
renaissance composition
renaissance digital painting
renaissance drawing
renaissance era
renaissance era clothing
renaissance fair
renaissance lighting
renaissance mural
renaissance nimbus overhead
renaissance oil painting
renaissance oil portrait
renaissance painting
renaissance painting of a knight
renaissance painting of evil men
renaissance painting of man
renaissance painting style
renaissance painting'
renaissance painting”
renaissance port city background
renaissance portrait
renaissance prince
renaissance religious art
renaissance religious painting
renaissance still life painting
renaissance style
renaissance style painting
renaissance themed
renaissance!!! painting!!!
renaissancepainting
renamon
renascentist
renata glasc
renato muccillo
renault
renault ultimo
render
render 3 d
render 8 k
render 8k
render amazing
render corona
render in blender
render in octane
render in pixar
render in re engine
render in unreal engine 5
render in vray
render naughty dog
render octane
render of a cute 3d anime girl
render of april
render of futuristic supercar
render of heavy fantasy armor
render of mirabel madrigal
render pixar palette
render to resolute perfection
render unreal engine
render unreal engine - h 7 0 4
render unreal engine - h 704
render unreal engine-h 704
render vray
rendered
rendered 3d model
rendered art
rendered illustration
rendered image
rendered in 3 dsmax
rendered in 32k huhd
rendered in 4 k
rendered in 8 k unreal engine
rendered in 8k unreal engine
rendered in arnold
rendered in arnold engine
rendered in blender
rendered in cgi
rendered in cinema 4 d
rendered in cinema 4 d octane
rendered in cinema4d
rendered in corona
rendered in cryengine
rendered in enscape
rendered in gouache painting
rendered in high octane
rendered in houdini
rendered in keyshot
rendered in lumion
rendered in lumion pro
rendered in maya
rendered in maya 4 d
rendered in maya and houdini
rendered in nvidia's omniverse
rendered in octane
rendered in octane 3d
rendered in octane render
rendered in octane render 32k
rendered in pov - ray
rendered in povray
rendered in redshift
rendered in renderman
rendered in rtx
rendered in sfm
rendered in the unreal engine
rendered in unity 3 d
rendered in unreal
rendered in unreal 5
rendered in unreal engine
rendered in unreal engine 3
rendered in unreal engine 3d
rendered in unreal engine 4 k hq
rendered in unreal engine 4k hq
rendered in unreal engine 5
rendered in unreal engine 6
rendered in unreal engine”
rendered in unrealengine
rendered in v-ray
rendered in vray
rendered in zbrush
rendered lighting
rendered on octane
rendered on unreal 3 d
rendered unreal engine 5
rendered with blender
rendered with octane
rendered with renderman
rendered with substance designer
rendered with unreal engine
renderer
renderfarm
renderhub
renderhub next2020
rendering
rendering 8 k
rendering 8k
rendering a blue rose
rendering of beauty pageant
rendering of checkmate
rendering of close up portrait
rendering of log pile factory
rendering of the windigo
rendering on unreal engine
rendering unreal engine 5
renderman
renderman render
rending on cgsociety
rendred in redshift
rene lalique
rene lalique and eddie mendoza
rene magritte. detailed
rene magritte. hyperdetailed
rene margitte
rene margitte illustrated
rene margritte
reneissance
renewable energy
rengen
rennaisance
rennaisance painting
rennaissance masters portrait
rennaissance painting
rennaissance style painting
rennes - le - chateau
renzo piano
rené laloux
repaired with kintsugi
repairing the other one
repeat pattern
repeatable
repeating
repeating 3 5 mm photography
repeating 35 mm photography
repeating 35mm photography
repeating fabric pattern
repeating pattern
repeating pattern. seamless
repeating patterns
repetition
repetitive
repetitiveness
repin
replica model
replicas
reportage
reportage photo
reportage photography
repoussoir
representation with abstraction
representative portrait
representing ed harris
representing liberty
representing the 4 seasons
repsol honda
reptil
reptile
reptile eyes
reptile face
reptile skin
reptiles
reptilian
reptilian cyborg
reptilian eyes
reptilian skin
reptilian slime
reptilian space alien
reptilian warrior
reptoids
republic of gamer
repugnant
repulsion
repulsive
rescalated 4 k
rescalated 4k
rescue from the underworld
rescue from the underworld!!!!!!
research center
research complex
research outpost
research station
resembling a crown
resembling a mix of grimes
reservoir dogs
reshift 3 d
resident evil
resident evil 1
resident evil 4
resident evil 7
resident evil inspired
resident evil virus concept art
residential
residential area
residential design
resignation
resin
resin and clay art
resin art
resin coated
resin miniature
resin painting
resin statue
resine figure
resistance
resistance is futile
resolute desk
resolute expression
resolution
resolve
resonance of fate tower art
resort
resources background
respirator
respiratory flap
resplendent
resplendent and proud of bearing
responsive
restaurant
restaurant exterior photography
restaurant in background
restaurant interior photography
restaurant menu photo
restaurant!
restaurant!!
restaurant!!!
resting
resting after a hard fight
resting after a hard mission
resting head on hands
resting on a pillow
resting on a tough day
resting on chest
resting on his throne
restomod
restoration
restored
restored color
restored colors
restored painting
restored photo
restrained
restrained. bloom
retail price 4 5 0
retaildesignblog
retaildesignblog.net
retarded
retarded wolf
retarded wolf portrait
retired barney
retopology
retouched
retouched in photoshop
retracing
retro
retro - futurism
retro - futuristic
retro - vintage
retro 1 9 0 0
retro 1 9 6 0 s sci - fi art
retro 1 9 7 0 s kodachrome
retro 1960 s sci - fi art
retro 3 d
retro 3 d graphics
retro 3d graphics
retro 5 0 s style
retro 6 0 s fashion
retro 9 0 s
retro 9 0 s japanese
retro 90 s japanese
retro ad
retro aesthetic
retro and 1 9 8 0 s style
retro and 1980 s style
retro and ethereal
retro and fantasy style
retro anime
retro anime girl
retro architecture
retro armor
retro art
retro art style
retro artwork
retro atmosphere
retro cartoon
retro color scheme
retro coloring
retro colors
retro colour
retro comic art style
retro computer graphics
retro cover
retro cyberpunk
retro dark vintage
retro dark vintage sci-fi
retro design
retro device
retro effect
retro fantasy
retro fantasy style
retro flat colors
retro future
retro futurism
retro futurism 1 9 5 0 s
retro futurism 1950 s
retro futurism art
retro futurism style
retro futurist
retro futurist style
retro futuristic
retro futuristic apartment
retro futuristic comics
retro futuristic fashion
retro futuristic illustration
retro futuristic style
retro game
retro game 1 9 8 0 style
retro game 1980 style
retro games
retro graphics
retro haze
retro illustration
retro label
retro lights
retro line art
retro machinery
retro movie poster
retro nintendo bitmap pixel art
retro pc graphics
retro photography
retro pink synthwave style
retro pinup model
retro poster
retro psychedelic illustration
retro pulp art
retro punk
retro sci - fi
retro sci - fi art
retro sci - fi movie
retro sci - fi picture
retro sci fi
retro sci fi film
retro sci-fi
retro sci-fi spaceship
retro science fiction
retro science-fiction book cover
retro scifi
retro space helmet
retro spaceships parked outside
retro style
retro style art
retro style ”
retro stylised
retro tech
retro technology
retro tv
retro vibe
retro vintage
retro vintage and romanticism
retro vintage art
retro vintage sci - fi
retro vintage screens
retro wave
retro-future
retro-futurism
retro-futuristic
retro-futuristic armor
retro-wave vibes
retrocore
retrofutistic
retrofutur
retrofuture
retrofuturism old rct screen
retrofuturism sci - fi old movie
retrofuturist
retrofuturist liminal space
retrofuturistic
retrofuturistic depressing
retrofuturistic digital painting
retrofuturistic female android
retrofuturistic science fantasy
retropunk
retroscifi
retrowave
retrowave ((synthwave))
retrowave art
retrowave atmosphere
retrowave color palette
retrowave color scheme
retrowave colors
retrowave colour scheme
retrowave epic art
retrowave noir
retrowave palette
retrowave style
retrowave synthwave
retrowave vibes
retrowave!
retrowaves
return of the jedi
return of the king. cinematic
return of the many to the one
returnal biome
reuben wu
reunion island
reunion island landscape
reuniting
reuters
revealing
revealing clothes
revealing outfit
revealing stylish dress
revealing wires and electronics
revelation
revellers
revenant
revenge
revenge of the sith
reveries
reversal film stock
reverse
revolution
revolutionary
revolutionary art style
revolver
revolver on chair
revolvers
revy black lagoon
revy from black lagoon
rex orange county
rey
rey and kylo ren
rey tracing
reyezuelo listado
reykjavik
reykjavik junior college
reylo kissing
reynold brown
reyyan
reza afshar
rgb
rgb color
rgb crt scanlines
rgb displacement
rgb ethereal lighting
rgb gamer toilet
rgb keyboard
rgb led
rgb led lights
rgb lighting
rgb lights
rgb shift
rgb wall light
rgb wires
rgba
rgba color
rgba colors
rgp artwork
rhads
rhads and lois van baarle
rhads and thomas kinkade
rhads watercolor
rhads!
rhads!!!
rhett and link
rhino
rhino beetle
rhino rendered
rhinoceros 3d
rhinoceros playing badminton
rhizomatic lifeform
rhizomatic network
rhizomorphs
rhizomuse
rhode island
rhys lee
rhythm
rhythm & hues
rhythm hues
rhythm of the wind
rhythmic
riabovitchev
rias gremory
rib cage
rib cage exposed
ribbed man face portrait
ribbon
ribbon chapel
ribbon in her hair
ribbons
ribbons and flowers
ribcage
ribs
ric flair
ricardo milos
riccardo federici
riccardo scamarcio
rice
rice paddies
rice paper texture
rich
rich and deep colors
rich and gaudily
rich aquarel
rich atmosphere
rich aztec jaguar armor
rich azure tones
rich blue color
rich blue colors
rich bright colors
rich bright colours
rich bright sunny colors
rich cinematic atmosphere
rich cold moody colours
rich color
rich color palette
rich colors
rich colour
rich colour and detail
rich colour scheme
rich colourful
rich colours
rich composition
rich contrast
rich decaying bleeding colors
rich decaying colors
rich decaying colors!
rich deep color scheme
rich deep colors
rich deep colors a masterpiece
rich deep colors masterpiece
rich deep colors. a masterpiece
rich deep colors. masterpiec
rich deep colors. masterpiece
rich deep colours
rich deep colours masterpiece
rich deep moody color
rich deep moody colors
rich deep pink
rich deep vibrant colors
rich deep vivid colours
rich detail
rich detailed pixels
rich details
rich details full of texture
rich diverse lush alien world
rich drapery
rich environment
rich estate
rich ethereal colours
rich evans
rich flower colors
rich forest
rich fresh colors
rich geometry
rich grainy texture
rich greenery
rich house
rich in details
rich in texture
rich in texture )
rich iridescent colors
rich iridescent specular colors
rich lighting
rich marbles
rich moody atmosphere
rich moody cold colours
rich moody colors
rich moody colour
rich moody colours
rich moody dark atmosphere
rich palette
rich pastel color palette
rich pastel colors
rich picturesque colors
rich red
rich red colors
rich style
rich texture
rich tree
rich vibrant colors
rich vibrant detailed textures
rich vines and verdant flowers
rich vivid color
rich vivid color scheme
rich vivid colors
rich vivid pastel colors
rich vivid vibrant colors
rich volumetric light
rich with epic details
rich woodgrain
richard amsel
richard corben style
richard garriott
richard iv the roman king
richard iv the roman king photo
richard kane - ferguson
richard mille
richard nixon
richard pearce
richard rider
richard scarry
richard sigamani
richard taddei
richly colored
richly decorated victorian house
richly defined face
richly detailed
richly detailed colored
richly textured
rick and morty
rick and morty in minecraft
rick and morty in real life
rick and morty pickle rick
rick and morty style
rick astley
rick baker
rick baker style
rick berry
rick dai
rick from rick and morty
rick grimes
rick grimes line art
rick guidice
rick owens
rick sanchez
rick sanchez in real life
rickroll
ricky berwick
ride horse
ride horse in saharan
ride on time
ride the wind and waves
rider waite card
rider-waite tarot
rides
rides a bike
ridiculous
ridiculous smile
ridiculously handsome
riding
riding a bike
riding a black horse
riding a camel
riding a cyborg raptor
riding a dragon
riding a futuristic motorcycle
riding a giant silver wolf
riding a hippo
riding a horse
riding a horse on the moon
riding a horse!!! in saharan
riding a motorbike
riding a motorbike down a street
riding a motorcycle
riding a skateboard
riding a skateboard in berlin
riding a unicorn
riding boots
riding horse
riding in the sky
riding in the town of doom
riding on a prancing horse
riding on the moped scooter
riding on the road
riding steampunk motorcycle
ridley scott and zack snyder
ridley scott film
ridley scott movie
ridley scott universe
riffle
riffle on chest
rifle
riflegun
rifles
riften
riften city in the background
right - half a cheerful face
right - side composition
right align
right anatomy
right angled view
right angles
right elevation
right hand side profile
right side composition
right side key light
right side profile
rigid
rigid bulky armor
rigidly defined abs
rigorous
rihanna
riichi ueshiba
rik oostenbroek
rim - light
rim highlights
rim lght
rim light
rim light and highlights
rim light on character
rim light smooth
rim light style tim burton
rim lighting
rim lights
rim lights and caustics
rim lights and glow
rim lights purple and green
rim lights wlop
rim lit
rim-light
rimight
rimlight
rimuru
rimuru and milim
rimuru playing chess
rimuru tempest
rin
rin tohsaka
rinat voligamsi
ring
ring - flash
ring flash closeup photograph
ring in the sky
ring light
ring lighting
ring lit
ring of fire
ringed planet in the sky
ringflash
ringflash lighting
ringlet
rings
rings asuka iwakura station game
ringworld
rinko kawaichi
rinko kawauchi
rino
rio de janeiro
rio de janeiro favela
rio de janeiro in an anime film
riot
riot background
riot entertainment
riot entertainment realistic
riot game
riot games
riot games concept art
riot in a cyberpunk city
riot shields
rioting
riotous
rip
rip and tear
ripley
ripley scott
ripped
ripped clothes holding whip
ripped clothing
ripped fabric
ripped flesh
ripped jeans
ripped pantyhose
ripped to shreds
ripped up field fatigues
ripped up white garment
ripping demons to shreds
ripping the fabric of spacetime
ripple
ripple effect
rippled white landscape
ripples
rippling
rippling dark cinnamon hair
rippling electromagnetic
rippling fabric of reality
rippling liquid
rippling magic
rippling muscles
rippling oceanic waves
rippling reflections
rippling trees
rippling water
rippling with magic
rips
rise above clouds
rise of the tomb raider
rising from a crypt
rising from mountain range
rising from ocean
rising from the void
rising in the air levitating
rising planet
rising smoke
rising steam
rising up from ocean
riske
risograph
risograph gradient
risograph poster
risograph print
risograph!
risograph!!!
risographic
risography print
risoprint
risque
risque fashion
ristan eaton
ritu kumar
ritual
ritual in a forest
ritual occult gathering
ritualistic
ritualistic tattoos
rituals
riven
riven from league of legends
rivendell
river
river and trees and hills
river at the bottom
river confluence
river delta
river flow through borneo jungle
river flowing beside the robot
river flowing through a wall
river god
river gorgeous lighting
river in a forest
river in front of him
river in the background
river in the wood
river island
river of blood
river of blue fire
river of wine
river otter dragon
river rapids
river running past the cottage
river running through it
river running thru the middle
river stour in canterbury
river stream
river styx
river thames
river with low flying parrots
river with low hanging plants
river with stunning water
riverdale
rivers
rivers and lakes
rivers of blood
rivers. space colony
riverside
rivets
rivuletpaper art
rivulets
riyahd cassiem
riyoko ikeda
rj palmer
rk post
rna bioweapon
road
road between hills
road between tall trees
road bike helmet
road california desert
road in a forest road
road into the forest with a lake
road street signs
road to perdition
road to perdition stillframe
road to the sea
road trip
road trip exciting
road warrior
roadhog from overwatch
roads
roads among fields
roadside
roadster
roald dahl
roaming entertainers
roaming the colony
roaring
roaring 20s
roaring blue lion. majestic
roaring fire
roaring mutant goat monster
roaring ocean in front
roaring twenties
rob
rob and hildebrandt
rob bottin
rob gonsalves and tim white
rob gonsavles
rob janoff
rob lee
rob mcnaughton
rob rey
rob rey and kentaro miura style
rob rey and kentarõ miura
rob rey and kentarõ miura style
rob ross
rob schneider
rob schneider is a stapler
robb cobb
robb stark
robbery
robe
robe. extremely high details
robe. perfect face
robe. perfect faces
robed
robed figures sat around a table
robed renaissance scholar
robert ayton
robert capa
robert cleminson
robert colescott painting
robert crumb photorealism
robert crumb style
robert de niro as medieval joker
robert downey jr
robert galbraith
robert hubert
robert kinoshita
robert kondo
robert lumley
robert maplethorpe
robert plant
robert richardson
robert rubert and john martin
robert sheehan
robert siodmak
robert t. mccall
roberto benardi
roberto bernardi
roberto ferri and ruan jia
robes
robes with golden characters
robes! intricate
robesintricate
robh ruppel
robin
robin eley
robin hood
robin williams
robin williams as the joker
robin williams is god
roblox
roblox avatar
roblox screenshot
robocop
robocop duck
robocop portrait
robot
robot anatomy elements
robot animal
robot arm
robot arm skeleton
robot arms
robot art cracking the road
robot barkeep
robot bird
robot bishop guards
robot body
robot cat
robot cat paws for feet
robot chicken
robot city ruins
robot concept art
robot cyborg
robot cyborgs
robot design
robot destruction
robot dog
robot dragon claws
robot dragon feet
robot dragon four fingered hands
robot dragon hands and feet
robot dragon head
robot duck concept portrait
robot eye
robot eyes
robot face
robot face bust
robot factory
robot ghost mask
robot girl
robot guards
robot hands
robot head
robot head and man head
robot helmet bust
robot icon
robot in a server room
robot in construction
robot in data center
robot killing man
robot lion
robot lurks in the background
robot machinery
robot made of a cardboard box
robot made of jet parts
robot made of truck parts
robot mecha female dragon head
robot metallic armor
robot mode
robot model kit
robot monster in background
robot ninja
robot overlord in the year 3499
robot overlords
robot painting a robot on canvas
robot parts
robot photography
robot pirate
robot pope
robot portrait
robot punk futuriste geometrie
robot religion
robot repair shop
robot repair workshop
robot revolution
robot seizes the forest crown
robot shadow
robot shaped like a rubber duck
robot skeleton
robot stand - off
robot suit
robot torso
robot warrior
robot with human face
robot wizard
robot!
robot's heart-shaped fingers
robotech styling
robotic
robotic anthro dolphin
robotic arm
robotic arms
robotic bionic technical
robotic body
robotic bust
robotic cactus design
robotic cat
robotic drones
robotic enhancements
robotic extended arms
robotic eye
robotic eyes
robotic face
robotic faces
robotic feeling
robotic head
robotic left arm
robotic limb
robotic limbs
robotic limbs on floor
robotic machinery
robotic machines
robotic mech parts
robotic octopus
robotic parts
robotic pig
robotic platform
robotic prosthetic arm
robotic prosthetic limbs
robotical
robotics
robots
robots drinking alcohol
robots fight
robots heart-shaped fingers
robots queue up for ice cream
robust
robust stocky body
robustly ordinated
rocco
rocha
rock
rock and dust
rock and sand around
rock arches
rock arcs
rock art
rock art drawings
rock band
rock cave painting
rock climber
rock climbers climbing a rock
rock climbing
rock columns
rock concert
rock details joints
rock edge
rock formations
rock monster
rock movie
rock music
rock musicians on the stage
rock n roll
rock painting
rock plateau
rock pools
rock quarry location
rock roof
rock star
rock texture
rock wall
rock walls
rockabilly
rockabilly band 1950s
rockabilly hair
rockabilly style
rocket
rocket launch
rocket launcher
rocket launchers
rocket launching
rocket launching into the sky
rocket league
rocket league tank
rocket raccoon
rocket ship
rockets
rocking out
rocks
rocks and metal
rocks coming out of the ground
rocks falling
rocks flying
rocks)
rockstar games
rockstar games art
rockwell
rocky
rocky beach
rocky cliff
rocky cliffs
rocky coast
rocky desert
rocky environment
rocky foreground
rocky grass field
rocky ground
rocky ground with a dirt path
rocky hills
rocky lake shore
rocky landscape
rocky meadows
rocky mountains
rocky mountains and a river
rocky mountains in background
rocky roads
rocky seashore
rocky shore
rocky terrain
rococco
rococo
rococo and art nouveau
rococo and art nouveau fusion
rococo and baroque styles
rococo architecture
rococo art
rococo art style
rococo baroque
rococo color palette
rococo color scheme
rococo cyber neon lighting
rococo cyberpunk
rococo decorations
rococo details
rococo dress
rococo elements
rococo fashion
rococo mechanical
rococo mechanical and electronic
rococo oil painting
rococo onyx headpiece
rococo ornament
rococo painting
rococo portrait
rococo queen
rococo ruffles dress
rococo style
rococo style portrait
rococo!
rod rays
rodent
rodger dean
rodin
rodin style
rodney mathews
rodney matthew
rodrigo duterte
roge antonio
roger deakin  s cinematography
roger deakin ’ s cinematography
roger deakin's cinematography
roger deakins cinematography
roger deakins filming
roger deakin’s cinematography
roger deal
roger dean and alexander jansson
roger dean and andrei tarkovsky
roger deankins
roger magrini
roger moore
roger moore as james bond
roger rabbit
rogue
rogue anime girl
rogue anime girl ayanami rei
rogue bard
rogue class warrior class
rogue thief
rogue trooper
roguelike
roguelike style
roguish
roguish smirk
rojava
rokoko
roland
rolands zilvinskis 3d render art
roleplay
roleplaying
roleplaying game art
rolex
rolf klep
roll 2 0
roll 20
roll20
rollei 3 5 camera
rollei 35 camera
rolleiflex
rolleiflex tlr
roller coaster
roller coasters
roller skating
rollerbladers
rollerblades
rollerblading
rollercoaster
rollerskaters
rollerskates
rolling dice
rolling fog
rolling foothills
rolling green hills
rolling hills
rolling mountain
rollingstone magazine
roma
romain jouandeau
roman
roman architecture
roman armor
roman bath
roman catholic icon
roman centurion
roman city
roman coliseum
roman columns
roman emperor
roman empire
roman face
roman festival backdrop
roman gladiator
roman goddess
roman historic works
roman legionnaire
roman monuments
roman mosaic
roman myth
roman nose
roman numerals
roman pillars
roman sculpture
roman setting
roman shipunov
roman style
roman toga
roman vlasov
romance
romance book cover
romance book cover style
romance fantasy movie
romance novel cover
romanesco broccoli
romanesque style
romanesque style statue
romanian
romanian heritage
romanized: al-kaʿbah
romantic
romantic ambiente
romantic atmosphere
romantic comedy
romantic couple
romantic dress
romantic era painting
romantic gown
romantic greenery
romantic impressionism
romantic landscape
romantic lead
romantic light
romantic lighting
romantic mood
romantic painting
romantic period
romantic scene
romantic simple path traced
romantic storybook fantasy
romantic style
romantic themed
romantic undertones
romantic!!!
romanticism art style
romanticism artwork
romanticism lain
romanticism landscape painting
romanticism painting
romanticism portrait
romanticism style
romanticist
romanticist oil painting
romanticist oil painting ”
romanticist style
romantisism
romantism
rome
rome backdrop
rome in background
romeo and juliet
romero ressendi
ron
ron cobb
ron cobb. cinestill
ron desantis as satan
ron english and eiichiro oda
ron hicks
ron mueck
ron perlman
ron weasley
ronald mcdonald
ronald mcdonald as a muppet
ronald reagan
ronald reagan wearing a bikini
ronaldo fenomeno
ronaldo luis nazario da lima
ronaldo luis nazario de lima
ronaldo nazario
ronaldo nazario fenomeno
rondel
ronen bekerman
rongier
ronin
roof background
roof garden
roof with vegetation
roofed forest
roofing tiles texture
roofs
rooftop
rooftop party
rooftop romantic
rooftop solar panels
room
room full of candles
room full of computers
room full of plants
room interior
room is full of people
room is on fire
room lighting
room mono window
room of a dark mansion
room of a spacecraft
room of the nameless painter
room shot
room with eerie feeling
roomies
rooster
rooster!!!!
root
root system
root trap
rooted lineage
roots
roots and hay coat
roots and thorns
roots dangling below
roots underwater
rope
rope bondage
rope bridge
rope bridges
ropes
ropes and chains
ropes and chains in background
roronoa zoro
rorsach path traced
rorschach
rorschach test
rosa bonheurn
rosalia
rosalia vila i tobella
rosamund pike as the doctor
rose
rose background
rose crown
rose garden
rose gold
rose gold heart
rose hair
rose in hair
rose of jericho
rose petals
rose petals flying in the wind
rose pink lighting
rose quartz
rose tattoo
rose thorn crown
rose tones
rose twining
rose-brambles
roseanne park of blackpink
rosen maiden
rosen zulu
rosenthal
roses
roses and lush fern flowers
roses and tulips
roses background
roses in cinematic light
roses in hands
roses in her hair
roses made of colorful smoke
rosette
rosetti
rosetti and monet
rosey cheeks
roshan
ross
ross and aivazovsky
ross draws
ross geller screaming
ross tan
ross tran 8 k
ross tran 8k
ross tran and artgerm
ross tran and bayard wu
ross tran and ilya kuvshinov
ross tran and makoto shinkai
ross tran and michael whelan
ross tran and studio ghibli
ross tran and wlop
ross tran style
ross tran!!!
ross tran. scenic background
rossbach
rossdraw
rossdraws
rossdraws  afrofuturism
rossdraws 1. 0
rossdraws 2. 0
rossdraws 2. 5
rossdraws and boris vallejo
rossdraws and jazza
rossdraws cartoon vibrant
rossdraws digital painting
rossdraws global illumination
rossdraws pastel vibrant
rossdraws portrait
rossdraws sakimimichan
rossdraws volumetric lighting
rossdraws | afrofuturism
rossdtaws
rosses
rossetti
rossetti bouguereau
rossier
rostov
rostov city
roswell air base
rosy cheeks
rosy cheeks with freckles
rot
rotary symmetrical
rotated
rotated left right front back
rotating
rotational symmetry
roth's drag nut fuel
rothko
roths drag nut fuel
rotoscope
rotoscoped
rotoscoping
rotten
rotten flesh
rotten gold
rotten green skin
rotten moldy black mold
rotten teeth
rotten textures
rotten wood
rotting
rotting black clay skin
rotting clay skin
rotting flesh
rotting skin
rottweiler dinosaur hybrid
rottweiler firefighter
rottweiler rabbit hybrid
rotunda
rough
rough abstract sketch
rough and moody
rough beard
rough brush strokes
rough charcoal sketch
rough color pencil illustration
rough concrete walls
rough draft
rough lines
rough oil paint
rough paper
rough paper texture
rough sea
rough seas
rough seas in background
rough sketch
rough texture
rough textures
rough water
rough watercolor painting
rough waters
rough waves
rough wood
rough wooden fence
roughled fur
rougue one
rougue one ( film )
round
round - face
round about to start
round and well-drawn eyes
round background
round base
round black eye pupils
round black glasses
round bottle
round buildings in background
round bulbous nose
round cheeks
round chin
round chin black eyeliner
round circle face
round clouds
round conch fractal horns
round corners
round cute face
round dance
round design
round doors
round ears
round elements
round eyes
round face
round faced
round form
round format
round gentle face
round glasses
round glasses potter
round goggles
round head
round headlights
round hood
round iris
round jaw
round jawline
round logo
round mirror on the wall
round narrow chin
round nose
round portruding chin
round pupil
round red m & m figure
round red m m figure
round robot
round round round nose
round shaped face
round shapes
round sunglasses
round symmetrical eyes
round symmetrical eyes!!!
round teeth and goofy face
round thighs
round window
round windows
round-cropped
rounded
rounded architecture
rounded beak
rounded ceiling
rounded corners
rounded eyeglasses
rounded eyes
rounded face
rounded forms
rounded house and cute character
rounded lines
rounded logo
rounded roof
rounded shapes
route 6 6
rowan atkinson
rowena morrill
rowing boat
rows of canteen in background
rows of doors
rows of lush crops
rows of razor sharp teeth
rows of windows lit internally
roxie vizcarra
roy krenkel
roy lichtenstein and jackie tsai
roy lichtenstein style
roy litchenstein
roy thomas
royal
royal academy
royal attire
royal attire akira
royal bird
royal blue colors
royal brocade dress
royal cape
royal commission
royal court
royal crown
royal dress
royal elegant pose
royal emperor
royal family during an argument
royal garden background
royal garden landscape
royal gown
royal green and nature light
royal insignia in background
royal interior
royal jewels
royal painting
royal palace
royal palace interior
royal palace near the lake
royal photo
royal photography
royal portrait
royal portrait painting
royal relief
royal robe
royal style
royal wedding
royal woman
royal workshop
royal-blue
royalcore
royality
royally decorated
royaltly
royalty
royalty kingdom
roygbiv
royo
rozalski
rpg
rpg art
rpg artwork
rpg background
rpg book portrait
rpg character
rpg character art
rpg character avatar
rpg character concept art
rpg character reference
rpg concept art character
rpg game
rpg game environment asset
rpg game inventory item
rpg game item
rpg game style
rpg illustration
rpg item
rpg item render
rpg landscape
rpg maker
rpg maker style bedroom
rpg portrait
rpg portrait concept art
rpg portrait full body
rpg portrait reference
rpg reference
rpg reference sheet
rpg rulebook illustration
rpg scene
rpg skill
rpg style
rpg_portrait
rpgmaker
rtx
rtx 3 0 9 0 ti
rtx 3080
rtx 3090
rtx 3090 ti
rtx 4090 ti
rtx engine
rtx geforce experience
rtx graphics
rtx on
rtx ray tracing
rtx reflections
rtx rendering
ru paul\'s drag race
ruan cute vtuber
ruan ji
ruan jia and artgerm
ruan jia and arthur rackham
ruan jia and brom
ruan jia and fenghua zhong
ruan jia and greg rutkowski
ruan jia and joao ruas
ruan jia and mandy jurgens
ruan jia and michael komarck
ruan jia beautiful
ruan jia beautiful!
ruan jia movie poster
ruan jian
ruan kia and john singer sargent
ruanjia
rubber
rubber and latex
rubber duck
rubber ducky
rubber hose
rubber hose animation
rubber hose style
rubber latex
rubber stamp
rubber suit
rubber undersuit
rubber waffle outsole
rubberhose style
rubbery
rubbery - looking body
rubbery-looking body
rubbing hands!!!
rubbish
rubble
rubble everywhere
rubble!!
rubbles
rubedo
rubenesque
rubens
rubi
rubies
rubrum lillies
ruched bodice
rude
rudolf belarski
rudolf béres
rudolf weisse
ruffled and tattered dress
ruffled fabric
ruffled wings
ruffles
ruffles tassels and ribbons
rug
rugged
rugged  stars
rugged and dirty
rugged beard
rugged black clothes
rugged details
rugged face
rugged male medieval knight
rugged male ranger
rugged male ranger in battle
rugged man portrait
rugged ranger
rugged ship captain
rugged soldier
rugged textured face
rugged | stars
ruggedly handsome ranger
rugrats
rugs
ruin
ruin despair apocalypse
ruined
ruined alien structures
ruined architecture
ruined buildings
ruined castle
ruined cities
ruined city
ruined city in background
ruined city in the background
ruined cityscape
ruined empire on the background
ruined gas station and cars
ruined gothic cathedral
ruined kingdom
ruined medieval architecture
ruined subdivision houses
ruined temple
ruined temple covered with moss
ruined town
ruins
ruins around
ruins in the background
ruins landscape
ruins of ancient rome
ruins of ancient technology
ruins of hell
ruins on the background
rukis
rukis. comic book style
rule
rule 34
rule britannia
rule for thirds
rule of 3 rds
rule of third
rule of third!!!!
rule of thirds
rule of thirds composition
rule of thirds fibonacci
rule of thirds golden ratio
rule of thirds highly detailed
rule of three
rule of threes
rule of thrids
rule-of-thirds
ruler of everything
ruler of inferno
ruler of the demons
ruler of the world
rules dream world
rules of composition
rum
rumble roses
rumiko
run
run down
run for your life
runaway photography
rundown
rundown buildings
rundown new york apartment
rune factory 5
rune magic
rune marks
rune-engraved armor
runes
runes in the air
runescape
runestone
runestones
rung
runic
runic arm tattoos
runic etching
runic inscription
runic rings
runic symbols
runic tattoos
runic words
running
running and falling on clouds
running at the edge of space
running cat
running dog in a library
running fast towards the camera
running freely
running from bear
running in savana
running in savannah
running lights
running pose
running robots in a marathon
running sequence
running shoes
running through italian town
running towards camera
running towards the camera
running water
runs away from men on the forest
runway
runway photo
rupert everton
rupert murdoch
rupert murdoch as the devil
rupert murdoch as the joker
rural
rural dystopian
rural japan
rural landscape
rural splendor
rural wastelands
rurouni kenshin
rush band
rushing water
russ abbott
russ mill
russ mills
russell dauterman
russia
russia in 2 0 2 1
russia in 2021
russian
russian academic
russian academic painting
russian academicism
russian and japanese mix
russian architecture
russian army infantry soldiers
russian cinema
russian city
russian city of the future
russian clothes
russian constructivism
russian costume
russian criminal tattoos
russian depression
russian flag
russian flags
russian folk fairytale
russian girlfriend
russian impressionism
russian lab experiment
russian lada car
russian landscape
russian national guard
russian neoclassicism
russian oil painting
russian opposition rally
russian orbit city cityscape
russian painters
russian revolution 1 9 1 0
russian revolution 1910
russian shaman
russian soviet motifs
russian style
russian temple
russian village
russian villages at background
russian ww 1
rust
rust and corrosion
rust and dust and fire and dirt
rust and plaster materials
rust background
rusted
rusted junk
rusted metal
rusted metal and sunflowers
rusted metal texture
rusted panels
rusted silent hill
rusted steel
rusted walls
rustic
rustic and weathered
rustic setting
rustic stone cabin in horizon
rustic throne room
rustic wood
rustic yet enormous scp (secure
rustic yet enormous scp secure
rusticated stone base
rusting
rusty
rusty armor
rusty biomechanical cyborg
rusty cars!!
rusty chain fencing
rusty colored long hair
rusty colors
rusty components
rusty helmet
rusty meat machine
rusty metal
rusty metal plating
rusty metal towers
rusty metal walls
rusty pipes
rusty red helmet
rusty shapes
rusty vehicles
rutkoswki
rutkovski
rutkovsky
rutkowski
rutkowski and caravaggio
rutkowski greg
rutkowski ilya krenz nixeu wlop
rutkowski |
rutkowsky
rutkowskyi
ruxing gao
rv
ryan church
ryan dening
ryan dyar
ryan glitter concept artist
ryan gosling
ryan gosling as black panther
ryan gosling as deadpool
ryan gosling as roman statue
ryan gosling fused with a goose
ryan hewett
ryan jia
ryan kiera armstrong
ryan mcginley
ryan meinerding
ryan renolds as batman
ryan reynolds
ryan reynolds as a bodybuilder
ryan reynolds as batman
ryan reynolds as thanos
ryan reynolds dead pool
ryan reynolds wearing a fat suit
ryden
rye (shishkin)
rye shishkin
ryohji hase
ryoji
ryokans and edo era houses
ryu from street fighter
ryuichi sakamoto
ryuko matoi
ryuta ueda artwork
ryuu
réalisme
réunion des musées nationaux
s baraldi
s clean lines
s line
s'mores
saab 39 gripen
saadane afif
saatchi art
sabattier effect
sabattier filter
sabbas
sabbas apterus
sabertooth
sabertooth cat
sabina klein
sables crossed in background
sachiko miyazaki
sachin teng
sachucci 9 5
saci perere
sacral chakra
sacrament
sacred
sacred ancient architecture
sacred architecture
sacred artifacts
sacred feminine
sacred fractal structures
sacred geometry
sacred geometry melting
sacred geometry pattern
sacred numbers
sacred perfect lighting
sacred skull
sacred skulls
sacred tiger
sacred vampire
sacredness
sacrifice
sacrificial altar
sacrilegious rite
sad
sad ambience
sad and happy
sad and introspective
sad and lonley
sad atmosphere
sad blue eyes
sad cat
sad cerulean eyes
sad christina hendricks
sad colors
sad cop looking at a
sad emoji
sad exasperated expression
sad expression
sad eyebrows
sad eyes
sad eyes tears
sad face
sad feeling
sad frown
sad green eyes
sad grumpy face
sad kawaii face
sad lighting
sad lonely mellow vibes
sad look
sad looking eyes
sad man
sad man puts on a bear mask
sad men
sad mood
sad motif
sad muppet eyes
sad prisoner holding ipad
sad scene
sad sky
sad steven seagal in jail cell
sad vampire
sad vibe
sad wizard
sad woman smoking a gigarette
sad!
sadamoto yoshiyuki
sadan vague
saddle up
sadie sink
sadistic
sadness
sadness look
sadness personified
safari
safari background
safe
safe for work
safebooru
safebooru anime image
safehavenhq
safi'jiiva armor
saga comic
sage
sage ( valorant )
sage green
sage smoke
saggy purple robes
saguaro
saguaro cacti
sahara
sahara comics logo
sahara desert
sail
sail boat on the background
sail boats
sail made of human skin
sailboat
sailboats
sailboats in the water
sailboats soaring in the wind
sailing boat
sailing boats
sailing ship
sailing ships
sailor clothing
sailor jerry tattoo flash
sailor moon
sailor moon aesthetic
sailor moon style
sailor moon!!!!!!!!
sailor uniform
sailpunk
sails
sails and masts and rigging
saint
saint homo neanderthalis
saint jerome as an hermit
saint man
saint michael the angel
saint of the pit
saint petersburg
saint seiya
saint skeleton queen
saint woman
saint womans
saintly
saints row
saints row fursuit tails mod
saitama
saitama prefecture
saito takao
saiyan armor
saiyan girl
sake
sakimi
sakimi chan
sakimichan
sakimichan and frank franzzeta
sakimichan and makoto shinkai
sakimichan frank franzzeta
sakimichan hdri
sakimimichan
sakuga
sakuga gunplay
sakuga mad gunplay
sakura
sakura bloomimg
sakura blooming on background
sakura flower
sakura flowers
sakura from cardcaptor sakura
sakura haruno
sakura haruno in slug sage mode
sakura kinomoto
sakura petals around her
sakura pigma micron
sakura season
sakura season dynamic lighting
sakura tree in background
sakura trees
salacious
salad
salad and white colors in scheme
salad fingers
salamander
salami
saleen s7
salems revenge
sales
saliva
sallon
sally
sally corporation
salma hayek
salma hayek as a barbarian
salma hayek as a sorceress
salma hayek in a hi vis vest
salma hayek portrait
salmon
salmon khoshroo
salome
salomon
saloon
saloon exterior
salsa dancing
salsa social dance
salsa tricks
salsa vendor
salt
salt and pepper hair
salt dunes
salt effects
salt flats with scattered ruins
salt shaker
saltwater
salustiano garcia cruz
saluting
salvador
salvador dali and frank frazetta
salvador dali painting
salvador dali style
salvation
salvia
salvia droid
sam
sam and max
sam gilliam
sam guay
sam hyde
sam hyde in god of war 4
sam hyde in gold suit
sam hyde in team fortress 2
sam leach
sam nassour
sam shearon
sam weber
sam yang
samadhi
samara weaving
samara weaving vampire
samarkand
samburu
samdoesart
samdoesarts
same body
samhain figure
samira from league of legends
samma van klaarbergen
samoan features
samorost
samoyed dog
samsara
samson pollen
samsung smartthings
samuel jackson
samuel l jackson
samuel l jackson posing in cafe
samuel l. jackson as a ballerina
samurai
samurai armor
samurai cat
samurai chains ink undead
samurai champloo
samurai deity with koi armor
samurai duel
samurai flight suit
samurai helmet
samurai jack
samurai jedi
samurai jetstream sam
samurai man vagabond
samurai man wrapped in chains
samurai mask
samurai movie poster
samurai outfit
samurai pizza cats
samurai portrait
samurai portrait photo
samurai style
samurai vinyl wrap
samurai warrior
samurai with afro
samurai with demon mask
samus
samus aran
samus aran bioorganic varia suit
samus aran fanart
san francisco
san francisco psychedelic poster
san mumford
sana takeda
sanctions in russia
sanctuary
sand
sand - colored walls
sand and desert environment
sand and sea
sand art bottle
sand banks
sand cat
sand color
sand desert
sand desert fantasy
sand dune
sand dune background
sand dunes
sand mists
sand particles
sand piled in corners
sand sculpture
sand sea
sand storm
sand storm approaching
sand storm enters
sand swirling
sand texture
sandals
sandcastle
sandfalls
sandman
sandman endless
sandman kingdom
sandra
sandra bullock
sandra chevier
sandra chevrier : : portrait
sandra chevrier and john hoyland
sandra pelser
sandro bottecelli
sandro botticelli style
sandro botticelli. very soft
sandstone
sandstorm
sandwich
sandwich with fried tofu
sandworm
sandy
sandy beach
sandy beige
sandy colors
sandy colours
sandy green
sandy white moon landscape
sangsoo jeong
sanguinary blood splatters
sangyeob park
sanic
sanja stikovic
sanji
sanjulian. detailed texture
sankakucomplex anime image
sanrio
sanrio ornaments
sans
sans from undertale
sans undertale
sansa
sansa stark
sanskrit
santa
santa claus
santa clause
santa inside a rustic barn
santa monica beach
santa's workshop
santiago calatrava
santiago rusinol
santorini
sao
sao paulo
sao paulo in the year 2 0 7 0
sao paulo in the year 2070
sao style anime
saoirse ronan
sap
sapphic art
sapphire
sapphire blue eyes
sapphire butterfly jewellery
sapphire earrings
sapphire waters below
sapphires
sara ali
sarah
sarah andersen
sarah cliff
sarah michelle gellar
sarah shahi
sarcastic
sarcastic and friendly
sarcastic evil smile
sarcastic evil smirk on face
sarcastic pose
sarcastic smile
sarcastic smile showing teeth
sarcastic smiling
sardax
sardine in a can
sarenrae
sargent
sargent and leyendecker
sargent and raffaelo monti
sari
sarong
sasai ukon masanao
sascha schneider
sasha grey
sasoura
sasquatch
sassy
sassy personality
sassy pose
sasuke uchiha
sat at a desk
sat at her desk
sat down in train aile
sat in an office
sat in his throne
satan
satan daughter
satan in hell
satanic
satanic body
satanic carl sagan
satanic church interior
satanic horror
satanic ritual
satanic states of america
satanic symbolism
satanic wings
satanism
satanistic sky
satchely
satchely and akihiko yoshida
satelite imagery
satellite
satellite dishes
satellite image
satellite imagery
satellite photo
satellite view
satin
satin ribbons
satin silver
satire
satirical painting
satisfactory screenshot
satisfied pose
satisfying
satisfying cable management
satisfying render
satoshi
satoshi - kon
satoshi con
satoshi kon and moebius
satoshi kon anime
satoshi kon artstyle
saturated
saturated color
saturated color scheme
saturated colorized
saturated colors
saturated colours
saturated pastel colors
saturated pointillism
saturated top light
saturated watercolor
saturation
saturation 40
saturday morning cartoon
saturday night fever ( film )
saturday night in a saloon
saturn
saturn and supermoon in the sky
saturn devouring his son
saturn in the background
saturn in the sky
saturn v
saturno butto
saturno butto. occult art
saturno buttò
satyr
sauce
saudi futuristic warrior mecha
saul bass
saul bass and paul rand
saul better call saul
saul bromberger
saul goodman
saul goodman as a muppet
saul goodman as captain america
saul goodman from breaking bad
saul goodman in ace attorney
saul goodman in fortnite
saul goodman in game of thrones
saul goodman in gta v
saul goodman in super mario 64
saul goodman in the backrooms
saul goodman in the simpsons
saul goodman!
sauna
saurians
sauroform hybrid
sauron
sauron fighting saruman
sausage
sausage hair
sausages
savage
savage cloak
savage monster
savage warrior
savana background
savanna
savannah
saving private ryan
saving the day again
savory
savrasov
savrasov levitan polenov
saw
saw teeth
sawblade border
saws
saxophone
saxophones
say
say ahh
sayem reza
saying
sayori
sazabi
sazed
saâdane afif
sbt
sc-fi
scabs
scaffolding
scaffolding collapsing
scale
scale model photography
scale of a city
scaled arm
scaled robes
scaled skin
scales
scales and fur
scales covering her chest
scales made of jade
scales on her chest
scales skin dog
scales with magic powder
scaly
scaly skin
scaly!!!
scamming
scan
scan lines
scan of codex seraphinianus
scandal sheet
scandelous fantasy armor
scandinavia
scandinavian
scandinavian / norse influenced
scandinavian design
scandinavian style
scandy and arender
scanlines
scanned
scanned 2400 dpi
scanned document
scanned in
scanning items with smartphone
scans from museum collection
scantily clad
scar
scar across face
scar across nose
scar across the nose
scar in one eye
scar on face
scar on the cheek
scar under left eye
scarab
scarab reploid
scaramouch
scarce sun rays peaking
scare
scarecrow
scared
scared emotion
scared expression
scared face
scarf
scarf made from spaghetti
scarf!
scarface
scarfolk
scariest looking man alive
scaring
scarlet
scarlet and yellow scheme
scarlet background
scarlet emerald
scarlet feather boa
scarlet johansen
scarlet johansson
scarlet tones
scarlet witch
scarlet witch costume
scarlet witch marvel
scarlett hooft
scarlett johanson
scarlett johansson
scarlett johansson as thanos
scarlett johansson black widow
scarlett johansson portrait
scarred
scarred eye
scarred face
scarred lip
scarred! d&d
scarry
scarry but bewitching
scarry castle)). mystical
scarry castle). mystical
scars
scars in the face
scars of battle
scars on eye
scars on face
scars on his face
scars will remain
scary
scary and brooding
scary and dark
scary angry pose
scary art
scary art in color
scary athmosphere
scary atmosphere
scary cartoons
scary clown
scary color art in 4 k
scary creature
scary creatures
scary creatures in background
scary dark
scary dark forest
scary detailed art in color
scary elmo with long
scary eyes
scary face
scary fish
scary ghost
scary gothic architecture
scary horrifying satanic rituals
scary knight
scary lighting
scary lightning
scary look
scary magical background
scary monster statues
scary mood
scary movie
scary night
scary photo
scary picture
scary picture in color
scary pines
scary portrait
scary pose
scary queen of death
scary scene
scary sea monster
scary shadow people
scary shadows
scary sharp icy
scary smile
scary stories
scary style
scary thunderstorm
scary vampire
scattered
scattered clouds
scattered glass shards
scattered glowing pink fireflies
scattered gold coins
scattered golden flakes
scattered islands
scattered light
scattered props
scattered rubbish
scattered rubbish and debris
scattering
scenario assets
scenary
scence of slam dunk
scene
scene : sunflower field
scene beautiful
scene from a 1 9 7 3 film
scene from a dinner party
scene from a movie
scene from a rave
scene from back to the future
scene from bladerunner movie
scene from church
scene from dune 2 0 2 1 movie
scene from fightclub movie
scene from kagemusha
scene from live action movie
scene from louisiana swamps
scene from prometheus movie
scene from star wars 1977
scene from starship
scene from supplizia
scene from the film
scene from the movie godfather
scene from the movie prometheus
scene from the voice of america
scene girl
scene in an open field. 1 8 9 0
scene in space
scene inside forest
scene render
scene set in a church
scene view
scene where she is in rivendell
scene with machine gun
scene!!
scene: sunflower field
scenery
scenery art detailed
scenery artwork
scenery game concept art
scenery wallpaper
scenery wallpaper aesthetic
scenic
scenic background
scenic colorful environment
scenic dystopian environment
scenic environment
scenic full shot
scenic landscape
scenic mountain setting
scenic prairie in background
scenic view
scenic view at night
scenic view of river
scepter
scetch
schematic
schematic in a notebook
schematics
scheme
schizophrenia
schizophrenic
schizophrenic hallucination
schlieren
schlieren flow visualisation
schmuck
schnapps
scholar
scholarly
schomburg
school
school bag
school class
school classroom
school courtyard
school curriculum expert
school girl
school girl in gothic dress
school of fish
school of fishes
school uniform
schools
schools of fish
schrodinger cat
schrodinger's cat
schrodingers cat
schutztruppe
schwarzenegger
sci - ci
sci - f
sci - fi
sci - fi -
sci - fi : :
sci - fi aesthetics
sci - fi and fantasy
sci - fi armor
sci - fi armour
sci - fi armour! muscular
sci - fi art
sci - fi art!!!!!!!
sci - fi artwork
sci - fi atmosphere
sci - fi biomechanical
sci - fi character
sci - fi city
sci - fi concept art
sci - fi details
sci - fi environment
sci - fi equipment
sci - fi face
sci - fi fantasy
sci - fi favela sculpture
sci - fi film color palette
sci - fi film still
sci - fi horror
sci - fi horror art
sci - fi illustration
sci - fi illustrations
sci - fi interior
sci - fi jewellery
sci - fi landscape
sci - fi look
sci - fi mongolian village
sci - fi monster hunters
sci - fi movie
sci - fi movie poster
sci - fi movie style
sci - fi pilot
sci - fi poster
sci - fi scene
sci - fi scene future new york
sci - fi setting
sci - fi spaceship in combat
sci - fi suit
sci - fi theme
sci - fi vending machine
sci - fi weapon
sci - fi!!
sci - fi!!!
sci - fi!!!!
sci - fy
sci fi
sci fi alien world
sci fi anime
sci fi art
sci fi artwork
sci fi character concept
sci fi characters
sci fi city
sci fi concept art
sci fi data readouts
sci fi digital painting
sci fi engine room living room
sci fi epic digital art
sci fi fantasy
sci fi female character
sci fi font
sci fi futuristic costume
sci fi horror
sci fi landscape
sci fi necklace
sci fi patterns
sci fi scenery
sci fi setting
sci fi world
sci if
sci-fi
sci-fi android female
sci-fi anime
sci-fi armor
sci-fi armour
sci-fi art
sci-fi artwork
sci-fi atmosphere
sci-fi building. isometric view
sci-fi car
sci-fi character concept
sci-fi cinematic movie still
sci-fi classic
sci-fi computer
sci-fi concept art
sci-fi concept art :: nixri
sci-fi crocodile alien
sci-fi cybernetic implants hq
sci-fi cyberpunk city street
sci-fi digital art
sci-fi digital art illustration
sci-fi digital painting
sci-fi face
sci-fi fantasy
sci-fi fantasy art
sci-fi fantasy desktop wallpaper
sci-fi fantasy wallpaper
sci-fi female
sci-fi film color palette
sci-fi futuristic
sci-fi helmet
sci-fi high fantasy
sci-fi highly detailed
sci-fi hollywood concept art
sci-fi horror artwork
sci-fi illustration
sci-fi illustrations
sci-fi landscape
sci-fi lizard alien
sci-fi lizardman
sci-fi magic highly detailed
sci-fi mech
sci-fi movie
sci-fi movie cinematic frame
sci-fi movie shot
sci-fi movie still
sci-fi nether world
sci-fi night club
sci-fi of iceland landscape
sci-fi setting
sci-fi skin
sci-fi soldier
sci-fi space game art
sci-fi steampunk
sci-fi style
sci-fi sword
sci-fi themed
sci-fi tibetan fashion
sci-fi vending machine
sci-fiish
sci-fiish landscape
sci-fy
science
science - fantasy
science - fiction
science - fiction matte painting
science background
science fantasy
science fantasy painting
science ficiton
science fiction
science fiction art
science fiction art ]
science fiction city
science fiction concept art
science fiction digital art
science fiction elements
science fiction fantasy
science fiction landscape
science fiction magazine
science fiction matte painting
science fiction painting
science fiction scene
science fiction space suit
science fiction spacecraft
science fiction style
science fiction suit
science journal cover
science lab
science magazine
science magazine cover
science magazines
science monthly photography
science poster
science-fantasy
science-fiction
science-fiction art wallpaper
scientific
scientific accurate
scientific depiction
scientific diagram
scientific document
scientific earth crust
scientific equipment
scientific glassware
scientific illustration
scientific instruments
scientific paper
scientific photo
scientific photography
scientific research
scientific schematics
scientific specimens
scientific study
scientifically accurate
scientist
scientist is a duck
scientists
scifi
scifi accessories
scifi art
scifi background
scifi base
scifi character
scifi character render
scifi colors
scifi concept art
scifi cyberpunk
scifi design
scifi environment
scifi fantasy
scifi farm
scifi film color palette
scifi futuristic vehicles
scifi gadgets in a pond
scifi horror
scifi horror setting
scifi illustration
scifi inspired
scifi junkshop
scifi landscape
scifi machine
scifi movie poster
scifi nanowires on skin
scifi nightscape
scifi platform
scifi pulp
scifi religious
scifi robot repair workshop
scifi robots
scifi room
scifi scene
scifi setting
scifi space station
scifi spaceship
scifi style
scifi woman
scimitar
scintillating
scissor people
scissors
scissors in hand
scobillyflup
scoia'tael)
scolding
sconces
scorched
scorched earth
scorching heat
scorching weather
scores
scorn
scorn game
scornful
scorpion
scorpion from mortal kombat
scorpion tail
scorpion whip
scorpions
scotland
scott adams
scott buoncristiano
scott burdick
scott fischer
scott m fischer
scott radke
scott roberston
scott robertson
scott the woz
scott wills
scott wozniak
scottish
scottish fold
scottish folklore
scottish highlands
scottish style
scout boy
scout police robot
scowl
scowling
scp
scp 173
scp 3008
scp foundation
scp-049
scp-106
scp-173
scp-914
scrambler
scrap
scrap metal
scrap metal headdress
scrap metal on workbenches
scrapbook
scrapbook paper collage
scrape off
scrape them
scrapyard
scrapyard architecture
scratched
scratched metal
scratched photo
scratched vial
scratched. biomechanical cyborg
scratches
scratches and burns on film
scratches on photo
scratching head
scratching post
scratchy
scratchy lines
scrathes
scream
screamer
screaming
screaming and crying
screaming and sad
screaming at the camera
screaming face
screaming fashion model face
screaming in agony
screaming in desperation
screaming in pain
screaming into air
screaming trent reznor
screaming with fear
screaming yelling
screaming. not realistic
screeching
screen
screen cap
screen capture
screen light
screen melting
screen print
screen print poster
screen print texture
screen printed
screen printing
screen short from mafia 2
screen shot
screen shot of cod
screen space global illumination
screen space reflections
screen tones
screencap
screencapture
screengrab
screenprint
screens
screensaver
screenshot
screenshot 4k
screenshot from a 2012s anime
screenshot from a movie
screenshot from an fps
screenshot from black clover
screenshot from bloodborne
screenshot from fallout (1997)
screenshot from fallout 4
screenshot from fortnite
screenshot from game
screenshot from gta v
screenshot from guro anime
screenshot from minecraft
screenshot from morbius (2022)
screenshot from nier automata
screenshot from overwatch
screenshot from simcity pc game
screenshot from skyrim
screenshot from the 1983 film
screenshot from the anime film
screenshot from the game
screenshot from the game'crysis'
screenshot from'fallout 4'
screenshot of cloudjumper
screenshot of found footage
screenshot of kingdom hearts 3
screenshot of pixel game
screenshots
screentone shading
screenwriter
scretch
screw
screwdriver
screwdrivers
screws
screws and bolts
scribble art
scribble sketch
scribbled
scribbled lines
scribbles
scribbles biopunk
scrolling computer mouse
scrolls
scrollwork
scrubs
scruffy beard
scruffy brown beard
scruffy facial hair
scruffy looking
scruffy man
scuba diving
scuba mask
sculduggery
sculls
sculpey diorama
sculpted
sculpted draconic features
sculpted in zbrush
sculpted out of candy
sculpting
sculptor
sculptural
sculpture
sculpture gardens
sculpture made of gold
sculpture made of piled stones
sculpture made of water
sculpture made of wood
sculpture of a woman
sculpture of carving marble
sculpture of cate blanchett
sculpture of moloch
sculpture of persephone
sculpture on display
sculpture work in progress
sculpture!!
sculptured
sculptured face
sculptures
scum and villainy
scumbled
scumbled thick oil paint
scumbling
scuta
scutari
scutoid
scylla
scylla and charybdis
scythe
scythe design
sd video
sea
sea - doo gtx
sea - green and white clothes
sea and sky
sea anemone
sea background
sea bottom
sea breeze rises in the air
sea butterflies
sea cloud
sea creature
sea creatures
sea dragon
sea enemies
sea floor
sea foam
sea green color theme
sea ground
sea horse
sea in background
sea in the background
sea kelp
sea king
sea like jelly
sea line
sea monster
sea monster looks like ship
sea monsters
sea of blood
sea of milk
sea of parfait
sea of souls
sea of thieves
sea of thieves screenshot
sea of thieves style
sea plants
sea queen
sea serpent
sea shell
sea sponges
sea spray
sea storm
sea storm and big waves cliffs
sea turtles
sea underneath
sea waves
sea weed
seafloor
seafoam green
seafood in preserved in ice
seaglass
seagull
seagull wearing luigis hat
seagulls
seahorse
seahorses
sealed since 1989
sealed super joe biden
seals
seamless
seamless fabric pattern 8k
seamless game texture
seamless micro detail
seamless pattern
seamless pattern :: symmetry
seamless pattern design
seamless texture
seamless wood texture
seamless wooden texture
seams
seams stitched tightly
sean connery
sean connery as big boss
sean connery as gandalf
sean harris
sean harris wizard
sean mcloughlin
sean yoro
sean young
seapunk
seapunk mecha
search lights
searching for eternity
searchlight
searchlights
searchlights in background
seas of mountain
seascape
seashell
seashell house
seashells
seashore
seaside
seaside backgroud
seaside victorian building
season 3 ( 1 9 9 7 )
seasonal
seasons of emotion
seasons!! : 🌸 ☀ 🍂 ❄
seated
seated at a table
seated in court
seated in royal ease
seated on a throne
seated on wooden chair
seattle
seattle completely wasted away
seaview
seaweed
seaweed and bubles
seaweed floating
sebastian kruger
sebastian luca
sebastian ludke
sebastian michaelis
sebastian pether
sebastian stan
sebastiao salgado
sebastien chabal
secluded
second colours - purple
second coming
second eagle head
second life avatar
second world war
secondhand
secondlife
secret
secret <
secret agent
secret agents
secret cypher
secret entrance
secret garden
secret holographic cyphers
secret illuminati cabal
secret lab
secret of mana
secret overgrown temple
secret romance
secret room upstairs
secret service photos
secret shady laboratory
secret society
secret tea society
secret underground lab
secret valley
secretive
secretly on a village
secrets
secrets inside the vatican
sect
section model
sectioned
sectoid
secuirty cam footage
security
security agent
security cam
security cam footage
security cam video
security camera
security camera footage
security camera photo
security footage
security rhox
security robots delivery
securityguard
sedlec ossuary
sedona's cathedral rock bluff
seducing
seducing expression
seducing eyes
seducing the camera
seduction
seductive
seductive and powerful
seductive anime girl
seductive camisole
seductive confident pose
seductive cyberpunk dark fantasy
seductive expression
seductive eyes
seductive eyes and face
seductive gaze
seductive glances
seductive lady
seductive lingerie camisole
seductive look
seductive pose
seductive reclining pose
seductive seated pose
seductive smile
seductive smirk
seductive stare
seductive tifa lockhart portrait
seductive. highly detailed
seductively looking in front
see
see - through
see fishes swimming
see no evil
see through
see through dress
see through glass hologram mask
see you on the other side
seedlings
seeds
seeds of despair tv documentary
seedy lighting
seeing a cat
seeing all sides a bit insane
seemless
seen from a distance
seen from a plane
seen from above
seen from afar
seen from behind
seen from below
seen from earth
seen from far away
seen from outside
seen from planet earth
seen from space
seen from straight above
seen from the back
seen from the long distance
seen from the side
seen in the distance
seen through a kaleidoscope
seen through a microscope
seen through a window
seen through broken glass
seething
sega
sega dreamcast
sega genesis
sega genesis box art
sega genesis video game
segmented 2d laser cut earrings
segmented armor
segmented armor and sashimono
segmented broken glass shards
segmented organic armor
seifuku
seinen
seinen manga portrait
seinfeld
seinfeld fancy tea party
sekiro
sekiro in bloodborne world
sekiro mountain
selective breeding
selective color effect
selena gomez
selena gomez made out of celery
selena quintanilla perez
selenar
self
self - aware
self - confidence
self - portrait
self - portrait!!!!
self - satisfied smirk
self - taught
self centered
self confidence
self deprecating
self erotic
self portrait
self-confidence
self-portrait
selfie
selfie of a dog
selfie of a man
selfie of a young woman
selfie of elon musk on mars
selfie photo
selfie photography
selfie shot straight on angle
selfie!!!!!
selfie!!!!! of a man
selfishness
selina
selk ´ nam god of the sun
sell out event
selling a gem
selling his wares
selling insurance
semi - realism
semi - realistic
semi - realistic anime
semi - realistic render
semi naive
semi realism
semi realistic
semi realistic anime
semi realistic gouache painting
semi-realism
semi-realistic
semi-transparent
semiabstract
semirealism
semirealistic
semirealistic anime style
sempe
sen no rikyu
senary
senator armstrong
sengoku - era art style
sengoku era
senior concept artist
senko-san
senna from league of legends
sennheiser
senor salme
sensational
sense of action
sense of awe
sense of decay given
sense of depth
sense of hope
sense of movement
sense of mystery
sense of mystery and loneliness
sense of perspective
sense of scale
sense of scale and awe
sense of space
sense of wonder
sensitive
sensor array
sensors
sensory processing overload
sensual
sensual and beautiful incubus
sensual bodies
sensual color scheme
sensual dancing
sensual features
sensual gloomy
sensual gloomy style
sensual lighting
sensual painting
sensual pose
sensual woman
sensuality
sensuous
sentience
sentient bird
sentient fruit
sentimental
sentinel
seoul
sep. e. scott
sepal helmet
sepals forming helmet
separated
seperated game asset
sephiroth
sephiroth from final fantasy
sephora
sepia
sepia colors
sepia photography
sepia sun
sepia sunshine
sepia tints
sepia tone
sepia toned
sepia tones
september 1937
septum piercing
sequel trilogy 80s
sequences
sequential
sequin top
sequins
seraphim
seraphine
seraphine ahri kda
serena malyon
serena williams
serendipity
serene
serene and peaceful style
serene beach setting
serene bedroom setting
serene colors
serene desert setting
serene emotion
serene emotions
serene environment
serene evening atmosphere
serene expression
serene field setting
serene forest setting
serene illustration
serene landscape
serene lighting
serene overcast atmosphere
serene post-nuclear background
serene scene
serene smile
serene vast landscape
serenity
serenity & calm
serenity calm
serge birault
serge lutens
serge marshennikov
serge minhulin
sergeant
sergey kolesov
sergey kolesov concept art
sergey krasovskiy
sergey vasnev
sergey zabelin
sergio leone
sergio toppi
serial experiments lain
serial killer
series
series of dreams
series on netflix
serine intense sunlight
serious
serious and stern expression
serious business
serious cartoon
serious composure
serious expression
serious expressions
serious face
serious faces
serious facial expression
serious focussed look
serious lighting
serious look
serious looking mainecoon cat
serious sad look in his eyes
serious sam as smash characters
sermon of philistine greed
serov
serpent
serpent body
serpentine curve
serpentine curve!!!
serpentine maze
serpentine pose
serpentine pose gesture
serpentine twisty maze
serpentine water monster
serrated point
servando lupini
servant of dark moth
servant squirrels
servants
served on a plate
served with pasta
server
server in the middle
servers
service robots
service ticket close up
serving big macs
serving body
serving burgers
serving fries
serving happy meals
serving rack of ribs
serving suggestion
sesame street
sesame street 1 9 7 8
sesame street 1978
seseon yoon
seshat
set 1 8 6 0
set 1860
set against a white background
set at night
set back dead colors
set design
set in 1 8 5 0
set in 1 8 6 0
set in 1850
set in 1860
set in 1982 tokyo
set in 1992 japan
set in 1998
set in 1999
set in 19xx
set in a steampunk world
set in bank vault room
set in c&c3 tiberium wars
set in cyberpunk bank vault
set in half-life 2
set in hell
set in observatory at night
set in post apocalyptic tokyo
set in the distant future
set in the future 2 1 5 0
set in the future 2150
set in the world of guildwars2
set in tokyo bank parking lot
set in tokyo rooftop
set in ww2 germany
set inside of office
set inside of parking garage
set inside of the bank
set of high quality hd sprites
set on fire
set on h. r. giger aesthetic
set on interstellar space
set on night
set on singaporean aesthetic
set photo
set photo in costume
set photograph
set photograph in costume
set photography
set pieces
set sail
seth aaron rogen
setoff
setteidreams colour design
setting
setting in nature
setting is bliss wallpaper
setting sun
setting sun. golden hour
settlement
settlers of catan
seu madruga
seurat
seven
seven deadly sins
seven deadly sins anime
seven pointed pink star
seven sins
seven stars in right hand
seven-dimensional
seventeen-dimensional
seventies
seventies cinema
seventies cinestill
seventies era
several character designs
several continents
several cottages
several dolls in one photo
several hearts
several layers of fabric
several soldiers
several waterfalls
severe - looking
severe expression
severe out of focus
severe snow
severe weather storms
severed limbs
severian of nessus
severus snape
severus snape dance in a bar
severus snape dances in a bar
seville
seville. byzantine mosaics
sewage
sewage falling from grates
sewer
sewer background
sewer pipe entrance
sewers
sexually attractive
sexuell
sexy
sexy :8
sexy black shorts
sexy black woman walks past them
sexy body
sexy body and face
sexy charlise theron
sexy dominant pose
sexy dress
sexy eyes
sexy face
sexy face with full makeup
sexy gaze
sexy girl
sexy girl wearing shorts
sexy girl with dark brown hair
sexy girl with dark complexion
sexy girl with green eyes
sexy girl with long blonde hair
sexy gown
sexy hot body
sexy lips
sexy lips :5 stylish
sexy look
sexy look at the camera
sexy masculine
sexy movie photo
sexy muscular body
sexy muscular upper body
sexy outfit
sexy painting of gal gadot
sexy pose
sexy pudica pose gesture
sexy push up bras
sexy red dress
sexy red lips
sexy sesame seed buns
sexy style
sexy young woman
sexy-s 100
sf
sf 5 ink style
sf5 ink style
sfm
sfm render
sfumato
sfw
sfw huge breasts
sfw safe for work
sfw version
sfx
sgi iris graphics
sha xi
shabab alizadeh
shack close up
shackled in the void
shackles in his hands
shaco from league of legends
shadar kai
shade
shaded
shaded animation cel
shaded face
shaded flat illustration
shaded ink illustration
shaded lighting
shaded out eyes
shaded perfect
shaded tattoo
shader
shaders
shades
shades green and red
shades of aerochrome
shades of aerochrome gold
shades of blue
shades of blue and grey
shades of gold display naturally
shades of green
shades of grey
shades of pink
shades of pink and blue
shades of purple
shades of red
shades of yellow
shading
shading in vray
shading study
shading unreal engine 5
shading!!!
shadow
shadow and light
shadow armor
shadow art
shadow beneath character
shadow cast of dark corridor
shadow depth
shadow effect
shadow filled room with gloomy
shadow gradient
shadow harsh lights
shadow of beard
shadow of catholic church cross
shadow of the colossus
shadow of the cross
shadow of the tomb raider
shadow of the tomb rider
shadow over innsmouth
shadow people
shadow play
shadow polaroid photo
shadow resolution
shadow the hedgehog
shadowbringers cinematic
shadowcreature
shadowed
shadowed face
shadowing
shadowrun
shadowrun character art
shadowrun splash art
shadows
shadows from trees
shadows of the past
shadows of zombies
shadows on side
shadows realism
shadows screaming
shadows. asian landscape
shadows. high fantasy
shadowverse
shadowverse character concept
shadowverse style
shadowy
shadowy and dark
shadowy and eerie character
shadowy area
shadowy castle background
shadowy cat under the bed'
shadowy creatures
shadowy figures
shadowy informant
shadowy lighting
shady
shady alleys
shady dark backalley at night
shady dull weather
shady look
shaft
shaft of light
shaft of sun through window
shaft studio
shafts of light
shafts of light god rays
shafts of lighting
shafts of sunlight in the centre
shag
shag cut
shag hair
shag haircut
shaggy
shaggy brown hair
shaggy hair
shaggy haircut
shaggy silver hair
shah rukh khan
shai hulud
shai-hulud
shakespeare
shaking
shaking hands
shakira
shakti
shaky
shaky camera
shaky lines
shallan davar
shallow d. o. f
shallow depth
shallow depth of field
shallow depth of field hdr 8 k
shallow depth of field hdr 8k
shallow depth of fielf
shallow depth of focus
shallow depth or field
shallow dof
shallow focus
shallow focus background
shallow perspective
shallow water
shallow waters
shalltear bloodfallen
shalltear from overlord
shaman
shaman witch
shamanic dmt horror art
shamanic horror lsd art
shamanic poste
shamanic poster lsd art
shamanism
shamanistic dark blue clothes
shamanpunk
shamans and strange creatures
shambala
shame
shame focus
shan shui
shane mcgowan
shane prigmore
shanghai
shanghai city
shanina shaik as medusa
shanty town
shanty townships
shap
shape
shape and form
shape language
shape of a circle
shape of frogs
shaped derriere
shaped focus
shaped like a yacht
shaped like torus ring
shaped picture
shapely
shapely derriere
shapely toned derriere
shapes
shapes and colours
shapeshifter
shaq
shaquille o'neil pregnant
shaquille oneil pregnant
shar focus
sharandula
shards
shards and fractal of infinity
shards of mercury
shards of time
sharing a pizza
sharing an oxygen tank
shark
shark - human face
shark and guitar
shark anthony
shark cat hybrid
shark face
shark head
shark man
shark teeth
sharknado
sharks
sharks surfacing from the lake
sharn
sharp
sharp )
sharp - focus
sharp - focus!!
sharp ages
sharp and
sharp and blocky shapes
sharp and clear
sharp and coherent
sharp and dangerous sleek design
sharp and detailed
sharp and focus
sharp and highly detailed
sharp and pointy vampire teeth
sharp and smooth
sharp angles
sharp angular features
sharp armor
sharp background
sharp beautiful face
sharp black armor
sharp black eyebrows
sharp black skin
sharp bone structure
sharp bright colors
sharp brushstrokes
sharp centred focus
sharp cheekbones
sharp claws
sharp claws and sharp teeth
sharp claws and tail
sharp claws close up
sharp claws for hands
sharp cliffs
sharp color palette
sharp colors
sharp contours
sharp contrast
sharp corners
sharp cyborg dragon head
sharp deep
sharp definition
sharp depth of field
sharp design spaceship sketches
sharp detail
sharp detail 8k
sharp detailed
sharp detailed claws
sharp detailed face
sharp detailed focus
sharp detailed reflections
sharp detailed robot dragon paws
sharp details
sharp digital painting
sharp dragon claws
sharp ears
sharp edge
sharp edged black armor
sharp edges
sharp edges and focus
sharp edges cartoon
sharp edges. octane render
sharp edges. poster
sharp edges. ultra detailed
sharp eyebrows
sharp eyes
sharp face
sharp facial features
sharp fangs
sharp fangs and tusks
sharp fashion
sharp features
sharp fine-face
sharp fingernails
sharp focc
sharp foccus
sharp foccus ilustration
sharp foccus ilustration hq
sharp focus
sharp focus - c 1 5. 0
sharp focus - h 8 0 0
sharp focus 3 d
sharp focus 4k
sharp focus 8k
sharp focus and octane render
sharp focus cinematic lighting
sharp focus dynamic light
sharp focus dynamic lighting
sharp focus high quality
sharp focus highly detailed
sharp focus illustration
sharp focus ilustration
sharp focus ilustration hq
sharp focus medium shot
sharp focus on eyes
sharp focus on face
sharp focus on houses
sharp focus on scenery
sharp focus on the face
sharp focus perfect horizontal
sharp focus photo centered
sharp focus uhd 8
sharp focus uhd 8 k
sharp focus uhd 8k
sharp focus vector centered
sharp focus w - 9 6 0
sharp focus w - 960
sharp focus »
sharp focus ”
sharp focus!
sharp focus!!
sharp focus!!!
sharp focus!!! coherent!!!
sharp focuscoherent
sharp focused
sharp focus”
sharp force
sharp foreground
sharp fox - teeth
sharp fucos
sharp geometrical squares
sharp glass shards
sharp high contrast
sharp high detail
sharp high detail face
sharp high detail illustration
sharp high detail ultra detailed
sharp high quality
sharp high quality anime
sharp high quality artwork
sharp high quality photo
sharp highlights
sharp hq rendering
sharp illustration
sharp image
sharp irregular shapes
sharp irregular spiraling wings
sharp jaw
sharp jawline
sharp jawline with a light beard
sharp lends
sharp lens
sharp lighting
sharp lighting. bright color
sharp lightning
sharp lineart
sharp lines
sharp linework
sharp long horns
sharp looking portrait
sharp luminescent focus
sharp mawshot
sharp metal claws
sharp metal crest
sharp metal dragon claws
sharp metal ears
sharp nose
sharp nose with rounded edges
sharp outlines
sharp paws
sharp photo
sharp polaroid photo
sharp purple eyes
sharp quality
sharp rain
sharp red eyes
sharp render
sharp robot cat paws
sharp robot dragon claws
sharp robot dragon paws
sharp rocks
sharp roofs
sharp shadows
sharp shapes
sharp silver armor
sharp silver armor fuchsia skin
sharp sleek cyborg dragon head
sharp sleek edged black armor
sharp smooth
sharp smooth focus
sharp soft focus
sharp spiky rocks
sharp spines
sharp sunray lighting
sharp teeth
sharp teeth and claws
sharp teeth grin
sharp teeth smile
sharp textures
sharp thick lines
sharp tip
sharp vampire teeth
sharp!
sharp-focus
sharp-jaw
sharp-lens
sharp. volumetric lighting
sharpe focus
sharped details
sharped nose
sharpen
sharpen high quality
sharpen lines
sharpened
sharpened depth of field
sharpened image
sharper luminescent focus
sharpfocus
sharpie
sharply detailed
sharply focused
sharply shaped
sharpness
sharpness applied
sharpness focus
sharpness. clean
sharps focus
sharpt teeth
shart focus
shatter
shattered
shattered abstractions
shattered crumbling plaster
shattered earth
shattered glass
shattered glass ( ( sunbeams ) )
shattered land
shattered sky cinematic
shattered visor
shattered wall
shattering
shattering expectations
shattering walls
shaun downey
shaun of the dead
shaun tan
shaun tan and peter mohrbacher
shaun tan. hyperrealism
shaved
shaved bald head
shaved beard
shaved face
shaved hair
shaved haircut
shaved head
shaved sides
shaved sides short top
shaved temple
shaven
shaven face
shaven stubble
shawl
shawn coss
she
she - hulk
she - ra
she arrived in a red dress
she expressing joy
she half human and half robot
she has a crown of dried flowers
she has a crown of flowers
she has a cute expressive face
she has a cute face
she has a diamond eye
she has a distant expression
she has a glow coming from her
she has a jiggly fat round belly
she has beautiful bone structure
she has black hair
she has black hair with bangs
she has blond hair
she has blue eyes
she has elf ears and gold eyes
she has feathers
she has fire powers
she has iridescent membranes
she has long orange brown hair
she has long redorange hair
she has messy hair
she has olive brown skin
she has pale blue skin!!!
she has perfect white teeths
she has purple hair
she has red hair
she has the head of a phoenix
she has tired eyes
she has two ponytails
she has white eyes!!!
she holds a glass of wine
she hulk
she is 2 3
she is a gourd
she is about 1 6 years old
she is about 2 0 years old
she is about 2 5 years old
she is about 20 years old
she is about 3 0 years old
she is about 30 years old
she is about 4 0 years old
she is about 40 years old
she is about 50 years old
she is about 6 0 years old
she is about 60 years old
she is about 7 0 years old
she is about 70 years old
she is approaching heaven
she is arriving heaven
she is attracting lightnings
she is dancing
she is dancing. realistic
she is distressed
she is dressed as a belly dancer
she is dressed in shaman clothes
she is easting a peach
she is eating a peach
she is facing the camera
she is floating in the air
she is frightened
she is happy
she is holding a cat in her arms
she is holding a katana sword
she is holding a long staff
she is holding a smartphone
she is holding a sword
she is in pure bliss
she is in the potions workshop
she is korean
she is laughing
she is laying on her back
she is looking at us
she is mexican
she is posing
she is quiet
she is ready to fight
she is redhead
she is smiling
she is smiling and excited
she is smiling and happy
she is tall
she is the center of the garden
she is the queen of black roses
she is walking on a river
she is wearing a black dress
she is wearing a black tank top
she is wearing a hat
she is wearing a wet coat
she is wearing streetwear
she looks like a mix of grimes
she wear gasmask
she wear red eyed gasmask
she wears a dark green dress
she wears a jacket
she wears boots
she wears harry potter glasses
she wears leather jacket
she's on my bed in bikini
she's sad
she-ra
sheath
shed
shed iridescent snakeskin
shed roof
sheen
sheep
sheep grazing
sheep wearing a suit
sheep wool
sheer
sheer cliffs surround the scene
sheer fabrics
sheet
sheet music
sheet of paper
sheet paper
sheik mohammad ruler of dubai
sheikh
sheikh mohammed ruler of dubai
sheild
shelf
shell
shell collar!!l
shell collarl
shell craters
shell shocked eyes
shells
shells and barnacles
shelter
sheltered
sheltering under a leaf
shelves
shelves filled with tomes
shelves full
shelves full of medieval goods
shenron
shenzhen
shepard fairey
shepard fairy style art
shephard fairey poster
shepherd's crook
shepherds crook
sherbert sky
sherek head design as a bottle
sheriff and cowboy
sherilyn fenn 2 2 years old
sherilyn fenn 2 6 years old
sherilyn fenn 22 years old
sherilyn fenn 26 years old
sherlock holmes
sheryl sandberg
sheryl sandberg at soulcycle
shes alone
shes on my bed in bikini
shes sad
shia labeouf
shiba figurine
shiba inu
shiba inu cosmonaut
shiba inu cosmonaut portrait
shiba inu dog
shiba inu face
shiba inu holding a baseball bat
shiba inu portrait
shibari
shibu inu
shibuya
shibuya crossing
shibuya prefecture
shibuya shibuya
shibuya street
shichiro kobayashi
shield
shield design
shield emblem
shield sunglasses
shields
shift photography
shift phtograpy
shifty
shigenori soejima
shigenori soejima illustration
shigeto hirai yuya
shigeto koyama
shih tzu
shikamimi
shikanosuke yagaki
shikishi
shilling
shimmer
shimmer detailed
shimmering
shimmering and prismatic
shimmering color
shimmering iridescent water
shimmering light
shimmering stars
shimmers
shin - hanga
shin godzilla
shin jeongho
shin megami
shin megami tensei
shin min jeong
shin-hanga
shine
shingeki no kyojin
shinichi fukuda
shinichi sakamoto
shinigami
shining
shining and happy atmosphere
shining armor
shining armour made of steel
shining claws
shining crescent moon
shining crystals
shining eyes
shining gold and black and red
shining golden hour
shining its light among stars
shining lamp
shining lamps
shining light
shining light and shadow
shining lights
shining lights on cliff side
shining metal
shining metal power armor
shining meteor
shining nordic runes
shining pink armor
shining plate armor
shining rainbow feathers
shining sea
shining silver katana sword
shining star
shining sun
shining sword
shinji
shinji higuchi and tomoki kyoda
shinji ikari
shinji kimura
shinjuku
shinkai
shinkai makoto
shinkiro
shinkiro toshiaki mori
shinning armor
shinning stars
shinning sword
shinny deep blue high heels
shinobi
shinsui ito
shintaro kago
shinto shrine
shintoism
shiny
shiny - skin
shiny - skin!!!
shiny and metallic
shiny and sparkling
shiny armor
shiny background
shiny black dress
shiny black lipstick
shiny bob haircut
shiny brass
shiny city in the distance
shiny colorful
shiny colors
shiny crisp finish
shiny eyes
shiny floors
shiny gems
shiny glitter / crystals
shiny glitter crystals
shiny gloss water reflections
shiny glossy mirror reflections
shiny glossy skin
shiny gold
shiny gold background
shiny golden
shiny golden bikini
shiny golden eyes
shiny hi tech armor
shiny jewels
shiny knobs
shiny layered geological strata
shiny lighting
shiny marble floor
shiny materials
shiny metal
shiny metal armor
shiny metallic glossy skin
shiny plastic
shiny plastic armor
shiny plastic bikini
shiny reflective surfaces
shiny scales
shiny silver
shiny silver armor engraved
shiny silver metal armor
shiny silver with gold trim
shiny skin
shiny skin”
shiny soft fur
shiny soft skin
shiny sparkling diamonds
shiny surfaces
shiny textured plastic shell
shiny wet skin
shiny wet skin!!
shiny white skin
shiny!!
shinya tsukamoto
shiori teshirogi
ship
ship at sea
ship control panel close-up
ship in a bottle
ship interior
ship on lake
shipfleet on the horizon
shipgirls
shipibo
shipibo patterns
shipping containers
shipping docks
ships
ships in the harbor
ships on fire
ships with sails
ships with sails underneath
shipwreck
shipwrecks
shipyard
shirabii
shire
shiro from deadman wonderland
shiro takatani
shirobako
shirow masamune
shirt
shirt art
shirt design
shirtless
shirtless  high detail
shirtless :: high detail
shirtless biden with tatoos
shishkin
shit
shit cobwebs and shit
shit grows
shiva
shkkeled in the voied
shmup
shock
shocked
shocked expression
shocked expression on her face
shocked face of putin demon
shocked look
shocking
shocking realization
shockwave
shockwaves are coming out
shodan
shodan from system shock 2
shodo
shoe
shoes
shogakukan
shoggoth
shogun audio
shogun era
shohei otomo
shohreh aghdashloo
shojo
shojo manga
sholim
shonen
shonen anime
shonen jump
shonen jump cover
shonen manga panel
shonen style
shooting
shooting a gun
shooting angle from below
shooting fire war
shooting ice
shooting laser
shooting pose
shooting star
shooting star in background
shooting stars
shootout
shop front
shop window for magical weapons
shopping cart
shopping cart icon
shopping carts full of groceries
shopping groceries
shopping mall
shops
shopwindows
shore
shore of the lake
shoreline
short
short - medium length hair
short antlers
short aqua blue black fade hair
short beard
short black curly hair
short black hair
short black hair with bangs
short black hair with gel
short black pixie cut hair
short black pixie like hair
short black pixie undercut hair
short black skirt
short blond hair
short blonde afro
short blonde hair
short blonde hair with bangs
short blue hair
short blue hair!!!
short blue haired woman
short bob hair
short body
short bookshelf
short braided curly blonde hair
short brown beard
short brown hair
short brown hair and large eyes
short brown hair with bangs
short brown leather jacket
short cartoon strip
short coat
short copper hair
short curly blonde haired girl
short curly hair
short dark blond beard
short dark hair
short dark haircut
short depth of field
short dof
short dreadlocks with beads
short dress
short exposure
short face
short facial hair
short flat hourglass slim figure
short focal
short focal length
short focus
short focus depth
short goatee
short golden curls
short green bobcut
short green hair
short grey hair
short hair
short hair cut
short hair on sides of head
short hair on top of his head
short hair style
short hair with gel
short haircut
short height
short horns
short in stature
short jacket
short jeans
short leather coat
short legs
short light brown hair
short light grey whiskers
short messy hair
short minidress
short miniskirt
short muzzle
short neck
short on sides
short pants
short person
short perspective
short pigtails hair
short pink hair
short platinum hair tomboy
short ponytail
short puffy pants
short puffy pants made of silk
short purple black fade hair
short purple hair
short purple hair with undercut
short red black fade hair
short red hair
short redhead
short robust woman
short scruffy beard
short shaved hair
short shorts
short silver grey hair
short silver hair
short size
short skirt
short skirt and a long jacket
short skirt length
short sleeves
short slicked - back hair
short smile
short spiked hair
short spiky blonde hair
short spiky hair
short spout
short straight hair
short stubble
short telephoto
short to medium length hair
short torso
short unkempt green hair
short wavy blond hair
short wavy hair
short white beard
short white hair
short white hair shaved sides
shortbow
shorter neck
shorting
shorts
shorts!!
shortsword
shot
shot at dark with studio lights
shot at golden hour
shot at night with studio lights
shot composition
shot for imax
shot for pottery magazine
shot from 5 0 feet distance
shot from a distance
shot from a drone
shot from a low angle
shot from above
shot from afar
shot from behind
shot from behind blades of grass
shot from below
shot from cinematic
shot from danis villeneuve movie
shot from drone
shot from far away
shot from game of thrones
shot from low angle
shot from movie
shot from professional camera
shot from roofline
shot from space
shot from the back
shot from the side
shot in 35mm
shot in canon
shot in canon 50mm f/1.2
shot in the air
shot in the photo studio
shot in the sky
shot in wide angle
shot of a highly detailed
shot of a space station at night
shot of face
shot of film
shot on 1 5 0 mm
shot on 1 6 mm
shot on 16mm film
shot on 3 5 mm
shot on 3 5 mm film
shot on 35 mm
shot on 35mm
shot on 7 0 mm
shot on 70 mm
shot on 70mm
shot on 85mm
shot on a 2 0 0 3 camera
shot on a 9.8mm wide angle lens
shot on alexa
shot on an iphone
shot on anamorphic lenses
shot on arri alexa
shot on canon camera
shot on canon eos r 5
shot on canon eos r5
shot on expired instamatic film
shot on expired kodak film
shot on film
shot on gopro9
shot on hasselblad
shot on imax
shot on iphone
shot on iphone 1 3 pro
shot on iphone 1 3 pro max
shot on iphone 6
shot on kodachrome
shot on kodak ektar
shot on kodak vision 200t
shot on large format film camera
shot on leica
shot on leica sl2
shot on nicon camera
shot on nikon d 3 2 0 0
shot on nikon z9
shot on polaroid
shot on red camera
shot on sony a 7
shot on sony a 7 iii
shot on sony alpha dslr-a300
shot on superia 400 film stock
shot on superia 400 filmstock
shot on webcam
shot onfilm
shot with a arriflex 35 ii
shot with a camera flash
shot with a canon
shot with a canon 20mm lens
shot with a canon 35mm lens
shot with a dslr
shot with a gopro
shot with a professional camera
shot with an analog camera
shot with canon 5 d mark ii
shot with canon eoa 6 d mark ii
shot with hasselblad
shot with hasselblade camera
shot with iphone 1 0
shot with premium dslr camera
shot with sigma f / 4. 2
shot with sigma f/ 4.2
shot with sony alpha
shot with sony alpha 1 camera
shot wounds
shotaro ishinomori
shotgun
shotguns
shots fired
shoujo
shoujo manga
shoujo manga character design
shoujo romance
should eyes
shoulder
shoulder - length
shoulder - length blonde hair
shoulder - length hair
shoulder eyes
shoulder length black hair
shoulder length hair
shoulder level shot
shoulder long hair
shoulder mounted gun
shoulder pads
shoulder patch design
shoulder tattoo
shoulder-length black hair
shoulder-length brown hair
shoulder-length hair
shoulder-long straight
shoulders
shoulders and head
shoulders can be seen
shoulders eyes
shoulders up
shounen jump
shouting
shovel knight as daft punk
show
show from below
show it
show light
show room scene
show-accurate
showcase
showcases full of embryos
showdown
shower cap
showers
showing a screen playing tetris
showing anger
showing curves
showing forehead
showing her face
showing her shoulder from back
showing his paws to viewer
showing kingdoms
showing off biceps
showing off his muscles
showing strong muscles
showing teeth
showing victory
showpiece
shows a leg
showstudio
shpongle
shrap focus
shredded
shrek
shrek as captain america
shrek as neo from the matrix
shrek doing ballet
shrek faced
shrek portrait
shrek with red eyes
shrimp
shrimps are all over the ground
shrine
shrines
shrinking spell
shrooms
shroud
shrouded
shrouded face
shrouded figure
shrouded in darkness
shrouded in fog
shrouded in mist
shrubbery
shrubs
shrubs and flowers. esao andrews
shrugging
shrugging arms
shui mo hua
shunga style
shungite bangle
shurikens
shusei nagaoka
shut down
shut eyes
shutter
shutter speed 1/1000
shutter speed 1/125
shutter speed is 8 0
shuttered mall store
shutters
shuttershock
shuushuu anime image
shy
shy and demure
shy beetles
shy looking down
shy smile
shy smiling face
si - fi
siamese twins
siamese twins made of spaghetti
siberia
siberia!!
siberian husky
siblings
sichuan
sicilian
sick
sick with a cold
sickened flesh decay
sickening
sickle
sickly
sickly colors
sickly green colors
sickness
sicko
sid
sid mead
sid meier
side
side  profile
side - profile painted portrait
side - scrolling 2 d platformer
side - scrolling 2d platformer
side - view
side angle
side boob
side centered painted portrait
side elevation
side fed
side fringe haircut
side front view
side hair
side light
side lighting
side lighting xf iq4
side lights
side of head
side parted combover brown hair
side perspective
side portrait
side portrait dark witch
side portrait imagery
side portrait of a girl
side portrait of a girl walking
side portrait of cute girl
side portrait of elven royalty
side portrait of imogen poots
side portrait rugged girl
side pose
side profile
side profile artwork
side profile cenetered portrait
side profile centered
side profile centered portrait
side profile in underwater
side profile painted portrait
side profile portrait
side profile shot
side profile view
side profile waist up portrait
side scroller
side scroller game
side scrolling
side shot
side view
side view centered
side view close up of a gaunt
side view from afar
side view future coder man
side view intricate details
side view of a gaunt
side view of her taking steps
side view profile
side view profile centered
side-view
side-view. highly detailed
sideburns
sidefx
sidefx houdini
sidelit
sideshow collectibles
sideshow figurines
sideview
sidewalk
sideways glance
sidious
sidney lugo
sie boob
siege
sienna
sierpinski gasket
sierra entertainment games
sierra games
sifi
siggraph
sight
sight proof
sighting
sigil
sigils
sigma
sigma 1 0 - 2 0 mm
sigma 1 0 5 mm f 2
sigma 1 0 5 mm f 2. 8
sigma 1.6
sigma 1/6
sigma 1/6. 50 mm
sigma 100mm
sigma 2 4 mm
sigma 2 4 mm f / 8
sigma 2 8 mm
sigma 200mm
sigma 24 mm f/8
sigma 24mm f/8
sigma 24mm f8
sigma 28mm
sigma 3 5 mm
sigma 30 mm f/1.4
sigma 35mm
sigma 35mm f/1.4
sigma 35mm f/2.8 dg dn art
sigma 35mm f/8
sigma 5 0
sigma 5 0 0 mm f / 5
sigma 5 0 mm
sigma 5 0 mm f 1. 4
sigma 5 5
sigma 50 mm f/1.4
sigma 500mm f/5
sigma 50mm
sigma 50mm f/1.4
sigma 50mm f/5.6
sigma 55mm f/8
sigma 55”
sigma 7 5 mm
sigma 75mm
sigma 8 0 mm
sigma 8 5 mm
sigma 8 5 mm f / 1 4
sigma 8 5 mm f / 1. 4
sigma 8 5 mm f / 8
sigma 8 5 mm f 1 6
sigma 8 5 mm f 1. 4
sigma 8 5 mm f 1. 5!!
sigma 8 5 mm f 1. 8
sigma 8 5 mm f _ 8
sigma 80mm
sigma 85 mm
sigma 85 mm f 1.4
sigma 85 mm f/1.4
sigma 85 mm f/14
sigma 85/1.2 portrait
sigma 85mm
sigma 85mm 1.4
sigma 85mm art
sigma 85mm f/1.4
sigma 85mm f/8
sigma 85mm f1.4 dg dn
sigma 85mm f_1.4
sigma 85mm lens f/1.4
sigma 85mm lens f/1.8
sigma art 135 mm f 1.4
sigma art 8 5 mm f 1. 4
sigma art 85 mm
sigma art 85mm f1.4
sigma art 85mm f1.8
sigma art lens
sigma female
sigma from overwatch
sigma lens
sigma lens photo
sigma look
sigma male
sigman 85mm
sign
sign that says 1 0 0
signature
signature on the bottom
signatures
signboards
signed
significant pebbles boundaries
signing a bill
signing autographs
signs
sigourney weaver
sigourney weaver smiling age 2 5
sil from species
silence
silenced pistol
silent
silent decay coloring
silent echo
silent hill
silent hill 1
silent hill 3 game screenshot
silent hill aesthetic
silent hill concept art
silent hill game
silent hill in real life
silent hill landscape
silent hill streets
silent hill style
silent movie
silent running
silent running ( film )
silhouette
silhouette :7
silhouette of a girl and her cat
silhouette of a man
silhouette of man
silhouette over sunset
silhouette!!!
silhouetted
silhouettes
silhouettes in field behind
silhouettes of people
silhoutte
silhuette
silicon and carbon
silicone cover
silicone patch design
silicone skin
siliconized
silk
silk belt
silk colors
silk dress
silk flowing in wind
silk hat
silk robes
silk screen
silk screen art
silk screen print
silk screen t-shirt design 4k
silk shoes
silk spectre
silk stockings
silk tarps hanging
silken blonde hair
silken hair
silks
silkscreen
silkscreen pop art
silkscreen print
silkscreen t-shirt art
silky
silky garment
silky hair
silky smooth
silky straight hair
silky texture
silly
silly and serious
silly cartoon
silly face
silly playful fun face
silmarillion
silo
siluette
siluettes
silvain sarrailh
silver
silver accessories
silver age of comics
silver and amethyst
silver and blue color schemes
silver and blue colors
silver and cool colors
silver and crimson ink
silver and emerald breastplate
silver and gold
silver and gold heavy armor
silver and muted colors
silver and sapphire
silver and yellow color scheme
silver angel wings
silver armor
silver armor and red clothing
silver armor with gold trim
silver armor with thin gold trim
silver background
silver bracelets
silver chrome color
silver color
silver crown
silver curly hair
silver cyber armor
silver dechroic details
silver details
silver earring
silver earrings
silver egg cup
silver eyes
silver eyes full body
silver filigree
silver filigree details
silver full beard
silver garment
silver gelatin print
silver gelatine realistic
silver glasses
silver gold details
silver gold fractal details
silver gold red details
silver hair
silver hair (ponytail)
silver hair and beard
silver hair girl
silver hair ponytail
silver hair so long
silver hair!!
silver haired
silver highlights
silver hues
silver insect legs
silver insignia
silver intricate armor
silver iodide
silver jewellery
silver jewelry
silver light
silver lining
silver long hair
silver metal armor
silver metallic moncler jacket
silver mist
silver monocle
silver necklace
silver nitrate photography
silver ornaments
silver palette
silver platter
silver ponytail hair
silver prince crown
silver red
silver red white details
silver shaggy hair
silver sharp streamlined armor
silver shimmering hair
silver silver glasses
silver skin
silver small glasses
silver small small small glasses
silver snakes
silver space suit
silver sports watch
silver steampunk gears
silver sword
silver very ornate jewelry
silver white and gold
silver white gold red details
silver white hair
silver white red details
silver wings
silver with gold accents
silver with gold trim
silvergill adept
silverplate
silvery skinned male elf
silver，ivory
silvio berlusconi
silvio santos
sim city 4
simba
simetric photography
simetrical medium shot
similar aesthetics to picasso
similar to hagrid
similar to legolas
similar to malfoy
similar to pokemon
similar to the bifrost
similar to the mona lisa
simin stalenhag
simon
simon birch
simon bisley and alphonse mucha
simon bisley and frank frazetta
simon bisley and paolo parente
simon bisley and richard corben
simon bisley!
simon bosley
simon cowell
simon holmedal
simon lee
simon pegg portrait
simon roy
simon stalberg
simon stalenhag highly detailed
simon stalenhag palette
simon stalenhag. masterpiece
simon stälenhag
simon stålenhag color scheme
simon stålenhag. digital art
simona sbaffi is the captain
simone graci
simonetti
simpe hands
simple
simple 2d flat design
simple aesthetic
simple airplane cockpit
simple anatomic
simple and clean illustration
simple art
simple background
simple bionic exploded drawing
simple brutalist architecture
simple but effective composition
simple cartoon
simple cartoon style
simple ceiling
simple chromatic xray
simple clean lines
simple clothes
simple clothing
simple clothing!!!!
simple clothing!!!!!
simple colors
simple composition
simple concept art portrait of
simple cream dress
simple curvilinear watercolor
simple design
simple design concepts
simple detail. greenish lighting
simple details
simple digital art
simple drawing
simple dress
simple elegant design
simple fantasy map
simple figures
simple flight instruments
simple form
simple futurist cyborg empress
simple gable roofs
simple gradients
simple illustration
simple line art
simple lineart
simple lines
simple logo
simple magic ring of poison
simple minimal
simple muted colors
simple nostalgic
simple path traced
simple primitive tube shape
simple red background
simple shading
simple shape
simple shapes
simple structure
simple style
simple stylized
simple tree fractal
simple water color
simple watercolor
simple white background
simple white veil
simple wood shelves
simple yet detailed
simplfied
simplicity
simplified
simplified forms
simplified shapes
simplified zentangle
simplistic
simplistic design
simplistic iconography
simplistic style
simply style
simpsons
simpsons and nendoroid mashup
simpsons style
sims 4 screenshot
simulacra
simulacrum of a space fungus
simulated space combat
simulation
simulation of water splashes
simón staalhag style
sin city
since
sincere
sincere face
sinew
sing for the laughter
sing for the tear
sing with me
singapore
singapore ( 2 0 1 8 )
singapore city
singapore esplanade
singer
singer - songwriter
singer beyoncé as she-hulk
singer in the voice show
singer maluma
singer songwriter
singer songwriter nin
singing
singing at a opera house
singing for you
singing into microphone
single
single !!tear!!
single area of attention
single aztec skull
single bangla farmer fighting
single body
single building
single centered subject
single chair
single chair brass
single character
single character concept art
single character full body
single character!
single clay museum sculpture
single color
single eye
single face
single figure
single figure composition
single file
single flat colour
single flooded tower
single floor
single head
single horn
single image
single light
single light source
single line
single line drawing
single logo
single long stick
single object scene
single pair of hands
single panel
single person
single person with umbrella
single pine
single plot
single point of light
single point perspective
single portrait
single ray of golden sunlight
single ray of moon
single ray of sun
single realistic face
single scary female vampire nun
single silhouette figure
single solid body
single steering wheel
single street
single strobe light
single subject
single tear
single tentacle
single tree
single vague light
singular
singular gigantic building focus
singular light source from below
singular lightning
singularity
singularity sculpted �ー etsy
singularity!!!
sinister
sinister ambience
sinister and stylish
sinister atmosphere
sinister background
sinister complexion
sinister dark fantasy
sinister demonic atmosphere
sinister eyes
sinister lighting
sinister looking
sinister mood
sinister photo
sinister portrait
sinister pose
sinister vibe
sink
sink ( ( ( fish tank ) ) ) ) )
sinkhole
sinkholes
sinking
sinking as if drowing
sinking into madness
sinking underwater
sins inc skyscraper front
sinuous
sinuous fine roots
sir joshua reynolds
sir lawrence alma - tadema
sir lawrence almda tadema
siren
siren song
sirens
sirius
sirius a and sirius b
sirius star system
sisters
sistina
sistine chapel
sistine chapel ceiling
sisyphus compostition
sit - com
sit on a bench
sit on the edge of swimming pool
sit on throne
sitcom
sitcom screenshot
sith
sith lord
sith lord. dramatic lighting
siting in a chair
siting on a toilet
sits on a finger
sits on a rooftop
sittin
sitting
sitting across the room
sitting alone
sitting alone at a bar
sitting alone in a cafe
sitting at a bar
sitting at a computer
sitting at a computer desk
sitting at a control center
sitting at a desk
sitting at a pond
sitting at a table
sitting at desk
sitting at desk at keyboard
sitting at his desk
sitting at his louis xiv desk
sitting at table
sitting at the bar
sitting at the beach
sitting at the edge of pool
sitting at the parlament meeting
sitting at the resolute desk
sitting at the table
sitting atop a dusty mountaintop
sitting behind desk
sitting cross-legged
sitting cutely on a mountain
sitting down
sitting down casually
sitting dynamic pose
sitting in a cafe
sitting in a cafe alone
sitting in a castle
sitting in a cemetery at night
sitting in a chair
sitting in a colorful forest
sitting in a crane
sitting in a dark prison cell
sitting in a field
sitting in a field of cannabis
sitting in a field of flowers
sitting in a fluffy cloud
sitting in a gilded throne
sitting in a lounge
sitting in a movie theater
sitting in a plastic throne
sitting in a prison
sitting in a reflective pool
sitting in a rocking chair
sitting in a small bamboo boat
sitting in a tree
sitting in a waiting room
sitting in a wheelchair
sitting in an armchair
sitting in an empty white room
sitting in bedroom
sitting in chair
sitting in dean's office
sitting in deans office
sitting in fancy chair
sitting in french apartment
sitting in front of a lake
sitting in front of a microphone
sitting in front of computer
sitting in her car
sitting in her room
sitting in her throne underwater
sitting in his chair. intricate
sitting in his highchair
sitting in his throne underwater
sitting in his tie fighter
sitting in office
sitting in rural living room
sitting in ten forward
sitting in the classroom
sitting in the forrest
sitting in the garden
sitting in the pool
sitting in the rose garden
sitting in tokyo
sitting near a river
sitting on a bed
sitting on a bench
sitting on a chair
sitting on a couch
sitting on a curly branch
sitting on a desk
sitting on a golden throne
sitting on a grand staircase
sitting on a lab table
sitting on a leaf
sitting on a log
sitting on a lotus flower
sitting on a martian rock
sitting on a metal throne
sitting on a miniature city
sitting on a mocha-colored table
sitting on a moon
sitting on a motorcycle
sitting on a park bench
sitting on a pile of gold coins
sitting on a red button
sitting on a reflective pool
sitting on a rock
sitting on a skyscraper rooftop
sitting on a sofa
sitting on a stone throne
sitting on a stool
sitting on a store shelf
sitting on a table
sitting on a throne
sitting on a tree
sitting on a window sill
sitting on a wood dock
sitting on a wooden dock
sitting on an armchair
sitting on an ornate throne
sitting on an royal throne
sitting on bench
sitting on bent knees
sitting on chair
sitting on couch
sitting on cyberpunk motorbike
sitting on designer chair
sitting on edge of bed
sitting on golden throne
sitting on green grass
sitting on her bed
sitting on her throne
sitting on his throne
sitting on ice throne
sitting on intricate throne
sitting on man's fingertip
sitting on obsidian throne
sitting on one knee on the grass
sitting on rocks
sitting on santa
sitting on temple stairs
sitting on the beach
sitting on the beach at night
sitting on the bed
sitting on the cosmic cloudscape
sitting on the couch
sitting on the edge
sitting on the edge of a bed
sitting on the floor
sitting on the ground
sitting on the iron throne
sitting on the porcelain throne
sitting on the sofa
sitting on the table
sitting on the throne
sitting on throne
sitting on tiger cavalry
sitting on top a table
sitting on top of a cryopod
sitting on top of a rainbow
sitting on vintage leather sofa
sitting pose
sitting relax and happy
sitting sad in spaceship
sitting under a tree
sitting under bridge
sitting with flowers
sitting with wrists together
situation
situationist international
siwoo kim
six arms
six eyes
six from little nightmares
six pack
six packs
six point perspective
six sided
six-dimensional
six-pack
sixteen-dimensional
sixties
sixties pinup
siya oum
size
size 1 0
size 10
size difference
sizes and colors
sk
ska skeleton and girlfriend
skary
skate park
skateboard
skateboard art
skateboarder style
skateboarding
skatepark
skatepark in flames
skater skirt
skaven
skeb.jp
skeksis
skeletal
skeletal figures
skeletal male figure
skeletal omens
skeletal with extra flesh
skeletal with extra fleshy bits
skeleton
skeleton - like creature
skeleton climbing
skeleton corpse
skeleton cowboy
skeleton drummer
skeleton face
skeleton face symmetrical face
skeleton girl
skeleton in a suit
skeleton king
skeleton knight
skeleton pirate
skeleton warrior
skeleton with eyes
skeleton-like creature
skeletons
skeletons on a pirate ship
skeletons on the ground
skeletons rising from the dead
skeletons smoking cigars
skeletor
skelleton
skeptical expression
sket - one x iamretro
sketch
sketch - up
sketch art
sketch black and white colors
sketch book
sketch drawing
sketch illustration
sketch of a caucasian face
sketch of a lucid dream
sketch of an ocean in ms paint
sketch of oculus rift
sketch style
sketch tattoo
sketch-like
sketchbook
sketchbook drawing
sketched
sketched 4k
sketches
sketchfab
sketchfab »
sketching
sketchlab
sketchy
sketchy artstyle
sketchy expressive linework
sketchy lines and brushstrokes
skeumorphism
skeuomorphic
skewed perspective
skewed shot
skewer
ski mask
ski masks
skidding
skies
skies behind
skiff
skiing
skill ability art
skilled
skilled geisha of the japanese
skilled homeless
skilled warrior of the apache
skimask
skimpy leather armor
skimpy shorts
skin
skin : tjalf sparnaay
skin blemishes
skin care
skin color
skin concept
skin detail
skin details
skin grain detail
skin made of led point lights
skin of flames
skin on the gaming pc
skin painted with green
skin pore detail
skin pores
skin reflections
skin reflective metallic
skin spikes
skin texture
skin texture details
skin texture like a brain
skin texture natural
skin textures
skin tinted a warm tone
skin tones
skin wounds
skincare
skindentation
skinless head
skinned
skinned alive
skinny
skinny breaking bad
skinny caucasian man
skinny caucasian young man
skinny face
skinny female artist back view
skinny girl in white boho dress
skinny grunt face
skinny male fantasy alchemist
skinny male fantasy scientist
skinny male mage
skinny male magician
skinny upper arms
skinny waist and thick hips
skins
skintight
skintight black bodysuit
skintight black clothes
skintight dress
skintight jet black leahter coat
skintight leather clothes
skintight rainbow body suit
skintight red leather armor
skintight silver armor
skintight suits
skinwalker
skirt
skottie young
skull
skull and bones
skull and crossbones
skull bones
skull bones flowers
skull bust
skull cane
skull cap
skull clown
skull clown inside a circus
skull design for a rock band
skull earings
skull face
skull face paint
skull faces
skull head
skull helmet
skull image on the vest
skull in broken space helmet
skull island
skull like
skull liminal void background
skull made of red roses
skull mask
skull on the chest
skull on the screen
skull paint
skull protruding from face
skull tattoo
skulled creature with black fur
skullish
skulls
skulls and bones
skulls and gas masks
skulls and skeletons
skulls are lying underneath
skulls around
skulls at her hips
skulls in hands
skulls made of algae
skulls on the ground
sky
sky - blue dress
sky - fi
sky - high view
sky and ocean background
sky background
sky blue
sky blue and white color scheme
sky blue eyes
sky blue hair
sky blue highlights in hair
sky blue straight hair
sky bridge
sky bridges
sky falling
sky focus
sky forest background
sky full of clouds
sky full of stars
sky gradient
sky high level of alcoholism
sky in a background
sky in background
sky in the background
sky is not visible
sky is orangish outside
sky island
sky line
sky made of ceiling panels
sky mural on the room ceiling
sky night
sky on fire
sky scrapers
sky setting
sky strewn with stars
sky swirling with black wind
sky theme in background
sky town
sky view
sky whales
sky whith plump white clouds
sky witch
sky with swirling clouds
sky!
sky!!!
sky-blue thick fur coat
sky-blue thick fur robes
skybox
skybridge towers
skybridges
skycrapers
skycrapers 1930s
skydiving
skydoll noa
skydome
skydsgaard
skye meaker
skyfall
skyfire
skylight open ceiling
skylights
skyline
skyline in back
skyline in the distance
skyline showing
skyline showing from the windows
skyline view from a rooftop
skynet
skyrim
skyrim armor
skyrim art
skyrim civil war
skyrim gameplay
skyrim inspired
skyrim intro
skyrim mod
skyrim pc screenshot
skyrim screenshot
skyrim setting
skyscape
skyscapers
skyscraper
skyscraper forest community
skyscrapers
skyscrapers and flying cars
skyscrapers in the background
skyscrapers in the distance
skyscrapers on the background
skyscrapers with greenery
skywind
slaanesh
slam dancing
slam dancing creatures
slam dunk
slanted amber eyes
slanted ceiling
slanted lighting from window
slapstick
slash and burn
slash page
slasher
slasher movie
slasher smile
slate
slaughterhouse
slav epic
slave
slave collar
slavic
slavic city. big mushrooms
slavic dog head man
slavic features
slavic folk fairytale
slavic folklore symbols
slavic mythology
slavic orcs in the steppe
slavic style
slavic!!!
slavoj zizek
slay
slayer
sleazy
sleazy dark future
sleek
sleek armor
sleek black and white
sleek blond hair
sleek blue visor for eyes
sleek bright white armor
sleek cool waterproof design
sleek curves
sleek dark fur
sleek design
sleek dragon head
sleek dragon legs
sleek eyes
sleek flowing shapes
sleek glass buildings
sleek glowing armor
sleek gold armor
sleek hands
sleek head
sleek interceptor profile
sleek legs
sleek mecha female dragon head
sleek metal ears
sleek metal head
sleek oled blue visor for eyes
sleek outfit
sleek purple armor
sleek purple eyes
sleek robes
sleek robot
sleek round shapes
sleek silver armor
sleek smooth white plated armor
sleek spines
sleek streamlined design
sleek streamlined white armor
sleek utopian design
sleek visor
sleek waterproof design
sleek white
sleek white armor
sleek yellow eyes
sleek!!!
sleep
sleep deprived
sleep paralysis
sleep paralysis demon
sleep paralysis monster
sleep with love
sleepers
sleeping
sleeping bag
sleeping beauty
sleeping beauty fairytale
sleeping cat on window
sleeping princess!!!!
sleepwear
sleepy
sleepy expression
sleepy eyes
sleepy fashion model face
sleepy feeling
sleet
sleeveless
sleeveless red dress
sleeveless tops
sleeveless turtleneck
sleipnir
slend body
slender
slender and muscular build
slender body
slender boy with a pale
slender figure
slender girl
slender man
slender nose
slender symmetrical body
slender symmetrical face
slender waist
slender woman
slenderman
slenderman in the woods
slenderman zombie
slice - of - life
slice of life
slice of life anime
slice of life”
sliced banana
sliced bread in slots
sliced grapefruit
slices of orange
slicing the air. pop surrealism
slick
slick clammy skin
slick design
slick elegant design
slick hair
slick leather armor
slick leds
slick pink armor
slick tires
slick wet walls
slick!!
slicked back hair
slicked black hair
slicked-back hair
slide show
slides
sliding glass windows
slifer the sky dragon
slight awkward smile
slight bloom
slight blur
slight bokeh
slight color bleed
slight cute smile
slight evil smile
slight fog
slight freckles
slight friendly smile
slight glow
slight grain
slight haze
slight lens flare
slight lens glare
slight motion blur
slight nerdy awkward smile
slight nerdy smile
slight overcast
slight overcast lighting
slight overcast weather
slight ruination
slight smile
slight smile and open eyes
slight smile expression
slight smirk
slight stubble
slight yellow hue
slightly - pointed ears
slightly abstract
slightly awkward smile
slightly awkward smile!
slightly blurred
slightly blurry
slightly buck - toothed
slightly colorful
slightly dark colors
slightly defined jawline
slightly desaturated colors
slightly dirty face
slightly drunk
slightly erotic
slightly fat
slightly foggy
slightly golden
slightly happy facial expression
slightly holographic
slightly larger nose
slightly minimal
slightly muscular
slightly open mouth
slightly out of focus
slightly overexposed
slightly overweight
slightly pixelated
slightly realistic
slightly red
slightly round chin
slightly rounded face
slightly set back jaw
slightly smiling
slightly smirking
slightly smooth
slightly sunny
slightly sunny weather
slightly tanned
slightly turned to the right
sligthly hard shadows
slik design
slim
slim aarons
slim and tall
slim body
slim build
slim face
slim female figure
slim figure
slim legs
slim shady tennis ball monster
slim waist
slime
slime and tentacles
slime mold
slime mold forest environment
slime monster
slime rancher
slimer
slimes
slimey
slimmer demeanor
slimy
slimy and hot
slimy and reflective
slimy fluid liquid
slimy fluid liquid blobs
slimy pus oozing specular
slimy shiny reflective joy
slimy tongue
slimy unreal engine
sling bikini
slip n slide
slipknot
slippers
slit - scan photography
slit pupils
slit-scan photography
sliver ice color reflected armor
sloped site
sloped street
sloppy
sloth
sloth as the king of cups
sloth stoned af
slovakia
slovenian
slow
slow - mo high speed photography
slow - shutter
slow exposure hdr 8 k
slow exposure hdr 8k
slow flash sync
slow motion
slow motion fashion
slow shutter
slow shutter speed
slowtime
slr
sludge
sludgy
slum
slum mountain futuristic urban
slums
slurping spaghetti
slush
slush!!
slush))
slutcore
sly
sly expression
sly smile
small
small and cosy student bedroom
small and dense intricate vines
small and sharp pupils
small and thick red lips
small animals
small astronaut looking up
small astronauts
small beard
small bed not made
small bedroom
small bees following the leader
small black eyes
small blond goatee
small blue eyes
small boat
small boat in foreground
small boat in the foreground
small breasts
small brush strokes
small buildings
small canoes
small castle in the distance
small chamber. hyperrealistic
small character. unreal engine 5
small chest
small chin
small city
small clocks as leaves
small cottage in the foreground
small cottage with red shutters
small creek
small crowd of people
small crown
small curvy loli
small dark grey beard
small demons
small depth of field
small details
small dock
small dog
small ears
small elongated planes
small eyebrows
small eyes
small fairies
small features
small fence
small finely stippled light
small fire
small fish swimming around
small flames
small flowing stream from wall
small freckles
small gadget
small glasses
small hairs
small hat
small head
small heart - shaped face
small hipster coffee shop
small horns
small houses
small human eyes
small in size
small jaw
small kitchen
small lake
small led lights
small legs
small library
small lips
small lips pointy nose
small loli girl
small man
small manufacture
small medium and large elements
small mouth
small necklace
small neon keyboard
small nixie tubes
small nose
small nose with freckles
small path up to door
small people with torches
small plants
small plants on the window sill
small plump lips
small pond
small port village
small portraits
small quills along it's back
small rectangular glasses
small red lights
small red roses
small reeds behind lake
small reflecting rainbow stars
small retro starship in the sky
small retro starship is near
small river
small river on the ground
small robots
small room
small room in tokyo
small round face
small scale
small scandinavian!!! houses
small scandinavianhouses
small settlements
small shoulders
small smile
small spacecraft in background
small spaceships
small spot light on robot
small square glasses
small starship near
small stature
small steps leading down
small straight nose
small streaks of light through
small stream
small studded earings
small style cue from mad max
small town
small town surrounding
small upturned nose
small vials and pouches on belt
small village
small waist
small water stream
small waterfall
small waterfalls
small wellness relaxation pool
small white dog at her side
small wide smile
small windows
smaller mouth
smaller nose
smallest waist
smallest waistline
smallest waistline ever
smart
smart ants
smart awkward and sexy
smart casual
smart design
smart hedgehog
smart looking
smart sharpen
smart tai warlord
smart textiles
smartphone
smartphone displays qr code
smartphone footage
smartphone photo
smartphone photography
smartphone resolution
smashed glass helmet
smashed wall
smaug
smeared acrylic paint
smeared flowers
smeared watercolours
smelling good
smelly
smelters
smelting pit'beeple
smg
smile
smile 1950s
smile coy
smile face
smile like a sphinx
smile on face
smile on her face
smile with large front teeth
smileeeeeee
smileing nright
smiles
smiles and colors
smiles in despair
smiles slightly
smiley
smiley face
smiley profile
smiling
smiling  attractive
smiling :: attractive
smiling amazed
smiling and dancing
smiling and looking directly
smiling as a queen of fairies
smiling at camera
smiling at each other
smiling at the camera
smiling at the viewer
smiling but has a temper
smiling cat
smiling confidently
smiling couple
smiling coy
smiling coyly
smiling down from above
smiling expression
smiling face
smiling faces
smiling fashion model
smiling fashion model face
smiling for the camera
smiling girl
smiling in heaven
smiling into the camera
smiling kindly
smiling laughing
smiling like a jerk
smiling male
smiling man
smiling maniacally
smiling mask
smiling mouth
smiling playfully
smiling seductively
smiling slightly
smiling smugly
smiling soft vibrant lips
smiling softly
smiling spartan
smiling sweetly
smiling warmly
smiling with confidence
smiling woman
smiling young woman
smiling!! fantasy
smilodon
smirk
smirk expression
smirk on face
smirking
smirking at the camera
smirking deviously
smirking face
smirking male bard
smithsonian
smithsonian american art museum
smithsonian museum
smog
smog on the floor
smogpunk
smoke
smoke - filled ， green hill
smoke :6
smoke all around
smoke and atmosphere
smoke and clouds
smoke and debris
smoke and destruction
smoke and dust
smoke and embers
smoke and fire
smoke and fog
smoke and gas
smoke and mist
smoke and orange volumetric fog
smoke and rubble
smoke and thick dust in the air
smoke and volumetric fog
smoke around her
smoke around him
smoke art
smoke atmosphere
smoke background
smoke behind
smoke behind wheels
smoke billowing
smoke billowing from chimney
smoke billows over the horizon
smoke boiling
smoke cloud
smoke clouds
smoke columns
smoke coming from tires
smoke coming out of her mouth
smoke coming out of the chimney
smoke debris
smoke effects
smoke energy
smoke everywhere
smoke explosion
smoke explosions
smoke filled room
smoke fills the area
smoke fills the room
smoke fire
smoke fog and crepuscular rays
smoke fog dust
smoke from chimneys
smoke from mouth
smoke from the fire
smoke grenade
smoke grenades
smoke in background
smoke in front
smoke in sky
smoke in the air
smoke in the background
smoke in the sky
smoke is flowing
smoke lasers
smoke machine
smoke mist rain
smoke on the street
smoke on the water
smoke on water
smoke out of eyes
smoke out of her eyes
smoke pit nebulas
smoke rising
smoke rising from the pipe
smoke rising in the background
smoke rising like clouds
smoke simulation
smoke swirling
smoke swirls
smoke tendrils
smoke trail
smoke trailing out the back
smoke trails
smoke under the ceiling
smoke war
smoked layered
smoker
smokes
smokes everywhere
smokestacks
smokestacks at night in the rain
smokey
smokey atmosphere
smokey background
smokey burnt envelopes
smokey burnt love letters
smokey cannons
smokey chimney
smokey eye
smokey eyes
smokey lights
smokey room
smokey tires
smokey water scenery
smoking
smoking a big cigar
smoking a blunt
smoking a bowl of hash together
smoking a cigar
smoking a cigarette
smoking a cigarette in the field
smoking a cigarette in the rain
smoking a cigarrette🚬
smoking a joint
smoking a magical bong
smoking a pipe
smoking a thick cigar
smoking a weed with snoop dogg
smoking a weed with snoop dogg!!
smoking and bickering
smoking and burning
smoking and holding a gun
smoking body
smoking cigar
smoking cigarette
smoking cigarettes
smoking cigars
smoking gun
smoking massive amounts of weed
smoking outside
smoking pipe
smoking rock
smoking soldering iron
smoking vessels
smoking weed
smoking with squat down pose
smoking woman
smoky
smoky atmosphere
smoky background
smoky chimney
smoky crystals
smoky eyes
smoky laboratory
smoky lighting
smoky sky background
smol
smol fluffy cat wearing smol hat
smoldering
smooggy
smooth
smooth 3 d illustration
smooth 3d cg render
smooth 3d illustration
smooth 4k
smooth and clean vector curves
smooth and rich color scheme
smooth and sharp
smooth and sharp focus
smooth andy warhol style
smooth anime cg art
smooth architecture
smooth arstation
smooth art
smooth art style
smooth background
smooth bevels
smooth bioluminescent skin
smooth blending
smooth blue skin
smooth body
smooth body features
smooth brushwork
smooth carapace
smooth cave rock
smooth chin
smooth chinese dragon
smooth clean surfaces
smooth clean texture
smooth color
smooth coloring
smooth colors
smooth composition
smooth concept art
smooth concrete
smooth contours
smooth curvature design
smooth curvatures
smooth curves
smooth curvilinear design
smooth defined edges
smooth defined outlines
smooth defined outlines!!
smooth design
smooth design spaceship sketches
smooth detailed
smooth details
smooth digital art
smooth digital artwork
smooth digital concept art
smooth draw
smooth draw with oil painting
smooth drawing
smooth edges
smooth eric zener cam de leon
smooth face
smooth face feature
smooth facial features
smooth feature
smooth focus
smooth fuschia skin
smooth glowing skin
smooth gold armor
smooth gold skin
smooth golden skin
smooth gradation
smooth gradient
smooth gradients
smooth graphics
smooth hair
smooth hd
smooth healthy skin
smooth illustration
smooth in 8k
smooth in _ the background
smooth in the background
smooth intricate
smooth light
smooth light from upper left
smooth light shading
smooth light skin
smooth lighthing
smooth lighting
smooth lines
smooth marble surfaces
smooth matte
smooth metal
smooth models
smooth noise
smooth noise 4k
smooth oil painting
smooth organic pattern
smooth oval head
smooth oval shape face
smooth paint
smooth painting
smooth pale skin
smooth panelling
smooth pink skin
smooth porcelain skin
smooth purple eyes
smooth purple skin
smooth red skin
smooth reflections
smooth render
smooth rendering
smooth rocks
smooth round rocks
smooth round shapes
smooth rounded shapes
smooth scales
smooth shaded
smooth shading
smooth shading techniques
smooth shadows
smooth shank
smooth shapes
smooth shapes and lines
smooth sharp focus
smooth shine texture
smooth shiny metal
smooth silver armor
smooth skin
smooth solid concrete
smooth streamline
smooth stylized shapes
smooth surface
smooth surface render
smooth surfaces
smooth symmetrical chin
smooth tan skin
smooth technology
smooth texture
smooth textured skin
smooth textures
smooth tiny details
smooth translucent white skin
smooth transparent visor
smooth utopian design
smooth vector curves
smooth vector lines
smooth vibrancy
smooth visual lines
smooth volumetric lighting
smooth waxy skin
smooth white skin
smooth white surroundings
smooth white tight clothes suit
smooth zenithal lighting
smooth!]
smooth-chinned
smooth. digital painting
smooth.sharp focus
smoothed
smoothed lip line
smoothened
smoothly
smores
smoth
smoth 3 d illustration
smoth 3d illustration
smothered in melted chocolate
smouldering charred timber
smudge
smudged
smudged edges
smudged face
smudged paint
smudges
smug
smug appearance
smug expression
smug face
smug facial expression
smug grin
smug look
smug smile
smug smirk
smuggler
smurf
snacks
snail
snail in the style of nfl logo
snail shell
snail vs knight
snails
snails vs knight
snails vs worms
snake
snake art
snake assassin
snake body
snake body. many large
snake eyes
snake fangs
snake from metal gear
snake hair
snake head
snake heads
snake human hybrid
snake is surrounding them
snake machine
snake man
snake mouth
snake oil
snake oil album
snake oil salesman
snake oil skin
snake river in the jungle
snake scales
snake skeleton
snake skin
snake skin armor forest dryad
snake tongue
snake van
snake woman
snake woman hybrid
snake-face female guard
snake-face lady
snake-head female assassin
snakes
snakes for hair
snakes in hair
snakes in place of hair
snakes on a plane
snakeskin
snap traps of dionaea muscipula
snapchat
snapchat photo
snapchat story screenshot
snapshot
snapshots
snarling
snarling dog teeth
sneaker
sneaker design
sneaker made out of lego
sneaker photo
sneaker shoes
sneakers
sneaking
sneaky
sneer
sneering
sneering at the camera
sneering roman legioneer
sneering. cinematic lighting
snes
snes game
snes graphics
snes screenshot
snes style
snining stars
sniper
sniper fire
sniper! middle age man
snk
snoop dog
snoop dogg
snoop dogg and donald trump
snoop dogg as a barbarian
snoop dogg as a mage
snoop dogg as doctor strange
snoop dogg doctor strange
snoop dogg in gta v
snoop dogg in mortal kombat
snoop dogg tennis ball head
snoop dogg tennis ball monster
snoring
snorlax
snot
snout under visor
snow
snow and ice
snow army war
snow blizzard
snow blizzard in woodland meadow
snow camouflage
snow capped mountains
snow cave
snow dunes
snow fall
snow falling
snow field
snow flurries
snow flurry
snow forest
snow globe
snow glow
snow is falling
snow landscape
snow landscape background
snow leopard
snow monkeys at the mountain spa
snow on the body
snow on trees and ground
snow outside the windows
snow scene
snow snow
snow storm
snow wasteland
snow weather
snow white
snow white as a d & d fighter
snow white as a d d fighter
snowboarding penguins
snowcrash
snowfall
snowfall at night
snowflakes
snowflakes falling
snowglobe
snowing
snowing frozen ice
snowing in the forest
snowing outside
snowstorm
snowstorm ::5
snowy
snowy apennines
snowy arctic environment
snowy background
snowy canyon at dawn
snowy craggy sharp mountains
snowy day
snowy environment
snowy field
snowy fjord
snowy forest
snowy hill at sunrise
snowy italian road
snowy landscape
snowy mountain
snowy mountain background
snowy mountain landscape
snowy mountains
snowy night
snowy peak
snowy peaks
snowy plains
snowy trees
snowy weather
snowy winter
snowy winter christmas night
snowy winter scene
snowy woodland meadow
snowy. by makoto shinkai
snub nose
so - s 1 4 8 ft light
so - s 148 ft light
so come on
so cute
so happy that her face hurts
so i advised her to get in line
so many plants
so many wires
soaked
soaking wet
soaking wet hair
soami
soap
soap bubble
soap bubble mind
soap bubbles
soap carving
soaring
soaring above a dense city
soaring over a lake in forest
soaring through the sky
soaring towers and bridges
soaring waves
soccer
soccer ball
soccer ball against her foot
soccer player
soccer player antoine griezmann
soccer player cristiano ronaldo
soccer player martin ødegaard
soccer player matthijs de ligt
soccer player timo werner
soccer players martin ødegaard
soccer players timo werner
soccer stadium
social commentary
social encounters
social media
social realism
socialist
society
society 6
sock cap
socks
soda
soda cans
soda themed girl
soey milk
sofa
sofia coppola
sofia vergara
sofie the giraffe
sofisticated
soft
soft - lighting
soft - warm
soft 3d render
soft aesthetic
soft airbrushed artwork
soft amazed smiles
soft ambient ilumination
soft ambient lighting
soft an diffuse lights
soft and blurry
soft and clear shadows
soft and detailed
soft and fluffy
soft and intricate
soft and muted colors
soft and warm
soft anime
soft anime illustration
soft art
soft artistic filter
soft atmosphere
soft autumn sunlight
soft backlight
soft backlighting
soft beautiful light
soft bioluminescent membranes
soft bloom lighting
soft blue and pink tints
soft blue light
soft blue lighting
soft blue living room
soft blue moonlight
soft blue texture
soft blue tones
soft blues and greens
soft blur
soft blur and glow
soft blur background
soft blur background light
soft blur light
soft blur lighting
soft blur outdoor background
soft blur outdoor lighting
soft blush
soft body
soft bokeh
soft box lighting
soft bright pastel
soft bright skin
soft brush
soft brush strokes
soft brushstrokes
soft but grumpy
soft butterfly lighting
soft calm warm neon atmosphere
soft chin
soft cinematic light
soft cinematic lighting
soft clouds
soft color
soft color dodge
soft color palette
soft color scheme
soft colore
soft coloring
soft colors
soft colors mono chromatic
soft coloured gel lighting
soft colours
soft colours scheme
soft contrast
soft cool colors
soft cool tones
soft cracks
soft curvy shape
soft cute colors
soft dark light
soft dark muted colors
soft delicate draconic features
soft details
soft devil queen madison beer
soft diffuse autumn lights
soft diffuse lighting
soft diffuse lights
soft diffused light
soft diffusion
soft digital painting
soft draconic features
soft dramatic lighting
soft edges
soft elegant gown
soft emotion
soft ethereal lighting
soft evening lighting
soft eyes
soft eyes and narrow chin
soft face
soft face features
soft facial features
soft facial traits
soft feather
soft features
soft femme
soft filmic tonemapping
soft filter
soft filtered outdoor lighting
soft flawless pale skin
soft flowers
soft focus
soft focus blur
soft focus hdr 8 k
soft focus hdr 8k
soft focus matte painting
soft focus oil canvas painting
soft forest background
soft freckles
soft frontal light
soft fur
soft fur texture
soft gaze
soft geometric 3d shapes
soft geometry
soft glow
soft glowing windows
soft golden hour lighting
soft golden light
soft gradient texture
soft gradients
soft gradients. baroque period
soft green lighting
soft green natural light
soft grey and blue natural light
soft grey and red natural light
soft hair
soft hair. light color palate
soft happy smile
soft highlights
soft illumination
soft ilumination
soft image shading
soft indoor light
soft internal light
soft iridescent membranes
soft lens
soft lighing
soft light
soft light - n 9
soft light 4 k
soft light 4 k in pink
soft light 4k
soft light 4k in pink
soft light 8 k
soft light 8k
soft light atmosphere
soft light dull mood
soft light effect
soft light from the side
soft light from the window
soft light misty
soft light misty yoshitaka amano
soft light of winter
soft light through blinds
soft light.4k
soft lightening
soft lighting
soft lighting 8 k resolution
soft lighting 8k
soft lighting 8k resolution
soft lighting album cover
soft lighting and focus
soft lighting colors scheme
soft lighting from above
soft lighting gradient. no text
soft lighting sold at an auction
soft lighting |
soft lightning
soft lights
soft lilac skies
soft lines
soft look
soft lulling tongue
soft luminescent glow
soft makeup
soft memories
soft mist
soft mood
soft moody
soft moonlight lighting
soft morning light
soft morning light. hyperrealism
soft morning lighting
soft motion blur
soft muted colors
soft natural light
soft natural lighting
soft natural volumetric lighting
soft neon
soft neon atmosphere
soft neon lighting
soft neon lights
soft neon purple lighting
soft opalescent membranes
soft orange and cyan highlights
soft organic abstraction
soft outdoor light
soft outline
soft outlines
soft oval face
soft pads
soft painting
soft pale golden skin
soft pale tone
soft pale white skin
soft pastel colors
soft pastel palette
soft pastels
soft pink lights
soft portrait
soft portrait shot 8 k
soft portrait shot 8k
soft purple glow
soft rainbow
soft red hair
soft red lights
soft red texture
soft red tone colors
soft reflections
soft render
soft rim light
soft round face
soft round features
soft rubber
soft scale texture
soft sepia tones
soft shade
soft shading
soft shadow
soft shadow transition
soft shadowing
soft shadows
soft shadows on the face
soft shapes
soft silk dress
soft skin
soft sm
soft smile
soft smoke
soft smooth skin
soft spot light
soft strokes
soft studio light
soft studio lighting
soft style
soft sun lights
soft sunbeam
soft sunlight dappling
soft sunset lighting
soft surface texture
soft surfaces
soft texture
soft textures
soft tones
soft top
soft top roof raised
soft translucent fabric folds
soft twirls curls and curves
soft vibes
soft vibrant colors
soft vignette
soft vintage glow
soft vinyl
soft volume absorbation
soft volumetric lighting
soft volumetric lights
soft volumetric studio lighting
soft warm light
soft warm lighting
soft white glow
soft white rubber
soft window light
soft yellow background
soft zen minimalist
soft!!
soft-sanded coastlines
soft. high quality
softair arena landscape
softair center landscape
softbox key light
softbox lighting
softbox studio lighting
softened diffuse light
softfocus
softglow
softlight
softly - lit
softly backlit
softly glowing control panels
softly lit
softly shadowed
softly swirling magical energy
softness
softplay
sofubi
sofurry
sofya emelenko
soggy
soil
soil landscape
sojourn from overwatch
sol lewitt
sol retreat
sola digital arts
solace
solar
solar beings
solar eclipse
solar eclipse in iceland
solar eclipse in the sky
solar field plains
solar flare
solar flare hd
solar flare unreal engine
solar flares
solar mythos
solar panels
solar power
solar powered
solar punk
solar punk city
solar punk product photo
solar rays
solar sail in space
solar sail infront of sun
solar sails
solar storm
solar system
solaris
solarised
solarized
solarpunk
solarpunk architecture
solarpunk cantine
solarpunk city
solarpunk futuristic utopia
solarpunk hovercar
solarpunk human
solarpunk metropolis
solarpunk space ship
solarpunk style
solarpunk utopia
solarpunk village
solarpunk!!!
sold at an auction
sold for
sold on sukebannyc
sold out piece
soldier
soldier 7 6 from overwatch
soldier 76 from overwatch
soldier clothing
soldier girl
soldier helmet
soldier outfit
soldier penguins
soldier under heavy fire
soldier with a gasmask
soldiers
soldiers and mech fight
soldiers charging in
soldiers charging the enemy
soldiers running
solemn
solemn and moody
solemn expression
solemn face
solemn gesture
solid
solid anatomy
solid bacgkround
solid background
solid background color
solid black #000000 background
solid black background
solid blue background
solid color
solid color backdrop
solid color background
solid color background intricate
solid coloring
solid colors
solid colour background
solid colour background”
solid coloured shapes
solid colours
solid colours material
solid concrete
solid cube of light
solid dark background
solid dark grey background
solid gray
solid grey
solid light grey background
solid massing
solid object in a void
solid shapes
solid smooth teeth
solid snake
solid snake bust portrait
solid snake portrait
solid white background
solid works
solidity and eternity
solidworks
solis sacerdotibus
solitary
solitary cottage in the woods
solitude
solitude seen in the distance
solitude under a waterfall
solo
solo 3 / 4 portait
solo female character
solo hiking in mountains trees
solo male weary soldier
solo performance unreal engine
solo photoshoot
solo portrait
solo portrait 🎨🖌️
solomon kane
solstice fire
soma
soma game art style
soma game style
somali attire
somali woman
somalia
somber
somber amber eyes
somber appearance
somber atmosphere
somber colors
somber expression
somber lighting
somber mood
somber turquoise eyes
somber white eyes
sombra from overwatch
sombre
sombre mood
sombrero
some are crying of joy
some background blur
some beard
some boats
some bulb lights
some chaotic sparkles
some chocolate sauce
some clouds
some cosmic angels
some curved armor
some dappled light
some dust in the air
some edges lost
some fallen trees
some fireflies
some fireflies flying in the air
some floating billboards
some glints and specs
some green
some green and purple
some grey hair
some grey hair in beard
some grungy markings
some have neon signs
some houses in the background
some ink drips
some light fog and fireflies
some light reflexions
some long
some marble statues can be seen
some mist grey smoke and fire
some mountains in the background
some oak acorns
some of the blacktop is showing
some of them use gas masks
some of them use gask mask
some orange and blue
some orange and purple
some people
some people are sitting
some people around
some people around ”
some pink
some plant life
some plants
some pouring techniques
some purple
some purple and blue
some purple and orange
some rays of light
some red
some red accents
some red and purple
some red and purple and yellow
some red and yellow
some red water
some reflexions
some rust
some sandy yellow pillows
some smoke and fire
some smooth
some spherical
some splattered paint
some square paintings
some stalls
some sun light ray
some sun ray of lights falling
some sunlight ray
some tanks destroyed
some tentacles are touching her
some trees
some trees in the background
some trees in the corner
some vagueness
some wrinkled
some yellow and blue
some yellow green and blue
some zoomed in shots
someone in home sits in bed
someone is screaming
someone lost job
someone sits in bed
something beautiful
something happened
somewhat androgenic
somewhat bent over
somewhere
somewhere in sands of the desert
son
son goku
son goku as the incredible hulk
son of sparda
son of the night
son of time
sona
sona is a slender
song
song dynasty
song hye - kyo
song joong - ki portrait
song nan li
song of the wind
songlines
sonic
sonic (series)
sonic boom
sonic game
sonic hedgehog
sonic looks like he is shocked
sonic oc
sonic power
sonic the hedgehog
sonic the hedgehog illustration
sonic the hedgehog in a surreal
sonic youth
sonichu
sony
sony 1 7 5 mm
sony 14mm f2.8
sony 175mm
sony 175mm”
sony 2 4 mm f 4. 0
sony 2 4 mm f 8. 0
sony 24 mm f 8. 0
sony 3 5 mm lens
sony 35 mm lens
sony 5 0 mm lens
sony 50 mm lens
sony a 6 3 0 0
sony a 6300
sony a 7 iii
sony a 7 r
sony a 7 r 3
sony a 7 r iv
sony a 7 s
sony a 7 siii
sony a3iii camera shot
sony a7
sony a7iii
sony a7r
sony a7r -
sony a7r iii
sony a7r iv
sony a7r3
sony a7z
sony alpha
sony alpha a6400
sony ar 7 ii
sony camera
sony fx 6
sony lens
sony nicon iphone 1 3 pro max
sony nicon iphone 13 pro max
sony pictures
sony pictures animation
sony produced
sony ultra camera
sony ultra camera hd+
sony world photography awards
soon will be dawn
soothing
soothing and cozy landscape
soothing colors
soothsayer
sophia lillis
sophia loren
sophie
sophie cover album
sophie turner
sophie turner girl
sophisiticated
sophisticated
sophisticated armor
sophisticated composition
sophisticated dress
sophisticated gravure idol
sophisticated hands // noir
sophisticated well rounded face
sophisticated young woman
sora
sora as a cat
sora from kingdom hearts
sora wielding a keyblade
sorayama
sorayama and moebius. occult art
sorayama. occult art
sorcerer
sorcerer magic witch
sorcerer of sand
sorceress
sorceress woman
sorcerous magic
sorcery
sorcery magic witch battle
sores
sores and scars
sorolla
sorrow
sorrow and despair
sorrow intense likely
sorrowful
sosaku hanga
sotn
sougetsu
sougo okita
soul
soul caliber
soul calibur
soul eater
soul frequency
soul hoarder!!!
soul journey
soul leaving body
soul penetrating invasive eyes
soul perculates
soul reaver
soul singer
soulful
soulful eyes
soulful irises
soulless
souls
souls of the dead
soulslike
sound
sound of music
sound wave
sound waves
sounds in the graveyard
soundwave
soup
source engine
source engine footage
source engine map
sous la pleine lune
sousaphone
sousaphones
south african coast
south beach colors
south east asian with long
south east asian with round face
south jakarta
south korea
south korean male
south park
south park style
southdale center
southern california
southern cross
southern european scenery
southern gothic
southern gothic art
southern gothic scene
southern slav features
southern wildflowers
soviet
soviet - era
soviet advertisement
soviet apartment
soviet apartment building
soviet apartment buildings
soviet architecture
soviet art
soviet artists
soviet brutalism
soviet bus stop
soviet cyberpunk
soviet era
soviet era propaganda poster
soviet flags
soviet futurism
soviet interior
soviet military
soviet nostalgia
soviet painting
soviet paintings
soviet poster
soviet poster of viktor orban
soviet propaganda
soviet propaganda art
soviet propaganda painting
soviet propaganda poster
soviet propaganda poster art
soviet propaganda poster style
soviet propaganda style
soviet realism
soviet style
soviet style cyberpunk
soviet suburbs
soviet town
soviet union
soviet yard
sovietic era
sovietwave
sovietwave aesthetic
sovjet
soy fan de quino
soyjack
soymilk
spa
space
space age pop
space alien
space and time
space armor
space art concept
space atmosphere
space backdrop
space background
space backround
space base
space battle
space battles
space between subjects
space cat
space civlization
space clouds
space color scheme
space colors
space cowboy
space craft
space dandy
space dandy style
space debris
space dream
space dust
space elevator
space exploration
space fantasy
space flower fairy
space fractal gradient
space galaxy
space galaxy background
space graphics art in background
space harbor
space helmet
space high school
space hive
space horror
space hulk
space in background
space in the background
space insect android
space jammers
space jellyfish
space junk
space landscape
space magical whale
space marine
space marines
space molecules
space molly
space nebula
space nebula background
space nebula in background
space oddity
space odyssey
space odyssey 2 0 0 1 ( film )
space opera
space opera and dystopian style
space opera gunfight
space photo
space photography
space pirate
space pirates
space plants
space port
space port city
space pressurized suit
space probe
space quantum death. deep space
space race
space satellites
space scene
space sci - fi
space seen outside from a window
space ship
space ship above an island
space ship cemetery outer space
space ship gribble
space ship in space
space ship in the background
space ship in the distance
space ships
space shuttle
space sky
space soldier on mars with a gun
space station
space station interior
space station light reflections
space station on the moon
space station planet afar
space station vore station
space suit
space suit with a modern helmet
space telescope
space theme
space themed
space tourism
space travel
space walk
space walk scene
space war
space war between china and usa
space warping and twisting
space western
space whale
space-station vuutun palaa
spacecore
spacecraft
spacecrafts
spaceengine
spacehip lands
spaceman standing looking
spaceport
spaceport docking bay at night
spaces
spaceship
spaceship battle
spaceship being repaired
spaceship concept art
spaceship design
spaceship exploding
spaceship far on the background
spaceship flies in the distance
spaceship from the movie dune
spaceship hallway
spaceship hallway background
spaceship hangar
spaceship hull texture
spaceship in a dry river bed
spaceship in a nebula
spaceship in background
spaceship in dark space
spaceship in space
spaceship in space in ruins
spaceship in the sky
spaceship interior
spaceship landing
spaceship night
spaceship sank
spaceship sketches
spaceship window
spaceships
spaceships flying
spaceships flying above
spaceships flying around
spaceships flying in background
spaceships in sky
spaceships in the cloudy sky
spaceships in the sky
spaceships landing
spacestation
spacesuit
spacesuit with small led lights
spacetime envelopment
spacex
spacex starship rocket launch
spacious
spades slick
spaghetti
spaghetti beard
spaghetti hair
spaghetti in the nostrils
spaghetti monster
spaghetti mustache
spaghetti western
spaghettification
spain
spain rodriguez
spangle
spangled
spanish
spanish alleyway
spanish ghibli alleyway
spanish moss
spanish princess
spanners
spare
spare parts
sparkels
sparking eyes
sparkle
sparkle in eyes
sparkle storm
sparklers
sparkles
sparkles all around
sparkles and glitter
sparkles and sun rays
sparkles in the air
sparkles on eyelids
sparkles sky
sparkling
sparkling atmosphere
sparkling bird eyes
sparkling blue eyes
sparkling cove
sparkling crystals
sparkling dark jewelry
sparkling dew
sparkling eyes
sparkling in the flowing creek
sparkling in the sunlight
sparkling light
sparkling magical girl
sparkling nebula
sparkling petals
sparkling spirits
sparkling water
sparkly
sparkly cat eyes
sparkly detailed cat eyes
sparkly eyes
sparks
sparks and liquid fire
sparks and smoke
sparks and thunders
sparks flying
sparks of fire flying
sparks of light
sparky
sparky metallic
sparky swimsuit
sparring
sparrows
sparse
sparse bare trees
sparse chest hair
sparse dark atmosphere
sparse detail
sparse floating particles
sparse freckles
sparse frozen landscape
sparse mountains on the horizon
sparse pine forest
sparse pine forest long shadows
sparse pine trees
sparse plants
sparse room
sparse trees
sparse vegetation
sparse winter landscape
sparsely populated
spartan
spartan armor
spartan rage
spartan warrior
sparth style
sparths
spasms
spatial phenomena
spatial phenomenon
spatial structure
spatialism
spatter
spatters
spatula
spawn
spawn of satan
speak no evil
speakeasy
speakeasy bar background
speakers
spear
spear in hand
spear in the right hand
spears
spec - ops head
spec - ops head with mask
spec ops mask
special
special agent
special attention to posing
special effects
special forces
special forces security
species fusion
specimens
specimens in glasses
speckled
specks
spectacle
spectacled
spectacles
spectacular
spectacular cinematic scene
spectacular composition
spectacular details
spectacular lighting
spectacular milky way
spectacular mountains
spectacular quality
spectacular quality torn paper
spectacular rocky mountains
spectacular splatter explosion
spectacular sunset
spectators
spectral
spectral color
spectral evolution
spectres
spectres of the night
spectrum
spectrum colours
spectular lighting
specular
specular colors
specular highlights
specular light
specular lighting
specular reflection
specular reflections
specularity
speculars
speculative biology
speculative evolution
speculative fashion
speculative futures
speculum
speech
speech bubbles
speed
speed (1994)
speed blur
speed grapher
speed lines
speed painting
speed painting and scribble art
speed racer
speeder
speeding on motorcycle
speeding through london
speedo
speedpaint
speedpainting
speedster
speedtree
speedy aircraft
speilberg movie
speilbergian lighting
speleothems
spell
spell book
spell casting
spell casting wizard
spellbreak
spellcasting
spellcasting pose
spellfire
spelljammer
spells
spells magic
spells practice
spelunky
sperical hdri map
sphere
spheres
spherical
spherical black helmets
spherical black pupils
spherical body
spherical lens
sphinx cat
sphinx in distance
spices
spicy
spider
spider - gwen
spider - man
spider - verse art style
spider abdomen
spider gag
spider gwen
spider legs
spider legs large
spider nest
spider sitting in chair
spider thorax
spider web
spider webbed body
spider webs
spider woman
spider zero
spider-gwen
spider-man
spiderman
spiderman as electro
spiderman as terminator
spiderman as wolverine
spiderman biting pizza
spiderman greek marble statue
spiderman in the moshpit
spiderman!!
spiders
spiders!!!!
spiderverse
spiderweb landscape
spiderwebs
spiderwoman
spiderwoman!!
spiderwoman!!!!!
spidery irregular shapes
spiel des jahres
spielberg
spike
spike - like branches
spike collar
spike painting
spike pit
spike shell
spike spiegel
spiked
spiked collar
spiked collar sunglasses
spiked collars
spiked metal armor
spiked scrap metal armor
spiked wristbands
spikes
spikes on the body
spikey hair
spikey rocks
spiky
spiky black hair and dark skin
spiky elf ears
spiky hair
spiky orange hair
spiky short hair
spiky skin
spiky tentacles
spilled milk
spilling juice
spilling ketchup
spilt coffee
spinal column
spine
spine-chilling
spines
spines and towers
spinning
spinning hands and feet
spinning records
spinning whirlwind
spinosaurus
spiral
spiral clock
spiral clouds
spiral eyes
spiral galaxy
spiral heavens
spiral horns
spiral horns!
spiral lines
spiral shelves full of books
spiral smoke
spiral staircase
spiral stairs
spiraling
spiraling design
spiraling upward
spiralling
spiralling bushes
spiralling fractals
spirals
spirals and swirls
spirals in eyes
spirals tubes roots
spire
spires
spirit
spirit fantasy concept art
spirit guide
spirit hugs
spirit of rage
spirit of the forest dwells
spirit photography
spirit warrior
spirited
spirited away
spirited water plants
spirits
spirits coming out of portal
spirits covered in drapery
spirits flying
spirits in the dark
spiritual
spiritual abstract forms
spiritual art
spiritual cryptid
spiritual eerie creepy picture
spiritual enlightenment
spiritual evolution
spiritual feeling
spiritual imagination of duality
spiritual journey into madness
spiritual light
spiritual masterpiece
spiritual scene
spiritual science
spiritual style
spiritual vibes
spiritualism
spirituality
spit flying from mouth
spitfire
spitting cushions from his mouth
splash
splash art
splash art anime loli
splash art brawlstars
splash aura in motion
splash comics
splash house
splash image
splash of color
splash of paint down center
splash page
splash painting
splash screen
splash screen art
splashcreen character art
splashed with graffiti art
splashes
splashes of color
splashes of colors
splashes of lightning behind her
splashes of liquid
splashes of neon
splashes of neon clouds
splashes of neon galaxies
splashing
splashing water
splashscreen
splatoon
splatoon 3
splatter
splatter paint
splatter paint on paper
splattered black goo
splattered goo
splattered paint
splattered tar
splattered vibrant paint
splattered with blood
splatterpaint
splatters
splendid
splendid haussmann architecture
splento
splinter cell
split - complementary - colors
split dye
split hair dye
split in half
split lighting
split near the left
split screen
sploch
splotch
splotches
spock
spock haircut
spoiler
sponge
sponge bob
spongebob
spongebob circle pants
spongebob cthulhu nightmare
spongebob in real life
spongebob squarepants
spongy
spontaneous
spontaneous linework
spontaneous portrait
spooked
spooky
spooky and scary atmosphere
spooky autumnal colours
spooky cartoon skull
spooky creepy liminal space
spooky filter
spooky fog
spooky forest
spooky found footage
spooky halloween fun
spooky halloween night
spooky halloween theme
spooky lighting
spooky mansion
spooky netflix still shot
spooky photo
spooky strange weird quirky
spoon
spoon pear slim loli figure
spoon placed
spoon slim figure
spores
spores floating in the air
sport
sport bra
sport bra and dark blue shorts
sport bra and shirt
sport bra and shorts
sport car
sport clothing
sport game
sport glasses
sport pants
sport photography
sport shorts
sport t-shirt
sportcar
sports
sports bra
sports broadcast
sports car
sports car in the room
sports clothing
sports illustrated
sports jacket
sports logo
sports mascot
sports photo
sports photography
sports setting
sports team mascot
sportscar
sportspalast amphitheatre
sporty
sporty physique
spot
spot illustration
spot light
spot lighting
spot lights
spot lit
spot-lighting
spotlight
spotlight at a 90 degree angle
spotlight from above
spotlight from face
spotlight in middle of face
spotlight lighting
spotlight on subject
spotlight on the singer
spotlight shining through
spotlighting
spotlights
spotlights from ceiling
spotlit
spots
spotted
spotted ultra realistic
sprawled out
sprawling
spray
spray art
spray brush
spray paint
spray paint texture
spray painted graffiti
spraying blood
spraying liquid
spraypaint
spraypainted bodypaint graffiti
spraypainted on a wall
sprays
spread wings
spreading her wings
sprial
spriggans
sprigger
spring
spring blooming flowers garden
spring color scheme
spring colors
spring day
spring early
spring early morning
spring evening
spring fashion
spring flowers
spring light
spring on saturn
spring season
spring season city
spring theme
spring time
spring vibes
spring vibrancy
spring winter nature melted snow
springs
springtime
springtime morning
springtime vibrancy
springtrap
sprinkles
sprinters in a race
sprinting
sprite
sprite 2 d
sprite 2d
sprite art
sprite sheet
sprites
spritesheet
sprong season
sprouting
spruce trees
spruce trees on the sides
spunky
spy
spy kangaroo
spy kids juni cortez
spy x family
spying
spying discretly
spyro
spyro the dragon
sqare-jawed in medieval clothing
squad
squad fighting enemy
square
square backpack
square black pupil centered
square enix
square enix cinematic art
square face
square facial structure
square glasses
square jaw
square jaw-line
square lines
square masculine facial features
square masculine jaw
square nose
square pictureframes
square rimmed glasses
square shapes
square sticker
squared border
squares
squaresoft
squarish face shape
squashed
squashed berries
squashed berries dripping
squashed berry stains
squatting
squatting down next to a pool
squatting pose
squelch
squid
squid attack
squid game
squid motifs
squid phoenix jellyfish
squidlike aliens
squids
squidward
squidward as a dark souls boss
squidward taking an acid trip
squint eyes
squinting
squinting at high noon
squinty eyes
squirrel
squirrel/tiger
squirrels
squirtle
squish
squishmallow
squishy
srgb
sri lanka
sri lankan landscape
sri lankan mad max style
ssao
ssao 8 k
ssao 8k
ssci - fi
ssci - fi and fantasy
ssci-fi
ssci-fi and fantasy
ssense
ssr card
sss
ssss.gridman
st : tng
st cirq lapopie
st jerome writing the vulgate
stab wound
stability
stability at last
stable diffusion
stable diffusion ai
stable diffusion ai as a human
stable diffusion self portrait
stack of books on side table
stack of comics on the floor
stacked
stacked buildings
stacked city
stacked computer screens
stacked houses
stacked image
stacking supermarket shelves
stacks
stacks of books
stacks of giant books
stadium
stadium full of people
stadium landscape
stadium setting
staff
staff of bones
staff wearing hazmat suits
stag beetle
stag wearing a crown
stage
stage at a club
stage background
stage lighrt
stage light
stage lighting
stage lights
stage photography
stage speakers
staggered depth
staggered depth)
staggered terraces
staggering in its beauty
stagnant water
stain glass
stained
stained and bleeding
stained antique copper car paint
stained dirty clothing
stained glass
stained glass art
stained glass background
stained glass futuristic church
stained glass style
stained glass tarot style
stained glass window
stained glass window geometric
stained glass window!!!!!
stained glass windows
stained glass wings
stained glass!!
stained paper
stained”
stainless steal
stainless steel
stains
staircase
staircases
stairs
stairs and arches
stairs and bookshelves
stairs from hell to heaven
stairs to an upper floor
stairs to the second floor
stairway
stairway to heaven
stairways
stalactites
stalagmites
stalenhag
stalenhag style
stalin
stalin and lenin in gay-bar
stalin as superman
stalingrad
stalinist architecture
stalk eyes
stalker
stalking
stamp
stamped
stamperia
stan lee
stan winston
stan winston studios
stanchions
stand
stand alone complex
stand off
stand on stone floor
stand up with the sea behind
standing
standing against the storm
standing alone
standing alone in grassy field
standing and posing elegantly
standing astride a gate
standing at the beach
standing at the resolute desk
standing athletic pose
standing atop a pile of rubble
standing before ancient ruins
standing beside a sea sheep
standing beside the ocean
standing bird
standing bravely on the road
standing close to volcano
standing confidently
standing elegant pose
standing elegantly
standing gracefully upon a lotus
standing here
standing in a barren field
standing in a botanical garden
standing in a buddhist temple
standing in a burnt forest
standing in a cantina
standing in a church
standing in a city center
standing in a city street
standing in a dark
standing in a dark alleyway
standing in a dark forest
standing in a desert
standing in a dimly lit room
standing in a field
standing in a field with flowers
standing in a flower field
standing in a forest
standing in a grassy field
standing in a grotto
standing in a heroic figure
standing in a hindu kovil
standing in a lake
standing in a large empty hall
standing in a maelstrom
standing in a parking lot
standing in a pond
standing in a pool of blood
standing in a restaurant
standing in a server room
standing in a shallow river
standing in a space ship wreck
standing in a stadium
standing in a starbase bar
standing in a township street
standing in a waterfall
standing in abandoned building
standing in an alleyway
standing in an apple orchard
standing in an arena
standing in class
standing in corner of room
standing in desert
standing in fire
standing in flower field
standing in front of a mirror
standing in front of a mountain
standing in front of a waterfall
standing in front of lowrider
standing in front of the altar
standing in gold foil
standing in grassy field
standing in hell
standing in his cluttered garage
standing in midground
standing in moonlight
standing in outer space
standing in road
standing in ruins
standing in sand
standing in shallow water
standing in street
standing in tall grass
standing in the desert
standing in the forest
standing in the forrest
standing in the grass at sunset
standing in the isle of harris
standing in the mystical forest
standing in the oval office
standing in the plains of rohan
standing in the rain
standing in the savannah
standing in the snow
standing in the solar system
standing in the street
standing in the streets
standing in time square
standing in times square
standing in township street
standing in wasteland
standing inside a magic library
standing inside a waterfall
standing inside of a church
standing lamp luxury
standing microphones
standing near a castle
standing near a waterfall
standing near a window
standing near the beach
standing next to a camel
standing next to desert oasis
standing next to joseph stalin
standing next to royal castle!!!
standing next to water
standing on 2 feet
standing on a battlefield
standing on a beach
standing on a beach in boracay
standing on a bridge
standing on a cliff
standing on a cliffside
standing on a cloud
standing on a desk
standing on a forest
standing on a hill
standing on a ladder
standing on a lotus
standing on a martian landscape
standing on a mountain top
standing on a rock
standing on a rooftop
standing on a shelf
standing on a ship deck
standing on a skateboard
standing on a skyscraper rooftop
standing on boulder
standing on mad max panzer tank
standing on mountain
standing on neptune
standing on rock
standing on rocky ground
standing on rooftop
standing on ship deck
standing on street corner
standing on surfboards
standing on the beach at sunset
standing on the edge of a cliff
standing on the field of battle
standing on the mast
standing on the moon
standing on the street
standing on the water ground
standing on top of a mountain
standing on top of a piano
standing on top of a violin
standing on two feet
standing on two legs
standing outside a house
standing over a fallen foe
standing over a tomb stone
standing pose
standing pose on stones
standing posture
standing sideways
standing still
standing straight
standing tall
standing tall invincible
standing together
standing triumphant and proud
standing under a beam of light
standing under the sea
standing up
standing upright
standing upright like people
standing water
standing with a black background
standing with a parasol
standing with her back to us
standing!!
standings defiantly
standoff
standout colours
stands at a his easel
stands at her easel
stands at the top
stands in a pool of water
stands in center with open arms
standup
standup comedian
stanely artgerm
stanger things
stanisław
stanisław szukalski
stanisław szukalski + moebius
stanley artgem lau
stanley artger m lau
stanley artgerm lau
stanley artgermm
stanley donwood
stanley kubrick
stanley kubrick movie
stanley kubrick movie frame
stanley kubrick the shinning
stanley kubricks
stanley kybric
stanley lau
stanley mouse
stanly kubrick
stannis baratheon
staples
star
star - gate of futurisma
star base
star born
star butterfly
star charts
star child
star citizen
star citizen concept art
star citizen digital art
star citizen halo
star citizen origin 100i
star craft
star destroyer
star destroyers
star dust
star flares
star fox
star guardian inspired
star guardians
star hatcheries
star in the sky
star inside
star killer
star lines
star lit sky
star map
star ocean
star platinum
star rain
star roof
star sharpness
star shining in space
star ships
star sky
star sparkle
star trails
star trails above
star trek
star trek : the next generation
star trek asian woman
star trek setting
star trek style
star trek the next generation
star trek tng
star trek voyager
star walk
star wars
star wars a new hope 1977
star wars alien faces
star wars architecture
star wars art
star wars art deco
star wars atmosphere
star wars cavern interior
star wars character
star wars clone wars
star wars concept art
star wars digital art
star wars expanded
star wars expanded universe
star wars film look
star wars film still
star wars imperial style
star wars inspired
star wars lasers
star wars movie
star wars movie poster
star wars movie still
star wars poster
star wars rebels
star wars revenge of the sith
star wars setting
star wars space battle
star wars style
star wars themed
star wars universe
star wars vibe
star(sky) starry_sky
starbucks
starbucks aprons and visors
starburst
starcraft
starcraft 2
starcraft 2 videogame character
stardestroyer in the background
stardew valley
stardust
stardust gradient scheme
stardust in atmosphere
stares at the camera
starfield
starfield background
starfield in background
starfighter
starfinder character
starfinder style
starfire
starfish
starfish monster movie
starfish pose
starfleet uniform
starfleet uniforms
stargate
stargate standing in desert
stargazer
staring
staring at camera
staring at the camera
staring at the viewer
staring at viewer
staring at you
staring directly at camera
staring directly into camera
staring eyes
staring hungrily
staring in the camera
staring intensely
staring into the camera
staring psychopathically
staring seductively
staring sofía vergara screaming
staring wide open eyes
staring you down
staring!!!!! into the camera
staringinto the camera
stark
stark chiaroscuro lighting
stark colours
stark composition
stark contrast
stark contrasting lighting
stark contrasts
stark landscape
stark light
stark light and shadows
stark lighting
stark shadows
stark sunrise lighting
starless space
starlight
starlit
starlit night sky
starlit shining eyes
starlit sky
starmaker
starport
starring chewbacca
starring dwayne johnson
starring geena davis
starring in thor ragnarok film
starring marty mcfly
starry
starry background
starry eyes
starry night
starry night sky
starry nights
starry skies
starry sky
starry sky 8 k
starry sky 8k
starry sky in background
starry sky!!
starry space nebula
starry tattoos
starry-night
starry-night!!!!!!!!!!!!!!!!!!!!
stars
stars and galaxies
stars and galaxies visible
stars and milky way and moon
stars and moons in the sky
stars and nebula
stars and paisley filled sky
stars and planets
stars and planets visible
stars and stripes
stars are hidden in the eyes
stars as eyes
stars as pupils
stars background
stars filled sky
stars glistening in the night
stars glittering in background
stars in background
stars in her eyes
stars in her gazing eyes
stars in sky
stars in space
stars in the background
stars in the distance
stars in the night sky
stars in the sky
stars in the sky above
stars on top of the crown
stars pour where blood boils
stars reflecting on the water
stars shining
stars strange perspective
stars visible
starscape
starship
starship cargo bay
starship enterprise
starship in background
starship troopers
starship-troopers
starship-troopers-rifle
starships
starsky starry_sky
start
start of the match
starting engines nitro jet drive
startled
startrek style
starts
starving artist wearing overalls
starwars
stary night painting
stary sky
state of melancholy
state of the art
state of the union
stately
stately robes
stathmore 2 0 0
stathmore 200
stathmore 4 0 0
stathmore 400
static
static colorful noise glitch
static noise
station
stats
statue
statue is a fountain
statue made of glass
statue of a cubes and rings
statue of a perfect woman
statue of a woman
statue of angela merkel
statue of carving marble
statue of david
statue of hercules
statue of hercules looking angry
statue of julius caesar
statue of liberty
statue of liberty at left
statue of the perfect woman
statue of venus callipygian
statue of zeus
statues
status icons
stay puft marshmallow man
steady look
steak
steakpunk
steal armors
stealth
stealth suit
stealthy
steam
steam and cyberpunk
steam clouds
steam community
steam deck
steam engine
steam necropolis
steam pipes
steam punk
steam punk grafitti
steam punk party
steam punk style
steam romance
steam train
steam trains
steam workshop
steam workshop maps
steam-punk
steam-punk illustration
steamboat willie
steamboat willy
steamcommunity
steamed buns
steaming coffee
steaming food on the stove
steampank style
steampunc
steampunk
steampunk acessoires
steampunk aesthetic
steampunk air haven
steampunk airship
steampunk airship!!!!!!!
steampunk airships fly overhead
steampunk angel
steampunk arcane shaman
steampunk armor
steampunk art
steampunk automaton
steampunk aztec owl pocketwatch
steampunk background
steampunk beautiful anime woman
steampunk bioarmor
steampunk blueprint
steampunk butterfly
steampunk car
steampunk cat
steampunk chandra queen of fire
steampunk city
steampunk city background
steampunk city on the background
steampunk clothes
steampunk concept art
steampunk cyberpunk
steampunk desert background
steampunk design
steampunk digital art
steampunk diving helmet
steampunk dog
steampunk engineer
steampunk era
steampunk eye
steampunk factory background
steampunk fantasy
steampunk fantasy style
steampunk ferret - shaped mech
steampunk fiction
steampunk forest
steampunk forest background
steampunk gears
steampunk girl
steampunk glasses
steampunk goggles
steampunk googles
steampunk guitar
steampunk hat
steampunk helmet
steampunk horse
steampunk hot air balloon
steampunk illustration
steampunk imagery themed
steampunk insect
steampunk inventor girl
steampunk iron man
steampunk jesus
steampunk katana
steampunk living room
steampunk machinery
steampunk male portrait
steampunk metropolis
steampunk monocle
steampunk motorcycle
steampunk owl inside a glass jar
steampunk owl pocketwatch
steampunk pin-up girl
steampunk rat
steampunk robot
steampunk robot ant
steampunk robot scorpion
steampunk rubber duck
steampunk setting
steampunk spider
steampunk stag beetle
steampunk stormtrooper
steampunk style
steampunk style weapons
steampunk submarine!
steampunk superhero
steampunk tech
steampunk tribal mask
steampunk trim
steampunk villages castles
steampunk warrior
steampunk watch
steampunk wizard
steampunk world
steampunk world spikes
steampunk zeppelin
steampunk!
steampunk! fiction
steampunk!!!
steampunk!!! and modern
steampunk!!!! grainy texture
steampunk-esque
steampunk-esque!
steampunk. intricate
steampunkand modern
steampunkfiction
steampunkgrainy texture
steamy
steel
steel and metal
steel archways
steel armor
steel armour
steel ball run
steel blades
steel bull run
steel choker
steel collar
steel gray body
steel inquisitor from mistborn
steel joint
steel mill
steel pipes
steel plating
steel skin
steel studs
steel sword
steel window mullions
steel wire
steelpunk
steep
steep cliffs
steering wheel
stefan gesell
stefan koidl
stefan koidl inspired
stefan kostic
stefan morrell
stefano brunesci
stefano tamburini
steffen sauerteig
steins ; gate
steins gate
steins;gate
stella alpina flower
stellar
stellar exploration
stellar formation
stellar sky
stellar space
stellaris
stellation
stempunk
stems
stencil
stencil art
stephan
stephane martiniere
stephanie welsh
stephen bauman
stephen bliss style
stephen conroy
stephen fabian
stephen fry
stephen gamell
stephen gammell style
stephen hawking
stephen hawking in rocket league
stephen hickman
stephen king
stephen king as pennywise
stephen king atmosphere
stephen lang
stephen lau and artgerm
stephen outram
stephen shore
stephen shore & john j. park
stephen silver
stephen wayda
stephen youll
steppe
steppe background
steppe landscape
stepping on a miniature city
stepping on the city
stepping on towers
stepping stones
steps
steps 50
steps leading down
stereogram
sterile
sterile background
sterile colours
sterile minimalistic room
stern
stern blue neon atmosphere
stern expression
stern face
stern like athena
stern look
stern looking fox in a lab coat
steroid use
steroids
stethoscope
stethoscope!
steve
steve belledin
steve buscemi
steve buscemi as the joker
steve buscemi with sunglass
steve buschemi
steve caldwell
steve carell
steve from minecraft
steve harvey as the flash
steve harvey holding lightsaber
steve henderson
steve henderson and mark brooks
steve huston
steve jobs
steve jobs as hokage
steve jobs vs bill gates manga
steve mccurry and irakli nadar
steve niles
steve oliff
steve purcell
steve wozniak
steve zheng
steven artgerm lau
steven jung
steven klein
steven mccurry portrait
steven meisel
steven meisel photography
steven outram
steven outram hd
steven outram highly detailed
steven seagal
steven spielberg
steven spielberg movie shot
steven universe
steven universe style
steven wiltshire
stew
stewart cowley
stick
stick and poke
stick figure
stick figures
stick poke
sticker
sticker - art
sticker - svg
sticker art
sticker concept design
sticker design
sticker design vector
sticker design vector art
sticker illustration
sticker of a home in the forest
sticker of a rock band
sticker sheet
stickers
stickers illustrations
sticky
sticky black goo
sticky black goo. photography
sticky tar. concept art
stiff necked
stigmata
stiletto heeled boots
stilettos
still
still art
still capture
still cinematic
still film
still form a silent film
still frame
still frame from a movie
still frame from prometheus
still frame from the simpsons
still frame the retro twin peaks
still from a 2001 pixar movie
still from a 2015 pixar movie
still from a fantasy movie
still from a live action movie
still from a movie
still from a music video
still from a nature documentary
still from a pixar movie
still from a ridley scott movie
still from a terence malik film
still from a wes anderson film
still from a wes anderson movie
still from akira (1988)
still from alien (1979)
still from alita
still from animated horror movie
still from anime
still from avengers endgame
still from better call saul
still from blade runner (1982)
still from blade runner 1982
still from breaking bad
still from despicable me 2010
still from film
still from game of thrones
still from half-life movie
still from harry potter
still from harry potter movies
still from horror movie
still from l'estate
still from lestate
still from loki  2021
still from loki ( 2 0 2 1 )
still from lord of the rings
still from marvel movie
still from mary poppins (1964)
still from movie
still from movie dune
still from nature documentary
still from pixar movie
still from riverdale
still from star trek
still from star wars
still from star wars (1977)
still from stranger things
still from stranger things movie
still from studio ghibli
still from the avengers
still from the dark knight
still from the film
still from the mandalorian show
still from the matrix
still from the matrix (1999)
still from the movie
still from the movie 8 mile
still from the movie aliens
still from the movie bladerunner
still from the movie ex machina
still from the movie predator
still from the movie saw
still from the movie sharknado
still from the movie terminator
still from the movie the arrival
still from the pixar movie
still from the show breaking bad
still from the the sopranos
still from the twilight zone
still from tv anime
still image
still image from batman movie
still image from game of thrones
still image from the movie
still image from tv series
still in package
still life
still life of an apple
still life of rotten flesh
still life of white xenomorph
still life painting
still life photo of a backdrop
still life photo studio
still life photography
still life vegetables
still life with flowers
still life with snow
still life with white vase
still moment
still movie shot
still of megan fox
still of rainbow ophanim
still photo
still photo of james bond
still photograph
still photography
still picture
still pond
still shot
still shot from movie
still water calm as a mirror
stillframe
stillness
stingray
stingrays
stinky
stipple
stipple art
stipple brush
stipple brush technique
stippled
stippled light
stippled lighting
stippled walls
stipples
stippling
stippling art
stippling dots
stitched together
stitches
stitching
stjepan sejic
stock
stock and two smoking barrels
stock art
stock color
stock footage
stock illustration
stock image
stock photo
stock photograph
stock photography
stockholm
stockholm city
stockholm city portrait
stockings
stockings and a tricorn hat
stockphoto
stocky
stoic
stoic and calm
stoic attitude
stoic face
stoic facial expression
stoic pose
stoicism
stolen footage
stomach
stomach skin
stomach vibes
stomping
stone
stone age
stone age rave in a cave
stone and glass
stone and glass and gold
stone and wood
stone art
stone brick
stone brick walls
stone bricks
stone bridge
stone bridge over brook
stone carving
stone carvings
stone colossus remains
stone dust
stone etchings
stone facade
stone face
stone floor
stone gate to the dark cave
stone golem
stone grotto in the center
stone hand raised up
stone marble
stone ocean
stone paths
stone pathways
stone pews
stone pillars
stone relief
stone roads
stone roof
stone runes on the front
stone sculpture
stone slab
stone stairway
stone statue
stone statues
stone steps
stone table
stone texture
stone tile hallway
stone wall
stone wall in background
stone walls
stone walls and pillars
stoned
stoned eyes
stonehenge
stonepunk
stoner
stoner eyes
stoner metal concert
stoner rock
stoner rock concert
stones
stones falling from the sky
stonework
stony
stony road
stood in a cell
stood in a factory
stood in a forest
stood in a lab
stood in a spooky forest
stood in a supermarket
stood in a tunnel
stood inside a futuristic lab
stood on top of a sky scraper
stood outside a corner shop
stood outside a wooden cabin
stools
stop - motion
stop frame animation
stop motion
stop motion animation
stop motion character
stop motion vinyl action figure
stop motion vinyl figure
stop sign
stop-motion film (1924)
stop-motion film 1924
stopmotion
stopmotion animation
stopped in time
store
storefront
storefronts
stores
storm
storm arriving
storm at night
storm at sea
storm background
storm clouds
storm clouds in the background
storm clouds in the distance
storm dragon
storm drain
storm egyptian god
storm in distance
storm in the background
storm in the distance
storm in the evening
storm of all storms
storm on horizon
storm outside
storm sky
storm trooper
storm weather
storm!
storm. bloom
stormcloaks vs imperials
stormclouds
storming
storming the beaches of normandy
stormlight archive
storms
stormtrooper
stormtrooper - helmet
stormtrooper fighting in ww2
stormtrooper in hot springs
stormtroopers
stormtroopers sitting around it
stormy
stormy and grand war scene
stormy atmosphere
stormy background
stormy clouds
stormy clouds on the horizon
stormy clouds outdoor
stormy coast
stormy day
stormy horizon
stormy landscape
stormy lighting
stormy night
stormy ocean
stormy overcast
stormy post apocalyptic desert
stormy sea
stormy seas
stormy setting
stormy skies
stormy sky
stormy sky with thunders
stormy snowy fiji mountain
stormy snowy weather
stormy weather
stormy weather at night
stormy weather with lightning
stormy weather. makoto shinkai
stormy wheater
story
story art
story board format
story book
story book design
story book for adults
story book illustration
story telling
story telling aesthetic
storyboard
storyboarding
storybook
storybook art
storybook design
storybook fantasy
storybook illustation
storybook illustration
storybook layout
storybook realism
storybook style
storybook wide shot :: hd
stove
stoya
stoya expressive oil painting
stp
strafing attack plane
straight
straight arms
straight bangs
straight black hair
straight camera view
straight dark outline
straight edges
straight eyebrows
straight face
straight fringe
straight grey hair
straight hair
straight hairstyle
straight jaw
straight jawline
straight lines
straight neck
straight nose
straight nose!!!
straight nose!!!!!
straight on
straight on portrait
straight shot
straight sky blue hair
straight smooth vertical
straight stiff dry damaged hair
straight teeth
straight white teeth
strained expression
straining
stranded
stranding straight
strands of being
strands of fog
strange
strange alien forest
strange and hauntingly beautiful
strange angles and spidery edges
strange animals
strange attractor
strange buildings
strange camera angle
strange clothes
strange clouds
strange craft above the horizon
strange creatures
strange creatures walking around
strange crown )
strange dimensions
strange emotions
strange ethereal being
strange fantastic
strange fauna
strange flora
strange flowers
strange foreign objects
strange formations
strange fruits
strange geometric facial tattoos
strange giant insects
strange ingredients on the table
strange levitating stones
strange lighting
strange magnetic anomaly
strange place
strange planet
strange plants
strange portrait with galaxy
strange poses
strange red particles
strange shapes
strange surrealist
strange symbols line the walls
strange trees and clouds
strange vegetation
strange vibes
strange weirdness
strangely mutated
strangely terrifying
stranger things
stranger things character
stranger things demogorgon
stranger things poster
stranger things vecna
strangled with rope
stranglehold
strapless dress
straps
strategy game
strathmore 2 0 0
strathmore 200
stratosphere
straw
straw hat
straw hat and overcoat
strawberries
strawberry
strawberry embellishment
strawberry fields forever
strawberry granules
strawberry ice cream
strawberry ninja
straya
streak lights
streaks
streaks of dust
stream
stream flowing through the house
stream of love and happiness
stream of water
streamers
streaming
streaming on twitch
streamline moderne
streamlined
streamlined matte black armor
streamlined pink armor
streamlined spines
streamlined white armor
streams
streams and rocks
streams of flowing light
streams of glowing hot lava
street
street - level
street art 8 k
street art : 4 masterpiece
street art style
street art:2
street art:2 highly detailed
street art:2 masterpiece
street art:2 sachin ten:4
street art:4 masterpiece
street at night
street background
street city
street city night
street clothes
street clothing
street corner
street elevation
street fashion
street fashion clothing
street fashion outfit
street fighter
street fighter iv
street fighting
street gang
street lamp
street lamps
street lamps with orange light
street lanterns
street lanterns are shining
street lanterns glow
street level
street level view
street life
street light
street lighting
street lights
street lights water refraction
street market
street moon landscape
street night
street of hong kong
street of moscow
street of new york
street of paris photography
street of teal stone
street outfit
street perspective
street photo
street photograph
street photography
street photography style
street pic
street printed poster
street samurai
street scene
street scene with water tower
street signs
street style
street top view
street tram
street vendors
street view
street wear
street wears
streetart
streetlamp
streetlamps
streetlamps with orange light
streetlight
streetlight at night
streetlights
streets
streets of calcutta
streets of heidelberg
streets of hong kong
streets of mumbai
streets of new york
streets of salvador
streetscape
streetscapes
streetview
streetwear
streetwear fashion
streetwear graphic design
strength
stress
stressed
stressed and burnt out
stressed expression
stressed out
stressful
stressful atmosphere
stressing out
stretch
stretching her legs on the grass
stretching to walls
striations
strict
stride
striking
striking a pose
striking artistic concept
striking artstyle
striking azure eyes
striking color
striking colors
striking colour
striking composition
striking detailed artstyle
striking eyes
striking features
striking lighting
striking manga artstyle
striking perspective
striking pose
striking visuals
string art
string lights
string puppet
string theory
strings
strings background
strings of pearls
stringy
stripe over eye
striped
striped orange and teal
striped pantyhose
striped shirt
striped socks
striped sweater
stripes
stripey pants
strobe and laser lights
strobe light
strobe lights
strobo lights
stroboscope
strogg
stroke painting
strokes
strokes visible
stromy clouds
stromy weather
strong
strong ambient occlusion
strong and aggressive
strong and ferocious
strong and imposing
strong and muscular
strong and tall
strong and vibrant colors
strong arms
strong artificial intelligence
strong atmosphere
strong aurora overhead
strong backlight
strong bad
strong bespoke shape language
strong blooded body
strong blue and orange colors
strong blue rimlit
strong body
strong bokeh
strong brush stroke
strong brushwork
strong but calm
strong chest
strong chin
strong cinematic light
strong colors
strong colours
strong composition
strong confident personality
strong contrast
strong contrasting shadows
strong contrasts
strong defined jaw
strong depth of field
strong design
strong dither
strong eerie back light
strong eggshell texture
strong emotional impact
strong eyes
strong facial expression
strong facial features
strong fantasy knight
strong fat bacchanalian body
strong features
strong fighter in leathers
strong grain
strong imagery
strong iridescent light
strong jaw
strong jawline
strong leader
strong legs
strong light
strong lighting
strong lights
strong line
strong line work
strong lines
strong lines and bold colors
strong linework
strong man
strong masculine features
strong moonlight
strong neon lighting
strong outline
strong personality
strong perspective
strong pixar wheat bread warrior
strong pose
strong presence
strong presense
strong rain night
strong red hue
strong rim light
strong rim light!!!!!
strong rimlight
strong shadow
strong shadows
strong shadows)
strong silhouette
strong spirit
strong spotlights
strong strokes
strong studio light
strong studio lights
strong subsurface scattering
strong sunlight
strong texture
strong walls
strong wind
strong woman
strong young man
stronghold strategy gameplay
strongman
strongshadows
stroopwaffel
structural
structural geology
structure
structure : kyle lambert
structured
structures
struggle
struggling
sttng
sttng ( television )
stu_dts
stubble
stubble beard
stubble on his face
stubbles
stucco walls
stuck
stuck in mud
stuck in the ground
student
studies
studio
studio 4 k
studio 4 k quality
studio 4k
studio 4k quality
studio 4°c
studio 54
studio ambient lighting
studio art
studio atmosphere
studio backdrop
studio background
studio backlight
studio bind
studio bones
studio camera
studio camera. 35mm lens
studio diffuse lights
studio disney
studio dramatic light
studio floor
studio gainax
studio gainax art
studio gainax illustration
studio ghibi
studio ghibili
studio ghibli
studio ghibli & james gurney
studio ghibli aesthetic
studio ghibli and dan mumford
studio ghibli and pixar and abzu
studio ghibli and shinkai makoto
studio ghibli animation
studio ghibli anime
studio ghibli anime screenshot
studio ghibli art
studio ghibli art style
studio ghibli artstyle
studio ghibli color cheme
studio ghibli color palette
studio ghibli color pallette
studio ghibli color scheme
studio ghibli composition
studio ghibli concept art
studio ghibli environment
studio ghibli film
studio ghibli filter
studio ghibli illustration
studio ghibli inspired
studio ghibli key visual
studio ghibli landscape
studio ghibli movie
studio ghibli movie scene
studio ghibli movie still
studio ghibli painterly style
studio ghibli painting
studio ghibli scheme
studio ghibli sky
studio ghibli smooth concept art
studio ghibli still
studio ghibli style
studio ghibli sunlight
studio ghibli!!!
studio ghibli!!!!
studio ghibli!!!!!
studio ghibli. artgerm
studio ghibli. intricate
studio ghiblil
studio ghiblo
studio ghibly
studio ghibly style
studio ghibly style mononoke
studio ghilbi
studio gibli
studio glibly makoto shinkai
studio harcourt
studio hdri soft lighting
studio junio black inking
studio junio color palette
studio kai
studio kyoto
studio la cachette
studio landscape
studio level quality
studio light
studio light photo realism
studio light style
studio lighting
studio lighting 4 k
studio lighting 5 0 mm
studio lighting ; photorealistic
studio lighting and shading
studio lighting”
studio lightning
studio lights
studio lit
studio madhouse
studio mappa
studio medium format photograph
studio microphone
studio mir
studio muti
studio octane render
studio orange
studio packshot
studio photo
studio photo of a ceramic figure
studio photo portrait
studio photo refined
studio photograph
studio photographic portrait
studio photography
studio photography dslr
studio photography portrait
studio photograph”
studio photoshoot
studio photoshot
studio picture
studio portrait
studio portrait photo
studio portrait photography
studio potrait
studio product photography
studio product shot
studio qualit
studio quality
studio quality effects
studio quality lighting
studio quality product
studio quality smooth render
studio recording
studio room
studio setting
studio setup
studio shaft
studio shoot
studio shooting
studio shot
studio shot in style
studio soft lighting
studio spotlight
studio trigger
studio trigger anime
studio trigger art
studio trigger style
studio ufotable
studio!! portrait lighting
studio-lighting
studiolight
studion lighting
studioportrait lighting
studious
studious chiaroscuro
studs
study
study of a flower fairy
studying
studying a hell open rift portal
studying in a brightly lit room
studyng in bedroom
stuff of nightmares
stuffed
stuffed animal
stuffed dragon
stuffed toy
stuido ghibli
stuning
stuning 3 d render
stuning 3d render
stuning fantasy 3 d render
stuning fantasy 3d render
stunnig
stunning
stunning 3 d render
stunning 3d render
stunning 3d render of a fairy
stunning 8 k
stunning action poses
stunning african princess
stunning alien landscape
stunning and rich detail
stunning anime face portrait
stunning appealing figure
stunning arcanum backdrop
stunning architecture
stunning armor
stunning art
stunning art style
stunning artwork
stunning atmosphere
stunning beautiful
stunning beauty
stunning cgsociety
stunning character art
stunning cinematography
stunning closeupheadshot
stunning colors
stunning composition
stunning concept art
stunning cover magazine
stunning design
stunning detail
stunning detailed picture
stunning details
stunning digital art
stunning digital illustration
stunning digital painting
stunning drawing
stunning dress
stunning elegant
stunning elegant pose
stunning eyes
stunning face
stunning fantasy horror
stunning features
stunning framing
stunning gradient colors
stunning grand architecture
stunning graphics
stunning high tech
stunning image
stunning inking lines
stunning intricate details
stunning ivory dress
stunning landscape
stunning large format photograph
stunning light
stunning light coming through
stunning light reflections
stunning lighting
stunning lines
stunning masterpiece
stunning matte painting
stunning model
stunning moody cinematography
stunning moonlight and shadows
stunning nature in background
stunning painting
stunning photo
stunning photo real concept art
stunning photo-quality
stunning photograph
stunning photography
stunning photoshot
stunning portrait
stunning quality
stunning realism
stunning render
stunning sasquatch
stunning scene
stunning scenery
stunning sci-fi concept art
stunning screensaver
stunning screenshot
stunning sight
stunning ski
stunning skied
stunning skies
stunning sky
stunning sunny lighting
stunning sunset
stunning vfx
stunning view
stunning vista
stunning visual
stunning visuals
stunning visuals with rtx on
stunning volumetric light
stunning volumetric lighting
stunning waterfall
stunning waves
stunning woman
stunning!
stunning! concept art
stunning!!
stunning-design
stunningconcept art
stunningly
stunningly beautiful
stunningly detailed
stunningly detailed artwork
stunningly mysterious
stunningly ominous
stunningly realistic
stupid
stupid idiot cryptid
sturdy
sturdy body
sturm und drang
stygian
style
style = retro-futurism
style alex gray
style anime
style arcane tv series
style art nouveau
style artgerm
style as nendoroid
style blend of burning man
style blend of hideo kojima
style blend of the vatican
style combination
style dan hillier
style digital painting
style edward hopper
style epic
style film
style from greg rutkowski
style game square enix
style game square enix life
style greg rutkowski
style hybrid mix of beeple
style in digital painting
style in ghibli anime
style in ghibli anime style
style is a blend of æon flux
style ivan talavera and artgerm
style jean giraud
style kim jung gi
style league of legends
style lettering
style mix of æon flux
style of
style of 3 d
style of a clint eastwood movie
style of ade santora
style of adrian ghenie
style of aenami alena
style of aetherpunk
style of akira motion movie
style of alan lee and john howe
style of albert bierstadt
style of alena aenami
style of alex grey
style of alex ross
style of alexander trufanov
style of alexandre chaudret
style of alphonse mucha
style of anato finnstark
style of ancient text
style of anders zorn
style of andrew baker
style of andy warhol
style of angela deane
style of anime
style of anton fadeev
style of arcane
style of arcane tv series
style of artgerm
style of arthur rackham
style of asher brown durand
style of ashley wood
style of atget
style of aubrey beardsley
style of banksy
style of beatrix potter
style of beautiful body
style of bernie wrightson
style of blade runner
style of blade runner 2049
style of bladerunner
style of borderlands
style of boris vallejo
style of caravaggio
style of carlos schwabe
style of carrivagio
style of cartoon
style of castaway ( film )
style of charlie bowater
style of chinese vase
style of chippy
style of chris foss
style of claude monet
style of conrad roset
style of constructivism
style of cute pokemon
style of cyberpunk
style of cyberpunk 2 0 7 7
style of da vinci
style of dali
style of daniel merriam
style of dante gabriel rossetti
style of dave mckean
style of david cronenberg
style of dc comics
style of denis villeneuve
style of disney animation
style of donato giancola
style of doug chiang
style of dragan bibin
style of duelyst
style of edward gorey”
style of edward hopper
style of eiichiro oda
style of emil melmoth
style of emoji
style of esao andrews
style of feng zhu
style of final + fantasy + 1 5
style of final + fantasy + 15
style of flavie audi
style of francis bacon
style of frank frazetta
style of frank miller
style of frank weston
style of frazetta
style of game of thrones
style of geof darrow
style of george bellows
style of gerald brom
style of ghost blade
style of giger
style of gilles beloeil
style of graphic novel
style of greg rutkowski
style of greg simkins
style of grim dark
style of gta v
style of gta v artworks
style of guido crepax
style of guo hua
style of gurren lagann (2007)
style of gustav klimt
style of gustave dore
style of h. r. giger
style of h.r. giger
style of hajime isayama
style of hajime sorayama
style of hayao miyazaki
style of hieronymus bosch
style of hiroshi sugimoto
style of hokusai
style of hr geiger
style of hubert robert
style of hydro74
style of ian hubert
style of ilya kushinov
style of ilya kuvshinov
style of ink
style of inside game
style of ivan aivazovsky
style of ivan shishkin
style of jakub rozalski
style of james gurney
style of james jean
style of jean baptiste monge
style of jean delville
style of jean giraud moebius
style of jeff soto
style of jeremy mann
style of jesper ejsing
style of jet set radio
style of jim burns
style of joel meyerowitz
style of john atkinson grimshaw
style of john bauer
style of john berkey
style of john blanche
style of john harris
style of john howe
style of john kenn mortensen
style of jojolion cover art
style of jonathan solter
style of jordan grimmer
style of julia razumova
style of junji ito
style of kanagawa
style of karol bak
style of kelly freas
style of kentaro miura!!!!
style of kieran yanner
style of kilian eng
style of lady frieda harris
style of laura sava
style of league of legends
style of leonid afremov
style of leonid afremov”
style of leonora carrington
style of lois royo
style of lord of the rings
style of luis royo
style of maciej kuciara
style of macross
style of madhouse anime
style of madhouse studio anime
style of magic the gathering
style of magical girl
style of makoto shinkai
style of manga
style of maple story
style of marc simonetti
style of marcin blaszczak
style of mark ryden
style of marvel
style of mary jackson
style of masami kurumada
style of masamune shirow
style of maurice sendak
style of maxfield parrish
style of michael schwan
style of michaelangelo
style of midjourney
style of mike jackson
style of minecraft
style of mirror\'s edge
style of mirrors edge
style of moebius
style of monet
style of monument valley
style of mortal kombat
style of nekroxiii
style of norman rockwell
style of nosferatu
style of old masters
style of old painting
style of olidon redon
style of paolo parente
style of paolo roversi
style of peter doig
style of peter mohrbacher
style of picasso
style of pinhole photography
style of pino daeni
style of pixar
style of pokemon
style of pusheen. comic
style of rafael pavarotti
style of ralph mcquarrie
style of ralph steadman
style of raphael lacoste
style of raymond swanland
style of red line anime movie
style of rembrandt
style of rene magritte
style of richard corben
style of rick & morty
style of rick morty
style of romanticism
style of ron cobb
style of salvador dali
style of salvator rosa
style of santiago caruso
style of satoshi kon
style of seb mckinnon
style of shepherd fairey
style of shoji kawamori
style of shuzo oshimi
style of simon bisley
style of simon stalenhag
style of ssss.gridman (2018)
style of stanley artgerm
style of stanley donwood
style of star wars
style of starfinder
style of stephen bliss
style of stephen jones
style of stone ocean cover art
style of stranger things
style of studio ghibli
style of style of robert mccall
style of super meat boy
style of surrealism
style of suspiria and neon demon
style of syd mead
style of takashi murakami
style of takato yamamoto
style of the fifth element
style of the game rimworld
style of the matrix
style of thomas kinkade
style of tim hildebrandt
style of tim white
style of titmouse animation
style of tolkien world
style of tom bagshaw
style of tony hawk
style of tony hawk pro skater
style of tyler edlin
style of van gogh
style of van gogh starry night
style of vento aureo cover art
style of vincent van gogh
style of virgil finlay
style of vogelsang
style of walter martin baumhofer
style of wayne barlowe
style of wayne haag
style of wes anderson
style of will eisner
style of william morris print
style of wlop
style of yoji shinkawa
style of yoshii chie
style of yoshitaka amano
style of zdislaw beksinski
style of zootopia
style sheet
style sheets
style simpsons
style steve mccurry
style thomas kinkade
styled food photography
styled hair
styled like ghost in the shell
styleframe
styling
stylised
stylised comic art
stylised design
stylised flat colors
stylised fox - like appearance
stylised graphic novel
stylised hand painted textures
stylised illustration
stylised military clothes
stylised painting
stylised storm
stylised textures
stylish
stylish armor
stylish coat for a rave
stylish cyborg armor
stylish dark dress
stylish decor
stylish deity
stylish dress
stylish hairstyles
stylish leather armor
stylish lighting
stylish make up
stylish navy blue heels
stylish pose
stylish shoe design
stylish suit
stylishly designed background
stylistic
stylistic blur
stylistic furniture
stylistic lighting
stylistic oval black eyes
stylistic painting
stylization for fallout 4
stylized
stylized 3 d
stylized 3 d graphics
stylized 3d graphics
stylized 3d render
stylized and expressive
stylized anime
stylized art
stylized as a 3d render
stylized background
stylized beauty portrait
stylized bold outline
stylized border
stylized cel shading
stylized character
stylized character design
stylized concept art
stylized cyberpunk minotaur logo
stylized digital art
stylized digital illustration
stylized dynamic folds
stylized face
stylized flowers
stylized fractal background
stylized game art
stylized geometric flowers
stylized grass texture
stylized illustration
stylized layered shapes
stylized layered textures
stylized linework
stylized liquid smoke twisting
stylized make-up on face
stylized material bssrdf
stylized motorcycle helmet
stylized neon
stylized oil painting
stylized painterly
stylized painting
stylized pbr
stylized pbr materials
stylized photo
stylized portrait
stylized portrait formal pose
stylized portrait h 1280
stylized portrait h 640
stylized portrait h 704
stylized proportions
stylized shading
stylized silhouette
stylized stl
stylized stl fantasy miniature
stylized stone cladding texture
stylized thin lines
stylized urban fantasy artwork
stylized vegetation
stålenhag
su fu
suave
sub - surface scattering
sub surface scattering
sub tropical
subaquatic photography
subaru
subatomic electrons! in love
subconscious
subdimensional
subdivision
subdued
subdued color
subdued color palette
subdued colors
subdued tones
subgenius
subject : kangaroo
subject action : holding sign
subject action: holding sign
subject action: smoking a cigar
subject center bottom of frame
subject centered
subject centered in frame
subject centered in the frame
subject detail: wearing a suit
subject in center
subject in center of frame
subject in middle
subject in the centre
subject is smiling
subject made of cracked clay
subject made of white mesh rope
subject: dog
subject: kangaroo
subject= chimp
subject= duck
subjugation of humans
sublighting
sublime
sublime ambience
sublime atmosphere
sublime but intricate design
sublime comforting ambience
sublime detail
sublime subsurface scattering
sublime-comforting ambience
sublime-comfy-elegant ambience
sublime-cool-hot-hyperadvanced
subliminal
sublittoral jellyfish schools
submarine
submarine camera imagery
submarine in background. dark
submecanophobia
submechanophobia
submerged
submerged dramatic
submerged in cranberries
submerged in water
submerged on titan
submerged pre - incan temple
submerged temple dance scene
submerged temple ritual scene
submerged temple scene
submerged to waste
submerged underwater
submission
submissive
subnautica
subreddit / r / whale
subsiding floodwaters
subspace emissary
substance
substance 3 d
substance 3 d painter
substance 3d painter
substance designer
substance designer height map
substance designer metal
substance designer render
substance material
substance painter
substance painter 3d
substance render
subsurface
subsurface cracks
subsurface diffusion
subsurface illumination
subsurface light transmission
subsurface scandering
subsurface scatter
subsurface scattering
subsurface scattering light
subsurface scattering skin
subterranean galaxy
subterranean homesick alien
subterranean sprawling galaxy
subtitles
subtle
subtle and compelling lighting
subtle and detailed
subtle anime style
subtle atmospheric lighting
subtle awkward smile
subtle blue
subtle chromatic aberration
subtle color variantions
subtle color variations
subtle colors
subtle confident smile
subtle depth of field
subtle detailing
subtle details
subtle earthy tones
subtle fog
subtle fog and mood lighting
subtle giger
subtle glitches
subtle gold accents
subtle junji ito
subtle lens flare
subtle lovecraftian vibes
subtle make up
subtle makeup
subtle multicolored light
subtle muted cinematic colors
subtle neon underlighting
subtle pattern
subtle patterns
subtle purple accents
subtle shadows
subtle smile
subtle textures
subtle tones
subtle vibrancy
subtle visual noise
subtle wear - and - tear
subtractive lighting
subtropical flowers and plants
subtropical plants
suburb
suburban
suburban garden
suburban home
suburban neighborhood
suburbia
suburbia street
suburbs
subway
subway station
subways
subwoofer
success
succubus
succubus in sundress portrait
succubus in tight short dress
succubus | medieval
such as arms & legs
such as arms and legs
such as arms legs
such as bacteria
such disappointment
sucker punch
suddenly
suddenly there came a tapping
suffering
sufism
sugar
sugar glider
sugar skull
sugar snow
sugar sprinkled
sugary sweet
suggestive
sui ishida
sui ishida art manga
sui ishida with black hair
suiboku - ga ink
suicide
suit
suit and tie
suit and waistcoat
suit made of stars
suit of armor
suit vest
suit vest and top hat and gloves
suit ， perfect face
suits
sukhasana
sukhothai costume
suki
sukkot
sulaco star destroyer drop ship
sulfur
sullen
sullen old maid  redhead
sullen old maid ( redhead
sultan
sultry
sultry and beckoning
sultry body with sexy belly
sultry digital painting
sultry expression
sultry look
sultry smirk
sumai-e artstyle
sumerian
sumerian goddess inanna ishtar
sumerville game
sumi - e
sumi - e art
sumi - e lighting style
sumi - e style
sumi ink style
sumi-e
summer
summer 2016
summer afternoon
summer camp
summer clear blue sky
summer clothes
summer color pattern
summer color scheme
summer day
summer dress
summer evening
summer feeling
summer festival in background
summer festival night
summer field
summer glau
summer glau as cortana from halo
summer lake setting
summer landscape
summer landscape with mountain
summer light
summer lighting
summer meadow
summer morning
summer morning dew
summer morning light
summer night
summer of love
summer olympics footage
summer palette
summer princess
summer rain
summer season
summer setting
summer shirt
summer siberian forest taiga
summer sky
summer street
summer street near a beach
summer sun
summer sunlight
summer sunset
summer swimming party
summer time
summer unreal engine 5
summer vibe
summer vibes
summer vibrance
summer vibrancy
summer weather
summertime
summetric detailed
summon
summoning a demon
summoning an eldritch god
summoning circle
summoning spell
summoning terrible odds
summoning undead friends
sumo japanese
sumo wrestler
sumptuous
sun
sun - drenched
sun - rays
sun - rays beams
sun - rays through canopy
sun after a storm
sun and shadow
sun and shadow over a city
sun at dawn
sun beam
sun beaming down on him
sun beams
sun beams across sky
sun behind her
sun behind him
sun coast
sun dappled
sun down
sun down golden hour
sun drenched
sun exploding on the background
sun filtering through trees
sun flairs
sun flare
sun flares
sun glare
sun glasses
sun glints
sun god
sun high in the sky
sun in the background
sun in the sky
sun in the zenith
sun is in the top
sun is shining
sun light
sun light rays
sun lighting
sun lighting from above
sun lit
sun on zenith
sun overhead
sun puddle
sun ray
sun rays
sun rays across sky
sun rays at sunset
sun rays beaming
sun rays penetrating water
sun rays shine through the water
sun rays through snow
sun rays through the trees
sun rays through trees
sun rise
sun rises between two mountains
sun rising
sun set
sun setting
sun setting in the background
sun shaft
sun shafts
sun shines down on the car
sun shines in the sky
sun shining
sun shining through
sun shining through clouds
sun shining through the leaves
sun shining through the trees
sun streaming in
sun sunset
sun system
sun through majestic clouds
sun through the trees
sun wukong
sun yunjoo
sun-hyuk kim
sun-ken rock
sunbathed skin
sunbathing at the beach
sunbathing. illustration
sunbeam
sunbeams
sunbeams at sunset
sunbeams blue sky
sunbeams in background
sunbeams. digital illustration
sunburn
sunburst
sunburst behind woman
sunday
sunday afternoon
sunday morning comic strip
sundial
sundown
sundown golden hour
sundown misty firefly wisps
sundress
sunfaded
sunflare
sunflares;back to camera
sunflower
sunflower background
sunflower field
sunflower stained glass
sunflowers
sunflowers in the background
sunglasses
sunglasses and a scarf
sunglasses on
sunken
sunken dark blue eyes
sunken eyes
sunken face
sunken recessed indented spots
sunken ship
sunken square
sunlight
sunlight and whimsical houses
sunlight beaming down
sunlight beams
sunlight breaking through clouds
sunlight filtering through skin
sunlight glistening
sunlight pouring through window
sunlight rays
sunlight reflected on the river
sunlight shining through
sunlight shining through windows
sunlight study
sunlight through cumulus
sunlight through windows
sunlights
sunlit
sunlit landscape
sunlit sky
sunlit windows
sunllight
sunny
sunny afternoon
sunny amber morning light
sunny at noon
sunny atmosphere
sunny bay window
sunny clear sky
sunny complementary palette
sunny day
sunny day at beach
sunny day background
sunny day in a park
sunny day in the forrest
sunny day time
sunny day with clear sky
sunny environment
sunny landscape
sunny leone
sunny light
sunny lighting
sunny meadow
sunny mid day
sunny morning
sunny morning light
sunny park background
sunny rainbow galaxy stormy sea
sunny sky
sunny sky background
sunny sky light
sunny summer day
sunny weather
sunny winter day
sunraise
sunrays
sunrays between clouds
sunrays breaking through clouds
sunrays from the left
sunrays shine upon it
sunrise
sunrise atmospheric phenomena
sunrise background
sunrise coloring the room
sunrise colors
sunrise dramatic light
sunrise light
sunrise lighting
sunrise over solar punk city
sunrise over solarpunk city
suns
suns and supernovas
suns rays
suns set on a desert planet
sunsest golden hour
sunset
sunset + hdri
sunset and big clouds behind her
sunset at golden hour
sunset at the beach
sunset atmosphere
sunset background
sunset backlight
sunset beach
sunset clouds
sunset colors
sunset dark dramatic
sunset dark dramatic day
sunset dramatic light
sunset evening lighting
sunset glow
sunset glow around head
sunset golden hour hues
sunset halo around her head
sunset halo behind her head
sunset illustration
sunset in a valley
sunset in background
sunset in the background
sunset in the clouds
sunset in the desert
sunset in the distance
sunset kanagawa prefecture
sunset light
sunset lighting
sunset lighting 8k
sunset lighting ominous shadows
sunset ligthing
sunset magazine
sunset mood
sunset on distant machine planet
sunset on mars
sunset on the beach
sunset panorama
sunset photo
sunset photo at golden hour
sunset psychedelic
sunset raked lighting
sunset red and orange
sunset sinrise
sunset sky
sunset sky in background
sunset south
sunset sunrise
sunset time
sunset view
sunset warm spring
sunset with cloudy skies
sunset with falling leaves
sunset!
sunset!!!
sunset. light shadow
sunsetting color
sunshaft
sunshafts
sunshine
sunshine light
sunshine lighting high mountains
sunshine rays
sunshine through window
sunstone
suntur
sup
super
super - detailed
super - detailed work
super - flat
super - resolution
super - resolution microscopy
super 8 mm
super 8mm
super 8mm film
super 8mm photograph
super accurate human anatomy!!!
super adorable
super aesthetic
super attractive
super bright colors
super buff and cool
super camera
super car
super clean
super clear
super clear detailed
super clear details
super close up
super close up shot
super coherent
super colorful
super complex and instruct
super conducters
super consciousness
super cool
super cool rocket
super cool!!!
super cute
super cute and friendly
super cute funky black girl
super cute robot face
super defined clouds
super detail
super detail of each object
super detailed
super detailed acrylic painting
super detailed and intricate
super detailed and realistic
super detailed color art
super detailed color graphics
super detailed color lowpoly art
super detailed detail
super detailed details
super detailed eyes
super detailed face
super detailed face and eyes
super detailed faces and eyes
super detailed image
super detailed intricate
super detailed octane render
super detailed oil painting
super detailed painting
super detailed picture
super detailed render
super detailed ”
super detailed”
super detailing
super details
super exaggerated proportions
super fine detail
super fine inking lines
super fluffy
super focus
super focused
super glitched
super hero
super hero art
super hero costume
super hero mask
super hero pose
super heroes
super heroine costume
super high definition
super high detail
super high detail picture
super high details
super high energy
super high quality
super high quality art
super high resolution
super high speed photography
super highly detail
super highly detailed
super intricate
super intricate jewelry
super intricate realism
super lens
super long shot
super macro
super mario
super mario 6 4
super mario 64
super mario bros
super mario bros 1 9 8 5
super mario in matrix
super mario in real life
super mario pixel art
super mario portrait
super mario theme
super math wizard cat
super merge
super model
super model-s 100
super moon
super natural
super nintendo
super nintendo cartridge
super nintendo game sprite
super nova
super nova octopus
super panavision 70
super photo realistic
super photo-realistic
super photorealistic
super power
super powers
super precise detail
super quality
super real
super realism
super realistic
super realistic facial features
super realistic food picture
super realistic gritty
super realistic painting style
super realistic photo
super realistic render vray
super realistic”
super rendered in octane render
super resolution
super revolution
super rich
super rich happy pepe
super robot
super robot wars
super saiyan
super saiyan 3
super saiyan blue
super saiyan donald trump
super saiyan goku
super saiyan joe biden
super saiyan sonic movie poster
super sayan
super sayian goku
super scale rendered
super serious facial expression
super sexy
super sharp
super sharp details
super sharp focus
super sharp image
super sharp images
super sharp teeth
super slowmotion
super smash bros
super smash bross
super smash brothers ultimate
super smooth
super smooth lines
super soldier
super sophisticated texture
super storm
super strong
super super dynamic dynamic pose
super symmetry
super technical
super villain
super wide
super wide angel
super wide angle
super wide angle lens
super wide lens
super wide shot
super wide view of a cityscape
super zoom
super-detailed
super-flat
super-hero girl
super-high quality
super-realistic
super-resolution
super-resolution microscopy
superb
superb detail 8 k
superb detail 8 k masterpiece
superb detail 8k
superb detail 8k masterpiece
superb resolution
superbia
superbowl
supercar
supercomputer
supercomputers text to images
superdetail
superfine ink detail
superfine inking
superfine inklines
superflat art
superflat art movement
superflat pop
supergiant games
supergirl
superhero
superhero body
superhero cape
superhero from the boys tv show
superhero movie
superhero portrait
superhero with a cat head
superheroes
superimposed composition
superior
superior detail
superior iron man
superior look
superior quality
superliminal 8 k uhd
superliminal 8k uhd
superman
superman >yelling<<<< screaming!
superman costume
superman emerging from the sun
superman is high
superman pose
superman yelling screaming
supermarionation
supermodel
supermodel body
supermodel in silent hill
supermodel! face!
supernatural
supernatural golden eyes
supernova
supernova explosion
supernova inside facility
supernovas
superpop ultrabright
superpowers
superrare trending
superrealism
superrealism 8k resolution
superrealistic
superres sharpening
superresolution
supersampled
supersharp
supersharp photo
supersonic trains and passengers
supervillain
supervillain sorceress witch
supervillains
superwide angle
superwide shot
supperrealistic
supple look
supply chain economics
support
supporting characters
supportive
suppressive fire
supra
suprematism composition
supreme
supreme pizza
supremely digital
surburb woman
sureal
surealism
surealistic
surf
surf photography
surface
surface blemishes
surface hives
surface imperfections
surface painter
surface reflections
surface scattering
surface with beer-texture
surfaces blemishes
surfing
surfing a barrel wave
surgeon
surgery
surgery table
surgery theatre
surgical equipment
surgical gear
surgical gown and scrubs on
surgical implements
surgical impliments
surgical iv bag
surgical iv drip
surgical mask covering mouth
surgical supplies
surikov
surprise
surprise me
surprised
surprised expression
surprised expression on her face
surprised frown
surprised pikachu
surprising
surreal
surreal +
surreal + highly detailed
surreal 3 d render
surreal 3d render
surreal abandoned buildings
surreal alien kingdom
surreal alien ribbed white fruit
surreal and allegorical
surreal and fantasy art
surreal architecture
surreal art
surreal atmosphere
surreal avant-garde style
surreal background
surreal beautiful young woman
surreal black and yellow
surreal canopy
surreal chess
surreal cinema
surreal cityscape background
surreal clouds
surreal collage
surreal colors
surreal composition
surreal concept art
surreal cyberspace
surreal dark art
surreal dark fantasy
surreal dark otherworldly mood
surreal design
surreal digital art
surreal dramatic lighting
surreal drawing
surreal dream
surreal dream landscape
surreal dreamscape
surreal dreamy poetic
surreal epic
surreal existentialism
surreal extra flesh and bones
surreal fantasy
surreal flat colors
surreal forest
surreal frozen landscape
surreal gediminas pranckevicius
surreal glass goblets
surreal glimpse
surreal gouache gesture painting
surreal gouache painting
surreal horror
surreal hybrid animals
surreal illustration
surreal landscape
surreal lighting
surreal material
surreal matte painting
surreal metal sculpture
surreal mystical atmosphere
surreal neil gaiman's sandman
surreal neuron city
surreal nuclear eclipse blast
surreal object photography
surreal oil on canvas
surreal oil painting
surreal painting
surreal photo
surreal photography
surreal portrait
surreal portrait photography
surreal psychedelic design
surreal ramifications
surreal realistic
surreal scene
surreal sci fi architecture
surreal sci fi set design
surreal sky
surreal space
surreal style
surreal tea party
surreal tears from the moon
surreal vfx
surreal waiizi flowers
surreal water art
surreal woodblock print
surreal woodcut engraving
surreal!!!
surrealcore
surreale
surrealism
surrealism - n 9
surrealism 8k
surrealism aesthetic
surrealism album cover
surrealism art
surrealism background
surrealism of francis bacon
surrealism oil on canvas
surrealism painting
surrealism portrait
surrealism style
surrealism!
surrealism!!!!
surrealism!!!!! concept art
surrealismconcept art
surrealisme aesthetic
surrealist
surrealist aesthetic
surrealist art
surrealist art style
surrealist artwork
surrealist conceptual art
surrealist landscape
surrealist landscape painting
surrealist oil painting
surrealist painting
surrealist photography
surrealist style
surrealistic
surrealistic bird
surrealistic digital artwork
surrealistic painting
surrealistic roman architecture
surrealistic style
surreality
surreautistic
surrender
surrounded
surrounded flower
surrounded in clouds and light
surrounded with fire
surrounding cinematic light
surrounding onions
surrounding the city
surveillance
surveillance camera
surveillance footage
sus
sus guy
susan hayward
sushi
suspended bridge
suspended bridge!
suspended ceiling
suspended in air
suspended in outer space
suspended in zero gravity
suspended underwater
suspenders
suspense
suspenseful
suspenseful tone
suspiria
sussy baka
sussy imposter
sustainability
sustainable
sustainable architecture
sustainable materials
sutro tower in san francisco
suzanne engelberg
suze randall
svarovsky
svend smital
svetlana belyaeva
svetlana tigai
svg
svg art
svg comic style
svg illustration
svg sticker
svg sticker art
svg vector
svg vector art
svg. technical
sviatoslav gerasimchuk
swag
swagger
swagger! lowrider culture
swallowtail butterflies
swamp
swamp forest
swamp gas
swamp land
swamp landscape
swamp monster
swamp monster of ice
swamp thing
swamps
swampy
swampy atmosphere
swan
swanland
swans
swarm
swarm of bats
swarm of fireflies
swarming in flowers
swarming swirling bats
swarming with insects
swarms of ravens
swarovski
swarovski and tiffany
swarovski crystals
swarovski style
swashbuckler
swashbuckler class pirate
swashbuckling and romantic
swat
swat helmet
swaying trees
sweat
sweat and labour
sweat drops
sweat flying
sweater
sweating
sweating hard
sweating intensely
sweating profusely
sweaty
sweaty 4 k
sweaty 4k
sweaty abs
sweaty and dirty
sweaty and gross pioneer work
sweaty face
sweaty insane
sweaty meat
sweaty mountain
sweaty skin
sweaty skin dripping down face
sweaty skin!!
sweaty wet skin
sweaty. steam in air
sweden
swedish
swedish countryside
swedish design
swedish forest
swedish house
swedish houses
swedish style
swedish urban landscape
swedish writing
sweeping
sweeping arches
sweeping landscape
sweeping ombre blonde hair
sweeping vista
sweeping wild blonde hair
sweet
sweet acacia trees
sweet almost oil
sweet and innocent
sweet artpiece
sweet color scheme
sweet dreams
sweet eyes
sweet home
sweet hugs
sweet looks
sweet night ambient
sweet sarcastic smile
sweet smile
sweets
sweltering
sweltering heat
swift
swim suite
swimming
swimming across the universe
swimming deep underwater
swimming in a pool of coffee
swimming in ocean
swimming in space
swimming pool
swimming pool in front
swimming through the ocean
swimming through time
swimming to surface
swimming underwater
swimmingpool
swimsuit
swimsuit model
swindling
swing on a tree
swinging a big sword
swinging on a vine over a chasm
swinging reflective katana
swings
swiping brushwork
swirl
swirled architecture
swirles
swirley clouds
swirling
swirling around
swirling bioluminescent energy
swirling black hair
swirling black magic
swirling clouds
swirling energy
swirling fabric
swirling fire flames cosmos
swirling flames
swirling flames in background
swirling flowers
swirling flows of energy
swirling fluid
swirling fluid smokey enigma
swirling fog
swirling galaxies
swirling gardens
swirling liquids
swirling magical energy
swirling mist
swirling nature magic
swirling paint
swirling paint colors
swirling red-colored silk fabric
swirling scene
swirling scene in forest
swirling schools of silver fish
swirling silver fish
swirling smoke
swirling smoke shapes
swirling thick smoke
swirling vortex of energy
swirling water cosmos
swirling wispy smoke
swirls
swirls of fire
swirls of magic
swirly
swirly body painting
swirly bokeh
swirly clouds
swirly clouds in the background
swirly curls
swirly dark hair
swirly eyes
swirly flower dress
swirly liquid fluid abstract art
swirly liquid ripples
swirly lunar ripples
swirly magic ripples
swirly ripples
swirly smoke
swirly tubes
swirly vibrant color lines
swirly vibrant colors
swirly vibrant lines
swirly vibrant ripples
swiss
swiss alps
swiss architecture
swiss design
swiss modernizm
switch
switches
switzerland
swollen muscles
swollen veins
swoosh
sword
sword & sorcery
sword and shield
sword and sorcery
sword and wand – water
sword antlers
sword art online
sword attack
sword design
sword fight
sword fighting
sword in hand
sword pointed at sky
sword slash
sword stuck in a pond island
swords
swords drawn
swords stuck in the ground
swordsman
sycamore
syd
syd mead 3/4 closeup portrait
syd mead and mark brooks
syd mead and raphael lacoste
syd mead cinematic painting
syd mead color scheme
syd mead concept art
syd mead style
syd mead. rich colors
syd meade
sydney
sydney hanson
sydney mortimer laurence
sydney opera house
sydney park
sydney sweeney
sylas
sylvain sarrailh and igor morski
sylvan glade
sylvanas windrunner
sylveon
sylvester stallone
sylvester stallone as batman
symatrical
symbiote
symbol
symbol for the word ersatz
symbolic
symbolic art
symbolic metaphor
symbolic mural
symbolist
symbolist painting
symbolist!
symbolization
symbology
symbols
symbols of live
symetric
symetric body
symetric character
symetric face and body
symetrical
symetrical composition
symetrical detailed faces
symetrical eyes
symetrical face
symetrical face structure
symetrical faces
symetrical facial
symetrical features
symetrical japanese pearl
symetrical portrait
symetrical scene
symetrical wings
symetry
symetry!!
symmetical face
symmetra from overwatch
symmetri
symmetric
symmetric and beautiful face
symmetric armor
symmetric art
symmetric azure eyes
symmetric balance
symmetric balance)
symmetric beautiful face
symmetric beauty
symmetric body
symmetric bright eyes
symmetric concept art
symmetric detailed
symmetric eyes
symmetric face
symmetric face and eyes
symmetric face!
symmetric facial features
symmetric form exploration
symmetric indian pattern
symmetric lights
symmetric lights and fog
symmetric lights and smoke
symmetric matte painting
symmetric ornaments
symmetric pattern
symmetric portrait
symmetric round detailed eyes
symmetric round detailed eyes!!
symmetric wings
symmetric!
symmetric!!
symmetrical
symmetrical 4k
symmetrical anatomy
symmetrical and centered
symmetrical and detailed
symmetrical and intricate
symmetrical artwork
symmetrical artwork. cinematic
symmetrical background
symmetrical balance
symmetrical baroque painting
symmetrical beautiful
symmetrical beautiful face
symmetrical beautiful human face
symmetrical beauty face
symmetrical body
symmetrical body features
symmetrical centered
symmetrical centered portrait
symmetrical cloaked figure face
symmetrical complex fine detail
symmetrical composition
symmetrical crown
symmetrical design
symmetrical detail
symmetrical details
symmetrical die cut sticker
symmetrical dieselpunk warrior
symmetrical digital illustration
symmetrical doorway
symmetrical dragon wings
symmetrical duality
symmetrical epic fantasy art
symmetrical eyes
symmetrical eyes and body
symmetrical eyes and face
symmetrical face
symmetrical face and body
symmetrical face and eyes
symmetrical face and full body
symmetrical face concept art
symmetrical face details
symmetrical face features
symmetrical face happy
symmetrical face illustration
symmetrical face orelsan
symmetrical face portrait
symmetrical face!!
symmetrical face!!!!
symmetrical face. beautiful eyes
symmetrical faces
symmetrical faces!!!
symmetrical facial
symmetrical facial feature
symmetrical facial features
symmetrical fantasy landscape
symmetrical features
symmetrical framing
symmetrical front
symmetrical front face portrait
symmetrical front view
symmetrical full body
symmetrical fullbody rendering
symmetrical hands
symmetrical head and body
symmetrical head-on centralized
symmetrical illustration
symmetrical image
symmetrical layout
symmetrical logo
symmetrical long head
symmetrical mechanical features
symmetrical mouth
symmetrical neon rim light
symmetrical nose
symmetrical nostrils
symmetrical ornament
symmetrical outpost
symmetrical painted face
symmetrical painting
symmetrical patterns
symmetrical perfect face
symmetrical portrait
symmetrical portrait rpg avatar
symmetrical portrait scifi
symmetrical portrait symmetrical
symmetrical pose
symmetrical proportions
symmetrical pupils
symmetrical realistic
symmetrical rim light
symmetrical shape
symmetrical shot
symmetrical shoulders
symmetrical sticker design
symmetrical tarot card
symmetrical tarot illustration
symmetrical vogue face portrait
symmetrical wings
symmetrical wings on back
symmetrical!
symmetrical! intricate
symmetrical!!
symmetrical!! face
symmetrical!! sci-fi
symmetrical!!!
symmetrical. sci - fi
symmetricalintricate
symmetrically
symmetrically centered
symmetrie
symmetry
symmetry ; photorealistic
symmetry accurate features
symmetry accurate human features
symmetry balance
symmetry body features
symmetry composition
symmetry concpet art
symmetry face
symmetry feature
symmetry features
symmetry features proportions
symmetry fractal
symmetry illustration
symmetry symmetrical
symmetry!
symmetry! futuristic robotic
symmetry! human fetus
symmetry!!
symmetry!! concept art
symmetry!! concpet art
symmetry!! full shot!!
symmetry!! portrait
symmetry!! portrait of a cowboy
symmetry!! portrait of a woman
symmetry!! portrait of akuma
symmetry!! portrait of cyborg
symmetry!! portrait of hades
symmetry!! solid cube of light
symmetry!! water
symmetry!! yellow ranger
symmetry!!!
symmetryconcept art
symmetryconcpet art
symmetryfull shot
symmetryhuman fetus
symmetrysolid cube of light
symmetrywater
symmetryyellow ranger
symmmetric design
symobolism
sympathetic
symphogear
symphony
synaesthesia
synched
syndicate corporation
syndicate(2012)
syndicate2012
syndrome from the incredibles
synesthesia
synth
synth - wave
synth feel
synth vibe
synth wave
synth-wave
synthesizer
synthesizers
synthetic
synthetic bio skin
synthetic cubism
synthetic curves striking pose
synthetic fur
synthetic light
synthetic materials
synthetic maw
synthetic polymer paint on linen
synthetic skin
synthewave
synthwave
synthwave aesthetic
synthwave art
synthwave art style
synthwave art style ]!!
synthwave background
synthwave city
synthwave color palette
synthwave color scheme
synthwave colors
synthwave colors!!
synthwave colorscheme
synthwave colours
synthwave digital art
synthwave image
synthwave inspired
synthwave neon retro
synthwave neon retrofuturism
synthwave style
synthwave sunset
syntwave
syphon filter
syringe
syringes
syrup
system
system shock 2
system unit
sythwave
sza
szekely bertalan and lotz karoly
szekely bertalan. atmospheric
szukalski
szukalski & blade runner
szukalski blade runner
szukalski ravi varma
são paulo
séance
t - 8 0 0
t - pose
t - rex
t - rex drinking a giant ipa
t - shirt
t - shirt design
t 1 0 0
t 100
t allen lawson and ian fisher
t artfully traced
t pose
t shirt design
t-1000
t-800
t-pose
t-pose of male magic wizard
t-pose of wizard
t-rex
t-shirt
t-shirt design
t-top
t100
ta ha
tabaxi
tabaxi :: rogue
tabaxi male
tabaxi monk
tabernacle deep focus
table
table in front with a cup
table is centered
table light
table set for second breakfast
table with microphones
tablecloth
tables
tables and chairs
tabletop
tabletop game board
tabletop game props
tabletop gaming
tabletop miniature
tabletop model
tabletop model buildings
tabletop role playing game
tabletop rpg
tabloid
tabloid photo
tachikoma
tachism
taciturn
tack sharp
tack sharp focus
taco
taco bell
tacos
tactical
tactical armor
tactical assault
tactical gear
tactical knife
tactical poncho latex rags
tactical squad :9
tactical squads :10
tactical squads :19
tactical squads :9
tactical team in hell
tactical vest
tactical vests and holsters
tactical warfare
tactile
tactile buttons and lights
tadema
taehyung eating gucci fries
taejune kim
tafy
tag
tag heur
tags: blonde hair
tai chi
tai costume
tai traditional bronze headdress
tai warlord
taiga
taiga landscape
tail
tail fin
tail lights
tail of a lemur
tail raised
tail slightly wavy
tailored clothing
tails worn
taisho roman
taiwan
taiyo matsumoto
taj mahal
taj mahal made of cheese
takada akemi
takaratomy
takashi murakami art style
takashi murakami artwork
takashi takeuchi
takata yamamoto style
takato yamamoto aesthetic
takato yomamoto
takato yomamoto. 4 k
takayuki takeya
take
take control of your data
take cover
take me away from the norm '
take my hand
take off
takehiko inoue
taken at golden hour
taken from a plane
taken from behind
taken from orbit
taken from the high street
taken in 1 9 9 7
taken in 2 0 2 0
taken in 2020
taken in 2022
taken in night club
taken in silver dollar city
taken in the 1970s
taken in the 2000s
taken in the early 1910s
taken in the early 1920s
taken in the early 1930s
taken in the early 1960s
taken in the early 1970s
taken in the early 1990s
taken in the early 2020s
taken in the late 1800s
taken in the late 1970s
taken in the late 1980s
taken in the late 2000s
taken in the late 2010s
taken in the mid 2000s
taken in the mid-late 1800s
taken in the night
taken in zoo
taken on 1970s kodak camera
taken on a 1960s kodak camera
taken on a 1970s polaroid camera
taken on a 1990s camera
taken on a 2000s camera
taken on a 2010s camera
taken on a field view camera
taken on a nikon
taken on a ww 1 camera
taken on a ww 2 camera
taken on a ww2 camera
taken on an iphone
taken on go pro hero8
taken on iphone 1 3 pro
taken on iphone 13 pro
taken on iphone 14 pro
taken through a telescope
taken with a canon dslr camera
taken with a canon eos 5 d
taken with a canon eos 5d
taken with a disposable camera
taken with a leica camera
taken with a pentax k1000
taken with a pentax1000
taken with canon 5d mk4
taken with canon 8 0 d
taken with canon 80 d
taken with canon eos 5 d
taken with canon eos 5 d mark iv
taken with kodak portra
taken with my nikon d 3
taken with polaroid kodak portra
taken with sigma 2 0 mm f 1. 4
taken with sony a7r camera
taken with sony alpha 9
taken with the best dlsr camera
takeuchi takashi
takeyuki kanda
taking a picture
taking a selfie
taking a smoke break
taking control while smiling
taking from above
taking mind altering drugs
taking selfies
taking tobacco snuff
takumi fujiwara
talaat harb square cairo
tales from the borderlands
tales from the crypt
tales of earthsea
taliban
taliyah
talk show
talking
talking animals
talking around a fire
talking creatures
tall
tall acid green grass field
tall and lanky skinny
tall and muscular
tall and slender
tall and slim
tall and slim figured
tall and small
tall and strong
tall and thin
tall angle
tall anime guy with blue eyes
tall arched stone doorways
tall arches
tall backlit waves
tall big rocks
tall bridge with city on top
tall broad oaks
tall building
tall buildings
tall buildings in background
tall buildings on the sides
tall castle enclosed palisaded
tall ceiling
tall ceilings
tall columns
tall corn in the foreground
tall cypress trees
tall door
tall emaciated man wolf hybrid
tall entry
tall factory
tall farmer
tall female angel
tall female emo art student
tall flowers
tall fluffy clouds
tall forehead
tall girl
tall gladiator sandals
tall golden heavenly gates
tall grass
tall grown reed on riverbank
tall handsome guys
tall hat
tall iced tea glass
tall kelp
tall large trees
tall man
tall metal towers
tall minimalist skyscrapers
tall mountain
tall mountains
tall mountains in the horizon
tall obsidian architecture
tall pine trees
tall plants
tall purple and pink trees
tall root tree background
tall shot
tall skeletal figure
tall skyscraper
tall skyscrapers
tall spires
tall stone spires
tall structures
tall terrace
tall thin
tall thin beautiful goddess
tall thin build
tall thin frame
tall towers
tall tree
tall trees
tall windows
tall windows lit up
taller than a skyscraper
taller than man
tallinn
tally hall
tally hall album cover
talons
tamagotchi
tamandua
tamara de lepika
tamborine
tamriel
tamron so 85mm
tan
tan and brown fur
tan complexion
tan shorts
tan skin
tan skin a tee shirt and shorts
tan skin)
tan suit
tan vest
tanaka suguru
tane skin
taned skin
tang dynasty
tang dynasty palace
tang mo
tangelos
tangerine dream album cover
tangible
tangled
tangled cables
tangled overhead wires
tangled trees
tangled vines
tangled wires
tangles of metallic cables
tanino liberatore
tanjiro kamado
tank
tank class
tank fires with dollar banknotes
tank girl
tank has a large solid cannon
tank top
tank with legs
tank-top
tankoban
tankobon
tanks
tanks crashed
tanned
tanned ameera al taweel
tanned beauty portrait
tanned body
tanned skin
tanned skintone
tannoy
tanny skin
tans
tansparent water
taoism
taoist master
taoist priest
taoist robe
taoist temples and monks
taormina amphitheatre
tap out
tape
tape deck
tapered hairline
tapestries hanging from ceiling
tapestries of dreams
tapestry
tapir
tapping in to something greater
taps with running water
tar
tar - like
tar pit
tar pits
tar roads
tara mcpherson
taran fiddler
tarantino
tarantino movie posters
tarantulas
taras shevchenko style
taras susak
tarbell
tardigrade
tardigrade in space
tardigrade statue in the forest
tardigrade wearing sunglasses
tardis
tardis console room
tareme eyes
targ nar
target reticles
tarkovsky
tarkovsky color palette
tarkovsky greatest scene
tarkovsky scene
tarmo juhola
tarnished and rusted metal
taron egerton as wolverine
tarot
tarot art
tarot card
tarot card art
tarot card background
tarot card design
tarot card enviroment
tarot card frame
tarot card goddess of death
tarot card layout
tarot card of a shirtless cowboy
tarot card style
tarot card the chariot
tarot card the empress
tarot card the fool
tarot card the hermit
tarot cards
tarot cards characters
tarot cards floating
tarot design
tarot style
tarot swords
tarot!!
tarsier
tarski fiume
tartakovsky
tartan cloak
tartan garment
tartan hoodie
tartan hoody
tartan scarf
tartan vestments
tartarian architecture
tarzan
taschen
tassels
tasteful
tasteful colors
tastes
tasty
tatami galaxy
tataru
tatoo
tatooine
tatoos
tatsuki fujimoto
tatsumaki
tatsumaki from one punch man
tatsumaki with green curly hair
tatsuro kiuchi
tatsuro yamashita
tatsuya endo
tatsuyuki tanaka hd
tatsuyuki tanaka movie poster
tatterdemalion
tattered
tattered cloth robes
tattered clothes
tattered clothing
tattered dress
tattered fabric
tattered gothic horror maiden
tattered green dress
tattered leather coat
tattered ragged gothic dress
tattered robes
tattered wings
tatto
tattoo
tattoo art
tattoo artist
tattoo design
tattoo design sketch
tattoo flash
tattoo graphics
tattoo ink
tattoo of a california scrub jay
tattoo on shoulder
tattoo on upper arm
tattoo parlor photo
tattoo sketch
tattoo sketch of a ocean
tattoo sketch of a sea
tattoo sketches
tattoo sleeve on her right arm
tattoo stencil
tattoo style
tattooed
tattooed back
tattooed body
tattooed face
tattooed man
tattooed pinup
tattooed skin
tattoos
tattoos all over the skin
tattoos and piercings
tattoos of cumulus clouds
tattoos of star wars symbols
taupe
tauren
taurus
taurus zodiac sign symbol
tavern
tavern background
taverns nighttime lifestyle
tawa trees
tawny frogmouth
taxi
taxidermy
taxis
taylor swift
taylor swift as a boy
taylor swift as a heavenly angel
taylor swift as captain america
taylor swift as princess leia
taylor swift carrying a rifle
taylor swift face
taylor swift made of purple ice
taylor swift modeling
taylor swift with green dress
tcg
tchock
te pae
tea
tea ceremony scene
tea cup
tea drinking and paper lanterns
tea party
teacher
teacher as angry zombie
teaching
teak table
teal
teal aesthetic
teal and orange
teal and orange color scheme
teal and orange colors
teal and orange colour palette
teal and orange colours
teal and pink
teal cloth
teal color graded
teal electricity
teal energy
teal ethereal tendril wings
teal eyebrows
teal eyes
teal gradient
teal hair
teal headband
teal landscape
teal lighting
teal lights
teal neon lights
teal orange
teal orange color palette
teal orange color palette 8k
teal orange color scheme
teal palette
teal palette. horror
teal palette.”
teal paper
teal silver red
teal skin
teal skirt
teal sky
teal studio backdrop
teal suit
teal tones
teal tunic
teal uniform
teal white gold color palette
teal-neon viking runes
teals
team fortress 2
team fortress 2 screenshot
team fortress 3
team fortress style
team ibuypower
team rocket
teamlab
teams
teapot
teapot : 1
teapots
tear
tear drop
tear drops
tear gas
tear gas and smoke
tear on face
tearaway
tearing
tears
tears drip from the eyes
tears dripping from the eyes
tears from the eyes
tears in eyes
tears in the rain
tears running down
tears running down face
teary eyes
tease
teaser
teaser trailer footage
teasing
teasing smile
tech
tech art
tech city in the background
tech demo
tech face
tech fantasy
tech glasses
tech helmet
tech noir
tech pattern
tech robes
tech war
tech wear
tech wear!!!
tech-noir
technical
technical atmosphere
technical detail
technical diagram
technical document
technical draw
technical drawing
technical drawings
technical environment
technical manual
technical sketch
technical suit
technical vest
technicolor
technicolor 4 k
technicolor 4k
technicolor color
technicolor film
technicolor film expired film
technicolor horror
technicolor!!!
technicolour
technicolour 1
technicolour film
technilogy
techno
techno artwork
techno concert advert
techno eye
techno eyes
techno gargoyles
techno music
techno mystic
techno mystic goddess
techno neon projector background
techno organic armor
techno party
techno-optimism
techno-punk
technocore
technocracy
technologic
technological
technological anguish
technological lights
technological longcoat
technological rings
technological screens
technological singularity
technological sunglasses
technologies
technology
technology and magic
technology and nature
technology and nature in harmony
technology cloak
technology design
technology flight suit
technology meets fantasy
technology sci - fi
technology vs nature
technomancer
technopunk
technouveau
technoviking male with no shirt
techwear
techwear clothes
techwear fashion
techwear look and clothes
techwear occultist
techwear!! intricate
techwearintricate
tectonic cityscape
tectonic sky
tectonics
ted cruz
ted cruz is the zodiac killer
ted danson
ted gore
ted naismith
ted nasmit
ted nasmith and marc simonetti
ted nasmuth
ted naysmith
teddy bear
teddy fresh
tee shirt graphic
teeming with life
teemo
teemo from league of legends
teen
teen boy
teen elf
teen elf girl
teen girl
teen magazine cover
teen titans
teenage
teenage boy
teenage engineering moad
teenage female schoolgirl
teenage girl
teenage girl riding a dragon
teenage grunge punk rock
teenage jughead jones
teenage mutant ninja turtle
teenage mutant ninja turtles
teenage no
teenage vanessa morgan
teenage viking shieldmaiden
teenager
teenager girl
teenager hangout spot
teenagers
teepee
teeth
teeth bared
teeth between bread and patty
teeth filled with cavities
teeth gritted
tehnicolor
tehran
tekken
tekken 7
tekkon kinkreet
tekkon kinkret
tekkon kinreet
tekkonkinkreet
tekla evelina severin
tel aviv street
tel aviv under rocket attack
tele lens
tele photo lens
telegram sticker
telegram sticker design
telekinesis
telekinetic aura
telepaths
telephhoto
telephone
telephone pole
telephone wires
telephoto
telephoto lens
telephoto lense
telephoto long distance shot
telephoto photography
telephoto shot
telephoto sigma 8 5 mm f / 1. 4
telephoto vacation picture
telephoto zoom
teleportation
telescope
teletubbies
teletubbies in the backrooms
television
television head
television screenshot
television show
television still
television sunglasses
televisions
telling jokes
telling stories
telltale games style
telltale style
tempera
tempera on wood
tempera paint
tempered in solitude
tempest
tempestuous
templar
template
template layout
template sheet
temple
temple background
temple fair
temple in the background
temple in the distance
temple made of flesh
temple of heaven
temple of the sun
temple ruins
temple run
temples
temples behind her
temporal dead zone
temporal super resolution
temporalization
temporary emergency shelter
temporary tattoo
tempting
tempura on panel
temuera morrison
ten flats
ten lee
ten-dimensional
ten-gallon hat
tender
tenderness
tending on arstation
tending on art station
tending on pinterest
tendons
tendrils
tendrils in the background
tendrils of colorful light
tends to have fractal structure
tendu pose
tenebrism
tenement buildings
tenet
tengri
tengu mask
tennis ball
tennis ball monster
tennis ball monsters
tennis court
tennis racket
tennis wear
tenochtitlan
tense
tense atmosphere
tense design
tense look
tensei shitara slime datta ken
tension
tensive mood
tent
tent architecture
tent camp in foreground
tentacle
tentacle beast
tentacle monster
tentacle motifs
tentacled creature mix
tentacles
tentacles and bones
tentacles around
tentacles beard
tentacles climb from the portal
tentacles growing from its face
tentacles in universe
tentacles rising from the sea
tentacles wrapped around burgers
tention
tents
teonanacatl glyph
terada
terada katsuya
terem
terence mckenna
terence nielsen
terminal
terminal text
terminals
terminator
terminator 2
terminator 2 movie
terminator art
terminator artifacts
terminator skeleton
terminator t 8 0 0
terminator tech
terminator without flesh
terrace
terraced
terraced orchards and ponds
terraces
terracotta
terraformation
terraformed mars
terraforming
terraforming jezero crater
terraforming mars
terragen
terrain
terran trade authority
terraria
terrarium
terrarium lounge area
terrazzo
terrence malick
terrence malick screenshots
terrestrial paradise
terrible
terrible atmosphere
terrific
terrified
terrified 👿
terrifying
terrifying :7
terrifying and dangerous
terrifying architecture
terrifying atmosphere
terrifying but fascinating
terrifying face
terrifying in its weirdness
terrifying masterpiece
terrifying monster
terrifying nightmare
terrifying!!!
terror
terror glow
terror glows
terrorist attack
terry davis
terry gilliam
terry moore
terry richardson
teruel city in 1989
tesla
tesla car
tesla coil
tesla coils
tesla cybertruck
tesla dune buggy
tesla logo on chest
tesla model 3
tessa thompson
tessa thompson inspired
tesselation
tessellated planes of rock
tessellating patterns
tessellation
tesseract
tessgarman
test
test screen
test subject
test subject supersoldier
test tubes
testing custom
teths and tentacles
tetrachromacy
tetradic color scheme
tetris
tetsuo from akira
tetsuo hara
tetsuo hara and katsuhiro otomo
tetsuya ichida
tetsuya nomura
teutonic knight
texas
texhnolyze
text
text : roborock
text and a pale young woman
text morphing into objects
text on paper
text paragraphs in left
text poster
text says: schwarzlicht
textbook
textbook illustration in clolour
textbook page
textbook pages
textbooks
textbooks and books
textile
textile print
textiles
textless
textural
texture
texture city at night
texture detail
texture details
texture electronic circuit
texture for 3 d
texture for 3d
texture map
texture of electronic circuit
texture of old rust and trash
texture of sand
texture pack
texture quality high
texture render
texture rust
textured
textured 3 d
textured 8k
textured base ; product photos
textured brush strokes
textured canvas
textured detailed skeleton
textured in substance
textured light
textured like a carpet
textured oil on canvas
textured paper
textured parchment background
textured photoshop brushes
textured plastic
textured skin
textured turquoise background
textures
textures. com
textures.com
texturing xyz
texturized
tezuka osamu
tf 1
tf 2
tf 2 maps. net
tf2
tf2 screenshot
tf2 screenshot colour palette
tfwiki. net photography
tfwiki.net photography
tgv
thai
thai architecture
thai temple
thailand
thailand art
thalassophobia
thallasophobia
thames river
thanatos
thancred waters
thancred waters in style of wlop
thangka
thank you
thank you very much
thanks
thanos
thanos as a starbucks barista
thanos as frodo baggins
thanos as vin diesel
thanos eating a big mac
thanos helicopter
thanos portrait
thanos style traits
thanos working at starbucks
thanshuhai
that has the form of a dragonfly
that is 1300 feet tall
that looks like a dragonfly
that looks like a insect
that resembles a bull\'s
that resembles a bulls
that violence breeds violence
that we would see in the essoldo
that's right
thatched houses
thatched roof
thatched roofs
thawan duchanee
thc
the
the 'american gothic' painting
the (void
the - dark - crystal
the 13th doctor regenerating
the 2000s
the 4th dimension
the 5 th element
the 9 0 s
the absolute worst
the actor
the addams family
the african painted dog
the age of discovery
the ai
the album is called tripmachine
the alien from the movie 'alien'
the allfather
the alps are in the background
the amazing spider man 2
the amazing world of gumball
the american dream
the american gothic painting
the anatomy of a head of lettuce
the ancient world
the angel of death
the angel of death with a halo
the angels are crying
the angry
the animatrix
the anime girl is crouching
the anime girl is running
the annunaki making humans
the answer to life
the antler people
the apocalypse
the apple and the moon
the argo
the art of athas and dark sun
the art of bionicle
the art of books
the art of pixar
the art of skinner
the artist
the artist has used bright
the artist is charles burns
the atmosphere is cheerful
the australian desert
the autumn plague gardener
the avengers
the average redditor
the ayahuasca spirit
the back rooms
the background is black
the background is blurred
the background is lush jungle
the background is misty
the background is on fire
the background is unsafe
the background is white
the backroom
the backrooms
the backrooms are empty
the backrooms liminal space
the bad
the bad and the ugly
the banana blue gang
the band name is roborock
the batman
the batman who laughs
the battle for the treehouse
the battle of hoth
the bauhaus
the beach
the beast of judgement
the beastlands
the beastmaster 1 9 8 5
the beatles
the beginning and the end
the beginning of the end
the beginning of the universe
the beginning of time
the best
the best composition
the best ever
the best lack all conviction
the best modern photo of august
the best of elephants
the best on cgsociety
the best photograph of august
the big bang
the big ben is in background
the big lebowski
the binding of isaac
the bird is wearing a bowtie
the bird's - eye view
the birth
the birth of venus
the black box
the black lioness made of tar
the blacksmith
the blacksmits’ daughter
the blade dancer
the blessed aisles
the blind liberty of the few
the blob
the blood meridian
the blue panther
the blue whale crystal texture
the bodhi tree at sunset
the body of ronda rousey
the bone crown
the bones came together
the boogie monster
the book is closed
the borg
the born fire
the boys
the brilliant dawn on the meadow
the british museum
the brittle. digital painting
the buddha
the building is a skyscraper
the buildings are on fire
the bunny has pink fur
the butterfly goddess of fire
the candle is on a wooden table
the car is corvette c2 1969
the card player man
the caretaker
the cars have faces
the castle
the cat cooks soup
the cat in the hat
the cat is drinking tea
the cat is fluffy
the cat is orange
the cat is smiling
the cat is walking
the cat looks like giraffe
the cat who could drive a car
the caterpillar
the catholic pope in his bed
the ceaseless waking nightmare
the centre cannot hold
the cepheid god of lost things
the character is a man
the cheshire cat
the chicken man
the children of the night
the chronic
the city iacon cybertron
the city is full of green plants
the city is on fire
the city of atlantis
the city of lisbon
the city of paris
the city of santa barbara
the city of toronto
the city that never sleeps
the clangers
the clear sky
the cloudy moonlit sky
the clown is far from the camera
the color black
the color red
the colors are bright
the colossus of rhodes
the colours of the sunset
the coming ai singularity
the computer gods ascend
the concept of infinity
the conjuring
the conjuring style
the console is tall and imposing
the contra space war
the cool colors
the council of spooky bitches
the cowboy in the weird west
the crawling chaos
the creation of adam
the creation of the universe
the creator
the creator of earth
the creator of everything
the creator of evil
the creator of the universe
the creature in the basement
the creature sells a used car
the creature sits in a car
the crowd flees the village
the crowd is cheering
the crown
the crown is very high
the cube
the culture
the cure
the cure for cancer
the cure for hate
the curse of monkey island
the cutest creature in the world
the cutest creature of the world
the cutest kitten ever
the cyberpunk apartment
the cyberpunk girl portrait
the cytoplasm”
the dark crystal
the dark god of cats
the dark knight
the dark lord
the dark lord sauron
the dark one
the dark side
the dark side of the moon
the dark souls knight
the dark sun
the dark sun of athas
the dark tower
the darkest hour
the darkness
the dawn of man
the dead sea
the death
the death of art
the decisive moment
the deck of many things
the depthless eye
the depths of depravity
the depths of joy
the desert
the desert is in color pencil
the desert is in watercolor
the designer of the universe
the designer's republic
the designers republic
the destroyer of worlds
the destruction of the universe
the devastating wise goat
the devil
the devil in hell as a dragon
the devil tarot card
the disease of imagination
the diskworld
the divine feminine
the doctor
the dog is doing a ballet dance
the doom slayer
the doomslayer
the doors
the dragon girl portrait
the dream
the dreaming
the dress\'s lower
the dresss lower
the drone king
the dude
the duke shrek
the earth
the earth from space
the earth sprouts lava
the ecolab industrial dishwasher
the edge of the universe
the edge of the world
the ego separates
the egyptian god
the elder scrolls
the elder scrolls iii: morrowind
the elder scrolls iv: oblivion
the elder scrolls v : skyrim
the elder scrolls v: skyrim
the elder scrolls vi
the elder scrolls vi hammerfell
the electric boy
the element woman
the emerald coast
the emerald herald
the emerald herald in the garden
the emperor
the emperor tarot card
the empress
the empress of licorice
the empress tarot card
the empress’ hanging
the empress’ swirling gardens
the encrypted metaverse
the end
the end of all things
the end of an organism
the end of days
the end of evangelion
the end of humanity
the end of the universe
the end of the word
the end of the world
the end of time
the end of times
the endless
the endless end beyond all ends
the energy is released
the energy of dreams
the entrance of valhalla
the essence of evil
the evil dead
the evil within monster
the expanse
the expanse tv series
the explosion from akira
the extremely hot and sexy
the eye of god
the eye of god rah
the eye of providence
the eye of sauron
the eye of the universe
the eye of the world
the eyes mouth are closed
the eyes of sharbat gula
the fabulous city of london
the face emerges from pamukkale
the face of absurdly beautiful
the face of god
the fairy queen
the fall of constantinople
the fall of gondolin
the fall of man
the fall of rome
the fall of summer
the fall of the galactic empire
the fall season
the falling soldier
the fashion of the time
the fast and the furious
the father of sorrow and revenge
the feeling of boredom
the feeling of dread
the fellowship of the ring
the fifth element
the fifth element artifacts
the fifth element movie
the fifth series
the final battle
the final battle in hell
the final frontier
the fire goddess
the fire is made of binary code
the fire king
the fire queen
the first and the last
the first landing on the moon
the first selfie on earth
the first steampunk dragon
the flamingo cafe
the flash
the flow of time. complex shapes
the flower crown
the flower prince
the flower tower
the flying dutchman
the flying spaghetti monster
the fool
the fool tarot
the fool tarot card
the fool tarot card rider waite
the fool tarot illustration
the forbidden city
the force
the forefoot to make a v gesture
the forest
the forest is on fire
the fork is on a plate
the former demon king
the fourth race of starcraft 2
the fox-like evolution pokemon
the front of a trading card
the funniest meme ever
the furry fursuit is running
the futility of overexertion
the future
the future ages
the galaxy
the game designer
the garden of eden
the gate to hell
the gates of argonath
the gates of heaven
the gates of hell
the ghost in the machine
the ghosts in the machine
the girl and the sun
the girl is scared
the girl made out of flowers
the girl on a windowsill
the girl plays the guitar
the glass bead game
the glimmering orange dawn
the glow of the moonlight
the glowing throne
the god anubis
the god athena
the god emperor of mankind
the god hades
the god hephaestus
the god hermes
the god of chaos
the god of mischief
the god of music
the god of oatmeal
the god of propane
the god of small scissors
the god of small scissors!!
the god of the forge
the god of the sea
the god of thunder
the god particle
the god poseidon
the god posideon
the god zeus
the goddess artemis smirking
the goddess hera looking angry
the goddess hestia
the goddess of art
the goddess of autumn harvest
the goddess of fertility
the goddess of love
the goddess of summer
the godess hera looking angry
the godfather
the golden cat armor knight
the golden hour
the golden humanoid robot
the golden ratio
the good
the grand budapest hotel
the grand steampunk piano
the grand temple of flowers
the grass
the graveyard
the graveyard!!
the great beyond
the great door of hell
the great marble wizards tower
the great wall
the great war
the great wave off kanagawa
the greatest cake
the greek god
the greek god of wine
the greek goddess aphrotite
the green goblin
the green lantern
the gremlins are too damn high
the grim reaper
the grim reapper
the grinch
the ground is dark and cracked
the grudge
the gruffalo
the guitar player
the gunslinger
the guy from a clockwork orange
the hair floats on the water
the hair reaches the ground
the handbag is over a wood table
the happiest moment
the harbour at stromness orkney
the harbringer of death
the hard and strong buildings
the harpoon is sharp
the harry potter trio (harry
the head of the man is a skull
the heat death of the universe
the hedgehog is holding a rifle
the high priestess
the hills have eyes
the hime cut
the hobbit
the hobbit art
the hobbit concept art
the hobbit orc concept
the holy grail
the homestuck trolls
the horror in the woods
the horse above
the horse and his boy
the horse is not mine
the house in the forest
the houses of parliament
the hulk
the hydra from path of exile
the ice queen
the idea
the image is futuristic
the image is refined with uhd
the imperial palace
the incal
the incredible hulk
the infinite
the infrastructure of humanity
the inside of a ufo
the internet
the iron giant
the iron lady
the iron throne
the jabberwock
the james webb telescope
the jaw showing the teeth
the jetsons
the jewelpunk aesthetic
the joker
the joker and patrick bateman
the joker as batman
the journey of life
the joy of life
the jungle
the jungle at the background
the justice tarot card
the key of life
the key of the nile
the king in the desert
the king in yellow
the king of death
the king of dreams
the king of hell
the king of rooster
the king of thieves
the kiss
the knight from hollow knight
the kraken
the lady of ash
the land of odrua
the land of the death
the lands littered with bodies
the lantern crown
the large birds of war
the last airbender
the last battle
the last day on earth
the last day on the earth
the last guardian skinny towers
the last human on earth
the last jedi
the last man on earth
the last of us
the last of us zombie
the last of us”
the last orchestra
the last photo ever taken
the last portrait of mac miller
the last rain on earth
the last selfie ever taken
the last selfie on earth
the last stand
the last supper
the last supper painting
the last v 8 interceptor
the last wanderer of earth
the leaning tower of pizza
the legend of zelda
the letter a
the letter w
the librarian
the library of babel
the lich king
the life
the life after death
the light is bright and wintry
the lighting is dark
the lights are on
the lion king
the little circus of horrors
the little mermaid
the little prince
the living room
the long shot
the longest night
the look of an elderly person
the lorax
the lord and savior
the lord of pestilence
the lord of the rings
the lost beach
the lost city of atlantis
the lost star wars film
the lost valley
the lovely hairy fox
the lover tarot card
the lovers
the lovers tarot card
the macho duck
the mad hatter
the madhatter
the madness of mono-yellow
the magic eclipsing
the magical ring of three wishes
the magician
the magician tarot card
the man
the man have a backpack
the man in the wall
the man is screaming and sad
the man looked up
the man riding is on the lion
the man stands out on the image
the man with robot head
the mandalorian
the mandalorian in gta v
the mane of a lion
the masjid al-haram in mecca
the mask covers her entire face
the mask is broken
the masks come off at night
the masters
the matrix
the matrix servers on fire
the mayo man
the meaning of life
the mekanik doll
the mekaverse
the merge
the met museum
the metaverse
the metropolitan museum of art
the microchip
the middle ages
the middle of a valley
the midjourney multiverse
the mighty donut
the milk way
the milk way up above
the milky way express
the milky way galaxy
the milky way in the sky
the mines of moria
the minions
the mock turtle
the mogul
the mome raths outgrabe
the mona lisa
the money of clowns
the monkey live here nigga
the monster under the bed
the moomins
the moomins in moominvalley
the moon
the moon and stars
the moon behind her
the moon cast on the man
the moon crashes into the earth
the moon crashing into the earth
the moon in the background
the moon is big an in the city
the moon is in the sky
the moon on the top right
the moon orbiting other moons
the moon presence
the moon reflects in the water
the moonlit dance of the fae
the morning river
the morning star
the most advanced humanoid robot
the most beautiful
the most beautiful landscape
the most beautiful scene
the most beautiful sunset
the most beautiful woman
the most bizarre hybrid animals
the most epic
the moth crown
the mother of a millions sounds
the motherland calls
the mothership
the mountain is steep
the mouth a bit open
the movie
the movie alien
the movie the thing
the multiverse
the mummy
the mummy astronaut
the munster in the background
the muppets
the music guy
the mysterious stranger
the mystery of the quotient
the narthex
the nature of existence
the nba finals
the neat and dense buildings
the netherlands
the neverending story
the new king of great britain
the new love
the next generation
the nexus portal
the night as vast as us
the night crown
the night sky
the night sky is a sea
the night war rages behind him
the nightmare before christmas
the nine circles of hell
the non-binary deity of spring
the nonlinear. digital painting
the normandy landings
the nucleus
the oak tree
the ocean
the ocean in the background
the odyssey
the office
the olympics footage
the omnipotent assassin
the one true god
the oracle of ancient wisdom
the oracle of the mayan elders
the oracle of trees
the orb of dreams
the orb of eternity
the orb of time
the orb of truth
the orbs of byob
the order of the burning shadow
the original conscious
the origine of cybertimes
the orville
the other a skeletal figure
the other is used for flight
the palace of ai
the palms come from the depths
the palms come from the ground
the panorama
the passage of time
the passing of time
the perfect human male specimen
the perfect woman
the phases of the moon
the phoenix
the photo shows a large
the photo was taken from a boat
the photo was taken from afar
the photography artwork
the pictish god of stags
the piercing stare of yuki onna
the pits of hell
the pixar adaptation
the pizza has a large cornicione
the place is just really small
the plague doctor
the plaguefather
the planet
the planet is warm with canyons
the planet mars
the plate is on a wooden table
the platonic ideal of flowers
the poolrooms
the pope
the pope is in his bed
the pope wakes up is his bed
the portrait
the portrait of a young man
the portrait of an elegant
the potatoes eaters
the predator
the president
the price of ncr
the prince of pleasure
the prince of thieves
the princess of power
the problem of evil
the prophet of the common people
the purge
the pyro
the queen of blades
the queen of hearts
the queen of longing
the queen of the sun
the rabbit has pink fur
the rad hatter
the raft of the medusa
the raining city of lisbon
the rapture
the reach
the real holy grail
the reaper
the reaper as a scary
the red citadel
the red ninja
the red queen
the red sox left fielder
the riddler
the rider carries a large sword
the rift
the right from wrong
the ring
the ring is horizontal
the ring movie
the ring of three wishes
the ringles
the rise of consciousness
the river is flowing its way
the river is full of lights
the river of time
the road
the robot has a fishing rod
the robot in her mechanical mask
the robot is captain of a ship
the robot wearing her human mask
the robot wearing the bone crown
the rock
the rock and roll emperor
the rock is a potato alarm clock
the rock is in the sea
the room is raucous and joyful
the rootless god of lost things
the ruins
the rules of proportion
the sacred cup of understading
the sailor galaxia. beautiful
the sailor jupiter. beautiful
the sailor moon. beautiful
the samurai is wrapped in chains
the sandman
the sandman from graphic novel
the sands of time
the scarecrow
the scary empty liminal spaces
the scooter ( edm band
the scream
the scream painting
the screaming fox
the sea
the sea and storms behind him
the sea in the background
the sea of sadness
the sea seen behind the city
the seal of fortune
the second… like a calf
the secret of monkey island
the secret seventh power ranger
the secrets inside the vatican
the see horse valley
the set of jeopardy
the seventh seal
the shackled
the shadow
the shaman of the subway
the shard
the shining
the shining 1980
the shining overlook hotel
the shinning hotel hallway
the shire
the shire castle
the shrike
the sigil of the mafia
the simpsons
the sims 4
the sims 4 texture
the singapore skyline
the singer
the singularity
the sky
the sky falls to the ground
the sky has the milky way
the sky is a faint misty red hue
the sky is a nebula on fire
the sky is a starry sky
the sky is all that illuminates
the sky is beautiful and clear
the sky is black
the sky is dark
the sky is full of stars
the sky is gray
the sky is grey 2 0 0 0
the sky is pink
the sky is red
the sky is the galaxy
the small house in the forest
the smooth black jaguar
the smooth black lioness
the solarpunk phoenix
the sopranos
the sorcerer's doll
the sorceress
the sorceress casting a fireball
the sour
the source of future growth
the space background
the space needle
the spaceship is on fire
the spear of odin
the sphinx
the spider thicket
the spirit of the bull run
the squirrel king
the stadium has a full crowd
the stanley parable
the star tarot card
the starry night
the stars
the stars are fish in the depths
the starship enterprise
the statue of liberty
the stone is rolling up
the stone is round
the store
the strange creature
the straw is in his mouth
the streamer
the streets of baldur's gate
the strongest superhero
the structure of galaxy
the stupendium
the style of andreas rocha
the style of andy warhol
the style of rococo
the style of syd mead
the style of wlop
the styles of escher and penrose
the sun
the sun at their back
the sun glitchart
the sun is blinding
the sun is exploding
the sun is setting
the sun is shining
the sun is shining wide shot
the sun is shining. photographic
the sun on the horizon
the sun reflecting on a window
the sun shines in
the sun up on the sky is strong
the sun's rays through the dust
the sun. beautiful
the suns light is consumed
the suns rays through the dust
the sunset
the super hot and sexy
the surface of the sun
the sydney opera house
the table is full of food
the tall man from phantasm
the taoist temples of huangshan
the tardis
the tarot card of earthly demise
the teddy bear is holding a gun
the temple of truth is white
the tentacle crown
the terminator
the terminator  1984
the terminator ( 1 9 8 4 )
the tesseract
the texas revolution
the thames is dry
the the man is wrapped in chains
the theme of alice in wonderland
the thing
the thing  1982
the thing ( 1 9 8 2 )
the thing 1 9 8 2
the thing 1982
the thing alien assimilation
the thinker
the third horseman
the third… had a face like a man
the three body problem
the three fates
the three marys at the sepulchre
the three moiras
the three suns
the tiger is smiling
the time machine
the time of day is dusk
the titanic is sinking
the toilet is shiny
the torch we all must hold
the tower of babel
the tower tarot card
the trappings of modern life
the tree is growing on a meadow
the tree is on top of a calm sea
the tree of life
the trees are angry
the treetops of giant oaks
the tudors
the tunnel into winter
the twilight zone
the ufo is over the city
the ugliest car in the world
the ugly truth
the ultimate gigachad
the ultimate question of life
the ultimate warrior
the uncanny valley
the undefeatable
the underworld
the universe
the universe and everything
the universe on the background
the upside down
the vanishing of ethan carter
the vatican
the vibrant echoes of the market
the victim is in the center
the video game
the village on the cliff
the vitruvian man style
the void
the void people
the wake of the unseen object
the walking dead
the walking man
the walls are pink
the walls purple and pulsing
the war multiverse
the warm
the wasteland
the watchers
the weather bright
the wednesday frog
the wendigo
the western wall
the white house
the white king
the white rabbit
the wicker man
the wild hunt
the wind moves her shirt
the window
the window is open
the windows are lit
the wise man is riding on top
the wisest of all owls
the witch conjure
the witch queen
the witcher
the witcher 3
the witcher concept art
the witness
the wizard
the wizard of oz
the wizard's magical tower!!
the wolf among us
the wolf howls at the moon
the wolf of wall street
the wolverine
the woman has long dark hair
the woman holds more toys
the woman is behind bars
the wonder of devotion
the wood between the worlds
the woods
the world
the world between death and life
the world ends with you
the world is insane
the world is on fire
the world of lost souls
the world on fire
the world tree
the world without form and void
the world's biggest idiot
the year 2089
the yellow creeper
the'other mother'from'coraline '
theater
theater access corridor
theater curtains are red
theater dance scene
theater dressing room
theater mask
theater stage
theatre equipment
theatre stage
theatrical
theatrical lighting
theatrical poster
theatrical scenery
theatrical!
thedieline
theft
their irises are red
thematic
theme :'icewind dale '
theme :icewind dale
theme is cyberpunk city market
theme park
themed after wine
themed on the stars and moon
then
then another
then leave it to god
theneedledrop
theodor kittelsen
theodore chasseriau
theodore ralli
theophanes
theophanic atmosphere
theophanic atmospheric vivarium
there are archways
there are birds on her head
there are flowers
there are two sides to the story
there for i am
there is a book next to the cat
there is a cat next to her
there is a cigar in his mouth
there is a glow coming from her
there is a loose wire mesh
there is a place in wales
there is a skull over a table
there is a window to the sea
there is blue sky
there is fire everywhere
there is full bedpan next to him
there is lightning
there is midnight sunset
there is one cherry
there is paint everywhere
there is something ugly
there is space around the figure
there is tall grass
there is ugliness in beauty
there is water splash
there was a noise
there were eight anime people
there were posters on the wall
there will be blood
there's flowers everywhere
theres flowers everywhere
thermal imaging
thermography
theron
therookies
thertrevkaiser
they all look terrified
they are all laying down
they are chasing a whale
they are close to each other
they are crouching
they are fighting very angry
they are in love
they are siblings
they are smoking
they are unhappy
they are very serious
they are watching
they look me in the eye
they might be crawling
they reach into his mind
they share one head. cinematic
they sit next to a pile of bones
thiago alcantara
thiago lehmann
thicc
thicc build
thick
thick - rimmed glasses
thick and bold black outlines
thick and dense vines
thick and thin lines
thick armor
thick atmosphere
thick beard
thick beard highly detailed
thick black beard
thick black lineart
thick black lines
thick black smoke
thick black smoke and fire
thick black smoke billowing
thick blue lines
thick body
thick bow
thick braided beard
thick brown beard
thick brush
thick brush strokes
thick brushstrokes
thick bushes
thick bushy straight eyebrows
thick cables on ground
thick cables whipping around
thick collar
thick colorful smoke
thick dark glasses
thick dark hair
thick dark smoke!
thick detailed smoke
thick dust and red tones
thick eyebrows
thick fancy eyeliner
thick fancy makeup
thick fluffy tail
thick fog
thick forest
thick furry neck and chest fluff
thick glasses
thick glowing chains
thick hair
thick heavy fog
thick heavy impasto
thick impasto
thick impasto paint
thick impasto technique
thick ink lines
thick jawline
thick jungle
thick legs
thick line art
thick line drawing
thick line ink
thick lineart
thick lines
thick lines highly detailed
thick lining
thick linings
thick lips
thick looping wires
thick mist
thick mustache
thick neck
thick neon lights
thick oil paint drip texture
thick oil painting
thick outline
thick outlines
thick paint
thick paint brush strokes
thick paint visible
thick painting
thick pigmented smoke
thick red lips
thick rim glasses
thick rimmed glasses
thick rimmed-glasses
thick round spectacles
thick set features
thick shining black hair
thick smoke
thick smoke around him
thick smooth warframe legs
thick smooth warframe thighs
thick squares and large arrows
thick strokes
thick swirling smoke
thick swirling smoke everywhere
thick swirling smoke tornado
thick swirling tornado
thick tail
thick thick thick outlines
thick thighs
thick thigs
thick vector line art
thick visible brush strokes
thick warframe legs
thick watercolor brush strokes
thick white detailed smoke
thick wires
thick wires looping
thief
thief red riding hood
thief warrior
thierry doizon
thierry mugler
thierry rouzier
thievery equipment
thigh focus
thigh gap
thigh high socks
thigh highs
thigh skin
thighhighs and skirt
thighs
thighs close up
thighs focus
thighs thighs thighs thighs
thighs!!!
thighs!!!! gorgeous
thighs!!!!! gorgeous
thighs!!!!!!
thin
thin aged 2 5
thin aged 25
thin antennae
thin antlers
thin beard
thin black lines
thin black lingerie
thin black robe
thin blue arteries
thin body
thin bodysuit
thin brush strokes
thin button nose
thin corpse
thin dof
thin eyebrows
thin face
thin face structure
thin film
thin fog
thin glowing devices
thin glowing lights
thin glowing wires
thin gold details
thin green glassy crystal shards
thin healing glowing devices
thin horizontal nebula
thin intricate art nouveau frame
thin light pink lips
thin line art
thin line work
thin lines
thin linework
thin lips
thin long auburn hair
thin long fine lines
thin lustrous auburn hair
thin lustrous hair
thin lustrous long auburn hair
thin lustrous long dark hair
thin male alchemist
thin moustache
thin nose
thin porcelain
thin pursed lips
thin red lines
thin red veins
thin round earrings
thin round glasses
thin scar on his forehead
thin sharp lines
thin skeleton
thin soft hand holding cigarette
thin spikes
thin straight lines
thin straight purple lines
thin stroke
thin strokes
thin waist
thin wires
thin young male
thin young male alchemist
thin-waist
things fall apart
things hanging from ceiling
think in 3 d
thinker
thinker pose
thinking
thinking pose
thinkpad
thinning hair
third - eye visions
third - person
third dimensional
third eye
third eye in middle of forehead
third eye middle of forehead
third eyes middle of foreheads
third lion head
third person
third person gameplay
third person perspective
third person view
third trimester
third-person shot
third-person view
thirst
thirteen-dimensional
thirtysomethins years old
this character has cryokinesis
this is a monster
this is beautiful
this is fine
this is hell
this is it
this is it style
this kind of smart
this painting is a large canvas
this person does not exist
this time
thisset colours
thisset style
thistle
thistles
thom browne
thom wasselmann
thom yorke
thom yorke (guitar)
thom yorke age 2 5
thom yorke lost
thom yorke singer songwriter
thomas
thomas blackshear and moebius
thomas cole - h 7 0 4
thomas cole and gustave dore
thomas danthony and malika favre
thomas dubois
thomas eakes
thomas eakins and beksinski
thomas edgar stevens
thomas ehretsmann
thomas jane
thomas jefferson
thomas kinade
thomas kincade
thomas kincaid
thomas kindkade
thomas kinkad
thomas kinkade
thomas kinkade and craig mullins
thomas kinkade and paul lehr
thomas kinkade and tristan eaton
thomas kinkade cfg _ scale 8
thomas kinkade cfg _ scale 9
thomas kinkade painting
thomas kinkade sharp focus
thomas kinkade style
thomas kinkade style painting
thomas kinkade. cute cozy room
thomas kinkade. forest room
thomas kinkade. highly detailed
thomas kinkadegreg
thomas kinkaide
thomas pynchon
thomas reimann
thomas river
thomas the tank engine
thomas the tank engine face
thomas veyrat intricate
thomasz alen kopera
thopter from magic the gathering
thor
thor 2 marvel film
thorax
thorn background. d&d
thorn crown
thorn everywhere
thorn rose crown
thorns
thorns and vines. detailed
thorns everywhere
thorough details
thors hammer
thot
thoth
thought provoking
thought-provoking
thoughtful
thoughtful )
thoughtful expression
thoughtful eyes
thoughtful pose
thoughts
thousand yard stare
thousands of crows
thousands of galaxies
thousands of teeth
thousands of tiny onlookers
thps 2 level
thrash metal
threadlike
threads
threatening
threatening pose
three
three - dimensional
three - dimensional rendering
three - point lighting
three - point perspective
three - quarter view
three - tailed fox
three animals
three bat head
three birds flying around it
three body problem
three cats drinking in a bar
three colors
three day growth
three dimensional shadowing
three dimensions
three dwarf brothers
three eyed
three eyed goddesses
three eyes
three fourths view
three futuristic princes
three guilders : a magician
three hairy fat cave people
three hairy neanderthal people
three handed god
three head one body
three heads
three kingdom
three kingdoms
three masts
three michelin star
three michelin stars
three moons
three people running a marathon
three piece suit
three point light
three point lighting
three point lighting bjork
three point perspective
three quarter angle
three quarter portrait
three quarter profile
three quarter shot
three quarter view
three quater notes
three towers
three views
three women
three-dimensional
three-dimensional image
three-quarter body
threea toys
threes
thresher shark human hybrid
threyda
thriller
thriller atmosphere
thrilling
thriving ecosystem
throbbing
throne
throne of olympus
throne room
throne worlds
through a sea made of ketchup
through clouds blue sky
through my eyes
through the windows
throw
throw pillows
throwing cards in the air
throwing spaghetti and meatballs
thrown tables
thrusters
thrustmaster t 3 0 0
thrustmaster t 300
thrustmaster ts pc racer
thug life
thugs
thumb up
thumbnail
thumbprint
thumbs up
thunder
thunder & lightning
thunder and lightning
thunder clouds
thunder clouds in the sky
thunder clouds modernism
thunder in the foggy jungle
thunder lightning
thunder man
thunder sky
thunder storm
thunder storm and forest on fire
thunder storm background
thunderbird 2
thunderbirds
thunderbolt
thundercats
thunderclouds
thunderclouds in the sky
thunderous
thunderous battle
thunders
thunderstom
thunderstorm
thunderstorm and fire
thunderstorm at night
thunderstorm background
thunderstorm dark night sky
thunderstorm in marrakech
thunderstorm in the sky
thunderstorm lightning
thunderstorm outside
thunderstorm supercell
thunderstorm the background
thunderstorms
thunderstrike
thundra ufo crash site
thus spoke zarathustra
thx sound
thy
thylacine
tia masic
tiamat
tian zi
tiananmen square
tiara
tiara with sapphire
tibet
tibetan book of the dead
tibetan inspired architecture
tibetan painting
tibetan skeleton dancer
tibetan text script
tibetan thangka
tibor nagy
tic tac ufo
tick helmet
ticket
tickle fight in the death tent
tie
tie - fighter
tie die shirt
tie fighter
tie fighters
tie fighters patrolling
tie-dye
tied - up shirt
tied bridge on local river
tied leather armor
tied with chair
tiefling
tiefling bard
tiefling female warlock
tiefling from d & d
tiefling from d d
tiefling rogue
tiepolo
tifa
tifa lockaert
tifa lockhart
tifa lockhart portrait
tifa lockhart with white hair
tifa lockheart
tiffany
tiffany dover
tiffany style
tiger
tiger - crab creature
tiger face
tiger head
tiger of fire flying
tiger paws as gloves
tiger pelt
tiger skin
tiger skin carpet
tiger stripes
tiger_beast
tigerman
tigers
tight
tight around neck
tight attire
tight black tank top and shorts
tight black tanktop
tight blue jeans and cool shoes
tight bone structure
tight bun
tight denim jeans
tight dress
tight face shot portrait
tight fit
tight fitted tank top
tight focus
tight full body suit
tight gigantic pink curls
tight light blue neopren suits
tight navy-blue leather outfit
tight outfit
tight push up bra
tight shirt
tight shot
tight shot of subject
tight wrinkled cloath
tightly framed
tights
tights skin
tights; on the street
tikal temple iv
tiki
tiktok
tiktok 4 k uhd image
tiktok video
tilda swinton
tilda swinton at burning man
tile
tile floor
tileable
tileable texture
tiled
tiled floor
tiled fountains
tiled roofs
tiled room squared waterway
tiles
tiles curtains
tileset
tileset asset store
tiling
tiling texture
till lindemann
tillamook cheese
tilt - shift
tilt - shift photography
tilt and orange
tilt angle uhd 8 k
tilt blur
tilt shift
tilt shift background
tilt shift desert background
tilt shift effect
tilt shift focus
tilt shift glass background
tilt shift h.r giger background
tilt shift lens
tilt shift library background
tilt shift mirror background
tilt shift photography
tilt shift suburban
tilt shift zaha hadid background
tilt shift”
tilt up angle
tilt-shift
tilt-shift effect
tilt-shift photography
tilt-shifted
tilted
tilted 35° frame
tilted angle
tilted camera
tilted camera angle
tilted frame
tilted head
tilted perspective
tiltshift
tim
tim booth
tim bradstreet
tim burton
tim burton comic book art
tim burton style
tim burton supersampled
tim burton's style
tim burtons style
tim hildebrant
tim jacobus
tim jacobus art
tim rees
tim walker
tim white art. 64 megapixels
timbered house with bricks
timbuktu
time
time + space + reality
time - lapse
time consumes us all
time displacement device
time does not exist anymore
time dragging on for too long
time elves
time exposure
time is running out
time machine
time magazine
time magazine 2 0 2 2
time magazine 2022
time magazine cover
time shift
time square
time to climb the mountain path
time travel
time travel machine
time travel theme
time traveler
time travelers
time travelers appear in a park
time vortex in the background
time warping
timelapse
timeless
timeless disturbing masterpiece
timeless masterpiece
timeline nexus
times magazine
times square
timestamp
timid
timid and vulnerable expression
timothee chalamet
timothy dalton
timothy kong
timothy rees
tin can
tin foiling
tin tin
tin type
tina belcher
tina belcher as a real person
tina turner in a turnip costume
tina turner portrait
tinder profile
tinkerbell
tinkercore
tinkerer
tinnitus
tins of food on the floor
tintal effect
tinted colours
tintin
tintoretto. detailed
tintoy characterdesign robot
tintype
tintype full body view
tintype photo
tintype photo of ice - t
tintype photograph
tintype photograph of florence
tintype photography
tiny
tiny bikini
tiny black bra
tiny cat riding a labrador
tiny charcoal smudges
tiny creature made of one brick
tiny crimson petals falling
tiny cute nose
tiny details
tiny evil alchemist gnome
tiny faeries
tiny feet
tiny fireflies glowing
tiny firespitter
tiny gaussian blur
tiny girl looking on
tiny glowbugs everywhere
tiny house
tiny insects
tiny mice living inside
tiny mouth
tiny nose
tiny ornate windows
tiny people devouring food
tiny people walking below
tiny person watching
tiny points of light
tiny rainbow triangles
tiny rats
tiny robots
tiny room with dirty wall tiles
tiny ships docking
tiny smokes from buildings
tiny space
tiny spaceship
tiny spaceship!!
tiny stars
tiny statue on display
tiny sticks
tiny thin mustache
tiny village
tiny villagers
tiny waist
tiny wires on the ground. narrow
tinyest midriff ever
tipping his fedora
tire
tired
tired and drunk
tired and haunted expression
tired appearance
tired bags around eyes
tired expression
tired eyes
tired face
tired half closed
tired haunted expression
tissue ornament
tissue paper art
titan
titanfall
titanfall 2
titanfall 2 game
titanfall irl
titanic
titanic sails near
titanic ship exterior
titanic ship interior
titanic tank with legs
titanium
titanium buff
titanium mechanical parts
titanium skeleton
titanium white
tite kubo
tite kubo and takashi murakami
tithi luadthong
titian
titian]
title
title - shift
title kanji
title-shift
titled'holiday at the beach '
tiziano vecelli
tjalf sparnaay
tjalf sparnaay 8 k
tkmiz
tlaquepaque
tlingit haida lithographic
tmnt
tmz
tn
tng
to
to be
to be or not to be
to destroy satan's kingdom
to every living being
to fathom hell or go angelic
to fathom hell or soar angelic
to hell with you
to honor jupiter
to infinity and beyond
to me
to protect us
to the moon
toad
toad head
toad philosopher the thinker
toad samurai
toads
toadstools
toast
toasted bread in toaster slots
toaster
toaster in the style of r2d2
tobey maguire
tocchini
tochigi prefecture
today's featured anime still
today's featured photograph
today's featured photograph 4 k
today's featured photograph 4k
today's featured photography 4k
today\'s featured photograph 4k
todays featured photograph
todays featured photograph 4k
todd hido
todd hido photography
todd howard courtroom sketch
todd macfarlane
todd mcfarlane
todd schorr
todd schorr highly detailed
todd solondz
toddler
todo hecho con papel de pinata
toei
toei animation
toga
together
toggles
toggling
toilet
toilet paper
toiletpaper magazine
toki doki
toki pona
tokio
tokio aoyama
tokio futuristic in background
toko fukawa
tokujin yoshioka
tokusatsu
tokusatsu suit vaporwave
tokyo
tokyo - esque town
tokyo akihabara street cityscape
tokyo alleyway
tokyo anime scene
tokyo at night
tokyo background
tokyo city
tokyo city in the background
tokyo drift
tokyo fashion
tokyo futuristic and clean
tokyo futuristic in background
tokyo ghoul
tokyo in a flood
tokyo in the background
tokyo inspired
tokyo izakaya scene
tokyo japan
tokyo kowloon
tokyo mural
tokyo prefecture
tokyo street
tokyo street background
tokyo street cityscape
tolkein art
tolkien
tolkien and michael komarck
tom
tom and jerry
tom anders zorn
tom badshaw
tom bagshaw
tom bagshaw ) ) )
tom bagshaw and sabbas apterus
tom bagshaw and tom richmond
tom bagshaw artstyle
tom bagshaw donato giancola
tom bagshaw inspired
tom bagshaw portrait
tom bagshaw style
tom bagshaw weta studio
tom bagshaw. oil painting
tom baker using his scarf to fly
tom bangshaw
tom brady
tom brady as che guevara
tom burke
tom chambers photography
tom cruise
tom cruise as iron man
tom cruise as ironman
tom cruise as kenshiro
tom cruise as the joker
tom cruise as tony stark
tom cruise handcuffed to a chair
tom cruise laughing super hard
tom cruise torso
tom cruise vs carnivore dinosaur
tom eerebout
tom finland
tom hammick
tom hanks
tom hanks as superman
tom hanks as tomato hanks mascot
tom hanks smirk
tom hanks tomato face
tom hanks with thick eyebrows
tom hardy
tom hardy as henry dorsett case
tom hiddleston
tom hiddleston as count dracula
tom holland
tom holland as a pirate
tom holland as peter pan
tom holland as the hulk
tom jobim
tom morello
tom of finland
tom of finland style
tom richmond
tom richmond illustration
tom waits as hellboy
tom whalen 8 k
tom whalen 8k
tomas kinkade
tomas sanchez
tomasz alen kopera and cgsociety
tomasz allen kopera
tomasz strzalkowski
tomato
tomato hat and a walking stick
tomato monster
tomato sauce
tomato splatter
tomatoes
tomatoes hanging on branches
tomb
tomb of elemental evil
tomb raidar
tomb raider
tomb raider (2018)
tomb raider beautiful
tomb raider setting
tomba
tomboy
tombs
tombstone
tombstones
tomcat raptor hornet falcon
tomcat replica
tomcruise!!!
tome
tome + concept art
tommy 1 6 years old
tommy gun
tommy wiseau
tomohiro shimoguchi
tomorrow
tomorrowland
tomoyuki yamasaki
tonal
tonal color
tonal color scheme
tonal colors
tonal colors outdoor
tonal separation
tonal topstitching
tonalism
tonalism illustration
tonalism painting
tonalism style
tonalism!
tonalist
tonalist landscape
tonalist painting
tonalist portrait
tonalist style
tone mapped
tone mapped william-adolphe
tone mapping
toned
toned arms
toned body
toned colours
toned derriere
toned down
toned legs
toned orange and pastel pink
toned paper
toned shape
tonemapped
tonemapping
tones of black
tones of black in background
tones of blue and green
tongue
tongue out
tongue sticking out
toni infante
tonic the fox
tons of details
tons of rooms
tony abbott as the joker
tony danza coloring book page
tony diterlizzi and brian froud
tony hawk
tony hawk crying
tony khan wearing a headset
tony matelli
tony montana
tony robert - fleury
tony roberts
tony sandoval
tony sandoval. 8 k realistic
tony sandoval. realistic
tony sart
tony sart and an anime artist
tony sart highly detailed
tony soprano
tony stark
tony taka
tony the tiger
too detailed sculpture
too late
too many eyes
too many hands
too many hands in all directions
too many teeth
too red
too sharp
too tired to care anymore
took on ipad
tool
tool band
tool band art
tool belt
toolbelt
tools
tools and junk on the ground
tools for science research
toon
toon - shading
toon aesthetic
toon boom render
toon render keyshot
toon rendering
toon shader
toon shading
toonami
toonix character
toony
tooth
tooth wu : : quixel megascans
toothless
toothless horror macabre face
toothless mutant
toothpaste blast
toothpaste refinery
top
top - down
top - down perspective
top - down photo
top - down photograph
top - down view
top - rated
top - side view
top - view
top 1 0 0
top 100
top 25 fantasy anime series
top 4 0
top 40
top 6 best movie ever imdb list
top 6 worst movie ever imdb list
top and side view
top angle
top angle view
top art-station
top boat racing simulator
top cinematic lighting
top down
top down angle
top down camera angle
top down drawing
top down extraterrestial view
top down lighting
top down perspecrive
top down perspective
top down photo
top down photo at 45 degrees
top down shot
top down spotlight lighting
top down view
top floor boss shit
top gear
top gun
top gun maverick
top half of body
top hat
top hat and luxurious moustache
top hats
top horrible creatures
top lid
top light
top lighting
top lit
top milk brands
top of a canyon
top of art
top of pinterest
top of the hill
top of the mountain
top production
top rated
top rated on pixiv
top secret
top secret space plane
top secret style
top secret style photo
top selection on unsplash
top shot
top tex bottom text meme
top view
top view of convertible
top-down shot
top-down view
top-rated
topaz
topaz ai
topaz and other jewellaries
topdown
toph bei fong
tophat
topiary
topknot
topless
topographic scan
topography
topological renders
topology
topview map
tor from marvel
torbjorn from overwatch
torch
torch - lit
torch light
torch lighting
torch shadows
torches
torches alit in the background
torches and fireflies
torches in ground
torches on the wall
torches on wall
torchlight
torchlight. sketch
torchlight. sketch art. roots
torchlights
torchlit
torii
torii gate
torii in a moutain with trees
torii in the background
toriyama akira
torment
torment and waves
torn
torn agazine
torn cape
torn clothes
torn clothing
torn cosmo magazine style
torn edges
torn fabric
torn magazine style
torn mesh
torn nebulas
torn overalls
torn paper
torn paper collage
torn paper edges
torn paper intricate texture
torn paper smouldering smoke
torn sails
torn shirt
torn shorts
torn!! clothing
tornado
tornado of fire
tornado twister
tornadoes
tornwing
toronto
toronto city
torquoise fantasy fanged medusa
torrent
torrential rain of blood
torri
torri gate
torso
torso and head
torso portrait
tortoise
tortured face made of wood
toru furuya
toru narita
torus energy
tosa school
total black outfit
total chaos
total darkness
total destruction
total eclipse
total lunar eclipse
total recall artifacts
total recall style
total recall tech
totalitarian
totalitarian dystopia
totalitarian prison island
totalitarian setting
totalitarian socialist angel
totally black eyes
totally gonzo
totally mad and yelling
totally radical
totally whack
totem
totem 2
totem pole
totoro
totoro as iron man
totoro from my neighbor totoro
totoro hiding behind tree
totoro sitting in a forest
totorrl
touareg
toucan
touch
touch of gold leaf
touches of gold leaf
touching
touching heads
touching her clothes
touching tree in a forest
tough
touhou
touhou character
touhou project
touhou project official artwork
toulouse lautrec
tour de france
touring
tourism
tourism photography
tourist
tourist destination
tourist map
tourist photo
tourists in background
tournament
tousled medium length hair
toward a planet
toward the sun rays and caustics
toward to the camera
towels
tower
tower bridge
tower of babel
tower of god
towering
towering above a small person
towering cumulonimbus clouds
towering giant made of zombies
towering high up over your view
towering over a city
towering over the camera
towering over your view
towering waves
towers
town
town background
town center background
town hall
town in the background
town square
townscape
township
toxic
toxic air
toxic cat
toxic clouds
toxic drips
toxic glowing smog in the sky
toxic rays of the sun
toxic slime
toxic smile. jan van eyck
toxic waste
toxicity
toy
toy advertisement
toy art
toy camera
toy commercial photo
toy design
toy guillotine
toy package
toy photo
toy photography
toy room
toy story
toy story style
toyko
toyota cresta
toyota jzx 1 0 0 drift
toyota supra
toys
toys figures
tpose
tracer
tracer from overwatch
tracer game character
tracer in a skintight dress
tracer overwatch
tracers
tracingstar trails
tracks of barbed wire
tracksuit
trade card game
trade offer meme
trademarks and symbols
traders
trading
trading art station
trading card
trading card fond
trading card front
trading card game
trading depots
trading illegal goods
trading stocks
tradition
traditional
traditional animation
traditional art
traditional beauty
traditional brushes strokes
traditional chinese
traditional chinese art
traditional chinese clothing
traditional chinese ink painting
traditional chinese painting
traditional chinese textures
traditional chinese watercolor
traditional clothes
traditional clothing
traditional corsican
traditional costume
traditional drawing style
traditional dress
traditional female hairstyles
traditional fireplace
traditional folk art style
traditional geisha clothing
traditional ink
traditional ink!
traditional japanese
traditional japanese art
traditional japanese colors
traditional japanese concept art
traditional japanese painting
traditional korean city
traditional korean interior
traditional makeup
traditional medium
traditional moon
traditional oil painting
traditional painting
traditional photography
traditional portrait
traditional roman armor
traditional romanian clothing
traditional russia
traditional tai costume
traditional tattoo
traffic
traffic accident
traffic in background
traffic light
traffic light on
traffic lights
traffic police woman
traffic signs
traffic with light trails
tragedy
tragedy of the mind - driven
tragic
tragic wraith
traian basescu
traidic color scheme
trail cam
trail cam footage
trail camera
trail camera footage
trail of blood follows behind it
trail of smoke
trailcam
trailcam footage
trailcam image of yoda
trailer
trailer park
trailer park boys
trailer-trash lifestyle
trailing off into the horizon
trailing white vapor
trails of ink follow movements
train
train far
train in a tunnel
train station
train station background
train station explosion
train station in summer
train window
train with maroon
training
training bra
trains
trains in the background
trainwreck
trakovsky greatest scene
tram
trampled
trampling an ancient city
trampling over pyongyang
trams
trams ) ) )
tran ross
trance
trance chaos
trance compilation cd
trance music
tranding on art station
tranding on pxiv
tranquil
tranquil scene
tranquility
tranquility of the endless stars
tranquillity
trans rights
transcendance
transcendence
transcendent
transcendental
transcending to a higher plane
transformation
transformation portrait
transformation scene
transformation scene-h 768
transformation sequence
transformer
transformer robots (2005)
transformer robots 2005
transformers
transformers : the last knight
transformers armada
transformers armada!!!!!
transformers beast wars
transformers cinematic universe
transformers fall of cybertron
transformers g1
transformers generation one
transformers movie style tech
transformers tech
transformers toy
transformers toys
transformers war for cybertron
transformers!!!!!!!!!!!!!
transforming
transforming into his final form
transforming into werewolf
transgender
transhuman
transhumanism
transhumanist
transhumanist dancing
transhumanist evolution
transhumanist hydration
transient
transistors
translucence
translucency
translucent
translucent algae ornate
translucent and slimy flesh
translucent body
translucent crystals
translucent cube
translucent dress
translucent eggs
translucent gills
translucent glass shine
translucent glowing jellyfish
translucent grapes
translucent grapes closeup
translucent greebles
translucent leaves
translucent material
translucent microchip ornate
translucent mushrooms
translucent neon
translucent neon skin
translucent orbs
translucent overtones
translucent pastel panels
translucent roses ornate
translucent silky dress
translucent skin
translucent sphere
translucent sss
translucent sss xray
translucent stone white skin
translucent white skin
translucent wings
translucid
translucid luminescence
translucid. biomechanical cyborg
translucidluminescence
transluscent
transluscent neon
transmetal
transmetal ii
transmetropolitan
transmitters on roof
transmutation
transparencies
transparency
transparency glass
transparent
transparent background
transparent backround
transparent black windshield
transparent body
transparent building
transparent carapace
transparent celestial light gels
transparent cloth
transparent corrugated glass
transparent crystal
transparent crystals
transparent droplets
transparent face
transparent feathers
transparent ghost screaming
transparent glass
transparent glass surfaces
transparent glass vase
transparent glass woman
transparent glasses
transparent goo
transparent gray dress
transparent gray skirts
transparent holographic being
transparent jellyfish
transparent labs
transparent liquid
transparent marble butterfly
transparent puffer jacket
transparent skin
transparent smoke from hell
transparent veil
transparent vibrant glowing skin
transparent water
transparent wings
transplanted hand to head
transport design
transportation design
transportation design render
transylvania
transylvanian castle
transylvanian folk art
trap
trap made of leaves
trapped ego
trapped egos in physical reality
trapped in my conscious
trapped in tall iced tea glass
trapped in the backrooms
trapped inside an hourglass
trapped on a hedonic treadmill
trapper keeper
traps
trash
trash ; basement
trash bag on head
trash barrels
trash can
trash polka
trash scattered everywhere
trash style
trauma
travel
travel ad
travel and adventure
travel guide
travel in time
travel photography
travel poster
travel upwards
traveler
travelers
travelers walking the streets
traveling clothes
traveling in france
traveling into a blackhole
traveling long dirt road
traveling through the mountains
traveling through time
traveller
traveller rpg
travellers
travelling across the stars
travelling through misty planes
traverse
traversing a shadowy city
travis bickle has a mohawk
travis charest
travis charest style
travis fimmel as a jedi master
travis scott
travis scott flying over earth
travis touchdown
treacherous road
treading above calm water
treading on calm water
treant
treasure
treasure artifact
treasure background
treasure chest
treasure chests
treasure hoard
treasure island
treasure on walls
treasure planet
treasure planet color scheme
treasure room
treasures
treasures of gold
trecking
tredning on art station
tree
tree and plants
tree bark texture
tree branches intertwine limbs
tree druid
tree ferns
tree house
tree in a galaxy made of stars
tree in the background
tree kangaroo
tree of life
tree of life brains
tree of life inside the ball
tree of life seed of doubt
tree palms in background
tree roots
tree stump
tree stumps
tree town
tree woodland atmosphere
tree's
tree-lined path at sunset
tree; on the tennis coat
treebeard
treehouse
trees
trees and bushes
trees and cliffs
trees and flowers
trees and pines everywhere
trees and plants
trees and stars background
trees around
trees bent over river
trees bent over the river
trees cast shadows on the wall
trees growing on its body
trees in background
trees in foreground
trees in the background
trees in the grassy hills
trees outside
trees reflecting on the lake
trees swaying to the beat
trees with faces
trees with lots of leaves
trees!!
trees. matte painting
trees. wide view
trekking in a forest
tremella - fuciformis
tremella fuciformis
tremella-fuciformis
tremendous mecha robot
tremors
trench coat
trench coat and suit
trench coat with many pockets
trench crusade
trench sandbags in background
trench warfare
trenchcoat
trenches
trenches bombs
trend
trend in art station
trend in behance hd
trend of artistation
trend on behance 3 d art
trend on behance 3d art
trend on behance illustration
trend on dribbble
trendin on artstation
trendind everywhere
trending
trending anime art
trending anime artwork
trending arstationhq
trending art
trending art station
trending artgerm
trending artistic art
trending artstaion
trending artstaition
trending artstation
trending artstayion
trending artwork
trending at art station
trending at cgstation
trending at gitmo
trending cgsociety
trending digital art
trending digital fantasy art
trending dribbble
trending dribble
trending in art station
trending in artsation
trending in artstation
trending in behance
trending in category epic
trending in cgsociety
trending in instagram
trending in japan
trending in pinterest
trending on
trending on 5 0 0 px
trending on 500 px
trending on 500px
trending on 8k
trending on aetstation
trending on arstation
trending on arstation w- 1024
trending on art
trending on art - sation
trending on art - station
trending on art station
trending on art-sation
trending on art-station
trending on artbreeder
trending on artforum
trending on artisation
trending on artist
trending on artistation
trending on artlist
trending on arts
trending on artsatio
trending on artsation
trending on artsstation
trending on artstaion
trending on artstarion
trending on artstartion
trending on artstatio
trending on artstatioin
trending on artstation
trending on artstation 4k
trending on artstation hd
trending on artstation hq
trending on artstation pixiv
trending on artstation.'
trending on artstation:3
trending on artstationh
trending on artstationhq
trending on artstation”
trending on artstaton
trending on artstatoon
trending on artstattion
trending on artstion
trending on artstration
trending on artststion
trending on artsy
trending on atrstation
trending on attestation
trending on bbwchan
trending on cgartist
trending on cgisociety
trending on cgsociety
trending on cgsociety art
trending on cgstation
trending on cgtalk
trending on character design
trending on cnn
trending on concept art
trending on conceptartworld
trending on derpibooru
trending on devian art
trending on devianart
trending on deviant art
trending on deviantarthq”
trending on dezeen
trending on dribbble
trending on dribbble.com mascot
trending on e 6 2 1
trending on etsy
trending on facebook
trending on fiverr
trending on flicker
trending on furaffinity
trending on gc society
trending on getty
trending on hollywood reporter
trending on imagestation
trending on imdb
trending on instagram
trending on instagram!
trending on interfacelift
trending on itch. io
trending on juxtapoz magazine
trending on kickstarter
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trending on mad magazine
trending on markets
trending on marvel
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trending on pinterest. com
trending on pinterest，maximalist
trending on pintrest
trending on pixart
trending on pixart”
trending on pixv
trending on pxiv
trending on r / mapporn
trending on r/mapporn
trending on r/streetwear
trending on r/techwearclothing
trending on saatchi art
trending on sketchfab
trending on social media
trending on society6
trending on spotify
trending on studio ghibli
trending on teemill
trending on textures. com
trending on tiktok
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trending on unreal 5
trending on unreal engine
trending on unreal engine 5
trending on vehicle design
trending on vsco
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trending on youtube
trending on zbrush
trending onstudio ghibli
trending photo
trending pinterest
trending top art
trending wallpaper
trending ，
trends at art station
trendsetter
trendy
trendy clothes
trendy food
trendy typography
trendy white clothing
trent parke
trent reznor
trevor brown style
trevor carlton
trevor henderson
trevor phillips
trex
trex dinosaur
trex from godzilla (2014)
trex from godzilla 2014
tri - gun
tri - x
tri - x 4 0 0
tri - x 4 0 0 tx
tri - x 400
tri - x 400 tx
tri - x film
tri - x pan
tri - x pan stock
tri-x
tri-x 400 side lighting
tri-x 400 tx
tri-x iso 400 film
triad of muses
triade color scheme
triadic
triadic chrome shading
triadic color
triadic color grading
triadic color palette
triadic color scheme
triadic colors
triadic colours
triangle
triangle inside circle
triangle makeup
triangle shape face
triangle shards
triangle to use spell
triangles
triangles in background
triangular elements
triangular face
tribal
tribal ancient imagery
tribal armor
tribal art
tribal clothing
tribal dance
tribal face paintings
tribal facepaint
tribal jewelry
tribal makeup
tribal mask
tribal mask inside mask
tribal masks
tribal paint
tribal patterns
tribal piercing and tatoos
tribal psychedelic
tribal red atmosphere
tribal sleeve left arm
tribal style
tribal tattoo
tribal tattoos
tribal tattoos right arm sleeve
tribal yurta
tribalism
tribalistic sci-fi
tribals
tribarrel
tribbles
tribe huts in the jungle
tribe members attacking
tribe members chasing
tribute to life
triceratops
trichromatic colors
trick
trick or treat
trickling water
trickster
tricky
tricolor background
tricorn hat
tricorne hat
trident
trident metal crown
triforce
trigger
trigger anime artstyle
trigun
trilliant
trim
trimmed
trimmed beard
trimmed with a white stripe
trinidad scorpion
trinity
trinity from the matrix
trinity matrix
trinity the matrix
trinkets
trio
trip
trip to legnica
triple h
triple six
triple white colorway
tripmachine
tripmachines
tripod
trippie redd
tripping
tripping on lsd
trippy
trippy art
trippy colors
trippy fractals
trippy mood
trippy mushroom
trippy vibrant colors
trippy visuals
trippy!!! symbolic
trippysymbolic
triptych
tripwire
tripy
trireme
triss merigold
triss merigold cosplay
tristan eaton
tristan eaton & greg rutkowski
tristan eaton's wallpaper
tristan eatons wallpaper
tristram
tritone
triumph
triumphant
triumphant fate
triumphant pose
triumphantly
troika games
trojan war
troll
troll face
trollface
trolls
tron
tron angel
tron legacy
tron legacy jesus
tron legacy jesus christ
tron legacy setting
trooper
troops searching the area
trophy truck
tropic climate
tropic plants and flowers
tropical
tropical atmosphere
tropical background
tropical beach
tropical beach paradise
tropical bird feathers
tropical birds
tropical climate
tropical coastal city
tropical color scheme
tropical fish
tropical flower plants
tropical flowers
tropical foliage
tropical forest
tropical fruit
tropical houseplants
tropical island
tropical jungle
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tropical leaves
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tropical location
tropical mood
tropical ocean
tropical palms
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tropical plants
tropical pool
tropical reef
tropical sea creatures
tropical setting
tropical style
tropical trees
tropical undertones
tropical vegetation
tropical vibe
tropical wood
tropicalism
tropics
trough the night
trout in pants
trout sticking out of pants
truck
truck racing into camera
trucker hat
trucks
true
true anatomy
true anatomy!
true color
true colors
true detective
true evil
true goth
true life
true love
true proportions
true realistic image
true romance
true to life
true unsimulated emotions
true-to-life
trulli
trump
trump as a minion
trump hair
trump in rdr2
trump kissing putin
trump praying in mosque
trump tower
trump training pikachu
trump with a gun
trumpet
truncated snout under visor
truss building
trustworthy
trustworthy eyes
truth
trying to escape
trying to read
trying to ride it
trying to study
trypophobia
trypophobia acne face
tryptamine
très détaillé
tshirt
tshirt design
tshirt!!
tsuaii
tsubasa chronicles
tsubasa hanekawa
tsubasa nakai's style
tsugumi ohba
tsukasa dokite
tsukasa hojo
tsunade from naruto
tsunami
tsunami behind him
tsurime eyes
tsuruda
tsurumaki kazuya
tsuruta kenji
tsutomo nihie
tsutomu kitazawa
tsutomu niehi
tsutomu nihei
tsutomu nihei art
tsutomu nihei gustav klimt
tsutomu nihei style
tsuyoshi nagano
tuareg
tuba
tuba knight
tube wave
tube-top dress
tubes
tubes and cables
tubes and gauges
tubes fused with the body
tubing
tubular creature
tucked out of view magic happens
tucker carlson
tucson arizona
tudor
tudor architecture
tuesday weld in a bubble bath
tuesday weld in a pink bikini
tuffle coat
tufted softly
tufty whiskers
tula
tulip
tulips
tulle and lace
tumbleweed
tumbleweeds
tumblr aesthetic
tumblr trending
tummy
tumors
tumours
tumultuous
tumultuous sea
tundra
tuned to a dead channel
tungstem color balance
tungsten
tunic
tuning
tunnel
tunnels
tunnels lead to different worlds
tunning
tuomas korpi and wlop
tuomas korpi bouguereau
tupac
tupac shakur
turaco morphing chicken
turban
turban of flowers
turban of vibrant flowers
turbid clouds
turbines
turbo
turbosquid
turbulence
turbulence filter
turbulent
turbulent alien vivid landscape
turbulent blood lake
turbulent lake
turbulent sea
turbulent storm clouds
turbulent water
turbulent waves
turf roof
turian
turkey
turkish and russian
turmoil
turnado
turnaround
turnaround world building
turned back to camera
turner
turner piranesi
turner whimsical
turner. piranesi. macro lens
turner. symmetrical
turning her head and smiling
turning yellow
turnip hair
turntables
turntablism dj scratching
turntablist
turqouise
turquoise
turquoise and orange
turquoise and pink lighting
turquoise and venetian red
turquoise blue face
turquoise color scheme
turquoise eyes
turquoise gold details
turquoise gradient
turquoise hair
turquoise horizon
turquoise jewelry
turquoise ocean
turquoise palette
turquoise pink and green
turquoise pink and yellow
turquoise rust
turquoise water
turrets
turtle
turtle shell
turtle warrior
turtle. pacific northwest coast
turtles
turtles all the way down
tuscany hills
tusks
tutorial
tutu
tuxedo
tuxedo cat
tv
tv capture
tv color test pattern
tv commercial
tv footage
tv frame
tv news
tv production
tv program
tv screens
tv screens in background
tv series
tv set
tv show
tv show still
tv static
tv still
tv still frame
tv studio
tv tokyo 2010s anime series
tv!
tvn
tvp
tvs
twas brillig
twee
tweed colour scheme
twelve arms
twelve-dimensional
twen magazine
twenty-dimensional
twice
twiddle
twiddle a plopple
twiddle a twoddle
twigs
twilight
twilight ; digital oil painting
twilight ; digital painting
twilight ; wide shot
twilight city on the background
twilight cool light
twilight in the city
twilight junkyard
twilight lighting
twilight princess
twilight ray
twilight sky
twilight skyline
twilight sparkle
twilight zone
twilight zone background
twin motion
twin peaks
twin peaks art
twin peaks movie poster
twin peaks movie poster art
twin peaks poster art
twin peaks poster artwork
twin peaks style
twin souls
twin towers
twink
twinkling and spiral nubela
twinkling distant stars
twinkling stars
twinmotion render
twins
twins playing video games
twintails
twintails hairstyle
twintails white_gloves
twirling
twirling glowing sea plants
twirling smoke trail
twirling smoke trails
twirls
twirls and curves
twirly
twist of time
twisted
twisted braid
twisted braids
twisted dark lucid dream
twisted energy flow
twisted flux
twisted gardens
twisted giant tower
twisted god with no face
twisted ivy vines
twisted metal
twisted rays
twisted shapes
twisted tentacles
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twisted trunk
twisted turn of fate abstraction
twisted vines
twisted waterway
twisted wire
twisted withering vines
twister
twisting
twisting leaves
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twisting streets
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twisting vapour
twisty
twitch emote
twitch streamer
twitch streamer / gamer ludwig
twitch tv
twitching and writhing
twitter
twitter pfp
twlight
two
two - handed sword
two - tone
two aboriginal elders
two aliens around
two anime handsome men
two arms
two arms and to legs
two arms that have sharp claws
two beautiful anime girls
two beautiful women in love
two black eyes
two blue braids
two braids
two buddies sitting in a room
two cats
two characters
two color hair
two colors
two cranes flying across the sun
two crutches near bench
two cups of coffee
two deer wearing suits
two different characters
two dogs
two exhausted
two eye art
two eyes
two eyes half closed
two fangs
two figures
two figures wearing gas masks
two finnish lapphunds
two frail
two giant towers
two girls
two hands
two hands reaching for a fish
two handsome men
two hang
two headed cat
two heads
two heads one body
two horns
two horns on the head
two hovering twin nuns
two identical symmetrical eyes
two in the void
two japanese schoolgirls posing
two kittens sleeping in a basket
two knights gauntlets
two large horns on the head
two legged with clawed feet
two legs
two legs two arms one head
two little horn on the head
two long braids blue
two magnificent jelly fish
two male
two medium sized islands
two men
two men hugging
two men in black
two models in the frame
two moons
two moons in the sky
two moons lighting
two mountains in background
two muscular men entwined
two old people
two onion rings
two organic looking towers
two people
two perfect eyes
two piece workout clothes
two pigtails hairstyle
two planets colliding
two plastic chair behind a table
two point perspective
two pointed ears
two points perspective
two pure moons
two shallow black eyes
two skinny figures
two skinny old figures
two skinny old people
two small horn on the head
two still figures facing camera
two stories
two suns
two suns are in the sky
two swans swimming on the lake
two swords
two tone
two tone dye
two tone hair dye
two tone hairdye
two towers
two women
two women kissing at a carnival
two wooden wardrobes
two young men
two-dimensional
two-point perspective
twoddle
twoface
tx
tyftt
tyler
tyler durden
tyler edlin and jean delville
tyler edlin and natasha tan
tyler edlin fantasy art
tyler jacobson style
tyler miles lockett
tyler the creator
tyler west
tyndall effect
tyndall rays
type - 2
type design
typical
typical anime classroom
typical cryptocurrency nerd
typical russian atmosphere
typing on a holographic keyboard
typing on laptop
typographic
typographic annotation
typographical experiments
typography
tyrannosarus rex
tyrannosaurus
tyrannosaurus rex
tyrant
tyre mark
tyrell wellick
tyrion lannister
tyrus wong
tzeentch
tzimisce
tzuyu from twice
u
u trending on art station
u. h. d
u.s. national security advisor
uav
ue
ue 4
ue 5
ue 6
ue marketplace
ue4
ue5
ue5 render
ue6
ueshiba riichi
ufc
ufo
ufo abduction
ufo aliens
ufo attack
ufo flying over paris
ufo in a forest
ufo in the sky
ufo landing
ufo lighting
ufology
ufos
ufos in the sky
ufotable
ufotable art style
ufotable studio art style
uganda knuckles
ugly
ugly appearance
ugly art
ugly face
ugly highly detailed
ugly look
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ugly monster
ugly vibrant colors
ugly woman
ugly!!!!!!
uhd
uhd - resolution
uhd 4 k
uhd 4k
uhd 4k image
uhd 4k image”
uhd 8 k
uhd 8 k cryengine
uhd 8k
uhd 8k cryengine
uhd 8k hidden message
uhd candid photo of dirty
uhd character details
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uhd hdr
uhd hyperdetailed
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uhd image
uhd photography
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uhd realistic faces
uhd resolution
uhd uhd uhd
uhd upscale
uhd ”
uhdr
uhd”
uhq
uhq scan
ui
ui and ux
ui card
ui design
ui intricate
uk
ukio-e style
ukiuo-e and studio ghilbi style
ukiyo
ukiyo - e
ukiyo - e art
ukiyo - e painting
ukiyo - e sculpture
ukiyo - e style
ukiyo e
ukiyo-e
ukiyo-e art
ukiyo-e print
ukiyo-e style
ukiyo-style
ukiyoe
ukiyoe painting
ukiyoe style
ukraine
ukraine. photography
ukraine. professional photo
ukrainian
ukrainian flag on the left side
ukrainian girl
ukrainian monk
ukrainian national clothes
ukrainian national costume
ukulele
ultima moongate
ultimate
ultimate collab
ultimate detail
ultimate fighting championship
ultimate octane render
ultra
ultra - detail
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ultra - futuristic architecture
ultra - hd
ultra - high detail
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ultra - high jump
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ultra - realistic faces
ultra - realistic painting
ultra - vivid
ultra - wide angle
ultra - wide shot
ultra - wide view
ultra 4 k
ultra 4 k concept turnaround
ultra 4k
ultra 4k concept turnaround
ultra 8 k
ultra 8k
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ultra _ realistic
ultra accurate
ultra ambient occlusion
ultra beautiful
ultra beautiful face
ultra clear
ultra clear and sharp focus
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ultra clear detailed
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ultra deep fog
ultra definition
ultra dense fog
ultra detail
ultra detail. digital painting
ultra detaild
ultra detailed
ultra detailed 16k
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ultra detailed 8k
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ultra detailed content : face
ultra detailed digital art
ultra detailed face
ultra detailed face and eyes
ultra detailed faces
ultra detailed fantasy
ultra detailed female android
ultra detailed game art
ultra detailed haunted house
ultra detailed hdr 8 k
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ultra detailed intricate ornate
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ultra detailed pen sketch
ultra detailed pencil art
ultra detailed pencil drawing
ultra detailed photo
ultra detailed photograph
ultra detailed portrait
ultra detailed rain drops
ultra detailed storm clouds
ultra detailed symbolism
ultra detailed textures
ultra detailed trees
ultra detailed water
ultra detailed wire decoration
ultra detailed-i
ultra detailed. style of arrival
ultra detailed”
ultra detailled
ultra details
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ultra enhanced faces
ultra facial detail
ultra fine colored inking lines
ultra fine detail
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ultra fine inklines
ultra futuristic
ultra glossy
ultra graphic settings
ultra graphics
ultra hd
ultra hd 4 k
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ultra hd anime wallpaper
ultra hd detaling
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ultra hd wallpaper
ultra hi resolution picture
ultra high contrast
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ultra humorous illustration
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ultra long lens
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ultra low poly modelling
ultra mega detailed
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ultra minimalistic
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ultra phonk
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ultra realistic ar 16:9
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ultra realistic soft painting
ultra realistic vfx
ultra realistic ”
ultra realistic!!!
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ultra realistic”
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ultra shallow depth of field
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ultra sharp - - ar 1 6 : 9
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ultra very long shot
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ultra violet highlights
ultra wide
ultra wide 1 0 mm
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ultra wide angle horizon
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ultra wide camera
ultra wide gameplay screenshot
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ultraclear intricate
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ultrafine detail
ultrafine detail ”
ultrafine detailed
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ultraman
ultramarine
ultramarine blue
ultramarine blue and gold
ultramarine metal
ultramarine space marine cat
ultraphotorealistic
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ultrarealistic octane render
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ultrarealistic photograph
ultrarealistic photography
ultrarealistic raytracing
ultrarealistic sweet bunny girl
ultrarealistic uhd faces
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ultrasharp
ultrasharp detail
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ultrastation hq
ultraterrestrial dragons
ultraviolet
ultraviolet and neon colors
ultraviolet colors
ultraviolet light
ultraviolet photography
ultraviolette color scheme
ultrawide
ultrawide angle
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ultrawide cinematic
ultrawide cinematic 3d render
ultrawide establishing shot
ultrawide image
ultrawide landscape
ultrawide lens
ultrawide lense
ultrawide lens”
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ultrawide shots
ultrawide watercolor
ultron
uluru
ulzzang
uma thurman
uma thurman in kill bill
umami
umamusume
umanosuke iida
umaru-chan
umber color scheme
umbra
umbrakinesis
umbrella
umbrella under a heavy rain
umbrellas
umineko
un and michael whelan
un dia de paz
un made bed
un meeting at the hague
un restaurant avec une terrasse
unarmed combat
unattractive
unaware of your existence
unaware of your tiny existence
unbalanced
unbearable anxiety
unbeatable coherency
unbeatable quality
unbelievable
unbelievably beautiful
unbiased render
unblur
unborn soul
unbothered
unbroken
uncanny
uncanny and scary atmosphere
uncanny atmosphere
uncanny creepy liminal space
uncanny smile
uncanny valley
uncanny valley!
uncanny valley!!!
uncany but fascinating
uncaring
uncertain look
uncharted
uncharted 4
uncharted 4: a thief's end
uncharted 4: a thiefs end
unclad
unclad against the storm
uncle aloysius
uncle sam
unclear
unclipped fingernails
uncluttered
unco corporate banner
uncomfortable
uncomfortable and anxious
uncomfortable crooked smile
uncompressed
uncompressed png
unconditional love
unconnected
unconscious
unconscious design
uncrop
uncropped
uncut
undead
undead cyborg head
undead facial features
undead knight
undead lich
undead mage
undead ronald reagan zombie
undead skeleton king
undead soldiers in background
undead winged hussar
undead. highly detailed
under a dark cloudy sky
under a full moon
under a gray foggy sky
under a microscope
under a night sky
under a pale dead moon
under a sea of stars
under a shower
under a spotlight
under a technicolor sky
under a waterfall
under artillery fire
under blue
under blue clouds
under bridge
under construction
under glass dome
under heavy fire
under large green umbrella
under light
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under outer world jungles
under rain
under repairs
under sakura tree
under street lamp
under studio lighting
under the moon
under the ocean
under the sea
under the silent night sky
under the soft shadow of a tree
under the stars
under water
under water deep sea laboratory
under water scenery
under water swimming
under water visual distortion
under waterfall
underbite
underbody
underbrush wash
undercut
undercut haircut
undercut hairstyle
underdeveloped
underdimensional
underexposed
underexposed grey
underexposed lighting
underexposed photography
underground
underground art
underground box office hit
underground bunker
underground cave
underground cavern
underground city
underground facility
underground lab
underground lake
underground metro
underground mine
underground party
underground room
underground scene
underground temple
underground tunnel
underground!!!!
underlight
underlit
underlying sense of dread
underneath the stars
underpainting
underrated
undersea
undersea environment
undersea temple
undersea temple with fish
underside
underside of a fox paw
understated
understated aesthetic
undertailed
undertale
underwater
underwater background
underwater bubbles
underwater bubbles background
underwater camera
underwater caustics
underwater city
underwater combat scene
underwater crystal caverns
underwater crystals
underwater edinburgh castle
underwater environment
underwater explosion
underwater face
underwater frog perspective
underwater glittering river
underwater glow
underwater home
underwater in the ocean at night
underwater ink
underwater ink env
underwater landscape
underwater leviathan
underwater life
underwater light rays
underwater lights
underwater looking up
underwater market
underwater mushroom forest
underwater ocean
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underwater photo
underwater photograph
underwater photography
underwater plants
underwater polaroid photo
underwater psychedelic smoke
underwater scene
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underwater sea dragon
underwater sea dragon full body
underwater sea monster
underwater shot
underwater shrine
underwater smoke
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underwater statues
underwater temple
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underwater westminster
underwater with coral and fish
underwater world
underwear ad
underworld
underworld boss
undulating nebulous clouds
unearthly
unearthly art style
unearthly gears
uneasy
uneasy atmosphere
unedited
uneven
uneven dense fog
uneven fog
uneven glass apple in the dark
unexpected
unexpected glitch art
unexpected windfall
unexplained phenomena
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unfathomable horror
unfinished
unfinished canvas
unfinished roots of white sand
unfocus
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unga bunga
unhappy
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unholy ceremony
unholy gathering
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unibrow
unicorn
unicorn flying in the sky
unicorn from the tusk movie
unicorn horn
unicorns
unicron trilogy
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uniform
uniform background
uniform off - white sky
uniform plain sky
uniform teeth
unigine render
unimaginable
unimaginable composition
unimaginably huge
union jack
unique
unique and intricate painting
unique architecture
unique architecture!
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unique hair designs
unique hairstyle
unique hairstyles
unique heads
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unique pot made for houseplants
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united kingdom
united kingdom flags
united nations
united states
united states air force
units
unity
unity 2 d
unity 2d
unity 3 d
unity asset store
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unity3d
universal
universal background
universal horror movie
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universe background
universe in a grain of sand
universe in the background
universe in the sky
universe life significance
university
unkempt
unkempt beard
unkempt hair and beard
unkept hair
unknowingly walking over you
unknown
unknown artist
unknown artstyle
unknown location
unknown pleasures
unknown space
unknown technology
unknown title
unknown zodiac sign
unkown year
unlife
unlike anything on earth
unlikely hero
unlimited blade works
unlimited detail
unlimited juice
unlimited potential
unlit
unmasked
unmistakably kenyan
unnatural
unnatural grin
unnatural lighting
unnatural movement
unnatural shapes
unnerving
unnerving anxiety
unnerving mist
unobstructed road
unopened
unorthodox
unpleasant face
unprocessed colors
unpublished photo of ufo
unrea 5
unreadable
unreadable text
unreal
unreal - engine
unreal 3 d
unreal 4
unreal 4 screenshot
unreal 5
unreal 5 8 k depth of field
unreal 5 engine
unreal 5 engine highlly render
unreal 5 engine render
unreal 5 engine rendered
unreal 5 render
unreal 5. rpg portrait
unreal 58k depth of field
unreal 6
unreal 6 breathtaking detailed
unreal detail
unreal engin
unreal engin 5
unreal engine
unreal engine - h 7 6 8
unreal engine 3
unreal engine 3 d
unreal engine 3 d render
unreal engine 3 d rendering
unreal engine 3d
unreal engine 3d render
unreal engine 3d rendering
unreal engine 4
unreal engine 4 k
unreal engine 4 render
unreal engine 4k
unreal engine 4k wallpaper
unreal engine 5
unreal engine 5 - w 1024
unreal engine 5 4 k
unreal engine 5 4 k uhd image
unreal engine 5 4k render
unreal engine 5 8 k rendering
unreal engine 5 : :
unreal engine 5 demo
unreal engine 5 detail
unreal engine 5 digital art
unreal engine 5 environment
unreal engine 5 full rendering
unreal engine 5 hdr
unreal engine 5 highly rendered
unreal engine 5 lighting
unreal engine 5 lumen
unreal engine 5 quality
unreal engine 5 quality render
unreal engine 5 render
unreal engine 5 render 16k
unreal engine 5 render dramatic
unreal engine 5 rendered
unreal engine 5 renderered
unreal engine 5 rendering
unreal engine 5 showcase
unreal engine 5 tech demo
unreal engine 5 »
unreal engine 54k
unreal engine 54k render
unreal engine 54k uhd image
unreal engine 58k rendering
unreal engine 5”
unreal engine 6
unreal engine 7
unreal engine 8 k
unreal engine 8k
unreal engine 9
unreal engine : : rave makeup
unreal engine ;
unreal engine ; romantic theme
unreal engine art
unreal engine art 5
unreal engine character art
unreal engine cinema4d
unreal engine cinematic
unreal engine cinematic smooth
unreal engine fantasy art
unreal engine film still
unreal engine generation
unreal engine hd render
unreal engine highly rendered
unreal engine lumen
unreal engine octane
unreal engine octane render
unreal engine quality
unreal engine realistic render
unreal engine render
unreal engine render + a goddess
unreal engine render concept art
unreal engine render saint seiya
unreal engine render style
unreal engine rendered
unreal engine rendering
unreal engine rendering 4 k
unreal engine rendering 4k
unreal engine retro film still
unreal engine screenshot
unreal engine style
unreal engine tech demo
unreal engine ”
unreal engine!
unreal engine'
unreal engine. 4 k
unreal engine. 8 k
unreal engine. film still
unreal engine. retro film still
unreal engine. sigma 35mm
unreal engine5
unreal engine: .2
unreal engine: 2
unreal enginedigital painting
unreal engineer 5
unreal engines
unreal engine”
unreal enging
unreal maya
unreal octane render
unreal post processing
unreal quality
unreal render
unreal rendered
unreal tournament
unreal ungine 5
unreal vision
unreal with on gradient
unreal-engine
unreal5
unrealengine
unrealengine 4
unrealengine 5
unrealengine octane render
unrealengine5
unrealistic
unrealistic character concept
unreality
unrefined gold nugget
unrefined sparkling gold nugget
unreleased
unrequited love
unsanitary
unsaturated
unsaturated and dark atmosphere
unsaturated colors
unsettling
unsettling atmosphere
unsettling creepy liminal space
unsettling found footage
unsettling grin
unsettling image
unsettling images
unsettling look in the eyes
unsettling moody vibe
unsettling photo
unshaded
unsharp mask
unshaven
unsheathing her katana
unsimulated real emotions
unskilled
unspeakable horrors
unsplash 4k
unsplash contest winning photo
unsplash photo contest winner
unsplash photography
unsplash transparent
unsplash transparent fractal
unstirred paint
untethered stelae
untextured
untitled
untitled mixed media
untouchable
untra realistic
unused design
unused sticker sheet
unusual
unusual beauty
unusual color palette
unusual composition
unusual place
unusually attractive
unusually unique beauty
unwind
unwind!
unzoom
up
up close
up close image
up close picture
up close portrait of mr bean
up close shot
up close shot shinji aramaki
up face with 1 9 2 0 s hairstyle
up face with 1920 s hairstyle
up light
up shot
up there
up to the elbow
up-angle view
up-close
upa style
upbeat
upclose
upcycled
update
updo
upfront
upgrade to max
uplifting
uplifting mood
uplight
uplighting
uplit
uploaded
upon a peak in darien
upon the clouds
upper and lower body
upper body
upper body 2d game avatar
upper body and face
upper body avatar
upper body close - up
upper body close up
upper body face shot
upper body image
upper body only
upper body portrait
upper body shot
upper body visible
upper body!!
upper half portrait
upper torso
upper torso included
upper volumetric lightning
uppercase letter
upright
upscale
upscale photo
upscaled
upscaled to 4 k
upscaled to 4k
upscaled to high resolution
upset
upset expression
upset the coffee does not work
upsetting
upshot
upside - down
upside - down building
upside down
upside down stranger things
upside-down
upsidedown
upturned nose
upward angle
upward cinematic angle
upward flowing long hair
upward perspective
upward shot
upwards
upwards perspective
uranium
uranus
urban
urban architecture
urban art
urban art style
urban atmosphere
urban background
urban city at background
urban city background
urban city photography
urban clothing
urban concept art
urban decay
urban dystopia
urban environment
urban exploration
urban exploring
urban fantasy
urban fantasy romance book cover
urban fantasy setting
urban fantasy style
urban girl fanart
urban graffiti
urban graffiti banksy
urban horror
urban horror aesthetic
urban house
urban in background
urban jungle
urban jungle plants
urban landscape
urban motifs
urban music studio
urban patrol
urban photography
urban planning
urban playground
urban plaza
urban samurai
urban setting
urban skyline
urban street
urban style
urban surroundings
urban view in the distance
urban warfare
urbex
urbex photography
ureal engine
urine collection bag
urs fischer
ursula the sea witch
urusei yatsura
us
us airforce
us army
us court
us flag
us in a vast night
us journalism ministry photo
us soldiers
usa
usa flag
usa volleyball
usa-sep 20
usb ports
use of negative space allowed
use rule of thirds
used future
used scifi style
used tape
user interface
user interface design
users
uses c4
uses explosives
using a exoskeleton
using a macbook
using a magical tablet
using a spade
using a vr headset
using dead lion costume jacket
using fork
using his desktop pc
using leather armour with bones
using magic
using synthesizer
using the degrade technique
using watercolor
uss enterprise
uss nimitz
ussa
ussg ishimura
ussr
ussr armor
ussr flag
ussr poster
ussr suit
usual color setting
ut 4
utagawa kunisada & salvador dali
utagawa kunisada salvador dali
utah
utamaro
utamaro kitagawa style
ute osterwalder
utilitarian
utilitarian cargo ship
utility
utopia
utopia art
utopia profile
utopia sci-fi style
utopian
utopian architecture
utopian art
utopian city
utopian galaxy
utopian jungle in space
utopian looking
utopian space ship
utra realistic
utradetailed
utrecht
uttarakhand
uv
uv blacklight
uv map
uwu
uwu hi-fructose
ux
uzumaki
v
v - fin
v - ray
v - ray collection
v - ray render
v - ray render 8 k uhd
v - ray render 8k uhd
v 8 k
v from devil may cry as an elf
v ray
v tuber
v wing
v-ray
v-ray 8k uhd
v-ray collection
v-ray engine
v-ray render
va-11 hall-a
vacation
vacation photo
vaccaro
vacuum
vacuum of space
vacuum tube-punk
vacuum tubes
vader
vader as captain america
vadim voitekhovitch
vagabond
vague
vale
vale encantado
valentin serov style
valentina remenar
valerian
valeriy vegera
valhalla
valiant
valiant comics
valkyrie
valkyrie fighter jet
valkyrie style character
valle dei templi
vallejo
valley
valley in the distance
valley mist
valley of the damned background
valorant
valorant character
valorant game style
valorant style
valter de morais
values as flat shapes
valve
valve official announcement
valve promotional splash art
valves
vamp
vampira
vampire
vampire bats
vampire cat
vampire fangs
vampire fashion
vampire girl
vampire hunter d
vampire lord
vampire nun
vampire of clan lasombra
vampire portrait
vampire queen
vampire teeth
vampire the masquerade
vampire white fox
vampires
vampires fantasy
vampires in space. moebius
vampre the masquerade
van
van allsburg
van cleef & arpels
van cleef & arpels style
van cleef arpels
van dyck
van gogh and m. c. escher
van gogh and monet
van gogh art style
van gogh painting
van gogh style
van herpen
van lieven
vanara
vancouver
vandalism
vanellope von schweetz
vanessa blue
vanessa morgan
vanguardist alternate timeline
vania zouravliov
vanilla
vanilla - colored lighting
vanilla smoothie explosion
vanillaware artwork
vanishing
vanishing point
vanishing point perspective
vanishment of the gods
vanity
vannessa ives
vanta blac
vanta black
vantablack
vantablack cape
vantablack chiaroscuro
vantablack cloak
vantablack cloth technology
vantablack gi
vantablack wall
vaponpunk
vapor
vapor punk
vapor wave
vaporware
vaporware aesthetic
vaporware style
vaporwave
vaporwave aesthetic
vaporwave aesthetics
vaporwave art
vaporwave artwork composition
vaporwave background
vaporwave cartoon
vaporwave city
vaporwave color scheme
vaporwave colors
vaporwave colors!
vaporwave jungle
vaporwave lighting
vaporwave lighting style
vaporwave lights
vaporwave mall aesthetic
vaporwave mansion
vaporwave nostalgia
vaporwave ocean
vaporwave pallette
vaporwave pixel sorting
vaporwave sci - fi
vaporwave style
vaporwave style masterpiece
vaporwave sunrise background
vaporwave sunset
vaporwave surreal ocean
vaporwave textures
vaporwave wallpaper environment
vaporwave!
vaporwave. black and white
vapour
vapourwave
varadero beach
vargas
varguyart style
varia suit
variable lighting
variable lineart
variation
variations
variations around female
variations of thom yorke
varied colors
varied trees in the back
variety of shapes and textures
various action poses
various angles
various animals
various art styles
various artists
various artworks
various backgrounds
various bending poses
various colors
various emotions
various eye shapes
various eyelid positions
various hair colors and styles
various items
various lacivious seducing poses
various lighting mood
various point of view
various pose
various posed
various poses
various poses shooting photos
various refine techniques
various refining methods
various refining techniques
various scenarios
various seducing pose
various seducing poses
various sizes
various styles
various subjects
varnished
varying angles
varying art styles
varying detailed skin
varying dots
varying ethnicities
varying locations
varying thickness
vascularity
vase
vase of flowers
vase with flowers
vase work
vases and a plaster bust
vases and bottles
vasily kandinsky
vasnetsov
vass roland
vast
vast cosmos
vast empty hall
vast expanse
vast expansive landscape
vast forest
vast ice dungeon
vast landscape
vast library
vast lush valley flowers
vast mountain landscape
vast nebula
vast power
vast seas
vast wheat fields
vastayan
vastness
vastus lateralis
vat
vatican
vatican in background
vatican map room
vaudevillian
vaughan bass
vaughan ling
vault
vaulted ceiling
vcr tape
vd
vdovenko
vdragan bibin
vecna
vecna from stranger things
vector
vector aesthetics
vector art
vector art for cnc plasma
vector art illustration
vector art panel for cnc plasma
vector art style
vector artwork
vector background
vector based
vector behance hd jesper ejsing
vector design
vector drawing
vector graphic
vector graphics
vector graphics forum badge
vector graphics icon
vector graphics with clean lines
vector icon
vector illustration
vector image
vector images
vector ink drawing
vector line
vector line - art style
vector line art
vector lineal
vector logo
vector patch logo of mermaid
vector shaded anime
vector shapes
vector sharp graphic
vector spline curve style
vector sticker
vector style
vector style drawing
vector svg
vector svg art
vector technical documents
vector trace
vector tracing
vector. 8 k
vectorart
vectorial
vectorial art
vectorial curves
vectorised
vectorized
vectorized logo style
vectors
vector”
vega mask
vegeta
vegetable
vegetable foliage
vegetables
vegetables on table and candle
vegetal architecture
vegetal dragon cyborg
vegetated roofs
vegetation
vegetation and flowers
vegetation tentacles
veggies
vehicle
vehicle concept photo!!
vehicle design
vehicle illustration
vehicle photography
vehicle profile
vehicles
vehicles on fire
veil
veiled
veiled face
veiled in mist
veils and jewels
veins
veins merged feet head
veins popping out
veiny
veiny hands
vektroid
vektroid album cover
velasquez
velly distant forest
velma
velociraptor
velvet
velvet art
velvet blazer
velvet couch
velvet frock coat
velvet stockings
velvet tablecloth
velvet with lace gown
velvety
velvia
velvia 100
velvia film
vending machine
vending machines
vendors
veneno
venetian glass
venetian mask
venetian red
vengeful
venice
venice at dusk
venice biennale
venice biennale's golden lion
venice biennales golden lion
venizian era
venom
venom costume
venom dunking on spiderman
venom fangs
venom symbiote
venomfang
venomize
venomized
venomous sorcerer
vent
ventail
ventilation shafts
ventricles and aorta
ventriloquist dummy
vents
venture brothers
venus
venus de milo with arms
venus effect
venus goddess
venus godness athena
venus of urbino
venus of willendorf
venus planet symbol
venus project
venus squid astronaut
venus surface
ver.ka mecha machinarium
vera wang couture
verbena
verdant
verdant and lush and overgrown
verdant field in the foreground
verdant forest
verdant gradient
verdant green fields
verdant plant wall
verdant plants
verdant plants green wall
verdant topiary
verdigris
vereschagin
vereshchagin
vergil
vergil from devil may cry
verisimilitude
vermeer
vermeer and caravaggio
vermeer lighting
vermeer painting
vermilion
vermilion and red lake
vermillion
vermillion and cyan
vermont fall colors
vernadskys noosphere
verner panton
versace
versace pattern
versailles
versatile
versimlitude
version 3
vert coherent
vertex
vertical
vertical architecture
vertical composition
vertical eyes
vertical gardens
vertical lines
vertical movie frame
vertical orientation
vertical orientation w 832
vertical portrait
vertical symmetry
vertical vegetable gardens
vertical wallpaper
vertically flat head
vertigo
vertigo - inducing
vertigo comic
vertigo comics
vervia
very
very - high - budget anime movie
very abstract
very accurate
very accurate and detailed
very accurate coherent image
very accurate face
very accurate photo
very aesthetic
very aesthetic leather jacket
very aesthetic!!!!!!
very aesthetic!!!!!!!!!!!!!!!
very aesthetically pleasing
very ahestetic
very angry
very angry expression
very angry squid
very anime
very anime style
very appealing
very artistic
very artistic pose
very asphalt
very atmospheric
very atmospheric lighting
very attractive
very attractive and beautiful
very attractive man with beard
very award - winning
very backlit
very beautiful
very beautiful ambient light
very beautiful ambient lighting
very beautiful and elegant
very beautiful angeline jolie
very beautiful anime cat girl
very beautiful cute catgirl
very beautiful cyberpunk samurai
very beautiful digital art
very beautiful elven top model
very beautiful enga style
very beautiful face
very beautiful fantasy art
very beautiful female barbarian
very beautiful fur
very beautiful girl
very beautiful goth top model
very beautiful lighting
very beautiful long slim legs
very beautiful masterpiece
very beautiful matte painting
very beautiful photo
very beautiful portrait
very beautiful scenery
very beautiful slim legs
very beautiful style
very beautiful woman
very beautiful young woman
very beautiful!! aerial shot
very beautiful. big muscles
very bensinski
very besautiful ambient light
very big eyes
very big eyes!
very big smile
very black sky
very bloody
very blurry
very bright
very bright brown eyes
very bright lights
very bright white light
very brightening eyes
very buff
very bushy brown hair
very busy place
very calm and wholesome
very chromatic aberration
very cinematic
very clean
very clean art
very clear
very clear face
very clear image
very clear picture
very clear view
very close detailed closeup
very close shot
very close to real nature
very close up foot pov shot
very close up foot shot
very closeup
very cloudy sky
very coherent
very coherent artwork
very coherent composition
very coherent image
very coherent stylized artwork
very coherent symmetrical
very coherent symmetrical work
very coherent symmetry
very coherent. cinematic
very coherent. high detail
very cohesive
very cohesive and vibrant
very colorful
very colorful heavenly
very colourful
very comfy
very comfy]
very complex
very consistent
very consistent bezier curves
very convincing
very cool
very cool pose
very cozy
very creepy
very crisp
very crisp details
very crisped colors
very crispy
very crowded
very curvy
very cute
very cute and childlike
very cute features
very cute purple dragon
very cute robot zen
very dark
very dark ambiance
very dark and abandoned
very dark background
very dark blue eyes
very dark brown skin!
very dark cave
very dark environment
very dark lighting
very dark night time
very dark room
very dark shadows
very dark with green lights
very deep
very deep depth of field
very deep sea
very deep stillness atmosphere
very defined
very design
very detaied
very detail
very detaile
very detailed
very detailed 4 k
very detailed 4k
very detailed 8 k
very detailed 8k
very detailed and beautiful face
very detailed and colorful
very detailed and high quality
very detailed and intricate
very detailed and realistic
very detailed and rich clothing
very detailed and sharp
very detailed animal hat
very detailed art
very detailed background
very detailed backgrounds
very detailed bd cover
very detailed beautiful face
very detailed birds
very detailed black feathers
very detailed cinematic
very detailed citycape at night
very detailed concept art
very detailed curve
very detailed dark super storm
very detailed design
very detailed digital art
very detailed digital painting
very detailed emerald green eyes
very detailed eyes
very detailed face
very detailed face and ears
very detailed faces
very detailed facial features
very detailed facial structure
very detailed feel
very detailed felt plushie
very detailed fine art
very detailed fur
very detailed illustration
very detailed intricate details
very detailed intricate intaglio
very detailed isometric
very detailed labeling
very detailed leaves
very detailed medieval
very detailed oil painting
very detailed painting
very detailed paradise
very detailed photo
very detailed photograph
very detailed photography
very detailed picture
very detailed portrait
very detailed render
very detailed shading
very detailed skin
very detailed stunning deep eyes
very detailed super storm
very detailed texture
very detailed toad
very detailed torso
very detailed unreal engine
very detailed »
very detailed ”
very detailed!
very detailed!!
very detailed. 4 k
very detailed”
very detailled
very detailled face
very details
very disturbing
very dramatic
very dramatic dynamic lighting
very dramatic light
very dramatic lighting
very dramatic volumetric light
very dull colors
very dull muted colors
very dynamic
very early film stock
very elegant
very elegant & complex
very elegant features
very elongated lines
very emotional
very epic
very epic atmosphere
very ethereal
very excited
very expensive
very expressive
very expressive detailed face
very expressive face
very extremely beautiful
very faded
very famous photo
very fancy whiskey bottle
very fantastic art
very far royal steampunk castle
very fashion
very fashionable
very fat
very fine artwork
very fine brush strokes
very fine brushstrokes
very fine detail
very fine detail and texture
very fine details
very fine inking lines
very fit and extremely muscular
very flat shading
very fluffy
very foggy
very foggy mist
very full detail
very full lips
very funny
very furry
very futuristic
very fuzzy
very geometrical
very glowing eyes
very grainy
very grainy film
very grainy image
very grainy texture
very hairy
very hairy bodies
very handsome
very happy
very hazy
very heavy vignette!!!
very high angle view
very high bloom ammount
very high complexity
very high contrast
very high detail
very high detailed
very high details
very high intricate details
very high quality
very high quality face
very high quality lights
very high quality photography
very high resolution
very high resolution images
very high view
very highly detailed
very highly detailed 8k
very highly intricately detailed
very hot
very huge smile
very huge waves
very humorous illustration
very humorous oil painting
very humorous!!!
very hyper realistic
very hyperrealistic
very impressive
very inspirational
very intense
very interesting
very intricate
very intricate art
very intricate braided hair
very intricate details
very intricate masterpiece
very intricate photorealistic
very intricate ultrafine details
very kenyan
very known photo
very large
very large basil leaves
very large bosum
very large eyes. symmetry
very large scales
very light freckles
very light snow
very little moss
very long
very long and unkempt hair
very long arms
very long bangs
very long black hair
very long black/red hair
very long blue hair
very long fingernails
very long flowing dark hair
very long flowing hair
very long flowing red hair
very long hair
very long neck
very long orange hair
very long shadows
very long shot
very long shot of a windmill
very long silver beard
very long silver hair
very long snow colored hair
very long spires
very long wavy azure blue hair
very long wavy fiery red hair
very long white beard and hair
very long white cloak
very long white hair
very longshot
very low - angle
very low angle
very low angle photograph
very low angle view
very low contrast
very low energy
very low light
very low quality
very low saturation
very luminous design
very magical and dreamy
very mediocre
very melancholic
very minimal
very minimal vector art
very minimalistic
very modern anime style
very moody lighting
very muscly
very muscular
very muscular male body
very muted colors
very neat
very nerdy
very nice pastel colors
very noisy
very nostalgic
very old
very old photo
very orange
very ornamented
very ornate
very pale
very pale skin
very pale white skin
very peaceful mood
very perfect position
very photorealistic
very poetic
very poor
very poor quality of photography
very powerful
very pretty
very pretty eyes
very pretty face
very pretty model
very professional
very rainy
very real
very realistic
very realistic 3 d render
very realistic cinematic
very realistic digital art
very realistic effect
very realistic face
very realistic film still
very realistic gemstones
very realistic looking
very realistic painting effect
very realistic photograph
very realistic render
very realistic. fog
very realistic. low dark light
very red colors
very refined
very reflective
very relaxing
very romantic
very round
very round headlights
very rust
very sad
very sad c 12.0
very sad emotion
very sad face
very sad medusa
very saul goodman
very scary
very scary photo
very seductive pose
very sexy
very sexy devil outfit
very sexy outfit
very sexy pose
very sexy woman with black hair
very shallow depth of field
very sharp
very sharp and detailed
very sharp and detailed image
very sharp and detailed photo
very sharp detail
very sharp details
very sharp focus
very sharp likeness
very sharp photo
very sharp vampire fangs teeth
very sharp!!!
very short curly hair
very short dark hair
very short depth of field
very short hair
very short messy hair
very short pants
very short slicked - back hair
very silly
very silly looking
very simple
very sleepy and shy
very slightly smiling
very small brushstrokes
very small eyes
very small lips
very smoky
very smoky cyberpunk paris bar
very smoky paris bar
very smooth
very soft diffuse lights
very soft diffuses lights
very soft shadowing
very sparse detail
very strong
very strong and muscular man
very stylish
very stylish fantasy armor
very stylized
very stylized character design
very sunny
very sunny weather
very surprised
very surreal garden
very sweaty
very sweet
very symmetrical
very symmetrical body
very symmetrical face
very tall
very tall and slender
very tall ceilings
very tasty
very terrifying
very thick and wer oil paint
very thin
very thirsty
very tight corset
very tight small push up bra
very tiny
very tired
very trippy and abstract
very ugly face
very vaporwave
very vascular
very very aesthetic
very very anime!!!
very very beautiful
very very beautiful art
very very beautiful furry art
very very beautiful scenery
very very beautiful woman
very very beautiful!
very very clean
very very coherent painting
very very creepy
very very curly blond hair
very very detailed
very very detailed face
very very elegant
very very happy
very very happy!
very very high detailed
very very highly detailed
very very intricate
very very kawaii
very very long blond curly hair
very very low quality
very very low quality picture
very very pale blond hair
very very pale white skin
very very precise
very very realistic
very very roberto ferri
very very saul goodman
very very small goat
very very small owl
very very surreal
very very unsettling
very very very aesthetic
very very very beautiful
very very very beautiful art
very very very beautiful scenery
very very very beautiful!
very very very beautiful!!
very very very beautifull face
very very very creative
very very very detailed
very very very dramatic lighting
very very very epic
very very very highly detailed
very very very hr giger
very very very pale skin
very very very pale white skin
very very very realistic
very very very rich
very very very saul goodman
very very very tall
very very very ultradetailed
very very very very beautiful
very very very very beautiful!!
very very very very detailed
very very very very electronic
very very very very famous art
very very very very scary
very very very very tall
very very well detailed image
very very wide shot
very vey very detailed face
very vibrant
very vibrant colors
very warm colors
very well detailed
very wet
very wide angle
very wide angle lens
very wide angle shot
very wide angle view
very wide shot
very wide view
very wide wide shot
very windy
very wow
vesa-matti loiri
vesalius anatomy
vesper lynd
vesper lynd from casino royale
vespertine
vessels
vest
vests
vests and corsets
vetements
veterinary medical diagram
veveltaria
vexillology
vey detailed faces
vf-1s jetfire
vfx
vfx action shot
vfx art
vfx espresso
vfx film
vfx film closeup
vfx movie
vfx movie closeup
vfx particle simulation
vfx portrait
vfx portrait of
vfx powers at night in the city
vfx render
vfx scene
vfx shot
vfx spritesheet!!!!!
vfxfriday
vga
vgbftk
vhs
vhs artefacts
vhs artifacts
vhs camcorder footage
vhs colour photography
vhs copy
vhs cover
vhs distortion
vhs distortions
vhs effect
vhs effects
vhs filter
vhs footage
vhs footage of a movie set
vhs footage still
vhs found footage
vhs glitch
vhs monster high
vhs noise static
vhs overlay
vhs quality
vhs recording
vhs screencap
vhs static
vhs static overlay
vhs style
vhs tape
vhs tape footage
vhs video
vhs video effect
vhsrip
vi from arcane
vi from league of legends
vial of stars
vials
vibe
vibes
vibing to music
vibrance scheme
vibrancy
vibrancy color
vibrant
vibrant & colorful background
vibrant - c 10
vibrant 3d textures
vibrant 8k rendering
vibrant accents
vibrant aesthetic
vibrant and colorful
vibrant and dynamic
vibrant and matching colors
vibrant and powerful
vibrant and rich colors
vibrant and stylized
vibrant and vivid
vibrant and vivid color
vibrant art
vibrant atmosphere
vibrant atmospheric colors
vibrant aura
vibrant autumn colors
vibrant background
vibrant backlit
vibrant bismuth material
vibrant blue
vibrant blue sky background
vibrant bright colors
vibrant bright colours
vibrant but dreary
vibrant but dreary blue
vibrant but dreary gold
vibrant but dreary orange
vibrant but dreary red
vibrant but muted colors
vibrant cartoon art
vibrant chromatic colors
vibrant cinematic lighting
vibrant city lights
vibrant cmyk dye overpainting
vibrant color
vibrant color details
vibrant color palette
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vibrant color scheme 8k
vibrant color with gold speckles
vibrant color!
vibrant colorful background
vibrant colorful green leaves
vibrant colors
vibrant colors americana
vibrant colors hyper realism
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vibrant colors smooth gradients
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vibrant contrast
vibrant cool colors
vibrant corals
vibrant dark hair
vibrant dark mood
vibrant dark wavy hair
vibrant darkness
vibrant deep colors
vibrant deep saturated colors
vibrant digital art
vibrant digital painting
vibrant epic
vibrant expressive colours
vibrant eyes
vibrant fan art
vibrant fantasy style
vibrant feel
vibrant foliage
vibrant fractal gems
vibrant glow
vibrant gouache painting scenery
vibrant green
vibrant greenery
vibrant greenery outside
vibrant hair
vibrant high contrast
vibrant high contrast coloring
vibrant hues
vibrant in color
vibrant inkbursts
vibrant iridescent
vibrant iridescent colors
vibrant light
vibrant light color scheme
vibrant light leaks
vibrant lighting
vibrant lights
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vibrant lush neon lighting
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vibrant movie poster
vibrant muted colors
vibrant nature
vibrant nebula
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vibrant neon inks painting
vibrant neon pastel
vibrant oil painting
vibrant orange
vibrant orange background
vibrant painting
vibrant palette
vibrant pastel color palette
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vibrant patterns
vibrant people
vibrant pink
vibrant powder paints
vibrant realistic
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vibrant red
vibrant red 8k
vibrant red and green colours
vibrant red background
vibrant red colors
vibrant red hair
vibrant red hibiscus
vibrant rich deep color
vibrant scattered light
vibrant scene
vibrant setting
vibrant shading
vibrant sunrise
vibrant sunset
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vibrant teal and maroon hair
vibrant threads
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vibrant tourism poster
vibrant triadic color scheme
vibrant uplifting color scheme
vibrant vegetation
vibrant vials
vibrant vibrations
vibrant vivid colors
vibrant volumetric natural light
vibrant water river
vibrant watercolor
vibrant watercolor painting
vibrant world
vibrant. fractal gems
vibrant.-h 704
vibrant: 0.75
vibrante colors
vibrantly colored
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vibrantly lush
vibrantly lush neon lighting
vibrating
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vibration
vibrent red lipstick
vic james
vice city
vicious
vicious appearance
vicious snapping alligator plant
vicotr hugo harmatiuk
victo
victo nagai
victo nagi
victo nga
victo ngai
victo ngai and surrealism
victo ngai and tristan eaton
victo ngai cyberpunk style
victo ngai fine texture
victo ngai style
victo ngai! cmyk palette
victo ngai! muted colors
victo ngai! muted sepia colors
victo ngai. unreal engine
victo ngaimuted colors
victo ngaimuted sepia colors
victo ngairich grainy texture
victor
victor antonov
victor brauner and moebius
victor charreton
victor einrich
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victor ngai
victor prezio
victor stone
victoria
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victoria secret runway show
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victoria's secret
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victorian
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victorian alchemists
victorian architecture
victorian arcs of sand
victorian armor
victorian blue dress
victorian britain 1 8 3 5
victorian britain 1 8 3 6
victorian britain 1835
victorian britain 1836
victorian buildings
victorian castle
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victorian clothing
victorian day of the dead
victorian dress
victorian england
victorian england style
victorian era
victorian era masterwork
victorian era painting
victorian era，jellyfish element
victorian female portrait
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victorian gothic lolita fashion
victorian harbour night
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victorian playing card
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victorian textiles
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victorious on a hill
victory
victory lap
video
video - game
video animation
video camera
video compression
video footage
video game
video game art
video game asset
video game asset file
video game assets
video game avatar
video game box art
video game character
video game character art
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video game consoles
video game control
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video game cover art
video game cutscene
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video game environment design
video game fanart
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video game graphics
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video game icon design
video game item
video game render
video game screenshot
video game screenshot>
video game skill ability
video game still
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video games
video glitch
video still
videogame
videogame 3d render
videogame art
videogame asset
videogame background
videogame character
videogame concept art
videogame cover art
videogame graphics
videogame inspired
videogame poster
videogame render
videogame screenshot
videogame sprite
videogame still
videogame style
videogames
videotape still from 1985
vienna
vienna city
vienna secesion style
vienna secession
vienna state opera house
vietnam
vietnam door gunner
vietnam soldier with skateboard
vietnam war
vietnam war soldier
vietnamese temple scene
vietnamese woman
view
view above the clouds
view for miles
view from a distance
view from a news truck
view from above
view from above from plane
view from above on seascape
view from across the street
view from afar
view from back
view from behind
view from behind mirror
view from below
view from bottom
view from bottom to top
view from distance
view from eyes
view from far away
view from front
view from ground
view from ground level
view from helicopter
view from high
view from inside
view from side
view from slightly above
view from space
view from the back
view from the bottom
view from the distance
view from the eyes
view from the ground
view from the lake
view from the sea
view from the side
view from the side”
view from the sky
view from the space
view from the street
view from the streets
view from the top
view from window
view from window on megapolis
view is centered on the robot
view of forest
view of houses in amsterdam
view of sea
view of the cosmos
view of the ocean
view of the one spiral galaxy
view of the spiral galaxy
view of villages
view out of a window
view over city
view through window
view up
view(full body + zoomed out)
viewed from a distance
viewed from above
viewed from afar
viewed from behind
viewed from bellow
viewed from below
viewed from bird's-eye
viewed from earth
viewed from far away
viewed from space
viewed from the ground
viewed from the harbor
viewed from the ocean
viewed from the side
viewed from very far away
viewed in profile
viewed in profile from far away
viewed through the cars window
viewfull body + zoomed out
viewpoint is to front and left
views front side and rear
views to the ocean
vignette
vignette effect
vignette illustration
vignette of windowsill
vignette tilt-shift
vignetting
vigo the carpathian
vijay jayant props
viking
viking and templar aesthetics
viking armor
viking attire
viking beard
viking berserker
viking boat
viking city
viking culture
viking face in profile
viking god
viking heaven
viking king
viking palace
viking runes
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viking style
viking warrior
viking warrior illustration
vikings
viktor antonov
viktor antonov concept art
viktor deni
viktor orban
viktor orban drinking champagne
viktor orban in a forest
viktoria gavrilenko
vile
villa
village
village far away
village girl reading a book
village house
village in the background
village in the woods
village square
villager
villagers busy farming
villages
villages castles
villages ， unreal engine
villain
villain pose
villain wearing a red oni mask
villainess
villainess has black angel wings
villany
ville valo
villeneuve
villian
vin diesel
vin diesel as thanos
vin diesel head in egg cup
vin diesel with a tummy
vinayak
vince vaughn as jack fenton
vincent
vincent callebaut
vincent callebaut composition
vincent desiderio
vincent di fate nausicaa
vincent maréchal
vincent van gogh
vincent van gogh painting
vincent van gogh style
vincent van gogh!!!
vincenzo catena style
vincenzo riccardi
vine
vine and plants and flowers
vine art
vine bridge silhouette over lake
vine covered
vine dress
vine headdress
vine twist
vines
vines along the jungle floor
vines and blue foliage
vines and cracked wood
vines and flowers
vines and thorns
vines everywhere
vines hanging down
vines hanging from trees
vines hanging over the water
vines on the walls
vines overflowing
vines wrap around the terrarium
vines. tiffany blue
vinesauce
vineyard
vinland saga
vinny from vinesauce
vinny vinesauce
vintage
vintage - w 1 0 2 4
vintage 1950s stamp
vintage 1960 print
vintage 1960s print
vintage 6 0 s print
vintage 60 s print
vintage 60s print
vintage 7 0 s anime watercolor
vintage 70 s anime watercolor
vintage 70s comic cover
vintage 70s print
vintage 8 0 s print
vintage 80 s print
vintage 80s print
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vintage 90 s print
vintage 90s print
vintage aesthetic
vintage art
vintage art deco
vintage associated press photo
vintage aston martin
vintage camera
vintage car
vintage cars
vintage cereal box art
vintage closeup photograph
vintage clothing
vintage clothing poster
vintage color
vintage color photo
vintage colors
vintage colours
vintage colours 1 9 5 0 s
vintage colours 1950 s
vintage comic
vintage comic book style
vintage dark sci fi
vintage disney
vintage doll
vintage dress
vintage electronics
vintage european folk art
vintage fantasy art
vintage fashion
vintage film
vintage film grain
vintage film photo
vintage film poster
vintage film stock
vintage footage
vintage footage on tokyo streets
vintage frame window
vintage fridge
vintage future
vintage glass plate photograph
vintage glow
vintage gothic gown
vintage halloween postcard
vintage horror
vintage horror art
vintage illustration
vintage infrared photograph
vintage inspired
vintage levi  s ad
vintage levi ’ s ad
vintage look
vintage magazine illustration
vintage makeup
vintage movie
vintage movie poster
vintage movie shot
vintage muted colors
vintage noir
vintage old
vintage old photo
vintage painting
vintage photo
vintage photo from 1890
vintage photograph
vintage photography
vintage pilot clothing
vintage pin up
vintage polaroid
vintage polaroid photo
vintage postcard
vintage postcard illustration
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vintage poster style
vintage pulp art
vintage race footage
vintage retro
vintage retro colors
vintage retro scifi
vintage revolution photograph
vintage robotics
vintage royalty
vintage saturation
vintage sci - fi
vintage sci - fi art
vintage sci - fi poster
vintage sci - fi soft grainy
vintage sci fi
vintage sci-fi book cover
vintage sci-fi soft grainy
vintage science fiction
vintage science fiction cinema
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vintage shading
vintage shapes
vintage shirt
vintage shoujo
vintage showcase of the 60s
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vintage space station logo
vintage style
vintage technicolor film photo
vintage theme
vintage transistors
vintage typography
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vintage!
vinyl
vinyl action figure
vinyl cover from 1988
vinyl cut ready
vinyl designer toy
vinyl material
vinyl on glazing
vinyl records
vinyl toy figurine
violence
violence blood
violence in her eyes
violence in his eyes
violencia
violent
violent action
violent and vicious appearance
violent gun action
violent protest
violent stormy waters
violent west
violent west )
violent zombie dance
violently fist fighting
violet
violet and aqua neon lights
violet and black
violet and pink palette
violet and yellow sunset
violet ants
violet battlefield theme
violet beetles
violet cockroach
violet color
violet color palette
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violet colors
violet colour palette
violet coloured t-shirt
violet evergarden
violet eyes
violet flower
violet flowers
violet hair
violet lighting
violet long hair
violet myers
violet planet
violet polsangi
violet polsangi pop art
violet skin
violet sky
violet spiders
violet spike smoke
violet theme
violet tight tanktop
violet tones
violin
vip room
viral
viral image
viral on instagram
viral on twitter
viral photo
viral post
virgil abloh
virgil finlaytim hildebrandt
virgin mary
virginie ropars
viridescent
viridescent at shoulder height
viridian
viridian and venetian red
virile
virtual
virtual engine
virtual engine 5
virtual installation
virtual metaverse room
virtual reality
virtual reality headset
virtual reality metaverse engine
virtual self
virtual set
virtually no peer or precedent
virtue
virtuosic level detail
virtuosic painting
virtuoso
virus
visa pour l'image
visa pour limage
visable sounds waves
visage
viscera splattered
visceral
viscous
viscous liquid
viscous rainbow paint
viscous smoke
viscous volumetric smoke
vishnu
visible and detailed face
visible belly
visible brush strokes
visible brushstrokes
visible eyes
visible face
visible face!
visible forehead
visible from afar!!
visible head
visible head and eyes
visible holy aura
visible layers
visible magic energy
visible nervous system
visible paint layers
visible paint texture
visible planets in the sky
visible pores
visible pupils
visible rain
visible sky and humid atmosphere
visible sky and sunny atmosphere
visible smoke
visible space
visible stitching
visible stomach
visible strokes
visible texture
visible veins
visibly angry
vision
vision of chaos
vision quest
visionary
visionary art
visionary art style
visionary arts
visionary fractal structures
visionary painting
visions
visiting saturn
visor
visor covering eyes
visor eyes
visor over face
visor screen for face
vista
vista of a city at sunset
vista of futuristic city
vista view
visual
visual art style
visual contrast
visual depth
visual design school
visual development
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visual fidelity and plasticity
visual identity
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visual novel sprite
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visual storytelling
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visualisation
visuality
visualize
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visually crisp & clear
visually crisp clear
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visually stunning scene
visuals
vitalik buterin
vitaliy bondarchuk
vitals visualiser
vitals visualiser!!
vitaly bugarov
vitaly bulgarov
vitaly bulgarov and mike nash
vito acconci
vitorugo
vitruvian man
vitruvian mecha
vitruvian woman
vittorio matteo corcos
viutiful joe
vivacious
vivarium
vivd
vivd colour
vivd details
vivec city
vivec robot
vivian westwood
vivic colors
vivid
vivid - colors
vivid 8 k
vivid 8k
vivid abstract landscape
vivid and balanced
vivid and colorful
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vivid and vibrant colors
vivid atmospheric lighting
vivid attention to detail
vivid background
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vivid blue eyes
vivid bright colors
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vivid broken glass
vivid cartoony colors
vivid caustics
vivid cinematic lightning
vivid coloes
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vivid color glow
vivid color hues
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vivid colorful comic style
vivid colorful comics style
vivid colors
vivid colors anatomical
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vivid colors!
vivid colors!!
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vivid colours. sharp focus. wow!
vivid colrs
vivid complementary colors
vivid composition
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vivid eyes
vivid flower crown
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vivid green lasers
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vivid illustration
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vivid imagery
vivid landscape
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vivid lights night time
vivid lines
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vivid painting
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vivid rainbow of colors
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vivid sky
vivid steampunk concept
vivid studio light
vivid tentacles
vivid tones
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vivid vegetation
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vivid vintage coloring
vivid!!
vivid)
vivide colors
vividly beautiful colors
vividly enhanced
vivienne westwood
vivienne westwood!
vivy
vixen
vladimir abat-cherkasov
vladimir krisetskiy
vladimir lenin
vladimir motsar
vladimir nikolov
vladimir pchelin
vladimir putin
vladimir putin amiibo
vladimir putin awe face
vladimir putin in minecraft
vladimir putin rides a unicorn
vladimir volegov
vlc screenshot
vlop
vman magazine
vmk myvmk
vocal
vocal tract model
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vodka
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vogue issue january
vogue italia
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void
void 1a
void eyeballs
void manifold
void of darkness
void of space
void vortex
voidless of the festival
voidless of the festival!
voidstar
voigtlander 3 5 mm
voigtlander 35 mm
voigtländer lens
voitv
volatile
volcanic
volcanic background
volcanic embers
volcanic eruption
volcanic landscape
volcanic skeleton
volcanic workshop background
volcano
volcano background
volcano eruption
volcano exploding
volcano fog
volcano in background
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volcano landscape
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volcano texture
volcano valley
volcanoes
volcanoes in the background
volcanos
voldemort
volegov
volley court background
volleyball
volleyball net
vollumetric lighting
volodymyr zelensky at war
volodymyr zelenskyy
voltron
volume
volume aesthetic
volume clouds
volume flutter
volume fog
volume light
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volume lights
volume metric lighting
volume noise
volume rays
volume raytracing fog
volumentric lighting
volumeric ghostly rays
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volumetric
volumetric 8 k
volumetric 8k
volumetric and perfect lighting
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volumetric cinematic light
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volumetric clouds
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volumetric dust
volumetric dust rays
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volumetric evening lights
volumetric fog
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volumetric fog and light
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volumetric fog resolution
volumetric fur
volumetric god rays
volumetric godrays
volumetric haze
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volumetric illumination
volumetric interior lighting
volumetric light
volumetric light - s 1 5 0
volumetric light - s 150
volumetric light and fog
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volumetric light clouds
volumetric light effect
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volumetric light highly detailed
volumetric light rays
volumetric light scattering
volumetric light water
volumetric light ， surreal
volumetric lightening
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volumetric lighting - h 7 6 8
volumetric lighting - n 9
volumetric lighting 4k
volumetric lighting 8 k
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volumetric lighting : :
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volumetric lighting — w 6 4 0
volumetric lighting — w 640
volumetric lighting!!
volumetric lighting. fantasy
volumetric lighting. red
volumetric lighting:.7
volumetric lighting”
volumetric lightning
volumetric lights
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volumetric mist
volumetric modelling
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volumetric noon lighting
volumetric object
volumetric octane render
volumetric outdoor lighting
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volumetric rainbow lighting
volumetric ray tracing
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volumetric render
volumetric rendering
volumetric search lights
volumetric shading
volumetric shadow
volumetric shadows
volumetric shadows and lighting
volumetric shapes
volumetric smoke
volumetric smoke and shadows
volumetric soft lighting
volumetric studio lighting
volumetric sun rays
volumetric sun rays and dust
volumetric sunlight
volumetric underwater lighting
volumetric water
volumetric wool felting
volumetrics
volumetrics lights
volumetrig fog
volumetry scattering
volumetry scattering into space
volumettic light
voluminous
voluminous clouds
voluminous light and light rays
voluminous sleeves
volummetric light
voluptuous
voluptuous and arousing
voluptuous body
voluptuous figure
voluptuous male
voluptuous sesame seed bun
volymetric light
vomit
vomiting blood
von gogh
voodo decor
voodoo
voodoo hat
voodoo!!
voodoo”
voque
vore
vore art
vore station borg hound
vorestation borg hound
voroni diagram
voronoi
voronoi pattern
vortex
vortex of plum petals
vortex portal banish the elders
vortex river
vortexes
vostok-1
vouge
vouge italy
vouge style photo
vox machina
voxel
voxel art
voxel based world
voxelart
voxels
voynich manuscript
vp of marketing
vr
vr game
vr glasses
vr goggles
vr googles
vr headset
vr helmet
vr helmet on man
vr iridium visor
vr sunglasses
vray
vray 4k
vray 4k render
vray 8k
vray 8k render
vray and arnold
vray beautiful
vray caustics
vray cinematic smooth
vray lighting
vray octane
vray render
vray render 4 k
vray render 4k
vray rendered
vray renderer
vray rendering
vray shading
vray smooth
vray tracing
vray. subsurface scattering
vraytracing
vrchat
vred
vril
vriska serket
vrubel
vs studio
vsco
vsco film grain
vsevolod ivanov
vshojo
vst
vsx
vtm
vtmb
vtuber
vue 3d render
vue render
vulcanic ground
vulcano
vulcaon machine gun
vulgar
vulture
vultures
vulvina queen of ecstasy
vvitch
vw bus on a street
vw microbus driving
vweto ii
vxf movie
vyverns
véronique meignaud
w
w 1 0 2 4
w 1 9 2 0
w 1024
w 4 0 k
w 40 k
w 7 6 8
w sieci
wabi - sabi
wabi sabi
wacky
wacky style
wad file
wadi rum
wadim
wadim kashin ultra realistic
wadim kashin. ultra realistic
wafflehouse
waifu
waikiki beach
waikiki beach skyline
waist - shot
waist - up
waist height
waist high
waist long hair
waist reaching ponytail
waist up
waist up portrait
waistcoat
waiting
waiting behind a wall
waiting patiently
waiting room
waiting to strike
waitress  girl
wakanda
wakanda background
wake cyborgs from hypersleep
wake initiated lucid dream
wake up
wake up samurai
wakfu colors + symmetry
waking up
waldo
waldo from where's waldo
waldo in the top right of frame
wales
walk
walk cycle
walk in a funeral procession
walkable
walking
walking above the clouds and fog
walking across ice planet
walking around in a forest
walking around in forest
walking at the garden
walking at the park
walking away
walking away from camera
walking away from the camera
walking boy
walking confidently
walking dead
walking down
walking down a marble stairwell
walking down a street
walking down the catwalk
walking in a bamboo forest
walking in a castle
walking in a forest
walking in forest
walking in high grass field
walking in the desert
walking in the forest
walking in the moon
walking in the wheat field
walking into a deep dark florest
walking on an old wood bridge
walking on grass
walking on ice
walking on the beach
walking on the sand
walking on the street
walking on top of a tiny city
walking on water
walking out of a the havens gate
walking out of flames
walking over a skeleton
walking over a tiny city
walking over sand dunes
walking over you
walking the wild west wastelands
walking through a field of wheat
walking through a forest
walking through a lush forest
walking through a lush jungle
walking through a suburb
walking through hell
walking through the forest
walking through the trees
walking to the right
walking to work
walking to work with a briefcase
walking together
walking toward you
walking towards camera
walking towards the camera
walking towards the full moon
walking towards you
walking up the sandy beach
walking upright in a forest
walking with a robot
walks down dark hallway
walkthrough
walkway
walkways
wall
wall - e
wall ]
wall art
wall corner
wall darkness
wall full of small art painting
wall hanging trophy taxidermy
wall mural
wall of eyes
wall of water either side
wall painting
wall paper
wall street
wall structure
wall to wall bookcases
wall torches
wall with colorful graffiti
wall with graffiti
wall wood fortress
wall-e
wallace and gromit
wallpaper
wallpaper - 1 0 2 4
wallpaper - 1024
wallpaper 4 k
wallpaper 4k
wallpaper aesthetic
wallpaper anime blue water
wallpaper art
wallpaper background
wallpaper d&d art
wallpaper design
wallpaper for monitor
wallpaper hd
wallpaper mobile
wallpaper on the walls
wallpaper pattern
wallpaper splash art promo art
wallpaper!
wallpaperflare
wallpapers
wallpaper”
walls
walls are covered with vines
walls are made of dry wall
walls of large moving images
walls with tone of yellow
walmart
walnut wood
walnuts
walrus
walt disney  1937
walt disney ( 1 9 3 7 )
walt disney (1937)
walt disney 1937
walt disney animation studios
walt disney style
walt disney world
walter
walter black
walter everett
walter gropius
walter payton
walter popp
walter white
walter white action figure
walter white and jesse pinkman
walter white as a bodybuilder
walter white as a funko pop
walter white as a jedi
walter white as a mii
walter white as a muppet
walter white as batman
walter white as captain america
walter white as doctor strange
walter white as gordon freeman
walter white as joker
walter white as thanos
walter white as the joker
walter white as thor
walter white botanical poster
walter white crying
walter white door knocker
walter white fortnite skin
walter white from breaking bad
walter white funko pop
walter white gigachad
walter white hiding in a sewer
walter white in animal crossing
walter white in attack on titan
walter white in dark souls
walter white in fortnite
walter white in gta 5
walter white in gta v
walter white in minecraft
walter white in roblox
walter white in skyrim
walter white in super smash bros
walter white in team fortress 2
walter white in the simpsons
walter white in the walking dead
walter white in valorant
walter white made of water
walter white portrait
walter white realistic portrait
walter white smiling
walter womacka
walton ford
walton's five and dime
waluigi
waluigi as a real person
wan adorable korean face
wand
wanda
wanda maximoff
wanderer
wanderer above the sea of fog
wanderers traveling from afar
wandering
wandering in the city
wandering the desert landscape
wanderlust
wands
waneella
wang liang
wangechi mutu
waning moon
wanted poster
waporware
war
war action
war and battle
war armor battle
war art
war blade weapon
war boys
war cry
war footage
war hero
war in background
war in ukraine
war journalism
war landscape
war machines from a gate in hell
war mechs fighting
war movie scene
war of colors
war of colorss
war of the worlds
war paint
war photo
war photograph
war photography
war robot
war scene
war scenery
war scenes
war theatre
war theme
war theme gauntlet
war thunder game
war torn
war zone
war-art style
warcaft art
warcore
warcraft
warcraft 3
warcraft 3 gameplay
warcraft architecture
warcraft artwork
warcraft blizzard weapon art
warcraft character
warcraft style
warehouse
warehouses
warfare
warforged
warforges
warframe
warframe and destiny fanart
warframe armor
warframe armor!!
warframe art
warframe concept art
warframe destiny
warframe destiny art
warframe destiny fanart
warframe fanart
warframe hound
warframe hound art
warframe infested art
warhammer
warhammer 4 0
warhammer 4 0 0 0
warhammer 4 0 0 0 0
warhammer 4 0 k
warhammer 4 0 k artwork
warhammer 4 0 k fantasy
warhammer 4 0 k setting
warhammer 4 0 k!!
warhammer 40
warhammer 40 k
warhammer 40 k fantasy
warhammer 40 k setting
warhammer 4000
warhammer 40000
warhammer 40000 in islam
warhammer 40000 space marine
warhammer 40k
warhammer 40k emperor of mankind
warhammer 40k setting
warhammer 40k style
warhammer 40k tyranid hormagaunt
warhammer 40k!!
warhammer 4k
warhammer art
warhammer chaos
warhammer emperium style
warhammer fantasy
warhammer fantasy art
warhammer fantasy battle
warhammer fantasy setting
warhammer fourty k
warhammer model
warhammer nagash haute couture
warhammer rogue trader
warhammer style
warhammer40k
warhammrer
waring a hawaiian shirt
wario
wario as the armored titan
warlock
warlock sumerge
warlord
warm
warm air
warm ambient light
warm ambient lighting
warm and calm
warm and cool colors
warm and gentle smile
warm and happy
warm and joyful atmosphere
warm and soft and subdued colors
warm and vibrant colors
warm atmosphere
warm azure tones
warm beautiful scene
warm bright white light
warm brown eyes
warm cinematic lighting
warm color
warm color clothes
warm color gradient
warm color highlights
warm color palate
warm color palette
warm color scheme
warm color scheme art rendition
warm color to move
warm color tone
warm colored furniture
warm colors
warm colors advance
warm colors scheme
warm colors--seed 1242253951
warm colour palette
warm colour scheme
warm coloured
warm colours
warm cosy colors
warm deep colours
warm dim light
warm dynamic lighting
warm fantasy lighting
warm features
warm fireplace
warm friendly expression
warm friendly face
warm glow
warm glow coming the ground
warm glow from the lights
warm golden backlit
warm hue
warm hue's
warm hues
warm illumination
warm interior
warm interiors
warm lantern lighting
warm light
warm lighting
warm lighting inside
warm lighting interior
warm lighting with cool shadows
warm lighting. movie poster
warm lightning
warm lights
warm living room
warm mood
warm moody lighting
warm muted colors
warm natural lighting
warm orange lighting
warm pink living room
warm saturated colors
warm saturated colours
warm saturated palette
warm saturatured colors
warm shades of blue
warm shading
warm shiny colors
warm skin tone
warm smile
warm space
warm spring
warm street lights store front
warm studio lighting
warm summer nights
warm sundown
warm sunlight shining in
warm sunset colors
warm sunshine
warm theme
warm tone
warm tone and ( cold tone )
warm tone gradient background
warm toned gradient background
warm tones
warm tri - color
warm volumetric lighting
warm volumetric lights
warm weather
warm wood
warm yellow lighting
warm yellow lights
warmer and softer colours
warmer colours
warmhole
warmly lit
warmly lit posh study
warmth
warning
warning lights
warp
warp lightning
warpaint aesthetic
warped
warped flesh
warped perspectives
warped reality
warped vhs
warpgate
warping
warping time and space
warrior
warrior armor and weapons
warrior body
warrior cats
warrior cats book series
warrior cats fan art
warrior character design
warrior dnd character
warrior dress
warrior face painting red
warrior fighting in a dark scene
warrior girl
warrior holding two swords
warrior in sci-fi armour
warrior man
warrior of light
warrior outfit
warrior platinum armor
warrior princess
warrior queen
warrior with metal owl armour
warrior woman
warriors
warriors and adventurers
warriors fan art
warsaw
warship
warships
wartime footage
wartorn
wartorn environment
wartorn landscape
warts
warwick saint
warzone
warzone background
wash
wash off in the rain
washed colors
washed out
washed out background
washed out color
washed out colors
washed up
washed-out
washing her dark long hair
washing machine
washington
washington crossing the delaware
washington dc
washington main street
washington state
washy brush
wasily kandinsky
wasp
wasps
waste
waste everywhere
waste processing machinery
wasteland
wastelands
wataru kajika
watch dogs game
watch photo
watch tower
watched
watcher
watches
watching
watching new york
watching night streets
watching the stars
watching the stars at night
watching the sun set. anime
watching the sunset
watching tv
watchmen
watchmen comics color scheme
watedrops
water
water - logged
water and power
water armor
water art manipulation
water art photoshop
water background
water bear
water bottle queen
water bottles
water bubble
water bubbles
water cascading
water caustics
water color
water color and pen
water color art on paper
water color nendoroid
water color on paper
water color painting
water color paper
water color splash
water colors
water colour
water colour 8k
water coming out of windows
water covers everything
water cuastics
water damage
water demon
water dragon
water dripping
water dripping from ceiling
water dripping off him
water droplet
water droplets
water droplets frozen in time
water droplets on lens
water drops
water drops on the lense
water element
water elemental
water everwhere fantasy
water everywhere
water eyes
water fairy
water fall
water falls
water feature
water fists of fury
water flow
water flowing
water flowing through the sewer
water flows inside the terrarium
water fog
water fountain
water gushing from ceiling
water in background
water is made of stardust
water jets
water landscape
water levels
water light scattering
water lilies
water line surface
water manipulation
water manipulation photoshop
water mirrored water
water mist
water mists
water on lens
water on the floor
water paint
water painting
water park
water particle in front
water particles
water particulate
water particules
water pipe
water powers water swirling
water puddles
water reflecting suns light
water reflection
water reflection on the floor
water reflection!!!!!
water reflections
water refractions
water refractions!!
water reservoir
water ripples
water running down the walls
water splash
water splashes
water splashes cascades
water splashing
water splashing cascading
water spray
water sprites
water stains
water stream
water surrounds the ship
water swirling
water temple
water texture
water to waist
water to waste
water tornado in the city
water torrent background
water type
water water
water wheel
water world
water-cooled
watercolor
watercolor and ink
watercolor and pen
watercolor art
watercolor art on paper
watercolor artstyle
watercolor artwork of exotic
watercolor background
watercolor colored painting
watercolor detailed art
watercolor digital painting
watercolor drawing
watercolor effect
watercolor expressionism
watercolor expressionist
watercolor illustration
watercolor illustration style
watercolor ink
watercolor ink illustration
watercolor ink painting
watercolor inpaint
watercolor landscape
watercolor masterpiece
watercolor on canvas
watercolor on paper
watercolor paint
watercolor painting
watercolor painting style
watercolor painting underwater
watercolor paper
watercolor pen drawing
watercolor pencil + ink drawing
watercolor sketch
watercolor splash
watercolor strokes
watercolor style
watercolor technique
watercolor texture
watercolor wash
watercolor wash over inks
watercolor-wash
watercolored
watercolors
watercolors and acrylics
watercolors on canvas
watercolour
watercolour on paper
watercolour painting
watercolour realism
watercolour realistic
watercolours
watercooling
watercore
waterdeep
waterfall
waterfall backdrop
waterfall background
waterfall below
waterfall cascades
waterfall cave
waterfall falling down
waterfall falling into a lake
waterfall flowing from the stone
waterfall in asteroid field
waterfall in background
waterfall in distance
waterfall in the background
waterfall walls
waterfall!!!!!
waterfall(beautiful
waterfall. fog
waterfalls
waterfalls and lakes
waterfalls in distance
waterfalls in the background
waterfals
waterfront houses
waterhouse
watering can
waterlily mecha nymphaea
waterlily pads
waterlily pond
waterline refractions
watermark
watermark:-1
watermarked
watermelon
watermeloncore
watermelons
waterpaint art
waterscape
watertank
waterway
waterways
waterwheels
watery
watery black eyes
watery blue eyes
watery caverns
watery crystal glow eyed
watery doe eyes
watery eyes
watery red eyes
wave
wave a hand at the camera
wave breaking
wave frequencies
wave function
wave of water particles
waveforms on top of square chart
waves
waves and particles
waves and splashes
waves crashing
waves crashing at rocks
waves crashing in the sea
waves of energy
waves of lights
waving
waving and smiling
waving arms
waving at the camera
waving hands
waving robe movement
wavy
wavy and short top hair
wavy beautiful hair
wavy big red hair
wavy black hair
wavy blond hair
wavy hair
wavy hair combed to one side
wavy hair spread out
wavy hair yellow theme
wavy hairstyle
wavy lingeries
wavy long - length black hair
wavy long black hair
wavy long black hair and glasses
wavy short hair
wavy short hazel hair
wavy shoulder-length hair
wavy vibrant red hair
wavy water
wavy white long hair
waw
wax
wax figure
wax skin
waxed
waxed beard
waxing moon
waxy
waxy candles
waxy skin
way of kings
wayne
wayne - barlowe
wayne - coyne
wayne barlove
wayne barlow
wayne barlowe and artem demura
wayne barlowe and james gilleard
wayne barlowe concept art
wayne barlowe detailed
wayne barlowe greg rutkowski
wayne barlowe pierre pellegrini
wayne barlowe's inferno
wayne barlowes inferno
wayne douglas barlowe
wayne haag
we
we all need control
we can do it
we can see the sea
we dehumanize ourselves
we didn't start the fire
we go
we see them from head to toe
we'll fall to pieces
we're all very tired
weak
weak and weary
weak demon
wealth
wealthy
wealthy women
weapon
weapon concept art
weapon damage
weapon design
weapon shop interior
weaponized
weaponry concept designs
weapons
weapons and armors
weapons arrays
weapons concept art
weapons in hand
wear an elegant mustach
wear and tear
wear ray - ban glass
wear spacesuits
wear's beige shirt
wearable art
weared in leather armor
wearing
wearing 1 8 5 0 s clothes
wearing 1 8 5 0 s era clothes
wearing 1 8 6 0 s clothes
wearing 1 9 2 0 s cloth hair
wearing 1 9 2 0 s fashion
wearing 1840s era clothes
wearing 1850 s clothes
wearing 1850 s era clothes
wearing 1850s era clothes
wearing 1860 s clothes
wearing 1860s era clothes
wearing 1890s era clothes
wearing 3 d glasses
wearing 3d glasses
wearing : tanktop
wearing a backwards baseball cap
wearing a baggy
wearing a baggy pajamas
wearing a bandana and chain
wearing a barca cape
wearing a baseball cap
wearing a baseball cap backwards
wearing a baseball hat
wearing a beanie
wearing a bejeweled mask
wearing a beret
wearing a bikini
wearing a black and red suit
wearing a black blazer
wearing a black bodysuit
wearing a black catsuit
wearing a black cloak
wearing a black cropped tank top
wearing a black dress
wearing a black hoodie
wearing a black jacket
wearing a black leather jacket
wearing a black leather vest
wearing a black noble suit
wearing a black robe
wearing a black shirt
wearing a black suit
wearing a black sweater
wearing a black t-shirt
wearing a black tshirt
wearing a black!! t - shirt
wearing a blazer
wearing a blouse
wearing a blue berries
wearing a blue dress
wearing a blue hoodie
wearing a blue jacket
wearing a blue qipao dress
wearing a blue robe
wearing a bomber jacket
wearing a bone gas mask
wearing a botanical gas mask
wearing a bowler hat
wearing a brown
wearing a brown cape
wearing a brown leather coat
wearing a bunny suit
wearing a business suit
wearing a camisole
wearing a camisole and boots
wearing a camisole and shorts
wearing a cape
wearing a cardigan
wearing a chain
wearing a chocker and cute hat
wearing a cloak
wearing a cocktail dress
wearing a colorful coogi sweater
wearing a colorful men's suit
wearing a colorful mens suit
wearing a colorful yukata
wearing a cowboy hat
wearing a crop top
wearing a cropped black tank top
wearing a cropped top
wearing a cropped tops
wearing a crown
wearing a crown and cape
wearing a crown and green cape
wearing a crown and suit
wearing a crown made of antlers
wearing a crown of vines
wearing a cute hat
wearing a cute top
wearing a cute white dress
wearing a dark armor
wearing a dark blue polo shirt
wearing a dark dress
wearing a dark heavy plate armor
wearing a dark hood
wearing a dark shirt and jeans
wearing a dark sweater
wearing a designer top
wearing a diamond crown
wearing a dragon mask
wearing a dress
wearing a dress made of beads
wearing a dress made of stars
wearing a dress made of vines
wearing a dress made of water
wearing a dress of gossamer gold
wearing a duster coat
wearing a fancy black jacket
wearing a fancy dress
wearing a fancy jacket
wearing a feather dress
wearing a fedora
wearing a fisher 🧥
wearing a flannel shirt
wearing a floral crown
wearing a flower headpiece
wearing a flowing cloak
wearing a flowing dress
wearing a flowing sundress
wearing a flying jacket
wearing a formal dress
wearing a french beret
wearing a full leather outfit
wearing a full-head gasmask
wearing a fur coat
wearing a gaming headset
wearing a gas mask
wearing a general\'s uniform
wearing a gold chain
wearing a golden crown
wearing a golden halo
wearing a gothic dress
wearing a green cloak
wearing a green sweater
wearing a grey fur robe
wearing a grey hooded sweatshirt
wearing a grey robe
wearing a grey wizard hat
wearing a haori
wearing a hat
wearing a hawaiian dress
wearing a head scarf
wearing a headband
wearing a headset
wearing a helmet
wearing a hood
wearing a hood with pointy ears
wearing a hooded cloak
wearing a hoodie
wearing a hoodie and flowers
wearing a hoodie and sweatpants
wearing a hoody
wearing a hospital gown
wearing a jeans jackets
wearing a kilt
wearing a kimono
wearing a king's cape
wearing a kitsune mask
wearing a kurta
wearing a lab coat
wearing a labcoat
wearing a laurel wreath
wearing a leather flight jacket
wearing a leather jacket
wearing a leather trench coat
wearing a lemon
wearing a light - pink suit
wearing a light blue shirt
wearing a light blue suit
wearing a light grey crown
wearing a light shirt
wearing a linen shirt
wearing a long beige trench coat
wearing a long coat
wearing a long dress
wearing a long flowery dress
wearing a long flowing robe
wearing a long flowy fabric
wearing a low cut tanktop
wearing a luxurious cloak
wearing a luxurious royal cloak
wearing a luxurious silk cloak
wearing a luxurious silk robe
wearing a luxurious velvet robe
wearing a luxury fur coat
wearing a magnificent dress
wearing a marijuana t - shirt
wearing a mask
wearing a melon
wearing a military uniform
wearing a modern yellow tshirt
wearing a monocle
wearing a muscle tee shirt
wearing a native american choker
wearing a navy blue utility cap
wearing a neon blue hoodie
wearing a neon green dress
wearing a nightgown
wearing a noblewoman's outfit
wearing a noblewomans outfit
wearing a noh theatre mask
wearing a paper crown
wearing a parker
wearing a party hat
wearing a pastel pink hoodie
wearing a patch over one eye
wearing a pink ballroom gown
wearing a pink dress
wearing a pink head band
wearing a pink hoodie
wearing a pink rabbit costume
wearing a pink romantic tutu
wearing a pink tutu
wearing a pink tux
wearing a pinstripe suit
wearing a plaid shirt
wearing a plastic blue dress
wearing a plastic garbage bag
wearing a plug suit
wearing a police uniform
wearing a punk outfit
wearing a purple breton cap
wearing a purple cap
wearing a purple detailed coat
wearing a purple frock coat
wearing a purple smoking jacket
wearing a purple sweatsuit
wearing a red backwards cap
wearing a red cape
wearing a red captain's uniform
wearing a red cheongsam
wearing a red dress
wearing a red gilet
wearing a red hoodie
wearing a red lumberjack shirt
wearing a red outfit
wearing a red plaid dress
wearing a red sundress
wearing a red turtleneck sweater
wearing a robe
wearing a round helmet
wearing a royal robe
wearing a santa hat
wearing a sari
wearing a scarf
wearing a scarlet hoodie
wearing a school soccer uniform
wearing a school uniform
wearing a sexy cropped top
wearing a shirt
wearing a shirt and a jean
wearing a shirt with a tie
wearing a silk kurta
wearing a silly hat
wearing a simple robe
wearing a skeleton armor
wearing a skirt
wearing a space helmet
wearing a space suit
wearing a spacesuit and helmet
wearing a sparkling dress
wearing a steampunk sari
wearing a stop sign on its head
wearing a straw hat and overalls
wearing a strict business suit
wearing a stylish men's suit
wearing a suit
wearing a suit and a tie
wearing a suit and glasses
wearing a suit and tie
wearing a suit of armour
wearing a suits
wearing a sun dress
wearing a sundress
wearing a sweater
wearing a t-shirt
wearing a tank top and shorts
wearing a tanktop
wearing a tanktop and skirt
wearing a tee shirt and combats
wearing a tiara
wearing a tie-dye shirt
wearing a tie-dye t-shirt
wearing a tight black dress
wearing a toga
wearing a toga and sandals
wearing a top hat
wearing a tophat
wearing a towel
wearing a track suit
wearing a tracksuit
wearing a travel hat
wearing a trenchcoat
wearing a tudor style dress
wearing a turban
wearing a turtleneck and jacket
wearing a tutu
wearing a tuxedo
wearing a veil
wearing a velvet cloak
wearing a velvet robe
wearing a vest
wearing a vest and a tie
wearing a vest top
wearing a volleyball jersey
wearing a vr headset
wearing a vr-headset
wearing a waistcoat
wearing a watch
wearing a wedding dress
wearing a wet white short dress
wearing a white bathing cap
wearing a white bikini
wearing a white blouse
wearing a white button up shirt
wearing a white dress
wearing a white flowing dress
wearing a white folkdrakt dress
wearing a white gi
wearing a white hospital gown
wearing a white lab coat
wearing a white robe
wearing a white shirt
wearing a white sundress
wearing a white sweater
wearing a white tuxedo
wearing a white winter coat
wearing a witch hat
wearing a wizard cloak
wearing a wizard hat
wearing a wonderful dress
wearing a worn out brown suit
wearing a worn out suit
wearing a yellow dress
wearing a yellow hoodie
wearing accurate clown makeup
wearing acne outfit
wearing adidas clothing
wearing adventure gear
wearing adventuring gear
wearing all black mempo mask
wearing along white dress
wearing amazing clothes
wearing an academic gown
wearing an african dress
wearing an ammo belt
wearing an apron
wearing an armor
wearing an astronaut helmet
wearing an aviator jacket
wearing an egyptian crown
wearing an elaborate helmet
wearing an elegant dress
wearing an elegant outfit
wearing an elegant tribal outfit
wearing an evening gown
wearing an eye patch
wearing an eyepatch
wearing an old tunic
wearing an orange jumpsuit
wearing an orange t shirt
wearing an orange t-shirt
wearing an ornate outfit
wearing an ornate suit
wearing an oversized hoodie
wearing an oversized sweater
wearing ancient armor
wearing ancient chinese clothes
wearing ancient cimmerian armor
wearing angel
wearing angel halo
wearing angel halo covered face
wearing animal skin clothing
wearing apocalyptic clothes
wearing aristocrat robe
wearing armor
wearing armor!
wearing armor!!
wearing armour
wearing astronaut outfit
wearing atompunk jumpsuit
wearing atsuko kudo latex outfit
wearing authentic attire
wearing bandit mask
wearing barbarian caveman pelt
wearing barn owl mask
wearing baseball cap
wearing basketball jersey
wearing battle armor
wearing beanie
wearing beautiful clothes
wearing big black circle glasses
wearing bihu dress mekhela sador
wearing bikini
wearing bionic implants
wearing bizarre clown makeup
wearing black
wearing black and purple robes
wearing black armor
wearing black boots
wearing black business suit
wearing black camisole outfit
wearing black choker
wearing black clothes
wearing black clothes and cape
wearing black coat
wearing black dress
wearing black dress and hat
wearing black frame glasses
wearing black glasses
wearing black grey suit
wearing black headphones
wearing black hooded cloak
wearing black latex outfit
wearing black leather trenchcoat
wearing black medieval robes
wearing black modern clothes
wearing black old dress and hat
wearing black open toe heels
wearing black overcoat
wearing black rimmed glasses
wearing black robe
wearing black robes
wearing black shorts
wearing black silk robe
wearing black silk robes
wearing black sith robes
wearing black sith uniform
wearing black stylish clothing
wearing black suit
wearing black tight clothing
wearing black tshirt
wearing black vest and skirt
wearing black witch hat
wearing black wizard robes
wearing blue
wearing blue dress
wearing blue jacket
wearing blue jean overalls
wearing blue robe
wearing blue sunglasses
wearing bone armor
wearing boots
wearing bra
wearing brown jedi robes
wearing brown leather jacket
wearing brown robes
wearing bullet-riddled armor
wearing business casual dress
wearing business suit
wearing camo
wearing cargo pants
wearing casual clothes
wearing casual clothing
wearing casual sweater
wearing cat ear headphones
wearing causal black suits
wearing cave man clothes
wearing celtic tattoos
wearing chain mail
wearing chainmail armor
wearing chains
wearing choker
wearing cloak
wearing cloak and hood
wearing cloak on blasted plain
wearing clothes
wearing clown makeup
wearing collar
wearing collar on neck
wearing cool sunglasses
wearing correct era clothes
wearing corset
wearing cowboy hat
wearing crop top
wearing crop top and miniskirt
wearing cross on robe
wearing crown
wearing crown of bright feathers
wearing crystal fractal tiara
wearing cultist red robe
wearing cybernetic bunny ears
wearing cyberpunk 2 0 7 7 jacket
wearing cyberpunk 2077 jacket
wearing cyberpunk leather jacket
wearing cyberpunk streetwear
wearing cylinder hat
wearing daedric armour
wearing dark blue suit
wearing dark cloak
wearing dark green bomber jacket
wearing dark green robes
wearing dark grey suit
wearing dark maritime clothing
wearing dark purple armor
wearing dark robe
wearing dark robes
wearing dark silk
wearing dark silk robe
wearing dark victorian goggles
wearing demobaza fashion
wearing denim
wearing denim short shorts
wearing desert poncho
wearing detailed leather collar
wearing diamond armor
wearing dirty flight suit
wearing dirty overalls
wearing dirty ripped flight suit
wearing dirty soldier uniform
wearing dirty travelling clothes
wearing disco suit
wearing double denim
wearing dramatic aristocrat robe
wearing dress
wearing dresses
wearing elaborate green and gold
wearing elegant casual clothes
wearing elegant jewellery
wearing elegant tudor clothes
wearing epic bionic implants
wearing eye shadow
wearing eyeglasses
wearing facemask
wearing facemask and sunglasses
wearing fancy clothes
wearing fantasy armor
wearing fantasy clothing
wearing fantasy formal clothing
wearing farm clothes
wearing fashion clothing
wearing fashion suit
wearing festive clothing
wearing fishnets
wearing fitness gear
wearing flight suit
wearing floral chiton
wearing flowing robes
wearing fluffy black scarf
wearing fuchsia silk robe
wearing full armour
wearing full metal armour
wearing full plate armor
wearing fur armor
wearing fur cloak
wearing futuristic
wearing futuristic armor
wearing futuristic clothing
wearing futuristic space gear
wearing futuristic white suit
wearing gaming headset
wearing gas mask helmets
wearing gas masks
wearing giant paper masks
wearing gilded red robes
wearing gilded red royal robes
wearing gilded ribes
wearing gilded robes
wearing glack glasses
wearing glasses
wearing gloves
wearing goggles
wearing gold armor
wearing gold chain
wearing gold detailed choker
wearing gold glasses
wearing gold jewellery
wearing golden armor
wearing golden cat armor
wearing golden crown
wearing golden laurel wreath
wearing golf shorts
wearing gorgeous clothing
wearing goth makeup
wearing gothic accessories
wearing greek palla
wearing green
wearing green armor and helmet
wearing green battle armor
wearing green cloak
wearing green clothing
wearing green jacket
wearing green suit
wearing green tophat
wearing gui
wearing hair
wearing hair bow
wearing hakama
wearing hat
wearing hay coat
wearing headmistress uniform
wearing headphones
wearing headset
wearing heavy armor
wearing heavy armor with cape
wearing heels and white dress
wearing helmet
wearing her helmet
wearing hero costume
wearing hi vis clothing
wearing hogwarts!!! robes!!!
wearing honey
wearing honey - themed miniskirt
wearing hood
wearing hoodie
wearing hoods
wearing huge golden crown
wearing huge straw hat
wearing human air force jumpsuit
wearing hunter coat
wearing ice crystals
wearing imperial gear
wearing in a summer dress
wearing in black cloak
wearing in black clothes
wearing in cardigan
wearing in shirt
wearing in stocking
wearing inka clothes
wearing intricate
wearing intricate black choker
wearing intricate fur armor
wearing intricate steel armor
wearing ivory colour dress
wearing jacket
wearing jacket and skirt
wearing japanese school uniform
wearing japanese techwear
wearing jeans
wearing jeans and a black hoodie
wearing jedi robes
wearing jedi robes and a sari
wearing jetpack
wearing jewellery
wearing jewelry
wearing kimono
wearing kimono armor
wearing kimono armour
wearing kings crown
wearing knee and elbow pads
wearing kneesocks
wearing knight  s armor
wearing knight armor
wearing knight ’ s armor
wearing lab coat
wearing lab coat and a blouse
wearing lab coat and glasses
wearing labradorite body armor
wearing labradorite veils
wearing latex
wearing leaf cloak
wearing leather
wearing leather armor
wearing leather armored vest
wearing leather assassin armor
wearing leather bikini
wearing leather coat
wearing leather jacket
wearing leather swim suite
wearing leotard
wearing light
wearing loincloth
wearing long black winter coat
wearing long gown
wearing long royal robe
wearing long silver robes
wearing long white robe
wearing louis vuitton armor
wearing mage robes
wearing maid uniform
wearing many medallions
wearing mask
wearing mass effect armor
wearing medieval clothes
wearing medium - sized glasses
wearing metal gauntlet
wearing military outfit
wearing military shoes
wearing military uniform
wearing mirrored sunglasses
wearing modern glasses
wearing modern gothic clothes
wearing modern headphone
wearing monster hunter armor
wearing nanotech honeycomb robe
wearing nba jersey
wearing netrunner clothing
wearing newsboy cap
wearing nike air mags
wearing noh theatre mask
wearing norse armor
wearing nothing
wearing oakley sunglasses
wearing off - white style
wearing only a green robe
wearing only pants
wearing open toe heels
wearing orange prison jumpsuit
wearing orange sundress
wearing orange sunglasses
wearing organza gown
wearing ornate armor
wearing ornate clothing
wearing ornate earrings
wearing ornate helmet
wearing ornate silk clothes
wearing overalls
wearing pajamas
wearing pants
wearing pants and a t-shirt
wearing pearl earrings
wearing pearl neon bikini
wearing pink flip flops
wearing pink floral chiton
wearing pink floral gown
wearing pink hair bow
wearing pink romantic tutu
wearing plaid shirt
wearing plate armor
wearing plate armour
wearing plumber uniform
wearing pointed hoods
wearing polo shirt
wearing presidential band
wearing prison jumpsuit
wearing professional makeup
wearing psychedelic wicca
wearing punk clothing
wearing purple corset and tutu
wearing purple headphones
wearing purple romantic tutu
wearing purple strapless dress
wearing purple undershirt
wearing ragged clothing
wearing rainbow kimono
wearing reading glasses
wearing red
wearing red and yellow clothes
wearing red and yellow hero suit
wearing red attire
wearing red clothes
wearing red converse shoes
wearing red dress
wearing red formal attire
wearing red fur
wearing red fur cloak
wearing red jacket
wearing red robes
wearing red shorts
wearing red sorcerer's robes
wearing red sorcerers robes
wearing red tainted glasses
wearing red tank top
wearing ripped dirty flight suit
wearing robe
wearing robes
wearing robes and neckties
wearing robes of silver
wearing round glasses
wearing royal kimono
wearing rr diner uniform
wearing russian ww 1 clothes
wearing samcrow leather jacket
wearing samurai armor
wearing santa hat
wearing sari
wearing school uniform
wearing sci - fi cloak with hood
wearing sci-fi armor
wearing sculpted textured armor
wearing sculpted textured armour
wearing seashell attire
wearing several pendants
wearing sexy lingerie
wearing shades
wearing shining armor
wearing shining plate armor
wearing shiny black goggles
wearing shiny breastplate
wearing shiny gold catsuit
wearing shiny stormtrooper armor
wearing shipibo tattoos
wearing shirts
wearing shorts
wearing shorts and t shirt
wearing shoulder cape
wearing silver dress
wearing silver hair
wearing silver silk robe
wearing simple robes
wearing sith hood
wearing skate helmet
wearing skating helmet
wearing skirt
wearing skirt and high socks
wearing sleek armor
wearing small round glasses
wearing sombrero
wearing space techwear
wearing spandex bikini
wearing spikes and piercings
wearing spiky
wearing square glasses
wearing star filled mage robes
wearing steampunk attire
wearing steampunk top hat
wearing steel collar
wearing stockings
wearing stockings. side-view
wearing stormtrooper armor!!
wearing straw hat
wearing strawberry backpack
wearing stripe shirt
wearing studded leather
wearing studded leather armor
wearing stunning ivory dress
wearing suit
wearing suit and tie
wearing suit vest
wearing suits
wearing suits!
wearing sundress
wearing sunglasses
wearing sunglasses and a cap
wearing sunglasses and a hat
wearing sweater
wearing sweatshirt
wearing tactical gear
wearing tall combat boots
wearing tank top
wearing tanktop
wearing teal beanie
wearing techwear and armor
wearing thanos armour
wearing the number 1 headband
wearing thin large round glasses
wearing thunder armor
wearing tiara
wearing tight shirt
wearing tight simple clothes
wearing tight suit
wearing toga
wearing top hat
wearing torn clothes
wearing torn white cape
wearing track and field suit
wearing traditional garb
wearing translucent sheet
wearing translucent veils
wearing transparent glass mask
wearing trenchcoat
wearing tribal armor
wearing tumultus flames
wearing turtleneck
wearing tuxedo
wearing two - piece swimsuit
wearing two metallic rings
wearing two silver bracelets
wearing two silver bracelets!
wearing unusual clothes
wearing urban techwear
wearing v - neck top
wearing versace sunglasses
wearing vibrant boxing gloves
wearing victorian brass goggles
wearing victorian clothes
wearing victorian dark goggles
wearing victorian goggles
wearing victorian rags
wearing viking clothes
wearing vr
wearing vr glasses
wearing vr goggles
wearing waistcoat
wearing war paint
wearing wheat yellow gauze
wearing white camisole
wearing white chef hat
wearing white clothes
wearing white cloths
wearing white dress
wearing white leotard
wearing white pajamas
wearing white robe
wearing white robes
wearing white robes!
wearing white shirt
wearing white silk
wearing white silk hood
wearing white silk robe
wearing white skirt
wearing white sneakers
wearing white suit
wearing white suit and glasses
wearing white tights
wearing white v - neck top
wearing wide sunhat
wearing winged helmet
wearing witchblade armor
wearing wizard hats
wearing wizard robes
wearing wooden mask
wearing wool hat
wearing wool suit
wearing xena armor
wearing yellow croptop
wearing yellow floral blouse
wearing! robes!! of silver
wearing!!! clothes!!!
wears a destroyed hat
wears a egyptian ankh necklace
wears a light grey crown
wears a long robe
wears a spacesuit
wears a suit of power armor
wears a watch
wears brown boots
wears glasses
wears oprah glasses
wears shorts
wears the ankh symbol
wears tiny spacesuit
weary
weary inside
weasel
weasel - ferret - stoat
weasel - ferret - stoat )
weasel - ferret - stoat ) ]
weather
weather photography
weather report
weather report style
weathered
weathered artifacts
weathered concrete
weathered drawing
weathered face
weathered olive skin
weathered pages
weathered polaroid
weathered skin
weathered surfaces
weathered ultra detailed
weathering armor plating
weathering with you
weave
weaving
web
web 3. 0
web design
web of life
web of tendrils
webbing
webcam
webcam footage
webcam screenshot
webcomic
webdesign icon for solar carport
webgl render
webs
website
website banner
website screenshot
webtoon
webtoons
wedding
wedding dress
wedding photo
wedding photography
wedjat eye
wednesday addams
wee whelp
weeb
weed
weed background
weed cutie mark
weeds
weeds and grass
weeds and ivy on the graves
weekly
weenie
weeping angels
weeping tears of black oil
weeping willows
weeping willows and flowers
wei wang
weibo
weightless
weightless in space
weightlessness
weird
weird abstract
weird alien trees
weird americana
weird and disturbing
weird angles
weird art
weird art on the wall
weird art piece
weird atmosphere
weird camera angle
weird creatures
weird expressionist pose
weird fashion
weird planet
weird pokemon
weird portrait angle
weird scribbles
weird silly thing with big eyes
weird simple fungus and tendrils
weird skies
weird space
weird stuff
weird stylish avant garde epic
weird west
weirdcore
weirdcore voidpunk fursona
welcome
welcome to night vale
welcome to the circus
welcome to the matrix
welcome to wonderland
welcoming
welcoming attitude
welcoming grin
welcoming smile
welder wings
welding helmet head
welding torches for arms
weldon casey
well
well - appointed space
well - decorated
well - designed
well - designed digital art
well - detailed outfit
well - drawn
well - dressed
well - lit
well - rendered
well armored
well armored mech dragon
well balanced composition
well built
well centered
well composed
well contoured smooth fair skin
well contoured smooth fair walls
well decorated
well defined
well defined jawline
well defined mechanical features
well designed
well designed female dragon head
well designed head
well detailed
well detailed head with led eyes
well done picture high quality
well drawn
well drawn eyes
well drawn faces
well dressed
well edited
well fed
well focused
well framed
well list
well lit
well lit 3 d render
well lit night in las vegas
well lit professional photo
well lit sky
well lit studio lighting
well made
well maintained
well preserved
well proportioned
well rendered
well rendered.:1
well shaded
well toned
well trimmed beard
well worn
well-appointed space
well-defined anatomy
well-designed digital art
well-designed masterpiece
well-detailed
well-endowed
well-gelled hair
well-groomed model
well-lit
well-rendered
wellington
wellness pool
welsh flag
wendigo
wenfei ye
wenjr
wenjun lin
wepa digital
werecrow
werewolf
werewolf man
werewolf?
werewolf”
wes anderson and gucci
wes anderson and wes anderson
wes anderson background
wes anderson color palette
wes anderson color scheme
wes anderson film
wes anderson film screenshot
wes anderson movie
wes anderson style
wes anderson)
wesley kimler
west africa mask patterns style
west slav features
west world show
western
western animation
western art
western background
western clothing
western comic
western comic art
western comic book art
western comic book style
western comic style
western cowgirl
western era
western european
western film
western gunslinger
western painting
western saloon theme
western setting
western steampunk cyborg
western town
westside
westworld
westworld style
wet
wet - on - wet technique
wet amphibious skin
wet and slimy
wet aslphalt road after rain
wet asphalt
wet atmosphere
wet body
wet boody
wet brush
wet clay
wet climate
wet collodion
wet collodion process
wet concrete
wet dark road
wet drapery
wet dripping
wet dripping hair
wet dripping long hair
wet eye in forehead
wet eye relections
wet fabric
wet face
wet feet in water
wet floor
wet floor on streets
wet floors
wet flowing hair
wet from rain
wet fur
wet grass
wet grass and black stones
wet grass and stones
wet ground
wet hair
wet hairy bodies
wet hdr refractions
wet highway chase
wet leaves
wet lips
wet look
wet lush jungle landscape
wet make - up
wet market street
wet metal reflections
wet mouth
wet mud
wet on wet
wet paint
wet pavement
wet plate
wet plate photo
wet plate photograph
wet plate photography
wet puddles on street
wet puddles reflecting
wet reflections
wet reflections in eyes
wet reflections in square eyes
wet reflective concrete
wet reflective ground
wet reflective tile floor
wet relections
wet relections in eyes
wet road
wet rocks
wet shiny skin
wet shirt
wet shredded red meat
wet sidewalk
wet skin
wet skin and windblown hair
wet street
wet streets
wet surface
wet swimsuit
wet t shirt
wet t-shirt
wet tshirt
wet-plate photography
weta
weta digital
weta disney
weta disney movie still photo
weta disney pixar
weta fx
weta studio
weta studio and james jean
weta studios
weta workshop
weta workshop concept art
weta workshop the hobbit
wetastudiofx
wetbrushes
wetcore
wetplate
wetplate photography
wh 4 0 k art
wh 40 k art
wh40k
whacky designs
whale
whale carcass
whale fall
whale monsters
whale skeleton
whalen tom
whales
whales showing from the waves
what
what a bumbler!
what depression looks like
what dreams may come
what happens after death
what music they make
what stable diffusion looks like
whatever
whatsapp
whealan
wheat field
wheat field behind the house
wheat fields
wheatly from portal 2
wheel
wheelchair
wheelie
wheels
when it's over
when its over
when kindness falls like rain
when mars attacks
when the wind is slow
where a large
where being rest in peace
where everyone is an npc
where is waldo
where lovecraftian horrors roam
where the ash gathered
where the planets are candy
where the wild things are
where's waldo
where's wally
wheres waldo
wheres wally
which are also skeletal & frail
which are also skeletal frail
which goes near the ground
which shows a beach at sunset
which splits in half into wings
whie tcolor
while holding a sword
while i pondered
while it's raining
while its raining
while marble
while posing in the same bed
while smiling for a photograph
while smoking a cigar
while tripping on dmt
whimsical
whimsical and cute
whimsical and psychedelic
whimsical art
whimsical beaver
whimsical demon with rainbow fur
whimsical fantasy landscape art
whimsical forest
whimsical fox
whimsical portrait
whimsical surrealism
whimsical tone
whimsical!!
whimsical!! intricate details
whimsical!!!
whimsicalintricate details
whimsy
whip
whip in hand
whipped cream
whipped cream on top
whips
whirling
whirling blue smoke
whirling death
whirling gasses
whirling green smoke
whirling nebulas
whirling plasma
whirling smoke
whirling smoke radiant colors
whirlpool
whirlwind
whirlwind of tarot cards
whirly
whiskers
whiskers hq
whiskey
whiskey bottle
whiskey glass with ice cubes
whisky
whispers
whistler
white
white  cat  girl
white ( cat ) girl
white + blue + gold + black
white - blond hair
white - haired fox
white accent lighting
white adidas pants
white alien squid
white ambient background
white american soldier
white and black
white and black clothing
white and black color palette
white and blood color scheme
white and blue
white and blue color scheme
white and gold
white and gold color palette
white and gold color scheme
white and gold dress
white and gold kintsugi
white and gold priestess robes
white and gold robes
white and grey
white and light-pink outfit
white and orange
white and orange breastplate
white and pale blue
white and pale blue toned
white and pink
white and pink cloth
white and purple
white and red armor
white and red body armor
white and red color scheme
white and red dresses
white and red roses
white and silver
white and teal garment
white and teal metallic accents
white and yellow scheme
white apron
white architecture
white armor
white around right eye
white ascot
white backdrop
white background
white background : 3
white background and fill
white background wall
white background with shadows
white background!!!!!!!!!!
white background”
white backround
white balance
white bandage tape on fists
white bandages on fists
white bangs
white baseball cap
white baseball hat
white beaches
white beard
white beautiful hair
white bed
white belt
white bg
white bikini
white biomechanical
white biomechanical details
white biomechanicaldetails
white bird skulls
white black fade braided hair
white block fence
white blonde hair
white blossoms
white blouse
white blouse and gothic boots
white bob cut hair
white body
white bones
white border
white border and background
white border frame
white borders
white box
white bra
white braided hair
white braids
white broom closet
white building
white buildings
white buildings with red roofs
white calacatta gold marble
white candles
white candles dripping wax
white canvas background
white cap
white cape
white carved abstract sculpture
white cat
white cat girl
white cat in a pink dress
white ceiling
white ceramic shapes
white cheeks
white church background
white clay
white cloak
white cloth
white cloth in wind shining
white clothes
white clothing
white cloud
white cloud hair
white clouds
white coat
white collar
white color
white color scheme
white colors
white concrete
white concrete floor
white cowboy hat
white crown
white cummerbund
white curly hair
white cyan
white cyborg fashion shot
white cyc
white cyclops portrait in sky
white daoist robes
white desert
white desert background
white detailed eyes
white details
white diamonds
white dove
white dress
white dress shirt
white dress!! of silver hair
white dressof silver hair
white eagle icon
white eclipse
white elbow gloves
white elegant baroque design
white eyebrows
white eyes
white eyes and eyelashes
white eyes without pupils
white face
white face makeup
white face paint
white facepaint
white feathers
white finish
white flesh
white floor
white flower
white flower crown
white flowers
white flowers on the floor
white fluffy cloud
white fluffy cotton shorts
white foam
white fog
white fog painting
white font on black canvas
white fox
white fox anime
white fox ears
white fractals
white frame
white frame border
white freckles
white french bob
white french bob hairstyle
white fringy hair
white fungal spores everywhere
white fur
white fur and dark skin
white furniture
white gallery
white ghosthulk
white girl
white glasses
white gloves
white glowing aura
white glowing eyes
white gold
white gold black
white gold color palette
white gold skin
white gossamer wings
white gown
white grey blue color palette
white grey color palette
white habitat 6 7
white hair
white hair and beard
white hair and white beard
white hair color
white hair dreads
white hair floating in air
white hair!!!
white hair!!!!
white hair!!!!!
white haired
white haired deity
white haired lady
white hairs
white halter top
white hanfu
white hat
white head
white helmet
white high waisted swimsuit
white highlights in hair
white hijab
white hime cut hairstyle
white holographic plastic
white horns
white horns from eyebrows
white horns queen demon
white horse
white house
white house archives
white houses
white hue
white human spine
white in color
white ink
white ink sketch on black paper
white irises and dark eye makeup
white jacket
white john berkey armor panels
white john berkey panels
white kitchen table
white l shaped couch
white lab coat
white labrador retriever face
white lace
white lace clothing
white lace dress
white lashes
white lava
white leggings
white light
white light halo
white light shining on her
white lighting
white lightning
white lights
white lilies
white limbo
white loincloth
white long beard
white long gloves
white long hair
white long hair!!
white long straight hair
white long tanktop
white machinery
white male
white man
white man with black fabric mask
white map library
white marble
white marble and gold
white marble buildings
white marble highly detailed
white marble interior photograph
white marble sculpture
white marble texture
white marble walls
white marble with gold accents
white mask
white mech bot
white mecha
white mechanical details
white metal
white metal armor
white metal neocubism armor
white metallic
white metallic armor
white metallic armour
white minimalist architecture
white minimalistic background
white miniskirt
white mist
white mohawk
white monochrome color
white monochrome color!!!!!
white moon
white moon and black background
white moon in the background
white mouse technomage
white mug
white muzzle and underside
white neck visible
white neon
white neon details
white neon lighting
white neon lights
white neon wash
white noise
white octopus
white on black
white orchids
white outfit
white outline
white outline border
white paint
white pale concrete city
white paled skin
white panels
white pants
white paper
white paper background
white peacock feathers
white pearlescent
white petal
white picket fence
white pillows
white plague doctor mask
white plank siding
white plastic
white plastic armour
white plated armor
white poet shirt
white polygonal dress
white ponytail hair
white porcelain skin
white powder bricks
white powder makeup
white prosthetic eyes
white puffy outfit
white rabbit
white reading glasses
white red
white regal gown
white ribbon
white robe
white robe with gold accents
white robes
white robot
white rocks made of bone
white room
white roses
white royal dress
white russian clothes
white salt
white sand
white sand beach
white sandy beach
white sarong
white satin gloves
white scarf
white scary skin
white sea cloud
white sheets
white shiny skin
white shirt
white shirt and blue jeans
white shirt and green skirt
white shirt and grey skirt
white shirt and jeans
white shirts
white shoes
white short hair
white shorts and hiking boots
white silky outfit
white silver bikini
white skeleton face
white sketch lines
white sketchbook style
white skin
white skin and reflective eyes
white skin color
white skin tone
white skirt
white skirt and barechest
white sky
white sleeves
white smoke
white smoke atmosphere
white snow
white soft leather model
white space
white space in middle
white sparkles everywhere
white sparkles sunlight beams
white spiral horns
white splendid fabric
white stars in the background
white steam on the side
white steel
white stockings
white stone
white stone arches
white stone temple ruins
white stone wall background
white stones
white straight hair
white straw flat brimmed hat
white stripes all over its body
white studio
white studio background
white suit
white suit and black tie
white suit and hat
white summer dress
white sundress
white superior iron man
white sweater
white sweeping arches
white t - shirt
white t-shirt
white t-shirt with red sleeves
white table
white tablecloth
white tank top
white teeth
white thick smoke
white tiger
white tights
white tights covered in stars
white tile on the floor
white tones
white top
white tracing
white transparent veil
white travertine terraces
white trendy clothes
white tshirt
white tunic
white unicorn
white uniform
white vase
white veil
white waist apron and undershirt
white waistcoat
white wall
white wall background
white wall coloured workshop
white wall complex
white walls
white warm illumination
white water
white water rapids
white wax
white wearing
white wheel rims
white white and gold kintsugi
white wine bottle
white wings
white witch
white with black spots
white with chocolate brown spots
white with gold accents
white wolf
white wolf with blue eyes
white woman
white wood
white wrinkled shirt
white xenomorph
white! dress
white! tank top with a print
white!!
white-hair pretty face
white-haired
white-space-surrounding
white: 0.5
whitebangs
whitebangsblackhair
whiteboard
whiteboards
whitedress
whitehorns
whiteout eyes
whiterun
whiterun city burning
whiterun hold
whiterun in the style of pixar
whites
whitesmith
whitespace
whitespace border
whitetank top with a print
whitewashed buildings
whitewashed housed
who
who can open the mind
who is a android
who is a male android
who is a robot
who is a robot”
who is born from the sea
who is looking up at it in fear
who was born in tokyo in 1964
whoa
whole
whole bill framed
whole body
whole body highly detailed
whole body in frame
whole body photography
whole card
whole cat body
whole earth
whole figure
whole head
whole page illustration
whole room
whole shoe is in picture
whole tattoo
whole-length
wholeness
wholesome
wholesome digital art
wholesome techno - shaman lady
whorl
whorl. clouds
whose mind is pure machinery
why
wicca
wicca symbols dnd
wiccan
wicked
wicked grin
wicked smile
wicked witch of the west
wicker art
wicker chair
wide
wide - angle
wide - angle film
wide - angle lens
wide - angle macro lens
wide - angle portrait
wide - angle shot
wide - angle view
wide - shot
wide aerial shot
wide angel
wide angel shot
wide angel shot from below
wide angle
wide angle + masterpiece
wide angle 15mm lens
wide angle 1981
wide angle 70mm lens
wide angle camera
wide angle cinematography
wide angle dynamic action shot
wide angle dynamic portrait
wide angle establishing shot
wide angle exterior 2022
wide angle fantasy art
wide angle full body
wide angle landscape
wide angle landscape photography
wide angle landscape shot
wide angle lens
wide angle lens 8k
wide angle lens glow in the dark
wide angle lens photography
wide angle lens. 8 k
wide angle lense
wide angle perspective
wide angle photo
wide angle photograph
wide angle photography
wide angle portrait of astroboy
wide angle river
wide angle scifi landscape
wide angle shot
wide angle shot 4 k hdr
wide angle shot 4k hdr
wide angle shot from above
wide angle shots
wide angle studio shoot
wide angle ultra - vivid
wide angle view
wide angle. rudolf herczog
wide angle. wayne barlowe
wide angle”
wide aperture
wide ayes
wide belt
wide belt scabbard
wide body
wide brush strokes
wide camera angle
wide chin
wide depth of field
wide establishing shot
wide evil grin
wide eyed
wide eyes
wide eyes shocked expression
wide face
wide field of view
wide film still
wide focus
wide forehead
wide fov
wide frame
wide frontal view
wide full body
wide golden eyes
wide golden eyes and red lips
wide grand staircase
wide greenways
wide grin
wide high angle view
wide hips
wide horizon
wide image
wide irises
wide landscape
wide leather boots
wide leg hakama trousers
wide lens
wide lens photography
wide lens shot
wide lense
wide lips and kind blue eyes
wide long shot
wide long view
wide low angle
wide mouth
wide neck
wide nose
wide nose!!!
wide nostrils
wide open city
wide open city ”
wide open mouth
wide open mouth in terror
wide open space
wide open wife mouth
wide open wings
wide opened eyes
wide overhead shot
wide panoramic shot
wide perspective
wide portrait
wide range of colors
wide ribbons
wide river and lake
wide roads
wide scene
wide screen
wide screen format
wide screenshot
wide set eyes
wide shoot
wide shot
wide shot 4k result
wide shot ; dynamic contrast
wide shot angle
wide shot of a cabin interior
wide shot of a spaceship battle
wide shot photo
wide shot photograph
wide shot!!!!!!
wide skirts
wide smile
wide strokes
wide torso
wide view
wide view cinematic lighting
wide view of a farm
wide views
wide wide angle
wide wide shot
wide windows
wide-angle
wide-angle lens
wide-angle lens vanishing point
wide-angle long shot
wide-angle photograph
wide-angle shot
wide-angle view
wide-perspective
wide-screen
wide-shot
wide-shots
wideangle
wideangle action
wideangle portrait
wideangle pov closeup
widelens
wider face
widescreen
widescreen 4 k
widescreen 4k
widescreen 8 k
widescreen 8k
widescreen ratio
widescreen resolution
widescreen shot
wideshot
widest hips
widest shot
widow maker
widowmaker
widowmaker from overwatch
widowmaker's former lover
width
width 1 0 2 4
width 768
wielding
wielding a battleaxe
wielding a bow
wielding a crowbar
wielding a dagger
wielding a decorated halberd
wielding a fireball
wielding a fish sword
wielding a flaming sword
wielding a huge great axe
wielding a katana
wielding a keris sword
wielding a knife
wielding a long halberd
wielding a longsword
wielding a magic staff
wielding a magical sword
wielding a spear
wielding a sword
wielding a sword and shield
wielding a whip
wielding a wrench
wielding an axe
wielding an axe on each hand
wielding assault rifle
wielding daggers
wielding fire
wielding knife
wielding kunai
wielding longsword
wielding machete
wielding scythe
wielding twin swords
wielding two daggers
wife
wifi icon
wig
wiggling a weasel
wiggly ethereal being
wikiart
wikihow
wikihow illustration
wikimedia
wikimedia commons
wikipedia
wilbur smith
wild
wild agony
wild angle lens
wild animals
wild beautiful donkey
wild berries
wild berry vines
wild black hair
wild brush strokes
wild colors
wild eyebrows
wild eyes
wild flowers
wild fluffy llama portrait
wild foliage
wild forest!!! vegetation!!!
wild forestvegetation
wild ginger hair
wild hair
wild hairs
wild hairstyle
wild jungle
wild look
wild ocean background
wild ocean storm at night
wild pencil style
wild power
wild rave outfit
wild rift
wild river in 1914
wild shape : raccoon
wild species photography
wild spiky black hair
wild spiky black saiyan hair
wild style
wild vegetation
wild water
wild west
wild west background
wild west setting
wild west theme
wildcards
wilderness
wilderness ambiance
wilderness ground
wilderness ground ; oil painting
wildfire
wildflowers
wildflowers and grasses
wildlife
wildlife documentary
wildlife drawing
wildlife illustration
wildlife photo
wildlife photograph
wildlife photography
wildlife photography canon
wildlife preservation
wildlife. atmospheric
wildly attractive
wildscapes
wildstyle
will
will arnett as beetlejuice
will ferrell
will graham
will murai
will smith
will smith anime style
will smith as neo in the matrix
will smith disney infinity
will smith portrait
will smith punching chris rock
will smith slapping chris rock
will terry
willem claesz heda
willem claesz. heda
willem dafoe
willem dafoe as a sith lord
willem dafoe as scarecrow
willem dafoe as spiderman
willem dafoe as the joker
willem dafoe in plague knight
willem wissing
william - adolphe bouguerea
william - adolphe bouguereau
william adolphe bouguereau
william adolphe-bouguereau
william blake and beksinski
william blake and repin
william blake art exhibit
william bouguereau
william dafoe
william eggleston
william eggleston style
william f. buckley graphic novel
william finn
william gibson
william goddard poster
william godward
william henrits
william henry hunt
william higginson
william james glackens
william joyce
william morris style
william murray
william open
william oxer
william penn state forest
william s burroughs
william shakespeare
william shakespeare crying
william warehouse
william waterhouse
william-adolphe
willie nelson on stage
willim blake
willing
willow 1 9 8 5
willow 1985
willow plant
willow smith young
willow smith zendaya
willow tree
willow tree and hill
willow trees
willows
willowy frame
willy lowman
willy wonka
willy wonka pandemic
wilson fisk
wilted flowers
wim crouwel
wim wenders
wimmelbild
wimmelbilder
wimmelbilder maze made of lego
wimmelbilder puzzle book
winamp skin
wind
wind - swept
wind and dust
wind blowing
wind blowing and hair flowing
wind blowing hair
wind blowing leaves
wind blown hair
wind blown trees
wind blows the leaves
wind chimes
wind egyptian god
wind in hair
wind in long hair
wind kissed picture
wind kissed pictures
wind magic
wind river valley
wind rose
wind sorcerer
wind sorcerer!
wind turbines
wind waker
wind wizard
windblown
windblown dark hair
winding around trees
winding branches
winding horn
winding rivers
windings
windmill
windmills
window
window  city
window  rain
window ( city )
window ( rain )
window and city background
window at the center
window glass reflecting
window in foreground
window in room
window into space behind them
window light
window lighting
window lights
window open
window reflections
window sill with plants
window to night time
window view
window with flower box
window with rio de janeiro view
window. netherlands tavern
windows
windows 7
windows 9 5
windows 9 8 logo
windows 95
windows and walls :5
windows lit up
windows vista
windows xp
windows xp background
windows xp bliss wallpaper
windows xp wallpaper
windows98 logo
windowsill
winds of winter
windsor knot tie
windstorm
windswept
windy
windy beach
windy day
windy floating hair!!
windy hair
windy mane
windy mood
windy weather
wine
wine bottle
wine cellar full of food
wine glass
wine label
wine red trim
wine-red and grey trim
wineglass
wing
winged
winged archer
winged boy
winged eyelashes
winged eyeliner
winged girl angel
winged head
winged helmet
winged human
winged insects and stems
winged victory
winged victory of samothrace
winged victory!!!
winged wolves
wings
wings are clouds of darkness
wings black lace wear
wings growing out of arms
wings lace wear
wings made of glass
wings made of light
wings made of pizza
wings made out of flowers
wings of a swan
wings of fire
wings on back
wings open
wings spread
wings spreading
wingspan
wingspan style
wining
wink
winking
winking at the camera
winking face
winking one eye
winner
winner of design award
winner of seven oscars
winner of the year's best photo
winner of the years best photo
winnie the pooh
winning
winning - award masterpiece
winning artwork
winning award image
winning award masterpiece
winning award photo
winning award piece
winning awards
winning illustration
winning photo
winning photo of the year
winning photograph
winning-award masterpiece
winnipeg skyline
winona ryder
winona ryder in repose
winston
winston churchill riding a t-rex
winston from overwatch
winston the ape from overwatch
winter
winter 1941
winter armosphere
winter atmosphere
winter blue drapery
winter concept art
winter forest
winter in the snow
winter lake setting
winter landscape
winter landscape outside
winter mist around her
winter night
winter painting
winter park background
winter photograph
winter princess
winter scene
winter scene fantasy
winter season
winter setting
winter snow
winter soldier mask
winter storm
winter sun
winter time
winter vibes
winter vibrancy
winter wonderland
wintermute
winterthorn blessing
wintertime
wintery scene
wintry rumpelstiltskin
winy
wip
wipe out
wipeout 2 0 4 8
wipeout 2048
wire
wire management
wire sculpture drawings
wired
wired landscape
wireframe
wireframe models
wireframes
wireless headphone stand
wires
wires and cables
wires and cables coming out
wires and cords
wires and lights
wires and resisters
wires and strings
wires and tubes
wires and veins
wires biopunk
wires cables skulls
wires connected
wires cybernetic implants
wires earth background
wires everywhere
wires flying in the air
wires for hair
wires from the matrix movie
wires hanging
wires hanging above street
wires hanging across windows
wires hanging down everywhere
wires in background
wires made of copper
wires with lights
wires. biopunk
wiring
wiry
wisconsin
wisdom
wise
wise appearance
wise forehead
wise old indian guru
wise old man
wisp lights
wisps
wisps of energy in the air
wisps of fog
wisps of smoke
wispy
wispy clouds
wispy clouds in a blue sky
wispy darkness
wispy fog
wispy gigantic wings
wispy ink horror
wispy ink horrors
wispy magical smoke
wispy smoke
wispy smoke and volumetric fog
wispy tendrils of smoke
wistful
wistful bosom
wistful expression
wit studio
wit studio anime
wit studio official media
witch
witch - doctor
witch academia
witch burning
witch clothes
witch cottage in the forest
witch fairytale
witch girl
witch hat
witch hut
witch in the woods
witch paying for her sins
witch runes in the deep forest
witch woman
witch's hat
witchcore
witchcore clothes
witchcraft
witchcraft!
witcher
witcher 3
witcher))
witches
witchlight carnival
witchs hat
witchy
witchy clothing
with
with 3 d render
with 3d render
with a backpack
with a beard and a black jacket
with a beard and a black shirt
with a beautifull smile
with a bewitching voice
with a big head
with a big parrot
with a black background
with a black dark background
with a blue background
with a bob cut
with a bottle of wine
with a bright smile
with a bright yellow aureola
with a brilliant
with a brown fringe
with a bruised face and bruises
with a bunch of stuff
with a bundt bundt pan face
with a bunk bed
with a business suit on
with a castle in the background
with a chinese temple
with a cigarette in its mouth
with a city in the background
with a cool pose
with a covered face
with a creepy secret temple
with a crow on her shoulder
with a crown
with a crown of skulls
with a cup of hot chocolate
with a curly perm
with a cute fluffy owl
with a cute toad
with a dark fringe
with a distant
with a dramatic looking
with a drink
with a few scars on the tree
with a few vines and overgrowth
with a figure in the background
with a fishpond and courtyard
with a french garden
with a fringe
with a front porch
with a full black beard
with a garden
with a garden as foreground
with a giant robot owl
with a glass eye and a top hat
with a gold crown
with a green cloak
with a gullet at the end
with a gun
with a halo of unkempt hair
with a happy expression
with a hurt expression
with a kid
with a lab coat
with a laptop on his lap
with a large breasts
with a large head and big eyes
with a light leather armor
with a long
with a long black cape
with a long white
with a lush grass lawn
with a male pirate partner
with a miniature indoor lake
with a mirror
with a mohawk
with a orientalist smileful face
with a park in the back ground
with a park in the background
with a parrot on his shoulder
with a patrician nose
with a pointed chin
with a ponytail
with a pouting smile
with a red eyes
with a red halo over her head
with a red skirt
with a river running through it
with a robotic arm
with a roof rack
with a round face
with a sad expression
with a seductive smile
with a skeleton army
with a sleek spoiler
with a small beard
with a small forest
with a snowy mountain and ice
with a soft
with a space suit on
with a spine crown
with a spooky filter applied
with a square
with a squid tattoo
with a squid tattoo on his arms
with a star - chart
with a stethoscope
with a straw
with a subtle smile
with a sunset
with a tall tree
with a tentacle - shaped tongue
with a tentacle tattoo
with a thin waist
with a touch of magic
with a tree in the background
with a twin
with a two handed axe
with a very large mouth
with a volcano in the background
with a walking cane
with a waterfalls
with a weird hat
with a white
with a white background
with a white complexion
with a white mug
with a white muzzle
with a white nose
with a whitish
with a wooden stuff
with a xerox machine
with a yellow beak
with a yellow green smog sky
with abs
with accurate face
with accurate features
with acient chinese clothes
with afro
with ai theme
with amber eyes
with an animal skull for a head
with an armor and a crown
with an ashtray on top
with an axe
with an eagle emblem
with an easter basket
with an elegant smile
with an explosion on the back
with an harp
with an intricate
with an iv drip
with an owl on her shoulder
with anamorphic lenses
with anchor man and woman
with ancient chinese aesthetic
with anthropology of wonder
with anti aircraft guns
with anxious
with anxious piercing eyes
with apple
with aqua neon dreadlocks
with aqua rapunzel dreadlocks
with archways
with armour
with arms bare
with arms up
with arteries as roots
with aurora borealis in the sky
with axe
with back to the camera
with backdrop of god rays
with backdrop of natural light
with backlight
with bangs
with beautiful colors
with beautiful exotic
with beautiful mosques
with beautiful volumetric light
with beautiful wings
with bewitching eyes
with big chrome tubes
with big eyes
with big rabbit ears
with big starfish
with black
with black beanie on head
with black eyeglasses
with black eyes
with black glossy lips
with black hair
with black horns instead of ears
with black magic powers
with black metal face paint
with black pigtails
with black sclera eyes
with black suns in the sky
with black vines
with black. magic powers
with blackness instead of eyes
with bloodshot eyes
with bloom ethereal effects
with blue eyes
with blue eyes!!!!
with blue fur and blue eyes
with blue hair
with blue light dark blue sky
with blue light inside
with blue skin
with blunt brown border
with book of science
with bow and arrow
with branches reaching the sky
with branches! reaching the sky
with branchesreaching the sky
with bread in the slots
with bright blue eyes
with broccoli hair
with brown hair
with brown skin
with bruce lee
with bunny ears
with bunny rabbit ears
with burning red eyes
with cables and wires coming out
with cactus plants in the room
with cape
with castle in distance
with cats on her side
with celadon glaze
with chicks
with cigar
with cinematic colour palette
with cinematic lighting
with claws
with clear glass
with closed eyes
with clothes on
with clothese
with clouds in the sky
with cobwebs
with colored flowers
with colorful flowers and plants
with colorfull jellybeans organs
with colourful intricate
with cool headlights
with crown
with cry engine
with cryengine
with crystals on the walls
with curls
with curly black and silver hair
with curly red hair
with cute - fine - face
with cute doting eyes
with damaged rusty arms
with dappled light
with dark brown bangs
with dark brown messy hair
with dark brown sunglasses
with dark clouds in the sky
with dark ghost smokes around
with dark grey hair
with dark hair tied up in a bun
with dark reddish hair
with dark trees in foreground
with deep green eyes
with depressive feeling
with depth of field
with detailed
with detailed facial features
with detailed wood
with differing emotions
with disturbing
with dogs
with donald trump
with donald trump hair
with dong son bronze artifacts
with dramatic lighting
with dramatic sky
with earth
with earth in the background
with eddie murphy
with eiffel view
with electric arc device
with electric arc devices
with elements of the f40
with ethereal light
with everything in its place
with eyes of flame
with facial tattoo
with familiar sprites
with fangs
with fashion clothe
with few fire red highlights
with few ultramarine highlights
with few vivid purple highlights
with fiery golden wings
with fiery golden wings of flame
with fingers
with fire
with fires and black smoke
with flower fields as foreground
with flowers
with flowers and plants
with fluent cloths
with fluo colored details
with fog
with fractal sunlight
with freckles
with fries
with frozen flowers around her
with fruit trees
with full bangs
with full descriptions
with full moon in the sky
with fully detailed faces
with funny feeling
with furniture overturned
with futuristic gear and helmet
with garfield the cartoon cat
with gears and tubes
with glasses
with glasses and goatee
with glasses on
with glitch and scribble effects
with glow on some of its parts
with glowing blue lights
with glowing eyes
with glowing lights at night
with glowing purple eyes
with glowing red eyes
with glowing runes on the body
with glowing windows
with glowing yellow eyes
with god blushing
with godray
with gold eyes
with gold teeth
with golden eyes
with gorgeous detailed eyes
with gradients
with great birds
with great domes and arches
with green cannabis leaves
with green eyes
with grey skin
with hand crossbow
with hands in pockets
with hat
with haunted eyes
with haunted eyes and crazy hair
with haunted eyes and curly hair
with haunted eyes and dark hair
with haunted eyes and glasses
with haunted eyes and wild hair
with hawaiian shirt
with head phones
with headphones
with height
with hellish devil wings
with her long
with high cheekbones
with high detail
with highly detailed
with his back to the viewer
with his back turned
with his hands in his hair
with his hyperactive little dog
with his long black hair
with his pet bird
with hooves and wings
with horns
with horse driven
with huge luminous sad eyes
with huge rpg
with hundreds of network cables
with ice powers
with illustrations and diagrams
with implied lines
with index finger
with infant jesus
with infinity
with influence of jeremy mann
with inspiration from beksinski
with inspiring feeling
with instagram filters
with interior potted palm trees
with intricate detail
with intricate details
with ivy
with jagged rocks & eerie
with jagged rocks and eerie
with jagged rocks eerie
with japanese inspiration
with japanese text
with kerala motifs
with kind face
with kitsune mask
with labels and notes
with labels. high quality
with large breasts
with large golden pipes
with large sword
with large wings
with laser-like focus
with lemon skin texture
with leonidas beard
with leviathan axe
with light-saber
with lightning
with lightning bolts
with lightsaber sword
with long
with long antennae
with long black hair
with long blond hair
with long curly
with long curly hair
with long dark hair
with long floppy rabbit ears
with long hair
with long hair and piercing eyes
with long red hair
with long thin antennae
with long turquoise hair
with long wavy red hair
with long white hair
with lots of dark grey rocks
with lots of details
with lots of fire coming from it
with lots of glittering light
with lots of scumbling
with lots of text and icons
with lots of thin ornaments
with lots of vegetation
with lotus flowers
with lovely look
with magical creatures
with malice
with many gold coins
with many mirrors
with many travelers
with massive
with matsu pine trees
with matte white angled ceiling
with mechanical arms that fix it
with merchant logo
with mirrorshades sunglasses
with morbid thoughts
with mountains as background
with mountains in background
with mountains in the background
with mountains in the distance
with mouth open
with multiple eyes
with multiple sharp
with mustache
with names
with narrow nose
with navigator shaped glasses
with neat stubble
with neon lighting
with neon lights
with neon night spots
with neon signs
with neon visor
with nerdy glasses and goatee
with nerdy! glasses! and goatee
with nerdyglassesand goatee
with nikon cameras
with no beard
with no derpy face
with no face
with no facial features
with no mouth
with no problems
with notes
with octane
with ominous shadows
with ominous storm clouds
with one small
with one vintage book on a table
with orange street lights
with organs
with organs labeled
with ornamental edges
with ornate jewelled
with out shading
with overhead cloudy!!!!! skies
with overhead cloudyskies
with pale skin
with palm trees and columns
with palm trees in the back
with paper lanterns
with parted lips and stunning
with pastel pink highlights
with people inside piloting it
with people watching in wonder
with photorealistic lighting
with piles of coins around it
with pink hair
with pipes attached to it
with pistol
with plasma guns
with playstation 1 graphics
with pointing finger
with pointy ears
with pop punk style
with potted palm trees
with prison clothing
with professional lighting
with professional makeup
with pterosaurs flying
with r2-d2
with radiating hill
with rain and lightning
with rap cap on head
with real wings
with red berries and icicles
with red eyes
with red glowing eyes
with red hair
with red hair and green eyes
with red haze
with red haze and a massive grin
with reflection and textures
with retro sci fi furniture
with rifle
with rifle in hands
with ripped crop t - shirt
with robot dragon head
with robotic arms
with robotic parts on his face
with rolling hills
with roulettes in the roof
with round cheeks
with round face
with rune tattoos on the face
with same hairstyle
with scaly-looking armor
with scars
with scepter and crown
with screens and silks
with seaweed
with serious face expression
with several broken statues
with sharp features
with shiny glass buildings
with shiny skin
with short bobbed white hair
with short hair
with short hair with bangs
with short straight brown hair
with shoulder pads
with silver runes on it
with skin of obsidian
with sleek lines and a powerful
with sleek silver armor
with sleek silver metal armor
with slender
with slight stubble
with small cat on lap
with small eyes
with small nose
with small object details
with small studded earings
with smoke
with snakes for hair
with snow covered colourful red
with snow on its peak
with soft bushes
with soft pink colors
with some hand written letters
with some sausages on the fire
with spaceships in the sky
with sparking circuits
with sparkling gems on top
with sparkling hazel eyes
with spikey short brown hair
with splatoon style
with sprouting rainbow hair
with square glasses
with stalinist style highrise
with stars
with stone henge on top
with straight black hair
with straw hat
with stray dogs
with street food stalls
with strong judging eyes
with stunning green eyes
with subtitles
with sunglass
with sunglasses
with sunset
with surreal architecture
with symbolic elements
with symmetrical facial features
with symmetrical head and eyes
with symmetry intricate detailed
with tail
with tall glass skyscrapers
with tattoos
with teal clothes
with tears
with techware
with text
with textured hair and skin
with the beak of an eagle
with the body of a goddess
with the face of emma watson
with the face of nicholas cage
with the head of a snake
with the mane of a lion
with the moon out
with the sky full of stars
with the sun shining on it
with thunderstorms
with tiny people
with towers
with trees
with trees and rivers
with trees and waterfalls
with trident and crown
with trimmed beard
with trimmed mustache
with tubes and wiring
with two arrows
with two characters
with two front pockets
with two pairs of wings
with two suns in the sky
with unreal engine
with vegeta head hair
with vegetation
with veins of magma and gold
with very  face
with very highly detailed face
with very long blonde hair
with very thin lips
with vestiges of rusty machinery
with vibrant sky
with victorian clothing
with village
with violence
with violence in his eyes
with vivid blue eyes
with vivid purple eyes
with vladimir putin
with vray
with walkways
with wart
with water and boats
with waterfalls
with waterfalls and river
with wet faces
with wet faces!!
with white
with white fluffy fur
with white kanji insignias
with white long hair
with white skin
with white streak in hair
with wide angle lens
with wide open mouth
with wild hair and haunted eyes
with wings
with wings. ultra-detailed
with wires and bandages
with yellow cloths
with yellow flowers around it
with young jedi army behind him
withered
withering autumnal forest
within a lake
within radiate connection
without anomalies
without background
without beard
without beard and mustache
without duplicate image
without duplication
without duplication content
without duplication noise
without eye
without eyebrows
without eyes
without fault
without frame
without glasses
without green grass
without hands
without helmet
without lightsaber
without makeup
without mustache
without nose
without people
without text
witness stand
witty
wiwek
wiz khalifa
wizard
wizard casting a spell
wizard casting acid splash
wizard cloak
wizard doing a tarot reading
wizard examining eggs
wizard fighting a golem
wizard hat
wizard hat cinematic lighting
wizard holding a cage
wizard magic staff
wizard man
wizard of oz
wizard pondering his orb
wizard reading a directory
wizard robe
wizard robes
wizard shuffling cards
wizard sits reading at a desk
wizard themed
wizard tower
wizard's tower
wizards laboratory
wizards of the coast
wizards tower
wlo and peter mohrbacher
wlop
wlop  artgerm
wlop 4k
wlop : :
wlop : : 5
wlop : : 6 octane render : : 3
wlop and andrei riabovitchev
wlop and artgerm
wlop and greg rutkowski
wlop and krenz cushart
wlop and rhads
wlop and ross thran
wlop and ross tran
wlop and rossdraws
wlop and sakimichan
wlop art
wlop glossy skin
wlop hdr
wlop jeremy lipkin
wlop loish and clamp style
wlop painting style
wlop rossdraws
wlop style
wlop |
wlop | artgerm
wlop! muted colors
wlop!!
wlop. 4 k
wlop. scifi
wlopmuted colors
wlup
woamn is curved
wojak
wojtek beksinski macmanus
wojtek fus
wok
wolf
wolf armor
wolf armor mech
wolf companion
wolf ears
wolf fursona
wolf head
wolf howling at full moon
wolf in a snowfield
wolf in hell
wolf like a human
wolf o'donnell
wolf odonnell
wolf pack following
wolf pedant
wolf pelt head
wolf portrait
wolf skulls
wolf template base lineart
wolfenstein
wolff olins
wolff olins |
wolfgang lettl
wolfgang lettl highly detailed
wolfgang novogratz
wolfgirl
wolfy nail
wolp
wolumetric lighting
wolverine
wolverine action pose
wolverine ate
wolves
wolves and their treasures
woman
woman / cat hybrid
woman and cat
woman and robot
woman astronaut
woman body
woman crying
woman crying rainbow paint
woman drinking coffee
woman his holding a sign
woman holding another woman
woman holding recurve bow
woman holding sign
woman human face
woman in a dark factory
woman in a dark forest
woman in a sheep costume
woman in black business suit
woman in black robes
woman in business suit
woman in dress
woman in flowers
woman in streetwear
woman is curved
woman is in a trenchcoat
woman is sitting
woman looks like gilda radner
woman looks like sharon tate
woman love woman
woman loves demon
woman made of black flames
woman made of plants
woman masterpiece
woman model
woman on the beach
woman portrait
woman portrait made out of paint
woman posing
woman riding a flying unicorn
woman silhouette
woman smoking a cigarette
woman smoking cigarette
woman standing in flower field
woman vampire
woman very tired
woman with black hair
woman with braided brown hair
woman with cat ears
woman with freckles
woman with hat
woman with porcelain skin
woman with red hair
woman with rose tinted glasses
woman's face
woman's face looking off camera
woman's portrait
woman's profile
womanhood
womanhood”
womans face
womans face looking off camera
womans portrait
women
women fighting men
women full body
women hand
women playing guitar
women's faces
womens faces
wonbin lee
wonder
wonder boy
wonder woman
wonderdraft
wonderful
wonderful compositions
wonderful dark hair
wonderful details
wonderful eyes
wonderful light
wonderful lighting
wonderful masterpiece
wonderful scene
wonderful shading
wonderful shadows
wonderful techno party
wonderfull techno party
wonderfully colorful
wondering about others
wonderland
wonderland at night
wonderland portal
wonderous
wonderous and magical
wondrous
wong kar wai
woo kim
wood
wood accents
wood and gold details
wood and paper
wood art
wood block pirnt
wood block print
wood block small hippo statue
wood blocks bottom hippo body
wood branch moss plants
wood bridges
wood burn
wood cabin in distance
wood cabinets
wood carved chair
wood carving
wood chair
wood cups
wood door
wood effect
wood elf
wood engraving
wood floor
wood furnishings
wood grain
wood headphones
wood headphones hyperrealistic
wood materials
wood ornaments
wood panel walls
wood paneling
wood panels
wood pier and houses
wood planks
wood print
wood sculpture
wood surface
wood table
wood texture
wood texture on top
wood texture overlays
woodblock
woodblock print
woodcut
woodcut art
woodcut print
woodcut style
wooded environment
wooden
wooden armor
wooden art nouveau swirls
wooden art toys
wooden art toys on base
wooden background
wooden banks
wooden bark armor
wooden boat
wooden bowl
wooden bridge
wooden buildings
wooden cabinet
wooden casing
wooden ceiling
wooden coffee table
wooden cottage
wooden crates
wooden crates and barrels
wooden decoration
wooden desks with books
wooden fence
wooden floor
wooden floor boards
wooden floors
wooden frame
wooden furniture
wooden furtniture
wooden headphones
wooden hippo
wooden house
wooden houses
wooden interior
wooden jagannath face
wooden jewerly
wooden logs
wooden magic wand
wooden match sticks 4k texture
wooden parquet
wooden platforms
wooden sailboats
wooden side table
wooden staff
wooden stairs
wooden statue
wooden structures
wooden supports
wooden table
wooden tables
wooden toilets
wooden trim
wooden walls brass panels
wooden walls with framed art
woodfired
woodland
woodland background
woodland backround
woodland creek
woodland grove
woodland location
woodland setting
woodland village
woodlands
woodlands style
woodlathe
woodpunk
woodrow wilson
woods
woods background
woods on fire
woodstock
woodturning
woody allen
woody foliage
woody from toy story
woody\'s homework
woof woof
wookie
wool
wool felting art
woolen torso in medieval clothes
wop
word
wordly
words
work
work clothes
work in progress
work of art
work wheels
work-wheels
workbench
worked stone
workers
workers revolution
working
working at mcdonalds
working clothes
working hard
working in a call center
working in an office
working in her science lab
working in the forge
working inside reactor room
working on a laptop at a desk
working on her laptop
working out
working out in the field
workout
worksafe
worksafe. 2000s
worksafe. cgsociety
worksafe. cinematic
worksafe. dramatic
worksafe. illustration
worksafe. instagram photo
worksafe.1990s
worksafe.2000s
worksafe.cgsociety
workshop
workshop background
world
world best photography
world boss kitten
world building
world championship fight
world curvature
world ending nuke
world machine
world map
world of darkness
world of fire and blood
world of lute
world of madness
world of only concrete
world of sleepers
world of the silence
world of war spaceships
world of warcarft
world of warcraft
world of warcraft armor
world of warcraft art
world of warcraft art style
world of warcraft concept art
world of warcraft elven druid
world of warcraft screenshot
world of warcraft spell icon
world of warcraft splash art
world of warcraft style
world on fire
world peace
world press photo
world press photo awarded
world record
world renowned artists
world seen only through a portal
world series of poker
world trade center twin towers
world tree
world war
world war 1
world war 2
world war 2 portrait photo
world war 3
world war 4
world war ii
world war ii military style
world war iii
world war one
world war one robot
world war three
world-bearing turtle
worlds collide
worlds within worlds
worlf of warcraft
worm
worm brown theme
worm hole
worm monster
worm mouth
worm round mouth
worm's - eye view
worm's eye view
worm's eye view from floor
worm's eye view from the floor
worm's-eye view
worm\'s eye view
wormhole
wormholes
worms
worms - eye - view
worms - eye view
worms eye view
worms eye view from the floor
worms intricated
worms-eye view
worms-eye-view
worn
worn black coat
worn clothes
worn decay texture
worn mono - yellow wallpaper
worn out
worn out clothes
worn paint
worn pants
worn traveling cloak
worried
worry
worrying eyes
worship
worship of the pope
worshipers
worst
worst place to live in europe
worth 1 0 0 0. com
worth 1000. com
worth1000.com
worthless
worthlessness
wotc
wotjek fus
would you let me dress you
wound
wounded
wounded soldiers
wounds
woven armour
woven with electricity
wow
wow 4 k detail fantasy
wow 4k detail fantasy
wow atmosphere
wow clouds
wow factor
wow i am artist now
wow it is beautiful
wow wow detail
wow!
wow!!!!!!
wpa
wpap
wpol and sarasti
wprost
wraith
wraith from apex legends
wraiths riding in the sky
wrapped
wrapped arms
wrapped blue background
wrapped in a black scarf
wrapped in black
wrapped in black tentacles
wrapped in cables and flowers
wrapped in flowers
wrapped in flowers and wired
wrapped in leather straps
wrapped in vines
wrapped in wires and piones
wrapped thermal background
wrath
wrath flame and ruin
wreath of ferns
wreathed in flame
wrecked
wrecked buildings
wrecked cars
wrecked technology
wrench
wrenches
wrestler
wrestler hulk hogan
wrestlers wearing vr headsets
wrestling
wrestling ring
wretched and corrupted knights
wretched camel
wrinkled
wrinkled big cheeks
wrinkled face
wrinkled muscles skin
wrinkled skin
wrinkles
wrinkles and muscle tissues
wrinkles and muscles
wrinkly
wrinkly forehead
writhing
writing a letter
writing in journal
writing on a clipboard
writings
written in a neat
wrong perspective points
wrote renaissance michelangelo
wrought iron
wrought iron architecture
wrx golf
wry smile
wry smirk
wsop
wtf
wu liu
wukong
wunderkammer
wuthering heights
wuxia
ww 1
ww 1 sith sorcerer
ww 2
ww 2 allied propaganda poster
ww 2 american propoganda poster
ww 2 historical photography
ww 2 normandy foy arnhem 1 9 4 4
ww 2 propaganda poster
ww 3
ww1
ww1 film photo
ww1 photo
ww1 trench
ww2
ww2 era
ww2 era photograph
ww2 historical photo
ww2 historical photography
ww2 normandy foy arnhem 1944
ww2 photo
ww2 space tech
ww3
wwe
wwf
wwi
wwii
wwii propaganda poster
wyatt earp and doc holliday
wyeth
wylops
wyndham lewis. behance
wyoming
wyvern
wētā fx
x
x - box
x - day
x - files
x - men
x - ray
x - ray black and white
x - wings
x = √ 4 5 y + 1 2 5 z
x logo
x man costume
x ray
x wing starfighter
x-man costume
x-men
x-men storm
x-ray
x-ray photography
x-wing
xbox
xbox 3 6 0
xbox 3 6 0 graphics
xbox 360 graphics
xbox series x
xcopy teeth
xenobiology
xenoblade
xenogears
xenomorph
xenomorph alien
xenomorph octopus in a swamp
xenomorphic
xenomorphs
xenon
xenophobia
xerography
xerox punk
xf iq 4
xf iq4
xhenomorph
xi jinping
xi jinping as winnie the pooh
xi jinping vs donald trump
xiang duan
xianxia
xianxia fantasy
xianxia hero
xiaofan zhang
xiaoguang sun
xiaolong wang
xintong chen
xision
xision wu
xix century military outfit
xixth century painting
xkcd
xkung work
xmen
xparticles
xqc
xqcow
xray
xray art
xray hd
xray melting colors
xray melting colors!!
xsullo
xue han
xxl
xxxtentacion
xylophone
y
y 2 k
y 2 k aesthetic
y 2 k cutecore clowncore
y 2 k cybercore
y 2 k fashion
y 2 k style
y 2k
y 2k aesthetic
y 2k cutecore clowncore
y 2k cybercore
y 2k style
y 3
y pre-raphaelite
y2k
y2k aesthetic
y2k design
y2k style
y2k!!!!!!
y2k”
y3
yael shelbia
yagami light
yah
yah! cultist journal cover
yahcultist journal cover
yakisugi
yakuza
yakuza 4
yakuza slim girl
yakuza tattoo on body
yama no susume
yamamoto
yamato
yami kawaii
yamy
yamy yamy
yan
yan gisuka
yan morala
yand.re
yandere
yandere intricate
yandere. tall
yang qi
yangjun chen
yanjun chengt
yann arthus - bertrand
yann blomquist
yantra
yaoi
yard
yarn
yarn ball
yaroslav tokar
yashica me - 1
yashica t 4
yasuhiko yoshikazu
yasuke 5 0 0 px models
yasumoto oka
yasunari ikenaga
yasushi nirasawa
yasushi nirasawa style
yasushi nirasawa syd mead
yautja
yaw 0 degrees
yawning
yayoi kasuma
ycbcr
ye mighty
yeah
year 1 9 0 0
year 1 9 1 4
year 1 9 2 0
year 1 9 4 4
year 1 9 7 0
year 1506
year 1850
year 1900
year 1920
year 1930
year 1968
year 1970
year 2 0 2 2
year 2 0 4 0
year 2 1 0 0
year 2 3 0 0
year 2 5 0 0
year 2 8 6 6
year 2040
year 2050
year 2099
year 2100
year 2134
year 2300
year 2447
year 3 0 0 0
year 3000
year 3022
year's best award
yearbook photo
years best award
years old
yeast
yee chong
yee chong silverfox
yeezus
yeezy
yeezy collection
yeg
yelena belova
yelling
yelling furiously
yellow
yellow - orange eyes
yellow and black
yellow and black color scheme
yellow and black grapes
yellow and black trim
yellow and blue
yellow and blue and cyan
yellow and blue color scheme
yellow and blue ribbons
yellow and charcoal
yellow and charcoal leather
yellow and cyan color palette
yellow and green
yellow and green scheme
yellow and greens
yellow and olive color scheme
yellow and orange color scheme
yellow and ornage color scheme
yellow and purple color scheme
yellow and purple tones
yellow and red
yellow and red color scheme
yellow apples
yellow artificial lighting
yellow aura
yellow aureole
yellow awning
yellow backdrop
yellow background
yellow background beam
yellow battlefield theme
yellow beak
yellow broad sword
yellow butterflies
yellow calibri font
yellow cap
yellow carpeted
yellow carrera glasses
yellow charcoal
yellow clothes
yellow clouds
yellow color scheme
yellow colors
yellow colours
yellow crystal gem
yellow cyborg eyes
yellow details
yellow dragon head festival
yellow eye
yellow eyes
yellow flash
yellow flowers
yellow fur
yellow fur explodes
yellow gemstones
yellow gemstones spikes
yellow glowing background
yellow hair
yellow hardhat
yellow helmet
yellow hue
yellow human-sized warforged
yellow infrared
yellow irises
yellow lanterns
yellow latex gloves
yellow light
yellow light spell
yellow lighting
yellow lighting from right
yellow lightning
yellow lights
yellow magic theme
yellow makeup
yellow mech
yellow mecha keetongu bionicle
yellow minion from despicable me
yellow mist
yellow navy teal black and gold
yellow neon cyborg eyes
yellow ochre
yellow ocre tint dominant
yellow orange
yellow ornate dress
yellow overall
yellow parasol
yellow pupils
yellow purple
yellow purple green
yellow purple green black
yellow radiant magic
yellow raincoat
yellow ranger
yellow rat pig
yellow red scheme
yellow robe
yellow robes
yellow rose
yellow scheme
yellow school bus
yellow seaweed
yellow skin
yellow sky
yellow space suit
yellow spiky hair
yellow sport glasses
yellow street lights
yellow sunshine
yellow theme
yellow tint
yellow translucent lace
yellow uneven teeth
yellow volumetric fog
yellow wallpaper
yellow walls
yellow windows and details
yellow x-man costume
yellow-orange
yellow-robed cultist
yellowed
yellowed paper
yellowed with age
yellowing wallpaper
yellowish full moon
yellowish light
yellows
yellows and blacks
yellows and reddish black
yelow
yeltsin
yen press
yennefer
yennefer of vengerberg
yes
yes it goes on and on my friends
yet unrecognizable
yggdrasil
yggrdasil
yihao ren
yin yang
yin zhen chu
ying and yang
yintion j - jiang geping
yinyang shaped
yiqiang and shurakrgt
ymmetrical
ymmm and that smell
yo
yo )
yoda
yoda as a zombie
yoda fantasy art portrait
yoda:-1
yog - sothoth! yah
yog - sothothyah
yoga
yoga meditation pose
yoga pose
yogurt
yoh yoshinari
yoh yoshinori
yohann schepacz and laurel burch
yohji yamamoto
yoichi hatakenaka
yoji shinakawa
yoji shinkawa : : portrait
yoji shinkawa and geof darrow
yoji takeshige
yokai
yoki shinkawa
yoko d'holbachie
yoko dholbachie
yoko matsugane as mai shiranui
yoko taro
yolie leif
yolo
yor forger
yorkshire
yorkshire terrier
yoruba body paint
yosemite
yosemite valley
yoshi
yoshiaki kawajiri
yoshida
yoshifumi ushima
yoshikata amano
yoshikazu yasuhiko
yoshiku
yoshimi versus the evil robots
yoshimura exhaust
yoshinari yoh
yoshio awazu
yoshitaka
yoshitaka amano and junji ito
yoshitaka amano character design
yoshitaka amano greg rutkowski
yoshitaka amano octane render
yoshitaka amano photorealistic
yoshitaka amano style
yoshitaka amano vibe
yoshitakka amano karol bak
yoshitomo nara
yoshitomo nara and aya takano
yoshiyuki sadamoto
yosuke ueno
yotobi
you
you are lost in the backrooms
you being dragon food
you better run
you can see all the passageways
you can see in the picture
you felt the edge again
you g face
you grow. then you focus on shit
you have yourself a very fun
you know
you looking into the maw
you may say i'm a dreamer
you may say im a dreamer
you see fishes
you wanted to
you were just like me
you won't believe it
you wont believe it
you're something special
young
young adorable korean face
young adult
young adult fantasy novel cover
young adult male
young almighty god
young and beautiful
young and cute
young and cute girl
young and slender
young angry woman
young anime girl
young anime man
young arab monica bellucci
young asian girl
young asian woman
young audrey hepburn
young beautiful
young beautiful amouranth
young beautiful face
young beautiful hippie girl
young beautiful woman
young black woman
young blonde boy fantasy thief
young blonde woman
young boy
young business woman
young carrie fisher
young child
young commoner
young cute face
young cute wan asian face
young david bowie
young dolly parton
young dolly parton portrait
young face
young female
young female face
young female firefighter
young female in black tuxedo
young female ww 2 soldier
young girl
young girl in steampunk clothes
young girl lies on a meadow
young girl playing flute
young girls
young glitched woman
young goddess
young gravure idol
young greek man
young half elf wizard
young handsome man
young handsome pale roma
young harpy-girl
young himalayan woman
young idol
young jennifer connelly
young jim caviezel
young lady
young lady cyborg
young lynx
young male
young male anthro dragon
young male with walking stick
young man
young man in a purple hoodie
young man with beautiful face
young man with medium - length
young man with short
young middle eastern woman
young natalie portman
young nicole kidman
young noble
young pretty gravure idol
young prince
young queen jezebel
young redhead girl in motion
young robert plant
young scarlett johansson
young sensual graceful
young sensual graceful woman
young sensual gravure idol
young sensual woman
young sexy elegant woman
young shadow mage male
young simon baker
young skinny gravure idol
young sophia loren
young southern woman
young spanish man
young swimsuit model
young teen
young thug
young tom hiddleston
young tsar
young victorian sad fancy lady
young wan angel
young with long hair
young wizard
young woman
young woman as medusa
young woman in a dress
young woman in her 20s
young woman looking up
young woman with antlers
young woman with long dark hair
young woman with lynx head
young woman's face
young womans face
young women
young!!!
younger brother vibes
your head turns back
your mom
your name
your name movie style
your personal data avatar
youre something special
youth
youthful
youthful appeal
youthful colours
youthful face
youthful taliyah
youtube
youtube clickbait
youtube logo
youtube thumbnail
youtube video
youtube video screenshot
youtuber
yoworld
yu - no
yuan - ti
yugioh
yugioh art style
yugioh artwork
yugioh card
yugioh style
yuichi kato
yuji himukai
yukata clothing
yukii morita
yukio - e
yukito kishiro
yukky
yuli ban
yulia nevskaya
yum
yume nikki
yummy
yun ling
yung lean
yuri shwedoff and tom bagshaw
yuru camp
yuruyuri
yusuke murata
yusuke murata and junji ito
yusuke murata and makoto shinkai
yusuke nakano
yutja
yuuka kazami
yuuki hagure
yuxiang chen
yuya nagai
yuyushiki
yvonne strahovski
yzy
yzy gap
z
z brush
z brushcentral
z ed
z nation
z'gok. 8 k hd resolution
z-brush
z-brush sculpt
z. beksinski
zaba style
zabrocki
zac efron
zac retz
zach galifianakis funko pop
zach hill
zachary corzine
zachary quinto
zack de la rocha
zack snyder 3 8 4 0 x 2 1 6 0
zack snyder 3840 x 2160
zack snyder cinematography style
zaha hadi
zaha hadid architecture
zaha hadid building
zaha hadid octane highly render
zaha hadid style architecture
zaku
zany
zaragoza gilberto soren
zarya
zarya from overwatch
zaun
zaush
zawadzki
zayn malik
zbrush
zbrush 3 d render
zbrush 3d render
zbrush art
zbrush contest winner
zbrush hard surface
zbrush model
zbrush render
zbrush sculpt
zbrush sculpt colored
zbrush!!
zbrushcentral
zdislav beksinsk
zdislav beksinsk - h768
zdislav beksinski
zdislav bexinski
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zdiszlaw beksinski
zdizslaw beksinski
zdzidaw
zdzislaw
zdzislaw bekinski
zdzislaw beksinksi
zdzislaw beksinsk
zdzislaw beksinski and canaletto
zdzislaw beksinski style
zdzislaw beksisnski
zdzislaw beksiński
zdzislaw belsinki
zdzislaw oil on canvas
zdzisław
zdzisław beksinski and giger
zdzisław beksiński
zdzisław beksiński style
zebra
zebra op art
zebra stripes
zee day
zeen chin and farel dalrymple
zeen chin and terada katsuya
zeiss
zeiss 1 5 0 mm
zeiss 1 5 0 mm f 2. 8 hasselblad
zeiss 14mm f1.8
zeiss 150 mm
zeiss 150 mm f 2. 8 hasselblad
zeiss 150mm f/ 2.8 hasselblad
zeiss 150mm f/2.8 hasselblad
zeiss 150mm f2.8 hasselblad
zeiss 18mm f2.8
zeiss 24mm f1.8
zeiss 24mm f2.8
zeiss 5 0 mm f 1. 8 lens
zeiss 50 mm f 1. 8 lens
zeiss 50mm f 10
zeiss 50mm f1.8
zeiss 50mm f2.8
zeiss 75mm
zeiss 8 0 mm f 2. 8 lens
zeiss 80 mm f 2. 8 lens
zeiss lens
zeiss lenses
zelda
zelda and link
zelda botw
zelda breath of the wild
zelda breath of the wild map
zelda style art
zelda twilight princess
zelda with triforce
zelensky having a tantrum
zellk
zen
zen aesthetic
zen atmosphere
zen concept
zen feeling
zen garden
zen ink
zen meditation
zen méditation cyberpunk
zen natural background
zen neon
zen prisms
zen sand carved lawn
zen temple background
zendaya
zendaya as she-hulk
zenescope
zeng fanzh
zeng fanzhi
zenith angle
zenith perspective
zenith view
zenithal lighting
zenithal lightning
zenithal view
zenobia
zenra taliyah
zentai suit
zenyatta from overwatch
zeppelin
zeppelin dock
zeppelin in the air
zerg
zerg hydralisk
zerg starcraft
zero - hour
zero dawn
zero g
zero gravity
zero suit samus
zero two
zerochan
zerochan art
zeronis
zeronis style
zettai ryouiki
zeus
zezhou chen
zhang daqian
zhang yimou
zhangjiajie
zhangjiajie in early morning
zhangjiajie national forest park
zhao yun
zhelong xu
zhong lin
zhongli from genshin impact
zhouzhuang ancient town
zhuoxin ye
zido
zig zag
zig-zags. mountains
ziggurat
zigor samaniego
zigor samaniego style
zillow interior
zinc white
zine
zine cover
zippered opening in skull
zippers
zirconium pants
zmonzheng
zochi
zodiac
zodiac libra sign
zodiac sign
zodiac signs
zoe kazan
zoe kravitz as an astronaut
zoe kravitz futuristic astronaut
zoe saldana
zoey deschannel
zoey frank
zoidberg
zoids
zoids art
zoltan
zombie
zombie apocalypse
zombie arms out in front
zombie chasing coffee
zombie coffee logo
zombie cyborg
zombie drinking coffee logo
zombie everywhere
zombie face
zombie from doom eternal
zombie ghouls
zombie holding coffee cup
zombie horde
zombie in horror concept art
zombie killer
zombie not enjoying coffee
zombie pirate
zombie reaching out of a grave
zombie ronald reagan
zombie with white eyes
zombie yoda
zombie's
zombies
zombies apocalipsis
zombies attack
zombies in the streets
zombified
zone of enders
zone of the enders
zoo
zoo photography
zooey deschanel
zoology! fantastical creatures
zoom
zoom blur
zoom in
zoom in on face
zoom lens
zoom on alien face
zoom out
zoom photograph
zoom shot
zoomed
zoomed in
zoomed in shots
zoomed out
zoomed out full body
zoomed out photography
zoomed out portrait of a duke
zoomed out shot
zoomed out to show entire image
zoomed out very far
zoomed out view
zoomed view of a banknote
zoomorphic
zoomorphism
zootopia
zootopia (2016)
zootopia 2
zootopia concept art
zootopia movie style
zootopia stile
zootopia style
zora
zoran janjetov
zordon from power rangers
zorua pokemon
zoë kravitz
zuckerberg
zuckerberg and bezos
zulu
zumi
zumidraws
zun
zurbaran
zx spectrum color palette
zyzz
zzislaw beksinski
{on edge}
{perfect body}
{perfect eyes}
{perfect face}
{uncertain look}
| 28mm |
| 35mm|
| demonic | horror themed
| fine detail anime
| | epic - fine - clean
| | very very anime!!!
|| very anime
« attractive
¯_(ツ)_/¯
ástor alexander
éclairage volumétrique
épaule devant pose
ƒ / 2. 5
ƒ / 3. 5
ƒ / 8
ƒ 1. 8
ƒ/2.5
ƒ/3.5
ƒ/5.0
ƒ/5.6
ƒ/8
ƒ1.8
ƒ5.6
а fantasy proto-slavic mythology
сastle on the rock
сinematic lighting
фото девушка курит
—ar 16:9
—n 9
‘luca’
‘raya and the last dragon’ etc
‘zootopia’
“ aerial view of a mountain
“ anime
“ ethereal
“ femme on a galactic shore
“ full body
“ golden chalice
“ golden cup
“ iron bark
“ painting
“ scarlett johansson portrait
“ sensual
“2019 bmw m5 wagon
“a dinosaur market
“air jordan 1
“anime girl
“berries
“derelict architecture buildings
“diamonds
“dnd dwarf
“early 1900s daguerreotype
“erebos’s titan
“fire breathing dragon
“gas station photography
“hockey team logo
“house music rave with dancers
“hyper realistic
“jeffrey” logo
“leonardo dicaprio
“loss of inner self
“megan fox
“meryl streep portrait
“modern calligraphy art
“natalie portman
“organic
“pig
“pixel art
“portrait of a cartoon animal
“portrait of leonard cohen
“puffy cloudscape
“scarlett johansson portrait
“the ultimate gigachad
“totoro
“uwu the prismatic person
“wide shot
“zendaya
”beautiful anime woman
”ultra realistic
→ ⃣ spectrum darkness prime
██full of expressions██
███beautiful face███
☁🌪🌙👩🏾
☕ on the table
✏️🎨
✨🕌🌙
❄️ corruption conceptart
❤🔥🍄🌪
【 sciart 💙💜 mson
あかさたなは on twitter
けもの
アニメ
中 元 节
中 国 鬼 节
千 葉 雄 大
奈良美智
王琛
🇺🇦
🌲🌌
🌸 🌼 💮
🌺 cgsociety
🌻🎹🎼
🍁 cute
🍂 cute
🍸🍋
🎀 🍓 🧚
🎀 🗡 🍓 🧚
🎀 🧟 🍓 🧚
🎀 🪓 🧚
🎨🖌
🎨🖌️
🐋 as 🐘 as 🤖 as 👽 as 🐳
🐎🍑
🐝👗👾
🐿🍸🍋
👁🌹👾
👅 👅
👰 🏇 ❌ 🍃
💋 💄 👠 👗
💣 💥
💣 💥💣 💥
📷 mungojerrie and rumpleteazer
🔞🤡
🔥 😎 🕹️ 👀
🔥 😎 🕹️ 👀 :2
🕹️ 😎 🔫 🤖 🚬
🕹️ 😎 🚬
😃😀😄☺🙃😉😗
😭 🤧 💔
😭 🤮 💕 🎀
😭🤮 💔
😱 chaos · nightmare resin
🚀🌈🤩
🚀🚀🚀
🚿🗝📝
🤠 using a 🖥
🤤 girl portrait
🤬 🤮 💕 🎀
🥥 🍕 hybrid
🦑 design
🦩🪐🐞👩🏻🦳
🧒 📸 🎨
🪔 🎨;🌞🌄


================================================
FILE: data/img2txt/mediums.txt
================================================
a 2 koma
a 2koma
a 3D render
a 4 koma
a 4koma
a 6 koma
a 6koma
a 8 koma
a 8koma
a black and white photo
a bronze sculpture
a cartoon
a cave painting
a character portrait
a charcoal drawing
a child's drawing
a color pencil sketch
a colorized photo
a comic book panel
a computer rendering
a cross stitch
a cubist painting
a detailed drawing
a detailed matte painting
a detailed painting
a diagram
a digital painting
a digital rendering
a drawing
a fine art painting
a flemish Baroque
a gouache
a hologram
a hyperrealistic painting
a jigsaw puzzle
a koma
a low poly render
a macro photograph
a manga drawing
a marble sculpture
a matte painting
a microscopic photo
a mid-nineteenth century engraving
a minimalist painting
a mosaic
a painting
a pastel
a pencil sketch
a photo
a photocopy
a photorealistic painting
a picture
a pointillism painting
a polaroid photo
a pop art painting
a portrait
a poster
a raytraced image
a renaissance painting
a screenprint
a screenshot
a silk screen
a sketch
a statue
a still life
a stipple
a stock photo
a storybook illustration
a surrealist painting
a surrealist sculpture
a tattoo
a tilt shift photo
a watercolor painting
a wireframe diagram
a woodcut
an abstract drawing
an abstract painting
an abstract sculpture
an acrylic painting
an airbrush painting
an album cover
an ambient occlusion render
an anime drawing
an art deco painting
an art deco sculpture
an engraving
an etching
an illustration of
an impressionist painting
an ink drawing
an oil on canvas painting
an oil painting
an ultrafine detailed painting
chalk art
computer graphics
concept art
cyberpunk art
digital art
egyptian art
graffiti art
lineart
pixel art
poster art
vector art


================================================
FILE: data/img2txt/movements.txt
================================================
abstract art
abstract expressionism
abstract illusionism
academic art
action painting
aestheticism
afrofuturism
altermodern
american barbizon school
american impressionism
american realism
american romanticism
american scene painting
analytical art
antipodeans
arabesque
arbeitsrat für kunst
art & language
art brut
art deco
art informel
art nouveau
art photography
arte povera
arts and crafts movement
ascii art
ashcan school
assemblage
australian tonalism
auto-destructive art
barbizon school
baroque
bauhaus
bengal school of art
berlin secession
black arts movement
brutalism
classical realism
cloisonnism
cobra
color field
computer art
conceptual art
concrete art
constructivism
context art
crayon art
crystal cubism
cubism
cubo-futurism
cynical realism
dada
danube school
dau-al-set
de stijl
deconstructivism
digital art
ecological art
environmental art
excessivism
expressionism
fantastic realism
fantasy art
fauvism
feminist art
figuration libre
figurative art
figurativism
fine art
fluxus
folk art
funk art
furry art
futurism
generative art
geometric abstract art
german romanticism
gothic art
graffiti
gutai group
happening
harlem renaissance
heidelberg school
holography
hudson river school
hurufiyya
hypermodernism
hyperrealism
impressionism
incoherents
institutional critique
interactive art
international gothic
international typographic style
kinetic art
kinetic pointillism
kitsch movement
land art
les automatistes
les nabis
letterism
light and space
lowbrow
lyco art
lyrical abstraction
magic realism
magical realism
mail art
mannerism
massurrealism
maximalism
metaphysical painting
mingei
minimalism
modern european ink painting
modernism
modular constructivism
naive art
naturalism
neo-dada
neo-expressionism
neo-fauvism
neo-figurative
neo-primitivism
neo-romanticism
neoclassicism
neogeo
neoism
neoplasticism
net art
new objectivity
new sculpture
northwest school
nuclear art
objective abstraction
op art
optical illusion
orphism
panfuturism
paris school
photorealism
pixel art
plasticien
plein air
pointillism
pop art
pop surrealism
post-impressionism
postminimalism
pre-raphaelitism
precisionism
primitivism
private press
process art
psychedelic art
purism
qajar art
quito school
rasquache
rayonism
realism
regionalism
remodernism
renaissance
retrofuturism
rococo
romanesque
romanticism
samikshavad
serial art
shin hanga
shock art
socialist realism
sots art
space art
street art
stuckism
sumatraism
superflat
suprematism
surrealism
symbolism
synchromism
synthetism
sōsaku hanga
tachisme
temporary art
tonalism
toyism
transgressive art
ukiyo-e
underground comix
unilalianism
vancouver school
vanitas
verdadism
video art
viennese actionism
visual art
vorticism


================================================
FILE: data/img2txt/sites.txt
================================================
Artstation
behance
cg society
cgsociety
deviantart
dribble
flickr
instagram
pexels
pinterest
pixabay
pixiv
polycount
reddit
shutterstock
tumblr
unsplash
zbrush central


================================================
FILE: data/img2txt/subreddits.txt
================================================
/r/ImaginaryAetherpunk
/r/ImaginaryAgriculture
/r/ImaginaryAirships
/r/ImaginaryAliens
/r/ImaginaryAngels
/r/ImaginaryAnimals
/r/ImaginaryArchers
/r/ImaginaryArchitecture
/r/ImaginaryArmor
/r/ImaginaryArtisans
/r/ImaginaryAssassins
/r/ImaginaryAstronauts
/r/ImaginaryAsylums
/r/ImaginaryAutumnscapes
/r/ImaginaryAviation
/r/ImaginaryAzeroth
/r/ImaginaryBattlefields
/r/ImaginaryBeasts
/r/ImaginaryBehemoths
/r/ImaginaryBodyscapes
/r/ImaginaryBooks
/r/ImaginaryCanyons
/r/ImaginaryCarnage
/r/ImaginaryCastles
/r/ImaginaryCaves
/r/ImaginaryCentaurs
/r/ImaginaryCharacters
/r/ImaginaryCityscapes
/r/ImaginaryClerics
/r/ImaginaryCowboys
/r/ImaginaryCrawlers
/r/ImaginaryCultists
/r/ImaginaryCybernetics
/r/ImaginaryCyberpunk
/r/ImaginaryDarkSouls
/r/ImaginaryDemons
/r/ImaginaryDerelicts
/r/ImaginaryDeserts
/r/ImaginaryDieselpunk
/r/ImaginaryDinosaurs
/r/ImaginaryDragons
/r/ImaginaryDruids
/r/ImaginaryDwarves
/r/ImaginaryDwellings
/r/ImaginaryElementals
/r/ImaginaryElves
/r/ImaginaryExplosions
/r/ImaginaryFactories
/r/ImaginaryFaeries
/r/ImaginaryFallout
/r/ImaginaryFamilies
/r/ImaginaryFashion
/r/ImaginaryFood
/r/ImaginaryForests
/r/ImaginaryFutureWar
/r/ImaginaryFuturism
/r/ImaginaryGardens
/r/ImaginaryGatherings
/r/ImaginaryGiants
/r/ImaginaryGlaciers
/r/ImaginaryGnomes
/r/ImaginaryGoblins
/r/ImaginaryHellscapes
/r/ImaginaryHistory
/r/ImaginaryHorrors
/r/ImaginaryHumans
/r/ImaginaryHybrids
/r/ImaginaryIcons
/r/ImaginaryImmortals
/r/ImaginaryInteriors
/r/ImaginaryIslands
/r/ImaginaryJedi
/r/ImaginaryKanto
/r/ImaginaryKnights
/r/ImaginaryLakes
/r/ImaginaryLandscapes
/r/ImaginaryLesbians
/r/ImaginaryLeviathans
/r/ImaginaryLovers
/r/ImaginaryMarvel
/r/ImaginaryMeIRL
/r/ImaginaryMechs
/r/ImaginaryMen
/r/ImaginaryMerchants
/r/ImaginaryMerfolk
/r/ImaginaryMiddleEarth
/r/ImaginaryMindscapes
/r/ImaginaryMonsterBoys
/r/ImaginaryMonsterGirls
/r/ImaginaryMonsters
/r/ImaginaryMonuments
/r/ImaginaryMountains
/r/ImaginaryMovies
/r/ImaginaryMythology
/r/ImaginaryNatives
/r/ImaginaryNecronomicon
/r/ImaginaryNightscapes
/r/ImaginaryNinjas
/r/ImaginaryNobles
/r/ImaginaryNomads
/r/ImaginaryOrcs
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/r/ImaginaryPirates
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/r/ImaginaryPrisons
/r/ImaginaryPropaganda
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/r/ImaginaryRobotics
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/r/ImaginaryScience
/r/ImaginarySeascapes
/r/ImaginarySkyscapes
/r/ImaginarySlavery
/r/ImaginarySoldiers
/r/ImaginarySpirits
/r/ImaginarySports
/r/ImaginarySpringscapes
/r/ImaginaryStarscapes
/r/ImaginaryStarships
/r/ImaginaryStatues
/r/ImaginarySteampunk
/r/ImaginarySummerscapes
/r/ImaginarySwamps
/r/ImaginaryTamriel
/r/ImaginaryTaverns
/r/ImaginaryTechnology
/r/ImaginaryTemples
/r/ImaginaryTowers
/r/ImaginaryTrees
/r/ImaginaryTrolls
/r/ImaginaryUndead
/r/ImaginaryUnicorns
/r/ImaginaryVampires
/r/ImaginaryVehicles
/r/ImaginaryVessels
/r/ImaginaryVikings
/r/ImaginaryVillages
/r/ImaginaryVolcanoes
/r/ImaginaryWTF
/r/ImaginaryWalls
/r/ImaginaryWarhammer
/r/ImaginaryWarriors
/r/ImaginaryWarships
/r/ImaginaryWastelands
/r/ImaginaryWaterfalls
/r/ImaginaryWaterscapes
/r/ImaginaryWeaponry
/r/ImaginaryWeather
/r/ImaginaryWerewolves
/r/ImaginaryWesteros
/r/ImaginaryWildlands
/r/ImaginaryWinterscapes
/r/ImaginaryWitcher
/r/ImaginaryWitches
/r/ImaginaryWizards
/r/ImaginaryWorldEaters
/r/ImaginaryWorlds


================================================
FILE: data/img2txt/tags.txt
================================================
04matsuko
100-percent-donmai
101-goushitsu
35-machi
39tei
3dlive
5505-project
5base
6channel
6no1
6tsuno87
774cake
a-kyokufuri
a-m-house
a-w-u
abortion
absorption
aburitoro-salmon-o-kawari
abuto-usui
adorablew
adventitious-mouth
adventitious-penis
adventitious-vagina
afro
age-progression
age-regression
agnes-digital
ahegao
ahoderasouryo
ahoge-kinoko
aidaruki
airpink
aizawa-anji
aizawa-seinikuten
aji-ponntarou
akaimelon
akari-sonozaki
akatsuki-sunsun
aki-aw
akigami-satoru
akubinium
akuma-no-memu-memu-chan
akumakko
akuyaku-warai-sandankatsuyou
albino
alicia-viewstream
alien
alien-girl
all-the-way-through
almirua
amagyappa
amamiya-kokoro
amane-hayabusa
amane-san
amputee
amuai
an-arc-xxx
anal
anal-birth
anal-intercourse
anal-prolapse
analphagia
angel
angel-ice
angelfish
animal-on-animal
animal-on-furry
animated
animegao
anorexic
anthology
aoi-riku
aosaki-hifumi
aoto-kage
apparel-bukkake
apron
araragi-himegiku
arasa-mama-no-watashi-de-iino
arcmothra
ari-and-mura
ariane-glenys-lalatoya
arisawa-touta
armpit-licking
armpit-sex
artbook
artistcg
arumaito
arurukaana7a
asagiya
asahina-mafuyu
asakura-r
asashina
asatsuki-aoi
ash-plus
ashina-mellow
ashino
ashitsubo
asia-kei-gang
asphyxiation
ass-expansion
assjob
atelier30
aunt
autofellatio
autopaizuri
ayase-mio
azamidon
azechi-kiyoshi
babu-no-ki
bald
ball-sucking
balljob
balls-expansion
bandages
bandaid
bankoku-ayuya
bara-manji
barghest
bat-girl
battenkyuuki
bbm
bbw
bdsm
bear
bear-girl
beauty-mark
bee-girl
bekobeko
bestiality
big-areolae
big-ass
big-balls
big-breasts
big-clit
big-lips
big-muscles
big-nipples
big-penis
big-vagina
bikai
bike-shorts
bikini
birth
biscuitone
bisexual
blackmail
blind
blindfold
blood
bloomers
blowjob
blowjob-face
blue-history
body-modification
body-painting
body-swap
body-writing
bodystocking
bodysuit
bondage
bonus-call
bori
boukensha-c
brain-fuck
breast-expansion
breast-feeding
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bride
brother
brr
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bukkake
bull
bump-mania
bunbuku-nagi
bunny-boy
bunny-girl
buranran
buried-stars
burping
burun-babon
business-suit
butler
calmflower
cannibalism
cappuccino
capsaicin
captain-mizuki
caption
capy-shuupan
cardfight-v-master
carter
cashier
casiopute
cat
catame
catboy
catfight
catgirl
catgirlwitch
catnip-s-tablet
cbt
cementite
censorship
centaur
cerbe3
cercis
cerecere
cervix-penetration
cervix-prolapse
cha-bou
chairo
chastity-belt
cheating
cheerleader
cherry-blossoms
chie-takemoto
chii-chan-no-oshinagaki
chikan
chikara-koso-power
chikichikin
chikumaya
chikushi-hirokazu
chikuwa-factory
chikuwa-soft
childbox
china-rose
chinese-dress
chinpan
chinpogge
chinpoki
chiparu
chirotata
chitsu-kara-liver
chiyou-yoyuchi
chloroform
chocobeam
chocolat-lilou
choma
chongera
christmas
chuuindou-shoukai
circle-guri
circle-ism
circle-takaya
circle-utumno
clamp
clit-growth
clit-insertion
clit-stimulation
clone
closed-eyes
closeranks
clothed-female-nude-male
clothed-male-nude-female
clothed-paizuri
clown
coach
cock-ring
cockphagia
cockslapping
cocoroach
collar
collon-club
comic
comjack-project
compilation
con-narita
condom
conejologia
conjoined
consent
coprophagia
corruption
corset
cosplaying
count-mecha
cousin
cow
cowgirl
cowman
cream-punch
croriin
cross-road
crossdressing
crotch-tattoo
crown
crying
cum-bath
cum-in-eye
cum-swap
cumflation
cunnilingus
cuntboy
cuntbusting
curry-aji
cyclosabakan
dainzia
daiya-owada
dakimakura
dakko-ja-rrs-2nd
dan-oniroku
dangomushi
dark-nipples
dark-sclera
dark-skin
darkness-sha
darkness-yuma
darymaya
date-roku
daughter
deaimon
deepthroat
deer
deer-boy
deer-girl
defaced
defloration
deko-ga-areba-boko-ga-aru
delicious-party-precure
delta-studio
deluxe-kougyou
demon
demon-girl
denki-anma
dennou-figure-alice
densetsu-guji
denyel
detached-sleeves
devils-tongue
diaper
dick-growth
dickgirl-on-dickgirl
dickgirl-on-male
dickgirls-only
dicknipples
dilf
dinosaur
dismantling
dmc-juukou
do-wa
doemutan
dog
dog-boy
dog-girl
dogaya
doggie-yu
doku-doku-kinoko
doll-joints
dolphin
domination-loss
domino-gumi
donkey
donmaikingdom
donshoku-echiechi-dragon
double-anal
double-blowjob
double-penetration
double-vaginal
dougi
doujinharuga
doujinshi
doyou-saburou
dragon
drd
drill-hair
drogod
dropwort-bell
dropwortbell
drugs
drunk
dummy-kaiko
e-note
eagle-land
ear-fuck
eba-uenihane
ebi-fry-teishoku
ebiken
ebio
echigonokuni
econokicks
eel
eggs
eihikanshi
el115
electric-shocks
electromonkey
elf
elizabeth-collar
els-tasmin
emiya-alter
emotionless-sex
emumalik
emuo
enami-nao
enari
endeaa
enema
enidou
enomaki
ere-2-aero
erika-amano
ero-cup
ero-trap-dungeon
eroiroe
erorhythm
erosheee
eroshiku
eruu
excellent-furo
exercise-abc
exhibitionism
exposed-clothing
eye-covering-bang
eye-penetration
eyebrows-born
eyemask
eyepatch
ezaki-yui
ezelith
facesitting
facial-hair
fairy
faketale
falcons-nest
farting
fate-grand-roder
father
fe3c
females-only
femdom
feminization
fengxi
fff-threesome
ffm-threesome
fft-threesome
figure
filming
fingering
first-person-perspective
fish
fishin-and-loli
fishnets
fisting
fixed-perpective
fliming
flour-spoon
focus-anal
focus-blowjob
focus-paizuri
food-on-body
foot-insertion
foot-licking
footjob
forced-exposure
forniphilia
fox
fox-boy
fox-girl
foxtail-project
freak-studio
freckles
free-de-la-hoya
frog
frog-girl
frottage
fudonofudou
fugurisensen
fuguta-ke
fujii-kasai
fujii-tei
fujino-kaede
fujisaki-chiro
fujiwarake
full-body-tattoo
full-censorship
full-color
fundoshi
furi-furi-youchien
furube
furukoto-takaichi
furutani
futaba-channel
futaba-tsukushi
futanari
futas-no-peanuts
futon-tatakiki
futsu
futsu-no-tapiocaya-san
futsuka-me
fuurindou
fuuzen-no-tomoshibi
fuwa-nozomi
fuwatoro-lion
fuwatoro-opanchu-cake
gachirin
gag
gairou
gaisei
gamanuntaka
game-manual
gamecg
gan-en
gan-sukii
gaping
garter-belt
gasmask
gatekeeper
gekimabu
gender-bender
gender-change
gender-morph
genkotsu-13-gou
ghost
giant
giant-sperm
giantess
gigantic-breasts
gijinka
gikkuri-toufuten
ginmaru
girigirimai
glasses
glory-hole
gloves
goblin
gokkun
gokudou-no-isshun
golden-bazooka
goldenbania
gomio
gorilla
gorilla-mountain-shikoku
gorou
gothic-lolita
goudoushi
grade-eight
grand-plie
granddaughter
grandfather
grandmother
great-akuta
great-chocolate
greenleona
group
growth
gunjou-croquette
guradesu
guro
gurotuki
guusuka
gyaru
gyaru-oh
gymshorts
gyoumuin-a
gyuunyuucha-uma
haburashi
hachikyu
hachimaru-extreme
haien-kikan
haigure
hair-buns
hairjob
hairy
hairy-armpits
hako-no-naka-no-imaginary
hakoten
hamidashi-kanitama
han-do-yoon
hanafubu
hananiarashi
handicapped
handjob
hanekkaeri
hanging
hardcore
hare
harekaze
harem
harness
harpy
haru-chan-kikaku
haru-hayate
haruharu-dou
haruhyan
harusame-kakigoori
hate-sate-club
hatsuyuki-sakura
hayabusa-takamaru
hazuki-younosuke
headless
headphones
heikou-sekai
henzutsuu
herb-cookie
hetare-drops
heterochromia
hida-naria
hidari-harai
hidden-sex
higehurai
high-metal
higuchi-shuu
higuti-syu
hiiro-uchiwa
hijab
hikaru-toyama
hikawa-ritsu
hinyari
hirame-hirayama
hirobi
hirocan
hironikuru-senga-anarogu
hironobu
hiropons
hirota-shimaguni
hisame-sui
hitozuma-lollipop
hiuh
hiyori-tomoe
hlord-recona
hogarakka-plus
hoine-shou
hokkete-sho
hokuyousuishi
hololive
homare-works
home-center-tenco
honeyroad
honjou-tatami
hood
hori-yusuke
horii
horishiki
hormone-shokudou
horns
horse
horse-boy
horse-cock
horse-girl
hoshigaki-seizoujo
hoshina-shinya
hoshino-iro
hoshino-takanashi
hotate-san
hotpants
hourusudatsu
houwa-aji
how-to
hoyhoy-colo
huge-breasts
huge-penis
hugo-baint-renxandt
hugo-baint-von-renxandt
human-cattle
human-on-furry
human-pet
humiliation
hyoui-no-jikan
hyoukorarin
hyouzan-daibakuha
hyper-uc-circle
ibara-saegusa
ice-medel
ichi-gsm
ichimatsu-dango
ichino-milk
ichinose-nyan
ichisuruga-tsuyuki
ido-nite
iikuni-george
ijou-shiniki
ikahotaru
ill
im-so-blue
imageset
imajin-kouji
imamo-too
impregnation
in-bed
incest
incest-inseki
incomplete
infantilism
inflation
insect
insect-boy
insect-girl
inseki
internal-urination
inverted-nipples
invisible
iolite
isashiimiyo
isekai-higaeri-manyuuki
ishihara-norihiro
ishiinu-tekkoujo
ishimari-yuuya
ishoku-dougen
isshiki-fuji
itabira-yasai
itagaki-hiro
itao-itsuko
itau
itokon-code
itou-nao
itsuka-yukihara
itsuki-itsuki
itsumo-no-yatsu
iwahisa-tsuki
iyo-no-kama
iyoudon
izumo-fuyusora
jaguchi
jaja
jamta
jar-of-elements
jellypudding
jero
jewelry-box
jikken-b-tou
jiko-moratorium
jikomanzoku-manga-shuudan
jokerkin
jokuseiken
josou-seme
jouyatou
juewang-yuyi
juki-kazu
jumyou-wa-isshukan
jun-sazanami
junyomaru
jureinomori
juuichi-gatsu
k-t
kaede-isami
kaedex
kaeru-douhu
kagaku-chop
kagamino-mochiko
kairakuochisuki
kaiten-paradogs
kaito-dokuringo
kakisaki
kakkou-no-iinazuke
kakoriri
kakucho-parts
kakuu-shoujo
kamatsukatei
kame-no-kotawashi
kamenoashi
kamiken
kamineta
kamipack
kamo-nanban
kamu-asa-sori-youtyuu-zyou
kancho
kanipon
kankitsudou
kaori-shimakura
kappa
karnatia-seri-anabald
karou
karouke
karude-taku
karuken-parea
kasai-yukiha
kasumi-kaori
kawai-hiraku
kawara-soba
kayou-arisa
kayukan
kazaoka
kazefuki-poni
kazu-ryuu
kazuhiko
kazukitodaemon
kedama-no-gonjiro
keienu
keiji-kurobi
keinv
keishouryaku
kemonomimi
kensaki-tsurugi
kepi
kichiku-ruijinen-bu
kido-jirou
kien-biu
kigurumi
kigurumi-pajama
kikakugai-extend
kiki-seiran
kikimoraru
kimetsutenshi-l
kimono
kimtoxic
kindergarten-uniform
kinei
kinginseki
kinmokusei-no-shiro
kinoko-youchien
kinoto-kanade
kirakou
kirikizu-kou
kiriko
kisetsu-fuu
kisetsufuu
kissatei
kissing
kitsune-bousouzoku
kitsune-no-nijibiya
kiyomiya-ryou
kiyoshi-gasuya
kneepit-sex
kobamiso
kodomo-oukoku
kodomokai
koeda-mameko
kogeta-neko
kogetsu
kogure
koikatsu
kojiin
kokomi
kokusai-hihoukan
komi-shouko
komikkukyassuru-junbi-iinkai
komyaku
kondou-kisaki
konton-kurabu
koonago
kosuke-poke
kouan
kouun-ryuusui
koyama-hayato
kuba-yodomu
kubo-minako
kuchibiru-chazuke
kudou-maimu
kujo-shima
kuki-isu
kumagawa-yuuichi
kumarin
kumata-katsumi
kunoichi
kure-no-genki-na-goaisatsu
kuremente
kureo-kingyo
kuri-dango-ya-sora
kurimaru-satoshi
kuroi-mello-yello
kurosaki-shun
kurotane
kuroyume-koubou
kutsudaru
kuusou-jikkenshitsu
kyuudai-manken-dennou
lab-coat
labo
lactation
lady-bat
lake-side
lalala-holiday
lander
large-insertions
large-tattoo
laserbeam
latex
layer-cake
leash
leg-lock
legjob
leotard
lestart
levi
lhr
lidenskap
limsett
lingerie
lion
little-note
living-clothes
lizard-girl
lizard-guy
loli-fan
lolicon
long-tongue
loon-koubou
lostdragon01
love-tan
low-bestiality
low-guro
low-incest
low-lolicon
low-scat
low-shotacon
low-smegma
ludonic-alien
lui-shirosagi
luo-xiaohei
lv-41
ma-maikea
maccho-morizou
machida-march
machine
madden
maggot
magical-girl
mahomaho
mahomaho-koubou
mai-mai-miracle
maid
maikage
maina-asakura
makai
makeup
makinomachi-park
makochiya
makolas
makoyuki
makusu
mala-xiao-longxia
male-on-dickgirl
males-onl
males-only
mameroku
manaita-koubou
manami-ogura
manbo
manga
maou-dante
mariel-legrand
maripaka
masaru
mase-ryuuki
mash
mashi-miyuki
masked-face
master-chikushoudou
masturbation
masuta
matsuda-val
matsumoto
matsurino-ato
matumoto-ke
maturbation
mature
mayu-ko
mecha-boy
mecha-girl
meimeikomei
melina
melonbooks
menstruation
mentoru
mermaid
merman
mesuiki
metal-armor
mian-mubo
miato
micchi
michiko
michio
midget
migi-hane
migi-ni-sasetsu
miginaa
miime
mika-misono
miki-hime
miko
mikomachi
mikoshiba
mikoshiba-kakyo
mikoshiro-nagitoh
milf
military
milking
milkyboy
milkyboy-inc
mill
mimamoriencyo
miminoura
mimitako-seijin
mimutan
minamo-ruri
minamori-noeru
minato-mirai
mind-break
mind-control
mine-mura
minigirl
miniguy
minotaur
miruki-amauri
misakitou
misc
misekiss
miso-tanuki-inka-teitoku
misoji-company
missing-cover
misstta
mistressmiko
mitari-gakuen
mito-mitsukuni
mitsuhide-rouen
mitsumura-hachi
miyahama-ryou
miyamon
miyamoto-smoke
miyazen
mizuhara-chitose
mizuhara-zakuro
mizuiro-penra
mizusawahiro
mizutamari-no-uta
mmf-threesome
mmm-threesome
mmt-threesome
mochapon
mochichimaru
mochinchi
mofuaki
moheji
mojihee
mokkindou
mokko
momendoufu
momoen
momomo-doumei
momomo-gasshuukoku
momoya
momoyamahato
mon
monimonimo
monkey
monkey-boy
monkey-girl
monoeye
monster
monster-girl
monster-ken
moodeter
moomoomilk
moonlight-laboratory
moral-degeneration
morimori-kissaten
morimoriya
moriyama-inu
morokochiffon-cake
mosaic-censorship
mosoya
mother
mouri-toushirou
mouse
mouse-boy
mouse-girl
mouth-mask
mr-bobo
mr-kurz
mt-sushi
mtf-threesome
muchuu-yakou
mugen-murasaki
mult-work-series
multi-work-series
multimouth-blowjob
multipanel-sequence
multiple-arms
multiple-assjob
multiple-breasts
multiple-footjob
multiple-handjob
multiple-nipples
multiple-orgasm
multiple-orgasms
multiple-paizuri
multiple-penises
multiple-straddling
mura-mura
murai-toyo
murakami-harumasa
murasaki-reika
muriyari
muroi
muscle
muscle-growth
mushypeas
musicamach
mute
mysterious-heroine-xx
myuu
nadesico-extacy
nagahama-kenji
nagisa-ran
nagomix
naisho-da
najimaarc
najimaru
naka
nakadashi
nakagome
nakahara-mizuimo
nakamura-denki
nakamura-kafka
nako-sir
namakopie
namaniku
namazu-chaya
namiitcho
naminori-kamome
nana-g
nanahamu
nananto
nanohito
nanonepio
nanquanzl
nappa-kaine
narumieru
natsukawa-kagari
natsuki-shinobu
natto-shiruko
nauvvii
navel-fuck
nazi
nazono
necrophilia
nedokoro-mitsuki
neko-no-tendon
neko-pan
nekoyama-service
neo-tokugawake
nerima-aki
nero-tarou
nerumesian
nestkeeper
netorare
nexton
nezumi-no-haha
nezumichiru
nf-freak
nicolai-no-yome
niece
nihon-brain-tou
niiruma-no-koya
nijihashi-sora
nijimaarc
nijimaru
nijisanji
nikuju
nikumayadorodou
ninja
ninomae
nintai-akira
nipple-birth
nipple-expansion
nipple-fuck
nipple-stimulation
nishieda-kinoe
nitohen
nitouhen
niwaka-daimyou
no-penetration
no-tengo-nada
nobiruko
nodo
noilpoisoncat
nokemon
nomad-joe
non-h
non-h-imageset
non-nude
noraneko-nicole
noriuma
nose-fuck
nose-hook
nudism
nudism-idiots
nudity-only
numata-shizumu
nun
nurse
nurugai
nya-tan
nyaa-no-esa
nyama
nyan-dorosei
nyantomo
nyowawa
ochi
ochichio
ocita-micawa
octopus
oekaki-ojisan
oil
oishii-oshiri
oishii-oshiri-cafe
oishinbo
ojarumaru
okadaya-tetuzoh
okamoto-nyohei
okemaruta
okugai
okuoku
okuribidokei
old-lady
old-man
omorashi
onahole
oni
onibi
onibi-kuudan
onigunsow
onome-shin
oohara-keiji
oohara-ya
ookamikaze-ryou
oosaki
ooya-nako
oppai-loli
orc
ore-teki-doremi-e
ore-to-kakuni-to-abura-soba
orgasm-denial
oritsuki-fumio
osaru-no-kai
oshino
oshiroiro
ostrich
otama-jacuzzi
otetsuki
otokofutanari
otona-no-sexy-ehon
oudo-mikito
oumi-keimaisha
ouroboros
out-of-order
outa-san
over-rice
oyakodon
p-craft
paedosho-nii-san
pai-genji
paizuri
pallapalla
panda-girl
pandaj
pangohan
pants-o-pants
pantyhose
pantyjob
parareyukicchu
parasite
pasties
pastime774
patapo
pavlov-no-neko
pawamaku
payochiyo
pegging
peimond
penchapen
penis-birth
penny-dreadful
penpenmaru
personality-excretion
petapeta
petplay
petrification
phase-e
phimosis
phone-sex
pichinki
piercing
pig
pig-girl
pig-man
pillory
pinez
pink-no-sukesuke
pinkpaper
pinochika
pirate
piss-drinking
pixie-cut
plant-boy
plant-girl
plug
poco-pene
pole-dancing
policeman
policewoman
ponchin-x
ponkotsu-afro
ponygirl
ponytail
poor-grammar
pori
possession
pottenpurincho-gyabin
poyojirou
pregnant
prehensile-hair
press-gear
priest
prismatic-boy
priysantheme
produce
project-take-4
prolapse
prostate-massage
prostitution
pubic-stubble
public-use
pulque-factory
puniket-junbikai
puniru-wa-kawaii-slime
purplian
pyonchi
pyonchi-koubou
qiujun
r-gakugu-nanagou-kenkyuushitsu
rabbit
raccoon-girl
race-queen
ragu
raid-socks
rainbow-zou
rainy-eternity
rakudai-ninja-rantaro
ramanda
ramuda
randoseru
rap
rape
rariart
rayasi
rayou
rayzhai
real-doll
realporn
record-of-ragnarok
red-light-red
red-miso-soup
red-plum-dianthus
redosukan
redraw
refrevo-comic
reitou-yakisoba
replaced
reptile
retort
retractable-penis
reversible
rim
rimjob
rin-nanagami
ritaomochi
rk-2
robo-z
robot
robot-ponkottsu
rof
roji-ya
room101
roppongi-nyugyo
rorichikku-izm
rough-grammar
rough-translation
ryona
ryou
ryuuguu
ryuunokke
s8403
saihou-yuki
saikime
saikoro
saita-katsutoshi
saitou-renji
sakakibara-bieru
sake-no-ate
sakeno-tsumami
sakuan
sakura-no-tomoru-hie
sakura-suishou
sakurai-mizuki
salad-oil
salad-resort
saliva
salmontofu
sanamaru
sanehara
sansan
santoka
sarashi
sasada-aki
sasaki-kazuyuki
sasaki-kudoo
sate
satoru-hoashi
satotsuki-chiyo
sawadai
sawaru-na-kiken
sayama-yukihiro
scan
scanmark
scar
scat
school-gym-uniform
school-swimsuit
schoolboy-uniform
schoolgirl-uniform
schwarz-kastell
schweinchen31
scrivener-lahn
scrotal-lingerie
sechi
seika-kyomachi
seiniku-aikoukai
seinyanko-gakuen
seisha-ryuso
seishokuki
sekaiju-no-hakoniwa
seki-hirame
sekito-sakurada
selfcest
semiemon
senoo-hibiteru
sesshouin-kiara
seto-natsutsuki
seto-ryouko
sex-toys
sexpistols
shachiku-san-wa-youjo-yuurei-ni-iyasaretai
shade-no-urahime
shako-punch
shared-senses
shark-boy
shark-girl
shaved-head
sheep-girl
shemale
shiawase-na-choushoku
shibaken-gorou
shibari
shibudaku-seisaku-iinkai
shiga-hanako
shigemaru-shigeru
shikanoo-derako
shimaidon
shimapan
shimejimo
shimenawa-to-onna-no-ko
shimizu-masaharu
shimon-ryuushirou
shimoshi
shin-arata
shinei-neko-hakase
shinitate-hokahoka-zombie
shinka-san
shioimo
shiokaze-toride
shipu
shiragi-mana
shiranai-uchi-ni-level-max-ni-nattemashita
shirasudon
shiratamaya
shiratorizawa-ai
shirishiri-denbu-club
shiromaru
shishio
shitsuren-restaurant-om
shittori-bouzu
shiyo
shizaki-tsukiyo
shizuko-kawawa
shizuku-yaegashi
shizukuno-reyu
shizuma
sho-utsumi
shokushu-teikoku
shokuyou-arikui
short-kami
shotacon
shotashikan-kyouwakoku
shoudama-potekora
shoujo-yoshitsune-den
shounan-rakujin-society
shrimp-cake
shrinking
shuhan
shunga-no-hassan
shuroop
shuusuke-takanashi
siansi
simasima
simaura
sink49
siruto
sister
six-at-tree
sk2b
skeb
skeleton-knight-in-another-world
sketch-lines
skinsuit
skunk-girl
slashpalooza
slave
sleeping
slime
slime-boy
slime-girl
slime-taoshite-300-nen-shiranai-uchi-ni-level-max-ni-nattemashita
slug
small-breasts
small-penis
smalldom
smegma
smell
smile-foran-company
smoking
snail-girl
snake
snake-boy
snake-girl
snow-rain
snuff
soboro
sockjob
sokushi
sole-dickgirl
sole-female
sole-femla
sole-fmelae
sole-male
sole-pussyboy
solo-action
sonna-bakana
sonora
soramame
sorya-anmarida
soudeiba
sound-project
soushuuhen
space-ship
spanking
sparkling-cookie
speculum
spider
spider-girl
squid-boy
squid-girl
squirrel-girl
squirting
ssbbm
ssbbw
stainless-night
stealth-koubou
stealthcraftwork
stellar-drop
stewardess
stirrup-legwear
stockings
stomach-deformation
story-arc
straitjacket
strand-infinity
stranded
strap-on
stretching
stuck-in-wall
studio-galapagos
studio-honey
sudou-raikuu
suehiro-machi
suelecia-zagan-analeit
suga-juurou
sugishin
suguru-getou
suihei-teil
sukezaemon
sukuna
sumairu
sumata
sumou
sundress
sunglasses
supuratta
sushi-yama
suzugaeru
suzuki-kenya
suzuki-takeshi
suzunashi-rei
suzuoka-chitose
suzurikawa-wasabi
suzushiro-rei
suzushiro-seri
sway
sweating
sweetie
swimsuit
swinging
syake-ama
syno
synolee
syntier13
syoumeiya
syringe
syunka-kikaku
syunkarow
ta-ta
table-masturbation
tachibana-ruri
tachiko
tadashi-akira
tadashigaki
tado-jou
tagajyou-grand-fleet
taiko-no-fang
tail
tail-plug
tailjob
tailphagia
taimanin
taishou-dokanism
taiyaki-pan
taiyou-no-nishi
takaedakanihasami
takeefu
takemitsu-tenta
takeshinshi
takuan-labo
takurowodou
tall-girl
tall-man
tana-kayushi
tankoubon
tankouyon
tanlines
taroimo-tarou
taroimobatake
tarutoru
tasuke-seinyuu
tatsuko
tattsuu
tawashi
tawawa-seimenjo
teacher
teaser-apron
teiryoku-lolita
teishibou-chichi
tekko
tekkou-bondo
tenga
tenkoinu
tenmusu
tentacles
tenuwi
tepen
terano-haruna
terasumc
terauchi-takahiro
teritama-potekora
teriyaki-sasami
teriyaki-sasami-donburi
tetsudou-hisakawa
texteater
the-future-retro-hero-story
the-genie-family
the-legend-of-luoxiaohei
themeless
thick-eyebrows
thigh-high-boots
thirty8ght
tiara
tickling
tiger
tights
time-stop
tiny-planet
tkf
tkosukotei
tnd
to-hegemonicon
toaru-kagaku-no-railgun
toaru-majutsu-no-index
toba-minami
tobiume-sayu
tokakukaku
tokunaga-poosaku
tokyo-kumitaisou-gumi
tomatoedge
tomboy
tomgirl
tomikadou
tominokozoku
tomonori-iwaki
tonari-toyama
tonezato-honu
tontorotei
toono-esuke
toorisugari-kari
tooth-brushing
toprush
tops
toranoana
torayarou
tori-seisakusho
toriaezu-sasahara
torigara-mayo
tornado
torobakoya
torotei
torotoro-yume-banana
torture
totorina
toubousha
touka-genteki
touya-toufuten
touyou-buzyutu-gakkou
toyoda-poem
toyono-kitsune
tracksuit
trampling
tranformation
transformation
transparent-clothing
tribadism
tricky
triple-anal
triple-penetration
triple-vaginal
tripura-shounen
truffe
tsp
tsujidou-ayase
tsuruga
tsurumaki-maki
ttf-threesome
ttm-threesome
ttt-threesome
tube
tuna-empire
turisasu
turquoise
tutor
tuxig
twinkyweeb
twins
twintails
tyontyon
u-ally
uchuu-jin-tanaka-tarou
udokurokujou
udon-attack
uenosuke
uguisu-se
ultimate-safaripark
umai
umeda
umeda-ku
umeda-nautilus
umizoko
umou
una-otomachi
unbirth
uncensored
uncle
underwater
uni18
unicorn
union-bust
universal-12
unusual-insertions
unusual-pupils
unusual-teeth
unya
unyakun
ura-chacha-seisaku-iinkai
urethra-insertion
uri-tan
urination
urushibara-koubou
urushibara-yasuhiro
usaman-senpai
usayama-nico
ushiking
ushio-kiyoshi
ushitora-kimon
ushitoya
utatane
uwatteru
uyuu
v-u-g
v-yuusha-no-kuse-ni-namaikida-r
vacbed
vaginal-birth
vaginal-sticker
vampire
variant-set
vegetablenabe
vel
very-long-hair
vesves
vidro
vildred
villain
virginity
virgo0901
vomit
vore
vorefection
vorevore
voyeurism
vtuber
waccha-primagi
wai-dei
waiter
waitress
wajima24
wakimiunten
wakiya
wal
walkure
war-devil
wararu
warrior-of-light
warui-hito
watermarked
watosu-mama
wazatsuma-kitsuna
webtoon
weight-gain
weixiefashi
western-cg
western-imageset
western-non-h
wet-clothes
whip
white-blood-cell-captain
white-tiger
widow
widower
wings
witch
wolf
wolf-boy
wolf-girl
wooden-horse
worm
wormhole
wrestling
wsplus
wushui
x-ray
xx-ya-tte-koto-wa-naisho-da
yagami-sai
yagen-nankotsu
yagitei
yakaze
yakaze-2000
yakumo-ryojin
yamada-shutaro
yamajo
yamano-murao
yamano-muraotto
yamanoya-honpo
yamazaki-takumi
yamazu
yami
yami-books
yamito
yan2252
yanagie-terasu
yandere
yaneurabeya
yang-nari
yaniku
yao-ha-ducy
yaoi
yaritsu
yariyo
yasai-seikatsu
yashima-tetsuya
yattsuhashi
yawaraka-aomame-no-on-salad
yawaraka-shouten
yioyituyu
yo-o-shinobu
yochiko
yoisaki-kanade
yokando
yokochichi-lingerie
yokota-kazuyuki
yonah
yongo
yoro
yorozu-company
yoshikichimaru
yoshikichiya
yoshinori
yoshiwara-tatsuhiko
yoshiyoshi-ya
yoshuku
youkai-club
young-farm
yue-yue
yugioh-arc-v
yukawa-asam
yukino-makoto
yukiusa
yukkuri
yumeharo
yumeki
yumemori
yumiko
yunoyu
yunusuko
yurayura
yuri
yutto-prime
yuuki-reina
yuuma-toutetsu
yuuna-asakura
yuzu-onsen
zakuro-gari
zakuro-myurasu
zawa-palette
zenobia
zenzai-monaka
zijou
zin-crow
zinkurou
zombie


================================================
FILE: data/img2txt/techniques.txt
================================================
Fine Art
Diagrammatic
Geometric
Architectural
Analytic
3D
Anamorphic
Pencil
Color Pencil
Charcoal
Graphite
Chalk
Pen
Ink
Crayon
Pastel
Sand
Beach Art
Rangoli
Mehndi
Flower
Food Art
Tattoo
Digital
Pixel
Embroidery
Line
Pointillism
Single Color
Stippling
Contour
Hatching
Scumbling
Scribble
Geometric Portait
Triangulation
Caricature
Photorealism
Photo realistic
Doodling
Wordtoons
Cartoon
Anime
Manga
Graffiti
Typography
Calligraphy
Mosaic
Figurative
Anatomy
Life
Still life
Portrait
Landscape
Perspective
Funny
Surreal
Wall Mural
Street
Realistic
Photo Realistic
Hyper Realistic
Doodle


================================================
FILE: data/scn2img_examples/cat_at_beach.scn2img.md
================================================
// blend it together and finish it with details
prompt: cute happy orange cat sitting at beach, beach in background, trending on artstation:1 cute happy cat:1
sampler_name:k_euler_a
ddim_steps: 35
denoising_strength: 0.55
variation: 3
initial_seed: 1

# put foreground onto background
size: 512, 512
color: 0,0,0

## create foreground
size:512,512
color:0,0,0,0
resize: 300, 300
pos: 256, 350

// select mask by probing some pixels from the image
mask_by_color_at: 15, 15,   15, 256,   85, 465,  100, 480
mask_by_color_threshold:80
mask_by_color_space: HLS

// some pixels inside the cat may be selected, remove them with mask_open
mask_open: 15

// there is still some background pixels left at the edge between cat and background
// grow the mask to get them as well
mask_grow: 15

// we want to remove whatever is masked:
mask_invert: True

####
prompt: cute happy orange cat, white background
ddim_steps: 25
variation: 1

## create background
prompt:beach landscape, beach with ocean in background, photographic, beautiful:1 red:-0.4


================================================
FILE: data/scn2img_examples/corgi_3d_transformation.scn2img.md
================================================
initial_seed: 2

// select background and img2img over it
mask_by_color_at: 64, 64
mask_invert: True

prompt: corgi
ddim_steps: 50
seed: 242886303

mask_mode: 0
denoising_strength: 0.8
//cfg_scale: 15
mask_restore: True
image_editor_mode:Mask

# estimate depth and transform the corgi in 3d
transform3d: True
transform3d_depth_near: 0.5
transform3d_depth_scale: 10
transform3d_from_hfov: 45
transform3d_to_hfov: 45
transform3d_from_pose: 0,0,0,  0,0,0
transform3d_to_pose: 0.5,0,0,  0,-5,0
transform3d_min_mask: 0
transform3d_max_mask: 255
transform3d_inpaint_radius: 1
transform3d_inpaint_method: 0

## put foreground onto background
size: 512, 512


### create foreground
size: 512, 512

mask_depth: True
mask_depth_model: 1
mask_depth_min: -0.05
mask_depth_max: 0.5
mask_depth_invert:False

####
prompt: corgi
ddim_steps: 25
seed: 242886303

### background
size: 512,512
color: #9F978D


================================================
FILE: data/scn2img_examples/corgi_at_beach.scn2img.md
================================================
// blend it together and finish it with some details
prompt: cute corgi at beach, trending on artstation
ddim_steps: 50
denoising_strength: 0.5
initial_seed: 2

# put foreground onto background
size: 512, 512

## create foreground
size: 512, 512

// estimate depth from image and select mask by depth
// https://huggingface.co/spaces/atsantiago/Monocular_Depth_Filter
mask_depth: True
mask_depth_min: -0.05
mask_depth_max: 0.4
mask_depth_invert:False

###
prompt: corgi
ddim_steps: 25

## create background
prompt:beach landscape, beach with ocean in background, photographic, beautiful:1 red:-0.4


================================================
FILE: data/scn2img_examples/corgi_at_beach_2.scn2img.md
================================================
// give it some polish and details
size: 512, 512
prompt: cute corgi at beach, intricate details, photorealistic, trending on artstation
variation: 0
seed: 1360051694
initial_seed: 5

# blend it together
prompt: beautiful corgi:1.5 cute corgi at beach, trending on artstation:1 photorealistic:1.5
ddim_steps: 50
denoising_strength: 0.5
variation: 0

## put foreground in front of background
size: 512, 512

### select foreground
size: 512, 512

// estimate depth from image and select mask by depth
// https://huggingface.co/spaces/atsantiago/Monocular_Depth_Filter
mask_depth: True
mask_depth_min: -0.05
mask_depth_max: 0.4
mask_depth_invert:False

#### create foreground
prompt: corgi
ddim_steps: 25
seed: 242886303

### create background
prompt:beach landscape, beach with ocean in background, photographic, beautiful:1 red:-0.4
variation: 3


================================================
FILE: data/scn2img_examples/landscape_3d.scn2img.md
================================================
size: 512,512
mask_blur: 6

prompt: fantasy landscape with castle and forest and waterfalls, trending on artstation
denoising_strength: 0.6
seed: 1
image_editor_mode: Mask
mask_mode: 0
mask_restore: True

# mask the left which contains artifacts
color: 255,255,255,0
blend:multiply
size: 100,512
pos: 50,256

# mask the top-left which contains lots of artifacts
color: 255,255,255,0
blend:multiply
size: 280,128
pos: 128,64

# go forward and turn head left to look at the left waterfalls
transform3d: True
transform3d_depth_scale: 10000
transform3d_from_hfov: 60
transform3d_to_hfov: 60
transform3d_from_pose: 0,0,0,  0,0,0
transform3d_to_pose: 4000,0,2000,  0,-50,0
transform3d_min_mask: 0
transform3d_max_mask: 255
transform3d_inpaint_radius: 5
transform3d_inpaint_method: 1

##
prompt: fantasy landscape with castle and forest and waterfalls, trending on artstation
seed: 1


================================================
FILE: data/tags/config.json
================================================
{
	"tagFile": "danbooru.csv",
	"maxResults": 5,
	"replaceUnderscores": true,
	"escapeParentheses": true,
	"colors": {
		"danbooru": {
			"0": ["lightblue", "dodgerblue"],
			"1": ["indianred", "firebrick"],
			"3": ["violet", "darkorchid"],
			"4": ["lightgreen", "darkgreen"],
			"5": ["orange", "darkorange"]
		},
		"e621": {
			"-1": ["red", "maroon"],
			"0": ["lightblue", "dodgerblue"],
			"1": ["gold", "goldenrod"],
			"3": ["violet", "darkorchid"],
			"4": ["lightgreen", "darkgreen"],
			"5": ["tomato", "darksalmon"],
			"6": ["red", "maroon"],
			"7": ["whitesmoke", "black"],
			"8": ["seagreen", "darkseagreen"]
		}
	}
}


================================================
FILE: data/tags/danbooru.csv
================================================
1girl,0
solo,0
highres,5
long_hair,0
commentary_request,5
breasts,0
looking_at_viewer,0
blush,0
smile,0
short_hair,0
open_mouth,0
bangs,0
blue_eyes,0
multiple_girls,0
skirt,0
blonde_hair,0
large_breasts,0
brown_hair,0
simple_background,0
shirt,0
black_hair,0
hair_ornament,0
absurdres,5
red_eyes,0
thighhighs,0
hat,0
gloves,0
1boy,0
bad_id,5
long_sleeves,0
white_background,0
dress,0
original,3
ribbon,0
bow,0
touhou,3
navel,0
bad_pixiv_id,5
2girls,0
photoshop_(medium),5
holding,0
animal_ears,0
cleavage,0
hair_between_eyes,0
bare_shoulders,0
brown_eyes,0
twintails,0
medium_breasts,0
commentary,5
jewelry,0
sitting,0
very_long_hair,0
underwear,0
closed_mouth,0
nipples,0
school_uniform,0
green_eyes,0
blue_hair,0
standing,0
purple_eyes,0
collarbone,0
panties,0
monochrome,0
tail,0
jacket,0
translated,5
swimsuit,0
full_body,0
closed_eyes,0
hair_ribbon,0
kantai_collection,3
yellow_eyes,0
weapon,0
ponytail,0
upper_body,0
purple_hair,0
ass,0
pink_hair,0
white_shirt,0
comic,0
braid,0
flower,0
ahoge,0
:d,0
short_sleeves,0
hair_bow,0
greyscale,0
hetero,0
white_hair,0
male_focus,0
heart,0
pantyhose,0
bikini,0
sidelocks,0
thighs,0
nude,0
red_hair,0
cowboy_shot,0
pleated_skirt,0
sweat,0
hairband,0
translation_request,5
small_breasts,0
earrings,0
multicolored_hair,0
boots,0
grey_hair,0
lying,0
censored,0
frills,0
parted_lips,0
detached_sleeves,0
one_eye_closed,0
outdoors,0
food,0
japanese_clothes,0
multiple_boys,0
green_hair,0
wings,0
open_clothes,0
sky,0
necktie,0
horns,0
penis,0
shoes,0
fate_(series),3
glasses,0
barefoot,0
shorts,0
serafuku,0
pussy,0
teeth,0
day,0
solo_focus,0
sleeveless,0
choker,0
alternate_costume,0
tongue,0
pointy_ears,0
socks,0
black_gloves,0
elbow_gloves,0
hairclip,0
fang,0
striped,0
midriff,0
puffy_sleeves,0
shiny,0
looking_back,0
belt,0
sword,0
collared_shirt,0
official_art,5
pants,0
cloud,0
artist_name,0
black_thighhighs,0
tears,0
fate/grand_order,3
indoors,0
cat_ears,0
silver_hair,0
white_gloves,0
3girls,0
hair_flower,0
virtual_youtuber,0
signature,0
dark_skin,0
hand_up,0
spread_legs,0
cum,0
2boys,0
hood,0
idolmaster,3
sex,0
miniskirt,0
wide_sleeves,0
tongue_out,0
fingerless_gloves,0
on_back,0
blunt_bangs,0
black_skirt,0
bowtie,0
armpits,0
pink_eyes,0
sailor_collar,0
kimono,0
english_commentary,5
black_legwear,0
pokemon,3
medium_hair,0
water,0
grey_background,0
necklace,0
chibi,0
off_shoulder,0
bag,0
hair_bun,0
clothes_lift,0
scarf,0
cape,0
from_behind,0
star_(symbol),0
twitter_username,0
bra,0
stomach,0
yuri,0
nail_polish,0
sweatdrop,0
orange_hair,0
white_dress,0
armor,0
holding_weapon,0
black_footwear,0
white_panties,0
rabbit_ears,0
mole,0
uniform,0
hair_over_one_eye,0
grin,0
:o,0
huge_breasts,0
character_name,0
black_eyes,0
apron,0
blurry,0
looking_at_another,0
vest,0
black_dress,0
high_heels,0
arm_up,0
twin_braids,0
vaginal,0
mosaic_censoring,0
red_bow,0
flat_chest,0
arms_up,0
side_ponytail,0
shiny_hair,0
bracelet,0
covered_nipples,0
collar,0
feet,0
dated,0
from_side,0
aqua_eyes,0
vocaloid,3
sweater,0
white_thighhighs,0
speech_bubble,0
two-tone_hair,0
leotard,0
red_ribbon,0
two_side_up,0
english_text,0
dark-skinned_female,0
sketch,0
tree,0
open_jacket,0
cup,0
lips,0
blue_sky,0
puffy_short_sleeves,0
zettai_ryouiki,0
blue_skirt,0
hololive,3
groin,0
fingernails,0
coat,0
wet,0
cat_tail,0
bad_twitter_id,5
v-shaped_eyebrows,0
white_legwear,0
neckerchief,0
head_tilt,0
black_jacket,0
official_alternate_costume,0
streaked_hair,0
genshin_impact,3
see-through,0
orange_eyes,0
azur_lane,3
hand_on_hip,0
gradient,0
gun,0
shiny_skin,0
crop_top,0
idolmaster_cinderella_girls,3
wrist_cuffs,0
pillow,0
book,0
animal_ear_fluff,0
artist_request,5
torn_clothes,0
plaid,0
maid,0
sleeves_past_wrists,0
looking_to_the_side,0
kneehighs,0
grey_eyes,0
military,0
sash,0
parted_bangs,0
pokemon_(game),3
maid_headdress,0
legs,0
petals,0
cosplay,0
black_panties,0
black_pantyhose,0
fur_trim,0
bare_arms,0
pubic_hair,0
hands_up,0
one-piece_swimsuit,0
gradient_background,0
symbol-shaped_pupils,0
fox_ears,0
loli,0
ascot,0
short_shorts,0
dutch_angle,0
clothing_cutout,0
black_shirt,0
eyelashes,0
open_shirt,0
bare_legs,0
no_humans,0
dress_shirt,0
lowres,5
sparkle,0
bar_censor,0
kneeling,0
window,0
mole_under_eye,0
pokemon_(creature),0
4girls,0
single_braid,0
hug,0
bodysuit,0
v,0
no_bra,0
sleeveless_shirt,0
bell,0
saliva,0
double_bun,0
strapless,0
aqua_hair,0
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?,0
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;o,0
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matoi_ryuuko,4
holding_another's_wrist,0
danganronpa:_trigger_happy_havoc,3
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persona_3,3
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serval_(kemono_friends),4
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!!,0
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okita_souji_(fate),4
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d:,0
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:/,0
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pixiv_fantasia,3
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kafuu_chino,4
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princess_zelda,4
bathtub,0
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3:,0
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third-party_source,5
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tail_wagging,0
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astolfo_(fate),4
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axis_powers_hetalia,3
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=3,0
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watashi_ga_motenai_no_wa_dou_kangaetemo_omaera_ga_warui!,3
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ssss.gridman,3
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tsukino_usagi,4
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>:(,0
notebook,0
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mahou_shoujo_madoka_magica:_hangyaku_no_monogatari,3
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lock,0
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whistle,0
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sailor,0
apex_legends,3
diamond_wa_kudakenai,3
hooded_coat,0
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monster_girl_encyclopedia,3
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mythra_(xenoblade),4
hk416_(girls'_frontline),4
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garreg_mach_monastery_uniform,0
verniy_(kancolle),4
no_mouth,0
fur-trimmed_dress,0
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detached_wings,0
aether_(genshin_impact),4
fullmetal_alchemist,3
maebari,0
letty_whiterock,4
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winter_coat,0
faceless_female,0
waking_up,0
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showgirl_skirt,0
cane,0
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pov_crotch,0
slime_(substance),0
amiya_(arknights),4
tower,0
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gloria_(pokemon),4
battle_tendency,3
kiryuuin_satsuki,4
orange_(fruit),0
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amagami,3
polka_dot_bikini,0
minase_iori,4
mizuki_hitoshi,1
brown_vest,0
throne,0
waterfall,0
inazuma_eleven,3
heart_pasties,0
kaguya-sama_wa_kokurasetai_~tensai-tachi_no_renai_zunousen~,3
business_suit,0
poking,0
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watson_amelia,4
unbuttoned_shirt,0
lightning,0
aikatsu!,3
mordred_(fate/apocrypha),4
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kikuchi_makoto,4
has_bad_revision,5
digimon,3
print_skirt,0
statue,0
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baby,0
wrist_ribbon,0
butterfly_wings,0
dark_blue_hair,0
monster_boy,0
squatting_cowgirl_position,0
crowd,0
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bandaid_on_nose,0
dark_elf,0
naruto_shippuuden,3
white_bloomers,0
crack,0
crotchless,0
machine_gun,0
towel_on_head,0
blue_fire,0
bow_hairband,0
bullet,0
ear_covers,0
medium_skirt,0
bb_(fate),4
pearl_necklace,0
thigh_grab,0
streaming_tears,0
boat,0
pocket_watch,0
surgical_mask,0
fate/prototype,3
collaboration,5
cropped,5
no_pussy,0
trench_coat,0
torn_dress,0
ex-keine,4
kasumi_(kancolle),4
iowa_(kancolle),4
bear,0
white_neckerchief,0
crab,0
letter,0
red_scrunchie,0
yellow_shorts,0
blood_on_hands,0
jeanne_d'arc_(ruler)_(fate),4
klee_(genshin_impact),4
tomboy,0
boy_on_top,0
ribbon-trimmed_legwear,0
kouji_(campus_life),1
blue_bodysuit,0
fatal_fury,3
spread_anus,0
skullgirls,3
pink_necktie,0
locked_arms,0
red_buruma,0
takanashi_kiara,4
bound_legs,0
whiskers,0
bodypaint,0
open_hands,0
dress_pull,0
saber_alter,4
ganaha_hibiki,4
snowman,0
toilet,0
vertical-striped_shirt,0
animal_on_shoulder,0
yuubari_(kancolle),4
drawing,0
ryouou_school_uniform,0
gintama,3
atago_(azur_lane),4
hot,0
cat_cutout,0
vertical-striped_thighhighs,0
landscape,0
no_legwear,0
idolmaster_1,3
one-punch_man,3
fusion,0
vehicle_focus,0
oshino_shinobu,4
tsundere,0
kitchen,0
yellow_theme,0
guilty_gear_xrd,3
hecatia_lapislazuli,4
imagining,0
kiso_(kancolle),4
green_pants,0
defeat,0
shimamura_uzuki,4
venti_(genshin_impact),4
nekomata_okayu,4
bra_visible_through_clothes,0
argyle_background,0
fire_emblem:_the_blazing_blade,3
tile_wall,0
towel_around_neck,0
soccer_uniform,0
locker,0
crotch,0
milk,0
steins;gate,3
toeless_footwear,0
orange_bowtie,0
flower_knot,0
hand_on_another's_cheek,0
imminent_rape,0
splatoon_2,3
evil_grin,0
frilled_pillow,0
jacket_removed,0
assisted_exposure,0
uruha_rushia,4
crescent_pin,0
wine_bottle,0
byleth_(fire_emblem)_(female),4
tiger,0
watanabe_you,4
zero_two_(darling_in_the_franxx),4
checkered_skirt,0
frilled_collar,0
cat_lingerie,0
asahina_mikuru,4
monster_musume_no_iru_nichijou,3
long_tongue,0
pirate_hat,0
road_sign,0
taihou_(azur_lane),4
oppai_loli,0
kiseru,0
bird_tail,0
phantasy_star_online_2,3
sagging_breasts,0
yellow_gloves,0
red_(pokemon),4
hatching_(texture),0
energy,0
gardevoir,4
death,0
fat_man,0
ash_ketchum,4
torn_skirt,0
starfish,0
library,0
suite_precure,3
virgin_killer_sweater,0
you_gonna_get_raped,0
yellow_headwear,0
print_bow,0
fingers_together,0
crow,0
bat_print,0
muted_color,0
unsheathed,0
hand_on_another's_chin,0
sleeves_pushed_up,0
yellow_panties,0
osomatsu-san,3
cat_hair_ornament,0
orange_dress,0
bikini_aside,0
resident_evil,3
gundam_build_fighters,3
back_cutout,0
magia_record:_mahou_shoujo_madoka_magica_gaiden,3
bikini_tan,0
teacher,0
1980s_(style),0
hiryuu_(kancolle),4
morrigan_aensland,4
defloration,0
alien,0
green_bowtie,0
anal_tail,0
game_console,0
heart_background,0
soul_gem,0
sideways_mouth,0
itomugi-kun,1
soap_bubbles,0
on_desk,0
costume,0
uchiwa,0
very_dark_skin,0
1koma,0
meltryllis_(fate),4
halftone_background,0
leg_tattoo,0
takagaki_kaede,4
prinz_eugen_(azur_lane),4
djeeta_(granblue_fantasy),4
formidable_(azur_lane),4
nintendo_switch,0
mechanical_wings,0
mask_removed,0
princess_peach,4
karyl_(princess_connect!),4
os-tan,3
two-sided_fabric,0
torn_pants,0
shirasaka_koume,4
shy,0
raised_eyebrow,0
planted_sword,0
track_pants,0
suitcase,0
bubble_blowing,0
stick,0
album_cover,0
graf_zeppelin_(kancolle),4
school,0
ultimate_madoka,4
colorful,0
romaji_text,0
purple_necktie,0
employee_uniform,0
command_spell,0
mandarin_orange,0
eromanga_sensei,3
musical_note_hair_ornament,0
unaligned_breasts,0
sagisawa_fumika,4
spoken_blush,0
gundam_seed,3
taking_picture,0
clover,0
pink_skin,0
dragon_wings,0
obijime,0
cum_pool,0
armchair,0
lifebuoy,0
gradient_eyes,0
tartaglia_(genshin_impact),4
doctor_(arknights),4
skadi_(arknights),4
pouring,0
striped_pantyhose,0
v_over_eye,0
pointing_up,0
kagerou_project,3
wakasagihime,4
blue_wings,0
tissue_box,0
holding_shield,0
school_chair,0
carrot_hair_ornament,0
dc_comics,3
shirogane_noel,4
clone,0
shingeki_no_bahamut,3
coffee_mug,0
holding_paper,0
winter_uniform,0
mountainous_horizon,0
infinite_stratos,3
male_masturbation,0
blue_butterfly,0
heart_necklace,0
husband_and_wife,0
yurucamp,3
cloud_strife,4
cropped_shirt,0
little_witch_academia,3
red_eyeshadow,0
koizumi_hanayo,4
:>=,0
demon,0
power_armor,0
off-topic,5
against_glass,0
barcode,0
feet_up,0
silk,0
smokestack,0
lamia,0
thong_leotard,0
multicolored_skirt,0
swimming,0
lake,0
half-closed_eye,0
medusa_(rider)_(fate),4
blood_on_weapon,0
blood_from_mouth,0
blunt_ends,0
leather_jacket,0
see-through_silhouette,0
backboob,0
fur-trimmed_boots,0
blue_sleeves,0
sode,0
kishin_sagume,4
texas_(arknights),4
2021,0
holding_candy,0
food_focus,0
the_pose,0
fisheye,0
red_moon,0
black_skin,0
jar,0
floppy_ears,0
long_bangs,0
komaeda_nagito,4
naka_(kancolle),4
kyon,4
takatsuki_yayoi,4
white_camisole,0
flexible,0
bandaid_on_knee,0
osomatsu-kun,3
orange_jacket,0
toradora!,3
hestia_(danmachi),4
holding_pen,0
american_flag_legwear,0
mitakihara_school_uniform,0
bremerton_(azur_lane),4
nanamori_school_uniform,0
puddle,0
cat_hood,0
multicolored_jacket,0
thorns,0
aiming,0
backwards_hat,0
dark_nipples,0
button_gap,0
pointing_at_self,0
holding_own_arm,0
kirby,4
houseki_no_kuni,3
paizuri_under_clothes,0
recording,0
black_cloak,0
inugami_korone,4
multicolored_nails,0
cheek-to-cheek,0
arm_hug,0
frilled_hat,0
pince-nez,0
demon_slayer_uniform,0
leaning,0
no_nipples,0
shrine,0
novelty_censor,0
black_suit,0
rei_no_himo,0
robot_ears,0
earth_(planet),0
underboob_cutout,0
green_legwear,0
black_rock_shooter_(character),4
hose,0
miyamoto_musashi_(fate),4
grey_cardigan,0
two-tone_skin,0
digital_media_player,0
fate/hollow_ataraxia,3
paradis_military_uniform,0
ear_bow,0
bowsette,4
sono_bisque_doll_wa_koi_wo_suru,3
blue_buruma,0
waist_bow,0
orange_headwear,0
banana,0
purple_bodysuit,0
flaccid,0
shop,0
kanzaki_ranko,4
sakura_miko,4
blue_socks,0
purple_hairband,0
skirt_suit,0
nude_cover,0
magazine_scan,5
selene_(pokemon),4
sleeveless_kimono,0
snout,0
ambiguous_gender,0
belfast_(azur_lane),4
showering,0
spider_lily,0
sirius_(azur_lane),4
super_sonico,4
shide,0
neon_trim,0
metal_collar,0
hoshimachi_suisei,4
white_ascot,0
aran_sweater,0
dolphin_shorts,0
what,0
yumemi_riamu,4
belt_collar,0
brown_scarf,0
lanyard,0
bikini_lift,0
spread_ass,0
rooftop,0
octopus,0
cu_chulainn_(fate/stay_night),4
kisume,4
seaport_princess,4
mini_wings,0
baggy_pants,0
cat_paws,0
yu-gi-oh!_duel_monsters,3
tani_takeshi,1
mahou_sensei_negima!,3
narukami_yuu,4
jougasaki_mika,4
improvised_gag,0
arm_around_shoulder,0
ishtar_(fate),4
chuunibyou_demo_koi_ga_shitai!,3
falling_leaves,0
fishing_rod,0
promotional_art,5
pole,0
framed,0
zero_no_tsukaima,3
horror_(theme),0
paimon_(genshin_impact),4
spider_web,0
lily_white,4
black_flower,0
film_grain,0
pompadour,0
fog,0
kunai,0
wizard_hat,0
rice_shower_(umamusume),4
grey_pantyhose,0
aerith_gainsborough,4
print_gloves,0
kissing_cheek,0
brand_name_imitation,0
trefoil,0
;q,0
glowing_weapon,0
naked_sweater,0
i-19_(kancolle),4
pokemon_frlg,3
kiryu_coco,4
laevatein_(touhou),0
fire_emblem:_mystery_of_the_emblem,3
imminent_vaginal,0
aqua_bow,0
steel_ball_run,3
furude_rika,4
rapier,0
fur_hat,0
fairy_(kancolle),4
toe_scrunch,0
ikari_shinji,4
painting_(object),0
wrench,0
macaron,0
dated_commentary,5
photo_background,0
cage,0
hair_tucking,0
mother_and_son,0
pentagram,0
red_sailor_collar,0
multiple_wings,0
interface_headset,0
adjusting_swimsuit,0
holding_cigarette,0
zouri,0
yellow_sweater,0
slave,0
eula_(genshin_impact),4
strapless_bikini,0
takebe_saori,4
purple_rose,0
food_print,0
paint.net_(medium),5
chicken,0
tsushima_yoshiko,4
tekken,3
energy_sword,0
pink_scarf,0
date_a_live,3
folded_fan,0
violin,0
mejiro_mcqueen_(umamusume),4
american_flag,0
gokou_ruri,4
holding_broom,0
needle,0
barnaby_brooks_jr.,4
zuihou_(kancolle),4
demon_boy,0
2others,0
excalibur_(fate/stay_night),0
boku_wa_tomodachi_ga_sukunai,3
perky_breasts,0
afloat,0
remote_control,0
too_many,0
leaning_on_person,0
amane_kanata,4
blue_hakama,0
open_door,0
hands_in_hair,0
steepled_fingers,0
snk,3
mascara,0
pantylines,0
netorare,0
sushi,0
real_life_insert,0
sanya_v._litvyak,4
swimsuit_aside,0
sheep,0
maria-sama_ga_miteru,3
v-fin,0
cat_boy,0
butt_plug,0
idolmaster_million_live!_theater_days,3
official_wallpaper,5
sailor_moon,4
green_sailor_collar,0
cream,0
print_dress,0
opaque_glasses,0
drum,0
kuroki_tomoko,4
calligraphy_brush,0
tokoyami_towa,4
armpit_hair,0
print_bowtie,0
hypnosis,0
lightning_bolt_symbol,0
brown_fur,0
contrail,0
left-handed,0
o-ring_bottom,0
vertical-striped_legwear,0
nitta_minami,4
kakyoin_noriaki,4
keyboard_(computer),0
collared_jacket,0
single_sleeve,0
lower_body,0
american_flag_dress,0
cake_slice,0
duel,0
elbow_rest,0
policewoman,0
figure,0
zombie_land_saga,3
holding_bowl,0
explosive,0
scope,0
food_on_body,0
eila_ilmatar_juutilainen,4
aqua_(konosuba),4
cammy_white,4
cynthia_(pokemon),4
kanon_(kurogane_knights),1
ereshkigal_(fate),4
heart_choker,0
unicorn_(azur_lane),4
hololive_indonesia,3
egg_vibrator,0
transformation,0
train,0
love_live!_school_idol_festival,3
floor,0
circle,0
frilled_legwear,0
grey_hoodie,0
sleeves_past_elbows,0
staring,0
fins,0
kaburagi_t._kotetsu,4
frilled_gloves,0
stylus,0
medical_eyepatch,0
concept_art,0
oozora_subaru,4
map,0
egyptian,0
orange_ribbon,0
hand_under_clothes,0
lower_teeth,0
fanbox_reward,5
office_chair,0
dungeon_and_fighter,3
ahri_(league_of_legends),4
string_of_fate,0
character_profile,0
two-tone_shirt,0
animal_crossing,3
final_fantasy_xi,3
ichinose_shiki,4
transformers,3
shiny_pokemon,0
i-58_(kancolle),4
capri_pants,0
spacecraft,0
loose_necktie,0
furisode,0
eiyuu_densetsu,3
bra_removed,0
orange_flower,0
elden_ring,3
binoculars,0
rudder_footwear,0
test_tube,0
striped_bra,0
stocking_(psg),4
striped_tail,0
envelope,0
paper_bag,0
horn_bow,0
medicine_melancholy,4
evangelion:_3.0_you_can_(not)_redo,3
lemon,0
green_leotard,0
kotomine_kirei,4
lion_tail,0
uneven_eyes,0
yahari_ore_no_seishun_lovecome_wa_machigatteiru.,3
lunasa_prismriver,4
rod_of_remorse,0
lucina_(fire_emblem),4
yagami_hayate,4
bkub,1
sonic_(series),3
basketball,0
bandaids_on_nipples,0
baozi,0
milestone_celebration,0
bad_feet,0
open_dress,0
maid_bikini,0
sennen_sensou_aigis,3
maya_(kancolle),4
after_anal,0
scan_artifacts,5
corrin_(fire_emblem),4
suction_cups,0
sake_bottle,0
multiple_crossover,0
meiji_schoolgirl_uniform,0
tight_pants,0
uranohoshi_school_uniform,0
breast_slip,0
asuna_(blue_archive),4
candy_apple,0
commissioner_upload,5
alice_in_wonderland,3
naganami_(kancolle),4
yuri!!!_on_ice,3
rubber_duck,0
haruno_sakura,4
ookami_mio,4
partially_undressed,0
commander_(azur_lane),4
hand_on_own_arm,0
blue_scrunchie,0
banner,0
hair_beads,0
fur-trimmed_headwear,0
dancer,0
serena_(pokemon),4
father_and_son,0
pussy_juice_stain,0
kokkoro_(princess_connect!),4
mouse_girl,0
pink_leotard,0
hair_strand,0
reverse_outfit,0
the_legend_of_zelda:_twilight_princess,3
chewing_gum,0
pussy_juice_trail,0
pizza,0
machikado_mazoku,3
hinata_hajime,4
footwear_bow,0
arm_belt,0
yamakaze_(kancolle),4
swim_trunks,0
holding_towel,0
taur,0
anastasia_(idolmaster),4
hiiragi_tsukasa,4
nightcap,0
submerged,0
stage,0
pumps,0
grey_vest,0
middle_finger,0
squirrel_ears,0
pink_shorts,0
mismatched_gloves,0
finger_gun,0
digimon_(creature),0
pink_cardigan,0
no_shirt,0
hooded_capelet,0
parfait,0
ouma_kokichi,4
pokemon_rse,3
copyright,0
holding_pokemon,0
2022,0
gae_bolg_(fate),0
shore,0
drop_shadow,0
murasame_(kancolle),4
two-tone_fur,0
holding_mask,0
lineup,0
obliques,0
son_goku,4
shotgun,0
tony_taka,1
grabbing_another's_ass,0
kirito,4
background_text,0
riding_crop,0
kindergarten_uniform,0
covered_collarbone,0
blue_capelet,0
bandage_over_one_eye,0
goggles_around_neck,0
holding_camera,0
arm_rest,0
blue_pantyhose,0
shirogane_naoto,4
penis_on_face,0
daiwa_scarlet_(umamusume),4
framed_breasts,0
flower-shaped_pupils,0
flashing,0
heart-shaped_eyewear,0
multiple_hair_bows,0
colored_pubic_hair,0
tales_of_vesperia,3
kitagawa_marin,4
gothic,0
polos_crown,0
mercy_(overwatch),4
gradient_sky,0
kuromorimine_military_uniform,0
pokemon_masters_ex,3
ejaculating_while_penetrated,0
kamisato_ayaka,4
lip_biting,0
nia_teppelin,4
green_bra,0
duck,0
piggyback,0
pokemon_adventures,3
doremy_sweet,4
sakurauchi_riko,4
kingdom_hearts,3
energy_gun,0
florence_nightingale_(fate),4
artbook,5
throwing,0
japari_symbol,0
lying_on_person,0
yukihana_lamy,4
red_headband,0
forked_eyebrows,0
hair_twirling,0
misty_(pokemon),4
sailor_bikini,0
edelgard_von_hresvelg,4
harpy,0
cuts,0
deep_penetration,0
reverse_suspended_congress,0
jack_the_ripper_(fate/apocrypha),4
obiage,0
leg_hair,0
metal_gear_(series),3
paint_splatter,0
clothes_grab,0
upright_straddle,0
project_sekai,3
giorno_giovanna,4
hiding,0
frilled_swimsuit,0
hairpods,0
shimada_arisu,4
ump45_(girls'_frontline),4
hands_in_opposite_sleeves,0
hyouka,3
katyusha_(girls_und_panzer),4
single_kneehigh,0
pink_neckerchief,0
tablet_pc,0
holding_another's_arm,0
kay_(girls_und_panzer),4
z1_leberecht_maass_(kancolle),4
squirrel_tail,0
gym_shorts,0
holding_underwear,0
sheryl_nome,4
black_armor,0
seagull,0
plaid_dress,0
shovel,0
tomoe_(symbol),0
kujo_jolyne,4
toy,0
scar_on_chest,0
earbuds,0
white_vest,0
reverse_bunnysuit,0
darkness,0
irrumatio,0
fresh_precure!,3
chips_(food),0
asymmetrical_footwear,0
gas_mask,0
hands_on_another's_shoulders,0
prosthetic_arm,0
dress_bow,0
bat_hair_ornament,0
body_markings,0
multiple_rings,0
yellow_scarf,0
happy_halloween,0
stirrup_legwear,0
leotard_aside,0
purple_umbrella,0
w_arms,0
partially_unzipped,0
hands_on_another's_face,0
egasumi,0
futaba_anzu,4
bandaged_hand,0
uzumaki_naruto,4
anus_peek,0
midoriya_izuku,4
bone_hair_ornament,0
blue-framed_eyewear,0
higuchi_madoka,4
galaga,3
slime_girl,0
z3_max_schultz_(kancolle),4
taihou_(kancolle),4
ayanami_(azur_lane),4
spitroast,0
magazine_(weapon),0
holding_box,0
speaker,0
purple_vest,0
grey_sailor_collar,0
pecorine_(princess_connect!),4
audible_speech,5
sandwich,0
treble_clef,0
street_fighter_v,3
loose_belt,0
waver_velvet,4
iskandar_(fate),4
alternate_legwear,0
viewfinder,0
suit_jacket,0
megaphone,0
stomach_tattoo,0
takarada_rikka,4
miniboy,0
kuma_(kancolle),4
evangelion:_2.0_you_can_(not)_advance,3
orange_bodysuit,0
miura_azusa,4
arm_around_waist,0
shikishi,0
layered_bikini,0
fire_emblem:_the_binding_blade,3
bamboo_forest,0
penis_awe,0
feather_boa,0
gold_ship_(umamusume),4
licking_finger,0
plantar_flexion,0
ear_ornament,0
surreal,0
letterman_jacket,0
holding_lollipop,0
humanization,0
tomato,0
bleeding,0
hyuuga_hinata,4
fire_emblem:_path_of_radiance,3
spice_and_wolf,3
green_neckwear,0
grenade,0
ruu_(tksymkw),1
bubble_tea,0
a1,1
pancake,0
jacket_around_waist,0
spoken_squiggle,0
bunny_print,0
hagiwara_yukiho,4
shirt_tug,0
newspaper,0
jonathan_joestar,4
soda_can,0
goggles_on_headwear,0
i-401_(kancolle),4
grinding,0
warspite_(kancolle),4
humanoid_robot,0
orange_hairband,0
magazine_cover,0
skates,0
shirai_kuroko,4
saihara_shuuichi,4
samurai_spirits,3
pubic_hair_peek,0
multi-tied_hair,0
shenhe_(genshin_impact),4
narmaya_(granblue_fantasy),4
akigumo_(kancolle),4
5koma,0
fighting,0
horned_headwear,0
xiao_(genshin_impact),4
izumi_sagiri,4
lion_girl,0
bag_charm,0
ghost_tail,0
shooting_star,0
fusou_(kancolle),4
jougasaki_rika,4
blade_(galaxist),1
scowl,0
murasaki_shion,4
snowflake_hair_ornament,0
finger_on_trigger,0
mario,4
lily_pad,0
pith_helmet,0
ryuuguu_rena,4
arms_around_neck,0
2020,0
hair_tie_in_mouth,0
mikasa_ackerman,4
mochi,0
chest_harness,0
lifting_person,0
chloe_von_einzbern,4
nami_(one_piece),4
left-to-right_manga,0
pyonta,4
traditional_youkai,0
idolmaster_side-m,3
piano,0
open-chest_sweater,0
in_tree,0
dark_souls_(series),3
non-human_admiral_(kancolle),4
audible_music,5
in_box,0
shishiro_botan,4
kanzashi,0
aisaka_taiga,4
eevee,4
crotch_rope,0
ribbed_dress,0
midorikawa_nao,4
food_on_head,0
elizabeth_bathory_(fate),4
beer_can,0
hand_on_head,0
uraraka_ochako,4
midair,0
deepthroat,0
common_raccoon_(kemono_friends),4
thumb_ring,0
uneven_gloves,0
leaf_print,0
fire_emblem:_radiant_dawn,3
reizei_mako,4
yugake,0
watson_cross,0
vegetable,0
yellow_rose,0
ranma_1/2,3
stitched,5
trainer_(umamusume),4
frying_pan,0
ankle_ribbon,0
ceiling,0
matsunaga_kouyou,1
yuki_miku,4
panda,0
spoken_interrobang,0
guro,0
motoori_kosuzu,4
thinking,0
working!!,3
guilty_gear_strive,3
undertale,3
bird_ears,0
heart_in_mouth,0
usami_sumireko,4
hand_over_own_mouth,0
winged_arms,0
neck_ruff,0
caterpillar_tracks,0
stained_glass,0
extra,0
gloom_(expression),0
pokephilia,0
chemise,0
leon_(pokemon),4
sekaiju_no_meikyuu,3
christmas_ornaments,0
satonaka_chie,4
hoto_cocoa,4
object_namesake,0
tiger_girl,0
tokai_teio_(umamusume),4
bike_shorts_under_skirt,0
hagoromo,0
lelouch_lamperouge,4
kumano_(kancolle),4
bodysuit_under_clothes,0
sayonara_zetsubou_sensei,3
breastless_clothes,0
kise_yayoi,4
ouro_kronii,4
heart_in_eye,0
beatrice_(umineko),4
numbered,0
sheep_ears,0
mankanshoku_mako,4
ruby_rose,4
holo,4
button_badge,0
peeing_self,0
torn_bodysuit,0
after_fellatio,0
monster_hunter_(character),4
cat_ear_panties,0
american_flag_bikini,0
flip-flops,0
hunter_x_hunter,3
raccoon_girl,0
gekkoukan_high_school_uniform,0
city_lights,0
flats,0
higashikata_josuke,4
writing,0
orange_sky,0
shakugan_no_shana,3
shared_clothes,0
puppet,0
four-leaf_clover,0
vertical-striped_dress,0
coat_on_shoulders,0
pretty_(series),3
barbara_(genshin_impact),4
making-of_available,5
bird_on_head,0
debris,0
aoba_(kancolle),4
public_nudity,0
wide_ponytail,0
sparkle_background,0
collage,0
spill,0
hyuuga_(kancolle),4
pink_scrunchie,0
maekawa_miku,4
charlotte_(madoka_magica),4
covering_eyes,0
rubber_boots,0
lotus,0
striker_unit,0
kote,0
pokemon_sm_(anime),3
happinesscharge_precure!,3
akizuki_ritsuko,4
boo_tao_(genshin_impact),4
raihan_(pokemon),4
naga_u,1
grey_panties,0
tokitsukaze_(kancolle),4
lotion,0
kama_(fate),4
shiranui_mai,4
chained,0
white_scrunchie,0
paint,0
shoulder_carry,0
shoe_dangle,0
emilia_(re:zero),4
orange_necktie,0
cupcake,0
diffraction_spikes,0
holding_axe,0
remote_control_vibrator,0
taimanin_(series),3
leopard_print,0
bento,0
fetal_position,0
single_leg_pantyhose,0
bar,0
cheese,0
gender_request,5
shell_casing,0
purple_pantyhose,0
kiyohime_(fate),4
keyhole,0
orange_nails,0
paw_shoes,0
layered_clothes,0
gaping,0
yunomi,0
yellow_kimono,0
world_war_ii,0
moonlight,0
tunic,0
ace_attorney,3
rabbit_hood,0
luna_child,4
hip_bones,0
arm_hair,0
pokemon_rgby,3
fennec_(kemono_friends),4
pinching,0
stuffed_cat,0
red_sleeves,0
thank_you,0
fate/prototype:_fragments_of_blue_and_silver,3
sword_of_hisou,0
urethra,0
hallway,0
ch'en_(arknights),4
hayate_no_gotoku!,3
lace_panties,0
reindeer_antlers,0
holding_scythe,0
ear_ribbon,0
star_sapphire,4
candy_cane,0
goldfish,0
kousaka_kirino,4
unzan,4
gem_uniform_(houseki_no_kuni),0
blue_gemstone,0
wedding,0
arms_under_breasts,0
corruption,0
oboro_(kancolle),4
quad_tails,0
breast_curtains,0
abstract,0
exercise,0
red_belt,0
medal,0
robin_(fire_emblem),4
quarter_note,0
long_eyelashes,0
tomoe_gozen_(fate),4
dragon_ball_super,3
furry_with_non-furry,0
red_rope,0
padlock,0
jealous,0
hand_in_panties,0
ashigara_(kancolle),4
pudding,0
fashion,0
au_ra,0
domino_mask,0
year_of_the_tiger,0
satsuki_(kancolle),4
boko_(girls_und_panzer),4
pirate,0
green_scarf,0
stethoscope,0
yohane,1
multiple_bows,0
jean_(genshin_impact),4
earclip,0
jintsuu_(kancolle),4
brush,0
illustrious_(azur_lane),4
black_ascot,0
kanon,3
helmet_removed,0
snot,0
volleyball,0
on_shoulder,0
holding_dagger,0
electric_fan,0
kogal,0
year_of_the_ox,0
kiyoshimo_(kancolle),4
kara_no_kyoukai,3
mogami_(kancolle),4
dice,0
bea_(pokemon),4
latin_cross,0
light_green_hair,0
pant_suit,0
weiss_schnee,4
bangs_pinned_back,0
hidamari_sketch,3
mega_man_(classic),3
ladder,0
crescent_earrings,0
skirt_tug,0
makise_kurisu,4
go!_princess_precure,3
happy_valentine,0
seamed_legwear,0
huge_nipples,0
kagerou_(kancolle),4
nanashi_mumei,4
leg_lock,0
aiming_at_viewer,0
urakaze_(kancolle),4
flaming_eye,0
hand_on_another's_chest,0
paddle,0
over-rim_eyewear,0
futa_(nabezoko),1
torn_sleeves,0
kyuubi,0
soldier,0
burn_scar,0
pink_apron,0
incredibly_absurdres,5
st._gloriana's_school_uniform,0
mobile_suit,0
bullpup,0
orange_footwear,0
uzuki_(kancolle),4
emiya_kiritsugu,4
zombie_pose,0
nitocris_(fate),4
fischl_(genshin_impact),4
single_detached_sleeve,0
sunburst,0
trash_can,0
takami_chika,4
coattails,0
sunny_milk,4
nakiri_ayame,4
duffel_bag,0
shower_head,0
ainu_clothes,0
feather_trim,0
green_thighhighs,0
fairy_tail,3
orange_scrunchie,0
stone_ocean,3
vibrator_under_clothes,0
miyafuji_yoshika,4
kallen_stadtfeld,4
erica_hartmann,4
makima_(chainsaw_man),4
2018,0
isokaze_(kancolle),4
turtle_shell,0
subtitled,0
green_hairband,0
wide_spread_legs,0
finger_to_cheek,0
melty_blood,3
purple_choker,0
artoria_pendragon_(lancer)_(fate),4
wet_swimsuit,0
negligee,0
2019,0
soulcalibur,3
made_in_abyss,3
ramen,0
pillarboxed,0
soccer_ball,0
purple_wings,0
official_alternate_hairstyle,0
tachibana_arisu,4
shark_girl,0
anime_coloring,0
alternate_headwear,0
two-tone_skirt,0
mole_on_thigh,0
come_hither,0
bra_peek,0
m.u.g.e.n,3
lappland_(arknights),4
mobile_suit_gundam,3
profanity,0
bass_guitar,0
yorigami_shion,4
pastry,0
heart_pillow,0
pig,0
seashell,0
horseshoe_ornament,0
cross_hair_ornament,0
natsuiro_matsuri,4
unryuu_(kancolle),4
2016,0
ears_down,0
carnelian,1
fur-trimmed_hood,0
shark,0
crossed_ankles,0
strap_between_breasts,0
koshimizu_sachiko,4
spade_(shape),0
owl,0
brown_theme,0
spandex,0
wrestling_outfit,0
sleepwear,0
diarmuid_ua_duibhne_(lancer)_(fate),4
eyepatch_bikini,0
untied_panties,0
rebecca_(keinelove),1
clitoral_stimulation,0
utau,3
mismatched_bikini,0
shiranui_flare,4
hatsuyuki_(kancolle),4
doorway,0
nipple_rings,0
single_sidelock,0
futari_wa_precure,3
2015,0
rose_print,0
suspension,0
star_in_eye,0
coffee_cup,0
fire_emblem:_genealogy_of_the_holy_war,3
mitsudomoe_(shape),0
looking_at_penis,0
two-footed_footjob,0
hishikawa_rikka,4
sigh,0
blade,0
hand_on_another's_hip,0
nessa_(pokemon),4
skirt_around_one_leg,0
g-string,0
alternate_universe,0
locker_room,0
shibari_over_clothes,0
clothes_in_mouth,0
orange_choker,0
warugaki_(sk-ii),1
snowflake_print,0
ido_(teketeke),1
exusiai_(arknights),4
qing_guanmao,0
lycoris_recoil,3
bandaid_on_pussy,0
corrin_(fire_emblem)_(female),4
red_one-piece_swimsuit,0
lifted_by_another,0
naoetsu_high_school_uniform,0
lights,0
embers,0
hayami_kanade,4
shirt_removed,0
cold,0
dizzy_(guilty_gear),4
akitsu_maru_(kancolle),4
breast_tattoo,0
final_fantasy_vi,3
asakura_ryouko,4
arm_held_back,0
mismatched_footwear,0
holding_panties,0
calendar_(medium),0
simon_(ttgl),4
flame-tipped_tail,0
raincoat,0
hisahiko,1
weibo_username,0
sweets,0
sun_symbol,0
shaved_ice,0
doll_hug,0
talons,0
zero_suit,0
mallet,0
sheep_girl,0
shiratsuyu_(kancolle),4
magazine_(object),0
chef_hat,0
cigar,0
diagonal_bangs,0
kitauji_high_school_uniform,0
walk-in,0
oshiro_project,3
wa_maid,0
yellow_scrunchie,0
head-mounted_display,0
brown_bag,0
holding_person,0
giving,0
taigei_(kancolle),4
tokyo_ghoul,3
black_cardigan,0
nakano_nino,4
houjou_satoko,4
project_diva_(series),3
star_ocean,3
mika_(girls_und_panzer),4
orange_gloves,0
white_theme,0
tuxedo,0
gertrud_barkhorn,4
frilled_kimono,0
photo-referenced,5
ooarai_military_uniform,0
wa2000_(girls'_frontline),4
green_coat,0
flat_ass,0
ram_(re:zero),4
2017,0
oda_nobunaga_(fate),4
sitting_on_face,0
cum_on_boy,0
ixy,1
skull_and_crossbones,0
aoki_reika,4
hand_grab,0
orgy,0
spacesuit,0
bow_(bhp),1
horizontal_pupils,0
look-alike,0
yellow_fur,0
pink_bodysuit,0
bloom,0
arm_around_neck,0
striped_jacket,0
sword_girls,3
ribbed_shirt,0
typo,0
grey_coat,0
undersized_clothes,0
blue_belt,0
multiple_horns,0
text-only_page,0
uneven_sleeves,0
kirisawa_juuzou,1
chest_sarashi,0
checkered_scarf,0
rice_bowl,0
see-through_shirt,0
frilled_hair_tubes,0
alice_(alice_in_wonderland),4
futami_mami,4
mouth_drool,0
clip_studio_paint_(medium),5
dragon_quest_iii,3
callie_(splatoon),4
o3o,0
dd_(ijigendd),1
hairy,0
red_gemstone,0
scar_on_arm,0
suspender_shorts,0
kirima_syaro,4
bit_gag,0
holding_flag,0
louise_francoise_le_blanc_de_la_valliere,4
millipen_(medium),0
muvluv,3
gundam_seed_destiny,3
toothbrush,0
puckered_lips,0
prone_bone,0
princess,0
swirl_lollipop,0
scar_on_nose,0
don-chan_(usada_pekora),4
updo,0
yoshida_yuuko_(machikado_mazoku),4
nervous_smile,0
d-pad,0
hand_on_another's_back,0
accelerator_(toaru_majutsu_no_index),4
room,0
nakano_miku,4
rubbing_eyes,0
convenient_arm,0
bomber_jacket,0
club_(weapon),0
white_feathers,0
santa_bikini,0
dark_magician_girl,4
nichijou,3
atelier_ryza,3
green_sweater,0
cum_on_stomach,0
nape,0
yellow_legwear,0
patterned_background,0
carrot_necklace,0
thigh_sex,0
harusame_(kancolle),4
orc,0
beach_towel,0
dog_boy,0
white_robe,0
open_shorts,0
hand_on_another's_arm,0
plastic_bag,0
tam_o'_shanter,0
saratoga_(kancolle),4
assault_lily,3
nagisa_kaworu,4
screaming,0
iesupa,1
spotlight,0
power_symbol,0
creator_connection,0
tombstone,0
flower_pot,0
orange_theme,0
jackal_ears,0
asuna_(bunny)_(blue_archive),4
makinami_mari_illustrious,4
super_saiyan,0
hamu_koutarou,1
hair_spread_out,0
golden_kamuy,3
bakugou_katsuki,4
anya_(spy_x_family),4
tachibana_kanade,4
asphyxiation,0
shouji,0
cameo,0
dusk,0
saenai_heroine_no_sodatekata,3
path,0
on_lap,0
multiple_belts,0
brown_ribbon,0
hadanugi_dousa,0
tied_up_(nonsexual),0
hatsuzuki_(kancolle),4
pokemon_(classic_anime),3
still_life,0
kani_biimu,1
flask,0
tally,0
small_penis,0
carpet,0
disembodied_head,0
evening_gown,0
sakamata_chloe,4
phimosis,0
red_sclera,0
merlin_prismriver,4
diagonal-striped_bow,0
kamijou_touma,4
female_pov,0
brave_witches,3
holding_smoking_pipe,0
re-class_battleship,4
takao_(azur_lane),4
sunrise,0
bow_bikini,0
red_sash,0
:i,0
smiley_face,0
bird_girl,0
faux_traditional_media,0
shana,4
mohawk,0
katsushika_hokusai_(fate),4
ichimi,1
arcueid_brunestud,4
michishio_(kancolle),4
side_cutout,0
diving_mask,0
triangle,0
roman_numeral,0
curry,0
hill,0
shinrabanshou,3
ushiromiya_battler,4
smell,0
red_pantyhose,0
cover_image,0
kou_hiyoyo,1
multicolored_legwear,0
drumsticks,0
russian_text,0
steam_censor,0
shinryaku!_ikamusume,3
retrofit_(azur_lane),0
heart_tattoo,0
shimakaze_(kancolle)_(cosplay),0
diadem,0
black_headband,0
cyclops,0
hexagram,0
ufo,0
number_tattoo,0
ranka_lee,4
yura_(kancolle),4
hamster,0
u-511_(kancolle),4
tree_shade,0
mouse_(computer),0
chihuri,1
red_pupils,0
broken_glass,0
hino_akane_(smile_precure!),4
talisman,0
fourth_wall,0
marie_(splatoon),4
monkey_tail,0
futa_with_male,0
egyptian_clothes,0
pink-framed_eyewear,0
shuuchiin_academy_school_uniform,0
lyrica_prismriver,4
the_legend_of_zelda:_ocarina_of_time,3
covered_abs,0
short_sword,0
new_game!,3
linea_alba,0
police_hat,0
tri_tails,0
kashiwazaki_sena,4
shared_scarf,0
animal_penis,0
purple_sleeves,0
aqua_bikini,0
cinderella_girls_gekijou,3
houtengeki,1
condom_on_penis,0
breast_bondage,0
downscaled,5
kantoku,1
sink,0
kanna_kamui,4
green_choker,0
blue_lips,0
anal_fingering,0
street_fighter_iv_(series),3
bad_hands,0
tennis_uniform,0
eraser,0
spatula,0
surtr_(arknights),4
yes!_precure_5,3
fur_coat,0
desert,0
ice_cube,0
kamado_nezuko,4
isuzu_hana,4
kyonko,4
torch,0
quill,0
chitanda_eru,4
hikarizaka_private_high_school_uniform,0
beak,0
lana_(pokemon),4
string_of_flags,0
satchel,0
yua_(checkmate),1
black_scrunchie,0
yahagi_(kancolle),4
scratches,0
kiana_kaslana,4
ankle_cuffs,0
kaenbyou_rin_(cat),4
bandaid_on_cheek,0
striped_socks,0
melting,0
driving,0
miyu_edelfelt,4
studded_belt,0
large_tail,0
d-pad_hair_ornament,0
cliff,0
gimp_(medium),5
multiple_earrings,0
ethan_(pokemon),4
toshinou_kyouko,4
jellyfish,0
feather-trimmed_sleeves,0
finger_in_another's_mouth,0
holding_arrow,0
mutsuki_(kancolle),4
octarian,0
strangling,0
yukine_chris,4
tippy_(gochiusa),4
enterprise_(azur_lane),4
ritual_baton,0
microdress,0
laffey_(azur_lane),4
big_belly,0
phone_screen,0
grey_sky,0
praying,0
yuuki_makoto,4
prayer_beads,0
hand_on_hilt,0
ribbed_legwear,0
magical_musket,0
haguro_(kancolle),4
torn_cape,0
oni_mask,0
masao,1
tribadism,0
samurai,0
keyboard_(instrument),0
oshiro_project_re,3
crossed_bandaids,0
cyberpunk,0
spider_web_print,0
shading_eyes,0
mechanical_parts,0
final_fantasy_iv,3
napoleon_bonaparte_(fate),4
insignia,0
red_umbrella,0
creature_and_personification,0
i-8_(kancolle),4
open_skirt,0
amamiya_ren,4
feather_hair,0
rectangular_eyewear,0
calendar_(object),0
shoulder_spikes,0
dynamax_band,0
ojipon,1
shiseki_hirame,1
final_fantasy_tactics,3
sleeping_upright,0
sitting_on_desk,0
sideless_outfit,0
matching_outfit,0
tamamo_cat_(fate),4
milk_bottle,0
cave,0
nisemonogatari,3
crotchless_panties,0
kirigiri_kyouko,4
restaurant,0
agnes_tachyon_(umamusume),4
yaegashi_nan,1
akai_haato,4
fine_art_parody,0
dirty_face,0
starter_pokemon_trio,0
horseback_riding,0
bb_(baalbuddy),1
shinku,4
houjou_hibiki,4
drawing_tablet,0
hanekawa_tsubasa,4
kousaka_tamaki,4
anzio_school_uniform,0
akizuki_(kancolle),4
dessert,0
no_eyewear,0
caught,0
durarara!!,3
cervix,0
purple_pants,0
karin_(blue_archive),4
trick_or_treat,0
kabedon,0
racket,0
final_fantasy_ix,3
tail_through_clothes,0
tone_(kancolle),4
curtain_grab,0
holding_sheath,0
broken_horn,0
flower_(symbol),0
hungry_clicker,1
seiken_densetsu,3
futami_ami,4
cooperative_fellatio,0
hands_on_own_head,0
abukuma_(kancolle),4
doki_doki_literature_club,3
st._gloriana's_military_uniform,0
sweater_around_waist,0
butterfly_print,0
puyopuyo,3
;3,0
green_cape,0
clitoral_hood,0
abubu,1
tankini,0
when_you_see_it,0
meitantei_conan,3
gundam_build_fighters_try,3
liquid,0
ear_tag,0
t-head_admiral,4
uchiha_sasuke,4
enpera,0
carrying_over_shoulder,0
town,0
festival,0
fur-trimmed_legwear,0
spoken_anger_vein,0
naked_coat,0
clothed_pokemon,0
abstract_background,0
smelling,0
plum_blossoms,0
standing_on_liquid,0
amber_(genshin_impact),4
aida_mana,4
??,0
yelan_(genshin_impact),4
akagi_(azur_lane),4
strapless_bra,0
light_bulb,0
holding_pom_poms,0
dreadlocks,0
tailcoat,0
lace-trimmed_dress,0
rocket_launcher,0
jeanne_d'arc_alter_(swimsuit_berserker)_(fate),4
constellation,0
blue_oak,4
open_collar,0
ano_hi_mita_hana_no_namae_wo_bokutachi_wa_mada_shiranai.,3
white_nails,0
bare_hips,0
black_fur,0
reisalin_stout,4
wire,0
takanashi_rikka,4
video_camera,0
plaid_bikini,0
black_camisole,0
attack,0
atlanta_(kancolle),4
junketsu,4
hews,1
gran_(granblue_fantasy),4
jet,0
black_tail,0
pulling,0
black_bag,0
blue_cardigan,0
hammer_and_sickle,0
surfboard,0
mini-hakkero,0
chain_necklace,0
spilling,0
kemono_friends_3,3
trumpet,0
single_vertical_stripe,0
black_rose,0
mega_pokemon,0
patterned_clothing,0
arabian_clothes,0
matsuura_kanan,4
budget_sarashi,0
pulled_by_another,0
graffiti,0
youkai_watch,3
sweatband,0
amagi_yukiko,4
shark_hair_ornament,0
kurosawa_dia,4
enoshima_junko,4
arch,0
product_placement,0
minecraft,3
god_eater,3
white_cloak,0
black_apron,0
church,0
vial,0
striped_pants,0
white_headband,0
>_o,0
gown,0
whistle_around_neck,0
colored_tongue,0
impossible_leotard,0
kisaragi_(kancolle),4
spread_wings,0
screencap_redraw,5
cat_ear_headphones,0
blue_ascot,0
chinese_knot,0
two-tone_bikini,0
yang_xiao_long,4
evolutionary_line,0
guilty_crown,3
tie_clip,0
hooded_cape,0
purple_cape,0
wild_arms,3
tegaki,0
hair_horns,0
shokuhou_misaki,4
yorigami_jo'on,4
street_fighter_zero_(series),3
french_fries,0
darker_than_black,3
strappy_heels,0
twitching_penis,0
nero_claudius_(swimsuit_caster)_(fate),4
briefs,0
fleeing,0
kagari_atsuko,4
action,0
bb_(fate/extra),4
manga_(object),0
azumanga_daioh,3
white_cat,0
green_lips,0
moss,0
ia_(vocaloid),4
egg_(food),0
bolt_action,0
veranda,0
belly_chain,0
nail,0
battleship_princess,4
nib_pen_(medium),0
thigh_ribbon,0
ikamusume,4
holding_sign,0
idolmaster_dearly_stars,3
glowstick,0
covering_nipples,0
single_hair_intake,0
denim_skirt,0
kurumi_erika,4
yoga_pants,0
tail_bell,0
cat_hat,0
spiked_armlet,0
scissor_blade,0
clothes_down,0
turban,0
naked_sheet,0
ueyama_michirou,1
gold_chain,0
stage_lights,0
nonna_(girls_und_panzer),4
spread_fingers,0
white_cardigan,0
monocle_hair_ornament,0
mop,0
eren_yeager,4
kunikida_hanamaru,4
akuma_homura,4
shako_cap,0
ump9_(girls'_frontline),4
pokemon_gsc,3
hilbert_(pokemon),4
perrine_h._clostermann,4
overlord_(maruyama),3
minaba_hideo,1
qiqi_(genshin_impact),4
kappougi,0
vector_trace,5
rowlet,4
charlotte_e._yeager,4
holding_condom,0
akari_(pokemon),4
instant_loss,0
clothes_hanger,0
bowl_hat,0
slugbox,1
aqua_skirt,0
chainsaw,0
dreaming,0
ankle_socks,0
mayuzumi_fuyuko,4
hands_on_headwear,0
setsubun,0
pool_ladder,0
finger_in_own_mouth,0
akaza_akari,4
vocaloid_append,0
cat_print,0
whale,0
ise_(kancolle),4
shikinami_(kancolle),4
raising_heart,4
head_grab,0
hoshizora_miyuki,4
star_wars,3
potato_chips,0
fire_emblem_echoes:_shadows_of_valentia,3
kamikaze_(kancolle),4
mudrock_(arknights),4
mechanical_legs,0
matara_okina,4
three-dimensional_maneuver_gear,0
oversized_animal,0
holding_doll,0
dyed_bangs,0
zounose,1
laurel_crown,0
ishiyumi,1
bloodborne,3
purple_scarf,0
tales_of_xillia,3
spanked,0
gusset,0
penis_grab,0
senjougahara_hitagi,4
jun'you_(kancolle),4
tamamo_no_mae_(swimsuit_lancer)_(fate),4
team_rocket,0
false_smile,0
purple_shorts,0
black_lips,0
dust,0
boris_(noborhys),1
fire_emblem:_the_sacred_stones,3
korean_clothes,0
pill,0
tusks,0
gambier_bay_(kancolle),4
crotch_rub,0
wind_chime,0
shuriken,0
teenage,0
sketchbook,0
ribbon-trimmed_skirt,0
winged_hat,0
hand_on_another's_thigh,0
see-through_legwear,0
nengajou,0
grey_bow,0
kid_icarus,3
vertical-striped_skirt,0
multicolored_wings,0
tsukudani_(coke-buta),1
turtle,0
kujikawa_rise,4
gangut_(kancolle),4
reverse_upright_straddle,0
nakano_yotsuba,4
miyuki_(kancolle),4
ankle_lace-up,0
sweet_potato,0
bad_proportions,0
fur_scarf,0
impossible_bodysuit,0
hands_on_lap,0
beamed_sixteenth_notes,0
anastasia_(fate),4
han_juri,4
tongue_piercing,0
long_pointy_ears,0
promare,3
devil_may_cry_(series),3
car_interior,0
nose_bubble,0
in_bucket,0
honda_mio,4
wreath,0
fou_(fate),4
dragon's_crown,3
rariatto_(ganguri),1
boxing_gloves,0
scepter,0
mechanical_horns,0
inflatable_toy,0
taiyaki,0
shinki_(touhou),4
wooden_bucket,0
energy_wings,0
afro,0
symmetry,0
fire_emblem_cipher,3
wrestling,0
baseball,0
lynette_bishop,4
saten_ruiko,4
sleep_molestation,0
clothed_masturbation,0
aria,3
warship,0
kal'tsit_(arknights),4
suiseiseki,4
two-tone_jacket,0
tsuda_nanafushi,1
hassan_of_serenity_(fate),4
poptepipic,3
bronya_zaychik,4
grimace,0
comb,0
feather_earrings,0
prehensile_hair,0
panty_(psg),4
crepe,0
kaga_(azur_lane),4
wide_image,5
clothed_animal,0
neck_tattoo,0
blur_censor,0
flower_earrings,0
two-tone_swimsuit,0
glowing_sword,0
diluc_(genshin_impact),4
school_hat,0
boxers,0
ryougi_shiki,4
shared_umbrella,0
ohtsuki_yui,4
bandaged_head,0
swim_briefs,0
plunging_neckline,0
ryuuou_no_oshigoto!,3
pola_(kancolle),4
shino_(ponjiyuusu),1
black_feathers,0
clothing_request,5
pink_sleeves,0
skewer,0
child_on_child,0
mod3_(girls'_frontline),0
cube,0
annotated,5
kizuna_akari,4
ribbon-trimmed_clothes,0
tripping,0
drawfag,1
transparent_umbrella,0
picture_(object),0
failure,0
spinning,0
wife_and_wife,0
median_furrow,0
union_jack,0
tsunako,1
hair_through_headwear,0
bb_(swimsuit_mooncancer)_(fate),4
drawer,0
between_thighs,0
cheek_pinching,0
borrowed_garments,0
high_contrast,0
have_to_pee,0
birthday_cake,0
galaxy_angel,3
tanuki,0
sayori_(neko_works),1
avatar_(ff11),4
katsuki_yuuri,4
i-168_(kancolle),4
flute,0
piplup,4
squid,0
aoki_hagane_no_arpeggio,3
sonozaki_mion,4
raiden_mei,4
shark_hood,0
irisviel_von_einzbern,4
lawson,3
yu-gi-oh!_gx,3
gegege_no_kitarou,3
tokkuri,0
barrel,0
ikkitousen,3
lion,0
turnaround,0
sports_bikini,0
pillow_grab,0
on_table,0
utawarerumono,3
white_male_underwear,0
purple_coat,0
soap,0
bunny_pose,0
harvin,0
shiroko_(blue_archive),4
juice_box,0
two-tone_bowtie,0
lyra_(pokemon),4
avogado6,1
cheek_poking,0
red_sky,0
counter,0
kurosawa_ruby,4
clover_hair_ornament,0
nude_filter,5
viktor_nikiforov,4
yu_mei-ren_(fate),4
yuugumo_(kancolle),4
corpse,0
stray_pubic_hair,0
shoebill_(kemono_friends),4
orange_kimono,0
nishikigi_chisato,4
sangonomiya_kokomi,4
spider_girl,0
toast,0
final_fantasy_v,3
blue_serafuku,0
spear_the_gungnir,0
kanikama,1
looking_at_object,0
hands_on_another's_head,0
head_bump,0
playing_with_own_hair,0
mallow_(pokemon),4
column_lineup,0
touhou_tag_dream,3
face_to_breasts,0
caustics,0
two-tone_gloves,0
akeome,0
christa_renz,4
gate,0
covered_face,0
mating_press,0
shadowverse,3
black_hakama,0
chaps,0
cocktail_glass,0
vita,4
polka_dot_bra,0
jakuzure_nonon,4
stone,0
pocky_day,0
back_tattoo,0
safety_pin,0
hanfu,0
heart_hands_duo,0
springfield_(girls'_frontline),4
balcony,0
fu_hua,4
holding_cat,0
lolita_channel,1
spider-man_(series),3
vending_machine,0
slime_(creature),0
chart,0
warcraft,3
nachi_(kancolle),4
alarm_clock,0
tatsumaki,4
makigumo_(kancolle),4
neptune_(neptune_series),4
bite_mark,0
macross_delta,3
naked_cape,0
koyama_shigeru,1
energy_ball,0
volleyball_uniform,0
blood_stain,0
watashi_ni_tenshi_ga_maiorita!,3
charizard,4
vivio,4
breath_of_fire,3
69,0
koihime_musou,3
inkling_boy,4
mushoku_tensei,3
tissue,0
ibaraki_douji_(fate),4
noumi_kudryavka,4
final_fantasy_x,3
daisy,0
la+_darknesss,4
idolmaster_2,3
meltryllis_(swimsuit_lancer)_(fate),4
nose_piercing,0
octoling,4
pangya,3
fried_egg,0
squirrel,0
mochi_au_lait,1
cosmetics,0
yellow_choker,0
hand_in_another's_hair,0
p-head_producer,4
tsukino_mito,4
grey-framed_eyewear,0
kochou_shinobu,4
licking_nipple,0
if_they_mated,0
head_back,0
burning,0
orange_pekoe_(girls_und_panzer),4
bisexual_female,0
elizabeth_bathory_(fate/extra_ccc),4
miyamoto_frederica,4
circle_cut,0
dominatrix,0
ujimatsu_chiya,4
ok_sign,0
industrial_pipe,0
light_trail,0
sleeveless_sweater,0
porkpie_hat,0
pointer,0
strawberry_shortcake,0
rainbow_order,0
torn_shorts,0
rabbit_house_uniform,0
brown_cape,0
homurahara_academy_school_uniform,0
keychain,0
checkered_necktie,0
kitsunerider,1
jack-o'_challenge,0
senran_kagura_shoujo-tachi_no_shin'ei,3
shared_food,0
hyur,0
shoujo_kageki_revue_starlight,3
sakura_miku,4
aqua_dress,0
lace_bra,0
lemon_slice,0
tedeza_rize,4
checkered_kimono,0
sofra,1
multicolored_gloves,0
under_table,0
train_station,0
hex_maniac_(pokemon),4
prostitution,0
whisk,0
amagi_brilliant_park,3
to_love-ru_darkness,3
choukai_(kancolle),4
traffic_light,0
aldnoah.zero,3
hairdressing,0
clover_print,0
w_(arknights),4
against_tree,0
striped_sleeves,0
censored_nipples,0
holding_paintbrush,0
watering_can,0
snake_tail,0
mysterious_heroine_xx_(fate),4
jeanne_d'arc_alter_santa_lily_(fate),4
nintendo,3
purple_sweater,0
:x,0
shrimp,0
johnny_joestar,4
impregnation,0
akagi_miria,4
skull_earrings,0
black_sports_bra,0
in_food,0
lyn_(fire_emblem),4
diamond-shaped_pupils,0
pearl_(gemstone),0
orange_shorts,0
rug,0
tsukishiro_saika,1
bound_ankles,0
nico_robin,4
dixie_cup_hat,0
saki_achiga-hen,3
large_hat,0
drill,0
sidesaddle,0
harem,0
yellow_bra,0
two-tone_ribbon,0
sesshouin_kiara,4
maru-yu_(kancolle),4
blake_belladonna,4
footprints,0
happy_tears,0
tape_gag,0
sleeping_on_person,0
board_game,0
capcom,3
love_plus,3
yuzuriha_inori,4
phantom_blood,3
looking_over_eyewear,0
finger_to_own_chin,0
multicolored_bodysuit,0
monkey,0
m4_carbine,0
flower_wreath,0
orange_scarf,0
holding_basket,0
fluffy,0
nero_claudius_(bride)_(fate),4
scar_on_forehead,0
nagatsuki_(kancolle),4
chasing,0
hara_(harayutaka),1
melonbooks,3
mash_kyrielight_(dangerous_beast),4
pokemon_journeys,3
magatama_necklace,0
soul_eater,3
mizuki_makoto,1
you're_doing_it_wrong,0
katsuragi_(kancolle),4
shinjou_akane,4
star_choker,0
brendan_(pokemon),4
gatling_gun,0
sinoalice,3
akamatsu_kaede,4
pale_color,0
kenzaki_makoto,4
refrigerator,0
bomb,0
identity_censor,0
railroad_tracks,0
minami_(colorful_palette),1
air,3
crossbow,0
bandaid_on_arm,0
plaid_necktie,0
beltbra,0
destruction,0
atelier_ryza_1,3
tennis_racket,0
nice_nature_(umamusume),4
android_18,4
strawberry_print,0
persona_3_portable,3
cloud_print,0
muvluv_alternative,3
takara_miyuki,4
kin-iro_mosaic,3
mostima_(arknights),4
color_guide,0
lysithea_von_ordelia,4
cockpit,0
dragon_quest_iv,3
hakos_baelz,4
loose_clothes,0
bardiche,4
tomose_shunsaku,1
poncho,0
ceiling_light,0
hitachi_magic_wand,0
earpiece,0
gloved_handjob,0
hiro_(darling_in_the_franxx),4
sakamoto_mio,4
mima_(touhou),4
body_hair,0
cafe,0
condom_packet_strip,0
ginhaha,1
pond,0
stripper_pole,0
yowane_haku,4
spoken_sweatdrop,0
faucet,0
taisa_(kari),1
index_(toaru_majutsu_no_index),4
butler,0
mabinogi,3
anti-materiel_rifle,0
akatsuki_kirika,4
dirty_clothes,0
haikyuu!!,3
koizumi_itsuki,4
senpai_ga_uzai_kouhai_no_hanashi,3
spitting,0
mirko,4
lilith_aensland,4
sinon,4
fishing,0
doyagao,0
holding_controller,0
aegis_(persona),4
nanashi_(nlo),1
low_twin_braids,0
tama_(kancolle),4
straitjacket,0
alternate_form,0
silence_suzuka_(umamusume),4
pepperoni_(girls_und_panzer),4
ornate_ring,0
saiguchi_otoufu,1
lucky_beast_(kemono_friends),4
no_wings,0
dove,0
birdcage,0
last_order_(toaru_majutsu_no_index),4
javelin_(azur_lane),4
kyoukaisenjou_no_horizon,3
nia_(xenoblade),4
cheating_(relationship),0
suzuran_(arknights),4
shinomiya_kaguya,4
noshiro_(kancolle),4
shima_rin,4
mace,0
champagne_flute,0
selfcest,0
multicolored_bikini,0
tsunomaki_watame,4
morpeko,4
enkyo_yuuichirou,1
teruzuki_(kancolle),4
variations,0
belko,1
bandage_on_face,0
surprise_kiss,0
full_nelson,0
morikubo_nono,4
sparks,0
winged_helmet,0
aqua_ribbon,0
alley,0
on_grass,0
freediving,0
tachibana_hibiki_(symphogear),4
gojou_satoru,4
deviantart_username,0
parted_hair,0
suzukaze_(kancolle),4
ink,0
looking_at_phone,0
dolphin,0
fewer_digits,0
cracked_skin,0
cowboy_western,0
mixed_media,0
grey_socks,0
drone,0
voyakiloid,0
after_kiss,0
futanari_masturbation,0
tickling,0
missile,0
crescent_facial_mark,0
hilda_valentine_goneril,4
black_lagoon,3
pain,0
sonia_(pokemon),4
rosalina,4
mizuno_ami,4
yume_nikki,3
artificial_vagina,0
purple_sky,0
briefcase,0
kannagi,3
yellow_vest,0
yayoi_(kancolle),4
thick_arms,0
mask_pull,0
on_person,0
brown_sailor_collar,0
spider,0
plectrum,0
hanamura_yousuke,4
circle_name,0
pocky_kiss,0
hina_(blue_archive),4
brushing_hair,0
roller_skates,0
kirino_ranmaru,4
tube,0
gorget,0
shin_megami_tensei,3
tanabata,0
signum,4
pussy_juice_puddle,0
torpedo_tubes,0
hands_on_own_thighs,0
mimikaki,0
aqua_bowtie,0
eastern_dragon,0
agawa_ryou,1
holding_syringe,0
milkpanda,1
shinsengumi,0
makoto_nanaya,4
hisona_(suaritesumi),1
confession,0
frilled_capelet,0
pripara,3
shirayuki_(kancolle),4
sitting_in_tree,0
miyo_(ranthath),1
green_gemstone,0
holly,0
kuromorimine_school_uniform,0
rubble,0
light_areolae,0
pokemon_go,3
gotland_(kancolle),4
white_horns,0
leg_belt,0
hooves,0
pole_dancing,0
yuuji_(and),1
pink_one-piece_swimsuit,0
love_live!_superstar!!,3
fellatio_gesture,0
holding_hammer,0
implied_futanari,0
hanasaki_tsubomi,4
yellow-framed_eyewear,0
dark_souls_i,3
avatar_(series),3
andou_(girls_und_panzer),4
asashio_kai_ni_(kancolle),4
skull_print,0
mattaku_mousuke,1
bird_on_hand,0
yasogami_school_uniform,0
free!,3
kuroshio_(kancolle),4
yuureidoushi_(yuurei6214),1
noel_vermillion,4
thighhighs_pull,0
sticker,0
pokemon_xy_(anime),3
character_print,0
patch,0
breast_curtain,0
asui_tsuyu,4
barcode_tattoo,0
wooden_sword,0
fine_fabric_emphasis,0
blue_apron,0
my_little_pony,3
frilled_ribbon,0
rotom,4
holding_drink,0
breasts_on_head,0
tales_of_graces,3
deer_ears,0
chiyoda_momo,4
impossible_dress,0
x_x,0
dress_tug,0
overskirt,0
beam_rifle,0
breast_smother,0
matou_kariya,4
kizuna_ai_inc.,3
kill_me_baby,3
seiken_densetsu_3,3
nail_art,0
producer_(idolmaster_cinderella_girls_anime),4
super_robot,0
phosphophyllite,4
scarlet_devil_mansion,0
purple_sailor_collar,0
alice_gear_aegis,3
the_legend_of_luo_xiaohei,3
eye_focus,0
oda_nobunaga_(koha-ace),4
wa_(genryusui),1
coral,0
dragon_ball_(classic),3
gundam_tekketsu_no_orphans,3
cabinet,0
high-waist_pants,0
cousins,0
matsuno_karamatsu,4
shiomi_syuko,4
helm,0
m-da_s-tarou,1
through_clothes,0
fanny_pack,0
multicolored_swimsuit,0
song_name,0
inuyasha,3
urusei_yatsura,3
pink_hoodie,0
aerial_fireworks,0
saber_lily,4
sen_no_kiseki,3
puffy_shorts,0
tracer_(overwatch),4
swing,0
buck_teeth,0
tokyo_7th_sisters,3
kasumi_(doa),4
ak-12_(girls'_frontline),4
baseball_uniform,0
star_halo,0
world_of_warcraft,3
biker_clothes,0
eye_mask,0
spring_(season),0
adjusting_gloves,0
shiomi_kotone,4
puchimasu!,3
zeta_gundam,3
reflective_floor,0
confused,0
st._louis_(azur_lane),4
luigi's_mansion,3
see-through_leotard,0
otonashi_kotori,4
leopard_ears,0
yellow_thighhighs,0
fate/extella,3
very_long_fingernails,0
flexing,0
tarot,0
palms,0
seigaiha,0
legwear_garter,0
fortissimo,0
sumiyao_(amam),1
upscaled,5
menu,0
asashimo_(kancolle),4
holding_pencil,0
ribbon_bondage,0
n_(pokemon),4
incoming_gift,0
kizuna_ai,4
starry_sky_print,0
red_armor,0
brown_capelet,0
shimazaki_mujirushi,1
matsuno_jyushimatsu,4
2014,0
\||/,0
grabbing_another's_chin,0
kalashnikov_rifle,0
tangzhuang,0
minamino_kanade,4
insect_wings,0
seiran_(touhou),4
plaid_background,0
my-hime,3
lord_el-melloi_ii_case_files,3
red_socks,0
naginata,0
beni_shake,1
hair_in_mouth,0
magatama_earrings,0
sword_art_online:_code_register,3
lio_fotia,4
hataraku_saibou,3
polka_dot_dress,0
in_the_face,0
aino_minako,4
horikawa_raiko,4
cucumber,0
matsuno_ichimatsu,4
matsuno_osomatsu,4
breasts_on_glass,0
moroboshi_kirari,4
hair_brush,0
german_clothes,0
naked_jacket,0
after_paizuri,0
green-framed_eyewear,0
radio_antenna,0
toga_himiko,4
worried,0
bow-shaped_hair,0
suzumiya_haruhi_no_shoushitsu,3
star_twinkle_precure,3
sugar_lyric,3
cherry_blossom_print,0
kamiya_nao,4
leaning_on_object,0
hanyuu,4
marker,0
pencil_dress,0
pokemon_on_head,0
beckoning,0
dragon_quest_v,3
tales_of_the_abyss,3
crotch_plate,0
stuck,0
kaedehara_kazuha,4
dowsing_rod,0
lucario,4
leaf_(pokemon),4
ningguang_(genshin_impact),4
gabriel_dropout,3
dodoco_(genshin_impact),4
messy,0
lip_piercing,0
white_suit,0
hand_on_thigh,0
role_reversal,0
oumae_kumiko,4
chocolate_bar,0
horned_helmet,0
mimura_kanako,4
star_necklace,0
moon_(ornament),0
autobot,0
rensouhou-kun,4
arashio_(kancolle),4
teasing,0
hugtto!_precure,3
skin_fangs,0
lounge_chair,0
cross_scar,0
o-ring_choker,0
poke_ball_symbol,0
tamako_market,3
senjou_no_valkyria_(series),3
faux_figurine,0
bondage_outfit,0
frozen_(disney),3
ha_akabouzu,1
projected_inset,0
monsterification,0
ayanami_(kancolle),4
komi-san_wa_komyushou_desu,3
untucked_shirt,0
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sorcerer's_sutra_scroll,0
gem_(symbol),0
patchwork_skin,0
sendai_hakurei_no_miko,4
jinx_(league_of_legends),4
yae_sakura,4
sendai_kai_ni_(kancolle),4
bad_drawr_id,5
claw_(weapon),0
crease,5
fish_girl,0
torn,0
pink_vest,0
hakurei_reimu_(cosplay),0
mechanical_pencil,0
heckler_&_koch,0
milk_carton,0
darkness_(konosuba),4
souseiseki,4
creayus,1
yorha_no._9_type_s,4
barbell_piercing,0
shikigami,0
hachimiya_meguru,4
bamboo_broom,0
wheel,0
peeking,0
isuzu_(kancolle),4
color_connection,0
tactical_clothes,0
misumi_nagisa,4
artoria_pendragon_(lancer_alter)_(fate),4
sakata_gintoki,4
red_hoodie,0
magi_the_labyrinth_of_magic,3
suzutsuki_(kancolle),4
strapless_shirt,0
tokusatsu,0
unconscious,0
chandelier,0
felicia_(vampire),4
blinds,0
ushiwakamaru_(fate),4
wooden_wall,0
chain_leash,0
polo_shirt,0
see-through_dress,0
striped_one-piece_swimsuit,0
two-handed,0
japanese_crested_ibis_(kemono_friends),4
i-class_destroyer,4
bishamonten's_pagoda,0
dagashi_kashi,3
ofuda_on_clothes,0
hikimayu,0
single_pauldron,0
sakurai_momoka,4
stiletto_heels,0
german_text,0
agano_(kancolle),4
toosaka_asagi,1
hand_on_own_leg,0
white_sash,0
yamamoto_souichirou,1
drill_locks,0
bikini_top_removed,0
park_bench,0
sunburst_background,0
danganronpa_3_(anime),3
orange_legwear,0
latex_bodysuit,0
icho_private_high_school_uniform,0
hagoita,0
video_game,0
watatsuki_no_yorihime,4
princess_king_boo,4
lusamine_(pokemon),4
rappa_(rappaya),1
old_woman,0
leg_hold,0
large_insertion,0
striped_gloves,0
high-waist_shorts,0
greatsword,0
nishizumi_shiho,4
leafa,4
purple_one-piece_swimsuit,0
circled_9,0
etna_(disgaea),4
kinugasa_(kancolle),4
ensemble_stars!,3
black_robe,0
staff_(music),0
neocoill,1
bowing,0
fireflies,0
harem_outfit,0
robin_(fire_emblem)_(female),4
elite_ii_(arknights),0
holostars,3
ushanka,0
cupless_bra,0
spiked_tail,0
northern_white-faced_owl_(kemono_friends),4
rosehip_(girls_und_panzer),4
fingers,0
futa_with_futa,0
red_cross,0
suppressor,0
health_bar,0
hojo_karen,4
groin_tendon,0
holstered_weapon,0
cuddling,0
plaid_bowtie,0
viera,0
lipstick_tube,0
pentacle,0
leotard_pull,0
instrument_case,0
konoshige_(ryuun),1
cosplay_photo,0
has_censored_revision,5
checkerboard_cookie,0
holding_whip,0
yu-gi-oh!_5d's,3
hexagon,0
green_hoodie,0
mars_symbol,0
holding_sack,0
eagle,0
ohara_mari,4
naked_hoodie,0
taut_dress,0
ribbon-trimmed_collar,0
gold_hairband,0
mei_(overwatch),4
kusazuri,0
ushiromiya_ange,4
hirasawa_ui,4
spine,0
choko_(cup),0
yuuhagi_(amaretto-no-natsu),1
gensou_suikoden,3
libeccio_(kancolle),4
shallow_water,0
pageratta,1
gold_earrings,0
mahjong,0
ramune,0
gamagoori_ira,4
ghost_in_the_shell,3
funami_yui,4
mikazuki_munechika,4
holding_stick,0
chanta_(ayatakaoisii),1
xenosaga,3
yoimiya_(genshin_impact),4
twitter,3
ayu_(mog),1
different_reflection,0
wisteria,0
single_gauntlet,0
monokuma,4
hatsune_miku_(append),4
crazy_smile,0
matsuryuu,1
head_between_breasts,0
saren_(princess_connect!),4
nontraditional_playboy_bunny,0
pacifier,0
sakugawa_school_uniform,0
edward_elric,4
red_cloak,0
notepad,0
visible_air,0
eternity_larva,4
little_red_riding_hood,3
levi_(shingeki_no_kyojin),4
kirigaya_suguha,4
fallen_down,0
whipped_cream,0
reach-around,0
fff_threesome,0
graveyard,0
puffy_detached_sleeves,0
japanese_flag,0
power_suit,0
strap-on,0
artoria_pendragon_(alter_swimsuit_rider)_(fate),4
painting_(action),0
yellow_cardigan,0
itadori_yuuji,4
kagura_(gintama),4
harukawa_maki,4
oshida_(girls_und_panzer),4
fate/unlimited_codes,3
neon_lights,0
tulip_hat,0
kawakaze_(kancolle),4
bear_hair_ornament,0
musashi_kai_ni_(kancolle),4
tokisaki_kurumi,4
sword_over_shoulder,0
love_letter,0
kissing_forehead,0
gundam_unicorn,3
office,0
morpeko_(full),4
mega_man_x_(series),3
beach_chair,0
dinosaur,0
tonda,1
special_week_(umamusume),4
pani_poni_dash!,3
bayonetta_(series),3
raimon,0
rotational_symmetry,0
ribbon_in_mouth,0
blueberry,0
vshojo,3
simplified_chinese_text,0
green_neckerchief,0
holding_leash,0
nyantype,3
mother_(game),3
sincos,1
honolulu_(azur_lane),4
lum,4
kibito_high_school_uniform,0
arm_between_breasts,0
truth,0
perineum,0
spiked_shell,0
kirakira_precure_a_la_mode,3
bremerton_(scorching-hot_training)_(azur_lane),4
angelina_(arknights),4
widowmaker_(overwatch),4
centaur,0
pussy_piercing,0
kamui_gakupo,4
playstation_portable,0
has_downscaled_revision,5
earth_(ornament),0
tera_online,3
ramlethal_valentine,4
other_focus,0
skyla_(pokemon),4
air_conditioner,0
sakura_taisen,3
wing_hair_ornament,0
dengeki_moeou,3
francesca_lucchini,4
mascot,0
onmyoji,3
tohru_(maidragon),4
cure_peace,4
reindeer_costume,0
collared_cape,0
ebi_193,1
caressing_testicles,0
sweatshirt,0
kiyosumi_school_uniform,0
tail_censor,0
isonami_(kancolle),4
manhattan_cafe_(umamusume),4
purple_heart,4
dog_days,3
tiger_boy,0
tan_background,0
title,0
aqua_theme,0
kashuu_kiyomitsu,4
stationary_restraints,0
frilled_cuffs,0
kishibe_rohan,4
thigh_cutout,0
2013,0
yd_(orange_maru),1
camilla_(fire_emblem),4
yang_guifei_(fate),4
kasugano_sakura,4
chibi_usa,4
wolf_boy,0
matsuno_choromatsu,4
kujou_sara,4
cotton_candy,0
ashiya_douman_(fate),4
inoue_takina,4
sanshoku_dango,0
boned_meat,0
leonardo_da_vinci_(fate),4
tsumiki_mikan,4
holding_needle,0
xenoblade_chronicles_3,3
garden,0
hooded_sweater,0
monkey_d._luffy,4
caution_tape,0
toranoana,3
morgan_le_fay_(fate),4
blood_from_eyes,0
stuffed_shark,0
yagasuri,0
gengar,4
bendy_straw,0
ymir_(shingeki_no_kyojin),4
age_progression,0
truck,0
eureka_seven_(series),3
katahira_masashi,1
candlestand,0
green_apron,0
camcorder,0
null_(nyanpyoun),1
buzz_cut,0
plant_girl,0
shirase_sakuya,4
overgrown,0
yellow_skin,0
white-framed_eyewear,0
spiked_club,0
handsfree_ejaculation,0
agahari,1
clothed_female_nude_female,0
martha_(fate),4
uzaki-chan_wa_asobitai!,3
hand_puppet,0
the_moon_studio,3
grey_necktie,0
helicopter,0
satou_kazuma,4
chocolate_on_body,0
ilya_kuvshinov,1
katori_(kancolle),4
fish_hair_ornament,0
palutena,4
halberd,0
2010,0
fushiguro_megumi,4
untying,0
tukiwani,1
2012,0
heart_tail,0
okamisty,0
fujibayashi_kyou,4
drying,0
uiharu_kazari,4
magnifying_glass,0
palette_(object),0
invisible_penis,0
viewer_holding_leash,0
arm_wrap,0
my_little_pony:_friendship_is_magic,3
donation_box,0
flame_print,0
makizushi,0
santa_boots,0
enemy_aircraft_(kancolle),4
hanging,0
aqua_shirt,0
sitting_on_object,0
holding_shoes,0
hand_on_another's_ass,0
mikage_takashi,1
morning,0
nijigasaki_academy_school_uniform,0
tall_female,0
pawpads,0
ar_tonelico,3
butcha-u,1
vibrator_under_panties,0
artoria_pendragon_(swimsuit_ruler)_(fate),4
wiping_tears,0
ringo_(touhou),4
head_chain,0
silver_fox_(kemono_friends),4
mechanization,0
fox_boy,0
puffy_pants,0
timestamp,0
tight_shirt,0
back-seamed_legwear,0
fiery_hair,0
as109,1
used_tissue,0
ceres_fauna,4
black_sash,0
inazuma_eleven_go_chrono_stone,3
tina_branford,4
buttjob,0
shirt_in_mouth,0
presenting_armpit,0
holding_innertube,0
spanking,0
sports_car,0
pink_blood,0
poke_ball_print,0
myoukou_(kancolle),4
ruffling_hair,0
spirit,0
striped_kimono,0
final_fantasy_xv,3
batman_(series),3
asakura_toru,4
handkerchief,0
tashkent_(kancolle),4
crane_(machine),0
jeanne_d'arc_(swimsuit_archer)_(fate),4
elsa_(frozen),4
konjiki_no_yami,4
expressive_hair,0
vegeta,4
petting,0
tree_stump,0
helena_blavatsky_(fate),4
noire_(neptune_series),4
column,0
ass_cutout,0
oral_invitation,0
mismatched_pupils,0
horns_through_headwear,0
holding_lantern,0
yuri_lowell,4
show_by_rock!!,3
ragna_the_bloodedge,4
alternate_skin_color,0
molestation,0
mouth_veil,0
yotsubato!,3
marie_antoinette_(fate),4
ib,3
sonozaki_shion,4
omaru_polka,4
onmyouji,0
tsukikage_yuri,4
cle_masahiro,1
penis_outside,0
curtsey,0
final_fantasy_viii,3
jessie_(pokemon),4
kamio_misuzu,4
shiba_inu,0
midna,4
side-tie_leotard,0
open_window,0
dirndl,0
raised_fist,0
takeuchi_takashi,1
multiple_condoms,0
kaeya_(genshin_impact),4
higashi_setsuna,4
pectoral_grab,0
ito_noizi,1
hino_rei,4
osakabe-hime_(fate),4
goblin_slayer!,3
after_ejaculation,0
le_malin_(azur_lane),4
yu-gi-oh!_arc-v,3
mizumoto_tadashi,1
xenoblade_chronicles_1,3
cropped_vest,0
suzuhira_hiro,1
gamepad,0
grey_wolf_(kemono_friends),4
niwatazumi,1
bowl_cut,0
nyantcha,1
pixiv_id,0
mysterious_heroine_x_alter_(fate),4
grey_scarf,0
\o/,0
window_shade,0
expressive_clothes,0
inflation,0
fujima_takuya,1
santa_dress,0
popuko,4
neck,0
beige_sweater,0
matsuno_todomatsu,4
jersey,0
triforce,0
bear_girl,0
party_hat,0
harem_pants,0
charlotte_dunois,4
monkey_ears,0
kemomimi-chan_(naga_u),4
leather_belt,0
anti_(untea9),1
furukawa_nagisa,4
skadi_the_corrupting_heart_(arknights),4
narrowed_eyes,0
ezo_red_fox_(kemono_friends),4
polka_dot_swimsuit,0
spell_card,0
weibo_logo,0
haruhisky,1
gym_storeroom,0
tk8d32,1
fukumaru_koito,4
ribbed_bodysuit,0
alternate_weapon,0
failure_penguin,4
scathach_skadi_(fate),4
plaid_headwear,0
endeavor_(boku_no_hero_academia),4
madotsuki,4
hand_on_another's_stomach,0
skyline,0
spread_toes,0
cowlick,0
panda_ears,0
jeanne_d'arc_alter_(ver._shinjuku_1999)_(fate),4
lipstick_mark,0
ovum,0
omurice,0
open_bra,0
bayonetta,4
furutaka_(kancolle),4
stone_lantern,0
hidden_eyes,0
radio,0
jervis_(kancolle),4
bloop_(gawr_gura),4
non-humanoid_robot,0
pet_play,0
thigh_pouch,0
39,0
animal_feet,0
summon_night,3
yamato-no-kami_yasusada,4
sangvis_ferri,0
konno_junko,4
h&k_hk416,0
karin_(bunny)_(blue_archive),4
baguette,0
cyberpunk_(series),3
jean_pierre_polnareff,4
tatsumi_kanji,4
mole_on_neck,0
nagi_no_asukara,3
brushing_teeth,0
mcdonald's,3
pointless_condom,0
diamond_button,0
medium_dress,0
index_fingers_together,0
hayashimo_(kancolle),4
coca-cola,0
fake_facial_hair,0
takafumi,1
nanairogaoka_middle_school_uniform,0
natsume_rin,4
yuffie_kisaragi,4
mega_man_legends,3
scene_reference,0
treasure_chest,0
eromame,1
cardfight!!_vanguard,3
kamina_(ttgl),4
mahou_girls_precure!,3
berserk,3
utility_belt,0
doughnut_hair_bun,0
sitting_on_stairs,0
holding_clipboard,0
dakimakura_(object),0
propeller,0
fangs_out,0
countdown,0
elesa_(pokemon),4
imu_sanjo,1
excessive_pubic_hair,0
redesign,0
di_gi_charat,3
2011,0
ishikei,1
pink_sailor_collar,0
micro_panties,0
nian_(arknights),4
excessive_cum,0
arcana_heart,3
pavement,0
eureka_seven,3
tapir_tail,0
fujiwara_chika,4
brown_kimono,0
cow_boy,0
kami_nomi_zo_shiru_sekai,3
yaoyorozu_momo,4
finger_to_face,0
bear_print,0
niiko_(gonnzou),1
orange_sailor_collar,0
mamiya_(kancolle),4
satono_diamond_(umamusume),4
hoshi_syoko,4
nanasaki_ai,4
tiki_(fire_emblem),4
yoshikawa_chinatsu,4
tribal,0
sakura_futaba,4
kawashina_(momen_silicon),1
alpha_transparency,5
yuno_(hidamari_sketch),4
carrying_under_arm,0
ajirogasa,0
nishi_kinuyo,4
happi,0
glomp,0
kanji,0
yellow_wings,0
holding_helmet,0
elin,4
fighter_jet,0
open_pants,0
takoyaki,0
sidepec,0
shiromanta,1
kamoi_(kancolle),4
forehead-to-forehead,0
cupping_hands,0
talking_on_phone,0
male_playboy_bunny,0
takamaki_anne,4
gundam_zz,3
mahou_shoujo_lyrical_nanoha_vivid,3
yuusha_de_aru,3
goat_ears,0
oguri_cap_(umamusume),4
murakumo_kai_ni_(kancolle),4
globe,0
mizuhara_aki,1
baseball_mitt,0
eyewear_on_headwear,0
byleth_(fire_emblem)_(male),4
blonde_pubic_hair,0
ark_royal_(kancolle),4
oversized_shirt,0
no_testicles,0
hand_on_breast,0
aaaa_(quad-a),1
masturbation_through_clothes,0
cat_teaser,0
overcoat,0
leg_between_thighs,0
venus_symbol,0
alice_margatroid_(pc-98),4
turtleneck_dress,0
saria_(arknights),4
vividred_operation,3
quiz_magic_academy,3
to_heart,3
priest,0
jirai_kei,0
tate_no_yuusha_no_nariagari,3
lotion_bottle,0
shin_guards,0
lisa_(genshin_impact),4
sakuma_mayu,4
people,0
hauchiwa,0
ultra_series,3
orange_thighhighs,0
twilight_(spy_x_family),4
cutesexyrobutts,1
roll_(mega_man),4
diving_mask_on_head,0
italian_flag,0
naked_kimono,0
claw_ring,0
twincest,0
kris_(pokemon),4
palm_leaf,0
cow,0
short_jumpsuit,0
lucifer_(helltaker),4
mixing_bowl,0
shantae_(series),3
impaled,0
tsukioka_kogane,4
symboli_rudolf_(umamusume),4
black_mask,0
ichikawa_feesu,1
blue_headband,0
sheet_music,0
debt,0
momosuzu_nene,4
aldehyde,1
cross-shaped_pupils,0
novel_cover,0
saga,3
gyee,3
holding_brush,0
snorkel,0
april_fools,0
clock_tower,0
tales_of_symphonia,3
u.a._school_uniform,0
jako_(jakoo21),1
m4a1_(girls'_frontline),4
haiyore!_nyaruko-san,3
spread_pussy_under_clothes,0
yukishiro_honoka,4
kimono_skirt,0
red-tinted_eyewear,0
reiuji_utsuho_(bird),4
screen,0
takodachi_(ninomae_ina'nis),4
looking_at_animal,0
grey_fur,0
foot_up,0
saku_usako_(rabbit),1
mishima_kurone,1
oshawott,4
onikobe_rin,1
yes-no_pillow,0
dei_shirou,1
black_buruma,0
black_theme,0
imizu_(nitro_unknown),1
asanagi,1
tsukumo_benben,4
cloth_gag,0
bulma,4
incoming_food,0
death_note,3
myoudouin_itsuki,4
okita_souji_alter_(fate),4
pokemon_dppt_(anime),3
production_art,5
school_briefcase,0
tropical_drink,0
stone_floor,0
ribbed_sleeves,0
glitch,0
kaguya_luna,4
prosthetic_leg,0
umikaze_(kancolle),4
cato_(monocatienus),1
ryu_(street_fighter),4
little_red_riding_hood_(grimm),4
dido_(azur_lane),4
pokemoa,1
brown_necktie,0
shining_(series),3
nike,0
stadium,0
frapowa,1
jaguar_ears,0
._.,0
albedo_(genshin_impact),4
katekyo_hitman_reborn!,3
sailor_moon_redraw_challenge_(meme),0
nekomusume,4
abigail_williams_(swimsuit_foreigner)_(fate),4
mihono_bourbon_(umamusume),4
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nekotoufu,1
pink_pants,0
joseph_joestar_(old),4
hagikaze_(kancolle),4
kettle,0
princess_principal,3
bruno_buccellati,4
shirosato,1
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nori_tamago,1
under_night_in-birth,3
reference_inset,0
komi_shouko,4
crosswalk,0
sekina,1
hand_on_eyewear,0
godzilla_(series),3
stomach_cutout,0
homare_(fool's_art),1
silver_(pokemon),4
karaagetarou,1
grey_bra,0
flower_request,5
potion,0
specter_(arknights),4
food-themed_clothes,0
hoshiguma_(arknights),4
drawing_bow,0
kemono_friends_v_project,3
fumio_(rsqkr),1
rainbow_gradient,0
elio_(pokemon),4
holding_handheld_game_console,0
stab,0
slapping,0
orange_slice,0
nishi_koutarou,1
gilles_de_rais_(caster)_(fate),4
cagliostro_(granblue_fantasy),4
plaid_pants,0
strike_witches:_suomus_misfits_squadron,3
lube,0
bodice,0
haramura_nodoka,4
murakami_suigun,1
dragon_ball_fighterz,3
oomuro_sakurako,4
squirrel_girl,0
nippleless_clothes,0
idol_clothes,0
kicchou_yachie,4
negom,1
intravenous_drip,0
incoming_attack,0
little_boy_admiral_(kancolle),4
multicolored_bow,0
tsurumaru_kuninaga,4
sengoku_nadeko,4
blanc_(neptune_series),4
scooter,0
print_bra,0
twitching,0
full-body_tattoo,0
log,0
7th_dragon_(series),3
blowing_kiss,0
my-otome,3
g11_(girls'_frontline),4
breathing_fire,0
sonic_the_hedgehog,4
lize_helesta,4
origami,0
an-94_(girls'_frontline),4
frottage,0
cleaning,0
komano_aunn,4
ranma-chan,4
tamura_yuri,4
k-suwabe,1
kino_makoto,4
brown-framed_eyewear,0
mayano_top_gun_(umamusume),4
holding_leaf,0
morino_rinze,4
lily_of_the_valley,0
soccer,0
makuwauri,1
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stone_wall,0
gekkan_shoujo_nozaki-kun,3
yuyushiki,3
riesz,4
thrusters,0
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ike_(fire_emblem),4
oouso,1
steampunk,0
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fertilization,0
grey_border,0
leather_gloves,0
kuwayama_chiyuki,4
yes,0
marina_(splatoon),4
humiliation,0
stream,0
pokemon_platinum,3
dr_rex,1
cheering,0
.live,3
tail_between_legs,0
tohno_akiha,4
platform_heels,0
ninomiya_asuka,4
twisted_wonderland,3
wrist_guards,0
altera_(fate),4
full-package_futanari,0
natsuki_subaru,4
kilye_kairi,1
torn_panties,0
hatsune_miku_(cosplay),0
bianca_(pokemon),4
horosuke,1
hand_net,0
prisma_illya,4
etorofu_(kancolle),4
world_flipper,3
sleeveless_hoodie,0
mithra_(ff11),0
hamaya,0
sweatpants,0
gloves_removed,0
tokiko_(touhou),4
theresa_apocalypse,4
bird_on_shoulder,0
harry_potter_(series),3
alphes_(style),0
happoubi_jin,1
artoria_caster_(fate),4
higuchi_kaede,4
su-san,4
yokochou,1
wooden_table,0
gaoo_(frpjx283),1
yano_toshinori,1
onee-loli,0
m4_sopmod_ii_(girls'_frontline),4
fumihiko_(fu_mihi_ko),1
cup_ramen,0
chest_belt,0
doraemon,3
blue_eyeshadow,0
poker_chip,0
pokedex_number,0
hakama_pants,0
akizuki_ryo,4
shinkai_no_valkyrie,3
balancing,0
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goblin,0
ootsuki_wataru,1
oreki_houtarou,4
camellia,0
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mechanical_tail,0
street_fighter_iii_(series),3
android_21,4
sponge,0
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scorbunny,4
striped_shorts,0
deer,0
hiroki_(yyqw7151),1
kaamin_(mariarose753),1
new_school_swimsuit,0
polka_dot_legwear,0
e.o.,1
kasumi_kai_ni_(kancolle),4
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sobu_high_school_uniform,0
shoelaces,0
dragon_quest_xi,3
arashi_(kancolle),4
beatmania,3
nishida_satono,4
stove,0
atalanta_(fate),4
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cat_on_head,0
alchemy_stars,3
multiple_sources,5
kimi_no_na_wa.,3
reflective_water,0
bad_end,0
chest_strap,0
phonograph,0
st_ar-15_(girls'_frontline),4
honma_meiko,4
partially_underwater_shot,0
purple_tail,0
kujou_karasuma,1
lord_camelot_(fate),0
koyanskaya_(fate),4
alisa_ilinichina_amiella,4
u_u,0
ankle_strap,0
traditional_clothes,0
tales_of_zestiria,3
nanase_nao,1
tail_wrap,0
kirin_(armor),4
cure_marine,4
mimikyu,4
diana_cavendish,4
bookmark,0
cooperative_paizuri,0
black_male_underwear,0
lace-trimmed_sleeves,0
fur-trimmed_skirt,0
hourai_doll,4
werewolf,0
jojolion,3
kuma_(persona_4),4
desk_lamp,0
green_flower,0
head_on_another's_shoulder,0
hana_kazari,1
roronoa_zoro,4
the_legend_of_zelda:_skyward_sword,3
namazuo_toushirou,4
kadotani_anzu,4
nepgear,4
panties_on_head,0
panty_straps,0
kourindou_tengu_costume,0
noelle_(genshin_impact),4
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gyro_zeppeli,4
pink_collar,0
maikaze_(kancolle),4
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araragi_koyomi,4
high_kick,0
brown_hairband,0
shower_(place),0
saint_seiya,3
minna-dietlinde_wilcke,4
breast_conscious,0
animal_ear_legwear,0
miyamoto_musashi_(swimsuit_berserker)_(fate),4
katawa_shoujo,3
enemy_lifebuoy_(kancolle),4
latex_gloves,0
sana_channel,3
kurokoma_saki,4
murasaki_shikibu_(fate),4
ivan_karelin,4
frederica_bernkastel,4
super_mario_galaxy,3
skateboard,0
clearite,1
hachikuji_mayoi,4
usashiro_mani,1
paper_airplane,0
tablecloth,0
2009,0
bridget_(guilty_gear),4
natori_sana,4
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ohisashiburi,1
undone_necktie,0
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himemori_luna,4
shinai,0
tokitarou_(fate),4
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engiyoshi,1
pink_bag,0
high_school_fleet,3
hand_on_own_shoulder,0
yordle,0
todoroki_shouto,4
yellow_hoodie,0
shin_jigen_game_neptune_vii,3
picnic_basket,0
age_regression,0
maturiuta_sorato,1
friends,0
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gundam_build_divers,3
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mitsumoto_jouji,1
honebami_toushirou,4
keroro_gunsou,3
ooshio_(kancolle),4
piers_(pokemon),4
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flash,5
kimi_kiss,3
wattson_(apex_legends),4
animal_bag,0
hoshino_fumina,4
gofu,1
marianne_von_edmund,4
sheya,1
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pote_(ptkan),1
misaki_kurehito,1
nanakusa_suzuna,1
colorado_(kancolle),4
aru_(blue_archive),4
diagonal-striped_necktie,0
sunscreen,0
bandaid_on_hand,0
curry_rice,0
playstation_controller,0
cocktail_dress,0
pokemon_on_shoulder,0
metal_gear_solid,3
shokugeki_no_souma,3
black_pubic_hair,0
club_(shape),0
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skull_mask,0
no_gloves,0
sonya_(kill_me_baby),4
kagamihara_nadeshiko,4
kimono_pull,0
sasaki_chie,4
odd_one_out,0
pravda_school_uniform,0
sailor_mercury,4
no_game_no_life,3
siege_(arknights),4
sunazuka_akira,4
unya,1
gold_armor,0
arch_bishop_(ragnarok_online),4
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decensored,5
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ijiranaide_nagatoro-san,3
tohsaka_tokiomi,4
grey_kimono,0
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doctor,0
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teamwork,0
blue_poison_(arknights),4
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blue_fur,0
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veiny_breasts,0
lace-trimmed_skirt,0
runes,0
koto_inari,1
nagi_(kannagi),4
maebara_keiichi,4
hop_(pokemon),4
mole_on_ass,0
bad_nijie_id,5
papakha,0
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robin_(fire_emblem)_(male),4
dark_penis,0
utano,1
kasumi_(skchkko),1
severed_head,0
girlfriend_(kari),3
alpaca_suri_(kemono_friends),4
soup,0
athletic_leotard,0
goma_(gomasamune),1
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peeping,0
impossible_swimsuit,0
sona_(league_of_legends),4
teruterubouzu,0
perpendicular_paizuri,0
stroking_own_chin,0
frilled_socks,0
sobble,4
furry_with_furry,0
mawaru_penguindrum,3
z23_(azur_lane),4
teireida_mai,4
gathers,0
kero,4
pants_rolled_up,0
busou_shinki,3
richelieu_(kancolle),4
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molten_rock,0
shiny_legwear,0
cyberpunk_edgerunners,3
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bulbasaur,4
sausage,0
furutani_himawari,4
dust_cloud,0
brown_bikini,0
beidou_(genshin_impact),4
caren_hortensia,4
amanogawa_kirara,4
schwarz_(arknights),4
christmas_lights,0
sick,0
shortstack,0
halloween_bucket,0
single_pantsleg,0
excalibur_morgan_(fate),0
slap_mark,0
power_(chainsaw_man),4
eurasian_eagle_owl_(kemono_friends),4
green_bodysuit,0
wading_pool,0
eyebrow_piercing,0
daidouji_tomoyo,4
futaba_channel,3
watermelon_bar,0
dead_or_alive_5,3
hand_on_lap,0
kanabou,0
yellow_pupils,0
shirobako,3
gweda,1
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ta-class_battleship,4
cellphone_picture,0
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ikuchan_kaoru,1
meito_(maze),1
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deerstalker,0
angel_and_devil,0
acrylic_paint_(medium),0
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tomoeda_elementary_school_uniform,0
wiping_face,0
ranni_the_witch,4
yomu_(sgt_epper),1
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sunflower_hair_ornament,0
da_capo,3
pie,0
zen_(kamuro),1
ladybug,0
naegi_makoto,4
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snivy,4
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masukuza_j,1
year_of_the_rat,0
medea_(fate),4
yabuki_kentarou,1
super_robot_wars_original_generation,3
two-tone_legwear,0
sandals_removed,0
wig,0
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dissidia_final_fantasy,3
oyashio_(kancolle),4
oikawa_shizuku,4
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ciel_(tsukihime),4
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nekopara,3
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warhammer_40k,3
fireplace,0
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kudamaki_tsukasa,4
ema,0
grey_bikini,0
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rook_(chess),0
ru-class_battleship,4
winged_footwear,0
native_american,0
magical_mirai_(vocaloid),3
digimon_adventure,3
tent,0
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final_fantasy_xii,3
ribbed_leotard,0
striped_sweater,0
ryona,0
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sekai_seifuku:_bouryaku_no_zvezda,3
machine,0
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karukan_(monjya),1
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seishun_buta_yarou,3
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yunamaro,1
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ankh,0
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jjune,1
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ramchi,1
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va-11_hall-a,3
kesa,0
leg_armor,0
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yellow_bodysuit,0
kururugi_suzaku,4
tulip,0
giving_up_the_ghost,0
monika_(doki_doki_literature_club),4
nanatsu_no_taizai,3
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potato,0
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kanroji_mitsuri,4
cum_on_self,0
uvula,0
nisekoi,3
abe_nana,4
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ferris_wheel,0
saniwa_(touken_ranbu),4
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crate,0
kagami_hirotaka,1
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asutora,1
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laser,0
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champion_uniform,0
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cheek_bulge,0
leather_boots,0
iphone,0
snail,0
ipod,0
blender_(medium),5
zeta_(granblue_fantasy),4
through_wall,0
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shidare_hotaru,4
spoken_character,0
bruise_on_face,0
tempura,0
hitting,0
tanaka_kusao,1
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twitter_logo,0
m16a1_(girls'_frontline),4
ryuuko_no_ken,3
art_brush,0
tima,1
aa_megami-sama,3
single_fingerless_glove,0
kusanagi_motoko,4
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bubukka,1
dot_mouth,0
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swd3e2,1
mismatched_pubic_hair,0
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fountain,0
bishop_(chess),0
artoria_pendragon_(alter_swimsuit_rider)_(second_ascension)_(fate),4
world_trigger,3
bus_stop,0
pink_pupils,0
turning_head,0
shantae,4
two-sided_cape,0
modeus_(helltaker),4
mikuma_(kancolle),4
breast_expansion,0
urushihara_satoshi,1
arm_blade,0
brown_panties,0
they_had_lots_of_sex_afterwards_(meme),0
park,0
james_(pokemon),4
white_mage,4
headwear_request,5
green_(pokemon),4
black_wristband,0
honeycomb_(pattern),0
chikuma_(kancolle),4
wrestling_ring,0
theft,0
blue_collar,0
jacket_partially_removed,0
holding_pillow,0
tensei_shitara_slime_datta_ken,3
seele_vollerei,4
emperor_penguin_(kemono_friends),4
super_sentai,3
kohaku_(tsukihime),4
meow_(nekodenki),1
dandon_fuga,1
minami-ke,3
kuromiya,1
leopard_tail,0
gladion_(pokemon),4
rydia_(ff4),4
voyeurism,0
hands_on_ass,0
marie_(girls_und_panzer),4
large_testicles,0
yuigahama_yui,4
aoshima,1
kid_icarus_uprising,3
bikesuit,0
tropical-rouge!_precure,3
utensil_in_mouth,0
emoji,0
i-26_(kancolle),4
archery,0
nagatoro_hayase,4
ballerina,0
yui_(angel_beats!),4
aqua_headwear,0
nikorashi-ka,1
hooded_cardigan,0
lapels,0
silver_trim,0
laundry,0
suzuki_toto,1
dorohedoro,3
hatsukaze_(kancolle),4
ready_to_draw,0
bai_lao_shu,1
makai_senki_disgaea,3
yukinoshita_yukino,4
beatmania_iidx,3
flashlight,0
battle_axe,0
hatsushimo_(kancolle),4
orange_panties,0
tsuruya,4
island,0
dream_c_club_(series),3
lizard_tail,0
final_fantasy_xiii,3
optionaltypo,1
vibrator_in_thighhighs,0
fuuin_no_tsue,0
minamoto_sakura,4
heartbeat,0
asagumo_(kancolle),4
huang_baoling,4
ikeuchi_tanuma,1
frankenstein's_monster_(fate),4
red_oni,0
uchikake,0
.hack//,3
candy_hair_ornament,0
bow_(music),0
jaguar_(kemono_friends),4
chalk,0
fumizuki_(kancolle),4
zara_(azur_lane),4
anilingus,0
motherly,0
behind_another,0
pink_coat,0
shuffle!,3
neckwear_grab,0
isshiki_(ffmania7),1
see-through_skirt,0
plate_armor,0
cum_on_hands,0
crystal_hair,0
fur-trimmed_cloak,0
baltimore_(azur_lane),4
monster_hunter_rise,3
victor_(pokemon),4
unicorn,0
nu-13,4
large_wings,0
kazenokaze,1
kamado_tanjirou,4
check_copyright,5
wraith_(apex_legends),4
grey_ribbon,0
namori,1
momozono_love,4
petals_on_liquid,0
sakuramon,0
haniyasushin_keiki,4
digitigrade,0
carrying_person,0
irys_(hololive),4
hidefu_kitayan,1
gift_bag,0
mismatched_sleeves,0
queen_(chess),0
boruto:_naruto_next_generations,3
fiery_horns,0
yin_(darker_than_black),4
disgust,0
ketchup,0
triangle_print,0
soda_bottle,0
penis_size_difference,0
newhalf,0
marvel_cinematic_universe,3
bow_earrings,0
ootachi,0
commander_(girls'_frontline),4
mizuno_ai,4
lettuce,0
pod_(nier_automata),4
akitsuki_karasu,1
satin,0
opening_door,0
izumi-no-kami_kanesada,4
iizuki_tasuku,1
dangle_earrings,0
drum_(container),0
oryo_(oryo04),1
spacezin,1
gym,0
fairy_knight_tristan_(fate),4
touhoku_kiritan,4
ruler,0
dark_aura,0
french_text,0
hair_ears,0
gakkou_gurashi!,3
sideways,0
blend_s,3
cat_day,0
noai_nioshi,1
littorio_(kancolle),4
ammunition_belt,0
slayers,3
archived_source,5
boudica_(fate),4
kawashima_momo,4
closers,3
kamille_(vcx68),1
lace-trimmed_gloves,0
furrification,0
ichigo_mashimaro,3
boo_(mario),4
sailor_venus,4
grimoire,0
off-shoulder_bikini,0
hickey,0
stuffed_winged_unicorn,0
jojo_pose,0
test_plugsuit,0
gradient_legwear,0
riding_pokemon,0
ribbon-trimmed_dress,0
bad_food,0
round_window,0
niijima_makoto,4
legs_over_head,0
satou_kibi,1
yamashita_shun'ya,1
oven_mitts,0
abuse,0
multiple_monochrome,0
yoshi_tama,1
akairiot,1
enkidu_(fate),4
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rising_sun,0
fake_wings,0
naked_bandage,0
vaulting_horse,0
comparison,0
ballet_slippers,0
berserker_(fate/zero),4
lalafell,0
implied_yuri,0
shared_bathing,0
g_gundam,3
torpedo_launcher,0
rance_(series),3
high_priest_(ragnarok_online),4
bat_ears,0
sword_art_online:_alicization,3
selen_tatsuki,4
nemoto_hina,4
adjusting_legwear,0
battle_rifle,0
shirt_grab,0
weapon_request,5
moisture_(chichi),1
luggage,0
boar,0
holding_water_gun,0
cum_on_penis,0
overcast,0
torn_jeans,0
futa_on_male,0
soap_censor,0
evangelion:_3.0+1.0_thrice_upon_a_time,3
cactus,0
guido_mista,4
orange_pantyhose,0
shards,0
yamashiro_(azur_lane),4
strap_lift,0
non_non_biyori,3
squirtle,4
kako_(kancolle),4
armin_arlert,4
guiding_hand,0
chimney,0
covering_ass,0
flock,0
dream_c_club,3
doitsuken,1
chat_log,0
upshorts,0
6_(yuchae),1
rias_gremory,4
passionlip_(fate),4
sundae,0
aestus_estus,0
red_neckwear,0
kitasan_black_(umamusume),4
fubuki_(one-punch_man),4
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tharja_(fire_emblem),4
shiina_yuika,4
tsukuyomi_shirabe,4
pom_pom_hair_ornament,0
chocolate_on_breasts,0
heart-shaped_lock,0
cure_sunshine,4
weight_conscious,0
knees_apart_feet_together,0
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salaryman,0
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lpip,1
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sailor_mars,4
swim_cap,0
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zara_(kancolle),4
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tokimeki_memorial,3
odin_sphere,3
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yuuki_yuuna_wa_yuusha_de_aru,3
luigi,4
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onbashira,0
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tamaki_iroha,4
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cure_beauty,4
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oribe_yasuna,4
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k/da_(league_of_legends),0
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irida_(pokemon),4
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pouty_lips,0
sky-freedom,1
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eve_(elsword),4
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caenis_(fate),4
haro,4
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azura_(fire_emblem),4
rachel_alucard,4
sword_behind_back,0
detexted,5
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clothes,0
kyouka_(princess_connect!),4
angora_rabbit,0
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ange_katrina,4
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angel_(kof),4
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eroe,1
dark_green_hair,0
cum_in_clothes,0
breastfeeding,0
miyamizu_mitsuha,4
tsunade_(naruto),4
iris_(pokemon),4
bicorne,0
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key_visual,5
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jumpy_dumpty,4
millia_rage,4
hisakawa_hayate,4
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inuyama_aoi,4
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eiri_(eirri),1
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licking_another's_face,0
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yuzuki_choco,4
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fkey,1
hokuto_no_ken,3
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tepig,4
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tonfa,0
cis_(carcharias),1
kaneki_ken,4
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kamikita_komari,4
tada_riina,4
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mirai_nikki,3
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3others,0
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sorceress_(dragon's_crown),4
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awa,1
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st._gloriana's_(emblem),0
the_legend_of_zelda:_a_link_between_worlds,3
nursery_rhyme_(fate),4
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maruki_(punchiki),1
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rocket,0
nintendo_ds,0
kaijuu,0
hair_flip,0
panzerkampfwagen_iv,0
wetsuit,0
holding_baseball_bat,0
cure_black,4
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love_live!_school_idol_festival_all_stars,3
prinz_eugen_(unfading_smile)_(azur_lane),4
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asa_no_ha_(pattern),0
omelet,0
tokino_sora,4
fantia_reward,5
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stealth_sex,0
soul_worker,3
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blue_cloak,0
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king_hassan_(fate),4
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getsuyoubi_no_tawawa,3
abigail_williams_(traveling_outfit)_(fate),4
akkijin,1
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torn_gloves,0
diaper,0
kureiji_ollie,4
clarisse_(granblue_fantasy),4
catstudioinc_(punepuni),1
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pointy_footwear,0
star_of_david,0
necktie_grab,0
vodka_(umamusume),4
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arisu_(blue_archive),4
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marth_(fire_emblem),4
torn_pantyhose,0
chan_co,1
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tokyo_ghoul:re,3
akali,4
6+others,0
yin_yang_orb,0
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ib_(ib),4
rokuwata_tomoe,1
grey_belt,0
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ferry_(granblue_fantasy),4
cocktail,0
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kooh,4
pearl_(splatoon),4
crocodilian_tail,0
dragalia_lost,3
7010,1
xingqiu_(genshin_impact),4
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rabbit_yukine,4
fushigi_no_umi_no_nadia,3
walkie-talkie,0
ushiromiya_maria,4
yellow_leotard,0
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frisk_(undertale),4
cure_march,4
mitake_ran,4
rectangular_mouth,0
covering_one_eye,0
glowing_horns,0
maoyuu_maou_yuusha,3
troll_face,0
kazama_iroha,4
yuri_plisetsky,4
rosaria_(genshin_impact),4
niconico,3
good_end,0
suzukaze_aoba,4
kazagumo_(kancolle),4
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reze_(chainsaw_man),4
takagi-san,4
elezen,0
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goma_(yoku_yatta_hou_jane),1
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king_(chess),0
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war,0
in_cup,0
piromizu,1
berry,0
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final_fantasy_vii_advent_children,3
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crayon,0
albedo_(overlord),4
bc_freedom_military_uniform,0
corn,0
mahou_tsukai_no_yoru,3
red_apron,0
tsukumo_yatsuhashi,4
loose_bowtie,0
embodiment_of_scarlet_devil,3
reiner_braun,4
asian,0
kugisaki_nobara,4
cure_happy,4
annin_musou,1
sleeveless_turtleneck_leotard,0
breast_focus,0
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aikatsu_stars!,3
lycoris_uniform,0
uchi_no_hime-sama_ga_ichiban_kawaii,3
telstar,0
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yanyo_(ogino_atsuki),1
odawara_hakone,1
goat_girl,0
sora_(kingdom_hearts),4
furigana,0
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yoroizuka_mizore,4
arisugawa_natsuha,4
takeba_yukari,4
saiyan_armor,0
tsushima_(kancolle),4
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dudou,0
vanishing_point,0
kuroko_no_basuke,3
body_freckles,0
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akitsushima_(kancolle),4
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gundam_wing,3
cd,0
uccow,1
anzio_military_uniform,0
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espeon,4
hina_ichigo,4
ninja_mask,0
intrepid_(kancolle),4
kitazawa_shiho,4
human_scabbard,0
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heart_balloon,0
sailor_senshi,0
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white_tail,0
scarf_over_mouth,0
brown_leotard,0
eyjafjalla_(arknights),4
easel,0
manabe_nodoka,4
bosshi,1
colored_shadow,0
mutsuki_(blue_archive),4
makaino_ririmu,4
doukyuusei_another_world,3
saotome_alto,4
toast_in_mouth,0
uzaki_hana,4
bowser,4
takanami_(kancolle),4
hoop,0
serizawa_asahi,4
puckered_anus,0
looking_at_mirror,0
raphtalia,4
kuchiki_rukia,4
tsukumo_sana,4
lactation_through_clothes,0
mo_(kireinamo),1
ink_tank_(splatoon),0
osaki_amana,4
yotsuba_alice,4
square_4koma,0
facepalm,0
ear_biting,0
misumi_(macaroni),1
kino_no_tabi,3
vikala_(granblue_fantasy),4
sakazaki_freddy,1
ririko_(zhuoyandesailaer),1
thighhighs_over_pantyhose,0
white_tiger,0
kujou_karen,4
gomennasai,1
cure_melody,4
moogle,4
karina_lyle,4
torn_jacket,0
ark_order,3
calico,0
shorts_under_dress,0
cum_on_pectorals,0
hat_over_one_eye,0
lucoa_(maidragon),4
shirayuki_hime,4
green_hakama,0
raimon_soccer_uniform,0
pornography,0
araragi_karen,4
penises_touching,0
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reins,0
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jin_(mugenjin),1
corded_phone,0
x-men,3
bibi_(tokoyami_towa),4
taneshima_popura,4
griffin_&_kryuger_military_uniform,0
trash_bag,0
team_rocket_uniform,0
thread,0
wood,0
miss_cloud,4
red_bag,0
sakuraba_yuuki,1
million_arthur_(series),3
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the_legend_of_zelda:_the_wind_waker,3
multicolored_cape,0
shirow_masamune,1
zabuton,0
drying_hair,0
momo_velia_deviluke,4
tateyama_ayano,4
ugly_man,0
tentacles_under_clothes,0
screw,0
pier,0
snowball,0
bubble_tea_challenge,0
print_headwear,0
sakurazawa_izumi,1
bandolier,0
meltryllis_(swimsuit_lancer)_(first_ascension)_(fate),4
toudou_shimako,4
from_outside,0
sucy_manbavaran,4
pet_bowl,0
kayneth_el-melloi_archibald,4
dildo_riding,0
tamamo_cross_(umamusume),4
bunny-shaped_pupils,0
huge_bow,0
rice_on_face,0
socks_removed,0
under_tree,0
setz,1
toilet_paper,0
white_snake,0
rotom_phone,4
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xenogears,3
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cevio,3
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benghuai_xueyuan,3
eto_(ikumika),1
obiwan,1
danua,4
keystone,0
hand_on_another's_leg,0
non_(z-art),1
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japan_(hetalia),4
onii-chan_wa_oshimai,3
no_tail,0
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shirabi,1
red_apple,0
eishin_flash_(umamusume),4
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voile,0
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chef_uniform,0
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kozakura_marry,4
pixiv_fantasia_5,3
slashing,0
meowth,4
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nanodesu_(phrase),0
heart_o-ring,0
rtil,1
aono3,1
chrono_trigger,3
folding_chair,0
kfr,1
cat_ear_legwear,0
multiple_riders,0
nanao_naru,1
sailor_jupiter,4
satou_yuuki,1
hanten_(clothes),0
afrobull,1
tanaka_mamimi,4
tsuruse,1
shopping,0
daruma_doll,0
alphonse_elric,4
saitama_(one-punch_man),4
alice_(alice_in_wonderland)_(cosplay),0
mega_man_zero,3
pokemon_lgpe,3
skirt_basket,0
koutetsujou_no_kabaneri,3
akinbo_(hyouka_fuyou),1
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frogtie,0
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bbb_(friskuser),1
divine_gate,3
geewhy,1
yagisaka_seto,1
sister_princess,3
eyewear_hang,0
kepi,0
musou_isshin_(genshin_impact),0
himouto!_umaru-chan,3
kemachiku,1
single_boot,0
traditional_chinese_text,0
anjou_naruko,4
okazaki_yumemi,4
blue_overalls,0
naked_scarf,0
vampire_costume,0
transparent_wings,0
grookey,4
purple_scrunchie,0
wiping_sweat,0
samneco,1
sonoda_chiyoko,4
tales_of_berseria,3
niwatari_kutaka,4
beige_jacket,0
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okabe_rintarou,4
kino_(kino_no_tabi),4
ogami_kazuki,1
popcorn,0
cu_chulainn_alter_(fate),4
otohime_(youngest_princess),1
flying_kick,0
kissing_hand,0
bandaged_neck,0
>o<,0
hoshikawa_sara,4
purple-framed_eyewear,0
hands_on_own_ass,0
tiger_stripes,0
cheshire_(azur_lane),4
kula_diamond,4
floating_island,0
triangle_earrings,0
animal_on_lap,0
amagi_(kancolle),4
green_one-piece_swimsuit,0
telekinesis,0
may_(guilty_gear),4
cleaver,0
wet_dress,0
nagian,1
rainbow_hair,0
blank_stare,0
baiken,4
arthur_pendragon_(fate),4
star_brooch,0
astolfo_(sailor_paladin)_(fate),4
takato_kurosuke,1
dotted_line,0
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kine,0
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kaisen_chuui,1
school_rumble,3
pixiv_fantasia_last_saga,3
bokken,0
backless_leotard,0
team_9,0
united_kingdom_(hetalia),4
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ibuki_fuuko,4
otter_ears,0
assassin_(fate/zero),4
barefoot_sandals,0
bee_(deadflow),1
hisui_(tsukihime),4
sousouman,1
dvd_cover,0
swimsuit_cover-up,0
barbed_wire,0
dark_souls_iii,3
pencil_case,0
chewing,0
amulet,0
aragaki_ayase,4
aquarion_evol,3
shark_costume,0
coin_hair_ornament,0
tokisadame_school_uniform,0
key_necklace,0
tentacle_pit,0
dandelion,0
boxer_briefs,0
the_legend_of_korra,3
yorha_type_a_no._2,4
elaina_(majo_no_tabitabi),4
sakino_shingetsu,1
sieg_(fate),4
yellow_pants,0
holding_fishing_rod,0
jmg,1
yellow_sash,0
oktavia_von_seckendorff,4
mota,1
instagram_username,0
vertical-striped_bikini,0
youkan,1
aztodio,1
falchion_(fire_emblem),0
shindou_takuto,4
fur-trimmed_kimono,0
holding_game_controller,0
hand_on_leg,0
red_border,0
keith_goodman,4
texture,0
sawa_azusa,4
wrist_wrap,0
multicolored_horns,0
shibasaki_shouji,1
counter:side,3
hand_on_another's_waist,0
lowleg_pants,0
foam,0
single_stripe,0
pink_pajamas,0
between_pectorals,0
brick,0
kinu_(kancolle),4
looking_outside,0
henshin,0
pushing,0
character_censor,0
yuuki_setsuna_(love_live!),4
komiya_kaho,4
hellsing,3
triple_penetration,0
ballet,0
diamond_(gemstone),0
fujisaki_chihiro,4
aquarium,0
studio_ghibli,3
commandant_teste_(kancolle),4
nakano_itsuki,4
analog_clock,0
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urin,1
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hataraku_maou-sama!,3
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yuuki_(princess_connect!),4
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enty_reward,5
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\n/,0
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miyako_(hidamari_sketch),4
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mahou_shoujo_madoka_magica_movie_1_&_2,3
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lord_el-melloi_ii,4
rainbow_mika,4
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maria_cadenzavna_eve,4
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diego_brando,4
ao_no_exorcist,3
thermos,0
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karaoke,0
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zara_(poolside_coincidence)_(azur_lane),4
caffein,1
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teana_lanster,4
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jeanne_d'arc_alter_(avenger)_(third_ascension)_(fate),4
ehoumaki,0
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pit_(kid_icarus),4
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sciamano240,1
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92m,1
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cure_moonlight,4
yuuji_(yukimimi),1
leafeon,4
mamaloni,1
silence_(arknights),4
justice_(helltaker),4
stained_panties,0
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elphelt_valentine,4
inline_skates,0
d.gray-man,3
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fairy_knight_lancelot_(fate),4
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sado_(kancolle),4
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high_school_dxd_born,3
pigeon,0
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akina_tsukako,1
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morino_hon,1
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7th_dragon,3
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animal_skull,0
super_soaker,0
diona_(genshin_impact),4
super_mario_bros._1,3
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scaramouche_(genshin_impact),4
m1911,0
ulrich_(tagaragakuin),1
2008,0
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onion,0
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tutu,0
tape_measure,0
wheat,0
senran_kagura_shinovi_versus,3
nironiro,1
echo_(circa),1
danganronpa_another_episode:_ultra_despair_girls,3
aqua_footwear,0
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kankan33333,1
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grey_cape,0
kukie-nyan,1
the_ring,3
kazuma_muramasa,1
starbucks,3
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jellytits-7,1
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frozen,0
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no_mole,0
green_tea,0
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multicolored_fur,0
nipple_clamps,0
sucrose_(genshin_impact),4
beltskirt,0
bib,0
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boa_hancock,4
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arcane:_league_of_legends,3
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splatoon_2:_octo_expansion,3
boy_sandwich,0
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senjou_no_valkyria_1,3
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dota_2,3
elephant,0
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kerchief,0
girls_und_panzer_senshadou_daisakusen!,3
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uchuu_senkan_yamato_2199,3
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yuni_(princess_connect!),4
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fantasy_earth_zero,3
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avatar:_the_last_airbender,3
elizabeth_bathory_(brave)_(fate),4
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moose_(kemono_friends),4
jack-o'_valentine,4
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akame_ga_kill!,3
saber_alter_(ver._shinjuku_1999)_(fate),4
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ara_haan,4
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cursor,0
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kamitsubaki_studio,3
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mon-musu_quest!,3
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rosmontis_(arknights),4
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pet,0
ssss.dynazenon,3
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carmilla_(fate),4
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eirika_(fire_emblem),4
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mary_(ib),4
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ivy,0
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pneuma_(xenoblade),4
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minato_ojitan,1
tsukumo_(soar99),1
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lavinia_whateley_(fate),4
strapless_bottom,0
mikan_(chipstar182),1
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steak,0
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osiimi,1
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pixiv_fantasia_fallen_kings,3
dog_penis,0
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gunblade,0
yamamura_sadako,4
nitocris_(swimsuit_assassin)_(fate),4
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oonusa,0
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noble_witches,3
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mast,0
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charlotte_(anime),3
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vsinger,3
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shin_(new),1
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setsuna_f._seiei,4
9law,1
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ingo_(pokemon),4
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ad,0
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polka_dot_ribbon,0
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dragon_boy,0
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sonia_nevermind,4
elizabeth_bathory_(first_ascension)_(fate),4
kazami_yuuka_(pc-98),4
meta_knight,4
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inazuma_eleven_go_galaxy,3
moyoron,1
40hara,1
super_creek_(umamusume),4
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muririn,1
paul_bunyan_(fate),4
toudou_yurika,4
rou-kyuu-bu!,3
knotted_penis,0
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ryuu_ga_gotoku_(series),3
culter,1
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mother_3,3
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ancesra,1
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nikaidou_saki,4
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himajin_noizu,1
eye_black,0
swordsouls,1
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shell_necklace,0
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dragonfly,0
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sion_eltnam_atlasia,4
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celes_chere,4
sd_gundam,3
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shiina_mayuri,4
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momo_no_sukebe,1
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team_fortress_2,3
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mat,0
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akita_neru,4
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sitting_on_bench,0
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soukou_akki_muramasa,3
yamato_(one_piece),4
homu,0
norasuko,1
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ame.,1
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final_fantasy_x-2,3
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kuririn,4
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benienma_(fate),4
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gallade,4
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sauce,0
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doom_(series),3
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fighter_(granblue_fantasy),4
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limiter_(tsukumo_sana),0
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pixiv_fantasia_new_world,3
squeezing,0
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fiammetta_(arknights),4
pipe_in_mouth,0
calligraphy,0
tokyo_(great_akuta),1
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tokiha_mai,4
seramikku,1
aqua_kimono,0
victorica_de_blois,4
tiger_costume,0
female_assassin_(fate/zero),4
mio_(xenoblade),4
animal_head,0
toraishi_666,1
kirero,1
nerv,0
blame!,3
jam,0
toujou_sakana,1
magikarp,4
tsugumomo,3
mechanic,0
hanadera_nodoka,4
tsubaki_(blue_archive),4
azur_lane:_slow_ahead,3
sweet_lolita,0
maya_fey,4
harley_quinn,4
kore_wa_zombie_desu_ka?,3
yonu_(yonurime),1
myama,1
persona_2,3
chabashira_tenko,4
lapel_pin,0
glass_bottle,0
cervical_penetration,0
food_request,5
horn_ring,0
black_vs_white,0
tokyo_mew_mew,3
seraph,0
dango_daikazoku,3
tsab_air_military_uniform,0
tatsumi_ray,1
miyama-zero,1
haunter,4
kuroi_moyamoya,1
oversized_breast_cup,0
ebiblue,1
tachibana_makoto,4
sekaiju_no_meikyuu_5,3
aoi_nagisa_(metalder),1
takayama_toshinori,1
harada_(sansei_rain),1
holding_heart,0
reshiram,4
daikon,0
mole_on_arm,0
gemu555,1
wavy_eyes,0
materia,0
infukun,1
siegfried_(fate),4
shuga_(soranote),1
cargo_pants,0
wild_tiger,4
horiguchi_yukiko,1
kirishima_eijirou,4
kadose_ara,1
nagasioo,1
boshi_(a-ieba),1
idolmaster_live_for_you!,3
fern,0
short_yukata,0
cum_through_clothes,0
hieroglyphics,0
ushiromiya_lion,4
moth_girl,0
puru_two,4
container,0
cure_beat,4
rucchiifu,1
cure_rosetta,4
barasuishou,4
ao_usagi,1
resident_evil_2,3
instagram_logo,0
cure_twinkle,4
blastoise,4
muku_(muku-coffee),1
gumroad_reward,5
kiryu_manzoku,1
puuzaki_puuna,1
eichi_yuu,1
hidari_(left_side),1
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rainbow_six_siege,3
foxyreine,1
alcremie,4
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shaking_head,0
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ao-chan_(ninomae_ina'nis),4
evoker,0
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rubber_gloves,0
komori_kuzuyu,1
ai-chan_(tawawa),4
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kantele,0
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sprinkles,0
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shan,1
white_heart,4
mg42,0
pawn,0
zettai_karen_children,3
monk,0
pixiv_fantasia_3,3
mordred_(swimsuit_rider)_(first_ascension)_(fate),4
tsurugi_kyousuke,4
iroyopon,1
white_hood,0
ejaculation_under_clothes,0
the_elder_scrolls,3
dumbbell_nan_kilo_moteru?,3
yuran,1
sameha_ikuya,1
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kekemotsu,1
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tantrum,0
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blue_rose_(tiger_&_bunny),4
ryouma_(galley),1
aroused,0
antonio_lopez,4
saiste,1
ayakura_juu,1
pecorine_(summer)_(princess_connect!),4
fold-over_boots,0
ao_banana,1
mint_blancmanche,4
wada_arco,1
piyodera_mucha,1
mizumoto_yukari,4
hello_kitty,3
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geshumaro,1
minigun,0
demonbane,3
senkou_hanabi,0
koiwai_yotsuba,4
pan_(mimi),1
vulpix,4
marill,4
akino_shuu,1
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after_masturbation,0
scanty_(psg),4
signpost,0
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kousei_(public_planet),1
anti-aircraft,0
arcade_cabinet,0
skating,0
saraki,1
hikage_eiji,1
hinata_yukari,4
jewelpet_(series),3
mechanical,0
face_in_pillow,0
kiran_(fire_emblem),4
nipple_cutout,0
ran'ou_(tamago_no_kimi),1
noses_touching,0
undone_neck_ribbon,0
arashio_kai_ni_(kancolle),4
asirpa,4
adam's_apple,0
mansion,0
keuma,1
tanaka_ryou,1
shizuki_hitomi,4
yui_(sao),4
crayon_shin-chan,3
koyubi_(littlefinger1988),1
bayonetta_1,3
fabulous,0
suzushiro_kurumi,1
weno,1
imazon,1
alice_carroll,4
shepherd0821,1
kawai_(purplrpouni),1
trigger_(company),3
senki_zesshou_symphogear_xd_unlimited,3
thighhighs_removed,0
rotary_phone,0
luicent,1
izumi_sai,1
amusement_park,0
hand_on_glass,0
hanafuda,0
wang_liu_mei,4
blazblue:_central_fiction,3
oversized_forearms,0
delmogeny_uniform,0
stained_clothes,0
myrtenaster,0
priestess_(goblin_slayer!),4
zaku_ii,4
sound_voltex,3
nakahira_guy,1
takatsuki_ichi,1
hinoa,4
corsage,0
oda_non,1
takitarou,1
lockon_stratos,4
sei_shoujo,1
nathan_seymour,4
nudist_beach_uniform,0
delicious_party_precure,3
nobita_(makoto7060355),1
yajirushi_(chanoma),1
satsuki_kai_ni_(kancolle),4
narciso_anasui,4
mononobe_alice,4
faustsketcher,1
cure_muse_(yellow),4
minarai,1
resident_evil_5,3
nipple-to-nipple,0
nu_carnival,3
mochizuki_shiina,1
shokuyou_mogura,1
glory_wall,0
grey_bowtie,0
xblaze,3
four_goddesses_online:_cyber_dimension_neptune,3
da_capo_i,3
masked,0
ssalbulre,1
genderswap_(otf),0
zuikaku_kai_ni_(kancolle),4
kallen_kaslana,4
tofu,0
yuuki_rika,1
mountain_of_faith,3
oda_nobukatsu_(fate),4
gae_buidhe_(fate),0
sw_(taco),1
ruton-niki,1
onigirikun,1
ideolo,1
white_garter_straps,0
shaymin,4
elizabeth_liones,4
dsmile,1
keenh,1
diploma,0
kuromiya_raika,4
aikatsu_friends!,3
coconut_tree,0
holding_microphone_stand,0
zorua,4
alphonse_(white_datura),1
hakozaki_serika,4
komori_kiri,4
pairan,1
nonohara_yuzuko,4
cream_(nipakupa),1
yamada_tae,4
yoshioka_yoshiko,1
rubbing,0
side-seamed_legwear,0
efukei,1
hanasaka_houcha,1
hat_belt,0
tsukasawa_takamatsu,1
min_min_(arms),4
izumi_tsubasu,1
sophia_(fire_emblem),4
buried,0
kutsuki_kai,1
pravda_(emblem),0
haneru,1
iwasaki_minami,4
soejima_shigenori,1
amisu,1
takao_(aoki_hagane_no_arpeggio),4
desert_eagle,0
naka_kai_ni_(kancolle),4
stopwatch,0
shaman_king,3
hand_on_floor,0
hakusai_(tiahszld),1
american_beaver_(kemono_friends),4
konoha_(kagerou_project),4
mdr_(girls'_frontline),4
laserflip,1
kissing_neck,0
bars,0
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forked_tail,0
ak-15_(girls'_frontline),4
wiping,0
kurapika,4
ayato,1
dusk_ball,0
shibuki_ran,4
lucas_(mother_3),4
gladiia_(arknights),4
bridle,0
ken_(koala),1
baccano!,3
sheep_costume,0
fujibayashi_ryou,4
puppet_rings,0
robert_e._o._speedwagon,4
magatama_hair_ornament,0
kujou_ichiso,1
gustav_(telomere_na),1
tobi_(one),1
project_diva_2nd,3
bedivere_(fate),4
kirisaki_chitoge,4
kanno_naoe,4
blocking,0
playing_sports,0
naizuri,0
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hangar,0
rory_mercury,4
amane_suzuha,4
finger_frame,0
nipple_chain,0
zekkyon,1
playing_with_another's_hair,0
pamiat_merkuria_(azur_lane),4
mob_psycho_100,3
two-tone_hoodie,0
star_driver,3
artsy-rc,1
masuishi_kinoto,1
shuzi,1
disco_brando,1
flying_button,0
stapler,0
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yami_yuugi,4
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rice_hat,0
sasaki_akebi,4
cobblestone,0
ikezawa_hanako,4
trophy,0
saipaco,1
kirakishou,4
brock_(pokemon),4
south_dakota_(kancolle),4
hitsugi_no_chaika,3
kasugano_sora,4
portgas_d._ace,4
holding_cross,0
ikeda_jun_(mizutamari),1
alvin_(tales),4
achilles_(fate),4
gazing_eye,1
nero_claudius_(bride)_(second_ascension)_(fate),4
eunie_(xenoblade),4
shermie_(kof),4
takafuji_kako,4
sword_on_back,0
mtu_virus,0
yu_yu,1
matsudaira_touko,4
rex_k,1
lying_on_water,0
beaker,0
neit_ni_sei,1
shiratsuyu_kai_ni_(kancolle),4
food_bite,0
hand_on_own_forehead,0
torn_coat,0
amezawa_koma,1
enlightened_byleth_(female),4
yakisobapan_tarou_&_negitoro-ko,1
misaka_worst,4
ear_grab,0
winged_hair_ornament,0
daive,1
nono_hana,4
arm_between_legs,0
maple_leaf_print,0
gradient_wings,0
living_hair,0
treecko,4
yakiimo,0
yukimura_aoi,4
elizabeth_(persona),4
falling_card,0
gunp,1
watching,0
metal_gear_solid_v,3
kekkai_sensen,3
black_garter_straps,0
carrying_bag,0
elbow_sleeve,0
armored_core,3
eho_(icbm),1
arm_on_head,0
dark_clouds,0
table_tennis_paddle,0
elma_(maidragon),4
ushiromiya_rosa,4
runny_nose,0
bilibili,3
f.s.,1
cure_passion,4
smartphone_case,0
itou_chika,4
ushiromiya_natsuhi,4
sasamori_tomoe,1
nucomas,1
luke_fon_fabre,4
phantom_ix_row,1
record_of_lodoss_war,3
grid_background,0
holding_another's_leg,0
kakure_eria,1
sangokushi_taisen,3
gensou_suikoden_v,3
circe_(fate),4
uncle_and_niece,0
hiburi_(kancolle),4
billboard,0
blue_oni,0
hanekoto,1
grey_apron,0
natsuki_rin,4
gunslinger_girl,3
grea_(shingeki_no_bahamut),4
garou:_mark_of_the_wolves,3
echidna_(re:zero),4
bacon,0
kawanishi_shinobu,4
oozora_akari,4
ayakashi_(monkeypanch),1
happy_facial,0
hitec,1
tsumami_kanzashi,0
party,0
caramelldansen,0
adaman_(pokemon),4
inktober,0
naruko_(naruto),4
uu~,0
hood_(azur_lane),4
monikano,1
cross-body_stretch,0
akino_sora,1
fever,0
black-tailed_prairie_dog_(kemono_friends),4
merman,0
flip_flappers,3
low-cut_armhole,0
red_nose,0
bad_leg,0
sugoi_dekai,0
tsurukame,1
miyasu_risa,1
carton,0
fumo_(doll),0
poring,4
astaroth_(shinrabanshou),4
shirayuki_chiyo,4
gundam_0083,3
tweyen_(granblue_fantasy),4
anti-aircraft_gun,0
american_flag_shirt,0
windsock,0
subaru_duck,4
hanauna,1
kunreishiki,0
judo_fuu,1
chocolat_(momoiro_piano),1
shimushu_(kancolle),4
northern_italy_(hetalia),4
courtney_(pokemon),4
kikuyoshi_(tracco),1
yukikaze_panettone,4
hange_zoe,4
dan_(kumadan),1
wakura_(gcdan),1
seele_(alter_ego),4
shibacha,1
udon,0
matsumae_ohana,4
kasuga_mirai,4
pepper,0
kitten,0
ookuma_nekosuke,1
rockhopper_penguin_(kemono_friends),4
wanaata,1
tripod,0
airi_(queen's_blade),4
killer_queen,4
l_hakase,1
enna_alouette,4
hope's_peak_academy_school_uniform,0
arisa_bannings,4
glitchedpuppet,1
barbara_(summertime_sparkle)_(genshin_impact),4
orange_headband,0
manhwa,0
dungeons_and_dragons,3
inconvenient_breasts,0
eudetenis,1
playground,0
pdf_available,5
curled_up,0
ashley_(warioware),4
buriki,1
cozy,1
fish_tank,0
medieval,0
roxas,4
elena_(street_fighter),4
holding_notebook,0
rosuuri,1
takozonesu,4
ace_of_spades,0
hyuuga_saki,4
noshiro_(azur_lane),4
usopp,4
pokachu,1
mdf_an,1
jessica_(arknights),4
shellvi,1
yoshizawa_tsubaki,1
kinta_(distortion),1
yamasaki_wataru,1
cherry_print,0
komori_kei,1
mazaki_anzu,4
reptile_girl,0
tanaka_kotoha,4
yuugiri_(zombie_land_saga),4
kiana_kaslana_(herrscher_of_the_void),4
makoto_(princess_connect!),4
shichigatsu,1
snake_print,0
maruzensky_(umamusume),4
ryo_(botugo),1
shikitani_asuka,1
holding_another's_foot,0
the_world,4
absol,4
nina_(fire_emblem),4
mai_(touhou),4
purple_collar,0
heart_panties,0
shopping_cart,0
mud,0
hedgehog,0
henshako,1
hitomaru,1
choujigen_game_neptune_mk2,3
nabeshima_tetsuhiro,1
yamagishi_yukako,4
lyn_(blade_&_soul),4
hisen_kaede,1
girl_holding_a_cat_(kancolle),4
heidimarie_w._schnaufer,4
aircraft_carrier_princess,4
atelier_sophie,3
anbe_yoshirou,1
miyamori_aoi,4
kazuma_(kazumav),1
kanpani_girls,3
gengetsu_(touhou),4
impossible_sweater,0
oar,0
fathom,1
making-of,5
a_channel,3
wedge_heels,0
rinotuna,1
striped_neckerchief,0
vignetting,0
croissant,0
toothpick,0
medaka_box,3
ookurikara,4
flagpole,0
blaziken,4
pola_(azur_lane),4
tank_helmet,0
hands_on_headphones,0
kaburagi_kaede,4
waddle_dee,4
breath_of_fire_v,3
tanaka_asuka,4
kinugasa_kai_ni_(kancolle),4
seneto,1
syhan,1
hiraga_matsuri,1
text_messaging,0
plaid_legwear,0
hijikata_toushirou,4
scp_foundation,3
chuunibyou,0
hugh_(pokemon),4
iron_saga,3
changing_room,0
heart_maebari,0
jasmine_(pokemon),4
son_goten,4
arin,4
makai_senki_disgaea_3,3
male_doctor_(arknights),4
choke_hold,0
a-chan_(hololive),4
amagai_tarou,1
tateha_(marvelous_grace),1
oryou_(girls_und_panzer),4
nero_claudius_(swimsuit_caster)_(third_ascension)_(fate),4
tsukemon,1
ho-oh,4
wanted,0
chobits,3
catura_(granblue_fantasy),4
bravely_default:_flying_fairy,3
usb,0
russian_flag,0
fumio_(ura_fmo),1
saburou_(hgmg),1
miura-n315,1
fakepucco,1
kishiyo,1
stiletto_(weapon),0
shirokami_project,3
baron_bunny_(genshin_impact),4
toilet_stall,0
huge_testicles,0
fudou_yuusei,4
hoozuki_no_reitetsu,3
nilitsu,1
omamori_himari,3
rouge_the_bat,4
ousama_ranking,3
fushigiboshi_no_futago_hime,3
shadow_the_hedgehog,4
yu-ta,1
damda,1
tokiani,1
anna_miller,0
cure_berry,4
oryou_(fate),4
anato_finnstark,1
zakusi,1
yellow_sailor_collar,0
yetworldview_kaze,1
torichamaru,1
kuresento,1
hairstyle_switch,0
fio_germi,4
ayanepuna,1
pregnancy_test,0
master_(vocaloid),4
sixten,1
nekomamire,1
lucy_(cyberpunk),4
deer_girl,0
fake_magazine_cover,0
watarase_jun,4
queen_of_sheba_(fate),4
shingyou_(alexander-13),1
kl,1
zodiac,0
kojo_anna,4
darjeeling_(reley),1
ning_hai_(azur_lane),4
47agdragon,1
haman_karn,4
bonkara_(sokuseki_maou),1
padlocked_collar,0
404_(girls'_frontline),0
ayasugi_tsubaki,1
hakika,1
errant,1
clamp_(circle),1
rolling,0
sasayuki,1
measurements,0
yone_kinji,1
beifeng_han,1
shin'ya_(nanp),1
gyarados,4
front-tie_bra,0
blue_pubic_hair,0
radish_p,1
cross_tie,0
square,0
jeanne_d'arc_(girl_from_orleans)_(fate),4
usoneko,1
sitting_on_water,0
e10,1
time_stop,0
tinker_bell,1
kamen_rider_fourze_(series),3
piano_keys,0
einhart_stratos,4
ewa_(seraphhuiyu),1
xion_(kingdom_hearts),4
aburaage,0
wacchi,1
indomitable_(azur_lane),4
reaper_(overwatch),4
too_many_frills,0
furyou_michi_~gang_road~,3
multicolored_neckwear,0
metal_gear_rising:_revengeance,3
grimsley_(pokemon),4
tsukareta_san,1
poop,0
eiwa,1
saber_(cosplay),0
peroponesosu.,1
hazawa_tsugumi,4
flintlock,0
ume_(plumblossom),1
hinata_yuu,1
okama,1
fur-trimmed_shirt,0
kneeless_mermaid,0
makio_(makiomeigenbot),1
pink_tank_top,0
kuronuko_neero,1
neco-arc,4
aliasing,5
grizzly_mkv_(girls'_frontline),4
texas_(winter_messenger)_(arknights),4
head_on_head,0
kingdom_hearts_birth_by_sleep,3
gummy_(arknights),4
covered_testicles,0
o-ring_belt,0
tomu_(tomubobu),1
exoskeleton,0
island_fox_(kemono_friends),4
weapon_case,0
yatogami_tooka,4
heart-shaped_boob_challenge,0
lain_paterson,4
sora_wo_kakeru_shoujo,3
witchblade,3
yuugen,1
big_eyes,0
unbuttoned_shorts,0
kedamono_kangoku-tou,1
kaminagi_(kaminagi-tei),1
ana_(overwatch),4
morag_ladair_(xenoblade),4
dog_hair_ornament,0
aqua_hairband,0
precum_drip,0
surrounded,0
cut-in,0
gabiran,1
eugeo,4
pixiv_fantasia_age_of_starlight,3
heart_facial_mark,0
warspite_(azur_lane),4
deviantart_sample,5
hikigaya_hachiman,4
kaname_buccaneer,4
twig,0
clear_sky,0
higurashi_kagome,4
ormille,1
notched_lapels,0
hijikata_toshizou_(fate),4
ginkgo,0
charisma_break,0
love_laika_(idolmaster),0
wamuu,4
nekoguruma,1
food_insertion,0
hikami_sumire,4
innerboob,0
sumisu_(mondo),1
anemone_(eureka_seven),4
madjian,1
hiiragi_yuuichi,1
shorts_tan,0
viral_(ttgl),4
komasan,4
a_(phrase),0
emma_verde,4
ameno_(a_meno0),1
akatsuki_akane,1
mutsuki_(azur_lane),4
wsman,1
shakujou,0
capura_lin,1
akemi_homura_(cosplay),0
gia,1
soulcalibur_iv,3
kiyama_hiroto,4
intestines,0
yoshizaki_mine,1
heanna_sumire,4
red_cardigan,0
peach_(momozen),1
uzumaki_boruto,4
metal_skin,0
maze_(gochama_ze_gohan),1
hunched_over,0
mega_man_star_force,3
c-button,1
kurata_rine,1
jema,1
yellow_border,0
raw_egg_lent,1
dirty_pair,3
flower_(vocaloid),4
takaman_(gaffe),1
eriko_(princess_connect!),4
otome_game_no_hametsu_flag_shika_nai_akuyaku_reijou_ni_tensei_shite_shimatta,3
urban_legend_in_limbo,3
cyan_(show_by_rock!!),4
drop_earrings,0
baba_konomi,4
shirogane_(platinum),1
grisaia_no_kajitsu,3
kalmahul,1
refraction,0
shampoo_bottle,0
sleigh,0
minakami_mai,4
warfarin_(arknights),4
mel/a,1
tiffy_(nottytiffy),4
himejoshi,0
alex_ahad,1
fran_(ff12),4
erina_pendleton,4
sankuro_(agoitei),1
goomy,4
rei_(cookie),4
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ree_(re-19),1
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hand_on_another's_knee,0
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pine_(bombergirl),4
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lei,0
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machamp,4
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119,1
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csyday,1
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brown_border,0
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akita_toushirou,4
kinona,1
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covered_anus,0
tomoe_gozen_(swimsuit_saber)_(first_ascension)_(fate),4
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suterii,1
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airi_(the_infernal_temptress),4
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ys,3
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tekehiro,1
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dragon_quest_vii,3
kyogre,4
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n:go,1
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22/7,3
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ar_tonelico_i,3
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ichiyou_moka,1
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greenmarine,1
enokuma_uuta,1
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agent_8_(splatoon),4
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germany_(hetalia),4
rohitsuka,1
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mu-12,4
428,3
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puck_(re:zero),4
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lucy_maria_misora,4
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ga-rei,3
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violence,0
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onineko-chan,1
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aki_(akikaze_asparagus),1
gundam_suisei_no_majo,3
suzuya_kai_ni_(kancolle),4
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maria_(hayate_no_gotoku!),4
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rayquaza,4
glass_table,0
zero_(code_geass),4
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maruyama_saki,4
finger_heart,0
bb_(swimsuit_mooncancer)_(first_ascension)_(fate),4
frostleaf_(arknights),4
sailor_swimsuit_(idolmaster),0
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sakurai_touko,4
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wild_arms_3,3
hater_(hatater),1
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eliwood_(fire_emblem),4
matsuoka_gou,4
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sakiryo_kanna,1
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animal_ear_headphones,0
amazon_warrior,0
yonomori_kobeni,4
uzuki_(cookie),4
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mineva_lao_zabi,4
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amino_(tn7135),1
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chii,4
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igawa_asagi,4
urara_meirochou,3
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saeki_hokuto,1
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fairy_maid_(touhou),4
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arc_the_lad_ii,3
sak_(lemondisk),1
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pa-15_(girls'_frontline),4
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ao_no_kiseki,3
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35p_(sakura_miko),4
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reu_(cookie),4
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sugue_tettou,1
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la_pucelle,3
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pom_pom_earrings,0
tsuki_ni_kawatte_oshioki_yo,0
tenkuu_no_shiro_laputa,3
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undyne,4
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guchico,1
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legend_of_the_cryptids,3
atte_nanakusa,1
crystalfly_(genshin_impact),4
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togami_byakuya,4
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top_pull,0
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oso_(toolate),1
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battleship_water_oni,4
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achiki,1
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.hack//g.u.,3
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christmas_present,0
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sen_(granblue_fantasy),4
gold_city_(umamusume),4
playing_piano,0
himeyamato,1
oda_nobuna_no_yabou,3
dragon_quest_x,3
majoccoid,1
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super_mario_odyssey,3
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sweater_tug,0
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teruui,1
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nakano_yuka,4
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lotte_no_omocha!,3
kumamiko,3
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devil_may_cry_3,3
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boulder,0
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pgm_hecate_ii,0
xingzhi_lv,1
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mashiro_aa,1
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kotozume_yukari,4
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riolu,4
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lee_(colt),1
if_(neptune_series),4
board,0
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kamen_rider_dcd,3
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minami_chiaki,4
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ansatsu_kyoushitsu,3
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sasairebun,1
sigurd_(fate),4
backless_panties,0
ghislaine_dedoldia,4
dragon_tattoo,0
pearl_hair_ornament,0
sakaki_yuuya,4
galaxy_angel_rune,3
kuroshitsuji,3
all_might,4
k/da_akali,4
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kananote,1
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sansei_muramasa,4
gryffindor,0
yan_(nicknikg),1
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ligne_claire,0
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rozalin,4
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kanna_(cookie),4
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american_mcgee's_alice,3
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neru_(bunny)_(blue_archive),4
macross:_do_you_remember_love?,3
subachi,1
yukiwo,1
xephyrks,1
miata_(miata8674),1
gengorou,1
litchi_faye_ling,4
maullarmaullar,1
zombie_fairy_(touhou),4
koumei_(twinameless),1
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princess_principal_game_of_mission,3
inkling_(language),0
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exlic,1
piripun,1
tsukimura_suzuka,4
iz_(asteroid_ill),4
kiawe_(pokemon),4
sakura_chiyono_o_(umamusume),4
mobu,1
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rpg,0
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oden,0
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raiou,1
ken_(coffee_michikusa),1
magnet_(vocaloid),3
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m_bison,4
suou_patra,4
suzuho_hotaru,1
mizushima_(p201112),1
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holding_cake,0
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tennis_court,0
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aty_(summon_night),4
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mp40_(girls'_frontline),4
yoako,1
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l_(death_note),4
majikina_mina,4
yaza,1
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pear,0
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nishizawa,1
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hanako_(blue_archive),4
redjuice,1
super_mushroom,0
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foo_fighters,4
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serena_(yu-gi-oh!),4
carol_malus_dienheim,4
tails_(sonic),4
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amuro_ray,4
maria_holic,3
risui_(suzu_rks),1
sasaame,1
shibari_under_clothes,0
female_ejaculation_through_clothes,0
animal_helmet,0
menacing_(jojo),0
demon's_souls,3
ooji,1
pokemon_mystery_dungeon,3
nanashin_naomi,1
yagyuu_(senran_kagura),4
narutaki_shin,1
admiral_(kancolle)_(cosplay),0
matoi_ryuuko_(cosplay),0
zuizi,1
hibiki_yuuta,4
sleeping_bag,0
caitlin_(pokemon),4
rojiura_satsuki:_chapter_heroine_sanctuary,3
hozuki_momiji,4
ayase_yue,4
lace_legwear,0
neropaso,1
rubik's_cube,0
voltron:_legendary_defender,3
girls_und_panzer_saishuushou,3
syunzou,1
irisviel_von_einzbern_(angel's_song),4
exiled_warrior_leina,4
aunt_and_niece,0
shimo_(depthbomb),1
katalina_(granblue_fantasy),4
vlad_iii_(fate/apocrypha),4
ayase_hazuki,1
gravel_(arknights),4
imperial_japanese_navy,0
levantine,4
star_ocean_the_last_hope,3
nokoppa,1
akagi_ritsuko,4
saitou_naoki,1
lucifero,4
kyuuba_melo,1
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fujitaka_nasu,1
dramatical_murder,3
aria_gakuen_school_uniform,0
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tabitha_(zero_no_tsukaima),4
miyako_hito,1
shark_print,0
knit_hat,0
kidou_yuuto,4
sylvain_jose_gautier,4
kabu_(pokemon),4
mimofu_(fullhighkick),1
momoji_(lobolobo2010),1
forbidden_scrollery,3
crescent_necklace,0
gayarou,1
kamen_rider_555,3
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iei,0
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jahy-sama_wa_kujikenai!,3
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jun_(aousa0328),1
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nori_(hidamari_sketch),4
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walkure_romanze,3
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catria_(fire_emblem),4
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amano_chiharu,1
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marcille,4
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sky:_children_of_the_light,3
virtua_fighter,3
lei_fang,4
mosako,1
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ipad,0
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:s,0
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gggg,1
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milly_ashford,4
pun2,1
kaibutsu_oujo,3
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houndoom,4
gumroad_username,0
akinaie,1
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hero's_daughter_(dq5),4
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nyoro~n,0
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mizushina_minato,1
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ringo-chan_(otokuyou),4
evolution,0
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kudelia_aina_bernstein,4
erio_mondial,4
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kim_eb,1
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71,1
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aku_no_musume_(vocaloid),3
siu_(siu0207),1
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nier_(granblue_fantasy),4
little_bel_(azur_lane),4
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seikon_no_qwaser,3
ina_(1813576),1
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:<>,0
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2ch,3
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re:act,3
joltik,4
lisia_(pokemon),4
white_headdress,0
bike_shorts_pull,0
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mad_hatter_(alice_in_wonderland),4
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xiao_wu_(douluo_dalu),4
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ilya_(princess_connect!),4
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tales_of_destiny_2,3
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minun,4
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shamir_nevrand,4
aogisa,1
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kao_no_nai_tsuki,3
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the_little_mermaid,3
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munna,4
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idaten93,1
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summon_night_2,3
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protean_assassin_melona,4
monado,0
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sword_art_online:_alicization_-_war_of_underworld,3
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io_(pso2),4
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lagann,4
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jon_(st05254),1
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ayumaru_(art_of_life),1
yusano,1
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chro,1
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quuni,1
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knight's_&_magic,3
deidara_(naruto),4
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samidare_(hoshi),1
no_smoking,0
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bou,1
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ump45_(agent_lop_rabbit)_(girls'_frontline),4
stan_marsh,4
cuphead_(game),3
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barasui,1
borrowed_hairstyle,0
bidoof,4
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tanigawa_yuzu,4
masamune_(ff7),0
sob_(submar1089),1
star_nun_(diva),4
star_cutout,0
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huangdanlan,1
oza_watto,1
bikini_bridge,0
extra_digits,0
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itsuka_shidou,4
high-low_skirt,0
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power_rangers,3
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sebastian_michaelis,4
uzuki_aki,1
malcorond,1
tenkuu_no_craft_fleet,3
kanbara_satomi,4
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orca_girl,0
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jasdavi,1
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fan_la_norne,4
hirota_(masasiv3),1
noble_academy_school_uniform,0
saruno_(eyesonly712),1
komekko,4
daitoshokan_no_hitsujikai,3
pointing_gun,0
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ore_no_kanojo_to_osananajimi_ga_shuraba_sugiru,3
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fenrir_(fenlil0316),1
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haiiro_teien,3
wewe,1
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taiga_(ryukyu-6102-8),1
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gradient_gloves,0
two-sided_headwear,0
mole_on_crotch,0
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uon_taraku,1
athrun1120,1
kingdra,4
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final_fantasy_crystal_chronicles,3
hodaka_natsumi,4
muhi11234,1
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castlevania:_portrait_of_ruin,3
sheriff_badge,0
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acea4,1
hougu_souji,1
murata,1
258n,1
ugeppa,1
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geodude,4
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leria_v,1
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tanishi_(tani4),1
munyuu,1
sisters_~natsu_no_saigo_no_hi~,3
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danaka,1
tun,1
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kokoro_connect,3
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scene_(arknights),4
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yu_ni_t,1
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dom,4
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agent_4_(splatoon),4
jun_(princess_connect!),4
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pouncing,0
komi_shuuko,4
leather_armor,0
kibadori_rue,1
wolflong,1
tasmanian_devil_(kemono_friends),4
tawashi1623,1
brionne,4
infi,1
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isshiki_momo,4
dusty_(gravity_daze),4
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nanu_(pokemon),4
wulazula,1
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tohsaka_rin_(cosplay),0
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yo-class_submarine,4
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noeru,1
ng_(kimjae737),1
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zhainan_s-jun,1
tsuruki_shizuka,4
miyasaka_naco,1
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granblue_fantasy_versus,3
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finn_the_human,4
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miwasiba,1
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kikuri_(touhou),4
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azumaya_hironaru,1
gotou_kenji,1
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chihayafuru,3
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senya_(dq11),4
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utane_uta,4
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aoi_suzu,1
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uganda_(ugandam_00),1
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polka_dot_scarf,0
type_95_(summer_cicada)_(girls'_frontline),4
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perrault_(last_origin),4
kasamoto_eri,4
akisa_(12023648),1
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iseki_(kuroshura_no_tabiji),1
sekiutsu_maria_tarou,4
barioth_(armor),4
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matsu_(kancolle),4
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northern_ocean_princess_(cosplay),0
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hacka_doll_1,4
cocked_eyebrow,0
sumia_(fire_emblem),4
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l85,0
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omc,3
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wadanohara,4
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armbinder,0
tenshinranman,3
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rangycrow,1
sivir,4
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yuigahama_yui's_mother,4
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pokemon_battle,0
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oda_nanami,4
ume-sensei,4
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ichinose_hajime,4
robot_girls_z,3
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koya_(koya_x_00),1
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el_(girls_und_panzer),4
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azen_(mntimcczgrtn),1
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nakamura_tetsuya,1
princess_leia_organa_solo,4
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yuusha_no_kuse_ni_namaiki_da,3
luozhu_(the_legend_of_luoxiaohei),4
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tales_of_rebirth,3
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jesus,4
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ryuumonbuchi_touka,4
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rin_(royal),1
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m-rs,1
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miyahara_takuya,1
kaya8,1
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dlanon,1
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super_robot_wars_og_saga_mugen_no_frontier_exceed,3
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sengo_muramasa_(touken_ranbu),4
kurofood,1
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nasu_no_yoichi,4
here's_johnny!_(meme),0
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my700,1
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sen_(sen0910),1
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phantom_(arknights),4
sora_(sky_s04),1
anna_nishikinomiya,4
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saaya_(suisei_no_gargantia),4
matsuki_(mikipingpong),1
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netoge_no_yome_wa_onna_no_ko_janai_to_omotta?,3
ymir_(queen's_blade),4
mukunokino_isshiki,1
tsube_aika,4
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isna_(footprintsofisna),1
lucidsky,1
dancing_stars_on_me!,0
gurasion_(gurasion),1
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last_exile:_gin'yoku_no_fam,3
boryeon_(last_origin),4
poyoyo_(nakiri_ayame),4
sin_kiske,4
amemiya_nazuna,4
obstagoon,4
takase_asagiri,1
quasarcake,1
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hoshino_ichika_(project_sekai),4
yodare_(3yami8),1
kyubey_(cosplay),0
yoshiwo,1
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asahina_mitsuru,4
yukke,1
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dragon_quest_i,3
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jiu_(sdesd3205),1
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ysk!,1
chariko,1
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taishou_yakyuu_musume,3
pig_girl,0
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vivy:_fluorite_eye's_song,3
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a.b.a,4
mullmull02,1
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touyama_nao,4
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monster_hunter_4,3
oniyama831,1
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mk23_(girls'_frontline),4
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hachiroku_(maitetsu),4
code:_empress_(elsword),4
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cheshire_(summery_date!)_(azur_lane),4
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amahane_madoka,4
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wynn_the_wind_charmer,4
tachibana_omina,1
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giant_otter_(kemono_friends)_(kuro_(kurojill)),4
paper_mario:_the_thousand_year_door,3
masaki_(msk064),1
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kapura,1
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applin,4
darius_(league_of_legends),4
kiryu_coco_(dragon),4
bison_(arknights),4
tsuchiya_ako,4
shion_(tensei_shitara_slime_datta_ken),4
akari_(princess_connect!),4
dainsleif_(genshin_impact),4
satou_samu,1
horz,1
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mr._mime,4
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wolf_o'donnell,4
kinsenka,1
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iron_tager,4
anew_returner,4
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jq,1
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namazu,4
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tsumetai_(tsurunoka),1
tamakaga,1
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taku1122,1
kurono,1
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sinaooo,1
lanturn,4
sewaddle,4
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steyr_aug,0
akagi_(warship_girls_r),4
hime_(kaibutsu_oujo),4
nogoodlife,1
unohana_retsu,4
hope_estheim,4
tayuya_(naruto),4
eria_(yu-gi-oh!),4
little_princess,3
tyoko_tanuki16,1
pimp,0
starkamisan,1
fortune_(last_origin),4
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jh,1
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canvas_2,3
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cul,4
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haku_(grimjin),1
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hi-nu_gundam,4
moreshan,1
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add_(fate),4
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resident_evil_3_(remake),3
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no_(xpxz7347),1
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anna_(drw01),1
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sv001_(metal_slug),0
ichika_(ichika87),1
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you06,1
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lene_(fire_emblem),4
shigure_(fire_emblem),4
izayoi_no_hanayome,3
eichan_(eichanidfi),1
ozaneko,1
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little_cocon_(umamusume),4
kokkoro_(real)_(princess_connect!),4
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rosmino,1
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moth1,1
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otona_no_moeoh,3
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inika,1
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star_of_lakshmi,0
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mo_(pixiv9929995),1
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johnathan_mar,4
honey_whip_(module),0
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picube525528,1
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bruno_(pokemon),4
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nise_(basilsis),1
paprika_private_academy_school_uniform,0
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yu_(bosshi),4
cu_chulainn_(second_ascension)_(fate),4
azure_striker_gunvolt_2,3
yuuki_terumi,4
jenny_wakeman,4
cross_akiha,1
sakuragi_rian,1
fumiko_(miruyuana),1
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a_(aaaaaaaaaaw),1
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heybot!,3
domestic_na_kanojo,3
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playboy_bunny_swimsuit,0
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venonat,4
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nanase_kurumi_(menhera-chan),4
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mars_(planet),0
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anti_(ssss.gridman),4
ren_(endscape20),1
detective_pikachu_(movie),3
satou_sasara,4
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suzu_(torikissa!),4
yae_sakura_(darkbolt_jonin),4
lino_chang,1
yonedatomo_mizu,1
sade_abyss,1
too_much_food,0
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cho!cho!,1
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diamond_sword,0
monogatari_series_puc_puc,3
lanmewko,4
eye_of_providence,0
olive_(laai),1
horned_girl_(jaco),4
dasdokter,1
ojo_aa,1
enchuu_kakiemon,1
noel_(cookie),4
turbo_engine_(rakugaki_tabo),1
print_camisole,0
cynthia_lane,4
kaijin_hime_do-s,4
sharla_(xenoblade),4
pieces_of_sweet_stars,3
adrien_agreste,4
chimerism,0
hinainf,1
alphen_(tales),4
misty_(pokemon)_(cosplay),0
mei_(adomanju),1
snickers,0
inferna_dragnis,4
leona_kingscholar,4
orange_brand,1
amano_mishio,4
mimizu_(tokagex),1
food_girls,3
koyanskaya_(lostbelt_beast:iv)_(fate),4
manaka_ao,4
hoe_satsuki,1
hikami_izuto,1
hellboy_(comic),3
shorts_lift,0
tona_(nekotte),1
harima_kenji,4
ayuhara_natsu,4
conjoined,0
kuromaru,1
numata_seiya,1
aquamarine,1
mikado_ryouko,4
exdeath,4
derringer,0
fei-yen,4
geister,1
tetsuji,1
abriel00,1
yamamiya_hiroshi,1
salamander_coral,4
tiro_finale,0
karaoke_box,0
shiina_takashi,1
shou_shishi,1
ayami_chiha,1
oku_no_shi,1
rhyhorn,4
otton,1
akosan,1
hanemiya_kazutora,4
jujunaught,1
faize_scheifa_beleth,4
iguana,0
bonjin,1
b.tarou,1
super_mario_bros._3,3
6_9,0
white_ensign,0
toku_(tokuhp),1
dr._mario,4
qontamblue,1
puniru_wa_kawaii_slime,3
nozaki_namiko,4
rl,1
nidoro,1
ryuusei_(kancolle),4
steeple,0
ri_kouran,4
heaven_condition,0
hyuuga_akito,4
kurozu_(crozu),1
ikurakun_(7th_dragon),4
overflowing,0
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dengeki,3
maido_mido,1
bosako_(haguhagu),4
akizuki_haruhi,1
ledge,0
sardegna_empire_(emblem),0
kilroylw,1
kagaya_hana,4
mimiko_(fuji_310),1
yamaishi_nohi,1
yuuki_nao_(pixiv10696483),1
shukurimu,1
ootori_gakuen_school_uniform,0
maruyama_akane,4
koharu_yoshino,4
komiya_hitoma,1
okome_(kome_kuma),1
mono_(jdaj),1
lostorage_wixoss,3
befu,1
murasame_shia,1
meowstress,4
bliss_(ferment_factory),1
grand_theft_auto_v,3
higashi_tarou,1
uemura_shun,1
suzunashi,1
ootori_mahiro,1
makarov_(girls'_frontline),4
gueira,4
stalker_(game),3
turkey_(hetalia),4
kagome_f,1
inaba_tewi_(cosplay),0
ayase_chihaya,4
okazaki_oka,1
seven_knights,3
myumi,1
hayarob,1
hanh_chu_(r_a),1
takanashi_hina,4
tsukimura_mayu,4
lshiki,1
ilima_(pokemon),4
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yasutange,1
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kuroishi_ringo,1
astraea_(atelierastraea),1
pumpkin_on_head,0
stantler,4
fuyumine_naoto,4
k.t.cube,1
ahobaka,1
colress_(pokemon),4
yuritamashi,1
forget-me-not_(flower),0
too_many_butterflies,0
karmiel,1
nurupo_(abooon),1
cacao_(chocolat),1
angel_flavor,3
hirafumi,1
tachi_(gutsutoma),1
dark_rhongomyniad,0
ka11_ca,1
riffraffhellyea,1
blanclauz,1
aibivy,1
yue_(arifureta),4
majorette,0
kog'maw,4
daniyyel+jennifer,4
biscuit_(bread),0
itefu,1
orchid_(orukido),1
nessen_(live_a_hero),4
slosher_(splatoon),0
thisuserisalive,1
shuto_aoi,4
twisted_fate,4
urtoriy,4
ann606,1
adam_taurus,4
kujya,1
kon_(fate),4
marlene_wallace,4
irudana,1
kaneda_mitsuko,1
nora_shinji,1
sakuraba_kaoru,4
takei_masaki,1
nonaka,1
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makihatayama_hana,4
garlock_simon,4
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tsubooi,1
qy73,1
taiki_kou,4
yowamushi_mont-blanc_(vocaloid),3
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zoey_(left_4_dead),4
nippon_housou_kyoukai,3
kosumo,1
kiichi,1
rope_train,0
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kiyuzuki_satoko,1
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very_long_tail,0
dkoro,1
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shikuro_(iorimiyakiyo),1
superboin,1
eu_(euspia),1
senomiya_akiho,4
nagi_(nagito),1
22_(tak_ja),1
hikaru_(houkago_no_pleiades),4
sutaa_dasuto-kun,1
edie_crop_hoodie,0
yuni_(real)_(princess_connect!),4
cobra_(space_adventure_cobra),4
liby_(libtnvd),1
sora_(summer_flowers)_(arknights),4
vital_sunflower_(idolmaster),0
nuppehofu_(nibuta),1
apoloniodraws,1
yuusha_support_center_maoujou_shibu,3
m1911_(girls'_frontline),4
takibi_(tamikyo),1
atago_(hallmarks_of_winter)_(azur_lane),4
shao_siming_(qin_shi_ming_yue),4
caren_hortensia_(amor_caren)_(third_ascension),4
aoki_ruri,4
fen_renlei,1
tali'zorah,4
sukedai,1
ellis_(toushinden),4
aina123,1
rounin_(amuza),1
aebafuti,1
inato_serere,1
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ikusotsu,1
tadano_kazuko,1
koimonogatari,3
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barufoa,1
tochinoko,1
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snover,4
suzu_(minagi),1
suncle,1
kohaku_q,1
s4_uniform,0
iroha_(akei0710),1
silque_(fire_emblem),4
andou_inari,4
yugo_(yugotme97),1
tsukishima_maira,4
cardfight!!_vanguard_g,3
koujouchou,1
girls_book_maker,3
cucumber_slice,0
oomura_karasu,1
zea_cornelia,4
dailykrumbs,1
akai_kagerou,1
cure_honey_(popcorn_cheer),4
shiranui_akeno,4
velahka,1
paine_(ff10),4
yagita_(astronomie),1
europa_(fate),4
katherine_mcbride,4
etori,1
takoyaki_(takoyaki-batake),1
achi_cirno,4
purumetal,1
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sashiyu,1
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intangible,0
across_the_stars_(idolmaster),0
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escort_fortress_(kancolle),4
azusagawa_sakuta,4
arms_note,3
minori_(faddy),1
maruco,1
yuzuri_ai,1
yuuki_arisu_(yuigi),1
devil_maker,3
ahen,1
suzuhara_misae,4
orie_h,1
tsubasaki,1
aritoki_ritsu,1
seedkeng,1
whis,4
snow_shovel,0
fukidamari_no_peke,1
sasakure_(mogunonbi),1
good_meat_day,0
dizdoodz,1
olaf_(frozen),4
otowa_(otoha4634),1
hanabusa_sumireko,4
squishing,0
jun_project,1
cyber_dragon-kun,1
minoseki_gakuin_uniform,0
white_polka_dots,0
bayeuxman,1
puui_(grandia),4
tendouji_musubi,4
gamecube_controller,0
ursula_(breath_of_fire),4
delores_(mvv),4
occhan_(11715),1
sword_art_online:_alicization_rising_steel,3
yuuki_chima,1
raraiya_monday,4
ms._smith,4
male_prostitution,0
kaoru,1
catalyst_scimitar,0
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maid-chan_(ramchi),4
tachibana_satomi,4
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nikusenpai,1
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izutsumi,4
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lolo_(kemonono),1
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culottes,0
remote_control_vehicle,0
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momose_narumi,4
wangzhe_rongyao,3
shirayuiii,1
dmt_(auburn),1
dosanko_gyaru_wa_namaramenkoi,3
holding_case,0
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mizuki_mau,1
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ichikushi_mojibake,1
omagacchu,1
suzuki_mikura,4
protagonist_(honkai_impact),4
jade_(gemstone),0
airool,1
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cyber_punked_wattson,4
alstroemeria_(idolmaster),0
itou_youichi,1
nair_(mindcreator),1
asla_yu,1
zaraki_kenpachi,4
kijima_shia,4
bkko,1
wemu_(ivycrown),1
cyoppu,1
getiao,1
roah,1
yumeria,3
siscon,0
aburame_shino,4
viper_gts,3
makibi_kiyone,4
onohara_asami,4
myonde,1
oppai_baka,3
kurogin,1
idashige_(walkietalkie),1
xemnas,4
ouka_nagisa,4
cyanomirahi,1
kawamura_tsukasa,1
koromon,4
splash_woman,4
live_a_live,3
hayami_tsurumasa,4
engineer,0
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tennouji_kotarou,4
neon_genesis_evangelion_anima,3
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sizucomaru,1
dmitrys,1
lotus_land_story,3
natsumi_hajime,1
oasis_(stand),4
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alfimi,4
mac_naut,1
hinabu,1
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strike_freedom_gundam,4
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zuburoku,1
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s.h.v,1
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hilda_ware,4
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purea,1
noto_mamiko,4
shibanme_tekikumo,1
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rk_(rktorinegi),1
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sitting_on_ball,0
ph.,1
yuki7128,1
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palanquin_d.va,4
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sunao_nako,4
melisaongmiqin,1
mechanical_boots,0
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menori,4
taric,4
shanghmely,1
usagi_poi,1
irisdina_bernhard,4
katsura_kokoro,4
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sorbet,4
walther_p38,0
goripan,1
minase_kaya,1
vending_cart,0
twinkle_crusaders,3
park_ongjol,1
susuwatari,4
cum_on_tail,0
nao_(syn_eaa),1
akanishi_erika,4
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gerbera,0
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mil_k_vanilla,1
stoutland,4
asura_(asr4000),1
ujiie_moku,1
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takase_yuu,1
gozaemon,1
zato-1,4
onionworkshop,1
shi-chen,1
wendy_testaburger,4
broiler,1
yuuya_(yu-ya's),1
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z.nov,1
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miyano_shiho,4
satetsu,1
dakuazu,1
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haruyuki_(gffewuoutgblubh),1
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itoji,1
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sagami_jun,4
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dywx_poison,1
tareko,1
assassin,0
nishi_juuji,1
turo_(pokemon),4
millia_jenius,4
girafarig,4
spectral_souls,3
andanon,1
aoi_minamo,4
minami_(minami373916),1
manggapaegtoli,1
nashiko_(nanaju_ko),1
skeleton_(helltaker),4
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yuu_(tiny_evil),4
uzaki_yanagi,4
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imminent_bite,0
yona_(akatsuki_no_yona),4
tonbo,1
hiiragi_mikoto,1
qixia,1
kito_(kito2),1
himajin_(starmine),1
maqin,1
ryusei_(ryusei_b),1
footjob_over_clothes,0
christmas_dress,0
shooting_gallery,0
kazami_yuuka_(cosplay),0
temari114,1
excel_saga,3
ogu_(oguogu0523),1
wheelie,0
neg_(101neg),1
heart_arrow,0
nano-nano_pudding,4
dragon_ball_xenoverse,3
bonk,0
yuunagi_(seventh_heaven),1
lrh0123,1
shitogi_eris,4
hilda_(series),3
sugisaka_umi,4
silent_sinner_in_blue,3
shiori_(xxxsi),1
yojouhan_shinwa_taikei,3
saikachi_(ogre_tree),1
matsuyama_kumiko,4
minari_(minari37),1
suko_mugi,1
bunches,0
neglect_play,0
yuuki_kazuhito,1
eureka_seven_ao,3
sou_(mgn),1
old-fashioned_doughnut,0
seong_mi-na,4
mori_kotarou,1
stellar_theater,3
feast,0
xiaoguimist,1
mass_effect_3,3
brynhildr_(swimsuit_berserker)_(fate),4
natsuiro_xx,1
pepe_the_frog,4
sa_ioio,1
jack_(darkshero),1
yorurokujuu,1
du_mogu,1
constellation_costume,0
rikorin,1
bashosho,1
takahashi_rie,4
bangom_r,1
nin_(lion),1
nazo_no_anime-dan,1
tomoe_(fdhs5855),1
nezha_(the_legend_of_luoxiaohei),4
ushimochi,1
urrrt,1
new_pokemon_snap,3
ikeda_emi,4
yutian_alice,1
jiraiya_(housamo),4
okosama_lunch_(sendan),1
xialuluo_(sharuro),1
zap_renfro,4
mib_mamizou,4
tkbn_r,1
shikaya,1
himiko_(third_ascension)_(fate),4
thors_military_academy_class_vii_uniform,0
aegir_(iron_blood's_dragon_maid)_(azur_lane),4
titan_(generic)_(shingeki_no_kyojin),4
tamuhi,1
amakusa_shino,4
odaodaoaoda,1
fujimaru_(bluebrand),1
satou_akane,4
kanimaru,1
meruru_(oreimo)_(cosplay),0
jake_muller,4
pochi_(poti1990),1
norinco,1
flat_top_chef_hat,0
elf_(dq10),4
yamoto_koki,4
eltnum,4
traveler_(journey),4
suzumiya_haruhi_no_tsuisou,3
sprout_(33510539),1
cole,1
mt._silver,0
torn_hakama,0
kayou_(kayou-bi),1
eruu_(eruuman),1
ai-kun,4
chicken99,1
negiwo,1
sana423,1
philipp_urlich,1
annoying_dog,4
seele0907,1
satoshi_(peso727),1
zeru_(wisel_army),1
oichi_(sengoku_basara),4
michiru_donut,1
2d_dating,0
tsumeki,1
ariaridoradora,1
rosenburg_engel_(idolmaster),0
hashimoto_takayoshi,1
gaogaigar,4
hayama_hayato,4
waist_hold,0
melty_(suisei_no_gargantia),4
nishi_minami,4
fuyube_gin_(huyube),1
sein_(nanoha),4
tiara_(fairy_fencer_f),4
nananana_nanana,1
moeru!_jiten,3
ennui_heiwa_miku,4
hinata_aki,4
petralka_anne_eldant_iii,4
yozo_(stanky),1
watashi_nouryoku_wa_heikinchi_de_tte_itta_yo_ne!,3
hououmaru_rei,4
triuni,1
takeda_yuuko,1
wendell,1
rafchu,1
wally99,1
yazawa_nico_(cosplay),0
kafuu_chino_(cosplay),0
muchabou,1
cuna_(qunya),1
supportasse,0
hitotsuki_no_yagi,1
viki_(suikoden),4
herme_(ohisashiburi),4
klasse14,3
mega_rayquaza,4
skull_hat_ornament,0
ellie_(the_last_of_us),4
arima_kousei,4
speedoru,1
arima_nana,4
sakura_ritsuki,4
margery_daw,4
thumbnail_surprise,5
miyashiro_karin,4
tomura2maru,1
kamome_yuu,1
totto_(naka),1
zaveid_(tales),4
meis_(promare),4
satonaka_narumi,4
mirai_suzu,4
kowata_makoto,4
roon_(dark_red_grin)_(azur_lane),4
yonic_symbol,0
tawapo,1
hiroki_(hirokiart),1
vomiting_cum,0
deerling_(spring),4
nagoooon_114,1
elbow_on_table,0
artery_gear:_fusion,3
deepcolor_(arknights),4
akure_ekuto,1
ayame_(tenchuu),4
curly_eyebrows,0
dodok_(gj77230),1
heart-shaped_cake,0
mishiranu_joshikousei_ni_kankin_sareta_mangaka_no_hanashi,3
beagle_(arknights),4
uu-tan_(hushigi10),1
sitting_on_drum,0
marnie_(pokemon)_(cosplay),0
yunweishukuang,1
leafa_(terraria),4
kashmir,1
althea_(sakiya0000),1
sherlock_holmes_(cosplay),0
bloodycat,1
dynamax_ball,0
rahxephon,3
irie_kyousuke,4
katou_emiri,4
awatake_takahiro,1
miche,1
yarawi,1
tarma_roving,4
maria_tachibana,4
huujyu,1
rinmei,1
arthur_(code_geass),4
succubus_(mabinogi),4
otosume_ruiko,1
hoshii_1213,1
nagahama_megumi,1
udder,0
doromizu,1
mana_(yu-gi-oh!),4
za_warudo,0
phanpy,4
matsuki_ringo,1
usumy,1
bummerdude,1
ajimu_najimi,4
kumajirou_(hetalia),4
chinjuu_hibakichi,1
jinguuji_ren,4
ironashi,1
gomibuta_kuzutarou,1
sea_cucumber,0
etzali,4
yuki_(cencoroll),4
la_corda_d'oro,3
mikanniro,1
watase_sanetoshi,4
shoujo_kakumei_utena_adolescence_mokushiroku,3
robinexile,1
hiking,0
murata_isshin,1
eggshell_hat,0
akthree0,1
kari_kenji,1
mayoln,1
aoi_yuuji,1
bands,0
dinosaur_king,3
koto_suomi,1
ashita_no_joe,3
iori_utahime,4
quadcopter,0
2c=galore,1
kamioka_saki,4
dachshund,0
blue_dragon,3
goal,0
super_metroid,3
beni_(bluebluesky),1
toshi_hiroshi,1
weepinbell,4
rose_(elsword),4
jiraiken,1
mamoswine,4
nitta_io,4
stoat_girl,0
audrey_hall,4
tachibana_rui,4
sinistea,4
hangleing,1
mofumofuen_(idolmaster),0
aqua_inc.,1
yatanukikey,1
release_the_spyce,3
kate_iwana,1
meister_otome_uniform,0
menomorute,1
mesushio,1
drum_master_(granblue_fantasy),4
slurping,0
friedbun,1
sgt-jz,1
pong_(vndn124),1
amitie_florian,4
x-overd,3
maru_(314),1
argyle_shirt,0
muvluv_alternative_strike_frontier,3
toki_(tokihakashi),1
noumu_(boku_no_hero_academia),4
yayin_(yayin233),1
kensight328,1
bisuke-tan,4
iren_lovel,1
snowstorm,0
vault_boy,4
warlock_(sekaiju),4
fractalmagnolia,1
gotointhepark,1
yameta_takashi,1
usuke_(u_skeeep),1
akiba's_trip,3
harikamo,1
earth_defense_force,3
eclair_(food),0
ginga_hyouryuu_vifam,3
futatsuiwa_mamizou_(tanuki),4
desmond_miles,4
rui_(rei_leyi),1
beauty_(pokemon),4
vomiting_rainbows,0
panzerkampfwagen_ii,0
gemini_seed,3
bunny_head,0
xkit,1
mute,0
jigoku_meguri,1
hiker_(pokemon),4
suffolk_(azur_lane),4
furfrou_(natural),4
divergenceok,1
kinpatsu-chan_(rucchiifu),4
achromaru,1
mirai_(happy-floral),1
renxzd,1
mikamin,1
hourai_ninjin,1
moonslanstudio,1
ms._marvel,4
lilirulu,1
orange_sash,0
skeleton_(minecraft),4
muscle_envy,0
removing_sock,0
leaphere,1
eve_online,3
honnryou_hanaru,1
cheng_xiaoshi,4
katade,1
cardigan_under_jacket,0
umenomori_chise,4
sunfish,0
crystal_maiden,4
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tina_(pixiv37050289),1
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blue_(happinesscharge_precure!),4
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yuuki_makoto_(ensemble_stars!),4
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mexico_salamander_(kemono_friends),4
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ranan,1
jtaka,1
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time,0
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ange_serena,4
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kise_(swimmt),1
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japanese_tankery_league_judge_uniform,0
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fjt_(fjym2),1
ai_xiao_meng,1
suno_(imydream),1
castration,0
nahanmin,1
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ooki1089,1
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surrender,1
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vinashyra,1
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oguri_cap_(miraculous_white_star)_(umamusume),4
super_masara_ahegao,0
toto_(twooz),4
flappy,4
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birman_h,1
asagi-so,1
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abemorioka,1
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l-gaim_mk_ii,4
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rizelmine,3
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sooya,1
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calvin_&_hobbes,3
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erementa,1
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sakana~_(meme),0
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l.j.,1
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palm_tree_print,0
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asura's_wrath,3
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world_teacher_-isekaishiki_kyouiku_agent-,3
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fencer_(sekaiju),4
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chikage_(bloodborne),0
alov,1
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nips_(ohnips),1
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ac/dc,3
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iru_may_(akairiot),4
muhyowo,1
kdkaikai,1
erhuo,1
sumiyoshi_rocket,0
lena_(fire_emblem),4
hayden_mackenzie,1
red_dragon_archfiend,4
magaqq123,1
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kirby's_dream_land_2,3
yukino_ko_(yukino_shiratama),4
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yuui1994,1
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kamen_rider_joker,4
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smelly_penis,0
peanut_butter,0
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aji77,1
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appare-ranman!,3
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xin_(zinc),1
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hs.50_(girls'_frontline),4
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st06,1
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xxxxakixxxx,1
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louise_francoise_le_blanc_de_la_valliere_(cosplay),0
asahina_suzuka,4
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kousaka_makoto,4
mine_fukaki_se_ni_tayutau_uta,3
miwa_hitomi,1
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rody_soul,4
juuken_sentai_gekiranger,3
maebari_teikoku_no_gyakushuu,3
archaic_sealed_heat,3
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yield,4
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suwi,1
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aymeric_de_borel,4
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nokia,4
canae0,1
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aya003030,1
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getter-3,4
battle_spirits:_brave,3
stashia,4
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wizardess_of_oz,4
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rena_geminis,4
pla0658,1
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laura_(houtengeki),4
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ryuujou_mashiro,4
riku_(kino_no_tabi),4
mikihiro,1
mayan_(macross),0
trimbil,1
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rosie99,1
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yeyuan33,1
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torture_dance,0
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secre_swallowtail,4
zealotus,4
muumin,1
creature_on_lap,0
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maki_makishima_(makimaki),4
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lost-phews,1
mmco,1
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sogdin,1
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lightning_ahoge,0
shima108,1
tenshi_no_shippo,3
orga_(orgacl),1
hanakoganei_hibari,4
kumegawa_botan,4
tamamura_tamao,4
dai_toro,1
jessie_gurtland,4
skirt_bow,0
siege_(legacy)_(arknights),4
mechanical_gloves,0
toyo_(c8),1
riding_animal,0
mitake_miyao,4
twintails_girl_(kamisimo_90),4
shibazaki_yousuke,4
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dortin,4
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white_magician_pikeru,4
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132,1
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xbox_series_x_(personification),4
fujimoto_shirou,4
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temptation_2,3
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jacky_bryant,4
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kono_bushitsu_wa_kitaku_shinai_bu_ga_senkyo_shimashita.,3
mikimura,1
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luna_(sailor_moon)_(cosplay),0
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taimanin_asagi_2,3
miura,1
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furubokko,1
rokuri-eva,1
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snow_white_evelysse,4
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wing_genome,0
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drowtales,3
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mikoshiba_rea,4
mariko_(elfen_lied),4
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fuyu_kitsune,1
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shibamura_mai,4
sana_(kerarino),1
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kaga_mine_2,3
culumon,4
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sunson,1
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yuuno_(hack_y),1
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biglove,1
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yuasa_satsuki,4
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time_gal,3
inomoto_hiro,1
tako_(pixiv8535),1
shinguuji_moena,4
kumaman,1
choco_holic,1
siukaukau24,1
imamiya_satoshi,4
ffr-31mr/d_super_sylph,4
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takayama_misa,4
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mable_(pokemon),4
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diggersby,4
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akagi_mio,4
sanjou_(weekly10),1
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chaos_(xenosaga),4
honda_yoshino,1
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ashley_ann_swaby,1
equuleus_shoko,4
alk_(world_flipper),4
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go!_go!_nippon!,3
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shinmon_benimaru,4
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eden_they_were_only_two_on_the_planet,3
madoi_shiroki_no_kamikakushi,3
happy_shower,0


================================================
FILE: data/tags/e621.csv
================================================
mammal,5
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4:3,7
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4:5,7
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rape,0
2012,7
undertale,3
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incest_(lore),-1
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forest,0
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buckteeth,0
big_dom_small_sub,0
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2:3,7
procyonid,5
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sciurid,5
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star,0
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chair,0
phone,0
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feline_humanoid,5
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sword,0
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insect,5
rope,0
crown,0
magic,0
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romantic,0
profanity,0
5:4,7
bow_ribbon,0
necktie,0
mario_bros,3
sweatdrop,0
breast_squish,0
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crying,0
pinup,0
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socks_(marking),0
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cute_fangs,0
loli,0
submissive_female,0
husky,5
macro,0
vehicle,0
were,5
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camel_toe,0
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cat_humanoid,5
intersex/male,0
armpit_hair,0
thick_penis,0
dominant_female,0
restrained,0
bite,0
short_stack,0
leaking_cum,0
pastoral_dog,5
bedroom,0
cunnilingus,0
gradient_background,0
excessive_cum,0
lipstick,0
fluttershy_(mlp),4
elbow_gloves,0
penis_lick,0
crouching,0
fruit,0
herding_dog,5
light,0
spotted_fur,0
human_penetrated,0
moan,0
intersex_penetrating,0
featureless_crotch,0
window,0
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cum_on_self,0
pink_tongue,0
striped_fur,0
one_eye_obstructed,0
rainbow_dash_(mlp),4
pregnant,0
onomatopoeia,0
crossdressing,0
blue_background,0
ambiguous_penetration,0
looking_up,0
blizzard_entertainment,3
lifewonders,3
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sleeping,0
multi_tail,0
blue_clothing,0
slit,0
double_penetration,0
nick_wilde,4
tapering_penis,0
scottgames,3
five_nights_at_freddy's,3
gynomorph/female,0
clothing_lift,0
cartoon_network,3
dipstick_ears,0
green_fur,0
male_on_anthro,0
tail_tuft,0
female_on_feral,0
black_nipples,0
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gun,0
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werecanid,5
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obese,0
head_tuft,0
star_fox,3
human_on_humanoid,0
missionary_position,0
red_clothing,0
mythological_avian,5
chain,0
lutrine,5
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thong,0
hair_ribbon,0
floppy_ears,0
pattern_clothing,0
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bone,0
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cum_on_breasts,0
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big_areola,0
loop,7
canine_humanoid,5
elemental_creature,5
3_fingers,0
musteline,5
fingerless_gloves,0
cum_on_butt,0
book,0
sound,7
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titfuck,0
bow_accessory,0
cuff_(restraint),0
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cum_inflation,0
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male/ambiguous,0
2011,7
bedding,0
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hair_over_eye,0
hands_behind_back,0
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open_topwear,0
cum_on_body,0
pink_areola,0
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spread_pussy,0
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warcraft,3
sweater,0
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rarity_(mlp),4
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christmas,3
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anthro_on_feral,0
pinkie_pie_(mlp),4
featureless_breasts,0
toony,0
choker,0
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hair_bow,0
bottomwear_down,0
mephitid,5
smirk,0
fin,0
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musclegut,0
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close-up,0
green_skin,0
alcohol,0
hand_on_leg,0
non-mammal_nipples,0
lucario,5
black_claws,0
multi_genitalia,0
foot_focus,0
skunk,5
model_sheet,7
crocodilian,5
human_penetrating_anthro,0
saliva_string,0
pink_pawpads,0
long_tongue,0
bouncing_breasts,0
white_skin,0
blue_penis,0
renamon,5
patreon,3
costume,0
hand_on_head,0
pivoted_ears,0
white_tail,0
bow_tie,0
male_on_human,0
cup,0
cum_string,0
duo_focus,0
nose_ring,0
helmet,0
bouncing_balls,0
amber_eyes,0
webcomic_character,4
countershade_torso,0
medial_ring,0
edit,7
male_on_feral,0
big_nipples,0
tokyo_afterschool_summoners,3
orgasm_face,0
foot_play,0
milk,0
genital_piercing,0
3:2,7
gagged,0
two_tone_tail,0
riot_games,3
league_of_legends,3
white_feathers,0
hands_behind_head,0
sheep,5
herm,0
against_surface,0
asian_mythology,3
rat,5
capcom,3
towel,0
sister,0
child,0
colored_nails,0
suina,5
heterochromia,0
multi_penis,0
furred_dragon,5
rimming,0
raised_arm,0
east_asian_mythology,3
sand,0
denim,0
ailurid,5
ring,0
werecanine,5
brother,0
applejack_(mlp),4
denim_clothing,0
animatronic,5
frown,0
asian_clothing,0
princess_celestia_(mlp),4
red_panda,5
cum_on_ground,0
mother,0
pants_down,0
fennec,5
east_asian_clothing,0
rainbow_hair,0
looking_away,0
urine,0
green_scales,0
imminent_sex,0
gryphon,5
werewolf,5
ineffective_censorship,0
dark_skin,0
mind_control,0
the_legend_of_zelda,3
princess_luna_(mlp),4
antennae_(anatomy),0
ursine,5
brown_nose,0
thick_tail,0
flora_fauna,5
leaning,0
ball_gag,0
suid,5
braided_hair,0
primate,5
cum_on_balls,0
multi_limb,0
leg_grab,0
frame_by_frame,7
looking_at_partner,0
genital_slit,0
translucent_hair,0
sea,0
anatomically_correct_genitalia,0
nipple_fetish,0
generation_8_pokemon,5
sexual_barrier_device,0
sus_(pig),5
genital_outline,0
condom,0
looking_back_at_viewer,0
nipple_play,0
boss_monster,5
raccoon_dog,5
snow,0
equine_pussy,0
sucking,0
spotted_hyena,5
looking_aside,0
knotting,0
4k,7
spread_butt,0
loincloth,0
story,7
micro,0
butt_focus,0
tanuki,5
censor_bar,0
toying_self,0
sex_toy_in_ass,0
hand_behind_head,0
self_lick,0
anal_masturbation,0
5:6,7
expansion,0
tree_squirrel,5
unknown_species,5
panting,0
retracted_foreskin,0
teats,0
kerchief,0
9:16,7
male_on_top,0
bandage,0
striped_clothing,0
square_enix,3
light_body,0
harness,0
slim,0
line_art,7
translucent_clothing,0
not_furry_focus,0
shaking,0
medium_breasts,0
cellphone,0
giant_panda,5
red_fox,5
fishnet,0
elf,5
japanese_clothing,0
standing_sex,0
pseudo_hair,0
under_boob,0
huge_thighs,0
tail_motion,0
jackal,5
blue_nipples,0
striped_markings,0
older_male,0
son,0
rock,0
chest_hair,0
2010,7
cutaway,0
anal_orgasm,0
spiked_collar,0
diaper,0
eye_patch,0
small_waist,0
human_focus,0
cum_on_chest,0
deep_throat,0
vibrator,0
shackles,0
open_shirt,0
pink_clothing,0
amphibian,5
cheetah,5
partially_submerged,0
lynx,5
rouge_the_bat,4
cape,0
suit,0
balls_deep,0
kobold,5
hunting_dog,5
vaginal_fingering,0
candy,0
clothing_aside,0
humanoid_penetrated,0
blue_scales,0
huge_muscles,0
growth,0
small_dom_big_sub,0
larger_anthro,0
pink_background,0
activision,3
eye_roll,0
biting_lip,0
tentacle_sex,0
red_penis,0
snow_leopard,5
smoking,0
species_transformation,0
story_in_description,7
fucked_silly,0
bandanna,0
mollusk,5
steam,0
high_framerate,7
sonic_the_hedgehog,4
larger_penetrated,0
penis_size_difference,0
shirt_lift,0
theropod,5
tools,0
building,0
purple_background,0
white_eyes,0
leaking,0
big_tail,0
gynomorph/male,0
cum_on_tongue,0
spikes_(anatomy),0
underwear_down,0
on_ground,0
oral_vore,0
reverse_cowgirl_position,0
barazoku,0
penile_spines,0
t-shirt,0
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cum_on_leg,0
flying,0
multi_arm,0
yellow_feathers,0
holding_breast,0
hypnosis,0
purple_skin,0
black_pawpads,0
long_penis,0
green_clothing,0
older_female,0
spirit,5
macropod,5
dock,0
artist_name,7
plump_labia,0
tan_skin,0
black_tail,0
teasing,0
blue_nose,0
reclining,0
city,0
jeans,0
holding_penis,0
black_skin,0
tailwag,0
talking_to_viewer,0
eating,0
leopard,5
cosplay,0
presenting_penis,0
warner_brothers,3
covering,0
nature,0
koopa,5
gaping_anus,0
gardevoir,5
dreamworks,3
athletic_anthro,0
gem,0
countershade_face,0
exposed_breasts,0
pikachu,5
high-angle_view,0
light_skin,0
striped_tail,0
helluva_boss,3
leaf,0
straddling,0
intersex_penetrated,0
true_musteline,5
canine_pussy,0
horned_humanoid,5
halloween,3
casual_nudity,0
mother_and_child,0
goo_creature,5
galliform,5
hand_holding,0
bouncing_butt,0
musk,0
shiny_pokemon,5
phasianid,5
bethesda_softworks,3
final_fantasy,3
andromorph,0
tail_accessory,0
ellipsis,0
royalty,0
gold_(metal),0
septum_piercing,0
grey_skin,0
egg,0
taur,5
what,0
penis_outline,0
v_sign,0
inverted_nipples,0
female_on_anthro,0
smoke,0
bent_legs,0
european_mythology,3
bottle,0
crop_top,0
kangaroo,5
marine_humanoid,5
grey_eyes,0
peeing,0
father,0
eyebrow_through_hair,0
arm_support,0
fox_humanoid,5
coat,0
german_shepherd,5
animate_inanimate,5
garter_straps,0
skull,0
cum_on_belly,0
plushie,0
blush_lines,0
hand_on_penis,0
bust_portrait,7
letterbox,0
dildo_insertion,0
half-length_portrait,7
being_watched,0
penetrating_pov,0
highlights_(coloring),0
shy,0
black_topwear,0
red_skin,0
red_scales,0
fully_sheathed,0
haplorhine,5
vest,0
obese_anthro,0
glass,0
unusual_anatomy,0
miles_prower,4
glistening_eyes,0
hand_on_thigh,0
looking_at_genitalia,0
intersex_penetrating_female,0
zebra,5
female_anthro,0
apron,0
red_sclera,0
greek_mythology,3
facesitting,0
sony_corporation,3
black_background,0
magic_user,0
wide_eyed,0
passerine,5
no_underwear,0
sony_interactive_entertainment,3
oscine,5
text_on_clothing,0
scared,0
dancing,0
undead,5
robe,0
talons,0
french_kissing,0
shower,0
amy_rose,4
feral_penetrating_feral,0
multi_eye,0
spread_wings,0
smaller_human,0
gangbang,0
plug_(sex_toy),0
black_scales,0
spitroast,0
white_countershading,0
inviting,0
briefs,0
footjob,0
green_background,0
krystal,4
intersex/intersex,0
jockstrap,0
blue_tail,0
penis_piercing,0
brown_tail,0
serpentine,0
long_playtime,7
tail_feathers,0
pink_pussy,0
precum_drip,0
impregnation,0
ghost,5
cigarette,0
chibi,0
dildo_sitting,0
flexing,0
capreoline,5
graphite_(artwork),7
three-quarter_view,0
umbreon,5
nervous,0
clothed_sex,0
penis_tip,0
ear_tuft,0
toy_dog,5
:3,0
pink_glans,0
black_balls,0
inflatable,0
rope_bondage,0
death,0
skinsuit,0
polar_bear,5
smiling_at_viewer,0
16:10,7
small_penis,0
white_topwear,0
questionable_consent,0
digital_painting_(artwork),7
mature_male,0
canid_demon,5
hand_on_stomach,0
cum_splatter,0
toriel,4
blue_tongue,0
smaller_anthro,0
changeling,5
big_eyes,0
wet_clothing,0
headphones,0
underwear_aside,0
spade_tail,0
alternate_version_at_source,7
frottage,0
drinking,0
faceless_human,0
pencil_(artwork),7
monster_girl_(genre),0
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feral_penetrating_human,0
sonic_the_hedgehog_(comics),3
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looking_at_penis,0
cephalopod,5
clitoral_hood,0
sparkles,0
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black_eyebrows,0
leather,0
blep,0
polearm,0
isabelle_(animal_crossing),4
white_claws,0
maid_uniform,0
purple_clothing,0
slime,0
intraspecies,0
cetacean,5
mountain,0
multicolored_scales,0
crocodylid,5
athletic_male,0
pigtails,0
eevee,5
non-mammal_balls,0
clothed/nude,0
crocodile,5
pink_skin,0
deltarune,3
hellhound,5
brown_nipples,0
father_and_child,0
annoyed,0
wood,0
watersports,0
upskirt,0
against_wall,0
white_scales,0
6:5,7
ball_fondling,0
arms_tied,0
cuddling,0
naughty_face,0
ears_back,0
alternate_color,0
ball,0
exhibitionism,0
spike_(mlp),4
underhoof,0
self_bite,0
forked_tongue,0
grope,0
forced_oral,0
cybernetics,0
blindfold,0
thought_bubble,0
buttplug,0
69_position,0
multiple_images,7
mustela,5
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brown_skin,0
dated,7
bathroom,0
bowser,4
lip_piercing,0
bangs,0
imp,5
crossed_legs,0
mtf_crossgender,0
gallus_(genus),5
uterus,0
monster_hunter,3
chicken,5
barely_visible_genitalia,0
anthro_penetrating_human,0
2009,7
erection_under_clothing,0
symbol,0
daughter,0
black_lips,0
blue_and_white,7
armband,0
notched_ear,0
brother_and_sister,0
bubble,0
humanoid_on_anthro,0
sequence,7
sergal,5
arm_tuft,0
hairband,0
biting_own_lip,0
slap,0
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walking,0
bed_sheet,0
legless,0
multi_nipple,0
car,0
alien_humanoid,5
detailed,7
insect_wings,0
scut_tail,0
circumcised,0
warrior,0
lamp,0
spread_anus,0
teenager,0
upside_down,0
coyote,5
toothed_whale,5
wraps,0
shocked,0
underwater,0
penis_accessory,0
raised_clothing,0
predator/prey,0
tusks,0
lilo_and_stitch,3
fist,0
gore,0
fur_markings,0
computer,0
navel_piercing,0
grey_tail,0
deity,5
shell,0
hot_dogging,0
delphinoid,5
ears_down,0
love,0
feces,0
multi_breast,0
humanoid_pussy,0
smug,0
panties_down,0
sharp_claws,0
pussy_ejaculation,0
leaning_forward,0
nickelodeon,3
eastern_dragon,5
mosaic_censorship,7
hyper_belly,0
source_request,7
candle,0
eyewear_on_head,0
purple_penis,0
anatomically_correct_pussy,0
fur_tuft,0
black_feathers,0
pole,0
one-piece_swimsuit,0
chastity_cage,0
long_image,7
beastars,3
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pinscher,5
yellow_scales,0
lighting,7
braixen,5
dobermann,5
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substance_intoxication,0
goatee,0
shih_tzu,5
curvaceous,0
thick_eyebrows,0
door,0
black_ears,0
tribal,0
alligatorid,5
cloaca,0
apode,0
sperm_cell,0
hourglass_figure,0
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penis_jewelry,0
tail_grab,0
flat_colors,7
eyebrow_piercing,0
archie_comics,3
arachnid,5
bird_feet,0
pattern_legwear,0
experiment_(lilo_and_stitch),5
triceps,0
sandals,0
lube,0
fingering_self,0
trans_(lore),-1
knife,0
fox_mccloud,4
sonic_the_hedgehog_(archie),3
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corvid,5
leggings,0
the_elder_scrolls,3
cock_ring,0
starry_sky,0
wild_boar,5
saliva_on_tongue,0
bat_wings,0
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carrying,0
selfie,0
male_on_humanoid,0
hyper_butt,0
ovum,0
draconcopode,5
lopunny,5
penis_grab,0
gender_transformation,0
scat,0
anal_beads,0
bodysuit,0
flash,7
christmas_clothing,0
black_bottomwear,0
bubble_butt,0
oral_masturbation,0
musical_instrument,0
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slave,0
suspension,0
trembling,0
metal_cuffs,0
bell_collar,0
motion_tweening,7
desk,0
yellow_skin,0
loona_(helluva_boss),4
sheepdog,5
the_amazing_world_of_gumball,3
human_penetrating_feral,0
huge_filesize,7
cave,0
accipitriform,5
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monkey,5
corset,0
sylveon,5
split_form,0
crash_bandicoot_(series),3
blanket,0
male_pred,0
reindeer,5
anal_fingering,0
purple_scales,0
charizard,5
purple_nipples,0
younger_penetrated,0
clothing_pull,0
vaporeon,5
obese_female,0
lepidopteran,5
yellow_tail,0
blurred_background,0
cum_on_hand,0
number,0
pointing,0
cum_on_own_face,0
pixel_(artwork),7
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female_pred,0
sun,0
female/ambiguous,0
ground_squirrel,5
mustache,0
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mohawk,0
younger_female,0
fti_crossgender,0
red_nose,0
shrub,0
cum_everywhere,0
zoroark,5
sanrio,3
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muscular_intersex,0
manly,0
outline,0
wardrobe_malfunction,0
covering_self,0
cum_in_uterus,0
puffy_nipples,0
curtains,0
collar_only,0
hair_over_eyes,0
bird_dog,5
strapon,0
headshot_portrait,7
monotone_hair,0
thick_lips,0
tall_image,7
space,0
muzzle_(object),0
chubby_female,0
breast_size_difference,0
long_neck,0
mother_and_son,0
2_penises,0
beauty_mark,0
feral_penetrating_anthro,0
furry-specific_piercing,0
frill_(anatomy),0
arm_warmers,0
obese_male,0
collaboration,7
urethra,0
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owl,5
striped_legwear,0
five_nights_at_freddy's_2,3
chipmunk,5
leotard,0
cowboy_hat,0
toe_curl,0
antelope,5
grey_clothing,0
controller,0
cum_from_ass,0
thrusting,0
tail_fetish,0
bouncing_penis,0
sunset,0
mega_evolution,5
facial_scar,0
dildo_in_ass,0
hands_on_hips,0
suggestive,0
high_heeled_boots,0
tail_play,0
tentacle_penetration,0
microsoft,3
green_penis,0
parody,0
on_lap,0
speedo,0
neck_bulge,0
mirror,0
swimming_pool,0
2008,7
text_on_topwear,0
anklet,0
chair_position,0
hybrid_genitalia,0
cyborg,5
panties_aside,0
2_toes,0
convenient_censorship,0
oviposition,0
glaceon,5
white_belly,0
mammal_taur,5
fundoshi,0
yordle,5
hanging_breasts,0
larger_feral,0
humiliation,0
regional_form_(pokemon),5
poking_out,0
drunk,0
running,0
android,5
urethral,0
digestion,0
white_shirt,0
visual_novel,0
sisters,0
mollusk_humanoid,5
christmas_headwear,0
hood,0
domestic_sheep,5
black_pupils,0
ftg_crossgender,0
hybrid_penis,0
police_uniform,0
blender_(software),3
one_leg_up,0
the_lion_king,3
clock,0
bovid_humanoid,5
sunlight,0
ball_slap,0
xbox_game_studios,3
mounting,0
two_tone_scales,0
humanoid_on_humanoid,0
underwear_only,0
brown_feathers,0
pink_anus,0
asinus,5
anatomically_correct_penis,0
santa_hat,0
cloven_hooves,0
pattern_background,0
cephalopod_humanoid,5
5:3,7
big_bulge,0
gynomorph/gynomorph,0
raised_topwear,0
donkey,5
dromaeosaurid,5
flower_in_hair,0
irrumatio,0
pokeball,0
bandicoot,5
baseball_cap,0
4_arms,0
brothers,0
laugh,0
molosser,5
cum_on_feet,0
pasties,0
rough_sex,0
spiked_bracelet,0
blue_bottomwear,0
toying_partner,0
fight,0
athletic_female,0
hairy,0
cum_in_hair,0
school_uniform,0
autofellatio,0
stretching,0
patreon_logo,0
skeleton,0
staff,0
black_pussy,0
red_background,0
deep_navel,0
huge_hips,0
roleplay,0
black_underwear,0
whip,0
accipitrid,5
gynomorph_penetrated,0
fishnet_legwear,0
frog,5
hemipenes,0
splatoon,3
red_markings,0
hand_on_chest,0
garter_belt,0
anubian_jackal,5
camera,0
sniffing,0
on_one_leg,0
10:16,7
sweetie_belle_(mlp),4
brown_ears,0
purple_feathers,0
male_focus,0
areola_slip,0
raining,0
queen_chrysalis_(mlp),4
puffy_areola,0
green_nipples,0
voyeur,0
handcuffs,0
badger,5
arms_above_head,0
ball_tuft,0
lagomorph_humanoid,5
muscular_humanoid,0
grey_feathers,0
chimera,5
wingless_dragon,5
looney_tunes,3
egyptian,0
pregnant_female,0
white_penis,0
asphyxiation,0
breast_suck,0
flexible,0
orange_skin,0
orange_tail,0
father_and_son,0
exercise,0
black_markings,0
spider,5
red_topwear,0
spines,0
flared_penis,0
humanoid_penetrating,0
legs_tied,0
transformation_sequence,0
happy_trail,0
food_fetish,0
hymenopteran,5
swallowing,0
spyro_the_dragon,3
glistening_skin,0
tentacle_hair,0
cum_from_pussy,0
oceanic_dolphin,5
bag,0
white_ears,0
dalmatian,5
brown_background,0
worm's-eye_view,0
arcanine,5
human_penetrating_humanoid,0
human_to_anthro,0
restricted_palette,7
breasts_frottage,0
raised_shirt,0
foursome,0
yellow_clothing,0
teal_eyes,0
blue_balls,0
multiple_poses,0
trixie_(mlp),4
wounded,0
goblin,5
soft_vore,0
blue_pussy,0
huge_areola,0
yellow_background,0
grey_scales,0
after_transformation,0
tongue_piercing,0
backpack,0
bare_shoulders,0
pain,0
locker_room,0
unusual_bodily_fluids,0
2007,7
bat_pony,5
hyper_muscles,0
male_penetrating_intersex,0
bench,0
filled_condom,0
brown_clothing,0
blue_sky,0
spiral_eyes,0
mascara,0
petplay,0
black_areola,0
empty_eyes,0
monotone_body,0
witch_hat,0
handgun,0
spear,0
black_legwear,0
stand_and_carry_position,0
collie,5
unusual_tail,0
spanking,0
huge_nipples,0
caught,0
pumpkin,0
butt_from_the_front,0
big_pecs,0
orca,5
multicolored_feathers,0
male_(lore),-1
crotch_tuft,0
gold_jewelry,0
mottled,0
bodypaint,0
cum_pool,0
ahoge,0
grey_balls,0
official_art,7
sign,0
text_on_shirt,0
humanoidized,0
tape,0
brown_balls,0
rubber_clothing,0
fingering_partner,0
collaborative,0
arctic_fox,5
cougar,5
flashing,0
goth,0
cake,0
anseriform,5
ambiguous_species,5
aged_up,0
improvised_sex_toy,0
apple_bloom_(mlp),4
blaziken,5
anatid,5
princess,0
princess_cadance_(mlp),4
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goggles_on_head,0
pink_tail,0
arm_grab,0
palm_tree,0
gaping_pussy,0
scootaloo_(mlp),4
blue_pawpads,0
mating_press,0
ring_(marking),0
brown_penis,0
prick_ears,0
hotpants,0
aircraft,0
breast_fondling,0
rainbow_tail,0
giraffid,5
multi_head,0
japanese_mythology,3
urethral_penetration,0
urine_stream,0
morbidly_obese,0
big_anus,0
organs,0
large_penetration,0
big_macintosh_(mlp),4
romantic_ambiance,0
apple,0
fence,0
sex_toy_in_pussy,0
shining_armor_(mlp),4
shiba_inu,5
holding_food,0
collaborative_sex,0
wetting,0
bent_arm,0
white_horn,0
hands_tied,0
holding_phone,0
logo,0
bikini_top,0
leporid_humanoid,5
muscular_gynomorph,0
blue_markings,0
quads,0
fingerless_(marking),0
countershade_feet,0
headgear_only,0
leash_pull,0
feline_penis,0
wall_(structure),0
foot_lick,0
box,0
rumbling_stomach,0
boxers_(clothing),0
blue_sclera,0
curved_horn,0
rabbit_humanoid,5
consentacles,0
head_grab,0
machairodontine,5
ears_up,0
gift,0
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photo,0
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tiara,0
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3:5,7
twins,0
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raichu,5
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frisk_(undertale),4
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paper,0
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legoshi_(beastars),4
inkling,5
animal_print,0
midna,4
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rose_(flower),0
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trans_woman_(lore),-1
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bioluminescence,0
grey_penis,0
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offscreen_character,0
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brush,0
2:1,7
kirby_(series),3
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2_horns,0
fakemon,3
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grey_nipples,0
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halo,0
rhinocerotoid,5
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animal_sex_toy,0
electricity,0
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animal_dildo,0
udders,0
final_fantasy_xiv,3
twili,5
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house,0
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dragon_ball,3
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yoshi,5
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painting_(artwork),7
2006,7
eagle,5
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leafeon,5
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pooping,0
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cowbell,0
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science_fiction,0
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wolf_o'donnell,4
fairy,5
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ape,5
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bowl,0
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kung_fu_panda,3
nonbinary_(lore),-1
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filth,0
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wine,0
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bird's-eye_view,0
gills,0
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kimono,0
pixel_animation,7
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axe,0
human_pov,0
?!,0
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police,0
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1:2,7
watch,0
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throne,0
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destruction,0
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female_(lore),-1
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icon,7
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sssonic2,1
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barn,0
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beckoning,0
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spanish_text,7
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lemur,5
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fuf,1
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uvula,0
rodent_humanoid,5
pen,0
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ring_gag,0
netflix,3
dark_nipples,0
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unavailable_at_source,7
greninja,5
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crowd,0
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cum_in_clothing,0
potion,0
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pokemon_legends_arceus,3
year,0
matemi,1
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bruised,0
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floor,0
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golden_retriever,5
centaur,5
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fatal_vore,0
cum_on_clothing,0
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rito,5
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anthro_penetrating_humanoid,0
herm_(lore),-1
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black_anus,0
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kittydee,1
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anthro_pred,0
card,0
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graffiti,0
airplane,0
tentacle_monster,5
peach_pussy,0
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kris_(deltarune),4
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public_restroom,0
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garter,0
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calico_cat,5
torture,0
alley,0
river,0
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fisting,0
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master_tigress,4
valve,3
backwards_baseball_cap,0
penguin,5
cuckold,0
ear_fins,0
horn_ring,0
red_bottomwear,0
prosthetic_arm,0
unzipped,0
purple_markings,0
belly_overhang,0
pink_ears,0
feral_pred,0
cute_expression,0
painting,0
base_one_layout,0
brown_stripes,0
camelid,5
ankle_cuffs,0
hare,5
two_tone_clothing,0
aquatic_dragon,5
brown_pawpads,0
horizontal_pupils,0
grabbing_from_behind,0
belly_scales,0
living_inflatable,5
freddy_(fnaf),4
orange_ears,0
hairless,0
suirano,1
cum_on_pussy,0
green_shirt,0
my_hero_academia,3
topless_anthro,0
fart_fetish,0
hip_grab,0
womb_tattoo,0
multicolored_eyes,0
black_eyelashes,0
pirate,0
nipple_tape,0
meat,0
ladder_piercing,0
cookie,0
looking_at_self,0
text_box,0
ball_grab,0
one_row_layout,0
beaver,5
straw,0
male_penetrating_gynomorph,0
unretracted_foreskin,0
kevinsano,1
tail_bow,0
white_face,0
poodle,5
ear_markings,0
anisodactyl,0
zp92,1
gulonine,5
basket,0
andromorph_penetrated,0
spot_color,7
wet_fur,0
bookshelf,0
cyberconnect2,3
humanoid_penetrating_humanoid,0
looking_at_object,0
cover_page,7
parrot,5
pencil_(object),0
medical_instrument,0
generation_9_pokemon,5
directional_arrow,0
dress_lift,0
holding_clothing,0
glowing_markings,0
2005,7
mink,5
photo_background,0
big_biceps,0
busty_feral,0
good_boy,0
villager_(animal_crossing),4
avian_humanoid,5
captured,0
back_boob,0
flying_sweatdrops,0
splits,0
coin,0
unknown_character,4
trunk,0
punk,0
petting,0
brown_spots,0
stuttering,0
visor,0
mouthless,0
4_breasts,0
black_handwear,0
rape_face,0
gauntlets,0
latex_gloves,0
purple_eyeshadow,0
black_wings,0
color_coded,0
pet,0
tailband,0
park,0
charr,5
scp_foundation,3
clothed_female_nude_male,0
dominant_gynomorph,0
bent_leg,0
cervical_penetration,0
doll,0
sketch_page,7
yellow_nipples,0
mane_hair,0
adventure_time,3
butt_shot,0
bracers,0
monotone_genitals,0
pseudo_clothing,0
mature_gynomorph,0
y_anus,0
closed_smile,0
purple_topwear,0
black_tongue,0
violence,0
big_hands,0
skinny_dipping,0
countershade_legs,0
living_vehicle,5
head_wings,0
imminent_death,0
featureless_feet,0
foot_grab,0
translation_check,7
roxanne_wolf_(fnaf),4
sculpture,0
quilava,5
mangle_(fnaf),4
marker_(artwork),7
lake,0
twitchyanimation,1
chinese_text,7
shoulder_pads,0
overwatch,3
holding_character,0
monstrous_humanoid,5
fully_bound,0
forehead_gem,0
pregnant_sex,0
chip_'n_dale_rescue_rangers,3
fusion,0
dark_fur,0
wine_glass,0
green_nose,0
petals,0
butt_squish,0
alien_(franchise),3
pendant,0
leaking_precum,0
fighting_ring,0
digiphilia,0
riding,0
tribal_markings,0
snivy,5
breast_growth,0
collar_tag,0
2_heads,0
purple_balls,0
motion_outline,0
hot_spring,0
hooved_fingers,0
derp_eyes,0
short,0
zombie,5
male_rape,0
scutes,0
same_size_vore,0
tied_hair,0
toe_ring,0
black_gloves,0
one_piece,3
gorilla,5
two_tone_face,0
maleherm,0
husband_and_wife,0
warhammer_(franchise),3
xenarthran,5
pegging,0
korean_text,7
butt_expansion,0
blue_horn,0
singing,0
overweight_intersex,0
blue_wings,0
trans_man_(lore),-1
extended_sound_effect,0
translucent_tentacles,0
for_a_head,0
tem,5
shaking_butt,0
caprine_horn,0
herm/male,0
virginia_opossum,5
3_heads,0
pinkamena_(mlp),4
blinking,0
ninetales,5
fire_manipulation,0
anthro_on_top,0
temmie_(undertale),4
pantyhose,0
minecraft,3
micro_on_macro,0
evil_grin,0
alternate_form,0
plug_insertion,0
pacifier,0
bottomless_female,0
three_frame_sequence,0
blue_theme,7
chica_(fnaf),4
:d,0
green_areola,0
tan_inner_ear,0
panty_pull,0
nurse_clothing,0
hand_on_knee,0
linked_speech_bubble,0
armpit_fetish,0
zzz,0
gatomon,5
asriel_dreemurr_(god_form),4
male_penetrating_anthro,0
daigaijin,1
rubbing,0
featureless_chest,0
easter,3
furball_(artist),1
writing_on_butt,0
atlus,3
middle_finger,0
kaiju,5
lock,0
cum_through,0
interactive,7
leather_topwear,0
dairy_products,0
hands_above_head,0
suspenders,0
larger_ambiguous,0
sweaty_legs,0
genie,5
buttplug_insertion,0
skindentation,0
charmander,5
fishnet_clothing,0
skyrim,3
octarian,5
kirlia,5
kindred_(lol),4
old,0
jungle,0
throat_swabbing,0
nun,0
mae_borowski,4
student,0
gengar,5
forced_orgasm,0
nose_to_anus,0
realistic,0
fog,0
hands_on_thighs,0
balls_in_underwear,0
turian,5
flirting,0
hyper_thighs,0
unimpressed,0
kitchen_appliance,0
frenum_ladder,0
freckles_(artist),1
female_raped,0
dingo,5
sauna,0
mienshao,5
princess_zelda,4
gender_symbol,0
jay_(bird),5
burger,0
night_fury,5
red_claws,0
super_smash_bros.,3
xenomorph,5
fluffy_hair,0
orange_penis,0
brick_wall,0
gynomorph_penetrating_gynomorph,0
exoskeleton,0
on_bench,0
anatomically_correct_anus,0
prequel_adventure,3
holding_beverage,0
t.u.f.f._puppy,3
cryptid-creations,1
beach_ball,0
shirtless,0
serratus,0
herm/female,0
nude_male,0
clothed_male_nude_female,0
megami_tensei,3
new_world_jay,5
looking_through,0
buttplug_in_ass,0
fucking_machine,0
octoling,5
latex_stockings,0
huge_penetration,0
spitfire_(mlp),4
extended_arms,0
paint,0
on_towel,0
holding_cellphone,0
limebreaker,1
bat_(object),0
cricetid,5
red_balls,0
zorua,5
red_footwear,0
tomboy,0
labrador,5
lamb_(lol),4
dominant_feral,0
marijuana,0
classroom,0
inkyfrog,1
super_crown,0
kloudmutt,1
nipple_penetration,0
cityscape,0
michiru_kagemori,4
serperior,5
gym_leader,0
foreshortening,0
long_sleeves,0
egg_vibrator,0
hand_on_own_leg,0
miqo'te,5
smudge_proof,1
buizel,5
castle,0
fox_tail,0
barbell_piercing,0
grey_bottomwear,0
egg_insertion,0
2004,7
holding_belly,0
spread_arms,0
white_wings,0
anubis,4
larger_pred,0
shivering,0
hladilnik,1
submerged_legs,0
male_raping_female,0
belly_expansion,0
color_edit,7
gilda_(mlp),4
chinese_mythology,3
prehensile_tail,0
zeraora,5
cleft_tail,0
floatzel,5
blue_jay,5
spiked_armband,0
brown_eyebrows,0
parallel_sex,0
partially_submerged_legs,0
reins,0
sunset_shimmer_(eg),4
penis_expansion,0
head_fin,0
sheriff_mao_mao_mao,4
planet,0
cum_through_clothing,0
why,0
tom_nook_(animal_crossing),4
purple_pussy,0
dagger,0
levitation,0
knight,0
ninja,0
white_eyebrows,0
pattern_footwear,0
summer,0
cum_on_own_penis,0
gaping_mouth,0
skull_head,0
younger_anthro,0
mastergodai,1
prisoner,0
ball_suck,0
muffet,4
landscape,0
tonguejob,0
sandwich_position,0
tail_clothing,0
striped_footwear,0
broom,0
legband,0
hook,0
holding_tail,0
living_aircraft,5
leather_jacket,0
lava,0
lightning,0
orange_sclera,0
monotone_breasts,0
tights,0
knock-kneed,0
adult_on_cub,0
holding_partner,0
veiny_balls,0
glistening_nose,0
wet_underwear,0
crustacean,5
ambiguous_form,0
gas_mask,0
unusual_genitalia_placement,0
gargoyle,5
warm_colors,7
glistening_fur,0
white_breasts,0
princess_peach,4
female_on_humanoid,0
white_bottomwear,0
butt_jiggle,0
wing_claws,0
lace,0
charmeleon,5
katia_managan,4
carpet,0
cloacal,0
fursuit,0
forest_background,0
3_eyes,0
afterglow,0
stairs,0
arm_tattoo,0
grizzly_bear,5
partially_inside,0
weavile,5
felino,1
canon_couple,0
perspective,0
spotted_tail,0
white_perineum,0
doughnut,0
monotone_face,0
humanoid_penetrating_human,0
breast_lick,0
overweight_gynomorph,0
grey_areola,0
stare,0
perching_position,0
guardians_of_the_galaxy,3
large_group,0
finnick,4
monotone_clothing,0
waiter,0
grey_horn,0
entwined_tails,0
blockage_(layout),0
morbidly_obese_female,0
herm_penetrating,0
all_three_filled,0
pussy_floss,0
hamster,5
male_penetrating_andromorph,0
cheek_bulge,0
legs_in_water,0
spoon_position,0
pearl_(gem),0
kick,0
raised_eyebrows,0
drawing,0
scorbunny,5
6_breasts,0
chalkboard,0
swat_kats,3
human_only,0
saddle,0
tile,0
rivet_(ratchet_and_clank),4
cooking,0
equid_humanoid,5
long_legs,0
rottweiler,5
hisuian_form,5
yellow_topwear,0
banana,0
bit_gag,0
kemono_friends,3
cool_colors,7
oekaki,0
body_part_in_ass,0
pattern_socks,0
blue_border,0
hollow_knight,3
team_cherry,3
love_handles,0
cheerleader,0
baseball_bat,0
gadget_hackwrench,4
information_board,0
fate_(series),3
birthday,0
reptile_humanoid,5
octopodiform,5
branch,0
translucent_penis,0
arm_hair,0
sweaty_breasts,0
crush,0
urine_pool,0
cum_overflow,0
satyr,5
excited,0
princess_twilight_sparkle_(mlp),4
falling,0
thecon,1
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multifur,0
topwear_only,0
hippopotamid,5
tail_aside,0
kirby,4
mightyena,5
muscle_growth,0
flustered,0
glistening_butt,0
darkner,5
pillarbox,7
dark_areola,0
don_bluth,3
human_to_feral,0
penis_tuck,0
aaron_(artist),1
plaid,0
ethereal_hair,0
ankh,0
cum_on_own_tongue,0
pussy_juice_on_penis,0
audible_creampie,0
toy_bonnie_(fnaf),4
jrvanesbroek,1
mario,4
grabbing_legs,0
metal,0
sonic_the_hedgehog_(idw),3
orgasm_denial,0
improvised_dildo,0
avante92,1
rocket_raccoon,4
blue_stripes,0
two_tone_feathers,0
yin_yang,0
ambiguous/ambiguous,0
spacecraft,0
4_eyes,0
looking_through_legs,0
harness_gag,0
striped_socks,0
illumination_entertainment,3
omorashi,0
barely_visible_penis,0
pumps,0
body_part_in_pussy,0
snarling,0
cleavage_overflow,0
lights,0
carmelita_fox,4
tan_ears,0
turtleneck,0
dominatrix,0
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moonlight,0
master,0
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topless_female,0
deep_skin,0
wayforward,3
compression_artifacts,7
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yawg,1
dota,3
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balls_expansion,0
sonic_boom,3
spiked_shell,0
sugarlesspaints,1
distracting_watermark,7
beetle,5
showering,0
exposure_variation,0
battle,0
king_sombra_(mlp),4
101_dalmatians,3
mlp_g5,3
depth_of_field,0
red_horn,0
red_dress,0
winick-lim,1
reshiram,5
papyrus_(undertale),4
lantern,0
suspended_in_midair,0
badge,0
trapped,0
pulling_hair,0
terrier,5
fennekin,5
beverage_can,0
piledriver_position,0
conjoined,0
pubic_mound,0
spyro_reignited_trilogy,3
ovipositor,0
train_position,0
guide_lines,7
zecora_(mlp),4
new_year,3
technical_incest,0
nightgown,0
stealth_sex,0
toho,3
chin_tuft,0
black_glans,0
reverse_missionary_position,0
pussyjob,0
ambiguous_pred,0
pauldron,0
bare_back,0
mineral_fauna,5
on_chair,0
color_coded_text,0
changed_(video_game),3
orange_nipples,0
argon_vile,1
camo,0
countershade_fur,0
katana,0
comparing,0
striped_topwear,0
spandex,0
leaning_on_elbow,0
doujinshi,7
under_covers,0
pink_lips,0
tiptoes,0
jumpsuit,0
soap,0
pajamas,0
gecko,5
alternate_costume,0
impmon,5
russian_text,7
avoid_posting,1
panel_skew,0
simple_shading,7
scent_hound,5
nipple_chain,0
paper_mario,3
green_bottomwear,0
submissive_pov,0
hugging_from_behind,0
face_paint,0
darkened_balls,0
precum_through_clothing,0
caprine_demon,5
multi_horn,0
faceless_anthro,0
pornography,0
belly_inflation,0
grey_border,0
arachnid_humanoid,5
sunglasses_on_head,0
fetlocks,0
koopaling,4
coffee_mug,0
penis_shaped_bulge,0
spaniel,5
dream,0
holding_book,0
oral_invitation,0
shantae_(series),3
frilly_underwear,0
spoon,0
penis_worship,0
humanoid_focus,0
dirty,0
bread,0
hairy_balls,0
mythological_firebird,5
stocks,0
las_lindas,3
bow_panties,0
mistletoe,0
green_markings,0
contact_onomatopoeia,0
text_on_headwear,0
brown_bottomwear,0
multicolored_antennae,0
sweaty_thighs,0
tent,0
clitoral_masturbation,0
vibrator_on_penis,0
breath_powers,0
balto_(film),3
blazblue,3
holding_bottle,0
dorsal_fin,0
christmas_lights,0
public_exposure,0
bottomless_anthro,0
night_elf,5
jackalope,5
punch,0
party,0
twitter,3
tail_pull,0
gym_clothing,0
retsuko,4
soda,0
raised_inner_eyebrows,0
cyclops,5
gui,0
haida,4
back_markings,0
family,0
overweight_humanoid,0
maid_marian,4
tail_masturbation,0
becoming_erect,0
ceratopsian,5
godzilla_(series),3
stage,0
partially_colored,7
ship,0
torn_topwear,0
jaeh,1
haru_(beastars),4
digimon_ghost_game,3
nipple_barbell,0
doneru,1
kirin,5
dark_hair,0
question,0
monotone_areola,0
scratches,0
double_leg_grab,0
oryctolagus,5
insect_humanoid,5
little_tail_bronx,3
simba,4
pencil_skirt,0
regular_show,3
all_the_way_through,0
trout_(artist),1
garchomp,5
atryl,1
public_transportation,0
white_sheath,0
restroom_stall,0
shaymin,5
penis_through_fly,0
common_hippopotamus,5
monitor,0
statue,0
advertisement,0
oni,5
towel_only,0
max_blackrabbit,1
herm_penetrated,0
cutie_mark_crusaders_(mlp),4
black_mane,0
anthro_pov,0
nidoqueen,5
domestic_rabbit,5
boxing_gloves,0
smaller_intersex,0
green_pussy,0
felicia_(darkstalkers),4
bowsette_meme,3
petruz_(copyright),3
dragon_ball_super,3
boat,0
chinese_clothing,0
dragonite,5
smug_face,0
smaller_humanoid,0
ambient_arthropod,5
nude_female,0
screencap,0
cute_eyes,0
silver_soul,4
rainbow_pride_colors,0
alternate_universe,0
pink_shirt,0
anthro_prey,0
nala,4
tokifuji,1
equine_humanoid,5
knee_pads,0
hand_on_arm,0
klonoa_(series),3
covering_mouth,0
living_plushie,5
walk-in,0
chain_leash,0
playing_music,0
red_collar,0
cetacean_penis,0
squeezing,0
flinters,1
slugbox,1
explosives,0
metallic_body,0
field,0
wing_boner,0
falconid,5
office_chair,0
albino,0
feral_penetrating_humanoid,0
quin-nsfw,1
pregnant_male,0
micro_bikini,0
strawberry,0
brown_claws,0
unusual_penis_placement,0
tickle_torture,0
sepia,7
falcon,5
tan_belly,0
pink_sclera,0
unguligrade_anthro,0
harpy,5
8:7,7
2003,7
purple_underwear,0
laboratory,0
grey_countershading,0
chalo,1
tube_top,0
imminent_anal,0
can,0
arm_wraps,0
pussy_blush,0
strip_club,0
electrostimulation,0
my_little_pony:_the_movie_(2017),3
virtual_youtuber,3
colorful,7
syringe,0
athletic_wear,0
double_handjob,0
toddler,0
chest_markings,0
gradient_hair,0
autumn,0
key,0
bad_parenting,0
megami_tensei_persona,3
human_penetrating_female,0
primarina,5
elder_dragon,5
handpaw,0
white_socks,0
foot_on_face,0
semi_incest,0
lifted,0
ram_horns,0
yellow_horn,0
draft_horse,5
dasyuromorph,5
larger_humanoid,0
plap,0
feet_on_balls,0
sing_(movie),3
fart_cloud,0
bikini_thong,0
implied_transformation,0
four_frame_sequence,0
intimate,0
detached_sleeves,0
button_(fastener),0
corpse,0
artist_logo,7
red_lips,0
fossil_pokémon,5
humanoid_face,0
big_pussy,0
small_balls,0
holding_controller,0
mottled_genitalia,0
screen_face,0
meowstic,5
insomniacovrlrd,1
projectile_lactation,0
street,0
playful,0
head_frill,0
long_eyelashes,0
orange_balls,0
portal,0
tea,0
shinx,5
nightclub,0
king,0
cynder,4
headset,0
ampharos,5
knotted_equine_penis,0
living_condom,0
serval,5
urine_in_mouth,0
pixar,3
alaskan_malamute,5
tail_mouth,0
scp-1471,4
mind_break,0
birth,0
hair_grab,0
black_bra,0
wood_floor,0
flygon,5
magic:_the_gathering,3
clitoral_fingering,0
abuse,0
sleep_sex,0
round_ears,0
sharp_nails,0
black_thigh_highs,0
ambiguous_on_human,0
nipple_lick,0
wizard_hat,0
scp-1471-a,4
lens_flare,0
symbol-shaped_pupils,0
replica_(artist),1
bareback,0
big_knot,0
conjoined_speech_bubble,0
harem,0
audino,5
wrinkled_feet,0
purple_sclera,0
flamedramon,5
meesh,1
spongebob_squarepants,3
hyper_nipples,0
submissive_intersex,0
raised_calf,0
monotone_penis,0
kadath,1
muzzle_piercing,0
chromatic_aberration,0
sonic_riders,3
barrel,0
echinoderm,5
melting,0
wrench,0
dipstick_antennae,0
hairless_cat,5
potbelly,0
monotone_nose,0
balls_on_face,0
twitching,0
hazbin_hotel,3
pointy_speech_bubble,0
picture_frame,0
yu-gi-oh!,3
explosion,0
twinkle-sez,1
triceratops,5
spider_humanoid,5
hole_(anatomy),0
smaller_prey,0
rug,0
brown_mane,0
vowelless_reaction,0
outside_border,0
toothless,4
twincest,0
hyper_bulge,0
colored_fire,0
prehensile_penis,0
cloacal_penis,0
side-tie_bikini,0
egyptian_clothing,0
impact_onomatopoeia,0
houndoom,5
shoes_only,0
overweight_feral,0
over_edge,0
wire,0
prison,0
motorcycle,0
muscular_legs,0
veil,0
white_diaper,0
hippogriff,5
final_fantasy_ix,3
library,0
where_is_your_god_now,0
butt_sniffing,0
fishnet_topwear,0
surfboard,0
visual_pun,0
latios,5
horizontal_blockage,0
juno_(beastars),4
breastplate,0
burmecian,5
carrying_partner,0
klonoa,4
mr._wolf_(the_bad_guys),4
wand_vibrator,0
deathclaw,5
louis_(beastars),4
snout_markings,0
torso_grab,0
crotch_lines,0
big_clitoris,0
black_dress,0
my_life_as_a_teenage_robot,3
clipboard,0
flesh_whiskers,0
belly_tuft,0
chest_spike,0
jingle_bell,0
sea_eagle,5
veemon,5
holding_both_legs,0
pantherine_humanoid,5
erobos,1
uraeus,0
absolute_territory,0
talespin,3
soarin_(mlp),4
feraligatr,5
fighting_pose,0
jack_savage,4
holding_glass,0
unguligrade,0
horn_grab,0
blue_inner_ear,0
huge_pecs,0
social_nudity,0
fire_breathing,0
deep_rimming,0
holly_(plant),0
glacierclear,1
brown_topwear,0
wedding_ring,0
story_at_source,7
harem_outfit,0
holding_head,0
pipe,0
wavy_hair,0
black_hooves,0
fork,0
©,0
hyper_pregnancy,0
shorts_down,0
green_yoshi,5
face_lick,0
dressing,0
crotchless_clothing,0
unconscious,0
straight_legs,0
zourik,1
brown_horn,0
peafowl,5
avali,5
maud_pie_(mlp),4
cloacal_penetration,0
red_areola,0
transparent_sex_toy,0
anthro_on_bottom,0
military_uniform,0
goat_demon,5
lavenderpandy,1
shocked_expression,0
freedom_planet,3
horizontal_diphallism,0
ruins,0
morning,0
flaming_hair,0
crystal-for-ever,1
short_story,0
merging,0
precum_on_penis,0
ratchet,4
banjo-kazooie,3
neck_bite,0
caprine_humanoid,5
willing_prey,0
loose_feather,0
miss_kobayashi's_dragon_maid,3
leucistic,0
gideon,1
fluff-kevlar,1
object_head,5
good_girl,0
scenery,0
purple_ears,0
plant_pot,0
five_nights_at_freddy's_3,3
doctor,0
soldier,0
purple_claws,0
female_pov,0
cross-eyed,0
twerking,0
knee_socks,0
black_shorts,0
:<,0
blood_elf,5
shantae,4
tailjob,0
bib,0
bigdad,1
bald_eagle,5
mawile,5
dumbbell,0
bad_metadata,7
somescrub,1
diane_foxington,4
shrinking,0
sprigatito,5
jailbird,1
impregnation_request,0
mottled_penis,0
tentacle_in_ass,0
ducktales,3
nuzzling,0
septum_ring,0
winking_at_viewer,0
phoenix,5
crash_bandicoot,4
dagasi,1
pink_balls,0
machoke,5
torn_legwear,0
elden_ring,3
zero_pictured,0
lizardlars,1
mandibles,0
aged_down,0
mongoose,5
imperatorcaesar,1
lifting,0
white_mane,0
green_underwear,0
bandeau,0
feretta,1
male_symbol,0
nurse_uniform,0
malacostracan,5
raphael_(tmnt),4
monster_musume,3
samus_aran,4
sphynx_(cat),5
stomping,0
geeflakes,1
silver_the_hedgehog,4
lip_ring,0
zora,5
detached_sheath,0
two_doms_one_sub,0
butt_heart,0
brother_penetrating_sister,0
marina_(splatoon),4
tail_piercing,0
text_on_hat,0
peg_pete,4
wind,0
hearts_around_body,0
countershade_neck,0
blue_highlights,0
mleonheart,1
united_states_of_america,0
inside_car,0
ruaidri,1
cum_on_anus,0
red_lipstick,0
hands_together,0
bike_shorts,0
jojo's_bizarre_adventure,3
id_software,3
artificial_intelligence,0
samurott,5
stomach_acid,0
sparrow_(artist),1
james_howard,1
raised_bottomwear,0
red_ears,0
bottomwear_pull,0
competition,0
pet_praise,0
beret,0
jerseydevil,1
mihoyo,3
red_glans,0
public_humiliation,0
clothed_anthro,0
vombatiform,5
ejaculating_cum,0
malo,5
handwear_only,0
doom_(series),3
night_sky,0
road,0
flower_creature,5
pattern_armwear,0
fenneko,4
slug,5
internal_monologue,0
health_bar,0
marten,5
imprint,0
:o,0
anibaruthecat,1
team_fortress_2,3
toenails,0
latex_clothing,0
pichu,5
tail_bondage,0
knees_together,0
condom_in_mouth,0
duel_monster,5
chara_(undertale),4
detailed_fur,0
step_pose,0
gargoyles,3
head_first,0
autotitfuck,0
anal_tugging,0
raised_skirt,0
alolan_vulpix,5
ōkami,3
stuffing,0
worship,0
huge_tail,0
friends,0
bloated,0
straitjacket,0
chaps,0
shark_tail,0
round_glasses,0
feathered_crest,0
hand_on_cheek,0
legs_behind_head,0
fox_spirit,0
cherry,0
gnoll,5
instant_loss_2koma,3
poolside,0
princess_ember_(mlp),4
sex_slave,0
ear_frill,0
ahri_(lol),4
floral,0
looking_forward,0
vertical_splits,0
textured_background,0
straight_hair,0
saluki,5
cupcake,0
tawna_bandicoot,4
teapot_(body_type),0
mutual_masturbation,0
noelle_holiday,4
justmegabenewell,1
lustylamb,1
human_prey,0
ambient_insect,5
colored_seam_underwear,0
campfire,0
against_natural_surface,0
purple_horn,0
bedding_background,0
furret,5
female_penetrating_male,0
feral_prey,0
human_on_bottom,0
multi_leg,0
suntan,0
guts,0
bea_santello,4
hands_on_own_legs,0
columbid,5
stirrup_socks,0
yo-kai_watch,3
wand,0
train,0
dominant_human,0
<3_censor,0
requiem_shark,5
red_headwear,0
ponyplay,0
tan_feathers,0
hyper_hips,0
horizontal_cloaca,0
bonbon_(mlp),4
the_weaver,1
extreme_size_difference,0
legwear_only,0
bare_chest,0
neck_grab,0
jenny_wakeman,4
big_nose,0
minerva_mink,4
transformation_through_sex,0
flashw,1
hyaku_(artist),1
sex_toy_background,0
virgin,0
baggy_clothing,0
frogtied,0
dark_souls,3
purse,0
flip_flops,0
snaggle_tooth,0
shirt_up,0
pie,0
leonardo_(tmnt),4
female_penetrating_female,0
ber00,1
bulging_breasts,0
species_name_in_dialogue,0
doorway,0
urine_on_ground,0
thunderouserections,1
pattern_stockings,0
after_vaginal,0
obscured_penetration,0
metro-goldwyn-mayer,3
attack,0
cosmic_hair,0
prehensile_feet,0
wolfdog,5
braces,0
raven,5
scooby-doo_(series),3
yoga,0
7:4,7
bottom_heavy,0
flesh_tunnel,0
anal_fisting,0
fifi_la_fume,4
store,0
miso_souperstar,1
teacher_and_student,0
australian_shepherd,5
brainwashing,0
moxxie_(helluva_boss),4
speed_bump_position,0
blue_panties,0
thinking,0
tristana_(lol),4
inteleon,5
sleeveless_shirt,0
teddy_bear,0
breasts_on_glass,0
cnidarian,5
goo_humanoid,5
big_hair,0
ballbusting,0
feathered_dinosaur,5
small_pupils,0
black_headwear,0
tan_line,0
grey_shirt,0
spacesuit,0
thumbnail,7
multicolored_topwear,0
gym_bottomwear,0
holding_sex_toy,0
glistening_balls,0
unwanted_ejaculation,0
meowth,5
tight_foreskin,0
arm_around_shoulders,0
grey_beak,0
grey_inner_ear,0
grandfathered_content,7
striped_armwear,0
bulbasaur,5
paintbrush,0
black_lipstick,0
leopardus,5
beak_fetish,0
growling,0
suction_cup,0
after_rape,0
tail_between_legs,0
succubus,5
2002,7
red_shoes,0
log,0
cum_on_bed,0
mostly_offscreen_character,0
watsup,1
long_claws,0
armless,0
red_cheeks,0
snakebite_piercing,0
muzzle_(marking),0
ultra_beast,5
sandwich_(food),0
ring-tailed_lemur,5
folded,0
sky_forme_shaymin,5
holding_thigh,0
canine_dildo,0
akita,5
symbiote,5
basitin,5
dirt,0
throat,0
pilosan,5
clothed_male,0
shelf,0
belt_buckle,0
potted_plant,0
encasement,0
wristwatch,0
red_nails,0
anal_beads_in_ass,0
print_underwear,0
helltaker,3
gym_shorts,0
viverrid,5
john_joseco,1
freya_crescent,4
joaoppereiraus,1
soiled_diaper,0
bgn,1
grumpy,0
prolapse,0
text_emphasis,0
sindoll,1
grabbing,0
skull_mask,0
hioshiru,1
five_frame_image,0
white_spots,0
black_stockings,0
wrists_tied,0
sploot,0
stoat,5
aloha_shirt,0
hard_translated,7
flag_(object),0
dragon_quest,3
stinger,0
sideburns,0
tunic,0
tenshigarden,1
bored,0
dreamkeepers,3
pearl_necklace,0
bite_mark,0
great_dane,5
degradation,0
musk_clouds,0
warframe,3
meerkat,5
glamrock_freddy_(fnaf),4
red_highlights,0
slosh,0
bridge_piercing,0
nervous_smile,0
sonic_adventure,3
sciurid_humanoid,5
two_tone_wings,0
ankk,1
orange_topwear,0
kihu,1
snapchat,3
tagme,7
comic_sans,7
waking_up,0
hand_on_own_thigh,0
poppy_(lol),4
kass_(tloz),4
lynian,5
daddy_kink,0
pink_nails,0
purple_theme,7
body_part_in_mouth,0
pouch_(anatomy),0
barely_visible_balls,0
keyboard,0
farm,0
golden_shower,0
open_mouth_gag,0
body_size_growth,0
after_kiss,0
arm_pull,0
kitsune_youkai,1
twilight_velvet_(mlp),4
oselotti,1
neon,0
remote_control,0
extreme_penetration,0
queen,0
darkened_penis,0
spotlight,0
folded_wings,0
sefeiren,1
springtrap_(fnaf),4
partially_translated,7
magazine,0
spider_web,0
cosplay_pikachu_(costume),0
decapoda,5
facial_spikes,0
striped_stockings,0
japanese_school_uniform,0
kitty_katswell,4
penis_everywhere,0
murid_humanoid,5
melon,0
grabbing_thighs,0
genital_focus,0
wet_topwear,0
breast_smother,0
crotch_shot,0
frilly_panties,0
felyne,5
beak_play,0
zero_suit,0
gynomorph/herm,0
colored_pencil_(artwork),7
russian,0
parasite,0
murine_humanoid,5
muffled,0
digital_creature,5
hiding,0
decapodiform,5
red_wings,0
cheerilee_(mlp),4
arctic_wolf,5
gloves_only,0
warhammer_fantasy,3
protagonist_(tas),4
hyper_anus,0
dewlap_(anatomy),0
cactus,0
wrapped_condom,0
black_shoes,0
decapitation,0
tag_panic,7
dragonair,5
modeseven,1
pince-nez,0
sly_cooper,4
stretched_anus,0
1-upclock,1
receiving_pov,0
dewott,5
caracal_(genus),5
barely_visible_anus,0
ossicone,0
backlash91,1
purple_pawpads,0
oral_knotting,0
knotted_humanoid_penis,0
beak_sex,0
after_oral,0
asterozoan,5
striped_skunk,5
yellow_nose,0
marjani,1
digestion_noises,0
ms_paint_adventures,3
blue_nails,0
double_vaginal,0
fireworks,0
text_on_tank_top,0
chief_bogo,4
blue_mane,0
wrestler,0
starfish,5
white_stripes,0
homestuck,3
ghost_hands,0
excessive_precum,0
mizzyam,1
big_teats,0
nidoking,5
flutterbat_(mlp),4
on_top_of,0
under_table,0
parappa_the_rapper,3
vallhund,1
black_swimwear,0
knee_tuft,0
red_kerchief,0
stolas_(helluva_boss),4
redishdragie,1
beads,0
orange_markings,0
theboogie,1
101_dalmatian_street,3
jar,0
natural_breasts,0
amaterasu,4
upskirt_sex,0
older_intersex,0
casual_sex,0
soulcentinel,1
morbidly_obese_male,0
restaurant,0
holding_writing_utensil,0
tailless,0
royal_guard_(mlp),4
backsash,1
truck_(vehicle),0
tan_nipples,0
fanged_wyvern,5
rainbow_symbol,0
godzilla,4
swimwear_aside,0
millie_(helluva_boss),4
multi_frame_sequence,0
skye_(zootopia),4
forked_tail,0
cane,0
pink_markings,0
kneeling_oral_position,0
hand_on_neck,0
blue_shorts,0
alternate_hairstyle,0
chubby_cheeks,0
plaid_clothing,0
autopenetration,0
tickling_feet,0
string_bikini,0
sitting_on_bed,0
penis_in_face,0
naruto,3
fairy_tales,3
derived_sound_effect,0
dark_nose,0
against_tree,0
cloth,0
true_parrot,5
glare,0
3mangos,1
viroveteruscy,1
wet_shirt,0
beakjob,0
heart_clothing,0
head_in_crotch,0
circlet,0
simultaneous_orgasms,0
bristol,1
playing,0
gynomorph_(lore),-1
cowboy,0
multi_balls,0
chest_fur,0
lari,5
these_aren't_my_glasses,0
calves_up,0
lipstick_on_penis,0
possession,0
blue_footwear,0
female_rimming_male,0
caracal,5
ducktales_(2017),3
blue_lips,0
mooning,0
living_room,0
killing,0
barely_contained,0
panties_only,0
nurse_headwear,0
alpaca,5
white_outline,0
ear_bite,0
male_penetrating_feral,0
covering_face,0
monocle,0
glowing_body,0
sink,0
scuted_arms,0
koala,5
easter_egg,0
midday_lycanroc,5
intersex_on_bottom,0
fuel_(artist),1
chinchillid,5
plant_hair,0
yellow_claws,0
spiked_penis,0
purple_wings,0
sonic_unleashed,3
f-r95,1
belly_grab,0
porcupine,5
twin_bows,0
tablet,0
badumsquish,1
young_domination,0
white_dress,0
audience,0
tea_cup,0
orange_nose,0
tentaclejob,0
patto,1
mature_feral,0
pink_claws,0
maid_headdress,0
citrinelle,1
lumineko,1
inanimate_transformation,0
dust:_an_elysian_tail,3
squirrel_humanoid,5
pawprint_marking,0
smug_grin,0
green_ears,0
pink_bottomwear,0
wedding_dress,0
noivern,5
dragon_ball_z,3
cheese,0
torn_shirt,0
halloween_costume,0
reduced_sound_effect,0
holstein_friesian_cattle,5
hooves-art,1
foreskin_play,0
vavacung,1
study_partners,3
personal_grooming,0
brown_face,0
business_suit,0
ribbed_penis,0
moritaka,4
hearts_around_head,0
underwater_sex,0
plushophilia,0
pink_theme,7
ayaka,1
marauder6272,1
sweaty_penis,0
crescent_moon,0
giga,0
multicolored_mane,0
nude_beach,0
troll,5
panty_shot,0
badgerclops,4
overweight_ambiguous,0
badgerben,1
e-01,1
curled_horns,0
feather_hair,0
penis_kissing,0
superbunnygt,1
viper,5
saltyxodium,1
cold-blooded-twilight,1
water_bottle,0
black_bear,5
michelangelo_(tmnt),4
fur_growth,0
cage,0
floating_hands,0
younger_human,0
trash_can,0
yveltal,5
red_theme,7
crossover_cosplay,0
odd_taxi,3
base_two_layout,0
slippers,0
hose,0
flower_petals,0
aoizuri,1
garland,0
warfare_machine,3
clothed_male_nude_male,0
rosy_cheeks,0
pom_poms,0
black_inner_ear,0
monotone_eyebrows,0
roly,1
dewclaw,0
space_jam,3
tan_clothing,0
fluffy_chest,0
tall,0
made_in_abyss,3
starbound,3
lego,3
vimhomeless,1
skidd,1
eldiman,1
pec_grasp,0
leg_hair,0
green_claws,0
green_theme,7
human_on_human,0
labia_piercing,0
mega_lopunny,5
blue_text,0
pentacle,0
feline_pussy,0
drone,0
male_lactation,0
takara_tomy,3
double_thigh_grab,0
ok_k.o.!_let's_be_heroes,3
between_toes,0
reverse_stand_and_carry_position,0
prison_guard_position,0
running_makeup,0
morgana_(persona),4
thin_tail,0
wave,0
fishnet_armwear,0
pomeranian,5
holding_clipboard,0
cum_between_breasts,0
transformers,3
basketball_(ball),0
penis_hug,0
bound_together,0
torch,0
scolipede,5
wavy_mouth,0
treasure,0
ritts,1
stars_and_stripes,0
humanoid_penetrating_anthro,0
kacey,1
moose,5
chinchilla,5
pikachu_libre,4
anus_only,0
light_beam,0
watermelon,0
multiple_prey,0
sticks_the_jungle_badger,4
purple_bottomwear,0
extreme_french_kiss,0
flick_(animal_crossing),4
ocelot,5
tempest_shadow_(mlp),4
liquid,0
tile_floor,0
shotgun,0
barbell,0
nipple_tuft,0
ambient_sealife,5
os,1
lactating_through_clothing,0
head_tilt,0
tailzkim,1
beach_umbrella,0
raymond_(animal_crossing),4
transparent_border,0
wolfblade,1
holding_arm,0
absorption_vore,0
pond,0
same-species_bestiality,0
grey_markings,0
long_nails,0
intersex/ambiguous,0
big_sheath,0
short_male,0
ponification,0
feather_tuft,0
bugs_bunny,4
glowing_nipples,0
spiral,0
waaifu_(arknights),4
pia-sama,1
cum_explosion,0
phalangeriform,5
sepiruth,1
mud,0
sitting_on_chair,0
party_hat,0
sun_hat,0
police_officer,0
seal,5
six-stripe_rainbow_pride_colors,0
brown_footwear,0
hornet_(hollow_knight),4
gideon_grey,4
chopsticks,0
serafuku,0
brown_inner_ear,0
peeping,0
braeburn_(mlp),4
joelasko,1
grey_pants,0
echo_project,3
nude_anthro,0
slit_penetration,0
zwitterkitsune,1
big_dildo,0
evolutionary_family,0
glowing_cum,0
kissing_bough,0
elpatrixf,1
user_avatar,0
horkeu_kamui_(tas),4
belly_rub,0
tribal_spellcaster,0
pattern_swimwear,0
the_truth,0
small_head,0
wet_hair,0
feral_focus,0
bottomwear_aside,0
sex_in_water,0
skaven,5
chinese_dress,0
diaper_fetish,0
steven_universe,3
half_naked,0
teal_hair,0
black_armwear,0
black_hoodie,0
cartoon_hangover,3
purple_stripes,0
transparent_dildo,0
legs_in_air,0
head_turned,0
comparing_penis,0
scrafty,5
blood_on_face,0
tiger_shark,5
double_v_sign,0
happy_tree_friends,3
bunnie_rabbot,4
sexual_competition,0
cuphead_(game),3
chinese_zodiac,3
blue_glans,0
throbbing_balls,0
fake_tail,0
comic_page,0
cumshot_in_mouth,0
smaller_gynomorph,0
litten,5
yellow_tongue,0
shower_head,0
nose_horn,0
narehate,5
wide_stance,0
cherry_blossom,0
pregnant_intersex,0
do_not_distribute,0
hitachi_magic_wand,0
rick_griffin,1
canid_taur,5
ankama,3
intestines,0
bloodshot_eyes,0
mythological_sphinx,5
penis_towards_viewer,0
lounging,0
yasuyori,4
green_pawpads,0
paddle,0
banjo_(banjo-kazooie),4
tan_face,0
tabby_cat,5
final_fantasy_vii,3
boop,0
fully_submerged,0
wind_waker,3
veiny_knot,0
auto_penis_lick,0
internal_vaginal,0
driving,0
flowey_the_flower,4
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resident_evil,3
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red_text,0
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aura,0
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onesie,0
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cum_on_head,0
harness_ball_gag,0
asthexiancal,1
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penis_in_penis,0
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rov,1
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wariza,0
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thefuckingdevil,1
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whisker_markings,0
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gazelle_(zootopia),4
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^_^,0
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conker's_bad_fur_day,3
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0r0ch1,1
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mudkip,5
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oil,0
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tojo_the_thief,1
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jack_(beastars),4
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fupa,0
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science,0
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python,5
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mommy_kink,0
mattel,3
tail_hug,0
faint,1
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haychel,1
drako1997,1
truegrave9,1
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au_ra,5
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politics,0
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wakfu,3
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slideshow,7
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gas,0
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vibrator_in_ass,0
penis_on_head,0
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pit_viper,5
laser,0
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toto_draw,1
chips_(food),0
studio_klondike,3
cardboard,0
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li_li_stormstout,4
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red_pandaren,5
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jet,0
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flurry_heart_(mlp),4
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lineup,0
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x-com,3
print_panties,0
cyancapsule,1
arms_by_side,0
sexfight,0
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blues64,1
posexe,1
long_horn,0
hand_on_own_hip,0
scabbard,0
laboratory_glassware,0
collaborative_titfuck,0
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winnie_the_pooh_(franchise),3
iskra,1
keyhole_underwear,0
loveslove,1
nurinaki,1
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street_fighter,3
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star_vs._the_forces_of_evil,3
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notebook,0
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koul,1
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my_little_pony_(idw),3
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9:14,7
javkiller,1
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artica_sparkle,4
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princess_connect!_re:dive,3
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mr.pink,1
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e621,3
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rwby,3
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lin_(changed),4
8:11,7
the_xing1,1
jinu,1
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diadorin,1
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iggi,1
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purring,0
tangle_the_lemur,4
narse,1
mina_mongoose,4
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sqoon,1
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soccer,0
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agumon,5
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tehbuttercookie,1
leaf_tail,0
whipped_cream,0
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dust,0
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aardwolf,5
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caption,0
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majin,5
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lekku_(anatomy),0
ori,4
danaume,1
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rainbowsprinklesart,1
yellow_face,0
vertical_cloaca,0
ivysaur,5
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may_(pokémon),4
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porsha_crystal,4
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black-rat,1
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armpit_lick,0
mega_charizard_x,5
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shooting,0
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longblueclaw,1
grey_fox,5
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viejillox,1
kanashiipanda,1
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green_stripes,0
destiny_(video_game),3
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teemo_the_yiffer,4
vivarium,0
kokuhane,1
high_heeled_sandals,0
howl,0
zeklullaby,1
kenno_arkkan,1
smell,0
karno,1
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sticker,0
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max_(sam_and_max),4
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awkward,0
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ms._harshwhinny_(mlp),4
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catty_(undertale),4
coach,0
ratcha,1
caress,0
vomit,0
nana_gel,1
freedom_planet_2,3
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ravoilie,1
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karabiner,1
balto,4
magic_cat_academy,3
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monotone_wings,0
chunie,1
sweatband,0
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san_ruishin,1
hattonslayden,1
1999,7
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roxanne_(goof_troop),4
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anthro_on_taur,0
trias,1
bastionshadowpaw,1
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volleyball_(ball),0
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feralise,1
negoya,1
enema,0
grypwolf,1
five_nights_at_freddy's_world,3
rayman_(series),3
courage_the_cowardly_dog,3
hairbrush,0
medusozoan,5
sketchybug,1
melthecannibal,1
momo_(google),4
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birdo,5
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whip_mark,0
weskers,1
penis_tongue,0
garfield_(series),3
sponge,0
cigarette_holder,0
meloetta_(aria_form),5
orange_shirt,0
bakuhaku,1
looking_down_at_partner,0
sonic_the_fighters,3
sloth,5
wrinkles,0
striped_thigh_highs,0
innersloth,3
among_us,3
vanny_(fnaf),4
eyes_mostly_closed,0
nexus,1
syuro,1
titfuck_under_clothes,0
gem_(species),5
volcano,0
heresy_(artist),1
sunny_way,1
scowl,0
flower_hair_accessory,0
pussy_juice_on_ground,0
muscle_tone,0
intersex_on_feral,0
kyatto_ninden_teyandee,3
oposa,1
elvofirida,1
simple_coloring,0
autocunnilingus,0
aka6,1
hotel,0
marci_hetson,4
scrunchy_face,0
training,0
finn_the_human,4
currency_symbol,0
daftpatriot,1
yellow_stripes,0
tapering_tail,0
averyshadydolphin,1
keadonger,1
hard_hat,0
ike_marshall,1
date,0
pushing,0
coconut,0
kissing_cheek,0
themefinland,1
handbag,0
cow_bikini,0
print_shirt,0
brandy_and_mr._whiskers,3
puzzle_and_dragons,3
mad_rat_(character),4
basset_hound,5
hairy_pussy,0
torogao,0
lotus_(mlp),4
big_bad_wolf,4
ambiguous_pov,0
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bandaged_arm,0
aloe_(mlp),4
azzilan,4
harnny,1
micro_organism_(organism),5
planted_leg,0
long_story,0
steampunk,0
whitekitten,3
forearms,0
grimart,1
reign-2004,1
rocko's_modern_life,3
flesh_creature,5
black_paws,0
devil_horns_(gesture),0
fti_transformation,0
arm_by_side,0
disposal,0
artdecade,1
fries,0
smelly_feet,0
dawn_(pokémon),4
parasitedeath,1
collage,0
red_gloves,0
zeriara,1
wearing_diaper,0
ambiguous_penetrating,0
cowlick,0
stretch_marks,0
bangle,0
white_canine_(marimo),4
lavilovi,1
cramped,0
alanscampos,1
roller_skates,0
pine_marten,5
clothing_cord,0
chest_grab,0
implied_oral,0
zooshi,1
t0l0k,1
liara_t'soni,4
dipstick_limbs,0
yellow_belly,0
super_planet_dolan,3
pochincoff,1
orange_horn,0
light_bondage,0
swing,0
animal_tail,0
skyrim_werewolf,4
champagne,0
magenta7,1
cannon,0
radio,0
butt_smother,0
angry_eyes,0
amicus_(adastra),4
screentone,0
leather_straps,0
inkling_girl,4
totesfleisch8,1
penis_ribbon,0
overlord_(series),3
fab3716,1
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blood_in_mouth,0
long_snout,0
puppet,0
parakeet,5
bikini_aside,0
cub_domination,0
mimikyu,5
nakoo,1
tight_orifice,0
feather_in_hair,0
dark_penis,0
purple_yoshi_draws,1
lovelesskiax,1
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mechanic,0
frying_pan,0
mrs._katswell,4
red_crown_(cult_of_the_lamb),4
nurse_redheart_(mlp),4
chakat,5
daybreaker_(mlp),4
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kig-yar,5
unborn_kicking,0
nobby_(artist),1
kyaru_(princess_connect!),4
doug_winger,1
trap_(contrivance),0
nightmare_rarity_(idw),4
dot_warner,4
diamond_dog_(mlp),5
marthedog,1
ponytail_ears,0
photomorph,7
tail_heart,0
wife,0
burning,0
ipan,1
14:9,7
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zerofox1000,1
moki,1
nika_sharkeh,4
pink_mane,0
world_flipper,3
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rapidash,5
selenophile,1
hair_mouth,0
star_marking,0
eon_duo,0
azur_lane,3
double_chin,0
dr._eggman,4
town,0
big_the_cat,4
strapon_sex,0
hand_in_pants,0
nintendo_console,0
reysi,1
glopossum,1
vkontakte,3
fizz_(lol),4
caviid,5
red_bow,0
swimwear_pull,0
sex_toy_in_mouth,0
hunter,0
hisuian_typhlosion,5
lamnid,5
xilrayne,1
foot_on_head,0
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drawstring,0
small_nose,0
fur_pattern,0
jealous,0
octavia_(helluva_boss),4
army,0
hands_on_belly,0
samurai,0
glistening_legs,0
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butt_crush,0
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lagotrope,1
streaming,0
blood_on_hand,0
hisuian_zoroark,5
leaning_on_object,0
style_parody,0
black_and_blue,7
masters_of_the_universe,3
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cum_vore,0
chochi,1
jester,0
castration,0
alarm_clock,0
the_giant_hamster,1
cum_trail,0
puffed_cheeks,0
supernumerary_breasts,0
pointing_at_another,0
blue_perineum,0
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light_fury,5
sunbathing,0
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aer0_zer0,1
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amwulf,1
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sisu_(ratld),4
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curious,0
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treecko,5
tremble_spikes,0
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uhoh,0
chair_bondage,0
excito,1
sailor_moon_(series),3
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chua,5
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halbean,1
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monster_girl_encyclopedia,3
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looking_at_phone,0
thong_straps,0
blue_hoodie,0
white_pubes,0
chef_hat,0
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rika_nonaka,4
buxbi,1
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bolt_(film),3
stockings_only,0
imminent_knotting,0
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spider-man_(series),3
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orange_theme,7
living_latex,0
harlem,1
bullet,0
tasteful_nudity,0
condom_wrapper,0
quill,0
pills,0
translucent_underwear,0
pillow_humping,0
stated_homosexuality,0
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iriedono,1
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spazman,1
eating_food,0
inktober,3
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white_stockings,0
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avali_(original),0
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cell_(organism),5
a-chan,4
raised_shoulders,0
orange_stripes,0
aeris_(vg_cats),4
cum_bubble,0
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marlon.cores,1
stadium,0
celebi,5
two-handed_masturbation,0
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engrish,7
tridark,1
brown_feet,0
backwards_arm_support,0
thighband,0
sexy_eyes,0
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oouna,1
bionicle,3
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delivery_employee,0
black_goo,0
feral_dominating_human,0
nipples_touching,0
dragon_tail,0
babydoll,0
elfdrago,1
night_light_(mlp),4
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towergirls,3
berry_punch_(mlp),4
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wolf_(petruz),4
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96panda,1
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himbo,0
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daiidalus,1
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plagueofgripes,1
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enookie,1
vocaloid,3
tile_wall,0
fleur_de_lis_(mlp),4
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ryekie_(live-a-hero),4
submachine_gun,0
bambii_dog,1
comedy_central,3
byondrage,1
pupbii,1
bravo,1
nut_(fruit),0
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short_ears,0
animal_skin,0
pokemon_champion,0
starfish_(accessory),0
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underline,0
wugi,1
hilda_(pokémon),4
black_legs,0
fake_cat_ears,0
hyper_pecs,0
anthozoan,5
jakethegoat,1
ear_pull,0
digitaldomain123,1
pier,0
toad_(frog),5
alishka,1
cross_fox,0
nazuna_hiwatashi,4
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arachnid_taur,5
facial_tattoo,0
catmonkshiro,1
playboy_bunny,0
cum_in_throat,0
microchiropteran,5
tooth_gap,0
gentle_giant,0
procyonid_humanoid,5
tale_of_tails,3
sponty,4
doomthewolf,1
sandwich-anomaly,1
lineless,7
detachable_head,0
pirate_eagle,4
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genet,5
annoyed_expression,0
wingedwilly,1
monsters_inc,3
ratofdrawn,1
bionics,0
legend_of_queen_opala,3
creepypasta,3
renekton,4
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clover,0
age_regression,0
purple_armwear,0
fivel,1
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skitty,5
aomori,1
fiona_fox,4
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feet_together_knees_apart,0
furlana,1
intersex_pred,0
rudolph_the_red-nosed_reindeer,4
tggeko,1
zinogre,5
frisky_ferals,3
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reina.,1
kyander,1
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turntable_(animation),7
pasaran,1
link2004,1
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hrothgar,5
marshal_(animal_crossing),4
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bittersweet_candy_bowl,3
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countershade_scales,0
jbond,1
goat_humanoid,5
cum_on_fur,0
communal_shower,0
vono,1
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glasses_on_head,0
chihuahua,5
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richard_watterson,4
tentacle_around_arm,0
inkit89,1
lighter,0
closet,0
snails_(mlp),4
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sitting_on_sofa,0
dsan,1
raccoon_humanoid,5
magic_wand,0
cum_on_own_chest,0
araivis-edelveys,1
firondraak,4
wet_dream,0
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barrel_(live-a-hero),4
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witch_(the_owl_house),5
commander_shepard,4
trials_of_mana,3
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heart_background,0
triforce,0
polly_esther,4
fuzzy,0
tower,0
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cubone,5
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cum_fart,0
cum_in_nipples,0
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redpixie,1
trace_legacy,4
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seamen,1
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red_anus,0
the_cabin_in_the_woods_(arania),3
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scepter,0
gums,0
jellyfish,5
philippine_eagle,5
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sticky,0
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award,0
angrypotato96,1
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yarn,0
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castbound,1
r3drunner,1
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angoramon,5
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mcsweezy,1
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blinds,0
bird_wyvern,5
clawroline,4
domino_mask,0
sake,0
manika_nika,1
bobbydando,1
technology,0
foxes_in_love,1
green_tentacles,0
grey_hooves,0
higgyy,1
007delta,1
shinysteel,1
yakovlev-vad,1
no_nut_november,3
mega_gardevoir,5
who_framed_roger_rabbit,3
background_character,4
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light_ears,0
sengoku_puzzle,3
revali,4
rairai-no26-chu,1
lulu_(lol),4
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green_pants,0
dizzy,0
jake_long,4
akunim,1
wyntersun,1
big_flare,0
green_fox_(foxes_in_love),4
super_mario_galaxy,3
skimpy_bikini,0
pussy_juice_on_hand,0
intersex_focus,0
front-print_panties,0
unusual_vore,0
writing_text,0
euplerid,5
thorns,0
handheld,0
brain,0
grisser,1
marceline_abadeer,4
bolt_(bolt),4
freckles_on_breasts,0
mature_humanoid,0
name_drop,0
pink_bow,0
red_necktie,0
uncle_and_nephew,0
blind_eye,0
air_bubble,0
slurping,0
wargreymon,5
suspension_bondage,0
full_nelson_(legs_held),0
tail_bell,0
creeper_(minecraft),5
bna_v5,1
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living_sex_toy,0
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horse_tail,0
heavy_thrusting,0
drill_curls,0
ice_cream_cone,0
the_simpsons,3
4koma,7
comic_panel,0
karol_pawlinski,1
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hyperstorm_h,1
foot_in_mouth,0
light_breasts,0
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zackary911,1
bleats,1
piñata,0
cornchip21,1
wiggle,0
blunt_bangs,0
immobilization,0
funtime_foxy_(fnaf),4
plantpenetrator,1
iudicium86,1
surface_piercing,0
miyu_lynx,4
claws_out,0
chaos_emerald,0
stella_(helluva_boss),4
dafka,1
canisfidelis,1
reverse_gangbang,0
aurora_borealis,0
pseudo-penis,0
triangle_position,0
baloo,4
blue_fox_(foxes_in_love),4
bully,0
dangpa,1
red_neckerchief,0
furafterdark,3
inverted_pentagram,0
ficficponyfic,1
mcfan,1
tsareena,5
pink_wings,0
incase,1
pata,1
5_eyes,0
picture_in_picture,7
pubic_tattoo,0
purple_tentacles,0
heart_pair,0
onsen,0
swallowing_sound_effect,0
monotone_tongue,0
christmas_present,0
exed_eyes,1
breeding_mount,0
cervid_humanoid,5
zi0808,1
self_service_pump,0
kovu,4
peppy_hare,4
translucent_wings,0
butt_tuft,0
pandashorts,1
brown_sclera,0
goonie-san,1
hadrosaurid,5
ralts,5
talking_to_prey,0
moondancer_(mlp),4
war,0
mettaton_ex,4
humanoid_pussy_on_feral,0
pyramid,0
casimira_(orannis0),4
qr_code,0
inuyasha,3
water_gun,0
disembowelment,0
cybernetic_leg,0
popplio,5
stethoscope,0
tongue_wrap,0
ketzio,1
glitch,0
top_heavy,0
cum_while_chaste,0
pink_knot,0
anterior_nasal_aperture,0
crepix,1
outside_panel,0
bleach_(series),3
azuma_minatsu,1
arm_spikes,0
lunate,1
multiple_subs,0
red_swimwear,0
taur_penetrated,0
long_labia,0
rainbow_body,0
assisted_sex,0
multicolored_markings,0
striptease,0
plushie-like,0
nawka,1
lizard_humanoid,5
rinny,1
lycangel,1
hooved_plantigrade,0
tokin_hat,0
hand_on_crotch,0
purple_dress,0
stuck_together,0
fat_rolls,0
plaid_topwear,0
through_clothing,0
scientific_experiment,0
blue_mouth,0
misty_(pokemon),4
model,0
scooby-doo,4
dedoarts,1
moreuselesssource,1
chesnaught,5
chikorita,5
pussy_juice_through_clothing,0
chat,0
cyndaquil,5
body_armor,0
spotted_legs,0
white_tank_top,0
kiseff,1
shell-less,0
dark_clothing,0
bone_frill,0
throwing,0
soina,1
tire,0
8_breasts,0
net,0
text_on_body,0
valkoinen,1
headkerchief,0
sparkling_eyes,0
narration,0
hotel_transylvania,3
digimorph,0
river_otter,5
yak,5
gold_ring,0
marsupial_penis,0
cum_on_food,0
looking_back_at_partner,0
mawplay,0
kotobuki,1
jet_the_hawk,4
black_and_white_fur,0
green_apron,0
submerged_tail,0
vibrating,0
yangochiropteran,5
cropped,7
feral_pov,0
sports_mask,0
artsy-theo,1
cloud_emanata,0
hand_on_another's_butt,0
brown_shoes,0
acstlu,1
the_other_half,1
isolatedartest,1
cabin,0
holowear_(pokemon),0
apartment,0
omari,1
josou_seme,0
prince_sidon,4
agamid,5
countershade_genitalia,0
ghoul_school,3
hypnotic_visor,0
intersex_pov,0
cracky,1
cum_strand,0
xbox,3
x_eyes,0
annelid,5
spirale,1
shirtless_male,0
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mario_and_luigi_(series),3
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obi,0
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ill,0
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fuga:_melodies_of_steel,3
opqhlak,1
angus_delaney,4
liveforthefunk,1
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xenoblade_chronicles_2,3
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magician,0
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webcam,0
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oral_while_penetrating,0
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crookedtrees,1
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piero03432812,1
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hoopa,5
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shota_deer_(berseepon09),4
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goo_tentacles,0
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puffin,5
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light_clothing,0
luigi's_mansion,3
jonas,1
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kinoshita-jiroh,1
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dakka,4
o-kemono,1
bangaa,5
sidnithefox,1
infinite_(sonic),4
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zerg,5
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piko_piko_hammer,0
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xaenyth,1
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delki,1
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hauringu,1
zingiber,1
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faraden,5
wolfkidd,1
nier_automata,3
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izzy223,1
heteromyid,5
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batman,4
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winnie_werewolf_(hotel_transylvania),4
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xenoblade_chronicles,3
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night_(dream_and_nightmare),4
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vu06,1
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sheela,1
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lara_croft,4
tezcatlipoca,4
reccand,1
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circus_baby_(fnaf),4
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omnic,5
kespr,1
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thewill,1
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pac-man,4
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thewilldpink,1
mykiio,1
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ictonica,1
etis,4
:>,0
pec_grab,0
bustier,0
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t72b,1
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bastriw,1
ladybug,5
yasmil,1
vtuber,3
black_cum,0
condom_suit,0
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discord_(app),3
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ms._tarantula_(the_bad_guys),4
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j._r._r._tolkien,3
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shoen,4
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chris_goodwin,1
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1995,7
heart_areola,0
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mia_(world_flipper),4
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zex,1
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18:17,7
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star_trek_the_animated_series,3
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chikiota,1
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aak_(arknights),4
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atori,1
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rakan,4
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harry_potter,3
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kin-shun,1
sugar_glider,5
middle-earth_(tolkien),3
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forceswerwolf,1
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imalou,1
stegosaurus,5
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sean_blackthorne,1
chasm-006,1
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panini_(chowder),4
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trioami260,1
osprey,5
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lady_nora_(twokinds),4
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gaiki,1
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sleeping_beauty_(1959),3
warden006,1
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sogaroth,1
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phallusbro,1
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prison_cell,0
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wadorigi,1
battle_angel,1
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ground_shark,5
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<3_tail,0
kaisura,5
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milkteafox,1
dinner,0
thunder,0
martial_arts,0
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examination,0
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aunt,0
grotesque,0
shoulder_tattoo,0
predator_penetrated,0
open_diaper,0
woodpecker,5
patchwork_creature,5
nun_habit,0
katarhein,1
mother's_day,3
borky-draws,1
peeing_on_furniture,0
sekiguchi_(odd_taxi),4
diner,0
namah_calah,4
light262,1
sonic_chronicles:_the_dark_brotherhood,3
pulling_pants_down,0
battery,0
jonas-puppeh,1
synge,4
translucent_panties,0
highleg,0
storage_media,0
glistening_anus,0
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wide_brim_hat,0
hinami,1
blue_ribbon,0
tigrex,5
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goat_horns,0
arm_around_waist,0
shot_glass,0
businesswear,0
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six_frame_sequence,0
caramel_(mlp),4
general_yunan,4
mesoamerican_mythology,3
neronova,1
kakuteki11029,1
hakiahki,1
misdreavus,5
azumarill,5
slob,0
dax_(daxzor),4
carrie_krueger,4
pony-berserker,1
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okioppai,1
ar-15,0
kaitou,1
penis_on_penis,0
horn_sex,0
wolfmask,1
feces_in_mouth,0
pink_sweater,0
hat_ornament,0
red_seam_underwear,0
lasso,0
yus-ts,1
fur_coat,0
raikoh-illust,1
manaphy,5
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gadget_the_wolf,4
zacatron94,1
drmax,1
vermelhatan,1
locosaltinc,1
notorious84,1
max_draws,1
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paladin,0
gloria_the_hippopotamus,4
he_wants_to_order,3
masshiro,1
pregoo,1
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dezz,1
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cthulhu,4
mango_(3mangos),4
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edjit,1
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toa,5
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anyare,1
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napstablook,4
shinerai,1
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berri,4
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vest_(artist),1
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menacing_(meme),3
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metroid_(species),5
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jou,5
penis_on_butt,0
alakazam,5
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rekin3d,1
game_cg,0
tuxedo_cat,5
frankly-art,1
hyhlion,1
purple_fire,0
yuman,5
tan_anus,0
chloe_sinclaire,4
haori,0
nuclearwasabi,1
legs_around_partner,0
shade_the_echidna,4
converse,3
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1ceberg,1
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waving_hand,0
fsmaverick,1
dogyd,1
rach_verity,1
dakimakura_pose,0
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black_suit,0
gyumao,4
nu_pogodi,3
eyeofcalamity,1
davidsanchan,1
draw_me_like_one_of_your_french_girls,3
ariannafray_pr,1
dove,5
alty,4
holding_spoon,0
gamba_no_bouken_(series),3
borderlands,3
shaman,0
neodokuro,1
solterv,1
anal_only,0
nyaswitchnya,1
rubber_boots,0
captainkirb,1
1:4,7
pink_flower,0
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takato_matsuki,4
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unistaart,1
kify,1
rin_kaenbyou,4
soft_focus,0
incredibleediblecalico,1
calvin_and_hobbes,3
luxar92,1
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teenager_on_teenager,0
arcade_machine,0
dilophosaurus,5
roz,4
tablet_pen,0
long_fur,0
sample,0
skye3337,1
doggo_(undertale),4
navitaserussirus,1
kobi_lacroix,1
cream_(disambiguation),-1
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rabblet,1
summer_wars,3
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imminent_oral_vore,0
hisuian_growlithe,5
metalpandora,1
synx_(synxthelynx),4
labelle_able,4
limb_slot,0
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vorusuarts,1
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leia_organa,4
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nightwing_(wof),5
russian_blue,5
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typhon,4
kadabra,5
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mamoru-kun,4
mt,1
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ziffir,1
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areye_(artist),1
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nedoiko,1
crackiepipe,1
yellow_hat,0
spacewaifu,1
akira_volfsar,1
legend_of_jenny_and_renamon,3
series,0
fel_(my_life_with_fel),4
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helixjack,1
telescope,0
clefairy,5
randomboobguy,1
jeanette_miller,4
thelionfish,1
breeze_in_the_clouds,3
sir_gallade,4
improvised_gag,0
cooner,1
rory_(ceehaz),4
rodrigues404,1
medusa,4
abubu,1
rayliicious,1
pouches,0
telleryspyro,1
evalion,1
navi,4
betelgammamon,5
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link_(rabbit_form),4
blue_sheath,0
taurin_fox,1
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java,1
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kobold_quest,3
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living_sex_doll,5
james_corck,1
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game_(disambiguation),-1
lagiacrus,5
rarakie,1
alexstrasza,4
carrot_pen,0
open_:3,0
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fake_wings,0
mr.tektite,1
adventures_of_sonic_the_hedgehog,3
loonatics_unleashed,3
junior_horse,4
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tauros,5
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nyar,1
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nekubi,1
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sumatran_rhinoceros,5
furred_wings,0
risk_of_rain_2,3
lizardman_(overlord),5
l-a-v,1
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scene_kid,0
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drake_(disambiguation),-1
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beast_(disambiguation),-1
loki_(bitterplaguerat),4
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mrscurlystyles,1
theycallhimcake,1
dezmine21,1
isabella_bandicoot,4
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on_roof,0
caedere,1
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kota´,1
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heel_claw,0
zerva_von_zadok_(capesir),4
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painted_balls,0
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globe,0
darkwing_duck,3
dora_the_explorer,3
kumatetsu,4
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ethersaga,1
ratih_(study_partners),4
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whitephoenix52,1
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aviator_cap,0
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adjatha,1
potato_chips,0
galaxxxart,1
tysontan,1
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damage,0
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cyn.,1
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nidoran♀,5
moblie,1
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zenu,1
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kiichi,1
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cooper_estevez,4
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syoee_b,1
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iontoon,1
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dr._k_(changed),4
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etheross,1
yellow_exoskeleton,0
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detective_pikachu,4
male_raping_male,0
marenlicious,1
sino_(furfragged),4
vale-city,1
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jooshy,1
david_lillie,1
altered_reflection,0
tail_hand,0
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kazushi,1
jellymon,5
zbrush,3
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vammzu,1
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hands_on_ankles,0
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duo_(artist),1
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lovespell,1
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ragnarok_online,3
tanutronik753_k,1
list,0
ssssnowy,1
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afternoon,0
blacksmith,0
vodka,0
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ivorylagiacrus,1
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tanio,1
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dedenne,5
heroes_of_the_storm,3
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re:zero,3
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dsw7,1
entei,5
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dracony,5
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ayn_(fluff-kevlar),4
aurorus,5
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farin,4
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lizheru,1
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x-leon-x,1
katt_(breath_of_fire),4
ronso,5
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ara_(genus),5
crystal_pony_(mlp),5
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thelunarmoon,1
gulusgammamon,5
artblush,1
matimus91,1
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rain_silves,4
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doraemon,3
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nic-m-lyc,1
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nyuroraxbigdon,1
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pang_(sdorica),4
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apatosaurus,5
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eikasianspire,1
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naruever,1
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aventis,4
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hyper_tail,0
yeti,5
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non-canine_knot,0
t'vaoan,5
brendan_(pokemon),4
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mega_milk,3
vibrantechoes,1
dude,0
pantherine_taur,5
lupe_the_wolf,4
chinese_crested_dog,5
hyrule_warriors,3
plushtrapboyuwu,1
hades_(game),3
jeniak,1
vasuki,1
patachu,4
delirost,1
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juantriforce,1
snowboard,0
dain_4201,1
shinobe,1
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wrappedvi,1
lobokosmico,1
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feet_in_water,0
crocdragon89,1
serval_humanoid,5
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shaze,4
sloppy_kiss,0
ikakins,1
docu_(doppel),4
castlevania,3
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excessive_saliva,0
darknetic,1
timothy_vladislaus,4
interrogation,0
living_weapon,5
winona_(mlp),4
fluffx,1
jooper,1
uma_musume_pretty_derby,3
golden_jackal,5
king_kazma,4
skimike,3
sash_(backsash),4
orange_and_white,7
shintori,1
chromamancer,1
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tan_spots,0
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pyravia,4
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antelon,1
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thatblackfox_(artist),1
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ronno,4
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numbat,5
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text_with_emanata,0
vhkansfweer,1
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jtveemo,1
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watermelon_slice,0
bigmaster,3
furoticon,3
squid_humanoid,5
ruby_savarin,3
bayonetta,3
candy_kong,4
arsalan,4
green_mouth,0
mean,0
miloff,1
nokuri,5
arufina_w,1
wbnsfwfactory,1
drerika,1
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brown_tail_tip,0
greymon,5
raptor_claws,0
r18alphawolf,1
pixelkitties,1
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retro_console,0
leek,0
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day-t,1
greg_panovich,1
snuddy,1
ouka,1
breast_shot,0
cum_on_eyewear,0
beezlebumawoken,1
luxurias,1
incense,0
smoking_weed,0
brokenwing,4
elemental_dragon,5
yaki_atsuage,1
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monotone_pawpads,0
kalita_(furryfight_chronicles),4
teal_nipples,0
nolegs_(oc),4
dracreloaded,1
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dappled_light,0
freefall_(webcomic),3
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piper_perri_surrounded,3
hal,4
santanahoffman,1
cast,0
chaos,0
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dippubear,1
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pink_hands,0
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ashnar,1
blue_neckerchief,0
tokugawa_ieyasu,4
jester_laughie,1
buwaro_elexion,4
spunkubus,1
foster's_home_for_imaginary_friends,3
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tekandprieda_(artist),1
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takeover,0
zi_ran,1
giggle,0
glowing_claws,0
mrs._nibbly,4
miosha,1
red_inner_ear_fluff,0
cyan_yoshi,5
penis_under_skirt,0
tengen_toppa_gurren_lagann,3
doctorpurple2000,1
ursofofinho,1
booth,0
balls_on_floor,0
two_tone_shoes,0
neeko_(lol),4
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betty_(weaver),4
corviknight,5
schwarzfox,1
portal_panties,0
manitka_(character),4
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anonipony,1
soda_bottle,0
shamrock,0
pussy_spreading_tape,0
kurohime,1
xanthor,1
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peskybatfish,1
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deervvitch,1
sparklyon3,1
icing,0
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lickitung,5
littlesheep,1
one_stormy_night,3
bright,0
tapir,5
ashwolves5,1
shamziwhite,1
game_boy,3
chimchar,5
shintatokoro,1
kamek,4
cum_circulation,0
auction,0
omikuro,1
marx_(kirby),4
brown_inner_ear_fluff,0
face_in_breasts,0
solaxe_(artist),1
doggie_kruger,4
queen_sectonia,4
damaged_clothing,0
hawlucha,5
holding_axe,0
feral_druid,0
echo_(game),3
tail_concerto,3
deku_(tloz),5
silver_dragon,5
raaz,1
mega_blaziken,5
blattarieva,1
bowser's_fury,3
desi,4
two_tone_hat,0
smartwhitefang,1
geier,1
thingshappen,1
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covering_pussy,0
grinn3r,1
concon-collector,3
cycling,0
darkshadow777,1
anubislivess,4
laylee,4
carmen_(animal_crossing),4
king_snugglemagne_xxv,4
roxythefoxy,1
twitter_logo,0
coatieyay,1
pidgey,5
exelzior,1
tentacle_around_penis,0
ponehanon,1
stunky,5
curly_brace,4
ozoneserpent,1
acta_(spacewaifu),4
cum_on_wings,0
pure_faraden,5
egg_from_ovipositor,0
preview,0
tales_of_rebirth,3
indeedee,5
hammer_and_sickle,0
hyoumaru,1
pink_ribbon,0
shaving,0
fcsimba,1
droid,5
papercraft,0
the-narutoshi,1
methados,1
cresselia,5
ivory-raven,1
urethral_tonguing,0
polyamory,0
gaming_chair,0
omegazuel,1
hand_on_own_arm,0
falla_(f-r95),4
mrdegradation,1
cavafly01,1
dusknoir,5
zhurong_(tas),4
raphtalia,4
rodenbeard,1
mynka,1
shin_guards,0
reisen_udongein_inaba,4
deck_chair_position,0
furromantic,1
aakashi,1
xchiseaxmargaritax,1
changeling_tale,3
mrs._otterton,4
xbox_360,3
fishnet_handwear,0
saw,0
sprinkles_(artist),1
dracovar_valeford,1
wallet,0
multicolored_hat,0
nduli,1
lighthouse,0
sethpup,1
kris_where_are_we,3
radiation_symbol,0
atlas-white,1
da_silva,3
gold_markings,0
small_nipples,0
hitmonchan,5
bottomless_intersex,0
nirvana3,1
afrobull,1
tooth_necklace,0
bottomwear_only,0
playboy,3
lupine_assassin,4
license_info,3
telehypnotic,1
eye_of_horus,0
olkategrin,1
yagi_b._(artist),1
spy_(team_fortress_2),4
ben_tennyson,4
slide,0
lorum_piercing,0
tod_(fath),4
geecku,4
bagheera,1
gold_crown,0
male_fingering_female,0
pulling_underwear,0
rapistwerewolf,1
pattern_scarf,0
sauce,0
dark_balls,0
looking_at_own_breasts,0
kara_resch,4
canvas,0
grey_gloves,0
arctic,0
glaring,0
zumjakal,1
elio_(pokémon),4
zombieray10,1
crystal_ball,0
brony,0
plates,0
aldea_(character),4
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devilbluedragon,1
feathered_arms,0
trigaroo,1
titan_a.e.,3
box_of_chocolates,0
duzt,1
untied,0
arms_out,0
skimpy_armor,0
worried_expression,0
alphonse_(james_howard),4
punk-pegasus,1
howl_echoes,1
snowball,0
light_text,0
beardofzoose,1
mumu202,1
peter_the_cat,4
discreet_user,1
tamagotchi,3
delibird,5
feet_tied,0
mrt0ony,1
eclipse,0
clockhands,1
moorsheadfalling,1
gynomorph_penetrating_ambiguous,0
larru-larru,1
resine,4
folwilliar,1
mikaduki_karasu,1
otterbits,1
okiyo,1
jug,0
male_penetrating_maleherm,0
altaria,5
jmg,1
unseen_male,0
orange_glans,0
charlie_(weaver),4
fwench_fwy_(chikn_nuggit),4
tgchan,3
delilah_(101_dalmatians),4
second_life,3
snappygrey,1
raburigaron,1
jimmy_(faf),4
basketball_court,0
warning_cream_filled,3
crimvael_(interspecies_reviewers),4
dynamite,0
bernard_(ok_k.o.!_lbh),4
bound_by_tentacles,0
machete,0
stoat_humanoid,5
pyotr_repkin,1
blue's_clues,3
pedipalps,0
push-up,0
v-neck,0
wasp_waist,0
lionsilverwolf,1
rohan_scribe,1
adamb/t2oa,1
tail_wrapped,0
thanu,1
margay,5
roommates_(comic),3
eliksni,5
pink_mouth,0
flannel_shirt,0
7:5,7
epona_(tloz),4
tribal_jewelry,0
presto_(artist),1
david_a_cantero,1
sitting_on_tail,0
listening_to_music,0
nepentz,1
elaine_(furryjibe),4
gatogenerico,1
hud,0
ori_and_the_will_of_the_wisps,3
plasma_blaster,0
deoxys,5
b-intend,1
silver_(ezukapizumu),4
superhero_costume,0
usagi_yojimbo,3
wimple,0
hyper_ears,0
beavernator,1
spotted_ears,0
zatch_(notkastar),4
badgengar,1
sunitai,1
emufu,1
lien-da,4
9:10,7
dimmi_(character),4
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skyward_sword,3
ambiguous_penetrating_male,0
zeriara_(character),4
mario_kart,3
bathroom_sex,0
naive_tabby,1
gang_xi_siyu,4
xenomorph_queen,5
polar_patroller,4
heart_above_head,0
clawing,0
unown_(pokémon),5
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harara,1
shendyt,0
torii,0
walk_cycle,0
teat_piercing,0
delga,4
rammy_aaron,4
boston_terrier,5
frostwyrm102,1
cockiestspaniel,1
tail_mane,0
penis_pump,0
km-15,1
lololo,4
sand_cat,5
godoffury,1
dabbling_duck,5
aeonspassed,1
lemurlemurovich,1
pembrokewkorgi,1
prey_penetrating_predator,0
awesomenauts,3
unie,1
imminent_pegging,0
bokustar_fox,1
symrea,1
fuzzy_handcuffs,0
marshadow,5
dreamcatcher,0
leopard_gecko,5
breastless_clothing,0
nelljoestar,1
exceed,5
black_toes,0
father_penetrating_daughter,0
crazy_eyes,0
living_cloud,5
kodardragon,1
the_three_caballeros,3
pink_hat,0
pumpkybutt,1
microsoft_windows,3
smiju,1
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infatuation,0
justwhite,1
x_navel,0
bng,1
twistedterra,1
black_thigh_socks,0
tassy_(artist),1
fishnet_leggings,0
zone,1
mothra_(series),3
everstone_guild,3
sword_art_online,3
dragonification,0
popsicle_melting,0
roommates:motha,3
ukent,1
tan_glans,0
poppy_opossum,3
telemonster,3
shoulder_bag,0
shorts_pull,0
hair_down,0
photo_finish_(mlp),4
munks_(character),4
cicada_(artist),1
jedi,0
sneeze,0
taco,0
sea_otter,5
ratatooey,1
enroshiva,1
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bodi_(rock_dog),4
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maeve_(twokinds),4
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quail,5
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roko_(cocolog),4
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legend_of_spyro,3
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nowykowski7,1
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amenlona,1
baalbuddy,1
text_in_heart,0
cake_the_cat,4
georgette_(disney),4
calm,0
dclzexon,1
ibuki_(beastars),4
wilbur_(animal_crossing),4
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lube_on_sex_toy,0
blanca_(nicky_illust),4
nocturnalfuzz,1
drockdraw,1
slimshod,4
school_days,3
buruma,0
pixelsketcher,1
fan_(disambiguation),-1
black_chest,0
antira,1
queenie_(shoutingisfun),4
dutch_(artist),1
dib_membrane,4
bunnicula,4
br'er_fox,4
suspended_via_penetration,0
missing_tooth,0
jake_(study_partners),4
casynuf,1
galarian_rapidash,5
dredjir,1
sprite_art,0
zackdadawg,1
implied_cannibalism,0
fafcomics,3
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cursed,0
endless_(artist),1
tail_stripes,0
pickup_truck,0
stagshack,1
upstairstudios,1
apex_legends,3
milking_self,0
aerodactyl,5
keys,0
long_skirt,0
holding_shin,0
lies,0
evelia_zara,4
layered_heart,0
in_one_ear_and_out_the_other,0
andromorph/andromorph,0
nes_controller,0
jojocoso,1
billiards,0
confession,0
hairjob,0
inflatable_transformation,0
bow_in_back,0
ahnik_(character),4
fanny_pack,0
yello,4
mouth_scar,0
leo_(twitchyanimation),4
monotone_panties,0
clothed_humanoid,0
uniped,0
condom_inside,0
prettypinkponyprincess,1
wolfiecanem,1
physical_list,0
raised_tank_top,0
drogoz,4
myenia,1
martha_lorraine,4
dolorcin,1
lowkey_nottoast,1
swelling,0
purple_hands,0
kamudragon,1
hand_on_another's_chest,0
garuda_six,1
electrikestorm,1
rareel,4
inter_schminter,0
bastiel,1
rayzoir,1
den255,1
angellsview3,1
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print_headwear,0
yellow_hands,0
sheath_grab,0
vaktus,1
jesse_collins,4
whiteabigail,1
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eggnog,0
daryl_vecat,4
tangobat,1
the_gory_saint,1
flabébé,5
angelauxes,1
avo_(weaver),4
pirin-apex,1
grillby,4
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katsuke_(character),4
cosmic_tail,0
amarihel,1
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fruitymilk,1
3dsmax,3
thevgbear,1
tupidwithouts,1
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blue_lightsaber,0
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police_baton,0
little_john,4
petaroh,1
skulldog_(species),5
drychicken,1
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nude_gynomorph,0
unknownlifeform,1
pussy_juice_splatter,0
kappy,1
leg_spreader,0
jasmine_(skidd),4
mei_(overwatch),4
invisible_sex,0
adrian_gray,4
macaronneko,1
zbornak,5
corner,0
sylvia_(wander_over_yonder),4
skintight_clothing,0
minxydoodle,1
the_legend_of_korra,3
megan_giles,1
long-tailed_weasel,5
neumono,5
cheesecaked,1
das,1
keltaan,1
chip_the_wolf,4
biffalo,1
herm_on_bottom,0
danawolfin,1
ikugo_(artist),1
mooing,0
16:21,7
pokémon_tcg,3
thechurroman,1
suggestive_shirt,0
tabhead,1
chicobo,1
penis_on_breast,0
sprout_cloverleaf_(mlp),4
koba,1
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redvais,1
self_cum_inflation,0
pink_butt,0
lizardfolk,5
beaten,0
rip,0
kobold_adventure,3
tail_curl,0
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in_food,0
shiver_(splatoon),4
wonder_boy_(series),3
135:256,7
mrs1989,1
marvol,4
feather_6,3
keyblade,0
photography,0
the_fatcat,1
pride_color_markings,0
jademerien,1
heart_tail,0
hexteknik,1
bolero_delatante,4
hentaib,1
soto_(freckles),4
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holtz,1
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twinning,0
rapid_strike_style_urshifu,5
goblin_slayer,3
cervine_humanoid,5
kanannbo,1
ittla,1
inkwell,0
chester_ringtail_magreer,4
fuzzywuff,1
kentowan,1
lanturn,5
bhtoss,1
leech,5
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ghislaine_dedorudia,4
servo117,1
sirphilliam,1
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muskydusky,1
breast_torture,0
kuroisumi,1
may825,1
earthworm,5
opera_kranz,4
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briskby,1
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mond_reyes,4
dots,0
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scarlet_(sequential_art),4
dusk_rhine,4
double_fisting,0
tan_beak,0
sanka-tetsu,1
kanada,1
kelly_hamilton,1
glados,4
cherushi,1
iron_will_(mlp),4
novus,4
spaulder,0
musician,0
pawalo,4
pokéball_anal_beads,0
sophie_(funkybun),4
beach_blanket,0
michikochan,1
looking_outside,0
magenta_hair,0
puffy_sleeves,0
arai-san,4
cock_warmer,0
ailaanne,1
two_tone_antennae,0
awesome_face,0
breast_blush,0
imminent_fellatio,0
ranadi,1
whitephox,1
orbitalis,1
shesamonkey,1
linda_wright,4
humanoid_domination,0
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alicia_acorn,4
vem,4
fire_emblem_awakening,3
peeing_into_cup,0
dreamspinner,5
flogger,0
tentacle_mouth,0
jaki-kun_(character),4
kiyo,1
coconut_drink,0
julie_(jhenightfox),4
spunkie,1
cyrillic_text,7
snowfyre,1
aipom,5
evals,4
baby_powder,0
veggie55,1
behniis,1
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aozee,1
harwick,1
furlyfe_redux,1
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lexxs,4
vixie00,1
suikuzu,1
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grimm_(rwby),5
alma_(vixinecomics),4
garruuk,1
nami_(one_piece),4
schnauzer,5
zygodactyl,0
luvon,1
crobat,5
taran_fiddler,1
anal_storage,0
rookieanimator210,1
iseenudepeople,1
ouginak,5
summon_night,3
rizonik,1
helmed_(helmed),4
ruff_(clothing),0
zombiecat,1
kelsey_sienna,4
bow_dress,0
touching_own_hip,0
cocothezebra,1
misterpickleman,1
kougatalbain,3
jeremy_mullins,1
berruchan,1
damingo,1
emoji_(race),5
spicedpopsicle,1
twstacker_(character),4
gimp,0
silicon_studio,3
scarlet-frost,1
souladdicted,1
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toco_(artist),1
phlegraofmystery,1
hitting,0
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c0nnerc00n,1
basketball_hoop,0
vivzmind,1
evolve_(copyright),3
drippy,0
durg,4
syaokitty,1
wolfrad,1
unregisteredcat,1
glorsh,0
flick-the-thief,1
eudetenis,1
mr._mist,1
taurusart,1
panty_bulge,0
xing_(the_xing1),4
gardening,0
grumpig,5
dolpix,1
moonabel,1
majin_buu,4
sanzo,1
ursula_vernon,1
sifyro,4
star_trek_lower_decks,3
naked_mole-rat,5
lovetaste_chica,4
diddy_kong_racing,3
mind_drive,1
deormynd,1
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water_reflection,0
gothorita,5
tfancred,1
jenni_(jennibutt),4
larger_cub,0
workshop,0
piercing_bondage,0
sevi_blaze,1
infection,0
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plus-alpha,1
konpeto,1
accidental_holly,0
crowned_shield_zamazenta,5
chaos-draco,1
swirl,0
maliketh_(elden_ring),4
nut-case,1
nessie_(disney),4
hair_dryer,0
yamisonic,3
omega56,1
hip_markings,0
hedge,0
nia_(xenoblade),4
artofthediscipline,1
grey_tail_feathers,0
opalescence_(mlp),4
jeffusherb,1
kiggy,4
elias_acorn,4
pastry,0
lets0020,1
death_knight,0
afevis_(character),4
niniidawns,1
anormaluser,1
hobbes_maxwell,1
towel_around_waist,0
yoshi_egg,0
doom_eternal,3
tahm_kench_(lol),4
beak_lick,0
okata,1
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chaoji_xiao_shou_he,1
glass_bottle,0
pawl_(fuze),4
balls_on_penis,0
shudayuda,1
triple_vaginal,0
tan_stripes,0
ollie_(pop'n_music),4
bondage_chair,0
futon,0
multi_clitoris,0
pillars,0
multicolored_panties,0
simple_nose,0
c-3matome,1
cirruskitfox,1
hoop,0
trial_captain_lana,4
healing,0
treebarkdragon,1
christopher_thorndyke,4
blue_toes,0
high_contrast,7
heart_accessory,0
desmond_(ceeb),4
luca_(pixar),3
mizuse_higomaru,1
grispinne,1
saria,4
plussun,1
lurkergg,1
shio_inu,1
nightmare_foxy_(fnaf),4
foxy_(cally3d),4
living_sword,5
wintersnowolf,1
anuv,4
dr._t'ana,4
utility_pole,0
barmaku,1
ttothep_arts,1
tchaikovsky2,1
female_rimming_female,0
tigra,4
monster_girl_profile,3
dark_samus,4
pxlfur,1
drxsmokey,1
penny_ling,4
lodi,1
xianjaguar,1
meloetta_(pirouette_form),5
sya,1
big-fig,1
neltharion290,1
zanamaoria,1
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white_ribbon,0
fashion_saddle,0
mitsuharu,1
son_dom_father_sub,0
amuzoreh,1
risky_boots,4
cozy,0
eamart,1
koffing,5
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cobalion,5
opening_door,0
komoroshi_(artist),1
bd,1
pumbaa,4
feral_to_anthro,0
qwaxi~lixard,1
loss_of_speech,0
marnie_(pokémon),4
reuniclus,5
amber_(fuf),4
blue_tuft,0
highoncoffee,1
giancarlo_rosato,4
greyhound,5
teen_titans_(television_series),3
yellow_inner_ear_fluff,0
rutmutt,5
inward_tail_speech_bubble,0
floebean,1
pvt._keron,1
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expression_cutaway,0
natsumemetalsonic,1
rt001,1
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filbert_(animal_crossing),4
rackun,1
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brontosaurus,5
el-yeguero,1
jeffron,1
zetsuboucchi,1
wonderbolts_uniform,0
lonely,0
spuydjeks,1
ryn_purrawri,4
hand_on_own_shin,0
jay-marvel,1
kalida,1
freya_howell,4
buster_moon,4
hyena_father_(pickles-hyena),4
dragonofdarkness13,1
urine_stain,0
kokiri,5
21:9,7
sugaryhotdog,1
amixeduppuppy,1
darkdraketom,1
dulcine,4
meisaikawaii,1
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noirbutt,1
holding_ankle,0
cream_tail,0
applebottom_family,4
bandaged_chest,0
happyending,1
maci_(ceehaz),4
hyper_lips,0
thaasteo,1
maddworld,1
porygon-z,5
haloren,4
mrpandragon,1
pink_glasses,0
death_by_penis,0
panchito_pistoles,4
perverted_bunny,1
digital_clock,0
ear_nibble,0
spike_(eg),4
hip_to_hip,0
ramdoctor,1
eelektross,5
penis_in_cloaca,0
sagging_diaper,0
small_iris,0
portal_penis,0
paintfox,1
malefor,4
redfred,1
elegant,0
danmakuman,1
bonbon_(animal_crossing),4
ink-eyes,4
ilovejudyhopps,1
azre,1
hanul,4
basket_position,0
luthien_nightwolf,1
happi_(clothing),0
oleander_(tfh),4
marble,0
silvia_(pullmytail),4
nevarrio,4
sharpedo,5
bastion_aduro,4
minum,1
on_one_hand,0
ookami-kun,1
t-kay,1
between_cheeks,0
mikeinel,1
doxxyl,1
board,0
chibi-marrow,1
pokkén_tournament,3
face_tentacles,0
macro_pred,0
kkoart,1
rise_of_the_guardians,3
coolryong,1
meru_(hhc),4
varied_multi_penis,0
wrestling_outfit,0
cosmic_wings,0
dagger_leonelli,1
standing_on_another,0
lunar_epitaph,1
lichgirlart,1
bee_haji,1
joycon_controller,0
sticker_pack,0
scarabaeid,5
paper_lantern,0
bow_hothoof_(mlp),4
gassy,0
fishimira,1
huge_abs,0
offering_condom,0
fade_out,0
inu-jean,1
white_collar,0
scratching_head,0
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nightmaremoons,1
blue_spikes,0
bagi_the_monster_of_mighty_nature,3
covertcanine,1
cake_sitting,0
food_container,0
radiantrealm,1
torterra,5
non_toxic_(oc),4
muddy,0
translucent_dress,0
sadcat16hrz,1
dog_days,3
beauceron,5
sly,0
gintaro,4
aunt_and_nephew,0
lizeron,4
marking_territory,0
mallow_(hhc),4
green_arms,0
crowbar,0
glowsticking,0
cranebear,1
chimera_ant,5
speedpaint,0
eti_(utopianvee),4
vf-01s,1
cutout_heels,0
fols,4
seph_(naughtymorg),4
pesty_skillengton,1
allesiathehedge,1
holding_fork,0
wii_fit_trainer,4
markydaysaid,1
monotone_tuft,0
fukurou_ya,1
stesha_di,1
collarbone_piercing,0
mylar_(discreet_user),4
inkudoragoon,1
superabsurd_res,7
kermit_the_frog,4
vertical_nipple_piercing,0
battycat,1
lolo_(klonoa),4
reef_shark,5
pyro29,4
leaning_on_edge,0
footjob_while_masturbating,0
color_partitioning,0
maya_(software),3
idoodle2draw,1
yamatoji201,1
lonelycharart,1
oo_sebastian_oo,1
grumblepluck,1
zamzi,1
pork,0
hands_on_back,0
salonkitty,1
iguanamouth,1
h-key,1
feralsoren,1
double_diamond_(mlp),4
a_fox_in_space,3
healingpit,1
trick,0
james_mccloud,4
cleaver_(weapon),0
stained_clothing,0
nerdmouse,1
amanda_(tcitw),4
gashiboka,1
eparihser,1
soutarou_(morenatsu),4
blood_on_penis,0
teddy_jack,1
jlullaby,1
theo_young,1
manya,1
victoria_viper,1
breast_tuft,0
animal_plushie,0
father_of_the_pride,3
horatio_svetlana,1
denzel_t_smith_(character),4
osira,4
tsukinori,1
feeldoe,0
birthday_sex,0
shopping_cart,0
zenmigawa,1
lobadelaluna,1
clothing_down,0
blade_(xenoblade),5
salt,0
gonewiththefart,1
ssirrus,1
call_of_duty,3
ken_ashcorp,4
star_pattern,0
petunia_(htf),4
the_walten_files,3
4pcsset,1
wen,1
bottomless_gynomorph,0
hutago,1
notesaver,1
patacon,1
thediscorded,1
gold_tiara,0
dark_hands,0
bucky_oryx-antlerson,4
ear_rub,0
bisamon,1
horn_magic,0
shun_imai_(odd_taxi),4
green_exoskeleton,0
tarian,1
goldmikun,1
pinocchio,3
lute_(zinfyu),4
nardoragon,5
james_auvereign,4
zak_(fvt),4
manfromearth,1
pat_(lapatte),4
rattatatus78,1
nightmare_bonnie_(fnaf),4
red_coat,0
flabbyotter,1
whitmaverick,1
growth_sequence,0
fixed_vibrator,0
vondranart,1
yellow_yoshi,5
ferris,1
heavensdoor,1
the-butcher-x,1
flinters_(character),4
universe,0
reinkorn,1
natasha_cat,1
soul_stealing,0
grostenmeiers,1
litterbox_comics,3
gym_bag,0
waitress_uniform,0
pregnant_herm,0
hate_sex,0
solardelton,1
gary_(zootopia),4
ofuda,0
klifflod,1
wishbone_mouth,0
yellow_scarf,0
forced_rimming,0
shwonky,1
90sigma,1
neayix,1
shido-tara,1
brand,0
jay-r,1
after_exercise,0
metapod,5
sera_(sera),4
voidtails,1
sasha_la_fleur,4
giggles_(htf),4
charlie_barkin,4
zal,4
gang,0
orcfun,1
kanizo,1
maid_hat,0
fire_salamander_(salamander),5
kinkypinkie,1
light_eyebrows,0
someone's_pc,3
kiddie_pool,0
sparkling_background,0
obijime,0
dirty_anal,0
impximon,1
petronoise,1
marvel_cinematic_universe,3
boxing_gloves_only,0
big_flexor_carpi,0
skippy,4
oatmealpecheneg,1
reddo12340,1
bycocket,0
witchking00,1
wolfarion,1
nullification,0
team_pose,0
zyria,1
dawnlux,1
taintedstar,1
revealing_clothes,0
gamercat_(character),4
kaiotawolf,1
officer_wolfard,4
knotted_condom,0
nuclear_throne,3
angewomon,5
jeremy_fitzgerald,4
name_in_internal_monologue,0
penis_on_glass,0
electric_shock,0
lucy_(hladilnik),4
klenerschluchti,3
hida,4
udder_balls,0
cum_inflated_breasts,0
monotone_paws,0
thread,0
tek_(tekandprieda),4
rubbercat,5
erect_nipples_under_clothes,0
shokan,5
grey_armwear,0
neltruin,1
nedroid,1
eulipotyphlan_humanoid,5
primal_reversion,5
burn_scar,0
snao,1
sunni_gummi,4
extraspecialzone,1
pasadena_o'possum,4
bent_over_furniture,0
mega_man_x_(series),3
mixi_elkhound,4
vyktor_dreygo,4
game_case,0
seismitoad,5
super_sentai,3
crushfang_(sdorica_sunset),4
dildo_sitting_reveal,0
poster_(object),0
tsukune_minaga,1
kitsunekotaro,1
evlampyshka,1
quagsire,5
pawtsun,1
lewdreaper,1
tan_exoskeleton,0
gaming_while_penetrating,0
rapid_pregnancy,0
redwall,3
leo_(whiteleo),4
spread_slit,0
fishnet_gloves,0
kalie,4
double_footjob,0
sunflora,5
neracoda,1
pink_panther,4
eye_mask,0
diana_(animal_crossing),4
ni_jikan,1
voluptuous_gynomorph,0
saturniid,5
fnaf_vr_help_wanted,3
saryn_(warframe),4
null_symbol,0
night_creep,1
intersex_symbol,0
gun_arm,0
orf,1
huge_clitoris,0
condom_crusader,4
dark_grey_fur,0
the_ballad_of_nessie,3
yuguni_(character),4
sharing,0
exposed_chest,0
thaz_(artist),1
lantha,4
chilling,0
thaine,3
bagi,4
"norithics_""nori""_kusemurai",4
blitzle,5
glacierk,1
lungless_salamander,5
brown_neck,0
daikitei,1
cutlass,0
helaviskrew,1
nicecream_man,4
king_shark,4
dj_bop,4
french_artist,0
richard_(james_howard),4
ziina,4
dodedo,1
cheetah_(dc),4
180_fov,7
daz_studio,3
ceiling_fan,0
hoverrover,1
lewnoli,1
md34,1
reverse_wheelbarrow_position,0
lexington_(gargoyles),4
racf92,1
green_paws,0
bambookat,1
parumpi,1
happylittlecloud,1
boxers_only,0
dark_topwear,0
gato_matero,1
russel_(pickles-hyena),4
snowyfeline,1
tails_doll,4
holding_glasses,0
shared_masturbator,0
wolfgang_(animal_crossing),4
throttle_(bmfm),4
kimero_kat,1
mishakun,1
arm_on_shoulder,0
skullfuck,0
grunt_(mass_effect),4
radcanine,1
little_witch_academia,3
hospital_gown,0
hand_under_chin,0
parking_lot,0
maho-gato,4
stormcutter,5
d.w._read,4
vikna_(fluff-kevlar),4
pokémon:_let's_go,3
brenna_jorunn,4
cultist,0
bebebebebe,1
nikubo,1
phess,1
duck_hunt_duck,4
music_player,0
purple_legs,0
ricky945,1
headscissor,0
thorny_devil,5
orange_headwear,0
metal_gear_rising:_revengeance,3
mcyt,3
featureless_arms,0
stan_(beez),4
bluekomadori,1
public_service_announcement,0
dandoo,1
white_exoskeleton,0
bayonetta_(character),4
furrypur,1
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blaketheflareon,1
pizza_slice,0
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lykranic_studios,3
box_(hajimeyou654),1
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mightycock_(artist),1
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alolan_sandslash,5
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ral,4
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eklund_daily_life_in_a_royal_family,3
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well,0
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pester,1
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goop,0
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red_high_heels,0
shaggy_hair,0
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piebunny,1
solar-slash,1
deeja,4
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vine_whip,0
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cate_wurtz,1
hthfp,1
steven_quartz_universe,4
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gabe_(mytigertail),4
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heat_(lilo_and_stitch),4
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gokhan16,1
caramel-kitteh,1
flame-lonewolf,1
sblueicecream,1
sentret,5
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amalgamate,5
kitty_(kimba),4
card_hand,0
contraption_concept,0
natalie_de_corsair,1
zlut385,1
nyuudles,1
zhang_fei_(full_bokko_heroes),4
lightingsaber,1
gyaru,0
tracey_tailor,4
twig,0
olive_(rawk_manx),4
ear_lick,0
henbe,1
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al-cat,1
itou_sora,4
dumbun,4
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filthy_rich_(mlp),4
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renoky,1
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draco_(artist),1
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pronk_oryx-antlerson,4
etsu_cuprumfox,1
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kannos,1
zigrock001,1
latex_panties,0
elyssa_(trinity-fate62),4
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lemon_smoothie,1
wynter,4
anna_(study_partners),4
e-123_omega,4
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coal_(samt517),4
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zkky,1
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garbodor,5
blue_submarine_no._6,3
vivillon,5
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toroko,4
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romeo_(leobo),4
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ravinosuke1,1
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amelie_(bunnybits),4
bijou_(hamtaro),4
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huepow,4
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sunagawa_(bonedra),4
miyuka,1
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cassie_(foxydude),4
nameless00,1
cyiakanami,1
lyre_belladonna,4
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firepawdacat,4
menyang,1
piratefoxbox,1
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lovers_of_aether,3
arun_(tokaido),4
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princess_vaxi,4
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first_orgasm,0
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heat_(disambiguation),-1
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mobian_prey,0
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zenfry,1
elbestia,1
darkminou,1
mrkipler,1
vyrn,4
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ikuta_takanon,1
no_symbol,0
chizi,1
aye-aye,5
purple_robe,0
swanna,5
tentacle_around_breast,0
kitsunami_the_fennec,4
jill_(james_howard),4
pedverse,1
vermilion888,1
indi_marrallang,4
pink_wolf_(ac_stuart),4
scott_otter,4
meeka_rose,4
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yoditax,1
amethyst_(steven_universe),4
jibanyan,4
biscuit_(biscuits),4
kiva_(amazon),4
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dragon_horn,0
hexxy,1
maximumpingas,1
o-ishi,1
skia,1
rooc,1
kdhynamo,1
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laundry_basket,0
pheeze,4
adhira_hale,4
yuzu_zuzu,1
raier_(unrealplace),4
scorchen,5
zygarde,5
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evenesko.d.fox_(artist),1
all_the_king's_men,3
bbdworks,1
tree_frog,5
juliagoldfox,1
kekeflipnote,3
makowolf1,1
jesse_(falcon_mccooper),4
trevart,1
garo_(garoshadowscale),4
stacey,4
spamjamz,1
harkrun,1
mechanical_dragon,5
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samuelwolfo,1
donkeypunch,1
6my,1
canopus335,1
farkhan,1
empskit,1
norataro,1
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sixsidesofmyhead,1
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brandi,1
sleepiness18,1
keron,4
sparkittyart,1
young_(lore),-1
doctorzexxck,1
ebonhorn_(foxxeh),4
jorge-the-wolfdog,1
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takarachan,1
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gwen_mason,4
feet_behind_head,0
relatedguy,1
noaharbre,1
kakuna,5
kimubota,1
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blizzard_(weather),0
nightmare_freddy_(fnaf),4
meheheehehe,1
mizky,1
lovemummification,1
cryphalen,1
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sobbing,0
fel,1
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ranara,1
vaporwave,0
wonderboy,3
fall_guy,5
squablodecomplash,1
ratbat,1
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xenochelle,1
brand_parody,0
brushart,1
tiesci,4
amanddica,1
matuska,1
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fizzz,1
kisera,1
daniel_kay,1
sigma_x_(character),4
frank_westerveldt,4
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ember_the_typhlosion,4
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gremlin,5
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river_(armello),4
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toot,1
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g1r0r0,1
dam_(artist),1
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loriinae,5
clam,5
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fek,1
axo_(fortnite),4
sudkampsin,1
54321awesome,1
jotaro_kujo,4
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psyredtails,1
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wit_1,1
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stamp,0
tim_o'rourke,1
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burrito,0
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groot,4
olimacdoodles,1
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ford_mustang,3
escopeto,1
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slop,3
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araru,1
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swadloon,5
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tribaldragon,1
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translucent_bra,0
twillight_(twillightskunk),4
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king_spade,4
mythical,0
tarkir,1
kumoko,4
rylee_(senimasan),4
gauged_snake_hood,0
anneke_(weaver),4
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maggie_applebee,4
kaislair,0
unyin,1
srriz,4
paloma-paloma,1
commandg,1
shark_week,3
eroticphobia,1
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greenmonkey,1
ruze,4
cosmicunicorn,1
mewdles,1
john_blacksad,4
squeaking,0
marzipan_(spottedtigress),4
nivy_(artist),1
kris_(zourik),4
reizo,4
rochelle_barnette,4
goat_horn,0
vest_only,0
banzai_(the_lion_king),4
hand_on_table,0
porldraws,1
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glaive,0
toe_scrunch,0
diaszoom,1
angryelanoises,1
roz_gibson,1
naga_(artist),1
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tower_duo,0
gold_skin,0
ais05,1
noctowl,5
:s,0
plant_print,0
spreader_toy,0
bagheera_(jungle_book),4
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olethros,1
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tapering_tongue,0
rental_mommy,3
marota,1
electro_current_(oc),4
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cheap-wazoo,1
pec_squeeze,0
black_scarf,0
alvano_amala,4
porygon,5
lipstick_on_butt,0
coontail_hair,0
wallpaper_(decoration),0
lewd_latte,1
neverwolf,1
larkdraws,1
zenonzard,3
garr_(breath_of_fire),4
picnic_table,0
dress_up,0
jiralhanae,5
gears_of_war,3
resachii,1
t.n.,1
keedot,1
team_aqua,3
tracy_j._butler,1
mother_and_father,0
danika_(wolflady),4
vandalistpikachu,1
tigerinspace,1
ace_stryker,1
tayronnebr,1
big_trapezius,0
oksara_(character),4
"""honest""_john_foulfellow",4
oswald_the_lucky_rabbit,4
gradient_eyes,0
kogeikun,1
paint_can,0
group_hug,0
fantasyblade,1
tektah,1
ahoge_(artist),1
mutio,4
piranhapettingzoo,1
fhyra,4
six_frame_staggered_grid,0
alori_dawnstar,4
toddrogue69,1
luigiix,1
malerouille,1
monkey_d._luffy,4
subnautica,3
two_tone_gloves,0
cra,5
da_polar_inc,1
wayesh,1
blueshark,1
popogori,1
cat-boots,1
leviantan581re,1
veiny_skin,0
rapid_digestion,0
owen,4
wet_bottomwear,0
tiki_san,1
two_side_up,0
hockey_mask,0
dlw,1
louis_wain,1
neo_hajime,1
pattern_necktie,0
kouh,1
twistedgrimtv,1
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norio_(pheromosa_times),1
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raemyshu,1
blue_pillow,0
mothman,4
giraffe_mom,4
scratch_(adventures_of_sonic_the_hedgehog),4
quest_for_glory,3
anivia_(lol),4
archon_eclipse,4
skink_(warhammer_fantasy),5
lucas_(sssonic2),4
ten_kodori,4
robedart,1
dry_bones,5
pepe_the_frog,4
muffy_(animal_crossing),4
mintyskin,1
kohtek,1
illusion,0
volcanins,1
toxic-mario,1
tsuyagami,1
flour,0
spy,0
sesame_akane,4
gore_magala,5
suri_polomare_(mlp),4
pudding,0
koosh-ball,1
red_pillow,0
light_areola,0
siege_(arknights),4
cinder_glow_(mlp),4
titfight,0
duster_(dustafyer7),4
17:22,7
wing_markings,0
cloudeon,1
enko_(mrt0ony),4
shinkaku,1
revenge_sex,0
fanfiction,0
ian_dela_cruz,4
saeko_art,1
sjevi,1
nose_spike,0
leqha,1
dorohedoro,3
keroro,4
vinnie_(bmfm),4
bearded_dragon,5
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montage,0
noguchi,4
hornbuckle,1
bowling_pin,0
samoji,1
aegislash,5
magiace,1
wilson_(brogulls),4
pantherlily,4
katty_katswell,4
toodledipsy,1
bokeh,0
fallen_(artist),1
david_hopkins,1
tamaranean,5
holding_penetrable_sex_toy,0
doodledoggy,1
madcow100,3
alex_kitsune,1
blowjob_beast,5
psibunny,1
youngster_(pokemon_sword_and_shield),4
knochka,1
asderzx,1
plugsuit,0
eurobeat,1
rat_god_(mad_rat_dead),4
purple_bonus,1
pom_(tfh),4
hicanyoumooforme,1
douglas_kim,1
greta_(mlp),4
plumber,0
noxy_(equinox),4
waterscape,0
reddrago,1
head_leaf,0
nogard_krad_nox,4
cat_o'_nine_tails,0
mendobear,1
unusual_heels,0
australia,0
pomacentrid,5
darksideofdiscovery,1
magearna,5
tan_toes,0
helmeted_lizard,5
bible,3
flim_(mlp),4
gas_station,0
diana_(bashfulsprite),4
marc_(smar7),4
furry_force,3
himachi,1
nightmare_fredbear_(fnaf),4
germany,0
solratic,1
boltund,5
species_name_variant,0
pitcher,0
tentacle_tail,0
pav,1
colarix,1
andromorph_focus,0
bovine_pussy,0
vani-fox,1
digi,4
monstro_village,3
genital_tattoo,0
venus_de_milo_(tmnt),4
spoonyfox,3
hijirinoyuta,1
geometryboymejia,1
loss,3
tre,4
hotbento,1
jace_(gasaraki2007),4
kokkoro_(princess_connect!),4
sighing,0
burrnie,1
chaindecay,1
goldeen,5
fyixen,1
tycloud,4
band_accessory,0
asterius_(hades),4
songbird_serenade_(mlp),4
jerro,1
hysk,1
multiple_impregnation,0
countershade_penis,0
hyper_foreskin,0
broody,4
tenynn,1
torn_body,0
huge_teats,0
vetisx,1
oldman_artist,1
hand_on_floor,0
one_shoe_on,0
stomped,0
gunpoint,0
kairunoburogu,1
utawarerumono,3
pop-tarts,3
foxxj,1
whiro,4
linahusky,4
slaking,5
lakehounds,1
blue_overalls,0
footless_socks,0
kyrio,1
jigglyjuggle,1
glass0milk,1
thevillager,1
annairu,1
student_mei_ling,4
mitti,1
1ndigocat,1
ruby_quest,3
naughty_skeleton,1
magayser,1
sudden,0
working_buddies!,3
excessive_drooling,0
8:9,7
stardep,1
anothercolouranon,1
nayel-ie,1
italian_text,7
dakota-bear,1
back_plant,0
caesar_(anglo),4
storm-tiger,4
4_panel_comic,0
cum_on_dildo,0
greeneyedwolfking,1
monster_prom,3
devilturtle,1
penis_mouth,0
falcon_mccooper_(character),4
taylor_knight,4
object_shows,3
trail,0
caribbean_blue,3
urine_in_own_mouth,0
bluedouble,1
daniel_porter,4
fleur-de-lis,0
scottred,1
kikunoya,1
ash_(ashkelling),4
snapping_fingers,0
twoscale,1
fennec_humanoid,5
urethra_worm,0
daz_3d,3
looming_over,0
dark_thigh_highs,0
mattie_(chimangetsu),4
nashiholy,1
glut,0
buffbumblebee,1
satoshi_nagashima_(odd_taxi),4
orange_handwear,0
black_antlers,0
toned_stomach,0
grovyle_the_thief,4
digo_marrallang,4
vicioustyrant,1
thatgryphonguy,1
the_wayward_astronomer,3
multi_body,0
ichkoro,1
white_sweater,0
fish_tank,0
puzzle,0
grimmsnarl,5
jockstrap_aside,0
delta_vee,4
kigoh-neko,1
sloth_bear,5
randt,1
fortune_telling,0
zera_(titsunekitsune),4
surgical_operation,0
keldeo_(resolute_form),5
ende,1
kajabuubuu,1
izrez,1
antti_remes,1
black_blindfold,0
guillotine,0
6_fingers,0
serving_food,0
padoga,1
aerith_gainsborough,4
urta,4
diamondbackdrake,1
pleasantlyplumpthiccness,1
luwei_(artist),1
non-humanoid_machine,0
hasana-chan,1
caution_tape,0
withered_chica_(fnaf),4
skdaffle,1
nestor_(spyro),4
dyson_(eldiman),4
mykendyke,1
buru_(jaynatorburudragon),4
robot_master,5
luraiokun,1
blue_(shiro-neko),4
neck_ribbon,0
alcelaphine,5
pyritie,1
kyuuhari,1
kissing_neck,0
featureless_eyes,0
fukuzawa,4
closet_coon,3
kincade,4
neonyx,1
clopician,1
rajah_(disney),4
croissant,0
cocaine,0
bowser_koopa_junior_(roommates),4
kom_(komdog),4
weighted_companion_cube,4
unit_no04,1
fused_legs,0
tiger_(monster_rancher),5
crystal_creature,5
sheriff_of_nottingham,4
slip,0
whiteperson,1
sassylebraix,1
suiting,0
mad_scientist,0
bitfly,1
homophobia,0
color_coded_hearts,0
draco_shokan,5
dr.bubblebum,1
emotionless,0
0227,1
daniel_(hladilnik),4
progressoftomorrow,1
hamstrings,0
glasses_askew,0
somewhatsketchy,1
longdanger,1
avalonsfm,1
anbs-02,4
splashtf,1
tharis,4
green_hill_zone,0
littledoll,1
mantle,0
roger_(mike_sherman),4
nobro,1
clownfish,5
bursting_out_of_clothing,0
dress_pants,0
kotetsu_rexen,1
pennant,0
strayserval,1
syandene,4
professor,0
doggieo_(character),4
herm/ambiguous,0
hawtmoon,1
water_slide,0
linsang,5
phat_pants,0
horn_ornament,0
livia_(dreamypride),4
ladyvenommyotismon,1
niko_(pkfirefawx),4
switch_console,0
doginacafe,1
nikorokumitsero,3
volodyanocturne,1
hamuga86,1
rariedash,1
lee_the_kec,4
pimpartist,1
hose_in_mouth,0
doctor_neo_cortex,4
holding_note_pad,0
arm_guards,0
tithinian,1
indigo_marrallang,4
stevenlew,1
pouring_onto_self,0
yogi_bear_(character),4
miroukitsu,1
law_(doggylaw),4
chupadore,5
white_heart,0
holding_collar,0
canyon_(artist),1
wffl_(artist),1
ferilla,1
rarewhoroastbeast_(artist),1
amethyst_(kitfox-crimson),4
ohakotome,1
monamania,1
ellie_saionji,1
tan_headwear,0
privvys-art,1
check_mark,0
inkanyamba_(artist),1
turnip,0
black_eyelids,0
licker_(resident_evil),5
kazoko_(artist),1
onmyoji,3
cyrus_(animal_crossing),4
kenjikanzaki05,1
thrakos,4
firelander,4
stowaway,1
kaitty,4
prinny,5
sheeva,4
gourd,0
averrisvis,1
thirstydraws,1
toxi_de_vyne_(artist),1
ledian,5
joyride_(colt_quest),4
bicorn,5
gradie,4
fluke,1
4:1,7
flame_markings,0
cacatua,5
kouseki0024,1
siriusdog,1
latex_boots,0
kaoru_(kitaness),4
lolly_(butterscotchlollipop),4
night_sky_body,0
x03,1
maxpany,1
miltonholmes,1
youngster_(pokemon),4
satisfied_look,0
blue_speedo,0
vannie,1
striped_wings,0
holstaurus,5
taboo_tails_(copyright),3
thor_(series),3
leather_collar,0
mizukithia,1
drake_drachenlicht,1
cum_on_sheath,0
saintcocoa,1
taracod,1
ryoko_(character),4
black_jackal,1
golf,0
taur_penetrating_taur,0
corinoch,4
2_mouths,0
animasanimus,1
starlow,4
kimmy_(felino),4
skunk_spray,0
wattchewant,1
honeyboyy,1
geodude,5
sawk,5
fatyogi,1
perspective_shot,0
firing_weapon,0
bungie,3
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robby_bunny,4
spiked_choker,0
kebi,1
day_count,0
cum_in_stomach,0
17:11,7
veiny_belly,0
shira_(ice_age),4
face_in_crotch,0
grey_highlights,0
meryl,4
strategically_covered,0
pawsve,1
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sum,1
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rynring,4
ethan_bedlam,4
realization,0
wrists_together,0
modelling,0
evillabrat,1
male_fingering_male,0
purple_arms,0
sleep_fetish,0
mechanical_penis,0
luna_paws,4
flaming_wings,0
sharperit,1
breeding_spree,0
lean_muscle,0
white_glans,0
yosioka_san,1
fol,1
cursor,0
cum_on_sofa,0
emmitt_otterton,4
firefly_(pre-g4),4
penny_flynn,4
prey_penetrated,0
elmelie,4
pelvic_fins,0
huegirl19,1
pastel_please,1
bindings,0
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anikifaux,1
wander,4
wormmon,5
elephant_penis,0
grummancat,1
traffic_light,0
invalid_tag,-1
combos-n-doodles,1
alpha_garza_(vimhomeless),4
tyranid,5
miss_bianca_(the_rescuers),4
felicity_longis,4
grandmother_and_grandson,0
hangover,0
the-wag,1
marcus_(rukis),4
pixile_studios,3
ssu_open,1
green_neckerchief,0
clothed_female_nude_intersex,0
kamyuelo,1
colored_pussy_juice,0
alric_kyznetsov,4
ipad,3
sihai's_legacy,3
halloween_decoration,0
parooty,1
pattern_headgear,0
nobody3,1
color_contrast,7
cuffs_(disambiguation),-1
throat_(artist),1
brunhilda_(dragalia_lost),4
princess_mononoke,3
mocha_latte,4
maron2475,1
lemonbizate_(artist),1
idolomantises,1
content,0
petri_(animal_crossing),4
presenting_sheath,0
robosylveon,1
suu_(monster_musume),4
whitepone,1
redrick_(erickredfox),4
digital_sketch,0
bo-gilliam,1
igazella,1
contrapposto,0
cortana_(halo),4
rookie_bear,1
red_glow,0
sebastian_cummins_(thechavicgerman),4
monkey_king,4
pinkaxolotl,1
two_tone_dress,0
solidasp,1
gameplay,0
majmajor,1
stephen_king_(copyright),3
pinkuh,1
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lucretia,1
saffron_masala_(mlp),4
manakete,5
krinkels,1
jason_voorhees,4
forceful,0
crimsonrabbit,1
aijou,4
heyitscousindave,1
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fennec_(artist),1
foxene,1
withered_foxy_(fnaf),4
kinaj,1
heavy_blush,0
pelican,5
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beast_(marvel),4
murdered,0
sleepyinu,1
tactical_gear,0
bob_(vju79),4
legs_above_head,0
the_avengers,3
autolactation,0
tanookicatoon,1
coel3d,1
snow_(matthewdragonblaze),4
on_counter,0
navel_tuft,0
dovecake,1
pepper_(fruit),0
dado463art,1
monotone_hat,0
tauntaun,5
emperorstarscream,1
blood_on_body,0
francis_misztalski,4
uno_yuuji,1
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handle,0
naz_namaki_(cynxie),4
skoll_(wolf-skoll),4
gas_tank,0
kapri,1
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female_dominating_female,0
brown_outline,0
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shanukk,4
leghorn_chicken,5
bluewhale127,1
onward_(2020),3
006_zerozerosix,1
tabatha_(samoyena),4
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dangan,1
adrian_(firewolf),4
tree_trunk,0
whitney_(pokemon),4
zeigram,1
pokemon_(anime),3
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mother_kate_(jakethegoat),4
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green_goo,0
demon-man,1
garrosh_hellscream,4
head_on_butt,0
ectoplasm,0
night_elf_(feral),5
miss_piggy,4
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nights,4
sasha_gothica,4
phursie,4
kink,0
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lucas_(earthbound),4
bieesha,4
kaiju_paradise,3
wolfsoul,1
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kasaler,1
s.leech_(oc),4
jagras,5
rosie_(roselynn_meadow),4
reflective_floor,0
vacation,0
furfnsfw,1
daestra_the_hedgehog,4
highwaytotartarus,1
boron_brioche,4
jeri_katou,4
garter_belt_socks,0
kunugi,1
heavy_metal,3
mn27,1
bodily,0
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olivia_hart,4
imminent_vaginal,0
wrong_hole,0
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meteor,0
triangle_nose,0
monotone_shoes,0
multicolored_feet,0
angoraram,1
hand_on_object,0
firefighter_helmet,0
biting_shirt,0
owo_sfm,1
unikitty!,3
horsen,1
fish_birb,1
pongldr,1
carnivorous_owl,1
nastypasty,1
lumina_(stargazer),4
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kl0ndike,1
alpi,1
infinity_train,3
teddy_conner,4
boomer_(nanoff),4
hydrabb,1
warskunk_(artist),1
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multi_head_kiss,0
whiteskyline,1
visark,1
wanderlustdragon,1
ochaco_uraraka,4
dusk_(tabuley),4
pussy_noir,4
queen_novo_(mlp),4
jazzotter,4
cinnamoroll,3
melody_(mellybyte),4
lounge,0
brass_knuckles,0
the_shadow_of_light,3
breezie_the_hedgehog,4
furred_shark,5
aiden_laninga,4
fellowwolf,1
ethriol,1
hazuki_mikami_(hasukii),4
yaudizz,1
pokémon_battle,0
ivan-jhang,1
snavel,4
rainbownspeedash,1
m4,0
presenting_cervix,0
mister_d,1
savemekitty,1
heart_shirt,0
yellow_flower,0
urethral_plug,0
scuba_gear,0
big_bun_burgers,3
sewn_pussy,0
nik_(sonicfox),4
balding,0
gouache_(artwork),7
overweight_semi-anthro,0
badwingm,1
barlu,1
dark_chao,5
grocery_store,0
krisha_russel,4
tsukigata_rossi,1
robin_(dc),4
caged,0
ultima_(oc),4
shema,4
kokkoman,1
tammy_squirrel,4
teal_mane,0
cam_collins,4
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filly_anon,4
ethereal_mane,0
maquenda,1
condiment,0
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light_arms,0
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garret_(rain-yatsu),4
sex_gesture,0
november,4
something_different,3
pink_panther_(series),3
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custom_species,5
yellow_skirt,0
only0neman,1
frill_(disambiguation),-1
recursive_vore,0
hotbrotkuroi,1
black_speedo,0
holding_single_card,0
game_boy_cartridge,0
flam_(mlp),4
scataraxia,1
kraft_lawrence,4
gtskunkrat_(character),4
diggersby,5
grievousfan,1
annonymouse,1
the_dreamstone,3
vandclash,1
dalley-the-alpha,1
karbo,1
cum_on_underwear,0
claydust,1
anutka,1
nipple_focus,0
white_fingernails,0
floral_clothing,0
ethiopian_wolf,5
holaxes,1
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kinojaggernov,1
zander_(zhanbow),4
kailys,1
mercury_shine,4
shadow_effect,0
hellbridge,1
flash_slothmore,4
converse_shoes,0
kanna_(joaoppereiraus),4
shoebill_(kemono_friends),4
irisarco,1
spiral_horn,0
twotail_(mlp),4
skunk_fu,3
monotone_stockings,0
friesian,5
ayana,4
harbour_princess,4
dave_(password),4
ankylosaurus,5
white_kerchief,0
biyemorph,5
hawkvally,1
6:7,7
wyverian,5
anna_(sailoranna),4
inake,1
khblacky97,1
mistaken_identity,0
american_bison,5
rabbitbrush,1
tilionmaia,1
mustelid_penis,0
caroo_(character),4
sex_toy_in_penis,0
naughty_smile,0
hb-viper,1
kaidzsu,1
endless_orgasm,0
jasmine_(pokemon),4
funky_kong,4
soraa,1
flamevulture17,1
ziggy_(dezo),4
bayonet,0
hands_on_feet,0
cum_on_glass,0
sweater_only,0
cat_(petruz),4
swordkirby,1
vi_(bug_fables),4
bambolbi,1
meowser,4
aoi_takayuki,1
7:9,7
usnarbit,1
extro,1
trolling,0
muto_(godzilla),4
year_of_the_rat,3
endtown,3
epic_soul,1
duo_radon,1
delia_(anglo),4
thesociallyawkwardpinguin,1
african_civet,5
dahs,1
shoelaces_untied,0
ryuakira,1
love_death_+_robots,3
rawdarkness,1
log_horizon,3
markiplier,4
blue_archive,3
abnarie,1
build_tiger,3
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inkune,1
violet_(artca9),4
stasis_delirium,1
road_sign,0
crocodilianbooty,1
magic_staff,0
vexedlupine,1
feirune,4
bow_(decoration),0
husk_(hazbin_hotel),4
by-sa,3
nissan,3
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female_shepard,4
wanderertamplior,1
hazel_(flittermilk),4
umbrian_(species),5
mabel_(1-upclock),4
yuki_motoe,4
blackjack_(fallout_equestria),4
squelching,0
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clear_sky,0
susaroo,1
xuan_(xuan_sirius),4
peanut_butter,0
tawny_otter,1
frill_piercing,0
bradley_(stylusknight),4
face_on_penis,0
hand_on_elbow,0
self-bondage,0
katie_kirster,4
dosh,1
nick_(beez),4
earplugs,0
mountain_range,0
tiffany_(animal_crossing),4
sly_asakura,4
lordflawn,1
drauk,5
aerth,1
jv,4
rytlock_brimstone,4
fist_pump,0
manyu,1
lethal_league,3
slushi_(chikn_nuggit),4
3_panel_comic,0
holding_marker,0
light_wings,0
digging,0
moses_(samur_shalem),4
two-handed_face_fucking,0
bucephalus,4
okdrsutsu,1
moon_knight_(series),3
rastaban_coal,1
lovebrew_(oc),4
predator_penetrating,0
teal_pussy,0
yin_yang_yo!,3
saillestraife,1
forepawz,3
leovictor,1
long_dress,0
reggie_(tokifuji),4
gold_penis,0
damian_(zoophobia),4
peeing_on_penis,0
pet_bed,0
arylon_lovire,4
aleu_moonshadow,4
noonie-beyl,4
sajik,1
nasa,3
pixelyteskunk,1
culpeofox,1
rubikon,1
pokey_pierce_(mlp),4
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blue_flower,0
loneclaw,1
neboveria,1
analog_clock,0
vibrator_hip_strap,0
will_delrio_(sketchybug),4
broken_glasses,0
caught_masturbating,0
sato_afroleone,1
hayven_celestia,3
diesirae,4
sijimmy456,1
shapeshifting,0
rider_of_black,4
angelface,1
object_shot,0
supergirl,4
yamato_iouko,4
frumples_(character),4
pinkie_peaches,1
sodiav,1
wolfgangcake,1
paguroid,5
9999gpera,1
milkjunkie,3
cum_on_own_feet,0
fudchan,1
nekoarashi,1
multicolored_boots,0
nosivi,4
saxophone,0
cum_on_pillow,0
coffeesheizen,1
selene_blackcat,4
gular_flap,0
bartok_the_magnificent,3
a_princess_of_mars,3
red_t-shirt,0
megumigoo,1
mottled_pussy,0
codpiece,0
natttttalina,1
sarana_(knotthere),4
pastelcore,1
faline,4
chrissy_(animal_crossing),4
red_tuft,0
sagging_pants,0
drpolice,1
palisal,1
deezmo,1
detailed_genitals,0
serilde,4
tionishia_(monster_musume),4
locker_bench,0
ember-dragoness,1
playing_piano,0
northernsprint,1
beavertyan,1
fleet_wing,1
micro_in_clothes,0
darwen,4
tenchi_muyo,3
margaret_(vetisx),4
green_slime,0
idrysse3,1
pinkie_pie_(eg),4
tres-apples,1
goattrain,1
neferpitou,4
quillu_(character),4
onyx_wasson,4
krayboost,1
purpleground02,1
landon,4
steel,0
coffinpaws,1
diablo_the_rex,1
swept_bangs,0
teenage_mutant_ninja_turtles_(1987),3
dylbun,1
chuurow,1
solii_(gizmo1205),4
elim_dorelga,4
dappled_fur,0
lio29227541,1
windborn,3
sowat-blend,1
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grey_dress,0
selective_coloring,0
amur_leopard,5
youl,4
hairless_tail,0
yrel,4
fisuku,1
chaise_lounge,0
knotted_tapering_penis,0
detailed_mouth,0
microphone_stand,0
dragontim,1
prehistoric,0
pinstripes,0
kubikitsune,1
clarabelle_cow,4
huckser,1
pattern_headwear,0
chibsarts,1
korean_jindo,5
ed_edd_n_eddy,3
tan_(artist),1
kamui_(kill_la_kill),5
yaoyasohachi,1
belt_bondage,0
tail_lick,0
jasentamiia,1
thong_only,0
koda-kota,1
dynoex,1
betsibi,4
beartato,4
infesticles,0
sitting_on_stool,0
go1den_(wanda_fan_one_piece),4
playstation_3,3
keith_(funkybun),4
mole_on_breast,0
e-zoid,1
scarfyace,1
12:13,0
american_eagle,4
blue_tail_feathers,0
agata_(beastars),4
longsword,0
anubii_(quin-nsfw),4
beholder,5
bibarel,5
ghostwolf,1
bran-draws-things,1
deusexequus,1
senketsu,4
strings,0
fan_comic,0
blue_neck,0
firebunny,5
dark_lips,0
dogma,1
used_diaper,0
multicolored_hands,0
steen,1
teleportation,0
crossbreed_priscilla,4
snuffy,4
kaeaskavi,1
felimon,4
two_tone_stockings,0
touching_chest,0
snack,0
head_out_of_frame,0
costom10_(character),4
orion,4
warning_symbol,0
nurse_joy,4
idena_(swordfox),4
tombola1993,1
sidekick,1
the_great_prince_of_the_forest,4
gf,1
yang_xiao_long,4
kowalski,4
admiralobster,1
danslittlefurs,1
pooping_on_another,0
d.angelo,1
human_to_taur,0
creamytea,1
jaggi,5
starswirl_the_bearded_(mlp),4
two_tone_paws,0
clip_accessory,0
freddy_(dislyte),4
huskyman24,1
malware,1
athlete,0
jace_(darknaig),4
ludmilla_(bartok),4
cattle_taur,5
vertical_staggering,0
thugji3,1
noke_(delicatessen),4
equine_legs,0
coby_(mao_mao),4
nastysashy,1
on_fence,0
derpyrider,1
ramiras,1
crested_porcupine,5
overweight_andromorph,0
space_dragon,5
goldie_pheasant,4
nullenigma,1
fat_mons,0
cyanzangoose,1
025aki,1
director_ton,4
mickey_mouse_shorts,3
jmsdf,3
planktonheretic,1
tapiko,1
swift_fox,5
green_light,0
swear,0
double_deck,1
nude_intersex,0
flower_ornament,0
vapor_trail_(mlp),4
watermelon_(artist),1
firetails,1
grey_paws,0
milking_cum,0
lyra_(pokémon),4
copper_(fath),4
alice_margatroid,4
shadowfenrirart,1
recon_scout_teemo,4
psychic_powers,0
metalhead97,1
pepperchan,1
shuuko,1
maya_henderson,4
ms._zard,4
amplifier,0
slipping,0
darkynsfw,1
rapid_transit,0
praise_kink,0
mayoineko,1
male_oviposition,0
myrilla,4
beholdervee,1
mine,0
perfume,0
shaggy_rogers,4
hisuian_samurott,5
black_wool,0
error,0
randall_boggs,4
torn_panties,0
imminent_rimming,0
straight_bangs,0
bumped_dildo,0
gooze_(sunibee),4
berserk,3
rough_sketch,0
mintyderg,1
leg_spikes,0
purple_necktie,0
zoom_lines,0
helelos_(artist),1
team_shuffle,1
ultrafem,1
edesk,1
toothpaste,0
mangobird,1
pansage,5
bourbon_(artist),1
gsphere,1
business,0
barzillai,4
doctorproxy,1
alfred_(umpherio),4
mike_(bcb),4
raft,0
veigar-chan,1
impidimp,5
black_tail_feathers,0
imabunbun,1
teryx,3
mega_sceptile,5
morpeko,5
dark_blue_body,0
poker_table,0
aglet,0
striped_necktie,0
nanukkluik,1
sheik_(tloz),4
kojiro-brushard,1
inner_monologue,0
mashiromiku,1
thaz_(character),4
eihman,1
deareditor,1
voviat,1
scombriform,5
kaeli_cedarfallen,4
riska_(artist),1
auria_jansson,4
caustics,0
slap_mark,0
begasuslu,1
snowboarding,0
bellies_touching,0
0k0j0,1
shoes_removed,0
davey_(diadorin),4
arm_on_thigh,0
monotone_swimwear,0
ryder_(striped_sins),4
fake_cow_ears,0
almost_fully_inside,0
glistening_gem,0
yuugi_hoshiguma,4
o-ring_bikini_top,0
heart_pawpads,0
oxfort2199,1
huge_brachioradialis,0
hallucination,0
pachycephalosaurus,5
rita_(fuf),4
dildo_mask,0
lube_container,0
spotted_wings,0
holding_basket,0
arm_around_leg,0
lotus_(flower),0
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amazon_(dragon's_crown),4
cum_on_lips,0
birthday_gift,0
female_penetrating_gynomorph,0
spray_bottle,0
political_cartoon,0
art-abaddon,1
gir,4
irish_wolfhound,5
drulttur,5
niban-destikim,1
sky_(youwannaslap),4
rwolf,1
alex_(phrannd),4
dipstick_fingers,0
iji,1
camping_tent,0
studio_chizu,3
hiddenmask18,1
armadillomon,5
cheese_wedge,0
bubbeh,1
ryu_masakaze,1
unwanted_creampie,0
savestate,3
riven_(lol),4
sasha_sweets,4
edi_(mass_effect),4
machgaogamon,5
year_of_the_pig,3
dmitrei,4
capdocks,1
blue_cape,0
prinisdead,1
nekonade,1
simul,1
momo_(dagasi),4
dibujito,1
fonyaa,1
seshed_circlet,0
tekahika,1
saucer,0
black-footed_ferret,5
incestaroos,3
holding_dagger,0
sakana8888888,1
quicktimepony,1
coco_nebulon,4
skarlett_cynder,4
asexual_pride_colors,0
true_hawk,5
schizy,1
ssvanti,4
ears_aside,0
green_speedo,0
cleric,0
blaze_(wolf),4
darkpotzm,1
diaper_creature,5
katie_tiedrich,1
flavored_cum,0
halu_1129,1
adjusting_clothing,0
disco_ball,0
sirenian,5
isisazza,1
implied_orgasm,0
apogee,4
annie_and_the_mirror_goat,3
moblie_(character),4
night_(nightfaux),4
modpone,4
misaki_furry515,1
anon_(snoot_game),4
magnetiorchid,1
gastropod_humanoid,5
dragonweirdo,1
neozoa,1
verbal_reaction_to_tf,0
tapestry,0
battlefield,0
lundi,1
lettuce_(artist),1
arm_around_shoulder,0
matt_(scratch21),4
south_park,3
sea_anemone,5
pugthe2ro,1
yakou,1
twilight_stormshi_(character),4
the_stag,4
ziggy_fox,1
denmoko,1
desktop,0
geng,1
battlerite,3
night_glider_(mlp),4
locked,0
creaturecorp,1
motmot,5
evening_gloves,0
relevancy,1
nightlycatgirl,1
mugshot,0
helemaranth,1
bat_symbol,0
queen_(deltarune),4
bunnie_love_(character),4
yellow_tank_top,0
annie_(anaid),4
reggie_(whygena),4
yellow_neckerchief,0
two_tone_balls,0
tentacles_from_pussy,0
beanish,5
viddharta-joshua,1
leila_snowpaw,4
cell_(dragon_ball),4
orange_gloves,0
groping_chest,0
sen_grisane_(artist),1
twister,3
heart_glasses,0
push,0
kunn_(artist),1
drooling_cum,0
foxyghost,1
zayats,4
kate_(jakethegoat),4
charlotte_(phurie),4
bludermaus,1
bsting,1
horngasm,0
teitoryu,1
harpy_humanoid,5
inkart_flow,1
super_monkey_ball,3
thescarletdragon1,1
older_herm,0
cowardlion,1
lizardman_(artist),1
impreza,4
kruth666,1
katxfish,1
body_jewelry,0
tail_on_furniture,0
boulder_(mlp),4
nose_kiss,0
by-nc-sa,3
purity_the_hedgehog,4
lotus_pose,0
vera_(frisky_ferals),4
narmaya,4
lock-wolf,1
canine_teeth,0
electivire,5
sila_dione,4
verbal,0
wooden,0
cum_in_own_ass,0
candy_(mrmadhead),4
security_camera,0
estper,1
passionatefloorromance,1
orange_hoodie,0
flushed,0
pattern_kerchief,0
tacet_the_terror,4
bilabee,1
gapao,1
arti4000,1
kaji_(karnator),4
necrozma,5
budgerigar,5
illuminatii,1
hair_tied,0
sheath_tuft,0
grace_mustang,4
wolfeed,1
maaia,1
powderkona,1
thunder-renamon,1
rj_(over_the_hedge),4
capt_hairball,1
foreskin_bite,0
weeniewonsh,1
warning_(fluff-kevlar),4
jumpjump,1
girls_frontline,3
skippy:_adventures_in_bushtown,3
zlatavector,1
maladash,4
abe_(mikrogoat),4
curls,0
penis_hold,0
cham22,1
sakimichan,1
anthro_penetrating_taur,0
samantha_whitten,1
hakuro_(onmyoji),4
tolpain,1
bowl_cut,0
shadowfirdark,1
coypowers,1
muhjob,1
red_carpet,0
ranok_(far_beyond_the_world),4
dark_bottomwear,0
gabe_(james_howard),4
male_and_female_submissive,0
villmix,1
dramatic_lighting,0
playing_violin,0
joss_(funkybun),4
little_cato,4
wavy_horn,0
cum_on_own_tail,0
punch-out!!,3
smaug,4
katta,5
ranni_the_witch,4
japanese_hare,5
green_lightsaber,0
mountain_dew,3
allannocte,1
twin_ear_bows,0
penis_on_belly,0
yellow_bow,0
damsel_in_distress,0
hiona,4
cdi,1
ambient_coral,5
hth_studios,3
drive-thru,0
feces_everywhere,0
monotone_socks,0
milkwyvern,1
regigigas,5
nongenerous,1
morgana_(lol),4
karin_(tetetor-oort),4
yu_chi_i,1
totally_spies!,3
nyurora,1
twintails3d,1
print_bottomwear,0
krista_(zillford),4
queen_(alfa995),4
mixed_cum_impregnation,0
dildo_shitting,0
panzerschreckleopard,1
foe:_project_horizons,3
cricket,5
cobb_(shade),4
nina_tucker,4
yellowroom,1
leafdog,1
alanottaviano,1
liz_(eye_moisturizer),4
milk-jug,1
toodles_galore,4
skull_earrings,0
magic_hands,0
satina,4
dodger_(disney),4
maguro,1
udder_breasts,0
royluna,1
pillow_fight,0
dragonmaid_(yu-gi-oh!),5
superhypersonic2000_(artist),1
amber_williams,1
the_smurfs,3
lunarlacepony,1
shlick,0
corset_dress,0
sdx3,1
wolverine_(marvel),4
school_bag,0
xxangeluciferxx,1
brown_coat,0
thiccwithaq,1
stranger_things,3
fursin,1
rosy_firefly,4
tabbycat,5
revoli,4
wall_bondage,0
malamar,5
game_grumps,3
window_sill,0
red_ear_fins,0
kabuki_homewood,1
worshiping,0
lewdookami,1
milo_nettle,1
saltwatertoffee,1
looking_around_corner,0
dingbat,1
kirin_(mh),5
racoonwolf,1
pink_chastity_cage,0
crocodile_(petruz),4
disembodied,0
non-euclidean_penetration,0
novus_(kitfox-crimson),4
human_on_female,0
yessys,1
forrest_(chump),4
nap,0
ginga_nagareboshi_gin,3
dipstick_horn,0
shira_kaisuri,4
cinna-tree,1
chav,0
lerico213,1
acreep,1
cover_them_up_slut,3
lc79510455,1
nongqiling,1
tush_(character),4
black_piercing,0
fvorte_(character),4
pheronoa,4
stuff_gag,0
blown-ego,1
adult_fink,4
clothing_theft,0
sakaeguchi_okarina,1
itomic,1
flight_attendant,0
thundergrey,4
cosmog,5
barrin84,1
amber,4
excessive_lactation,0
zhekathewolf,1
rattfood,1
orange_lesbian_pride_colors,0
ren_amamiya,4
clown_makeup,0
uokkom,1
flailing,0
kevintheradioguy,1
hokku_(artist),1
assisted_peeing,0
mitsuko_(spacescape),4
moirah,1
deer_prince,4
glowing_ears,0
back_wings,0
rubik's_cube,3
tamfox,1
wing_hug,0
suspended_via_tentacles,0
multicolored_text,0
barbonicles,1
gorou_(genshin_impact),4
diving_suit,0
steelix,5
hyu,4
pallas's_cat,5
chani_(ajdurai),4
rz54,1
tehbluebubble,1
daycare,0
sverhnovapony,1
offering_to_another,0
bussaca,1
magical_masturbation,0
kitt_kitan_(character),4
boke_(artist),1
jolt_(fuze),4
rubberskunktoo,1
yurari_yr,1
vitaly_(madagascar),4
plunging_neckline,0
la-frugele,1
blooper,5
the_loud_house,3
flay_(wingedwilly),4
thash,4
blue_thong,0
submarine,0
lux_(lol),4
machimotetaikou,1
faggotrat,1
multi-word_onomatopoeia,0
gammamon_(ghost_game),4
yopy,1
sea_turtle,5
wolf_children,3
dark_magic,0
yaya_panda,4
jumperkit,1
duplicitousmachine,1
scott_ramsoomair,1
blade_wolf,4
wdh3905,1
walking_away,0
xoti_(artist),1
d-wop,1
motogen,1
raidramon,5
shushup,1
nipple_band-aid,0
muh-arts,1
fortunefox,1
zinzoa,1
nekojita,1
homura_(homura_kasuka),4
kevemperor,1
marge_simpson,4
sarafina,4
cm26,1
pewdiepie,3
idlecil,1
yano_(odd_taxi),4
raptorroper,1
unknown_worlds_entertainment,3
black_corset,0
zexyprof,1
contrast,0
kaya_(knockedoutdragon),4
princess_samoyed,1
axis_(character),4
fen_(lagotrope),4
elise_(sousuke81),4
rog_minotaur,1
malekart,1
ictonychine,5
mare_do_well_(mlp),4
tora_chitose,4
chaoz_(chaozdesignz),4
matt_donovan,4
austin_(night_physics),4
sonichu_(character),4
anal_kiss_mark,0
forced_masturbation,0
man_bun,0
light_chest,0
botak,1
karkat_vantas,4
shirokuma,4
niviox,1
zehn,1
mo_to_i_chi,1
dreke,1
terrabutt,1
savagelyrandom,1
owahi_ego,1
doktor-savage,1
baterco,1
denatontr,1
syl_(fvt),4
mononi,1
skipsy_dragon_(character),4
eddie_puss,4
miu_vamcat,1
bistup,1
goatse,0
thank_you,0
dancer_outfit,0
gender_symbol_penetration,0
kaiju_girls_(webcomic),3
tofuubear,1
frostedchase,1
tokumori_kaisen,1
female_on_taur,0
longma,5
deviljho,5
akuma_gaoru,4
spunky_mutt,1
kifyun2,1
leon_henderson,4
studying,0
shashe'_saramunra,4
theo_hightower,4
liard,1
hexami,1
harem_pants,0
lindong,1
neylatl,1
lactaid,3
zzx,1
m16,0
gloom_(pokémon),5
clifford_the_big_red_dog,3
hazard_symbol_print,0
elesa_(pokémon),4
muramasa:_princess_commander,3
breast_creature,5
spot_(arknights),4
lonewolffl,1
cosplay_lolita,0
cumjarbunny,1
kemonono,1
yukari_yakumo,4
qtipps,1
teal_pawpads,0
backless_dress,0
insignia,0
zilla,4
jiandou,1
loyse,4
irony,0
salmy,4
beelzemon_(artist),1
miri,1
tephros,4
star_dragon,5
jodira,4
ambrosia,4
tarzan_(disney),3
theothefox,1
rope_gag,0
seljhet,4
green_robe,0
allie_von_schwarzenbek,4
impala,5
panko_(lawyerdog),4
do_obmmm,1
pantheggon,1
eraquis,1
modo_(bmfm),4
perspective_text,0
camcroc,1
elbow_fin,0
g-cat,1
humanoid_on_bottom,0
latch,4
clothed_masturbation,0
4_tails,0
dabelette_(character),4
cum_from_ear,0
faunoiphilia,4
bounty_hunter,0
ralek_(oc),4
slingshot,0
crustacean_humanoid,5
muetank,1
20:29,7
tania_tlacuache,4
annham,1
hexagram,0
sek-raktaa,4
vinesauce,3
orange_jacket,0
body_slider,0
orca_(artist),1
kira_nerisu,1
holding_carrot,0
underwater_view,0
tron,3
syntech,1
shermugi,1
headdress_only,0
kalimah,1
audiophilliac,1
2020_tokyo_olympics,3
bruised_eye,0
toucan_sam,4
shake,0
drew_dubsky,4
love_ball,0
back_scar,0
fuse,0
wrinkled_soles,0
anal_juice_drip,0
haley_sturmbringer_(character),4
vibershot,1
combat_helmet,0
furmessiah_(character),4
theslimedragon,1
queen's_stallion,5
xpray_(character),4
ravindra,1
hair_dye,0
cupcakes_(mlp_fanfic),3
mkcrown,1
gofa,1
yuureikun,1
corndog,0
bored_user,1
tetraodontiform,5
prometheus_(franchise),3
thomas_ii_(zer0rebel4),4
trixie_(jay_naylor),4
tamacolle,3
helena_(bonk6),4
void_elf,5
platform_shoes,0
gamecube_console,0
forced_autofellatio,0
denied,0
big_udders,0
greey,1
butterchalk,1
side_b,4
no3512,1
firebadger,1
kabrro,1
dnk,4
hammerhead_(petruz),4
4ears_5eyes,1
desert_eagle,0
monster_world_(series),3
urine_on_arms,0
gravelurkertsk,1
wetsealky,1
feline_tail,0
requestfaeg,1
cyberia,3
aoniya_yuudai,4
huckle,4
coderenard,1
hair_sex,0
idolmaster_cinderella_girls,3
dark_spots,0
air_conditioner,0
calolily,1
male_non-humanoid_machine,0
mowgli,4
squidapple,1
xd,0
calvin_mcmurray,4
carving,0
throat_biting,0
echinozoan,5
lactaid_cow,4
nate_(8chan),4
inukon_geek,1
quetzalcoatl,4
swiftsketchpone,1
nood1e,1
sallandril,1
agent_classified,4
vela_(kagekitsoon),4
akhlys,4
fuzzikayu,1
marco_(adastra),4
ryukawa,1
shoe_sniffing,0
jazzabelle,4
mayan_mythology,3
cowification,0
cum_from_eyes,0
cobu,1
conjoined_thought_bubble,0
oata_rinsky,4
experiment_627,4
howard_(james_howard),4
concern,0
male_fingering,0
provocative,0
gastropod_shell,0
sixpathoffriendship,1
naivintage,1
yarpi,1
snacks,0
hax-the-monster,1
whitev,1
beartouma_0517,1
antiander,1
foot_on_crotch,0
disc,0
white_bow,0
surfing_charizard,1
kleinvoimond,1
jay-r_(character),4
wiredhooves,1
dish,0
disney_fairies,3
classified-crud,1
cheetah_print,0
cinko,1
sweetie_bot_(mlp),4
transformers:_animated_(2007),3
luca_paguro,4
red_heels,0
slime_rancher,3
cydergerra,1
styx_(jelomaus),4
riipley,1
harry_amorós,1
nightmare_chica_(fnaf),4
janitor,0
vrchat,3
skulldog,1
the_vore_house_of_klyneth,3
datfurrydude,1
monster_mind,3
flair_projects,1
shuckle,5
banglow,1
zoey_(jwinkz),4
warning_sign,0
skull_necklace,0
ricky_landon,4
sonia_(pokémon),4
orangusnake,4
rex_(disambiguation),4
yellow_sweater,0
angel_the_catgirl,4
hidden,0
chooy64,1
narrow_legs,0
eruprior,1
natsuki_takano,1
uncle_and_niece,0
zone-tan,4
stake,0
gardenminttea,1
trypophobia,0
nero_eternity_(artist),1
cumminham,1
kaiyonato,4
kiss_the_carrot,1
dandee_(character),4
nekoyuu,1
tala_(fluff-kevlar),4
moomba,5
xeono,1
richarddeus,1
the-hydroxian,1
flashing_panties,0
necroizu,1
eye_creature,5
chatting,0
deli_(delirost),4
the_red_prince,4
fchicken,1
taku,1
symbianl,1
shuppet,5
sfmoclock,1
weisswinddragon,1
club_penguin,3
ikasama,4
sheath_fingering,0
udder_growth,0
penis_drawing,0
kobu_art,1
rt0no,1
nicolaowo,1
donk_sis,4
punkinbuu,1
pingpenne,1
blue_heart,0
brain_(inspector_gadget),4
katsuki_bakugou,4
icy_heart,4
grey_membrane,0
looking_at_panties,0
kazerad,1
jayjay_(zoophobia),4
hat_on_penis,0
under_balls,0
kalasiris,0
ren_(daikitei),4
blue_antennae,0
snowy_(creatures_of_the_night),4
saffira_queen_of_dragons,4
hel_(shiretsuna),4
two_tone_nose,0
quote_(cave_story),4
shaking_breasts,0
shadyadi1,1
fullfolka,1
shyvana,4
master_emerald,0
zephyr_(dragon),4
toraya_inugami,1
sethrak,5
dany-j,1
pharynx_(mlp),4
kiseru,0
nsfwronin,1
reiko4835i,1
dirtyrenamon,3
petted_silly,0
karzahnii,1
hoof_heels,0
gloryworm,1
shephira_(cert),4
ligoni,1
edgar_rice_burroughs,1
bunnyfeet,1
dimonis,1
supernatural_stimulation,0
hoppip,5
sidni,4
ace_of_hearts,0
boa_constrictor,5
monotone_shorts,0
greenlinzerd,1
tail_around_neck,0
hand_on_hat,0
xnassyx,1
awkwardzombie,3
cream.pup,1
ken_(claralaine),4
mobian_hedgehog,5
tasque,5
football_field,0
dracozolt,5
fighterjet,1
blacklite,1
biobatz,4
skelbely,1
:q,0
dangling_legs,0
latex_elbow_gloves,0
braided_mane,0
transformation_potion,0
belt_bra,0
temtem_(species),5
luther_denholme,4
disturbed,0
ruby_(rubyluvcow),4
football_uniform,0
xasyr,4
pencilsdgr,1
morrowind,3
silentravyn,1
zebra_print,0
zony,5
heart_formation,0
john_doe_(artist),1
drpickelle,1
master_shifu,4
asymmetrical_transformation,0
tach8,1
momorodent,1
joel_mustard,4
striped_gloves,0
red_jewelry,0
primeape,5
meshpet,1
poduu,1
diego_abel,4
energy_wings,0
saurus,5
renard_queenston,4
brachydios,5
racing,0
tennis_court,0
huge_moobs,0
band_shirt,0
diamondstorm,1
triuni,1
rayman,4
anal_object_insertion,0
coconut_bra,0
lyon_carter,4
changle_(ffjjfjci),4
gold_clothing,0
bingo_heeler,4
causationcorrelation,1
flukes,0
pelvic_thrust,0
water_break,0
kitchen_stove,0
leatherhead,4
wurae,5
lesser_dog,4
dark_pawpads,0
item_box,0
living_appliance,5
ham,0
momdadno,1
thistle_(frisky_ferals),4
zaboom,1
bookmark,0
magnus_(spyro),4
holding_each_other,0
vexus,4
zajice,1
arkanman,1
thegreatrouge,1
raised_sweater,0
solutionwcs,1
lazuli_delarosa,4
pussy_slip,0
condom_in_pussy,0
xc404,1
tongue_wrapped_around_penis,0
azurei,1
qwertyigloo,4
toejob,0
fate_valentine,4
rose_(skybluefox),4
heart_shaped_box,0
glamrock_bonnie_(fnaf),4
akai,1
tanithjackal,1
jin_macchiato,4
al_(weaver),4
almos-tiroshi,1
zevex,1
blue-eyes_white_dragon,4
krunch_the_kremling,4
jay_(oc),4
baconfetish'ntomato,1
hinata_hyuga,4
dropping,0
breastwizard,1
sinibun_95,1
spitfire_(hideki_kaneda),4
bri_(ennismore),4
screwball_(mlp),4
rim_light,0
terrakion,5
mama_maria,4
vinejob,0
lyre,0
rokito,1
snowcheetah,4
wolfdawg,1
g_brigander55,1
spotted_butt,0
wildwolfproduction,1
cubicle,0
ಠ_ಠ,0
think_mark_think!,3
the_great_warrior_wall,3
kato200_0,1
sha_(twf),4
five_nights_at_freddy's_ar,3
chemistry,0
themeshow101,1
choo-choo_(top_cat),4
fth_transformation,0
green_armwear,0
stains,0
forastero,1
crocs,3
dishka,1
ear_nom,0
pandawa,5
ayden_feuer,1
plesiosaur,5
cat_food,0
adam_caro,4
yumei,1
pavement,0
snickerlewdles,1
forced_to_penetrate,0
reallydarkandwindie,1
locksto,1
pink_legs,0
7:6,7
solid_hooves,0
red_neckwear,0
game_background,0
bayron_(artist),1
vambrace,0
time_travel,0
bending,0
resting_arm,0
stilettopink,1
horn_ribbon,0
bio-android_(dragon_ball),5
gift_tag,0
mutt_(wagnermutt),4
rotalice2,1
sexylinguini,1
mei_(artist),1
planeswalker,4
shlorp,0
night_time,0
badquinque,1
workplace,0
cardiac_monitor,0
moth_sprout,1
holding_ice_cream,0
top_in_heels,0
holding_comb,0
vck,1
blue_fin,0
homeless,0
ember_(warframe),4
ashlee_hurwitz,4
tongue_around_penis,0
dragon_quest_v,3
flower_hair,0
skull_grunt,4
gear_(mlp),4
fully_submerged_legs,0
fuzefurry,1
fadingsky,1
thetyrant,1
pixxy_fizzleclank,4
sergeantbuck,3
francine_(ruanshi),4
white_muzzle,0
glistening_thighs,0
belly_on_glass,0
anthony_(goldiescales),4
cotton_swab,0
hung_bottom,0
glistening_ears,0
harpoon,0
urine_meter,0
snus-kun,1
darth_talon,4
dj-rodney,1
amber_(disambiguation),4
urethral_fisting,0
egg_belly,0
mr5star,1
orange_hat,0
fire_eclipse,4
muscular_maleherm,0
adelar_elric,1
skeletonkid5,1
riley_(s-nina),4
jasmine_ivory,4
fuf_(character),4
dragroon,1
wood_table,0
shrink_ray,0
sphere,0
terrenslks,1
boobipede,5
kekpafrany,1
taking_selfie,0
corsola,5
isaki,1
blazedscarf,1
bootydox,1
zahra_(airheart),4
spinneborg,1
skeletal,0
athus,1
tbfm,1
cosmo_(artist),1
gilgash,1
juniper_(freckles),4
kerun,1
notepaddy,1
cum_on_hoof,0
guided_penetration,0
dolphydolphiana,1
golisopod,5
bullet_hole,0
skull_symbol,0
great_horned_owl,5
marsha_twilight,4
felicia_(tahlian),4
bethesda,3
knight_(towergirls),4
ashchu,4
fatzoimp,1
purple_briefs,0
high_elf_archer_(goblin_slayer),4
james_killian_spratt,1
the_nightmare_before_christmas,3
big_obliques,0
graedius_(character),4
multicolored_nose,0
ajani_goldmane,4
xia_(cydonia_xia),4
marisa_kirisame,4
fursat,1
spiderweber,1
ibgross,1
zolombo,1
aura_spark,4
black_kerchief,0
extracurricular_activities,3
liquefactiophilia,0
nanogrrr,1
retros,1
lobar,1
shouta_magatsuchi,4
azurill,5
cupless_bra,0
mudkipful,1
millennium_dragon,5
morning_after,0
shaking_hips,0
darkereve,1
diaper_squish,0
blue_antlers,0
rainbow_stockings,0
juggling,0
semple,4
tucker_chimera_(fma),5
rekuuhh,1
licking_nose,0
minotaur_hotel,3
haziq_(hazumazu),4
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tom_(ehs),4
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tan_rouge,4
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xeila,4
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cherri_topps,4
delicioussoup,1
chabett,4
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guwu,1
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xianos,4
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kali_(kilinah),4
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horsie,4
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ranma_1/2,3
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sanlich,4
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nemi,4
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bandit_(disambiguation),-1
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1991,7
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jc_(artist),1
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rocky_and_bullwinkle,3
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gemini_the_otter,4
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coop_(wrng),4
felix_(striped_sins),4
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ice_wolf_(undertale),4
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antoreakk,1
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pac,1
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dreaj1,3
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ko_(fabio_paulino),4
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velvet_remedy,4
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boris_(theboris),4
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song_(kung_fu_panda),4
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starscream,4
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supermarket,0
fazbear_fanverse,3
tila_sunrise,4
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rodemaru,1
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abigail_roo,4
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skan_drake,4
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noinipi,1
jlazarus,1
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peko,4
inscryption,3
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tjloog,1
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taross,4
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ghetto,0
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bb,1
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ann-jey,1
troodon,5
saphayla_(zelianda),4
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pipyaka,1
momobeda,1
moomaid,1
guitar_pick,0
james_(james_howard),4
shephard,4
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dg254,1
lo,4
kaepotassium,1
barney_the_dinosaur,4
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mindkog,1
ann_gora,4
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arith,4
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krosbar_(artist),1
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milo_(catastrophe),4
american_pit_bull_terrier,5
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uni,1
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the_cyantian_chronicles,3
daniela_idril,4
kaohto,1
manizu,4
tracy_(linker),4
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thehenwithatie,1
oracle_of_seasons,3
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twisted_table_lotus,0
nugget_(diadorin),4
dynamite_(kadath),4
hudson_(gargoyles),4
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alan_white,4
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kam,1
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chubby_intersex,0
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august_moon,4
blade_and_soul,3
ammy,4
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purple_tank_top,0
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unico,4
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niur,1
taffyy,1
nyquil,1
tony_(tonytoran),4
chinpo,1
ashcozy,1
malt_marzipan,4
psychpsyo,1
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confusedraven,1
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nargle_(nargleflex),4
kcn,1
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fox_(minecraft),5
seikeikei2015,1
devergilia,1
mrs._amp_(mramp),4
vivian_rose,4
bunny_raven,4
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fkey,1
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nelson88,1
trish_(invasormkiv),4
sans_(underfell),4
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oricorio,5
faliet,1
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pharah_(overwatch),4
waifu_chart_meme,3
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marcus_mccloud,4
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gwon,1
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sabitare,1
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elements_of_harmony,0
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martha_wakeman,4
jorge_san_nicolas,4
perenthis,1
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maximus_(tangled),4
horse_(centaurworld),4
phione,5
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trigger12,3
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nemo2d,1
majiro_the_hedgehog,4
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xigma,1
miracle_star,3
gummy_bear_(food),0
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shorebird,5
kilix,4
the_spiner,3
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rainbow_armwear,0
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shed,0
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two_tone_swimwear,0
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wolfsecret,1
arm_over_head,0
aperture_science,3
kipchin_2nd00,1
bbc,3
jake_hart,4
tawny_otterson,4
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li_shan_(kung_fu_panda),4
blue_formalin,1
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twymouse,1
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sebun_(beastars),4
grey_piercing,0
headband_only,0
white_thong,0
wolfwood1,1
southern,0
the_huckleberry_hound_show,3
yellow_boots,0
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dustedpollen,1
spe,1
jinku_56,1
stygimoloch_(artist),1
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trash_anon,1
akamaru,4
umikazenet3,1
maxx-ros,1
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bolts,0
dressuptober,3
pussy_juice_pool,0
dvd,0
froggy_(sonic),4
command_and_conquer,3
zera_stormfire,4
rui_nikaido_(odd_taxi),4
metal_chain,0
yuuuki_desu,1
osiris_henschel,4
ork,5
pilitan,1
caprine_pussy,0
blue_leash,0
ultrazeta120,1
rtzero,1
friskalpox,1
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julian_(animal_crossing),4
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ole,1
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foxydude,4
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torso,0
escape_to_nowhere,3
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flipyart,1
keto,1
soonico,1
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vanara,5
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lully_pop,4
orang111,1
komane,4
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tail_tattoo,0
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keinos,4
messy_eater,0
max_(notkastar),4
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chimera_humanoid,5
natu,5
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arkomeda,1
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kinar_(amocin),4
thelazydreamer,1
justice_(helltaker),4
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tiffany_brewwer,4
wonder_boy,4
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strapon_in_pussy,0
glowing_areola,0
sintronic,1
xion_archaeus,4
luna_the_eevee,4
dunmer,5
veronica_(securipun),4
pups_of_liberty,3
lonefox,1
sakura_haruno,4
geld_(that_time_i_got_reincarnated_as_a_slime),4
shenki,1
fenra,5
hisashiyo_0705,1
emil_(funkybun),4
wavern,4
bat_(petruz),4
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mako_(rudragon),4
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aeolius,1
diegotan,1
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myrl,4
begging_to_stop,0
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forsburn,4
samaraka,1
elisa_(maddeku),4
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vermine,1
mathew_kelly,4
vanessa_(fnaf),4
udtf,0
terribly_british,0
keeperseen,4
vetarix,1
notpcd,1
hidoritoyama,1
lovelyneckbeard,1
double_thumbs_up,0
zandermanith,1
zoophilic,1
cassiopeia_(lol),4
gynecological_chair,0
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ragnacock,4
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reik_(peritian),4
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thephantompornographer,1
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z00p,1
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gila_monster,5
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litchi666,1
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miraculous_ladybug,3
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icariusunlimited,1
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horumu,1
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reynard_the_fox,4
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inkybeaker,1
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kiba24,1
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tsbellatre,1
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woong,1
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exenthal,4
fuko,4
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anna_(luke154),4
tatara94,1
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rakihiro,1
:c,0
deepfriedlasereyes,1
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pussy_juice_on_balls,0
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mina_(mina_the_hollower),4
sochi_(lynx),4
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chell,4
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amalj'aa,5
kittykuscell,1
stinger_tail,0
silver2299,1
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vensicle,1
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tobacco,0
gaghiel,4
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scottytheman,1
wwredgrave,1
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dal_(joelasko),4
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phant0mhat,1
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chaco_(cave_story),4
gaz_membrane,4
tan_hooves,0
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tozamtr,1
vexyvoo,1
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gustav,4
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tristan_knight,4
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cyberlord1109,1
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chun-ni,4
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tamaskan_dog,5
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nataly-b,1
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cool.,1
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itsuki_(hane),4
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basian,4
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;p,0
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isher,4
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soarinlion,1
rayne_blanc,4
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ronin-nc,1
32:9,7
skull_(marking),0
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tuki_(shantae),4
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acchan_(arkaid),4
thehoneybutter,1
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together_(disambiguation),0
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iva_(cornchip21),4
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fvorte,1
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leskaviene,4
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adian_(moki),4
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d3mo,1
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zel_(interspecies_reviewers),4
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drop,0
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hair_on_shoulders,0
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11:8,7
xlyuz,1
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jakensitou,1
asyr,4
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sharla_(zootopia),4
guttybee,1
ezo_red_fox_(kemono_friends),4
sagging,0
luuriolu,1
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rosheatfreak,1
nhl,3
handsdownhound,1
pride_color_collar,0
wrist_on_leg,0
thelivingtrashcan,1
pale,0
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myett-wrath,1
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shadart,1
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lizanne,4
tanashi,4
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idea,0
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illuminati,0
yu-gi-oh_card,0
batter,0
neoncel,1
zairiza,1
aira_kokonatsu,4
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lassie_lunaris,4
minikane,4
bandana_waddle_dee,4
erika_(ambris),4
creature_sp,1
makemon,3
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billy_&_friends,3
striped_tongue,0
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sammy_(hambor12),4
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linlvjia,1
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stella_(vivzmind),4
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xiaolin_showdown,3
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oni_(artist),1
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lillymoo,1
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my_little_pony:_a_new_generation,3
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chihiro_ogino,4
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julian_(kazecat),4
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dongwongtang,1
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xbuimonsama,4
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dannydumal,4
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total_war:_warhammer,3
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snoot_(trinity-fate62),4
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hel,1
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littlest_pet_shop_a_world_of_our_own,3
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mommomma114,1
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tr.anonymous.h,1
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ryusuke573,1
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cjzilla88,1
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virus.exe,1
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arki,4
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anemonemisc,1
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ritika448,1
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joji_(@jojipando),4
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00freeze00,1
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starman75042,1
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liz_(lizzycat21),4
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raymond158,1
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thebonezonedeluxe,1
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rovik_(rovik1174),4
rena_(rena0107),4
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gael_the_scrafty,4
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spheal,5
leo_alvarez,4
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sam_(sawolf151),4
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heiko_(domasarts),4
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abyssinian_cat,5
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jet_(quin_nsfw),4
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the_incredibles,3
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3cir_cle,1
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violavirus,1
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fru_fru,4
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policewoman,0
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bagu,1
ookami_(aggretsuko),4
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ty_arashi,1
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genji_(overwatch),4
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^w^,0
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mario_smoking_weed,3
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0600hours,4
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kiiko,1
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sky_(umbry_sky),4
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vortex_(vivzmind),4
tales_of_tabira,3
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yu_yu_hakusho,3
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>w<,0
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motherly,0
rileyomalley,1
forbiddenknights2,1
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feilen,4
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go!_go!_hypergrind,3
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funeral,0
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19:25,7
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jem,4
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vd-dv,1
d0nk,4
reddit,3
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sapsan,1
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ryndion,4
narwhal69,1
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ifgirl,1
stephen,4
binocszoomx10,1
round_tail,0
button_underwear,0
schitzofox,1
fox0808,1
young_intellegent_furry_friend,1
wynaut,5
cheating_wife,0
lylla,4
high_chair,0
hexadec,1
shrek_(character),4
satanic_leaf-tailed_gecko,5
rafiki,4
angel_loveridge,4
dog_girl_(berseepon09),4
torgore,1
aesir_(artist),1
gamermac,1
wes_(pokémon),4
bent,5
alxias,3
kippy,1
hero_chao,5
shutter_shades,0
to_be_continued_meme,3
lunicent,1
smaller_fingered,0
elma_(dragon_maid),4
roodaka,4
pumpkinspicelatte,1
mr._mephit,4
a0nmaster,1
lucas_(lucasreturns),4
flat-tailed_gecko,5
diablos_(mh),5
holding_leaf,0
dirk_aukea_(artist),1
tobacco_pipe,0
infected_(species),5
p-sebae,1
anubis_and_the_buried_bone,3
lemendigogoat,1
jewel-thief,1
neck_to_tail_bondage,0
glimglam,1
newfoundland_dog,5
ellie_cooper,4
paimon_(helluva_boss),4
radar,0
vivi_ornitier,4
razzper,1
sk8th,1
targso,1
saerro,4
fender,4
lentejota6,1
supermoon,0
bluebun,1
seyorrol,1
belly_hug,0
wiping_mouth,0
zoroa553,1
skeleton_markings,0
theartmanor,1
pokéball_gag,0
white_antlers,0
patterned_fur,0
alice_in_wonderland_(1951),3
steve_gallacci,1
r_ex,1
eyes_in_darkness,0
poisoned,0
reverse_v_sign,0
supersoupnova,1
free_(beastars),4
sex_through_clothing,0
tomdantherock,1
blocky_snout,0
pang4746,1
casey_jones,4
yungoos,5
ballet_leotard,0
evil-sprite,1
chipfox,4
public_pool,0
caravan_palace,3
drampa,5
olga_(here_there_be_dragons),4
herald_hearth,4
tulip_(flower),0
clitoris_slip,0
family_guy_death_pose,3
shukin,1
geno_e._vefa_(coyotek),4
elisedae,4
pink_neckerchief,0
tractor,0
grimfaust_(nightterror),4
multicolored_thigh_socks,0
budew,5
tom_sawyer_(2000_film),3
:i,0
arf-fra,1
porah,4
mudshark,1
elise_(lustylamb),4
qmanshark,1
ghost3280,1
fluffymare,1
dating_sim,0
tan_nails,0
vanimate,1
roxanne_(spikedmauler),4
lucky_(luckyabsol),4
yellow_briefs,0
kurosuke0755,1
kagetora_(pop'n_music),4
dupli,1
park_kr,1
hung_clothing,0
big_female,0
whale_tail,0
mylafox,1
nonksoit,1
decorative_pin,0
red_sheath,0
uno,3
missing_limb,0
jamila_queen_cobra,4
coconutmilkyway,1
doritomon,1
akari_jamisson,4
pawmi,5
blue_pseudo_hair,0
executioner,0
russell_ferguson,4
oro_(ungoliant),4
ewr_115,1
hashimoto-chan,4
david_(bcb),4
pidove,5
record_of_lodoss_war,3
kodi_(sqink),4
wooden_spoon,0
phos_(phosaggro),4
rokuke_shiba_(character),4
izuku.,1
vermintide,3
ouroboros,0
fluffy_wings,0
lumieart,1
gabriel_(fuze),4
potion_label,0
fuzzled,1
vulpa,4
lime_ade,4
teckit,1
bluewavecon,1
gao-lukchup,1
humongous_entertainment,3
aaaa,1
spartadog,1
gi_(disambiguation),-1
hylian_shield,0
mitachurl,5
moneychan,1
red_leotard,0
gordon_freeman,4
snips456fur,1
katibara,1
gameroo,1
strelizia,4
fi_(blen_bodega),4
norakaru,1
green_necktie,0
lulubell,1
plague_knight,4
oh_fugg,0
day-tripper-guy,1
sorez,4
sam_(the_sunfish),4
nes_console,0
krixata,1
rainbow_crash,4
greenhouse,0
dan_bluestone,1
silver_fang,4
gausscannon,1
hooks,0
firr,4
ankle_crossing_leg,0
theserg,1
polygons,0
pukao,1
tasticstarlight,1
tales_of_arcadia,3
multicolored_inner_ear,0
quaise_(doneru),4
zorym,1
momo_(doodle_dip),4
mia_perella,4
orange_bikini,0
pepipopo,1
cha_hieun,4
diaper_package,0
airborne,0
fern_walters,4
tenderegoist,1
zeir0,1
morrigan_the_marwari,1
hand_on_bed,0
chloeelaw,1
buzzing,0
duragonart,1
tongue_sheath,0
zebra_taur,5
stumpeg,1
ali_gray,4
metal_table,0
everstone,0
anatomically_correct_balls,0
thunderbird_(tas),4
light_bottomwear,0
kovaltiel,1
pastelmistress,1
smrtka769,1
visible_underwear,0
notsafeforhoofs,1
nausea,0
inker_comics,1
gligar,5
varknakfrery,1
team_ico_(game_developer),3
thunder_ring,4
riproarrex,1
bottle_insertion,0
zephyr_the_hyena,4
daxratchet,1
sapphwolf,1
fiztheancient,1
deeroni,1
greyshores,1
sidonie,1
aiko_stable,1
impact,0
leopard_shark,5
kaltren,4
garcia,4
aevere_(anotherpersons129),4
kokobiel,1
atoh_darkscythe,4
j3t,1
mauro_skyles,4
living_tank,5
centipeetle,4
tiril,4
kiss_mark_on_balls,0
scanlines,0
toofer,4
birthday_candle,0
against_rock,0
dennhaze,1
biri,1
tumble_the_skunk,4
hibari,4
manic_the_hedgehog,4
rex_rabbit,5
eoks,1
amelia_(ryuakira),4
alphamoonlight,1
backbend,0
lodish,4
goroguro,1
ailaiweiyala,1
dj50,3
secretkitsune,1
poegryn,1
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pink_high_heels,0
zaxstar,1
topaz_(lipton),4
arms_on_legs,0
pichu90,1
thetroon,1
king-kakapo,1
ryukyur,1
conical_hat,0
owen_(repeat),4
beverly_(sailoranna),4
pool_chair,0
zander_oro,4
nina_flip,4
piglet,4
bart_simpson,4
stunfisk,5
face_panties,0
fang_asian,1
cade_(the_human_heart),4
roly_(roly),4
lavender_(flower),0
slither_(slither),4
trainer_aliyah,4
black_kitty,1
neyukidou,1
sun_wukong,4
gibbons,1
wraith,5
sammehchub,1
zebra_finch,5
emerald_(katai),4
agape_(petruz),4
elvis_(animal_crossing),4
calico-chan_(akamu_ver20),4
uc77,1
puffypinkpaws,1
the_next_mutation,3
yellow_pubes,0
margevonn,1
lugus,1
disembodied_mouth,0
synxirazu-niam_(character),4
skiaskai,1
glitch_pokémon,5
scene_interpretation,0
peeing_on_viewer,0
dan_(dan_vs.),4
sparrowl,1
exit_sign,0
maruzyrukun,1
vortixx,5
sairaks,4
winter_(wof),4
bloo_(character),4
supearibu,1
droplets,0
choking_on_penis,0
hokkyoku_kuma,1
dominique_(bionichound),4
dullyarts,1
besped,1
onmyoji-mama-rama-sama_(nightfaux),4
zake,4
cosmic_being,5
butt_bite,0
side_bangs,0
flesh_fang,0
santafire,1
dagos,1
bonita_(gaturo),4
northfelle,1
hollow_knight_(character),4
holding_pizza,0
vaginal_foot_play,0
sai_(weapon),0
chimera_synx,1
nursery,0
nording,1
alcitron,4
tail_belt,0
predatory_look,0
tagging_guidelines_illustrated,0
redwolfalang,1
employee,0
necrobern,1
juanomorfo,1
vehicle_destruction,0
beatriz_resont,4
sar,4
sybervenn,1
holding_bikini_top,0
geralt_of_rivia,4
sueli_(joaoppereiraus),4
green_blood,0
ghostblanketboy,1
bottomless_human,0
red_knight_(sirphilliam),4
squean,0
sephaloid,1
sambal,4
timblackfox_(character),4
blorb,0
yafya_(beastars),4
arche_kruz,1
oral_fixation,0
sinyxstra,4
neve_vecat,4
skin_tight_suit,0
broken_arm,0
panda_po,1
taped_mouth,0
jail_placard,0
dark_gloves,0
minor_arcana,0
okayado,1
stern,0
bl4ck-cl1n1c,1
txlegionnaire,1
angle_(copperback01),4
bookman_v,1
amaryllis_(amy)_sharmila,4
plot_twist,0
pink_antennae,0
spanking_paddle,0
elbow_on_knee,0
shoulder_scar,0
haze_the_giraking,4
large_pecs,0
green_diaper,0
r-fenrir,1
varenvel,4
tight_panties,0
kale_(angellove44),4
cum_from_cloaca,0
sentai_rabbit_(marimo),4
pointed_feet,0
bluethebone,1
shitsu_kushi-mono,1
bribe,0
lemy,4
kemoppai,1
dyspo,4
white_bars,0
cassandra_hart,4
countershade_thighs,0
maleherm/maleherm,0
8k,0
shrimp_(uk_brony),4
maly_paczek,4
avj_(artist),1
woopu_(zoohomme),4
reiko_(goonie-san),4
seldom,1
anus_lick,0
whalegourd,1
yard,0
rikamon,4
biker_jacket,0
9:7,7
green_roon,1
ear_ribbon,0
ozy_and_millie,3
crazedwd,1
riku_tavash,4
warship,0
getting_up,0
dark_tail_feathers,0
anthro_only,0
windpaw,1
brown_head,0
paramour_(artist),1
lectern,0
usb_necklace,0
asylum,0
dante_(101_dalmatians),4
linked_piercing,0
sledgehammer,0
orange_neckerchief,0
yoda,4
in_pouch,0
dantae_meoi,4
chuck_(braford),4
hm3526,1
mentalcrash,1
moonagvaze,1
trinity_(farran_height),4
waist_ribbon,0
presenting_belly,0
country,0
fujiokaaika,1
polartoons,1
asphyxia_lemieux,4
leopard_humanoid,5
voss_(beastars),4
mole_humanoid,5
victim,0
levi_west,4
panty_bow,0
adonis_(redimplight),4
thousandarms,1
fynath,4
spica_(aoino),4
teien,1
pink_latex,0
t-shirt_only,0
lap,0
cubi_(characters),3
balaclavacat,1
mungo_(housepets!),4
sarah_van_fiepland,4
lordzbacon,1
iris_(ratcha),4
kittmouri,1
mutemyth,1
korrina_(pokemon),4
baron_von_jackal,4
luenas,4
skadjer,1
purple_elbow_gloves,0
pluvian,4
hazelkisses,1
shixiexiaomo,1
kokuhane_(character),4
penis_arm,0
sulcus,0
rufus_(mao_mao:_heroes_of_pure_heart),4
puinkey_(character),4
dark_paws,0
dipstick_toes,0
jem_(hornedproxy),4
rann,1
hydoor,4
diamobster,1
shirakami_fubuki,4
andersonicth,1
deimos,4
pecorine_(princess_connect!),4
rookie425,1
duskie-06,1
size_shaming,0
matar,4
pink_sheath,0
vincenzo_moretti,4
historical,0
hornybunny,1
vapor,0
rayu_(kamui_shirow),4
vaginal_storage,0
feces_on_hand,0
graph,0
how_to_talk_to_short_people,3
georgesabertooth,1
floorninja,1
jayecho,1
anubiskruger,1
husky_(artist),1
fur_hire,4
osiriix,1
ball_bite,0
muraachi2gou,1
ruff_bull,4
alice_lovage,4
nickolai_alaric,4
justin_(aaron),4
yukizard_(evov1),4
grrrwolf,1
orange_seam_underwear,0
circe,4
crest_(disambiguation),-1
lopin,4
iratu_elexion,4
ear_fur,0
bee_the_cat,4
monotone_tank_top,0
musorok,1
trunkjob,0
masturbator,0
goldie_(animal_crossing),4
mametanu_tanuki,1
mochi-bun,1
happiny,5
hammerface,4
fuleco,4
orenjisalmonpaw,1
recursion,0
bella_mouse,4
beowolf_(rwby),5
fluffybardo,1
text_on_pillow,0
wob,1
ralan_(thepatchedragon),4
grappling,0
eliotak,1
diver,0
glacierdragon,1
plated_scales,0
hajime_sorayama,1
cowfee,1
drybone,1
grabbing_wrists,0
slashner,1
using_magic_on_self,0
toilet_pov,0
jmeo1230,1
copymirror,1
serth,1
fireflufferz,1
character_description,0
fur_gauntlets,0
omegabrawl,1
luxury_ball,0
rosiesquish,4
penance,4
allstatus3,1
tail_spots,0
qweeli,1
bianca_(dragon_quest),4
uthstar01,1
dragonmanmike,1
polesitting,0
drapion,5
alvar_miles_udell,4
autoknotting,0
11:9,7
n3f4str10,1
kitsuumi,1
reylin,4
bubbles_(powerpuff_girls),4
daxx_(shirteater18),4
estemilk,1
hypnotized_prey,0
roger_mello_(dynamitegrizzly),4
lordosis,0
ratarang,4
huge_trapezius,0
sensual,0
teateastuff,1
kaio_kincaid,4
tindalos_(tas),4
seyloid,5
kalei_(dreamkeepers),4
light_handwear,0
data_(wouhlven),4
huge_tongue,0
aven-fawn,4
fay,4
berlin,1
jakescorp,3
50percentgrey,1
amber_(mancoin),4
jack_(mass_effect),4
clei,4
juanonyx,1
user_cpsf8285,1
black_arm_warmers,0
u-turn_penetration,0
brown_sweater,0
hose_in_urethra,0
snorunt,5
shomunona,1
selker,1
flir_(panther),4
solapi_(artist),1
orthros,5
anklav,4
greedmasterh,1
pool_noodle,0
tentacle_dildo,0
seaking,5
nightmare_(species),5
stepping_on_balls,0
daisy_chain,0
mazzy_techna,4
accidental_bow_mutant_(resident_evil),5
friendly_fire,0
jake_(fuze),4
vinoda,1
fitletter,1
miyuki_(helzimgiger),4
double_pov,0
duncan_(kitfox_crimson),4
curry,0
championship_belt,0
callmedaddy,1
escaping_heart,0
linette_noir,4
iceshadow13,1
navel_lick,0
fruit_slice_(yurusa),4
juri_han,4
lyn_(z-ray),4
tongue_in_sheath,0
grabbing_hips,0
dough,0
predacon,5
cloaca_ejaculation,0
salmacisreptile,1
shiro,4
fur_pile,3
rolly_(maple_town),4
biohazard_symbol_print,0
parchment,0
red_teeth,0
prea,4
rocking_chair,0
chigui_(character),4
7010,1
redgreendied,1
udder_lactation,0
mary_magdalene,4
nyjasca,4
teragabba,1
redmn,1
olive_(disambiguation),-1
back_bow,0
daniel-sg,1
one_eye_visible,0
amara_(jwinkz),4
daloaf,1
jack_dragon_(character),4
friita,1
stan_borowski,4
graph_paper,0
pimple_(battletoads),4
sinister-jordex,1
lying_on_pool_toy,0
flip_phone,0
jeffrey_taggart,4
octomaid,5
kira,4
naoki_saito,1
freckles_(nukepone),4
lindsay_(funkybun),4
licking_teeth,0
kewon,1
gem_eyes,0
zach_(zer0rebel4),4
blaquetygriss,1
tsukiyo,4
kukkiisart,1
dreemurr_reborn,3
sovulsen,1
jorden,4
sookmo,1
fenrik_(artist),1
cosmo_(beastars),4
garbagioni,1
shishiro_botan,4
yellow_t-shirt,0
orange_tuft,0
pussy_juice_on_tentacle,0
distracted_boyfriend,3
exeggcute,5
swarm,0
mina_(gaturo),4
meowcephei,1
milo_(captain_nikko),4
jessesmash32,1
oola,4
koruro,1
runway,0
box-s,1
barraqda,1
su_wu,4
mstivoy,1
rouken,3
yellow-billed_magpie,5
zantyarz,1
team_plasma,3
scarlet,4
durabelle,4
arrow_through_heart,0
empty_scrotum,0
cula,1
hairy_belly,0
duke_nukem_(series),3
dera_kimidori,1
shadow_of_the_colossus,3
radwolf,4
heart_pendant,0
gtoyaannno,1
mrw32,1
adena_(ewgengster),4
on_vehicle,0
kneeling_position,0
suger_phox,1
dobie_(animal_crossing),4
xevv,1
shadify,1
gem_(babystar),4
kevak_(castbound),4
selune_darkeye,4
salamandr,1
souga,4
diana_digma,4
emmy_dook,4
earning_chastity_removal,0
wiess,1
ambiguous_rimming_male,0
sir_cums_a_lot,1
dio_(diorexity),4
ethan_(thiingamajiig),4
gopher,5
glistening_armor,0
v-invidia,1
himimi,1
atsuya_baba_(odd_taxi),4
green_head,0
bondage_harness,0
maple_(maplegek),4
coffeeslice,1
dreamyart,1
dillon's_rolling_western,3
whywhyouo,1
analon_(character),4
tuwka,1
e-ward,1
caroxmaster,1
hisuian_sneasel,5
lunette_(lunebat),4
imflain3d,1
butt_flap,0
homer_simpson,4
nuxinara,1
mysterious_mew,1
kollerss,1
star_panties,0
5danny1206,1
keitronic,1
breast_freckles,0
weewizzylizzy,1
trish_(winter),4
cinnamomo,1
munchkin_cat,5
reverse_forced_cunnilingus,0
wen_kamui_(tas),4
multicolored_neckwear,0
sidotama,1
niuxii,1
kayle_(lol),4
krorenshima,1
reverse_prince_albert_piercing,0
unbuttoned_shorts,0
dj_mixer_(character),4
rikuta_tp,1
blunted_horn,0
straxus,4
rottencanines,1
holding_chopsticks,0
neomorph,5
feebas,5
datbadger,1
rawchomp,1
speaker_ears,0
erokaiga,1
funeral-paws,1
on_penis,0
lotad,5
symmetra_(overwatch),4
gazer_(mamono_girl_lover),4
lung,0
huskii-s,1
howlite,1
sickbelle,1
nazebesu_hobby,1
orangy,1
tales_of_the_ashes_(series),3
catfight,0
viken_welopl,4
herdier,5
notched_tail,0
alexclimax,1
dashthefox,1
foot_on_foot,0
explosive_orgasm,0
saliva_on_pussy,0
vorekingdom,1
shadow_mewtwo,5
arm_over_edge,0
prawn_(district_9),5
galacta_knight,4
ripping_sound_effect,0
queen_bee,5
fused,0
usb_compatible,0
finnish_text,7
nosch,1
dragon_village_m,3
nipple_tattoo,0
black_widow_spider,5
workout_equipment,0
alternate_breast_size,0
kedama,1
brian_o'connell,1
oguchi_magami_(tas),4
stormfront,4
two_tone_speedo,0
olexey_oleg,1
taking_order,0
miss_rain,1
fossa_penis,0
nekokai,5
huge_udders,0
mrfoxees,4
the_thing,3
white_wolf_publishing,3
charles_entertainment_cheese,4
sulid,5
gammauzumaki,1
drago_(bakugan),4
dorobo_39,1
furukara,1
koopie_koo,4
two_tone_claws,0
destiny-yuuei,1
impstripe,1
pansear,5
watermelon_crushing,0
tenseiani,1
scruffymusk,1
katsurokurosaki,1
otterscience,1
maxy,4
magic_dildo,0
sephiroth,4
bender_bending_rodríguez,4
certedia,1
star_(shape),0
purple_dragon,5
lyn,5
amarian,1
grove_(regalbuster),4
sidern_brethencourt,4
sabine_(lykenzealot),4
raptoral_(character),4
black_ball_gag,0
yuuh1212,1
implied_anal_penetration,0
carnivorousvixen,1
zoe_heartfields,4
kodaibun,1
blazkenzxy,1
ice_queen,4
toony_expression,0
pocketmob,1
uzaki-chan_wa_asobitai!,3
latea_hyena,1
realistic_fur,0
cheerie,4
m2vcjtad5hsdvla,1
boom_boom,5
katamra_(spazman),4
kittyrosie,1
tea_party,0
raymondfoxxx,1
imminent_unbirth,0
necktie_between_breasts,0
bottomless_humanoid,0
mojaloo,1
zoom,0
watership_down,3
bound_breasts,0
plank,0
xetajts,1
acino,4
venus_(trias),4
fidzfox,1
squish_(artist),1
duop-qoub,1
tigress_(disambiguation),-1
maleherm/female,0
stronkfurry,1
atmongoose,1
chappey,1
vrock,4
pussy_growth,0
kittydogcrystal,1
dogson,1
rosanne_hayes,4
stimulation-free_orgasm,0
lovingwolf,1
kajinman,1
the_queen_(fvt),4
grind3r,1
bastardpalace,1
white_tipped_tail,0
ps4_console,0
jonfawkes,1
tamani_(coc),4
gildedbonexfm,1
holding_paws,0
vulpesvant,1
shinyluvdisc,1
west_of_heaven,4
robyn_(kaylii),4
zeflex,1
tattoo_on_belly,0
snowstorm,0
stantler,5
vao_(coffeechicken),4
blackrabbit-13,1
bow_on_head,0
tan_border,0
puffylover69,1
toy_car,0
juniper_(egocider),4
plejman,1
stickysheep,1
nooshy_(sing),4
toe_lick,0
licking_object,0
princess_(nicoya),4
virovirokun,5
tdtbaa,1
extyrannomon_(artist),1
orchid_mantis,5
the_pirate's_fate,3
blankieplush,1
groping_butt,0
walter_moss,4
blinx,4
relatable,0
devon_(furball),4
haley_(elysian_tail),4
amused,0
galvinwolf,1
roscoe_(animal_crossing),4
frosmoth,5
krita_(program),3
coiled_up,0
dreamous_(writer),1
latrans,1
rayne_(quin-nsfw),4
zoey_lavender,4
zraxi,1
delet_this,3
mia_(eag1e),4
fishine,1
listening,0
caeleste,1
satellite,0
grey_helmet,0
cum_enema,0
aureola,0
kissing_with_both_sets_of_lips,0
chameleos,5
purple_pillow,0
oreo_(food),3
duke_corgi,4
sirartwork,1
bfw,4
diathorn,1
asuri_(brawlhalla),4
taro-fox,4
gingy_(gingy_k_fox),4
shrinking_breast,0
charming,0
thundurus,5
aysu,4
boxing_shorts,0
sugarbandit,1
japanese_badger,5
koyanskaya,4
bondage_wrist_cuff,0
text_on_shoes,0
andybunny,1
zeecibee,1
twistingtoxic,1
fuka_(kantoku),1
watchog,5
bel_group,3
deer-spangle,4
cyoa,4
therapist,0
snuckums,1
grandpaced,1
hsp,1
xenoblade_chronicles_1,3
rine,1
louie_(bomberman),5
darth_vader_sanchez_(housepets!),4
evangellos_(artist),1
aisha_(neopets),5
synn,4
akylie,1
epic_journeys_and_random_encounters,3
photon_(ratte),4
ishigray,1
virginity,0
o-ring_collar,0
euphorica,1
luga_(ut134567),4
offering_hand,0
slydragoon16,1
mimi_(playkids),4
paw_grab,0
donkey_humanoid,5
arturo_juarez,1
jotaku,1
tosx,1
pinkierawr,1
freakyed,1
rainven,1
monobe_yuri,1
jepso,1
bondage_mitts,0
casual_knotting,0
vulpesvant_(character),4
unicycle,0
erebonbon,1
ohohflamethrower,1
daisy_(doom),4
stepmother,0
mitsuhide_vulpes,4
altowovurr,1
padunk,1
journal,0
blowout,0
fish_hook_piercing,0
damian_(funkybun),4
skyican,4
keiko_tachibana,4
eukayoticprokaryote,1
shorttail,4
kloogshicer,1
teatfuck,0
trampoline,0
submission_hold,0
bellecandie,4
pandora's_fox,4
shawnthegirl,1
trilby_(hat),0
wing_grab,0
foreign_girl_lovers'_association,1
pinkushika,1
octavian,4
tolbi,1
munty,4
toon_patrol,0
lucy_(wherewolf),4
stargate525,1
carbink,5
smash,0
pollen,0
zaigane,1
neon_(artist),1
scuted_legs,0
multicolored_highlights,0
gevind,1
strawberrycucumber,1
negative_number,0
us_flag,0
scarlett_vithica,4
bianca_(sheep_and_wolves),4
tagovantor,1
tapping_foot,0
tom_(mlp),4
cum_on_screen,0
pro_bun_(hladilnik),4
zoroj,1
ambus_(grimart),4
laosduude,1
girthy,0
cyote,4
frackhead,1
lewdcreamy,1
littlepawz,3
confrontation,0
batibatizekkyou,1
toodles_(springtime_for_thomas),4
wrinkle,0
anthro_to_human,0
toothy_smile,0
taiden2,1
xylious,1
malberrybush,1
quadruple_anal,0
holding_both_ears,0
drake1dragon,1
9:17,7
andrefil360,1
nunutaro3,1
krabby_(artist),1
shijin,4
raven_darkfur,4
pink_bed,0
tangle_(fnaf),4
assisted_undressing,0
grey_fingernails,0
green_egg,0
tom_(animal_crossing),4
improbable_(artist),1
orthros_(mlp),4
stein,0
lilithmon,5
koi-chan,4
porn_studio,0
noisy_oral,0
kenobear,1
hithog,1
heatboom,1
yacht,0
darksilver,1
chizu,1
thornbeast,1
banned_from_equestria,3
younger_fingered,0
dungeon_fighter_(franchise),3
seuchenkater,1
blacky-moon,1
playing_sport,0
hit_ton_ton,1
frostbone,1
rasalhague,1
lesser_hedgehog_tenrec,5
volac,1
area_(artist),1
aurelina_canidae,4
souleatersaku90,1
glistening_gloves,0
limebeatz,1
pteruges,0
shaybecke_(character),4
qunari,5
peachymewtwo,1
plum_(insomniacovrlrd),4
conqista,1
mariah_veiethe,4
mathew_(srmmk_mce),1
sneel,1
korokke,1
10:11,7
inked-waffle,1
sorlag,4
red_bull,3
code_name:_hunter,3
o-ring_bikini_bottom,0
morgdl,1
kmd023,1
furry_problems,0
soundwave,4
nata_rivermane,4
rtil,1
anthro_to_humanoid,0
nitrosimi96,1
kanzashi,0
lampent,5
lilina,4
butt_transformation,0
mnementh,4
santana_the_dewott,4
lauren_faust,4
xhyra,4
rudderg33k,3
case,0
dezo,3
recklessarts,1
spiderpony,5
improvised_bondage,0
supermegatopia,3
purplefondue,1
ratharn,4
redo_of_healer,3
shira-hedgie,1
monstercat,4
bcpony,1
lesang,1
zōri,0
adrian_(crovirus),4
meru_(biggreen),4
threewontoo,1
morosuhe,1
beating,0
nose_lick,0
chorsinnell39,1
s_mino930,1
allie,4
umeguru,1
tail_embrace,0
setsuna_(redo_of_healer),4
shai,4
grace_kaiser,4
blazeymix,1
toy_chica_(eroticphobia),4
sheep_taur,5
band-aid_on_face,0
pouring_on_breasts,0
butt_pillow,0
paperdrop,1
tribute,0
pecha_berry,0
lost,0
spamcat,1
pepper_(crushpepper),4
dragonclaw36,1
tiggs,4
cold_quarantine,1
maisie_whisk,4
paintchat,0
tony_tony_chopper_(horn_point_form),4
base_four_layout,0
amiibo,3
ear_clip,0
zartersus,4
carrioncat,1
heart_body_writing,0
jana_(jana's_lab),4
rogers_(alfredblaze),4
multyashka-sweet,1
evan555alpha,1
nova_(anonym0use),4
excessive_fluff,0
hose_in_penis,0
vernid,5
aj_(ajisthebest),4
jack_(webcomic),3
cseed,1
playstation_1,3
jackhammer_position,0
tam_o'_shanter,0
cheesecake,0
morpeko_(full_belly_mode),5
professor_kukui,4
kotaro_(leobo),4
psychedelic-lemur,1
halloween_2021,0
tundra,0
bear_nuts,3
lewdicrousart,1
ribr0t,1
undersized_clothing,0
kadoo,4
tickling_nipples,0
wrestling_clothing,0
sunrays,0
pato_(bastriw),4
red_tail_tip,0
ktandoku,1
spook_(top_cat),4
mayor_pauline,4
covered_mouth,0
tara_(taranima),4
tepigfan101,1
recca,4
sweater_dress,0
soccer_field,0
chocolate_syrup,0
stegz,4
sofi_(aygee),4
blue_poison_dart_frog,5
on_tail,0
fighting_game,0
tigran_the_werewolf,1
coff,1
sit_up,0
amalgam,0
orange_cape,0
faldarea,4
akaunkel,1
amyth,1
pananovich,1
tinywag,1
gardie_(otukimi),4
belgian_shepherd,5
kitara_cydonis,4
penis_in_ear,0
lil-heartache,1
holding_coffee_mug,0
dr._mario,3
wi,4
carotte666,1
moonwhitetiger,1
spilled_liquid,0
pink_exoskeleton,0
ariel_(james_howard),4
sharkvore,1
morain_xs,1
nebulabat,1
penis_dildo,0
pumpkin_belly,0
alister_azimuth,4
seiko_(chewycontroller),4
evilthabad,1
grappling_hook,0
gundam_build_divers,3
fingerless_armwear,0
ettie,4
jmf,1
shell_necklace,0
anal_kiss,0
inviting_cumshot,0
altenusy,1
palm_town,3
kennzeichen,1
just_right,3
jessie-katcat_(artist),1
maxim_theos,1
g-virus_mutant_(resident_evil),5
tauski,4
explaining,0
cai_yuwang,1
handjob_through_clothing,0
dragonmaid_sheou,4
shepherd,0
9air,1
pill_bottle,0
nekogarasu,1
dromedary,5
hexerade,4
loving_gaze,0
mattie_(pokefound),4
kitt-n-valentine,4
5_nails,0
railroad_mejic,1
charity_(sanders),4
partially_(disambiguation),-1
aunt_orange_(mlp),4
sindaj,1
wtf_face,0
wattacat,1
horn_size_difference,0
domatriuz,4
jess_(capdocks),4
meeka_(spiko),4
sunspotfish,1
siam_fox,1
armpit_worship,0
thumbclawz,1
monotone_bikini,0
annie_(lol),4
1-up_mushroom,0
multiple_genders,0
vio023,1
nenerhea,1
framed_picture,0
bulb,0
icarus_skyhawk,4
earband,0
isaiah,4
the_wonderful_wizard_of_oz,3
pathfinder_(apex_legends),4
meowth_(team_rocket),4
tritscrits,1
maxwellshiba,1
university,0
cheek_frill,0
underwear_around_ankles,0
junglepony,1
lycalopex,5
notsafeforweh,1
travel,0
tribes_of_tanglebrook,3
mordekaiser_(lol),4
ariozyn-soulcatcher,1
amber-aria,1
miss_brush_(brushfire),4
film_(disambiguation),0
muffin_top_(bottomwear),0
blue_gem,0
unusual_font,0
homework,0
warrnet,1
cum_flow,0
luna_(domovoi_lazaroth),4
usagin,1
terror,0
redvelvetbat,1
cantio_(lawyerdog),4
teedraws,1
nikohowlii,1
cherry_the_trainer_(redimplight),4
arms_around_legs,0
italy,0
teslajellymon,5
kiba_(kiba32),4
garble_(drowsyboi),4
unzipped_jacket,0
kayasamix,1
ra,4
jayrunner_(kitfox-crimson),4
oral_footjob,0
jersey_devil,5
drboumboom32,1
tarakanovich,1
the_testimony_of_trixie_glimmer_smith,3
head_arms,0
hermione_granger,4
celine_(vinfox),4
sega_genesis,3
hitori09,1
piccolo,4
momorawrr,1
asestrada157,1
stench,0
maxine-dragon-787,1
aleximusprime,1
triceraton,5
mosstheopossum,1
pink_clitoral_hood,0
salt_shaker,0
tenides,1
nuadha,4
lunarez,1
rothar,4
>o<,0
toph_beifong,4
theterm,1
starburst,3
christie_(felino),4
hanzo_(overwatch),4
yogan,4
flag_on_structure,0
gigawix,1
kami_(kamicheetah),4
barboach,5
thick_foreskin,0
alduin,4
wild_west_c.o.w.-boys_of_moo_mesa,3
ranillopa,1
aralyre,1
copper_(animal_crossing),4
solomon_(johnithanial),4
jiggydino,1
jogging_pants,0
orangebox,1
gombee,1
weather_trio,0
mp5,0
shamu_meruruusa,4
tacticalmagpie,1
kamos_(sylvanedadeer),4
nafeon,4
dronesuit,0
pointer_(dog),5
countershade_feathers,0
splendid_(htf),4
mental_transformation,0
bucker,4
shen_(archshen),4
fang_the_weavile,4
chirros,1
scratte_(ice_age),4
imaranx,1
protest,0
brown_thigh_highs,0
varollis,1
selmers_ann_forrester,4
nearu-senpai,1
badcat,1
shiuk_(character),4
text_on_jockstrap,0
adonis,1
pawronica,1
shadowthespirit,1
shoulder_armor,0
cum_disposal,0
isal,1
carhop,0
angus_(critterclaws),4
hooves-art_(oc),4
dragonith,1
cups,0
stepson,0
mostlymlpanthroporn,1
rope_leash,0
angelic,0
amniotic_sac,0
srfx18,1
z-lion,1
conto,1
aquila_chrysaetos,1
neoarcadianx,1
jolt_(wm149),4
ickleseed,1
despina,4
glowing_spikes,0
mardi_gras,3
flame_(spyro),4
elwynofastora,1
jegc,1
starfox_adventures,3
lemon_shark,5
rainbow-flyer,1
playjoe2005,1
avali_underfluffies,0
nick_(ulfhednar),4
justpony,1
pooding,1
lan_(zeta-haru),4
eve_(1pervydwarf),4
musket,0
shon_arts,1
holding_pool_toy,0
partial_transformation,0
okatana,1
ian_r_soulfox,1
cole_(colelucario),4
reptile_tail,0
viking_helmet,0
marflebark,4
pit,0
zoodystopia,3
liz_art,1
claudia_(averyshadydolphin),4
hynvale,1
letsdrawcats,1
tojo_the_thief_(character),4
cloacal_kiss,0
delivery_vehicle,0
holding_ankles,0
penelope_(sly_cooper),4
buck_(ice_age),4
jenna_kutu,4
caroline_waters,4
midnight_meowth,1
kickboxing,0
otter_grafitsnake,1
tatotake_mono,1
saikorodekimeru,1
coppertone_girl,4
shenron,4
white_cat_legend,3
pattern_cape,0
colin_(anonymous3355),4
raevi10,1
revealing_penis,0
myosotis_(gym_pals),4
mara_kilpatrick,4
broken_rape_victim,0
marilyn_monroe,4
leopold_visette,1
bow_collar,0
emaciated,0
denton,1
creamydonuts,1
fiszi,1
lone_digger,3
umbrella_cockatoo,5
cchilab,1
deadlift,0
rodinsinker,1
nft,0
theember,1
monotone_eyewear,0
pile_(breeze_in_the_clouds),4
himbofication,0
red_loincloth,0
orange_tank_top,0
dogget_(doggettdouglasmcdog),4
lemonkyubun,1
headcrab,5
high_collar,0
pip_focus,0
river_lakes,4
mollish,1
mephitid_humanoid,5
trevor_henderson,3
toad_(disambiguation),-1
ruby-kila,1
rainbow_wings,0
black_arms_(sonic),5
falcon_graves,4
nose_ring_pull,0
watermelon_pattern,0
spring_sawsbuck,5
sand_creature,5
original_content,0
lying_on_floor,0
goh_(pokemon),4
hydralisk_(starcraft),5
teslawolfen,4
kingbriarturtle,1
omithefox,1
king_andrias_leviathan,4
gekko-seishin,1
modern_bird,1
glistening_underwear,0
leaves_in_hair,0
rainbow_topwear,0
japanese_tally_marks,0
foxiful,1
blue_rose,0
unf,0
preciousrat,1
sonic_the_comic,3
soap_bar,0
maya_del_phox,4
mynerva,1
fast_sex,0
judylavernehopps,1
exhibit,0
biohazard_tattoo,0
slate_(slates1n),4
quadruple_amputee_portal,0
vaultlad,1
spicetail,1
anitwenty,1
kitzy_(character),4
chocobo_tales,3
red_(redpixie),4
o-ruff,1
cabinets,0
croft,1
squirrelman,1
hyaenid_humanoid,5
the_last_unicorn,3
red_headgear,0
elytron,0
swinub,5
switcher-roo,3
claire_(ponehanon),4
regice,5
choke_hold,0
stoaty,4
human_on_robot,0
fist_of_the_north_star,3
school_for_vampires,3
riversausage,1
brown_pseudo_hair,0
im51nn5,1
rayfkm,1
track_and_field,0
low_twintails,0
newtype_hero,1
stirrup_clothing,0
dinosaur_planet,3
lucentleader,1
mira_(animal_crossing),4
entwined,0
fievel_mousekewitz,4
helen_dish,4
cynicalmoose,1
epsilon,4
oneeyewolf_(artist),1
methodia_rascal,1
lameboast,1
jesse,4
cum_on_boots,0
tooneyd,1
bridgie_the_heligator,4
ryken,4
lei_long,4
kaelyn_idow,4
vanilla_(buta99),4
pattern_shorts,0
blort,0
brooks_(anti_dev),4
heart_a._chicago,4
selucilia,1
seadra,5
qunteki,1
savalkas,1
xvvajra,1
martha_(roly),4
leering,0
alyona_the_floof,4
race_car,0
grey_head,0
hexatoy,5
inbetweening,0
skunkfrakker,1
disarten,1
thestrider,1
theo_the_zebunny,4
crimsonmercury7192,1
monsterfuzz,1
ryeono_kemo,1
argo_(knights_college),4
marema_kishin,1
dianna_(komponi),4
jaquin_s,1
sheazu,4
zilla_(airlea),4
basicbiscuit,1
holly_(fvt),4
kipwolf,4
sheen,4
hun-yi_(wherewolf),4
kratos,4
slut_dragon,5
nightmarebearpuff,1
jenette_neils,4
thel_'vadam,4
nekonohige,1
mudbray,5
united_kingdom,0
winter_deerling,5
tapu_fini,5
blak-dragon-boymk2,1
urine_on_body,0
toothy_(htf),4
multi_face,0
wolfwalkers,3
skutchi,1
dantera_rina,4
muq,1
pucker,0
amalia_sheran_sharm,4
shiver_(shivereevee),4
saint,0
ventox,1
yuki_(wolf_children),4
horny_blue_bowlingball,4
buyobutt,1
li_bing_(white_cat_legend),4
ambient_jellyfish,5
aleron,1
kipo_and_the_age_of_wonderbeasts,3
wildmutt,4
fnafng,3
sue-chan_(artist),1
slug_humanoid,5
moisteaglevent,1
mythological_bird,5
jugg4,1
vincentcc,1
caricature,0
quivster,1
melo,1
hhazard,1
taykoe,1
lewi_(toomuchdynamite),4
nanuna,1
tmnt_1987,3
booker_(animal_crossing),4
senka-bekic,1
glistening_hooves,0
belly_pop,0
;d,0
plugged,0
tfwnocatgirlgf,1
remert,1
kyder,1
scuta_patch,0
fullbodywolf,1
smoliv,5
tammy_(animal_crossing),4
darkfang100,1
vicious_kitty,4
matsuura,1
resinger17,1
lamika,4
standing_on,0
finvi,4
spier17,1
tail_bag,0
bael_(tas),4
nucahfox,1
hikagenootimsya,1
kelvin_hiu,1
rhoda,4
hidden_orosubi,4
drawalaverr,1
nero_(naruever),4
harder,0
fuwayuuko,1
megatron,4
brekkist,1
rokumaki,1
posing_for_picture,0
thickick,1
ryn_(stargazer),4
full_body_suit,0
triforce_print,0
uselesslizard,1
side_saddle_position,0
solar_gush,1
qibli_(wof),4
angeloid003,1
shampoo_bottle,0
antinous_(character),4
jitenshasw,1
drake_inrelal,4
heart_as_word,0
alegrimm,1
garret_(pickles-hyena),4
vicente_(romeodog),4
sheriff_badge,0
hanna_fondant,4
patriotic_clothing,0
popka,4
doctor_dee,4
rujirublkdragon,1
brown_cowboy_hat,0
troy_lesage,4
nsfwquynzel,1
ipsywitch,4
voodoo_masturbation,0
puniwanko,1
mestiso_(character),4
sipping,0
windwttom,1
life_preserver,0
dweet-tea,1
at_work,0
combat_gear,0
proboscidean_humanoid,5
yellow_frill,0
pasiphae_structure,0
arokha,4
fenix31_(artist),1
pretzel_(piemations),4
charlotteray,1
goldfur,4
rosie_(heavensdoor),4
faisonne,1
mazedmarten,1
real-life_pornography,0
drakkor,4
temporalwolf,1
dicephalic_twins,0
epididymis,0
lamia_(lamiaaaa),4
soapy,0
arwokom,1
reverse_penetration,0
finding_nemo,3
tigon,5
foxelyn_tyson,4
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clover_(totally_spies!),4
requested_sex,0
tail_on_tail,0
baleinebleue,1
angelise_reiter,4
examination_room,0
beautyfromabove,1
chub_(disambiguation),-1
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rozga,1
bastion_(bastion),4
cempe_(caelum_sky),4
benson_dunwoody,4
milkie,1
hotcoffeecat,1
wepawet,4
new_world_oriole,5
bouncing_pecs,0
zentai,0
flashingfox,4
lyrebird,5
white_neckerchief,0
theloanwanderer216,1
telem,1
latenightsexycomics,1
rokesys,1
signhereplease,1
valstrax,5
skunk_humanoid,5
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star_(babystar),4
xenarthran_humanoid,5
chelsea_(meesh),4
koto0v0haru,1
white_tattoo,0
dombrus,1
srymaimon,4
sinisterheaven,1
casey_hartley,4
cinder_(cinderfrost),4
dillon_(dillon's_rolling_western),4
green_flesh,0
doughtea,1
suiro,1
leg_restraint,0
convent_of_hell,3
emberwick,1
synthia_vice,4
nyagirl,1
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shoji,0
haik,1
hot_sauce,0
sherwood,1
boywiththebrokenhalo,1
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mirokuj7,1
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zangief,4
multicolored_membrane,0
ol'_blue,4
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kai_(fuze),4
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en_pointe,0
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pink_inner_pussy,0
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sunflowmon,5
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blue_(limebreaker),4
sanaki,1
gazer,5
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upov,1
quentin_(zylo24),4
sebtheredpanda,1
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cosia,1
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dr_rabbit_(tomtc),4
riot41,1
arctic_hare,5
verakultura,1
lexi_bunny,4
ral-jiktar,4
whitecrow,1
saruki,1
netto_(uk-brony),4
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the_lareme_triplets_(battler),4
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bubblewolf,1
toraisix,1
worn_down_children,3
nil,4
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photocopying_self,0
frosti_loxxxe,4
cobalt_(tatsuchan18),4
timmothy_arts,1
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chimiko,4
ivy_pepper,4
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furred_snake,5
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nir_(old_design),4
ranthfox,1
b00mt00b,1
zeglo-official,1
memory,0
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ashleyzombie,1
hiroyko_art,1
berenstain_bears,3
dark_dragon_(american_dragon),4
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jager-darklite,1
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sakuyako_kyora,1
a5wagyu,1
wanda_werewolf,4
dinosaur_train,3
carracosta,5
formalinov,1
blackpassion777,1
joyful,0
smile.dog,4
hypohate,1
rosechu_(character),4
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virul,4
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amboyna,1
valkyrie,0
rasile_murdock,4
cordell,4
voff_akita,4
gus_the_gator,4
doumon,5
two_tone_head,0
mirroid,5
maxwell_(reign-2004),4
ziggy_zerda,4
sev_(batartcave),4
ragey,4
m@k,1
x-38_(maddeku),4
ahekao,1
finnegan_lyonhardt,4
max_(sci),4
amy_(canisfidelis),4
philomaeus,4
sagaris_uwu,1
moromorowephi,1
karipup,1
nachos,0
diary,0
kuroeda_(plus-sized_elf),4
irving_(100_deeds),4
12-tf,1
purple_speedo,0
masked_matter-horn_(mlp),4
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daisy_maybelle,4
scarr_ruddlocke_(harmonydescent),4
urethral_sound,0
dar-kuu,1
hat_flower,0
lolidonk,0
the_smooze,4
scrubbing,0
princess_fiona,4
lenses,0
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spike_wolf,4
rough_the_skunk,4
baek-myo,1
jodero,1
sunderlovely,1
purple_diaper,0
tanis_(ghoul_school),4
wawik,4
arabian_goggles,0
head_growth,0
holding_baseball_bat,0
marcus_red,4
maria_whiteblood,4
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drapery,0
tau_maxim,4
foxy101,1
peng,1
abbey_(bcb),4
input-command,1
chikiot,4
neiliousdyson,1
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green_clitoris,0
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gem_(doubledog),4
tied_drawstring,0
wood_mouse,5
vaatari,4
sen_grisane_(character),4
walrider_(outlast),4
rainbow_(artist),1
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cass_(nekomimiranger),4
oreocakes,1
poncle,5
sheep_mom,4
jaspixie,1
blue_dragon_(character),4
bato_the_cyborg,1
shoe_fetish,0
ino_makani,4
kemo_nuko,1
head_wound,0
yilx,1
kittie,4
taus,1
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pachislot,1
mike_(sigma_x),4
primal_dialga,4
covered_navel,0
multicolored_speedo,0
tsuderou,1
kelkessel,1
dirty_face,0
white6000,1
eye_makeup,0
konomichi,4
rick_(dream_and_nightmare),4
intermundano,1
miles_(anti-tails),4
old_man,0
incarnata,1
touching_breasts,0
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alivia,4
dog_bed,0
wavyrr,1
kiriban,0
hanger,0
erroremma,1
smeargle20,1
wally_(pokémon),4
kosperry,1
slime_core,0
wobble,0
fisting_partner,0
mangolynx,1
vallycuts,1
ball_penetrating,0
kamukamu6392,1
sukarabe,1
reishuseyu,1
caution_stripes,0
paralee_(character),4
spaceship_interior,0
the-rasp-b,1
supermattym8,1
star_guardian,0
senatorwong,1
catmuti,1
ninon_(artist),1
teddy_porter,4
lucas_arynn,4
furvie,1
moozel,4
lanya_(shian),4
goblimon,5
southern_belle,4
tuba,0
desiree_(jmh),4
pepperoni_pizza,0
midnight_sparkle_(eg),4
comment,0
deepest_sword,3
amy_pratt,4
bra_aside,0
clicking,0
merengue_(animal_crossing),4
straps_across_chest,0
pencil_in_mouth,0
quil,4
radixpanther,1
cheshirecatsmile37,1
astremine,1
washcloth,0
writing_utensil_in_mouth,0
looking_into_mouth,0
tubasa,1
air_vent,0
anotherpic,1
black_headgear,0
itsmorti,1
whiscash,5
j.fbelen,1
dokuga,4
arwen,4
gattatta,1
harvin,5
locomotive,0
gallar_(nnecgrau),4
merystic,1
tribal_paint,0
gatox_(artist),1
abigail_shire_(platylot),4
uken_l,1
qew123,1
pocky_day,3
tempestryder,1
st4rsbucks,1
jose-497,1
bounsweet,5
claw_pose,0
kajhiro,1
dmann892,1
evolution_chart,0
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eurotica,3
renki_blackwolf,4
pubic_hair_peek,0
mochagato,1
boxer_briefs_only,0
mordetwi,3
rygel_(rygel_spkb),4
gab,4
uzi,0
title_screen,0
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repzzmonster_(character),4
carrot_(carrot),4
jack_(disambiguation),-1
shirano,4
cobalt_the_unicorn,4
kadomarco,1
riff_(riff34),4
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mike_(mrmellow),4
remmyfox,1
lovelytrashpanda,1
frilly_legwear,0
erakir,4
felicia_(krekk0v),4
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bow_(artist),1
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foot_claws,0
lexy_the_sheep_taur,4
cilok,1
hugo,4
baelfire117,3
fallen_angel_(artist),1
drawn-sex,1
species_sheet,0
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ladypixelheart,1
shaak_ti,4
amonomega,1
cierras,1
nahadon,1
alex_(loobka),4
atlas_(jelomaus),4
world_of_final_fantasy,3
queen_rain_shine_(mlp),4
kyain,1
crotch_rub,0
mazilion,1
raised_forearm,0
kuma25-ya,1
turtle_neck,0
arctix_(talaniss),4
ball_tattoo,0
dark_shoes,0
touching_balls,0
imeow,4
brewheardt,1
pheanir,1
text_on_gloves,0
looking_at_computer,0
caramelthecalf,1
tyson_(password),4
virgil_(apoetofthefall),4
phasianus,5
big_hamstrings,0
parcly_taxel,4
belly_to_belly,0
ninjapony,1
gracie_(vixeyhuskybutt),4
sierraex,1
grey-headed_flying_fox,5
fully_shaded,0
ritkat,1
lie,0
shiny_fakémon,5
tickling_balls,0
clothing_around_legs,0
mosaic,0
drafthoof,1
hourlessmage,1
dipo_(artist),1
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brindle,1
long_the_dragon,4
pussy_juice_stain,0
steelph,1
xlr8,4
science_lab,0
mamandil,1
adc1309,1
malcontentus,1
obsidiana,4
amber_(femsubamber),4
krinele_fullin,4
xenstroke,1
alex_(nsf),4
hisuian_arcanine,5
master_mantis,4
nailed_bat,0
charbold,1
dunes,0
kurube,1
pebble_(letodoesart),4
green_ribbon,0
lewdzure,1
minna_sundberg,1
viktorvaughn,1
african_grey,5
mits_(character),4
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pride_heart,0
pawniard,5
srw225we,1
yvem,1
mathythemathy,1
karlo,1
dark_tongue,0
saygoodbye-sfm,1
fenris64,1
maxizz,1
ko-gami,1
dracokaitonsfw,1
melonpussy,1
alchiba,4
lupiarts,1
base,0
syrios_(character),4
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fukashinx,1
preyfar,4
thydris,1
corruption_of_champions_2,3
dark_underwear,0
implied_pregnancy,0
fauxsquared,1
zziowin,1
complex_vortex,1
deadman_joe_velasquez,4
obedient,0
coalt,4
scratazon_leader,4
spaffy,4
andrew_oikonny,4
3doss,1
drakxs,1
unluckyoni,1
joushi,1
rel,4
lazybutt_(artist),1
winged_dragon,5
shadowfenris,1
cautioncat,1
jamie_(boosterpang),4
lt._john_llama,4
vier_punksterne,1
shirokuro0,1
q-blackbun,1
mommyosha,1
mud_wrestling,0
manor_(bottlebear),4
football_gear,0
ravebounce,4
isna,1
mankini,0
tail_penetrating,0
pink_light,0
cats_(musical),3
ciwi,1
underwear_around_ankle,0
spookaboo,1
live,0
johara_mombasa,4
wigwoo1,1
multi_tone_scales,0
myrsi,4
long_shirt,0
tight_balls,0
syntek_(character),4
gankgoat,1
laserkitten,1
knives4cats,1
jonathan_(character),4
selicia,4
marching,0
riley_(disjachi),4
elewdfurs,1
yen_rin,1
snazzamazing,1
15:19,7
blitzen,4
radaq,1
yagi-no-baka,1
leg_around_pole,0
doeboi,1
hares_(unit_no04),4
sagejwood,1
fili-second_(mlp),4
lolo_(lomidepuzlo),4
taillessbunny,1
hukeng,1
extry,1
horror_story_(character),4
rylie_(hypernovagm),4
fetal_movement,0
sega_controller,0
badday28,1
foxx.dott,1
rome_(feuerfrei),4
audrey_(woofyrainshadow),4
bling,0
asmodeus_(helluva_boss),4
skittle_(oc),4
ginglymostomatid,5
fillyscoots42,1
blackblood-queen,1
poppy_(justkindofhere),4
aziel_softpaw_(character),4
profec,1
iron_aegis,4
junior_(playkids),4
foxie,4
pangurban91,1
billmurray,1
glowing_stripes,0
trimming_(grooming),0
cinemagraph,0
suki_yamamoto,4
betta_(genus),5
flood,0
reddishmaroon,1
orgasm_count,0
alduin_hearth_(character),4
medical_play,0
flappydog,1
lf,1
the_isle,3
dalthia,4
becca_(bernielover),4
gerson,4
florence_flask,0
hand_over_breast,0
keiren_(twokinds),4
wolf_j._samuel,4
skeptical,0
cherry_(macmegagerc),4
microwave_oven,0
bobby123,4
graduation,0
wendy_corduroy,4
argit,4
etherial_bitch,1
zacharoff_(anothereidos_r),4
zedzar,1
brandon_(cookiefoxbrandon),4
katxlogan,4
a-wh-b,1
rod_steele,4
clef,4
humanoid_fingers,0
dingaling_productions,3
heir-of-rick,1
swift_(sleekhusky),4
einom,1
bundle0sticks,1
sparklepupfaeri,1
greyscale_background,0
mrs._bull,4
thorsoneyja,3
jana's_lab,1
deilan12,1
anne_barnetson,1
gourami,5
ludicolo,5
snake_(animal_crossing),4
penny_(plantpenetrator),4
esterr_(artist),1
joh29,1
magenta_(magenta7),4
dottipink,4
jimahn,1
blue_tail_tip,0
time_stop,0
wooxx,1
thokk_(invincible),4
multicolored_bracelet,0
bakap,1
wolferno,1
dick_slip,0
simon_(evane),4
half-dude,1
gavin_(spyro),4
ezalias,1
ggv,4
aqua_teen_hunger_force,3
ratatoskr,4
larynkir,4
arkanoego,1
roasting_spit,0
star_censor,0
senpuuki-chan,1
thespicypaprika,1
felix_joyful,4
nyong_nyong,1
pilz_moos,1
givuon_(artist),1
claire_(bunnybits),4
gojho,1
shadow_(copperback01),4
salem_(discordthege),4
silas_blackwing,1
mira_bloodstar,4
celesse_souris,4
costume_transformation,0
marie_(oughta),4
looking_at_crotch,0
inverted_(disambiguation),0
avarice_panthera_leo,4
mash_kyrielight,4
knight_(nightfaux),4
purple_hood,0
deejayhan,1
turkey_meat,0
ultra_nyan,4
fiamme_(citruscave),4
shippun,4
bubblegum_(cuddlehooves),4
dillon_(aaron),4
laughing_at,0
atlas_(fusion_h0ss),4
fermusieboir4,1
under_foot,0
duct_tape_gag,0
orange_t-shirt,0
custardalvis,1
sangie_nativus,4
tiberius_creations,1
firefighter_uniform,0
torrent_(elden_ring),4
starbirbz,1
springbok,5
donkey_(shrek),4
mr._frenzy,1
sweet_apple_acres,0
afk_arena,3
saria_legacy,4
red_mask,0
hanako_(lyorenth-the-dragon),4
tongue_taco,0
to_fat_to_fly,1
yellow_(character),4
tailsmight,1
wobblelava,1
mourn_the_buck,1
mounted_sign,0
algae,0
mirialan,5
kilika,4
two_tone_neckerchief,0
golf_ball,0
embereon,5
distress,0
rayoshi,4
vincian_pride_colors,0
fury,0
rifykoh,1
paintrfiend,1
elbow_fur,0
siman-clifton,1
dav-19,1
australian_flag,0
brown_spikes,0
popo_(ice_climber),4
thedragonred12,1
claudette_dupri,4
brawl_stars,3
entsk,1
bicycle_helmet,0
cali'fon_(sinister),4
kabbu_(bug_fables),4
gym_equipment,0
fermata_suzuka,1
kaptainarr_(character),4
nantaimori,0
gat_(character),4
kourourin,1
otter_humanoid,5
wood_fence,0
leafwing_(wof),5
tape_over_penis,0
narinder,4
sylas_(sylasdoggo),4
naginata_katsurino,1
wizard_robe,0
mask_with_sex_toy,0
ripper_torsent,1
french_accent,0
green_tunic,0
orchard,0
crows_feet,0
sammy_(buxbi),4
andy_(nekomimiranger),4
black_eye_patch,0
gojiro_(pak009),4
presenting_to_viewer,0
common_pheasant,5
oldgreg,1
projector,0
twi_paww,1
chance_(bad_dragon),4
teal_wings,0
oolong_(dragon_ball),4
satsuki_kiryūin,4
nurse_shark,5
rancid,1
sunglasses_on_face,0
nix_(ceehaz),4
alibiwolf,1
harp_seal,5
brickoven,1
riju,4
leg_ring,0
alonsocas15,1
women_livestock,0
popped_collar,0
hand_on_own_foot,0
customer_service_wolf,3
pitu_le_pew,4
clair_(seel_kaiser),4
ball_crush,0
sexilydrawn,1
aldo_(my_life_with_fel),4
butt_fondling,0
kappa_spark,1
cepheus_(lieutenantskittles),4
harami,1
anymouse1968,1
dori-to,1
slacks,0
agony_(copyright),3
batgirl,4
abigfathen,1
plop,0
..._(artist),1
neck_floof,0
siretoko_panda,1
paivio_selanne,4
kibbleztheyeen,1
it_(stephen_king),3
jukebox,0
kavaeric,1
rane_(fluff-kevlar),4
two_tone_skirt,0
lace_(lacethecutegoat),4
wanko0487,1
ghast_(minecraft),5
male_fingered,0
chef_kawasaki,4
clothed_human,0
feelferal,1
beanie_(roommates),4
neversoft,1
hit,0
lyga,4
cutebold,5
niko_(artist),1
kaffii,1
nayrin,1
blackmorass,1
snout_grab,0
kiss_my_ass,0
m0000rgn,1
amaterasu_(artist),1
genesect,5
beastmen_forest,3
doodle_champion_island_games,3
body_takeover,0
sound_effect_pair,0
acru_jovian,1
green_sky,0
shakes_heartwood,4
hand_on_knot,0
mech_(mechedragon),4
gosalyn_mallard,4
medivh_(soundvariations),4
impressed,0
comfortable,0
han_moz,1
san_renard,1
walton_(vdisco),4
urine_on_feet,0
caspar_the_frog,4
compression_tools,0
baseball_field,0
martin_ballamore,4
temils,1
orangeflyingfox,1
wing_wu,4
nim-nim,1
indian,0
santino_rosato,4
kira_redpaw,4
floramon,5
blossom_(powerpuff_girls),4
chestburster,5
disk_(daftpatriot),4
kusunagi,1
dave_(notkastar),4
deaver,1
moon_studios,3
smokeyocity,1
presenting_armpit,0
pinklop,1
semi-anthro_on_semi-anthro,0
tattoo_on_chest,0
flapper,4
yeenstank,1
yugia_(evov1),4
sayakat,5
hacatiko,1
jasonwerefox,4
tezcatl-ayauhtli,1
duolingo,3
blade_runner,3
kameri-kun,1
giant_food,0
grishnax,1
tail_hair,0
sugarboy,1
quarko-muon,1
gab0o0,1
hair_loss,0
sheldon_j._plankton,4
daven_(dado463art),4
merena_(azaleesh),4
furtshi,1
thaman,1
wasabitea,1
kuatabami,1
deihnyx,4
erie51,1
exawolf,1
robertfiddler,1
cosmicvanellope,1
armored_boots,0
shado3,1
sralivewolfox,1
death_note,3
exotic_dancer,0
trixie_hardfuse,4
earthworm_jim_(series),3
mr.guinea_pig,1
kama_sutra,3
translucent_armwear,0
death_stranding,3
lobodelamuerte,1
nijisanji_en,3
griotte_(armello),4
8_ball,0
bronyant,1
jellybats,1
stereoplair,1
castitas,1
margaret_de_campos,4
dawn_(ymbk),4
multiple_hands,0
redmok,1
eep,0
titanic,3
rape_rack,0
fierce,0
insect_taur,5
famished_(artist),1
word_substitution,0
beruzumi-m,1
print_diaper,0
mitoro,1
3rr0rartz.stud10,1
jasmine_(bcb),4
emerald_swift,5
tikki_(zonkey),4
graduation_cap,0
uncut_with_slit,0
wicke_(pokémon),4
taleir,4
yiffalicious,3
revealing_(disambiguation),-1
american_badger,5
moon_(marking),0
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leo-wolf,1
mega_man_star_force,3
totolove35,1
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fortuna_(unknownlifeform),4
maaty-s,1
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tales_of_sezvilpan_(copyright),3
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hriscia,1
tears_of_orgasmic_joy,0
watering,0
kokuhaku,4
independence,4
imaani,1
tomiwoof,1
spotlights,0
mrrshan,5
silver_(shiro-neko),4
espent,1
squirmle,5
tina_(tonytoran),4
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lykenzealot,1
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garwaron,1
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jomaro_(artist),1
m_ikky,1
junker,4
censored_breasts,0
harmonie_goof,4
under_tree,0
h56_(hikkoro),1
lanhai,1
dractaco,4
dragon_ball_xenoverse,3
kc_(kingcrazy),4
rybiok,1
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mellos,1
tart_(eto_rangers),4
phoberry,1
pathia,4
matomonstera,1
kaiser_(ayaka),4
theburningdonut,1
feet_everywhere,0
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tygre,4
velvet_roe_(weaver),4
albinoturtle,1
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dravening,1
purple_neckerchief,0
maddi_beller,4
grossarts,0
blackriver,4
toony_eyes,0
filia,4
fejess96,1
smallfryspy,1
genji_(animal_crossing),4
supersonicsoup,1
bleeding_eyes,0
breech_loader,1
6_hands,0
skiploom,5
phantasy_star_online_2,3
nanozell_spirit,5
dragonlove,1
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lxander191,1
manwiththemole,1
throwing_knife,0
akiya-kamikawa,1
awrpie,1
mayin,1
with_you_and_the_rain,3
spiked_boots,0
hilda_(warcraft),4
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raising_leg,0
constance_everheart,4
zeke_(crocdragon89),4
patrat,5
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niis_(character),4
deathheadmoth00,1
dasher,4
k'sharra,4
moki_(species),5
mkz_kabeuchi,1
glowing_glans,0
audie-gryph,1
zac_heartfields,4
okko,1
mox_sapphire,0
nikora_angeli,1
ausjamcian,1
annie_(the_sunfish),4
birdy,1
hana_(jishinu),4
lulu_(zerach),4
ian_(gothicskunk),4
pheel,1
rack_(disambiguation),-1
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forest_of_the_blue_skin,3
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hodiaa,1
ravios,4
revolverwing,1
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meat_hook,0
toonstruck,3
vibrator_under_clothing,0
hexdragon,4
alef_(shining),4
king_of_sorrow_(artist),1
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sd_(artist),1
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orange_armwear,0
nemesisprime909,1
pawssfm,1
fauxpawe,1
shari_(tiger1001),4
zipper_jacket,0
citrinelle_(character),4
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bozi,1
hewhoerasesmost,1
shinolara,1
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goldenwolf,1
mute,0
dickbitch_molly,4
deserter_(artist),1
queen_of_spades,0
panpour,5
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kkron_m,1
nylonlyon,1
calamity_(fallout_equestria),4
red-crowned_crane,5
jess_(kinaj),4
mikah_miller_(character),4
roomba,3
gintama,3
sdjenej_(artist),1
deamondante,1
lizzie_green_(dreamworks),4
misokemo,1
relaxed_expression,0
hellanoided,1
glow_ring,0
fred_(fredrick_brennan),4
shena,4
panthro,4
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anhmaru,1
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foshu_(character),4
buttershe,4
safeword,0
aimi_(sleepysushiroll),4
grand_councilwoman,4
card_deck,0
vuljath,5
omni_(sonicfox),4
a-10,0
dragonblue900,1
tubswubadubs,1
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ceylon_(stitchy626),4
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living_balloon,5
riushi,1
j-fujita,1
susiebeeca,1
giaru_(artist),1
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may_sanderson,4
jay_(drredhusky83),4
pawpadpup,1
triangular_anus,0
american_dad,3
pulling_up_pants,0
fauxtellno1,1
benji_(bng),4
haifisch_ehrlich,1
sr-71_blackbird,0
brown_membrane,0
on_hands_and_knees,0
making_love,0
protoqueen_bountiful_(oc),4
peppercat,5
flipaclip,3
veo,1
berry_pinch_(mlp),4
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amelia_(yips),4
frost_wolf,4
vareoth,4
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buns,0
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breast_piercing,0
amatsucat,1
wolfscar2810,1
simple_eyebrows,0
taiga_(bungotaiga),4
nazimi00,1
daenerys_targaryen,4
metal_wings,0
sheep_magik,1
partially/partially_submerged,0
cement,0
sex_shop,0
finger_wag,0
kanic,4
tyrant_(resident_evil),5
dallas_(gingersnaps),4
feces_on_genitals,0
matchesyashi,1
lumpy_space_princess,4
kotsu,1
blueberry_jam_(viroveteruscy),4
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holding_beer,0
bruno_rheinbear,4
hands_on_shins,0
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gemkin,1
torchy,4
themed_object,0
tunic_(video_game),3
krystal_(dinosaur_planet),4
ayuukuro,1
lockpick,0
jamesab,1
robo-fortune,4
barryfactory,1
finch_(xenoblade),4
b1_battle_droid,4
vezerem,1
cyanu,1
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fyr,4
aiko_(infamousrel),4
100_percent_wolf,3
foxia,1
randolph_(randt),4
king_tangu,4
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omar_mercado,4
meg_griffin,4
touching_hand,0
verde_the_snivy,4
deathlight,1
titanium,0
wan_wu,4
holo_doks,1
levin_rhekunda,4
fraxure,5
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killian_joy,1
ri_denueth,1
grotesque_genitals,0
eerie_(thekite),4
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shigehisa_nakaue,1
apostle,1
matthew-x,1
klefki,5
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elizabeth_báthory_(fate),4
celestialwolfen,1
mad-project,1
aoi_(ukenya),4
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heart_collar,0
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scp-049,4
evil_celestia_(idw),4
silverclaw1,1
relight,4
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muttasaur,1
lei_(skecchiart),4
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charem,4
bulkmon,5
jalle,1
panties_removed,0
walrein,5
toonpimp,1
knell_bell,1
head_lick,0
raised_claws,0
mojo_jojo,4
joey-darkmeat,1
watery_eyes,0
fae_(disambiguation),-1
shen_shepa,1
katherine_(caelum_sky),4
cow_and_chicken,3
leaning_to_side,0
tiifu,4
g-ma,4
miga,1
john_silver,4
western_dragon_humanoid,5
hondra,4
tygra,4
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clairen_(rivals_of_aether),4
alexander_grayhaven_(characters),4
fiddagracepaws_(artist),1
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guppy_(artist),1
javeloz,4
salz_sop,1
dark_clefita,1
unusual_tongue,0
friisans,1
preparation,0
delos,5
ashsky,1
slasheramaya,1
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metal_teeth,0
chuuta_(pixiv),1
meier_tennant,4
fabian,4
homeless_dog,4
tentacle_pregnancy,0
prosthetic_tail,0
clerian,1
bucky_o'hare,4
tai_(changing_fates),4
gnell_(inkgoat),4
dhstein,1
breasts_are_the_best,3
sorayasha,4
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flowing_mane,0
riku_tachibana,4
irma_langinstein,4
hee-na,4
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lottie_(animal_crossing),4
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caltsar,4
aryll,4
liquiir,4
freeedon,1
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wereursine,5
dahsharky_(character),4
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balthromaw,4
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lizzy,4
johnofe,1
fileossur,4
koalcleaver,1
surf's_up,3
united_states_forest_service,3
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purple_outline,0
yukimushi6,1
shane_(shane),4
milenth_drake,4
ruttingstrap,1
krystal_appreciation_month,3
george_(george701),4
talking_angela,4
blue_ball_gag,0
shikaruneko_(series),3
soot_(bleat),4
large_tongue,0
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coronta_(tenshoku_safari),4
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erise_(talarath),4
umber,4
dawntrotter,1
varian_nye,4
scas,1
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she-hulk,4
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apollo_(caldariequine),4
nikozoi,1
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gen_kiryu,4
stretch_(sound_effect),0
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zeke_the_zorua,4
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pinny_lane_(mlp),4
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xxzero,1
onoskelis,4
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aldus,4
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arthur_(lapinbeau),4
oscar_(braford),4
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metal_slug,3
portugueselynx,1
littletale,3
sonson-sensei,1
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kofomy,1
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gharial,5
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chubbyjam,1
kebab,0
jay_wolfe,4
lifty_(htf),4
ruby_(ultilix),4
chiptune,0
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yae_miko,4
partially_visible_vulva,0
seviyummy,1
thendyart,1
cum_from_spanking,0
polo,4
dot_executables,1
syrup_(artist),1
paw_shot,0
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luca_(doubledog),4
ritt_(character),4
lapinousmalice,1
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chaossabre,1
itreyu,4
bryce_(angels_with_scaly_wings),4
lotte_(cobalt_snow),4
johnny_(fuze),4
pokelai,1
baking_tray,0
deebo_(zoohomme),4
albelwolf,4
alexalaz,1
davidsone,1
18_(artist),1
zeckle,1
lilo_pelekai_(experiment),4
shred_(shonuff),4
purpleakumu,1
ayrrenth,4
pramanix_(arknights),4
kajin-sama,1
rebecca_(jamearts),4
shale_(insomniacovrlrd),4
nageruamado,1
tallion,1
invertebrate_proboscis,0
green_neck,0
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benny_(kibaru),4
the_dragon_prince,3
cali_(nastycalamari),4
roostax_wilddog,1
dark_flesh,0
siamese_fighting_fish,5
sterr,1
booshie,1
ms._pennypacker,4
tome,0
abasirisippo,1
two_best_friends_play,3
fallinnight,1
zitz_(battletoads),4
millicent,4
nystemy_(artist),1
aliscik,1
el_shaka,1
tardar_sauce,4
puuzo,1
daniel156161_(character),4
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dukey,4
sega_dreamcast,3
five_nights_at_candy's,3
obsidian_(character),4
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the_cajun_fox,4
ariesredlo,1
rnarccus,1
mon311,1
daria_arbuz,1
gin_(ginga),4
degen3d,1
jetta_the_jolteon,4
green_outline,0
crocobu,1
yaoimeowmaster,1
appaloosa,5
yosh!_comics,3
aftermath,0
everest,4
dare,0
thomas_james_o'connor,4
ipomoea_(oc),4
mrflaptastic,1
alcohol_enema,0
forsen,1
efudek,1
dragon_fruit,0
wall-e,3
specterdragon,4
tharja_(justkindofhere),4
dragon_ball_gt,3
fidough,5
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sessa,1
nirsiera,1
miss_sunflower,4
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sadida,5
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fatchaos,1
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schmozy,4
arabic_text,7
kwzu,1
dogg,1
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garuganofu,1
hypno_eyes,0
multi_taur,5
bro_wolffox,4
wallflower_blush_(eg),4
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flute_dragon,5
yacteruc1,1
chris_t._snuggleskunk,4
natricle,1
elbrar,4
kincaid,4
puzzle_piece,0
poleaxe,0
yvette_(kilinah),4
kobraa,1
u.s._navy,3
mommy_alpaca,4
fivethirtyeight,3
daxterdingo,4
inhale,0
konarofu,1
minion_(despicable_me),5
service,0
holding_beach_ball,0
konda,1
marlon_(marlon.cores),4
silvaentys,4
calendar_graphic,0
fishjob,0
pickle-pee,4
white_wall,0
ultra_necrozma,5
peeps,3
blepwep,1
demon_royal,1
tfsubmissions,1
mechanicalstage,1
scale_growth,0
jellcaps,1
evzen_(dreamkeepers),4
black_hanekawa,4
gb_of_bs,1
fannfare,1
trip,4
sore,0
threeworlds,1
lovie_(anjuneko),4
force_field,0
audrey,4
omnii34,1
low-leg_panties,0
cjfurs,1
blushing_ears,0
tippy_toes,0
lessthan3,1
katherine_(appledees),4
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dive,0
niucniuc,1
foxretro,1
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muffinshire,1
dissolving,0
koba_(koba),4
boris_the_friendly_wolf,4
cairo_(armoredkangaroo),4
punxsimon,1
saddle_rager_(mlp),4
thermos,0
aciag,1
nievelion,4
vanillayote,1
iandragonlover,1
sadayoshi,4
white_sky,0
cirno,4
can't_enjoy,0
terrible_terror,5
ninjara_(tmnt),4
monotone_high_heels,0
logo_parody,0
koginoro_28,1
fancomic,0
mr._krabs,4
blue_beard,0
green_spines,0
peanut_(food),0
looking_at_foot,0
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anubis_(smite),4
carmine_embershard,4
rusty_(darkrusty),4
evgenydion,1
cottongryph,1
renee_(jasen_tamiia),4
thejinxess,1
richardrodney,4
gigantic,3
snap_clip,0
game_poster,0
tonitrux,4
melnitsa,3
issilora_(artist),1
chris_(totesfleisch8),4
thistle_(evilymasterful),4
water_drops,0
sergioghi,1
lullaby_of_the_lost,1
rouge_the_werebat,4
forebucks,0
v-i-e,1
pornthulhu,1
enorach_(0laffson),4
vader120,3
blacksaikou,1
black_head,0
tyunre,1
galactabee,1
39coit,1
torfur,1
bonfire_(bonfirefox),4
child_abuse,0
arms_restrained,0
kagase-san_(sakamata),4
glacial_(wintrygale),4
protoss,5
bleok,4
drilbur,5
inika,4
roast,0
stun_gun,0
nell_two,1
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okawari_jiyuu,1
nite_(danandnite),4
oneobese,1
dork,0
pig-fish,1
sirtroglodon,1
jellyfishjulep,1
kog'maw_(lol),4
nobody_(artist),1
nolow,1
shizuka,4
aria_(walnut225),4
garam,1
kissing_booth,0
donphan,5
ladygreer,1
eye_penetration,0
mochizuki_tooya,1
osiris,4
pygmy_goat,5
gladion_(pokémon),4
glowing_weapon,0
9-puzzle,1
sabrina_(housepets!),4
camazotz,4
eclipse_silverwolf,4
washing_dishes,0
avezola,1
revolver64,1
korafuro,1
rizzyofen,1
mrscrambled,1
papa_soul,1
zinpatink_m,1
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corablue_(character),4
footless_legwear,0
sitting_on_back,0
beastmen_(warhammer),5
riley_(rileypup),4
indigo_(artca9),4
norse,0
slanted_eyebrows,0
soarinarts,1
criminalbunnydraws,1
musky_armpit,0
geebie33,1
tolnga,1
tasmanian_devil_(looney_tunes),4
cane_(fairy_musketeers),4
fish-ears,1
dark_nails,0
yukiharu_nyawate,1
pattern_leggings,0
ttherandom,1
sasayama_akira,4
robaato,1
light_thigh_highs,0
bafbun,1
arrow_(anatomy),0
courtroom,0
beatriz_rebollo,1
marijuana_blunt,0
ogremon,5
jotunneko,1
bluewolfavenger,1
kyte_frost,4
licheart,1
burk_(benzo),4
raster_dreams,1
flowing_tail,0
doc_icenogle,1
after-der,1
zoy,1
yax,4
stajan,4
the_deep_dark,3
denike,1
gohami,1
golden_ticket,4
andalite,5
holding_nose,0
irgramfox,1
bravest_warriors,3
twisoft,1
john_(bunybunyboi),4
exsys,1
pinsandquills,1
o3o,0
opblaaskrokodil,1
rhade,4
paperwork,0
calua_napage,4
god_rays,0
pettankon,1
xxxx52,1
dante_lovett,4
seregios,5
galactic-overlord,1
misty_the_mouse,3
grandmasterfdc,1
moi,4
clauschristmas2,1
tim_(rain-yatsu),4
adagadeprata,1
crosswalk,0
burger_king,3
implied_nudity,0
mikhail_alkaev,4
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asiri,4
arofexdracona,1
eidilon,4
latex_underwear,0
night_light_(species),5
littlegeecko,1
pants_pooping,0
mindes_(grimart),4
loss_of_self,0
yellow_glasses,0
azzunyr,1
dominant_in_chastity,0
william_birkin_(resident_evil),4
pink_genitals,0
sweaty_clothing,0
lerose,4
pinkmoth,1
mini_flag,0
skully_(skully),4
sakuri_rainne,4
crushed_object,0
kandi_(bracelet),0
jason_(mr5star),4
leaf_print_clothing,0
toye2004,1
snow_leopard_taur,5
grabbing_partner,0
koray_(clockwork_journeys),4
nicky_(thea_sisters),4
athom,1
birch_tree,0
checkered_wall,0
dukebarbatos,1
maty,4
caedis_animus,4
glistening_collar,0
bel_(cyancapsule),4
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gumiboie,1
timothy_(zer0rebel4),4
contortionism,0
a-pony,1
silverfang_the_steel_wolf,4
kela_ariolima,4
mdwines,1
shadi_(0laffson),4
tomukemono,1
imprisonment,0
atomic_wedgie,0
censored_anus,0
flamingtitania,1
laundry_room,0
nezha_(warframe),4
back_at_the_barnyard,3
aquabunny,1
twirling_hair,0
apple_macintosh,3
group_kissing,0
saitama_(one-punch_man),4
10:13,7
devon_(misterstallion),4
menacing_approach,3
neerishia,4
thwackey,5
raised_underwear,0
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sonic_rainboom,0
grammeowster_chef,4
anamosus,1
lotion_cat,4
kursed_(star_fox),4
pina,1
lolzneo,1
pink_speech_bubble,0
happy_feet,3
bakamaokun,1
anchors_(artist),1
tizoc,4
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squeezing_butt,0
lilandy,1
stomach_bulging,0
mafekoba,1
morbidly_obese_intersex,0
izumi_(artist),1
nari_oakes,4
rawr_(tendril),4
narrow_beak,0
smokey_bear,4
stick_in_tail,0
jared_(peculiart),4
romanginzburg,1
pux,1
twopapers,1
alynna,4
north_shepherd,4
bleating,0
hook_penetration,0
miranda_arqayla,4
thor_(marvel),4
djijey_hellfire,4
cleo_(theredghost),4
rockyhardwood,1
duncan_roo,4
fi,4
bri-sta,1
perching,0
gorsha_(character),4
kestrel,5
side,0
debra_diaz,4
dark_end_(stitch!),4
shroomish,5
chevy_dahl,4
hatsuen_kikan,1
logan_the_fox,4
tire_swing,0
axel_(lazydergenboi),4
monsterdongles,1
pusheen,4
dakota_(dark_stallion),4
geisha,0
meeting,0
thegalen,1
ikugo_(character),4
virmir,1
beautifly,5
african_elephant,5
lunatone,5
moody,0
cutiepoppony,1
vikalh,1
crab_stealing_clothing,0
tsuchi_dash,1
rivalry,0
mess1ah,1
inuki_(character),4
losian,4
shadow-anubis,4
lure,0
hornystorm,1
email,0
black_dragon_kalameet,4
sydneysnake,1
biglovealicia,1
shiarra,4
exhaust,0
locks,0
mandarax,4
kra-ra,4
incendax,1
e-series,5
hazelmere,1
rerepop,1
archway,0
genderfluid_pride_colors,0
bush_viper,5
lotion_bottle,0
hillside,0
skyler_(fuze),4
kanekuo,1
bearp_(artist),1
koalarobot_(artist),1
temmie_(deltarune),4
goo_penetration,0
papayamist,1
mennnntaiko,1
cum_in_top's_ass,0
inakamichi,1
cruel_pred,0
ashleigh,4
ukrainian_flag,0
nion,1
it_ain't_gonna_do_itself,3
scuttlfish,1
felicity_(animal_crossing),4
sheycra,1
maude_(bts),4
hazel_(the_sword_in_the_stone),4
dobu_(odd_taxi),4
bent_horns,0
licking_mouth,0
mobian_bat,5
basilllisk,1
zdrada_(helltaker),4
scratching_butt,0
medieval_armor,0
drackonthanri,4
aruse,1
shanher_(character),4
camo_(breeze_in_the_clouds),4
big_arms,0
bray_(lucyfercomic),4
flirting_look,0
penis_horn,0
ducky_(the_land_before_time),4
heart0fink_(artist),1
alex_(totally_spies!),4
imminent_fight,0
grey_wall,0
meteor_shower,0
super_mario_world,3
snowball_fight,0
throat_hug,0
candykittycat,1
avielsusej,1
barber,0
megumin_(konosuba),4
veterinarian,0
thispornguy,1
inflatable_ride,0
felicesta,1
dixie_seterdahl,4
cum_on_shoes,0
ashfur_(warriors),4
nargle,4
lisp,0
hollow_(bleach),5
vaiderman,1
kuroneko_waltz,1
tamamo,4
zephery_hughes,1
shandra_(renthedragon),4
spider26,1
ruhje,1
mappy_(character),4
implied_erection,0
celebrated_earl,1
falcomon,5
donguri,1
tokenworks,1
coochan,1
dressing_up,0
ltr300,1
less_(artist),1
abe_(artdecade),4
scott_ryder,4
cowering,0
ily_sign,0
leg_humping,0
twisteddragon,1
drawstring_bottomwear,0
fozuna,1
hilda_(series),3
yellow_necktie,0
mcerror,1
bow_shirt,0
bahn_(slapstick70),4
weketa,1
theseus_(hades),4
brown_back,0
lapel_pin,0
kurt_the_thunderfloof,4
gao_(gaokun),4
shinnie_(shinigamigirl),4
rainforest,0
tavin,1
poof_effect,0
velvet_(amberdrop),4
callitrichid,5
manji_luo,4
sam_(totally_spies!),4
pride_color_face_paint,0
hat_over_eyes,0
warlordhunter01,1
sasha_(trigger12),4
kobold_dellarte,1
jangmo-o,5
tanner_james,4
pocky_in_mouth,0
ansel_(anaid),4
ray_(disambiguation),4
tatiana_(sorcererlance),4
cytrusawa,1
python_nu_kappa,4
boxtop,1
katlek,1
ixkouu,1
slapping_self,0
instrument_(disambiguation),-1
epitaphkho_(artist),1
galen,1
pixie_(thekite),4
feralbeans,1
jacques_(fuze),4
fellatio_request,0
arteficialtrees,1
scribs03,1
straw_(cstrawrun),4
ord_(dragon_tales),4
rb,1
4ere4nik,1
wetchop,1
light_inner_ear_fluff,0
lumixdragonfey_(artist),1
ibis,5
topaz_(sonic),4
genital_jewelry,0
integlol,1
elvor_xaetri,4
butt_massage,0
blooregard,4
eldrick_pica,4
stoutland,5
thumbs_down,0
floverale-hellewen,1
female_operator,4
rimworld,3
poofy_hair,0
purple_flesh,0
dazen_(character),4
toque,0
pinky_(animal_crossing),4
annabelle_(all_dogs_go_to_heaven),4
tachi_(kaptcha),4
blue_eyelids,0
eliott,4
sawyer_(ferobird),4
wolfluffyfly,1
kofi_(kofithechee),4
garoh,4
coffeesoda_(fursona),4
squint_(leobo),4
passchan,1
indoors_sex,0
coffaefox,1
lsd,0
suce,1
holding_bouquet,0
ferdinand_(film),3
alamander,1
flan,0
flyer_(pixiv),1
bleak_ambiance,0
wafflebond_(artist),1
tiny_feet,0
red_frill,0
coal_(maririn),4
artemis_jackalope,4
jen_foxworth,1
seaweed_toast,1
cafe_plaisir,3
food_shot,0
bacon_(baconbakin),4
rudolph_the_red-nosed_reindeer_(tv_special),3
torn_sleeves,0
rorero,1
scobionicle99,1
h_futako,1
buckshot_(metal),4
shark_puppy,5
yogurpodrido,1
eura,1
sirocco_zephyrine,4
removing_underwear,0
schnuffel,4
ramon_nemeris,4
zofia,4
humanoid_tail,0
swingset,0
hecking,1
gnoll_(monsterpunk),5
gundanw,1
bedroll,0
daisy_mae_(animal_crossing),4
boosterpang_(character),4
woody_woodpecker,4
furvidd,1
han,4
strikedramon,5
sylvari,5
millie_(sayuncle),4
stripey,0
jason_takayama,4
guss_(vju79),4
nekonote,1
mavezar,1
sahara_(nicnak044),4
writing_on_foot,0
neck_bell,0
astromech_droid,5
smokedaddy,1
two_tone_membrane,0
isatan,1
lionkinen,1
ruark,1
emetophilia,0
pranky,1
fluffins,1
bf_109_(hideki_kaneda),4
creation_trio,0
bunny_ears_(gesture),0
shimm,1
kaxiota,1
quasart,1
zenia,1
stroller,0
pearboy-ooh,1
35:18,7
kivu,4
mummified,0
mina-mortem,1
mistake,0
attached_sheath,0
gomdepp,1
ahornsirup,1
tillie_(vimhomeless),4
soul-silver-dragon_(character),4
tech_control,0
plushkinn,1
dahbastard,1
rchammer,1
the_eye_of_ramalach,3
lillya_(biggreen),4
11:17,7
bub,4
xxmileikaivanaxx_(artist),1
cc0,3
dakota,4
redeyedgazer,1
chick,5
doug_(101_dalmatians),4
romancruzzz,1
banicsidy,1
lexorez,1
theroflcoptr,1
oasis_(character),4
smog01,1
sami_demarco,4
two_tone_spots,0
rino99,1
character_chao,5
scar_reach,4
nisharu,1
undnift,1
bluemaster,1
pussy_to_ass,0
toe_talons,0
wash_rotom,5
hakya11,1
chomper_(the_land_before_time),4
kooper,4
ceratosaurid,5
heart_between_text,0
pussycat_(o-kemono),4
text_on_container,0
dekatanu,1
havesomemoore,1
drowsy,0
kyo_(stargazer),4
nara,4
waidan_kai,4
hand_on_another's_belly,0
tender_taps_(mlp),4
chalk_(oc),4
anal_threesome,0
blaine_edan,4
titty_vixen_(character),4
text_with_iconography,0
jinn,5
maomi_(doomdutch),4
mnty_(character),4
mr_happy,1
egg_in_uterus,0
devilman_(series),3
cum_on_legwear,0
plna,1
yuudai_(character),4
twintailssfm,1
roman_clothing,0
spinnerets,0
moob_grope,0
poison_lily_(kazudanefonfon),4
deceased_bunny_(artist),1
flying_kick,0
pico_(f-zero),4
sitting_on_branch,0
big_spikes,0
brick_break_after_dark,1
yellow_speech_bubble,0
areku,4
riolutiny,1
fossil,0
braided_fur,0
cassidy_(ruth66),4
lu123,1
eon_(typhlosion),4
finneon,5
x3no,4
body_scars,0
clownboy1,1
mono_ka_tarl,1
ivnis,1
fergus_(101_dalmatians),4
kaceuth,1
handwritten_text,0
dewstar_(artist),1
creamsicle,0
holding_elbow,0
immortality,0
holding_platter,0
drawain,1
elma_(tenchi_muyo),4
carmen_(patrol_03),4
living,5
9de-light6,1
scarlet_rose,4
arcshep,4
milkymaiden,4
weimaraner,5
blade4649,1
runar_(ffxiv),4
jollyferret,1
zaccai,4
the_story_of_ferdinand,3
broken_sword,0
conductor,0
juandelcoyote.inc,1
orange_sweater,0
helium,0
paw_tattoo,0
seraphie,5
mazda,3
limp_arms,0
tahoma,1
tennis_ace,3
glance,0
nat_the_lich,1
rainbow_stripes,0
sky_bison,5
cybergoth,0
swadpewel,1
zoey_(senip),4
raised_bra,0
r.o.b.,4
fluorite,1
alystaircat,1
moomintroll,4
erika_(meesh),4
ruka_vaporeon,4
chumpy,1
sky_(sky),4
cans,0
dallas_(101_dalmatians),4
stickmanwww,1
poron53,1
teo_(hayakain),4
artimus_(character),4
sun_dragon_bal,4
deanka,1
laobai,1
cynthia_(david_siegl),4
holding_riding_crop,0
jessie,4
de2w,1
gracie_films,3
sonokido,1
lakeside,0
kuro-gane,1
chad_(meme),3
text_on_sports_bra,0
pizza_delivery_carrier,0
hitmaru,1
xbox_logo,0
viki_(vikifox),4
ruslorc,1
x-tian,5
intense_stutter,0
red_blanket,0
senorkah,1
magniym,1
hair_bow_(anatomy),0
penetrable_sex_toy_in_mouth,0
nosugarnohoney,1
llamama,3
cloudburst,4
coltsteelstallion,1
skunkdude13,1
yuffie_kisaragi,4
474,1
purrl_(animal_crossing),4
krokodos,1
book_whitener,4
shiko,4
spy_fox_(series),3
crypticfigurines,1
diamondhead,4
tamura_kazumasa,1
magic_jackal,4
goozie,1
haunted,0
ally_(alxias),4
whiteleonard,1
burstfire,1
poem,0
noodle-lu,1
gaytor,5
fluffy_butt,0
silver_draw,4
schpicy,1
aas,1
black_mouth,0
harriet_(harry_amoros),4
bose_(character),4
niko_(animal_crossing),4
ace_(tuftydoggo),4
beauwolfhusky,4
spilling_drink,0
docmoreau,1
mona,4
gattles,1
green_speech_bubble,0
choi_yujin,4
inkmaven,1
pink_bow_tie,0
danwolf15,1
cum_feeding,0
jorunna_parva,5
hand_on_own_elbow,0
two_tone_text,0
kaizooki,1
red_mouth,0
felix_(disambiguation),-1
hiroyko,4
mabaya,1
quivers-for-arrows,4
precum_from_penis,0
saliva_on_finger,0
open_belt,0
tex_(desertmotels),4
enya_(littlemutt),4
kanaya_maryam,4
broken_antler,0
demeterservant,1
velveteksotica,1
xscar10,1
netsuko,4
konata_izumi,4
katida,1
foxbtails,1
ferrari,3
iotran,3
jessica_elwood_(character),4
flufflix,1
shadowweasel,4
bruh,0
blazingifrit,1
kai_the_cervine_ghost,4
tyra_reckks,4
cosmicdanger,1
kimber,4
loceri,1
lip_stud,0
the_humbler,1
arcnod,1
forefinger,0
dandruff,0
girdled_lizard,5
rainbow_boa,5
soledad_(atomic417),4
jennifer_nyte,4
casual_urination,0
bubble_lee_(character),4
mascara_maroon,4
marina_datillo,4
despairchanpu,1
multicolored_eyewear,0
gold_eyeshadow,0
ophiotaurus,5
doolyboobieboy,1
arkveveen_(artist),1
rita_skopt,4
haduko1,1
scuba_diver,0
titanfall_2,3
andisun,4
zan_(zantanerz),4
lewdango,1
drip_(dripponi),4
violet_seren,4
sakura_wadagaki_(odd_taxi),4
bulluppa,1
jenny_(capt_hairball),4
poppy's69,1
dark_talons,0
dua_(bahnbahn),4
lunarclaws,1
azura_(azura_inalis),4
sori_(ara_chibi),4
saloo,1
melkah,4
doraemon_(character),4
plessie,4
yuji_uekawa,1
abby_sinian,4
cymek,1
grey_(character),4
poland_(artist),1
lunara_(warcraft),4
adoption,0
erickredfox,1
inspection,0
azura_racon_(fursona),4
beri_(sqoon),4
miralle,4
crystal_empire,0
almar,1
mod,0
vault_boy,4
stu_(artist),1
sora_takenouchi,4
meepo_the_geomancer,4
ero-bee,1
inubashiri,1
ancient_greece,0
big_gag,0
ultrafox,1
reyes,4
awarebear,1
toontown_corporate_clash,3
ginsingoo1,1
talehnyan,5
h_rin0u0,1
skyspeardw,1
midnight_snowstorm,4
vex_(44vex),4
hoya82,1
robot_penis,0
snackcracker,1
radioactivemint,1
zipper_shorts,0
yong-sil_(ryonggay),4
treble_clef,0
ty_the_tasmanian_tiger,4
bear_2key,1
sashaviel,1
ashesg,1
kunaru,1
crushed_micros,0
translucent_socks,0
tricorne,0
nightglider,4
beau_(luxurias),4
julia_autio,4
pouch_(clothing),0
spoogiehowl,1
light_paws,0
underwear_crabs,0
egg_from_cloaca,0
holt-odium,1
namielle,5
marcus_of_the_cloud_forest,3
omochiwotakusan,1
unseen_partner,0
flag_underwear,0
pickle_juice,1
jolly_jack_(character),4
featureless_penis,0
orange_bunny,4
osamodas,5
kp-yoshi,1
yorkshire_terrier,5
kinkmasternero,1
20pixels,1
sexy_doll,1
dshou,1
randochris_(character),4
fighting_foodons,3
chloroform,0
tryst_entangled,1
1_arm,0
liebeslied,1
there_she_is,3
alucard_(hellsing),4
goldy_(golden.dragon),4
ky-jane,1
incogneato,1
sasukex125,1
gs-fox,1
lui-ra,1
yuki_sairenji-noroi,4
cherry_(dredjir),4
agito-savra,1
yaoi-no-mikan,1
love_letter,0
deroichi,1
buster_sword,0
ouji_(fashion),0
malina_(athiesh),4
corgidor,5
wolf_wolfindeir_(thewolfycreator),4
eringi,1
mizumi_(pyrojey),4
arm_in_water,0
vibrator_on_clitoris,0
heartman98,1
severed_leg,0
huge_hair,0
legion_(mass_effect),4
glowing_gem,0
bivalve_shell,0
basedvulpine,1
light_underwear,0
whinge,1
sarina_doyle,4
tan_theme,7
blackwhiplash,1
dotte_(gunwhale),4
markazian,5
22:17,7
canine_penis_tongue,0
artofmander,1
apprehensive,0
rokacake,1
open_onesie,0
arkailart,1
ahatas,1
mkvero,1
moodytail,1
alex_nightmurr,4
subjectnumber2394,1
randoseru,0
aetherxov,1
re:_strained,3
head_tail,0
space_furry_(copyright),3
tongue_expansion,0
cross_country_detours,3
star_wars_rebels,3
early_pregnancy,0
jessie_(disambiguation),4
fawn_deer,4
eruru,4
cayugo,1
kinkykeroro,1
yuta_agc,1
syrena_(riptideshark),4
tribal_armor,0
fever-dreamer,1
pussy_nipples,0
baba,4
sweetpupperoo,1
la_lune_rouge,1
iop,5
punipuri,1
ryujisama,1
midsection,0
terry_(roanoak),4
almatea,1
pokemania,1
dogmanart,1
seirva,1
summer_solstice_(oc),4
maruyama_(artist),1
redfoxsoul,1
wheezie_(dragon_tales),4
kingdomxathers,1
snickering,0
zull,5
creatures_of_sonaria,3
hc_hybriddave,1
ninja_kaiden,1
rape_play,0
pinkamena_diane_pie,4
bluffy,4
armpit_musk,0
kissing_pov,0
primal_(series),3
taxidermy,0
diablo_3,3
ceratosaurus,5
rebouwu,1
borni,1
intest,1
porunga,4
moyashi,1
sady,4
gradient_feathers,0
matoi-chan_(mamoru-kun),4
heart_after_name,0
rainbow_lass,5
taryn_crimson,4
kyyanno,4
giik,1
eztp,1
cela,4
tyviania,1
jack_reston,4
forced_cunnilingus,0
watte,1
incestuous_fantasy,0
bymyside,1
randoramble,1
blackguard,1
slurk,0
akaro_(lukiro),4
heckded,1
remy_(angels_with_scaly_wings),4
roguecolonel303,1
dromaeosaurus,5
buffalo_(disambiguation),-1
snaketeeth12,1
freely_redistributable,0
ares_(devilenby),4
matthewwoodward,4
evil_luna_(idw),4
virus_(noronori),4
pazymomo,1
june_greenfield,4
victor_(brushfire),4
pigburger,1
arctic_fox_masseuse_(zootopia),4
nex_anima_canis,4
needleplay,0
skarltano,1
thorne_(jurassic_beauties),4
north_wind,4
sketchygarden,1
carcharodontosaurus,5
veela,4
shelby_(aquasnug),4
breasts_on_ground,0
pticelov,1
soranoiro_(artist),1
rockyrcoon,1
body_art,0
flintlock_pistol,0
edhel,4
roxyrex,1
moosh,4
aurin,5
night0wi,1
steven_magnet_(mlp),4
navel_gem,0
kemonokun,1
nekomata_(disgaea),4
greatwuff,1
sanura,4
anthor,4
zarizav,4
wander_crown,3
rikitsu,1
susan_long,4
sooperman,1
jeacn,1
skunk_(skunk_fu),4
fyoshi,1
plankton_(species),5
glenskunk,1
shenanigan,1
spurt_(disambiguation),0
jaybeyyy,1
supported_tail,0
vulpamon,4
clover_(kitfox-crimson),4
heart_meter,0
switch_(disambiguation),0
sona_(yuio),4
topless_intersex,0
yes-no_pillow,0
slitherpon,1
frame_blending,7
quirachen,1
galdon,4
blakefox,1
sewage,0
floor_sex,0
lox_(artist),1
cokesero,1
fjorge,1
tree-kangaroo,5
soulsplosion,1
cumu,4
memories,0
akferal,1
appa_(avatar),4
surrealatorium,1
pinupgirl,0
heartgear,4
lewdlemage,1
jodyinkfist,1
shiho_ichimura_(odd_taxi),4
alhedgehog,1
ysera,4
kingraam,1
blueflarefox1,1
yellow_tail_tuft,0
haruki,1
miyao_yu,1
flags,0
radicles,4
bokuman,1
neck_bow_(anatomy),0
adr,1
gear_(artist),1
tiki,0
peeing_into_bowl,0
tongue_scarf,0
franciscassidy,1
whimpering,0
omega-xis,4
snowdrop_(character),4
jerkcentral,1
ribbontail_(character),4
folvondusque,1
feniks_felstorm,4
soranotamashii,1
vanilla,4
cleopatra_in_space,3
bailey_(fluff-kevlar),4
edgar_munroe,4
xlkev,1
normi,1
pengu_(ratld),4
razorfox,1
outdated_model,0
bean_(legume),0
pinned_to_bed,0
harmaagriffin,1
ruolina,1
sentharn,1
crystal_bloom,4
freeda_(himynameisnobody),4
yellow_fingernails,0
sombra_(overwatch),4
pink_crop_top,0
ao_(pixiv_145813),1
soul_release,0
precum_in_ass,0
unknownspy,1
magnus_(pasc),4
granolapunch,1
anchor_tattoo,0
brittany_(roushfan5),4
hana,4
finalroar,1
dim,4
hizzacked,1
guitar_case,0
cock_limbs,0
insole,0
hands_in_lap,0
carcar,4
fast,0
butt_smack,0
mr._small,4
spix's_macaw,5
goddess_of_madness,4
samurai_armor,0
shazzi,1
missingno.,5
flash_sentry_(eg),4
vixikats,1
birdcheese,1
majora,4
blaker,1
the_dread_fall,3
impa,4
heater,0
chonmage,0
kierstal,1
sookta,1
musicsheep,4
crystal_clarity,4
tteyuu,4
ollicantskate,1
schattenklaue,0
sensh_the_cat,4
flauschdraws,1
fulvus,4
ch_ia_ki,1
scraptrap_(fnaf),4
humanoid_penetrating_female,0
thepsychodog,1
rosanne_(artist),1
leg_fins,0
clover_(happytimes),4
misoden_(artist),1
grace_(sssonic2),4
ghoul_(fallout),5
hands_on_own_ankles,0
kyle_the_fox,4
tofu-doggo,1
kiro_(warcraft),4
xìngyùn,4
raiden_shin'emon,4
paint_splatter,0
sourlemonade,1
red_toes,0
bakudanarare,1
leafrunnerk,1
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final_fantasy_vi,3
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cheri's_dad_(atrolux),4
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kin-cishepholf,1
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red_(sethkeidashi),4
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mag_(warframe),4
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666,0
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famir_(thebestvore),4
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9:8,7
bit-bite,1
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yay_(milkytiger1145),4
sweetchu,4
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majinvoir,4
kat,4
zoey_(berk_rider),4
xiavier_(cydonia_xia),4
xkax,1
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muramasa:_the_demon_blade,3
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reshsfm,1
desir,5
illyasviel_von_einzbern,4
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kiki_the_cyber_squirrel,4
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lucy_(jcdr),4
raik_(glacierponi),4
inspector_arai,4
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kinoko,1
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danielle_o'hare_(akiric),4
feyyore,4
reaper_(overwatch),4
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nintendo_logo,0
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eliza_(canisfidelis),4
fazbear's_fright,0
sketchyboi08,1
amphydamph,1
lanoya_(artist),1
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bunnielovesyou,1
hailey_(kittyprint),4
batmanholecover,1
bepis,3
sirbossy1,1
takeru_takaishi,4
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lewd_can,1
shy_ayu,1
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ruka_landberg,4
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dian_(jewelpet),4
noodle_(domovoi_lazaroth),4
silver_(pokémon),4
miss_pearl,4
cum_from_nipples,0
alphabet,0
scent_play,0
polka_dot_bikini,0
melissara,4
xinaelle,1
furnace,0
taurus_demon,5
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deltadrawz,1
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sango_(jewelpet),4
yata,1
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odin_(wulframite),4
amras_lelliath,4
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tat_(klonoa),4
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kira_adelay,1
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kalianne,4
toasty,4
yogurt,0
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jamie_(klausd),4
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sexybigears69,1
thefreckleden,1
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ulvbecker,1
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sveemon,1
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akella,1
nzuuure,1
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xxmidknightxx,1
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moob_suck,0
skipper,4
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eric_dream_everyotherheart,1
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dj-black-n-white,1
vulpessentia,1
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noet,1
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trail_arnak,1
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ray_doug,4
brazier,0
rowrow,4
jericho_(disambiguation),-1
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ten_frame_image,0
monster_girl_1000,3
crazy_water,1
ah'momo,4
vulkin,4
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rotom_phone,4
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ultimate_custom_night,3
doctor_monifa,4
wingdavon,1
elara,4
jyxa,4
darkwufflez,1
kiit0s,1
chirin_(ringing_bell),4
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the_depths,3
oney_plays,3
messyszop,1
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lucky_(mcnasty),4
lotus55,1
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mystical_stratus,4
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delicious_in_dungeon,3
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lovely_tail_(dlrowdog),4
buzya,1
cornflakes_(derek_hetrick),4
agent_0730,1
six~,4
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tism_rabbit,5
helena_(paledrake),4
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diarmaidhutchence,3
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kendall_(wastester),4
miriam_hutchins,4
dergum,1
tyler_3d_(artist),1
ciervorobot,1
tommybunz,1
vance_(zephyrnok),4
taur_to_humanoid,0
ray_(takemoto_arashi),4
desk_lamp,0
fenoxo,1
kathleen,4
hairy_ass,0
tripping_balls,0
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sydea,4
leaf_umbrella,0
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psycrhen,5
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gauge,0
kmf97,1
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mistress_mare-velous_(mlp),4
sekotta,4
phoebe_(animal_crossing),4
dogtanian_and_the_three_muskehounds,3
mad_dummy,4
fuck_the_police,0
taneysha,1
patio,0
app,0
clover_(madagascar),4
dingbat_(character),4
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diddydoo,1
ghost_of_a_tale,3
bololo,1
tay_(powfooo),4
cyanne,4
craymin,1
draneas,1
by,3
mekko_rarekko,3
si_ra_ta_ma3,1
burnbuckie,1
mad_moxxi,4
luminyu,1
sebastian_greninja,4
turkojar,1
jiffic,1
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teal_belly,0
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maneki-neko,0
catsmeow,1
sonicgamer,1
daenanguis,5
kilo_(kilocharlie),4
chelsea_(zombieray10),4
rainihorn,1
demon_beast,5
sallie_may_(vivzmind),4
henrietta_hound,4
ayabemiso,1
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nycteus,4
nomuralok,1
gylala_(artist),1
panickingad,1
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brown_hoodie,0
sammyphan,1
mood_lighting,0
vamux,4
syx_(symbiote),4
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blue_(among_us),4
elpizo_(artist),1
tail_covering_crotch,0
ying_li_(lucariored),4
kokoro-tokoro,1
leg_squeeze,0
zen_(grimart),4
amo_(amocin),4
sarah_(the_last_of_us),4
vertical_standing_split,0
eleniel,4
final_fantasy_tactics_advance_2,3
dairy_air,3
kt80,1
zahk_(knight),4
aleks_howes,1
hyenajack,4
eroskun,1
pink_eyelids,0
lucheek,1
mask_transformation,0
elfuda_(plus-sized_elf),4
skull_boss_guzma,4
melanie_cow,4
feral_transformation,0
blackbatwolf,1
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rifle_sling,0
kishna,4
teal_claws,0
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golde,1
moles,0
comet,0
cassie_cage,4
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sober_(character),4
dean.winchester,1
arilen,4
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ganassa,1
1970s,0
meegol,1
calamity_(averyshadydolphin),4
rosie_(cyancapsule),4
emmapresents,1
blackcat,1
wet_breasts,0
nalica,4
aria_t'loak,4
lbt9000,1
anapnea,1
stapler,0
reginaprimata,1
kharnak,1
laurel_(freckles),4
heligator,5
harumati_ituko,1
sapphire1010,1
eluna_odis,4
hamsteroftime,1
home-cooking,1
venauva,1
emerald_raven,1
daiha,1
septum,0
alt-b-shephelie,4
8-bit,0
tyranos,4
seras_greil,4
anaktis,1
tygar_tiger,1
whatsalewd,1
shaadorian,1
patsy_smiles,4
amethyst_star_(mlp),4
diaper_bag,0
maple_(animal_crossing),4
simon_belmont,4
takatmadisney,1
head_accessory,0
frisbee_in_mouth,0
wittleskaj,1
frankescobarx,1
anal_impregnation,0
menstruation,0
malegardevoir,1
slightly_chubby_gynomorph,0
waving_arms,0
helga_(world_flipper),4
pantvore,1
itzalisix,4
between_feet,0
cjweasle,1
hilwu,4
ah_yes._me._my_girlfriend.,3
maylah,4
movie_accurate,0
kindergarten_uniform,0
sockodrawing,1
mindfucklingskelly,1
marcosaurus_(imperatorcaesar),4
ascord,4
belly_kiss,0
catmakinbiscuits,1
black_sky,0
rorik_(the_human_heart),4
xylerthewolf,1
tongue_out_piercing,0
cleo,4
door_frame,0
gabriel_(luckyabsol),4
monotone_back,0
zylenox,1
poochybig,1
svi,4
momo_92698,1
garou:_mark_of_the_wolves,3
multicolored_sclera,0
stickyguts,1
minamikoboyasy,1
topless_gynomorph,0
lorena_(artist),1
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diana_linda,4
legend_of_the_werehorse,3
soroxel,1
vasha_vinodragova,4
mars_light_carbuncle,4
plover,5
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japanese_flag,0
charmin,3
terra_macro,0
green_cheeks,0
syncope,1
dining_room,0
aniel,4
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yeenbitez,1
light_armor,0
countdown,0
renrailos,1
makuta,5
orange_glow,0
multicolored_briefs,0
luz_(the_owl_house),0
cait_sith_(ff7),4
alice_(gold97fox),4
kyubicat,1
britz_strudel,4
burninggryphon,1
milftoon,3
tawnix,1
roninsong,1
diana_(lol),4
cutedeer,1
shun_shirai,4
jinbei_(clothing),0
subaru,3
darunia,4
ganson,1
two_tone_t-shirt,0
fire_flower,0
ello_(anaugi),4
clara_(canisfidelis),4
kuroneko_(doukutsunezumi),4
pnoll,1
iguanodontid,5
the_bottomless_district,3
cheetabbit,0
ftnranat,1
angel_patoo,1
touching_ear,0
katlovesshrimp,1
the_neopets_team,1
havoc63,1
monotone_inner_pussy,0
axentooth,1
survival_of_the_fittest,3
canyne_khai_(character),4
feodaron_sd,1
xinwan_(artist),1
papyrus_(underswap),4
lacy_panties,0
norihito,1
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ike_(fire_emblem),4
hochune_meowku,4
parfait_(yesthisisgoocat),4
raina_(goopyarts),4
olive_(olive1155),4
churroshiba,1
pak_choi,4
holding_tablet_pen,0
shiratzu,1
sleepysluff,1
kenron_toqueen,1
red_swimming_trunks,0
tail_nom,0
shopping_mall,0
human_on_machine,0
small_beak,0
tight_jeans,0
ada_wong_(resident_evil),4
green_saliva,0
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hornet's_needle,0
wildworks,3
dart_(nuttynut93),4
luckymax2012,1
drinking_blood,0
icaron,1
ashley_williams,4
stonecircle,1
auro_vee,4
holding_back,0
pixie_(monster_rancher),5
penny_(tits),4
khurje,1
chikkibug,1
mirkrali,1
squirrel_girl_(marvel),4
dulcinea,4
remitost_(bigrottiedawg),4
charlotte_(bzeh),4
hacking,0
anatomically_correct_cloaca,0
fleet_wing_(fursona),4
host,0
takemoto,4
curry_(copyright),3
foreskin_piercing,0
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gus_(flir),4
lagombi,5
shulk,4
ys,3
qinus_axia,4
kizion_ksharr,1
chet_(anti_dev),4
initiation,0
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hamgie,4
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harb_freton,4
gabu2929,1
fur_jacket,0
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sirus,4
demon_pony,5
heart_speech_bubble,0
tennessee_kid_cooper,4
huge_latissimus_dorsi,0
werepony,5
tentacle_lick,0
apple_pie,0
brushing_tail,0
khanyvor,1
xuteng_(tokifuji),4
srmko,1
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concoction_(artist),1
maka_(anthemoftears),4
suerte,4
hermann_(knights_college),4
wireframe,0
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girokemo,1
jina_choi,4
lewdstuffstack,1
luxury_gin,1
katie_tiedrich_(persona),4
clitoris_rubbing,0
bastet_(puzzle_and_dragons),4
outcrop,0
shina_(artist),1
valheru,4
body_wraps,0
k-draws,0
tasteydonuts,1
celes_traydor,4
touching_pussy,0
tofu_(food),0
rare_ltd,3
multicolored_necktie,0
banded_tail,0
wortox,4
soft_lighting,0
ignitus7,1
cyd_(animal_crossing),4
ananafruit,1
beans_(rango),4
one_glove,0
dai.dai,1
nibe.a,1
danilokumriolu,1
natsu_(natsu_o3o),4
ace_(kamex),4
sefuart,1
iudi_(iudicium86),4
panda-kun,4
back_plates,0
black_circus,3
maewix_(artist),1
butts_everywhere,0
hyena_girl_(study_partners),4
mexifurfoof,1
sachidog,1
bmf_6666,1
coal_(tawny_otter),4
leakyroot,1
saaxon,1
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hoedeer,1
jessica_ainsley,4
woolier,1
sarybomb,1
diana_(jewelpet),4
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efrejok,1
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mistresssable,1
trombone,0
jakkal,1
rubilocks,4
vress_(artist),1
duez,4
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conkeldurr,5
freebird11,1
ruzha,4
kimihito_kurusu,4
mavi,4
mixing_bowl,0
congalala,5
jenny,4
hindu_mythology,3
hoot_(character),4
ron_spencer,1
blanche,4
the_surgeon,4
starbuck,4
cat_taur,5
smexyoryx,1
kurokawasudou,1
dog_costume,0
ftl:_faster_than_light,3
mirromy,5
my_singing_monsters,3
decay,0
iuana,4
christine_day,4
gouguru_(artist),1
rainypaws,1
wheat_field,0
deadjackal,1
hibiki2,1
1950s,0
ida,4
banding,0
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evay,4
sadistic_smile,0
voice_actor_joke,0
clover_(lost-paw),4
cabrony,1
hayden_(solfies),4
dekotf,1
rubiont-47,1
brian_(thespiderbunny),4
avalee,4
lexiedraw,1
litho_sandoval,4
kastoluza,1
wet_anus,0
mighty_switch_force!,3
imminent_death_by_snu_snu,0
danny_(funnybox),4
ash_bunny_(skeleion),4
lionel_(fluffedwings),4
wolfthatmeows,1
long_term_chastity,0
lizardmane,1
trainer-kun,4
spikysketches,1
melusine_(final_fantasy_xiv),4
flaffy_(viskasunya),4
jenery,1
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kloe_(artist),1
mek,1
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duo_(character),4
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germainethevixen,1
two_ton_tina,4
richard_conrad,4
tachyon,1
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deckland_(tokifuji),4
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cacnea,5
shading_eyes,0
apex_(starbound),5
legend_of_krystal,3
larathen,1
konani,1
soroka-ne-soroka,1
granberia,4
shempu,1
millie_(ozy_and_millie),4
kyoshinhei,1
tavros_nitram,4
yukino_kouta,1
digital_hysteria,1
miz_ma'm'selle_hepzibah,4
crying_blood,0
stefan_(smove),4
kaaly_(notbad621),4
tatiana_tuschenko,4
/co/,3
calico_pattern,0
entering,0
steve_martin,1
snow_white_and_the_seven_dwarfs,3
askadolesentspikewebcam,1
katja_(tigertau),4
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selection_menu,0
separation_(layout),0
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pingumarci,1
gold_hooves,0
torou,1
koji_koda,4
throh,5
ari_yellow_bell,4
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lonnyk,1
goopy,0
monotone_neckerchief,0
monobutt,0
keira_niobe,4
on_container,0
sammy_(dogfluid),4
kimera,5
dimitri_(xelaaredn),4
nanniras,1
soleil_(keffotin),4
nikki_(nikkithetanuki),4
nsilverdraws,1
puss3250,1
devon_(nikora_angeli),4
licking_popsicle,0
king_cobra,5
akira_(shane),4
pj_(pittiepj),4
nautilus,5
dark_stockings,0
else_(masterful),4
pink_speedo,0
metal_balls,0
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pink_necktie,0
iaian_luxo_fox,1
uiokv,1
in_glass,0
tsunami_(wof),4
kleika,5
four_(bfb),4
gossip,0
deathclaw_humanoid,5
nose_steam,0
falling_over,0
erection_under_towel,0
diraulus,1
kloe_kitty,4
rhiannon_(gingerm),4
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oscar_statuette,0
jukan_ace_no.007-2,3
streaked_tenrec,5
crazygreyrabbit,1
belle_fleur,4
elsword,3
sectoid,5
high_kick,0
air_force,0
tsukihime,3
aether_foundation,3
valdis_the_bard,4
rabbitexe,1
purple_antennae,0
vanilluxe,5
rek'sai_(lol),4
shay_(hladilnik),4
counter-strike,3
yow,1
amy_(dicknation),4
spritzee,5
drakengard,3
unktehila,4
kyros_(dowski),4
garr_(artist),1
amanda_(artist),1
arcturusx1,1
oviduction,0
draconid,5
glancojusticar,1
dragon_hybrid,5
martin_(qckslvrslash),4
amedama12321,1
57mm,1
blue-rum,1
steele_(accelo),4
tenugui,4
bread-kun,4
blub,0
gatorbeast,1
ps1_controller,0
meltan,5
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komajiro,4
kunako,1
garth,4
ruua,1
cum_on_grass,0
izzy,4
scourge_(warriors),4
q-nik,1
thrasian,1
skibby,1
rollingslash,1
paraplegic,0
bug_chasing,0
yabuinu,1
marie_rose,4
accidental,0
captainninja,1
flashspot,1
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foxey_(artist),1
winner,0
fyrre,4
bloodstone,4
chimeratechspyro,1
dangerking11,1
carbon_(carbon-draws),4
red_(redeye),4
character_cipher,7
tympole,5
nemo_(simplifypm),4
evan_(lavilovi),4
madkrayzydave_(artist),1
vulpamon_x,4
pear_(don_ko),4
ouse,1
con_badge,0
lily_(ralenfox),4
pinchibird,1
cinn_delafontaine,4
gusta_(gusta),4
clarabellecrow,1
vesna,4
kitket,1
kyron-ladon,1
daryl_(cringebird),4
bumblewish_(artist),1
umbryte,1
styrling,4
reloading,0
saurian_(mortal_kombat),5
holding_plant,0
multicolored_spots,0
leotard_pull,0
aka_leopard,1
unwilling_vore,0
colson_grainger,4
white_pseudo_hair,0
tsillah,4
pink_undergarments,0
training_pants,0
blue_sex_toy,0
buckler,0
kadith,1
small_fangs,0
raozone,1
bellhop,0
voider_(artist),1
fur_rug,0
tosin,4
namugriff,4
galaxy_fight,3
zero_escape,3
molly_(zootopia_fan_character),4
jack_frost_(rotg),4
pasikon,1
toyger_chives,4
head_plate,0
primal_rage,3
edward_elric,4
jinzhan,1
angelickiddy,1
fc32,1
ecstasy,0
cyana_(code-blocker),4
nina_neckerly,4
tsunderplane,4
gasoline,0
sw1tchbl4de,1
lifted_by_neck,0
enro,4
ice_cave,0
rock_band,0
charlotte_(puella_magi_madoka_magica),4
peterson,1
strawberry_dragon,4
highborn,3
fox_(bloody_roar),4
suit_and_tie,0
mennsuke,1
sable_antelope,5
satellite_dish,0
skyraptor,1
fundles,1
pat,0
silverlonewolf,1
shimille,1
nightwind,4
aita,4
pattarchus,4
c-v-m,1
24cakes,1
draringoa,1
minosuke_mino,4
jasmine_(arizel),4
mahalo_(mahalocheetah),4
stepmother_and_stepson,0
rocket_grunt,4
wickedcake,1
biomutant,3
hope_blossoms_(colt_quest),4
big_blue_bubble,3
goldyura,5
filler_shmazman,1
beak_growth,0
lara_(daniel156161),4
toto_(character),4
female_monster,5
honey-beest,1
marty_shepard,4
hornyvoir,4
lash,4
frozen_solid,0
submerged_arm,0
interrobang_exclamation,0
buttpalace,1
ice_humanoid,5
glalie,5
rawk_hawk,4
goro_(leobo),4
lick_zack,4
page_(jay-r),4
omori_(game),3
natadeko_kitsune,1
leaf_pattern,0
sofia_(jagon),4
ekoi1995,1
chevronfox,1
doctor_stable_(mlp),4
dio_brando,4
lavender_(arizonathevixen),4
rhonbon,1
snoval,5
big_stomach,0
nut_(hardware),0
supercell_(company),3
puppychan48,1
anthro_domination,0
lola_(lewdua),4
e4hargus,1
vivian_varker_(herny),4
darthhell,1
hat_ribbon,0
mama_rabbit_(tmftw),4
black_knee_highs,0
cddi_h,1
disclaimer,1
flowerdino,1
toucannon,5
covering_erection,0
nabi,4
barutan,1
uncensored_version_at_source,7
frontbend,0
blob_creature,5
dotsfa,1
buttercup_(powerpuff_girls),4
orion_mckracken,4
flay,4
henkeiusagi,1
glistening_egg,0
monotone_sweater,0
spike_bulldog,4
tvcomrade,1
teddy_bear_(species),5
mankor,1
smollypoli,1
rocking_out,0
fluffylambbooty,1
cum_on_command,0
the_man_from_the_window,3
belly_on_ground,0
tsarina,4
sealguy,1
squealydealy,1
witch_(puella_magi),5
xing_the_cheetah,4
okoge_16,1
assisted_oral,0
shinywark,1
snatcher_(ahit),4
ari_(caudamus),4
flushthebatsanta,1
danny_(no3512),4
ennard_(fnafsl),4
miu_(aas),4
fofa,4
abyssal_(kancolle),5
evelynn_(stargazer),4
hot_skitty_on_wailord_action,3
berserker_tamamo_cat,4
hakumimis,1
shamerli,1
kay_rox,4
chiderg,4
libri,4
skyler_(diegojhol),4
sei'ven,4
sinsxie,1
lock_down_(monowono),4
eluku99,1
blenderknight,1
terikressner,4
nsfwshamecave,1
manokit,5
blue_shell,0
ukimori,5
showers,0
sasha_(maloo),4
thirsty,0
gahowolf,1
geckzgo,1
air_freshener,0
bindi,0
connec,4
purple_rope,0
andalusian_horse,5
belle_(cainesart),4
sky_stinger_(mlp),4
princess_sherry,4
lemoncore,1
turbine,0
neoneon,1
oliverfox,1
moonlight_raven_(mlp),4
suel,4
photo_booth,0
plum_(fruit),0
floating_crown,0
iizuna,1
naked_ribbon,0
president,0
amethystdust_(character),4
hunter_(father_of_the_pride),4
kajex_surnahm,4
popori,5
fink_(artist),1
ailure,4
sonatadragon,1
ari_guardian,1
snekkuu,1
vaginal_threading,0
naomi,4
wafflecat,1
spotted_nose,0
revenant_(supersoupnova),5
hands_on_own_butt,0
darkburst_(artist),1
denim_(artist),1
gina_(lightsource),4
masquerain,5
delorean,3
skyeder0,1
t-bone_(extreme_dinosaurs),4
smokedpone,1
ember_(angstrom),4
melissa,4
anila_(granblue),4
cloudjumper,4
maximilo,1
balls_through_fly,0
misplaced_spigot,1
cliffs,0
sharing_scarf,0
firewood,0
jagged_teeth,0
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reggie_(thenowayout),4
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f.l.u.d.d.,4
johanna_(paledrake),4
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alliteration,0
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therealf1rebird,1
ethrk,1
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kullax,4
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marsupial_humanoid,5
dart_(dewwydarts),4
mysteryboy18,1
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jess_(azathura),4
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kaite,4
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the_mask,3
dung_beetle,5
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kit_fox,5
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axelegandersson,1
pink_rathian,5
cheetahpaws_(character),4
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slushie,0
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mina_(mlp),4
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triangle_(disambiguation),-1
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shiny_(disambiguation),-1
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snowers_(fan_character),4
kaniku,1
sad_eyes,0
inari_okami,4
acacia,4
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pommyn64,4
noichi_53,1
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0711kdes,1
elysianelly,1
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11:15,7
tinky,1
realistic_anatomy,0
artamid,5
multicolored_pseudo_hair,0
pride_color_jockstrap,0
draconian,5
fatehunter,4
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idel,1
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blaze_(mglblaze),4
exotic_species,5
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tanquito,1
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purmoral,1
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rurik_(metalmilitiaman),4
red_apple,0
justdavefnd,1
koko_(kishibe),4
lying_on_partner,0
honey_(stagshack),4
shiori_shi,4
yumi_yama,4
kougra,5
bluepanda1,1
jim_hawkins,4
penny_squirrel,4
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shiki_taigen,4
utilizator,1
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obi-wan_kenobi,4
ale,0
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timburr,5
pagoda,0
aurora_selachi,4
bravestarr,3
seductivesquid,1
flagging,0
tokyo_mew_mew,3
drcockula,1
lucknight,1
darkeshi,1
drawfag,1
rylai_the_crystal_maiden,4
greldon,4
serareldeer,1
paintbrush_tail,0
translucent_balls,0
ornate_hawk-eagle,5
33:50,7
sabbyth,4
akittu,1
tanya_rhyne,4
combat_ready_(artist),1
tahoma_(evergreendrgn),4
russo-ukrainian_war,3
andythetanuki,1
jamie_(zootopia_fan_character),4
tervos_(character),4
bunny_enid,4
dragonlordfluffyxd,1
thediyemi,1
elephant_humanoid,5
rockstar_bonnie_(fnaf),4
cerberus_(resident_evil),5
franset,1
kastral,4
saucy_(sunhuiz),4
sheriff_hayseed,3
bǐnggān,4
hiveswap,3
renabu_(character),4
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spartan_armor_(roman),0
lying_sex,0
pat_(bluey),4
sog_(squishy),4
yoshu_makato_(character),4
pbs_kids_(copyright),3
short_hair_with_long_locks,0
silverscarf,1
empress_jasana,4
lori_jackrabbit,4
baroque,4
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zeph_boone,4
lauralien,1
tylowell,1
katamari,3
aohren,4
scream_(artist),1
6_toes,0
thenomeking,4
tonomori_suguru,4
dragonflora,5
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the_ring,3
curiousferret,4
teal_anus,0
topony,1
noogie,0
vilous,3
jstarwolf13,1
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template93,1
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killer_croc,4
kgh786,1
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mrincred,1
mireska_sunbreeze_the_dark_willow,4
mklxiv,1
iguanodon,5
yuhbuh,1
emille_selachi,4
milk_jug,0
glistening_head,0
squirting_dildo,0
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clarice_(rudolph_the_red-nosed_reindeer),4
las_plagas_host_(resident_evil),5
rush_(mega_man),4
quizzical_(artist),1
glistening_shoulders,0
head_wraps,0
vjmorales,1
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lazy_(elvche),4
daryl_nimble,4
will-o-wish~,1
caltroplay,1
chronos_(dragalia_lost),4
kisses,0
melony,4
chuck_(angry_birds),4
equestria_trainers_society,3
relax_lion,1
sturmovik_(hideki_kaneda),4
mnty,1
chiro_(skweekers),4
cum_drenched,0
goldenfur,3
ampz,4
marine_explorer_buizel,4
oraderg,1
phyrexian,5
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scottieman,1
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eurotrish,4
aafiya_khalil_(spiff),4
dvenadtsat_(character),4
prisma_faerdo,4
contemplating,0
kitsuakari,1
mira_(wetchop),4
nsfwulf,1
duck_(sarah_and_duck),4
permagrin,0
tinky_(character),4
zylothefusky,1
linkaransfm,1
ottmutt,1
meowscular_chef,4
australian_magpie,5
takakei1,1
female_penetrating_herm,0
d._e._belton_(artist),1
galidor-dragon,1
neon-chan,1
green_backpack,0
pie_(food),0
pearl_bracelet,0
kurorak,4
jennifer_(abluedeer),4
holding_mask,0
pesky12,1
flashequestria,1
anakitsune,1
beth_gabriel_(lemonfont),4
shota_orca_(marimo),4
zirilon,1
fenefell,1
commando_(risk_of_rain),4
llama_llama_(character),4
whitesky,1
camelid_taur,5
shadow2007x,1
wrath_(changing_fates),4
maxillopod,5
mipsmiyu,1
sakisukem,1
kyla_thompson,4
koko_(youki029),4
aladdin_(disney),4
drake239,1
iridiu,4
7oy7iger,1
short_beak,0
kuruko,1
rimentus_(character),4
valerie_veronica_winchester,4
jameslewis,1
groucho_(animal_crossing),4
dark_brown_hair,0
gossifleur,5
stephen_wintre,4
washing_hair,0
clara,4
simon_7617118,1
back_stripes,0
veiled616,1
wear,0
masso_nullbuilt,1
fizition,4
cracticinae,5
one_shoulder_out,0
double_anal_fisting,0
breast_curtains,0
keith_wilson,4
purple_clitoris,0
hauhau_mg,1
kane-neko,1
feeling_up,0
leashed_female,0
ivka,4
star_decoration,0
thehuskydragon,1
half-lidded_eyes,0
stars_around_body,0
glitch_(glitch308),4
loki_rel_zephyr,4
blue_boxer_briefs,0
omoi,1
hstudios,1
tacoghastly,1
haley_baxter,4
gold_accessory,0
the_fangs_palace,3
tropicana,1
giant_squirrel,5
planks,0
60_fps,0
tracy_(sugarnutz),4
fluffyslipper,1
eonbound,3
reallyreallybigbang,1
solarbyte,1
idess,1
waffleirony,1
zavian,1
celtic_knot,0
thirty_thirty,4
ten,1
rage_face,0
blacklight,0
descension,4
touching_self,0
mm_(kilinah),4
supernatural,0
kampyo,4
hguy44,1
curled,0
supremekitten,1
mac-10,0
ta-ek,1
lumineon,5
harusupu,1
oshaki,4
adelbert,4
manoreo,1
butcher_knife,0
sharemyshipment,1
genchi,1
zana6_(artist),1
affablesinger6,1
freshly_uprooted,1
terryeternity,1
quiritum,1
scarred_for_life,0
sallie_may_(helluva_boss),4
artariem,1
gwen_stacy,4
amelia_raevert,4
socko,1
alsoan,5
aerotransor,1
daao_(petruz),4
erynerikard,1
merengue_z,1
aikan_(sisco),4
detra,1
realistic_feral,0
nezubunn,1
x-red,1
mocha_(mochalattefox),4
noctis_(kinkmasternero),4
moba_(artist),1
purple_neck,0
snivybatt,1
flag_in_mouth,0
chakat-silverpaws_(character),4
ydart,1
nekosnicker,1
yom_mido_plus,1
lip_fang,0
jewel_(whitekitten),4
touching_knee,0
teal_shirt,0
jaspian,4
dekonsfw,1
spanklet,1
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gozso-alako,1
eri_(feral.),4
souffle_murdock,4
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peregrine,1
tamara,4
p.chronos,1
aklazzix,1
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bailey_rosworth,4
duke_komarovski,4
emerald_(yuureikun),4
venom_snake,4
elmo,4
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okabe_masatsuna,4
mordecai_heller,4
ginger_(elysian_tail),4
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kaotikjuju,1
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irie-mangastudios,1
squirrelpony,1
garnet_til_alexandros_xvii,4
ami_(oonami),4
ffl_paris,1
val_mal,4
gunfire,0
darc_sowers,1
gakuran,0
xelvy,1
young_justice,3
dansyron,1
bigbang,1
chubby_beach,3
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exploud,5
articfox,1
orange04,1
benj24,1
drood,4
himitsudragon,1
swalot,5
heinkel,4
draconic,5
hidro_(nekuzx),4
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morrigan_(dragon_age),4
waver-ring,1
icelyon,1
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verra,4
projectile_cum,0
creepy_susie,4
shyama,4
kobold_thief,4
fairdahlia,1
prince_arthur,4
chat_window,0
aedbc,1
arrowdark,1
light_grey_fur,0
sakayasaka,1
glacierdragoon,1
gwen_lee,4
ivy_(paganee),4
talen,4
stories:_the_path_of_destinies,3
touching_belly,0
foughtdragon01,1
foxcyen,1
mommyulysses,1
katzun,4
archie_otterdog,4
shu-hoocooh,1
nelewdy,1
mint_(disambiguation),-1
dragomar,4
professor_venomous,4
dubindore,1
fang_(primal),4
gang_orca,4
kiro,4
medjed,4
axelwolf,1
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maxine,4
mayhem_(artist),1
matzzacre,1
ruby_(chowdie),4
home_on_the_range,3
b.cat,1
apple_fritter_(mlp),4
teresa_brisby,4
diesel_(ralarare),4
lorliz,1
saltedtea,1
rammus_(lol),4
sweden,0
ayumi_(ayumixx),4
meken,1
blancmark,4
azumanga_daioh,3
thebravelittle,1
strength,0
bug_net,0
kara_(raptor007),4
kylie_koopa,4
trianglepapaya,1
diaper_disposal,0
onionsan,4
destiny_blackmoon,1
enslaved,0
diantha_(pokémon),4
grizzly_(animal_crossing),4
momo_(avatar),4
mark_(evane),4
back_focus,0
repgg,1
sonic-mj,1
nejumipro,1
neck_collar,0
crispcactus,1
alvaz,1
pom_poko,3
sugar_cube,0
huge_sternocleidomastoid,0
shaved_ice,0
aafox196,1
doooo2424,1
courtney_brushmarke,4
message_box,0
lucky_(artist),1
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amara_(windborn),4
garter_snake,5
sea_angel,5
wronglayer,1
pride_color_penis,0
sickeleye,1
cherrii_pop,1
head_only,0
dorsal_crest,0
shoulder_angel,0
dragonsnakeowo,1
furrholic,1
jackie_moo,4
feces_on_balls,0
tuntematon,4
giving_up_the_ghost,0
frostbound,1
gooseberry_kobold,4
nicole_(foxnick12),4
george_(bottler),4
gobbo_(kooni),4
holding_tea_cup,0
miss_cougar_(new_looney_tunes),4
grennadder,1
mind_(disambiguation),-1
tobyfox,3
lupisvulpes,3
yawning_position,0
black_toe_claws,0
svadil_(character),4
ketu,4
desire_yoshi,4
predator_city,0
bed_frame,0
jabba_the_hutt,4
iridescent_fur,0
fishbone,0
mega_altaria,5
boo_(boo3),4
oki_(okami),4
stamina_bar,0
edgar_vladilisitsa,4
canon_grimaldy,4
tight_swimsuit,0
zaaru,1
ridleymorph,5
helga_(iskra),4
dart_gun,0
jet_set_radio,3
tranquill,5
red_sky_fruit_strawberry_dragon,4
osansyoup,1
yoshioka_haru,4
panel_gag,0
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crow_demon,5
welding,0
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jorts_(artist),1
olive_cow,1
marauder_(doom),5
bdmon,1
yellow_light,0
foogyakumo,1
gooborg,5
alphabet_(mike_salcedo),3
red_pseudo_hair,0
john_(johnithanial),4
sabah_(radarn),4
arttukorppu,1
white-faced_owl,5
revertigo,1
kitsune-2000,1
ankle_markings,0
cam_(artist),1
kanagawa_kitsune,4
zebrov,1
thedinosaurmann,1
vorochi,1
wood_elf,5
marbearis,1
cole_the_shark,4
9:11,7
mohitopaw,1
artdexo2000,1
discarded_bra,0
misty_(lewdfruit),4
alduinred,1
petal_(petal11),4
tobias_wolf,4
kate-nikki,1
cold_warrior,4
lickagoat,1
zeym,1
kiva_(partran),4
ms._fortune_(toonstruck),4
ryan_(ryanwolfeh),4
tip,0
kid_buu,4
toha_a'nassura,4
internal_kiss,0
zulf,4
jazzwolf,1
severed_hand,0
martina_(weaver),4
small_moo,4
aquaticas,1
brask_vovik,4
rad-lizer,1
mechanical_bull,0
stubbornstallion,1
danger_dolan,4
flat_texture,0
rockstar_freddy_(fnaf),4
stellizard,1
drawindonkey,1
poz,0
andromorph_impregnation,0
brooke_(simplifypm),4
maggie_(kitty_bobo),4
sharing_dildo,0
syn_(neurodyne),4
f_(mike_salcedo),4
xzorgothoth,1
mutant_(franchise),3
sagadreams,1
mokko,3
pussywillow_moonsugar,4
usada_pekora,4
wreckage,0
nightmareroa,1
yulia_(bakedbunny),4
air_bound,3
krotsgier,4
doral,4
rorix,1
lemonynade,3
normal_castform,5
critter,0
the_walking_dead,3
dracolich,5
starberyl,1
hc_svnt_dracones,3
razor_blade,0
glury_the_unown,1
reese,4
xennie,4
lucifer_the_doom_bringer,4
cone,0
aurora_(haven_insomniacovrlrd),4
apexyotie,1
foongus,5
camillia_(camychan),4
blake_(desidobie),4
bonicfan123,1
goatjeph,1
expression_print,0
hyper_deltoids,0
dive_ball,0
artfight,3
heart_clip,0
sin_cyan06,1
tumugiv,1
ncr_ranger_(fallout),4
piper_(smuttysquid),4
glowing_orb,0
den_(zerofox1000),4
xvii_(artist),1
sentry_gun_(team_fortress_2),4
wolke,1
mrs.mayberry_(helluva_boss),4
zinni,4
buns-n-spurs,1
whyte_(daemon_lady),4
lina_the_slayer,4
fawkesdrox,1
i_will_dance_only_if_the_beat_is_funky,1
bikini_bottom_removed,0
lugiaberry,1
tricolor_fur,0
spanklet_chocoa,4
br'er_rabbit,4
kyouman,1
signal_line,0
f0rever13,1
nekodon_san,1
cafe_(coffeefly),4
sifaka,5
holding_(disambiguation),-1
diesis_schmitt,4
tamandua,5
st.boogie,1
bixiekz,1
electric_razor,0
booty_ass_meme,3
lacrimal_caruncle,0
arm_ring,0
antylamon,5
shaymee_(lunaris_parukia),4
megan_(animal_crossing),4
caprice_art,1
jessica_(fejess96),4
tytus_the_arcanine,4
avocado_seed,1
menma911,1
file,0
thanris_(thanris),4
gemerency,1
takuya_yamashita,4
patches_ito,4
accidentalaesthetics,1
boleroofbooty,1
shiftry,5
wet_butt,0
disenchantment_(series),3
arokha_(human),4
regys_(carp),4
fart_tubes,0
rune,0
standing_on_tail,0
white_jewelry,0
hamatnk,1
cloroxformius,1
ruki_(character),4
neiiio,1
voxell,4
cuisse,0
willemsvdmerwe,1
against_locker,0
nerond,5
trundle,4
doberbrothers_comic,3
fatalsyndrome,1
cum_on_hips,0
raykayra,1
puppypaws,1
eixin,1
mixxiwuff,1
justduet,1
kitsunekit,1
flicky,5
tko-san,1
beta_ray_bill,4
feather_bangs_(mlp),4
jarek,4
franklin_(series),3
sloth_(kuroodod),4
emily_chan,1
cuddlesong_(mlp),4
dakota_(tartii),4
matthew_taranto,1
eek,0
akasch,4
mega_gallade,5
haxton,4
abstractshadow,4
hyper_condom,0
deus_ex,3
parakarry,4
the_dark_crystal,3
no_navel,0
kagamine_len,4
silby_(silbyaws),4
mass_rape,0
skylar_wing,4
gigi_(sheepuppy),4
baku,1
freenzy-plant,1
akam,1
giant_squid,5
rio_(miu),4
matidramon,1
sylene_(blissey1),4
tail_pouch,0
kettlebell,0
hokin_the_bear,1
cheloniid,5
mobian_rabbit,5
lewdbones,1
pappel,4
caravan_stories,3
foot_on_shoulder,0
calvin_bowling,1
kunaboto,1
leostar,4
darou,4
shiny_celebi_(eotds),4
chloe_von_einzbern,4
outstretched_leg,0
maroon_hair,0
shinyfoxguy,1
kirena-kaya,1
fangs_bared,0
nurah,4
burgess_shale_(lucidum),4
hasukichi,1
surrealism,0
tharakaos,1
puquanah,4
norang94,1
moose_(kemono_friends),4
snowshoe_hare,5
darknader,1
akuro,4
scp-173,4
blueberrysnow,1
ann_gustave,4
mrmilky,1
police_tape,0
painful_penetration,0
spell_tag,0
lynn_(twokinds),4
cheep_cheep,5
hideo_katashi,4
mikituu,1
redrosid,4
panty_(pswg),4
blitza,4
fluffyboobs,1
sexual_exploitation,0
kaleesh,5
scottish_terrier,5
sam_(samwiding),4
hand_spanking,0
shaffer,1
jack_skellington,4
treat,0
endie,4
padme_amidala,4
shep_(animal_crossing),4
hot_dog_bun,0
canoe,0
kaiberu,1
sedyana,4
terra,4
monette,4
fr0stbit3,1
furry_sb,1
zortie,1
fanch1,1
komi_shouko,4
eraxa_(character),4
avery_(vir-no-vigoratus),4
kotaro_kurokawa,1
tavyapl,1
lost_(series),3
meo_the_meowth,4
rocky_mountain_goat,5
rearing_up,0
scaled_dragon,5
werehusky,5
paraphore,3
dollification,0
sonja_wusky,4
white_earbuds,0
xiaoyaozhi,1
lisyra_(avelos),4
milo_(aliclan),4
dororo_(sgt._frog),4
powfeen,1
daelyhelaxon,1
opaline_(mlp),4
darkito420,1
yaarp,4
smores_(ribbontini),4
slime_sex,0
magnadramon,5
mochi,0
theartsygirl94,1
blemishine_(arknights),4
black_thigh_boots,0
maruku,1
hood_up,0
knightferret,1
kia,4
fishyboner,1
foulfrost,1
familiarsaint,3
chana,1
lemon_bread,4
ak-74,0
awilddrawfagappears,1
race_track,0
scaroused,0
cotton_(locosaltinc),4
gibbon,5
4_hands,0
communicator,0
pizza_hut,3
sokajou,4
helix_piercing,0
the_roop,1
joshy_furret,4
pulling_down_pants,0
borahorn,4
marines,0
brawl_in_the_family,3
boxer_(disambiguation),-1
everfree_forest,0
snover,5
schism,4
cote,1
saberon,5
sienna,1
shinooka,1
enico,4
wolf_(minecraft),5
gibberish,0
blastgoggles,4
long_toes,0
colton,4
tapping,0
c-smut-run,1
cornelius_(commissioner),3
malcolm_douglas,1
hitting_the_showers,3
coiling_another,0
pericings,0
whooperscooper,1
javier_(photolol.03),4
sath15,1
mel_price,4
reyn_goldfur,1
morbidly_obese_gynomorph,0
light_perineum,0
kif_yppreah,4
come_and_learn_with_pibby,3
takorin,1
katiedragonart,1
buttonverse,3
holding_dress,0
wisp_(wispeon),4
satanbear,1
implied_popping,0
blitz_the_seahawk,4
disaster_dragon,4
two_tone_dildo,0
black_(among_us),4
anjing_kampuss,1
raedainfossa,1
james_di_benedetto,4
mcmadmissile,1
choco_monaca,1
green_gem,0
partially_submerged_arm,0
bloon,0
towelthebear,1
humiliating,0
tails_touching,0
headphones_only,0
donation,0
chigusa_amano,1
sophia_hellstrand,4
the_dark_brotherhood_(the_elder_scrolls),4
whiner,1
giselle_(open_season),4
max_(dominussfm),4
mpanda_kuma,1
kirby_star_allies,3
vampirsttg,1
ursula_(furronika),4
caust,4
under_paws,0
asiahao520,1
the_legend_of_zelda:_twilight_princess,3
ichiri,1
rockhoof_(mlp),4
spider-man:_into_the_spider-verse,3
deepak_(101_dalmatians),4
alex168wang,1
strawbear,1
appl,1
fizintine,1
edgeartstation,1
rogialos,5
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pokebraix,1
toumoro_22,1
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sam_(cuchuflin),4
joyce_(totesfleisch8),4
takeshi_kemo,1
mosasauroid,5
skye_primis,4
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thenaysayer34,1
cornelia_(glacierclear),4
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rena_(raptor007),4
heads_and_tails,3
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flying_saucer,0
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hobo,0
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davos,4
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odium,4
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insect_(petruz),4
maurice_(nexus),4
kayz_(snepkayz),4
epsi110,1
minoss,1
space_dog,5
alvh-omega,1
ferri_(enti123),4
anhotep,4
wolf552906,1
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aeldari,5
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kaya_(twin_dragons),4
kingcreep105,1
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17:20,7
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rei_(breath_of_fire),4
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heatmor,5
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ket_lesh,4
darmanitan,5
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shin_(negger),4
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1jaz,1
ivan_(blackmailz),4
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ann_takamaki,4
shoebill,5
dzk,1
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zecho,1
nyonyoimpact,1
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mordechai,4
plum_(viroveteruscy),4
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dexter's_mom,4
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roshea_(rosheatfreak),4
larretthekinkyferret,1
d4,0
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9/11,3
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vice_the_raccoon,4
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heyyy_it's_the_king!,3
octii,4
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mysticaldark,1
likkezg,1
siqko,1
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emma_(shouk),4
ollie_(pokefound),4
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penny_(ceeb),4
black_eye_(disambiguation),-1
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ravirr,1
slowaf,1
parker_(theredhare_and_demicoeur),4
kabuki_trim_furfrou,5
star-nosed_mole,5
zynn,4
11:10,7
featheryboy,1
queen_haven_(mlp),4
sodo_(breeze_in_the_clouds),4
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sakuroma_(retrospecter),4
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oncha,1
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monotone_headgear,0
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neeshi_(vorekingdom),4
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firael,1
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aptonoth,5
blacky,4
mentaiko,1
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mathew_porter,4
daft_punk,3
what_a_twist,0
c-trigger,1
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nikita_(oc),4
requiem,4
snapping_turtle,5
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malamin,1
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jamie_(ldr),4
gohan,4
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gia_the_jaguar,4
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ripster,4
vore_planet,3
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gully,1
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men_in_black,3
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kxflyer,1
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sheori,4
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rantanatan,1
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mauricebeverle2,1
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sonic_channel,3
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todd_(joelasko),4
hello_nurse,4
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connor_(snakemayo),4
masked_fox_(kame_3),4
fully_clothed_humanoid,0
glistening_teeth,0
belladonna_(all_dogs_go_to_heaven),4
ami_(personalami),4
pondering_my_orb,3
tenyo_cat,4
self_exposure,0
hedgehogblue02,1
ruby_(ghostth39),4
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hugging_plushie,0
temmie_chang,1
greed,0
runawaystride,1
goat_demon_humanoid,5
calib_peterson,4
cringebird,1
queen_regina,4
nowego_(artist),1
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tringa,1
splitalien,1
reabault,1
scraffyraccoon,1
thelightsmen,1
biped_taur,5
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twisted,0
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username_(character),4
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smewed,1
gordon_(fuze),4
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dirk_aukea,4
scott_(rosheatfreak),4
shiftylook,3
daphne_dress,3
zuri_(tlg),4
usagichi_(vkontakte),4
gunther_(mating_season),4
lynn_(deltav),4
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kodiak079,3
joel_(adondis),4
prisma_(fr0gv0re),4
light_back,0
great_horned_bunwolf,5
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styracosaurus,5
mx99926,1
mimi_(mr.smile),4
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tysuga,1
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shindorei,4
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vespur,1
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raine_kirijo,4
pudding_(plaga),4
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woobat,5
janet_q,4
shurya,4
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rita_(sugarnutz),4
prison_suit,0
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kit_mambo,4
tala_tearjerk,4
kiwa,4
entwined_tongues,0
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flechette,4
spiritfarer,3
noonun,1
justsantiago,1
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apache_(artist),1
meleon,1
arnius,1
eve_hawthorne,4
orange_dildo,0
a2,1
ramzymo,1
autumn_sushi,1
kitterjitters,1
cinnamon_buns_(oc),4
alexis_(hacken),4
w0lfmare,1
lionmom,1
erin_(kawfee),4
artz_(eevee),4
arching_back,0
kiryu,1
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photo_frame,0
turquoise_blitz,4
in_hand,0
hannibalazul,4
five_nights_at_freddy's:_the_day_shift,3
1985,7
mimirin,4
korbinite,5
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qiao,4
darin,4
jon_sanders,4
nook-lom,1
solaire_of_astora,4
esk,5
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amy_untold,3
niko_(kitroxas),4
badwing,1
razter,1
turtlechan,1
deuce,1
vixy,4
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caoscore,1
ursine_ears,0
zweihander,0
tolng,4
rozelyn_the_floatzel_(mischievous-skunk),4
religious_symbols,0
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sammy_(disambiguation),-1
orcasanddragons,1
usul_(neopets),5
nyasplush,1
kitnip,1
jona_kazuo,1
mouse_(maynara),4
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sky_(xxskyxx),4
snegireva,1
charlie_peaches_(raunchyhaunches),4
haoming,1
crat_(connivingrat),4
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holding_arrow,0
exadian_hybrid,5
neumokun,1
glittermiilk,1
khaki_body,0
savvel,4
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spaicy,3
angelfish,5
takum,4
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conch,0
fetishsketches,1
randomobliquity_(artist),1
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zedwin,4
m6,1
vonnir,1
phantom_freddy_(fnaf),4
exmoz,1
deliciousq,1
mutant:_year_zero,3
olive_(xylious),4
akyko_(ceehaz),4
gulasauce_(artist),1
surprised_pikachu,3
axela,4
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avalondragon,4
patrigue,4
kracker,1
macro710,1
sticki_bun,1
virgil_(maxydont),4
maharen,1
femlek_(elektr0),4
davidkyatt,1
pomopuchi,1
liam_(tempestus_vulpis),4
lennonblack,1
disembodied_pussy,0
rd-rn00,1
silipinfox1298,1
decapodian_(futurama),5
spotted_pawpads,0
discardingsabot,3
marleybraun,1
toxel,5
eipon_tarou,1
lumpy_(htf),4
teenagebratwurst,1
jack_pot_(mlp),4
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rehzi_(fluff-kevlar),4
mike_(doomhelm),4
grey_headphones,0
tontaro,1
sniffing_self,0
aerothefox,3
multi_tone_skin,0
silver_fox_cn,1
peeing_out_of_fear,0
game_item,0
phantomx999,1
yuki_(quin-nsfw),4
lifting_belly,0
keisatsu_dog_(sususuigi),4
malcolm_(applelover22),4
vampiricpig,1
mars,0
10ne-v,1
callie_(furry_force),4
wislow,4
nedrik,1
aruru,4
awoogasm,1
unsatisfied,0
elisasharky_(artist),1
fallflys,1
blue-wolfy,4
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jimsdaydream,1
splatter,0
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chloe_thompson,4
nova_spark,4
red_alert_(series),3
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chinchilla3333,1
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mega_gnar_(lol),4
oliver_hornski,4
leaf_censor,0
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abigrock,1
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thatphatbun,1
carly_bear,4
minimalism,0
zary_(zarycolour),4
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kalivero,1
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mei_ambers,4
axel_the_tepig,4
abum,5
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doctor_ark,1
tech_the_renamon,4
hidoihito,1
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rivas,4
momma_(ice_age),4
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maxhe,1
rotfell_the_fox,4
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bastien,4
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fenalia,1
tiredanon,1
daclusia,1
seedot,5
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spots_(spotthecat),4
lana's_mother,4
faye_(ipan),4
eternatus,5
otsu,1
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rangetsu_(onmyou_taisenki),4
super_robot_monkey_team_hyperforce_go,3
doctor_starline,4
buck_(disambiguation),-1
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chillhop_raccoon,4
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jasmine_miller,4
tem_(beastars),4
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kopk,1
miklia,1
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aurus_(endertwinks),4
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tails_gets_trolled,3
limon_picante_(oc),4
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pink_rabbit_(huitu_c),4
khan_maykr,4
the_seven_year_itch,3
yoru_vida,1
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kitty_(meyou),4
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my_little_goat,3
gabiru_(that_time_i_got_reincarnated_as_a_slime),4
ark_celosar_(polokun),4
tongue_stuck,0
pornstar_(meme),3
tremors,3
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merci,4
blue-eyed_black_lemur,5
mia_(nanodarkk),4
vixen_(reindeer),4
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thatwhiskeredthing,1
gear_(guilty_gear),5
mikazuki162,1
opal_weasel,1
fantajii_dm_(artist),1
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oranguru,5
skizu,1
lair,0
wo262,1
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renga_sato,4
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dreadbear_(fnaf),4
lamont786,1
kennehbreen,1
divdurvart,1
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rammionn,1
moonblood,5
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star-rod,1
zed_(scales),4
thewizardstick,1
living_puppet,5
magnus_(bristol),4
tj_hess,4
steve_(rumble),4
delbin_(spyro),4
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kolu_(koluthings),4
alabasterlion,1
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rooty_(leakyroot),4
milia_wars,3
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vanimateapp,3
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silverqueen0,1
sasha_(ariveil),4
zimabel,1
tahara_(altrue),4
tod_d,1
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cynlonyth,1
tonza,4
triple_oral,0
noahsense,1
cheri_(animal_crossing),4
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helium2,1
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mechagodzilla,4
meatspin,3
callmebisky,1
jaal_ama_darav,4
lana_(arnethorn),4
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knightthewolfal,1
gaping_cervix,0
couldnotparseline,1
verd,4
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void_(sssonic2),4
bossy_the_bat,4
splatterhouse,3
toriko,4
cesium,1
david_kawena,4
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kavat,5
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black-kalak,1
xaigatomon,1
sweet_strokes,4
dio_leila,4
permavore,0
muskox,5
magic_aura,0
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azzy,4
jig,1
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leanna_fennec,4
reindeer_humanoid,5
bucky_(the_sunfish),4
amon_bars,4
tao_(beastars),4
sleeveless_turtleneck,0
chloe_culson_(ryltar),4
gouki_(the_spicy_ninja_scrolls),4
marcusunderwood,1
wasabi_(aimbot-jones),4
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arin_(daxhush),4
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keranas,4
xane,4
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nick_(disambiguation),4
group_transformation,0
kyrie_(eleison),4
ale_vananice,1
adeleine_(kirby),4
devyshirehell,1
tuberosekotoki,1
sven_(frozen),4
evgheshkafx,1
squishymare,1
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meiju_(ashnurazg),4
sheela_(rajak),4
jones_boi,1
ralefov,1
stardust_(tricktrashing),4
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orange_stockings,0
musical_keyboard,0
listening_to_belly,0
tina_(vrabo),4
tomilixak,4
wenisberry,1
aerocats,3
furry_mom_(wbnsfwfactory),4
kenshiro_daimon_(odd_taxi),4
ankle_crossing_thigh,0
the_whiteboard,3
fanatic-heretic-101,1
aurora_(kamikazekit),4
aresanchor,1
white_sneakers,0
the_alby-cat,4
tylwing,1
ijpalette-color,1
skippyarts,1
kimero_(kimero_kat),4
bellenightjoy,1
kneeing,0
john_egbert,4
great_old_one_(h.p._lovecraft),5
nylla,4
severe_wound,0
torn_leg,0
dagmar_(totesfleisch8),4
elote_(enginetrap),4
alejandro_(xenoforge),4
pattern_bedding,0
andy_(kaboozey),4
fantum_fox,1
tueetch_ambersnout,4
rottentuttifrutti,1
topaz_lareme_(battler),4
azel_(nickii),4
mxxnkiddo,1
dildo_riding,0
pink_butterfree,5
tank_top_only,0
lifting_up,0
nilina_(felino),4
pekoneko,1
white_necklace,0
voyuer,0
experiment_101,3
flam_kish,4
kuma_richards,1
elf_costume,0
maddencreatures,1
vis_(bob0424),4
kazashino,1
samantha_wolf,4
lazyollie,1
zibanitu6969,1
dragostewolf,1
holly_(abluedeer),4
balls_shot,0
sandstorm,0
curvy_anthro,0
walked_in_on,0
christian_cross,0
virginal_blood,0
casual_vore,0
indramon,5
holding_helmet,0
navel_close-up,0
storyboard,0
grub_(insect),5
spaboofy,1
ryoku_(character),4
xc,1
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crona_(artist),1
beronon,4
lewdydoo,1
luditima,1
curry_(parker_simmons),4
party_cannon,0
asekeu,1
gyee,3
m3nsfw,1
alex_(xanderblaze),4
ayatori,1
pale_scales,0
fozzey,1
corwolf,4
crossed_wrists,0
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desoto_(disney),4
ponegranate,1
letigre,4
everquest_2,3
rosianna_rabbit,4
shad,4
magic_book,0
molten_corgi,5
slakoth,5
camazotz_(smite),4
pigpen,0
noiz,1
pegibruno,1
adebola_melesi,4
princess_tiana,4
snapper,5
animal_hood,0
softcore_works,3
pafull,1
sahash,5
jadenkaiba,1
shou_(salkitten),4
nyani_ga_nyander_nyander_kamen,3
kyubun_(character),4
kryn,4
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mrpenning,1
masmas,1
cd-i,3
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sophitia_alexandra,4
face_covered,0
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randomdragon_(character),4
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dancer_(reindeer),4
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shrike_alvaron,4
brazilian_flag,0
sleufoot,1
pulp_fiction,3
danica_(daniruu),4
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marmelmm,1
gumball_machine,0
kukulkan,4
xiin,1
nogicu,1
mackenzie_(modeseven),4
sen_(looneyluna),4
miharushoka,1
compression_shorts,0
asanagi,1
spiked_club,0
amazon_milk_frog,5
orchidpony,1
tail_wiggle,0
valz,4
anteater_humanoid,5
red_rose,0
purplemantis,1
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thomas_(shane_frost),4
dark_ron,0
predalien,5
idlecum,1
pyramids,0
enzi,4
demi-glenn,4
chromosomefarm,1
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jane_porter,4
nikku_(ra),1
tek,4
coliser,1
heiken,4
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tail_slap,0
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xilimyth,4
rax_zenova,4
reptar,4
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mey_mey,4
lintu_(character),4
sinder,4
milochu,4
shingeki_no_bahamut,3
mashato_basuka,4
magiculation,0
extinct,0
corriezodori,1
altera,4
johnsergal,1
kaley_(lynxer),4
learning_curves,3
hebiko,4
cuteosphere,1
sir.smiley,4
spiracles,0
pocky_game,0
poor_yorick,1
drake_tigerclaw,1
lordwolfie,4
hentagon,1
rei,4
sukemyon,1
greenlion,1
vincent_(vincent),4
pashmina_(animal_crossing),4
derigrowl,1
cogwheel,0
prinplup,5
pussy_juice_vore,0
sylvia_(disambiguation),4
arty_stu_(fursona),4
klavinova_(mlp),4
blue_quills,0
shy_smile,0
jde10,1
rudgy_(aquilak),4
tree_time,4
master_hand,4
kutar_g,1
basculegion,5
iridescent_scales,0
jack_(gunwhale),4
supporting_breasts,0
rurua,1
drude,5
gyrodoodle,1
lara-su,4
prismatic_wolf,1
roxie_(pokemon),4
striped_headwear,0
garrett_(gamegod210),4
storage_device,0
ggan_ggandi,1
cupping_breasts,0
lady_kluck,4
ambar_black,4
hill_(father_hill),4
wilson_(furry_force),4
scale_hair,0
nathradas,1
t_rex_(weaver),4
fionafortunecookie,1
sunamori,1
shoroch,1
ainro,1
otis_(darkpenguin),4
dr_emery,1
sea_creature,5
takoyaki,0
ron9,1
eviljrgr,1
terrajou,4
xiel_milano,1
on_bedding,0
partially/fully_submerged,0
thylacine_penis,0
mr._jellybeans,1
quangdoann,1
gekko_(character),4
pink_hairband,0
owl_beast_(the_owl_house),5
rajak_(rajak),4
black_apron,0
revy_(revy),4
koufun,1
pansol_(kochapatsu),4
harmony_(splatoon),4
raptie_(caedere),4
300_(film),3
cakeinferno,1
observing,0
zacks123,1
winserferret,1
penis_leg,0
shax_(artist),1
ars_goetia,3
shironaga67,1
mikey_roo,4
minka_mark,4
rachel_wilson,4
razi_(covertcanine),4
chmunk,4
octonnibal,1
carmen_herrera,4
drowned,0
ango76,1
chris_(chris13131415),4
chrimson,4
graboid,5
balthazar_haxter,4
kudamon,5
padraig_(masterofall),4
perish_(artist),1
sharkinwaves,1
unknown_artist_(artist),1
kith0241,3
queen_octavia_(teathekook),4
kanachirou,1
zaos_(anidra),4
flashing_light,0
pokebreeder_(mabit),4
seated_carry_position,0
orenji_salmonpaw,4
lunarpanda8686,1
mouthmoth,1
boggartowl,1
wooden_door,0
anglerfish_humanoid,5
ao_bai_(gunfire_reborn),4
theunokcringe,1
iijackie3678ii,1
leaf_print_topwear,0
blue_necklace,0
rho_(warg),4
chillhop,3
smaller_taur,0
gothstoat,1
kerry_(character),4
small_claws,0
helpy_(fnaf),4
zterry,1
kaa_bakensobek,1
vixel,1
tug_(brother_bear),4
traknoch,1
laspyke,1
dtella,5
strauburi,1
gardenia_(pokemon),4
brazilian_carnival,3
cottonsocks_minkelson,4
button_skirt,0
rixx_(yawg),4
robin_(submarine_screw),4
pufftor,1
feet_on_desk,0
imaaahorny,1
casiika1,1
precum_on_chest,0
tiger_lily_(tito_lizzardo),4
engie_(liebro),4
vimmy_(squishy),4
covered_in_goo,0
herzog_silverfang,4
hands_in_mouth,0
herba_(hasukii),4
honey_pie_(character),4
my_little_pony:_pony_life,3
rawgreen,1
cat_peach,4
bamboodog,1
catch,0
reaper_bird,4
tina_(caribbean_blue),4
irregular_fetishes,1
large_feet,0
dotrook,1
kotetsu_redwood,4
nme,1
harlequin,0
xrayzebra4,1
elaine_applebottom,4
commander,4
lurelin,1
drazil,4
marionette,0
cyndan,4
bigtyme,1
ongoing,0
blissful,0
butt_scar,0
kemono_michi:_rise_up,3
fluffybunz,1
room_kinoko,1
tassets,0
split_color_hair,0
smug_eyes,0
holding_device,0
wishy_washy_(clexyoshi),4
ihcaris,1
hades_(thehades),4
forced_knotting,0
rick_(rain-yatsu),4
quinlan_(halcyon),4
leo's_father_(vg_cats),4
atil,4
molly_(hendak),4
white_high_heels,0
tahu,4
posthuman,5
kouda_(kemokin_mania),4
cousin_dave,4
gage_(ft522),4
monotone_bandanna,0
leloli,1
becquerel_(timidauxiliator),4
miyu_(rimefox),4
kiara_(blacky),4
chubby_penis,0
purple_genitals,0
suzuran_(arknights),4
fox_next_door_(horokusa),4
objects_in_condom,0
russo,4
davirook,1
ilpion_(hyilpi),4
gordon_the_otter,4
fzst,4
jasmine_(kevinsano),4
azulejo,1
elyzalizah,1
oral_on_backwards_penis,0
hellbrain,1
hookturn_(kitfox-crimson),4
yellowparrottw,1
dischimera,1
thigh_belt,0
logan_(misterstallion),4
nearl_(arknights),4
maverick_(lonekeith),4
isiica_(ishton),4
ad-games,1
emoticon_on_clothing,0
twentles,4
hairy_feet,0
foot_on_tail,0
grey_bandanna,0
vitaj,1
feruda_(farstaria),4
ward0r,1
kadeki_(character),4
shikame_akihiro,4
bow_bikini,0
pre-transformation,0
speed_forme_deoxys,5
water_shuriken,0
black_chair,0
ice_breathing,0
big_mama_yena,1
iorarua,4
mandyfoxy,1
caressing_head,0
tetisuka,1
vale,4
skye_(skyebubblez242),4
language,0
adultpartypunch,1
bitting,0
kowbold,5
akuna,4
eve_(wall-e),4
mu_plus,1
timeless,3
brave_fencer_musashi,3
ebi_193,1
papaya_(artist),1
orcinus,4
slot_machine,0
the_rookie_(character),4
beret_girl,4
diego_brando,4
pudú,5
silver_shadowheart,1
phoenixe_(character),4
foreskin_stretching,0
performing,0
black_dragon,5
fidget_spinner,0
hat-kun,4
parenthood,0
avalugg,5
xj-6,4
bewbchan,1
blocking,0
red-eyes_black_dragon,4
pop_(htf),4
seylyn,1
implied_handjob,0
badtiger,1
xwhitedreamsx,1
imax,0
wandering_nipple,0
basketball_shorts,0
jyta,4
sierra_lowe,4
liara_(microphone),4
ayumi_(whitephox),4
head_bump,0
huntail,5
feryl,4
pucacorgi,4
taira,4
tiny_panties,0
wacom,3
huge_eyelashes,0
egsaku,1
gullet,0
ribbed,0
maternal,0
tumiohax,1
dragon_princess_ii,4
snowdrake,5
one_way_mirror,0
raspberyl,4
katie_koopa_(character),4
mio_(powfooo),4
emmonsta,1
arsemaus,1
stereotype,0
ahobaka,1
legends_of_chima,3
epithet,3
mayor_swinton,4
duck_penis,0
purra,1
untitled_goose_game,3
chikorita_moon,1
zuwardo,1
chiubear0214,1
heartberry,4
ozzy_otter,4
aether_employee,4
shiori_(anklav),4
glitter_trap_boy_(character),4
kiva_(kiva),4
moonlynxx,1
franko_(negger),4
irria,4
dust_the_otter,4
fiona_gummi_(fan_character),4
pierre_collins,4
log_(fox),4
chrona_(cat_busters),4
mojito_(novusnova),4
r-z,1
kirikaze_(eclipseprodigy),4
sky_dragon_(cave_story),5
autumnflower_(game),3
circuitry,0
tomatoztp,1
solosis,5
kodiakwolfy,4
eruption,0
phillip_(repeat),4
quagsireofvp,1
uncertain,0
anal_toy,0
scheknul,4
big_daddy_(sing),4
sanic,4
cup4eney,1
kaizeh,4
fray_(twinfoxes),4
rizcifra,1
kaczy,1
damian_(maim),4
britt,4
rina_von_dan,4
andres_chung_jr,1
askfrosty,1
ast,1
understall,0
daroach,4
lemonscent,1
elvaan,5
nana_noodleman,4
yonezmi,1
bull_shark,5
darkartskai,1
mason_(suck_mcjones),4
aurawolf,4
lier_magician,1
star_trek_the_original_series,3
deathwing,4
riko,4
acme,3
moodraw,1
sliv'oth,4
xandrah,4
monokini,0
fooga123,1
mob,0
deep_insertion,0
gamblefur,1
bano_akira,1
bunny_(dure),4
rhianna_ravenclaw,4
yume_nikki,3
lemon_hearts_(mlp),4
laionss,1
bluespaceling,1
dean_blitz,4
dark_queen_oriale,4
team_galactic,3
yoo_oona,1
hypnofur,1
leina_(sandwich-anomaly),4
garmina_(curiousgarchomp),4
pcraxkers,1
lopmon_(survive),4
towel_on_lap,0
negativedye,1
lightningwolt,1
lorenzo_(royluna),4
iridium_gloom,4
einrotica,1
yuki_(side_b),4
jz-jake,4
balinese_cat,5
hunibun,4
felhound_(warcraft),5
roy_(chuki),4
william_adler,4
bruce_morgan,1
demonstration,0
yogin,1
floating_hair,0
lileep,5
coolmaster98,1
tribez,3
curved_claws,0
riko_saikawa,4
connie_savannah,4
redphlannel,1
rusoide,1
yukkoo,1
pengi_nsfw,1
torchembers,1
pink_bedding,0
souls,0
torturedtorchic,1
blue-senpai,1
soy_sauce,0
nu_(bikupan),4
snowsnow11,1
cowprint_lingerie,0
in_vehicle,0
cyclostomata,5
santagiera,1
esmeia,1
doodle_(doodledoggy),4
mustard_(fek),4
teddie_(persona),4
raeford_(brogulls),4
nova_(purplebird),4
avalon,4
todd_hayseed,4
harem_clothing,0
level_drain,0
balaa,1
grapploct,5
fluffy_penis,0
gina_cattelli,4
blushing_at_viewer,0
berry_juice,0
masked_dragon,5
shane_boder_(shaneandco),4
snapps,1
two_tone_headgear,0
yellow_(among_us),4
netogra,4
cinnabyte,4
marikuishiyutaru,1
wafflemouse,4
krystamon_(character),4
long_braid,0
wrist_fluff,0
axiomtf,1
discarded_condom,0
vuaisu,1
leaf_wings,0
patricia_wagon,4
gnoshy0,1
peggle,3
iryx,4
alchemical_symbol,0
molly_(disambiguation),-1
viktor_(paladins),4
moxie_the_mouse,4
eric_sacae,4
keith_(black-kitten),4
ben_(sssonic2),4
lorekeeper_zinnia,4
sheerheart,1
chuck_(pokemon),4
agent_3_(splatoon),4
amelia_(petruz),4
unicuck,1
lidia_(hyperstorm_h),4
moonrunes,1
stakeho_lder,1
large_weapon,0
against_bar_counter,0
smoke_heart,0
ikaika,4
nonyammychoko,1
yoshikage_kira,4
leporid_taur,5
pride_color_body,0
nottanj,1
venus_noire,1
valuthar,1
enen666,1
red_tail_tuft,0
amputated_arm,0
origin_story,0
railcar,0
strapped_in_dildo,0
extremius_amphibious,1
great_knife_(silent_hill),0
black_widow_(marvel),4
bobby_pin,0
mr_person,1
electronic,0
stephanie,4
bladed_weapon,0
asymmetry,0
grubber_(mlp),4
dearmybrothers,1
kiruki,4
aquagalaxy,1
crotch_fur,0
sereth,4
sinful-spector,1
penmark,1
animal_bride_1,3
sora_(tokifuji),4
mimic_dragon,5
salkafar,1
thri-kreen,5
trooper_(yifftrooper501),4
denim_skirt,0
bluebear_(animal_crossing),4
millipede,5
shugr,5
tabaluga,4
armored_vehicle,0
safi_(safiru),4
kitchen_sink,0
quill_wonderfowl,4
cerealharem_(artist),1
spiked_sex_toy,0
hand_over_shoulder,0
mala_(malafox),4
annie_hole,4
rigel_(kivwolf),4
pillow_talk,0
chris_(chrisbmonkey),4
razia_(narej),4
arimura_taishi,1
mrbroomstic_(artist),1
liz_(draconder),4
beth_bunny_(ota),4
gator_girl,4
trapped_in_bubble,0
rabbit_taur,5
cutefckngbunny,1
izzybsides,1
fenris_williams,4
generalgodzilla,1
madame_broode,4
hittoga,1
roadkill_(zackdadawg),4
yeehawt0wn,1
manifest_harmony,1
musi,4
guildmaster_wigglytuff,4
geometrian_(species),5
13:15,7
kresto,1
cobra_(petruz),4
leaning_on_tree,0
gaku_kodori,4
seras_victoria,4
icy_breeze,4
surprise_transformation,0
jasper_(bgklonewolf),4
light_stripes,0
daycare_attendant_(fnaf),4
smithing,0
syntheticimagination,1
popsicle_stick,0
resting_bitch_face,0
cicada,5
salim_latif_soman,4
sprite_(soda),3
bumpty_(artist),1
duke_nukem,4
kalida_(character),4
strredwolf,4
aphexangel,1
cottonee,5
aster_rhodes_ravier,4
sex_pet,0
culpeo_fox,5
xenogon,5
lionfish,5
graphicbrat,1
scarlett_(furball),4
s'more,0
khyaber,1
foolish,4
dv-ch,1
castanic,5
arta_aere,4
heartszora,1
kit_ballard,4
ukiyo-e,0
priley,4
head_on_arm,0
one_in_one_out,0
ayerart,1
william_(misterstallion),4
rayla_(the_dragon_prince),4
penis_on_cheek,0
extreme_inflation,0
masterofwolves99,3
dress_removed,0
polygonal_thought_bubble,0
foot_grinding,0
madjoker,1
hunicast,3
head_in_hands,0
kyu-bum_lee,1
kyoko_(cocolog),4
funktilda,1
lotus_(seuchenkater),4
genie_(disney),4
markhor_(artist),1
magic:_the_gathering_card,0
totem_pole,0
eva_(disambiguation),4
kakula,4
inushishigami,1
foxtide888,1
carrie_(s2-freak),4
bullwinkle_j._moose,4
chili_(zummeng),4
elliot_(fuf),4
sweepyy,1
patchwork_monster,5
pdart,1
neck_tied,0
august_(artist),1
barriss_offee,4
virushunter,1
fantasy_creature,5
inflatable_bondage,0
desmond_(zootopia_fan_character),4
regina_(dino_crisis),4
mary_blissany,4
garrote,0
shibasaki_saki,4
rayjay,1
paint_(artist),1
ctrl-z,1
agnes_(animal_crossing),4
sharu,4
cookie_(animal_crossing),4
fender_mcbender,4
caribun,1
baresenio,4
akiru01,1
centrifuge,4
bruxish,5
tales_of_symphonia,3
helen_parr,4
steam_(software),3
bravely_default,3
klump,4
sock_puppet,0
zedeki,1
noiverus,1
caleb_(kokuhane),4
general_barcode,1
pleasure_bon_bon,3
star_ko,1
wakan_tanka,4
xionxiii,1
plache6,1
akweer,1
mannysdirt,1
reku,1
karasu_chan,1
zagiir,1
jazzax,1
holding_katana,0
post_vore_scat,0
unusual_sperm_cell,0
nyaongssi,1
repeated_internal_monologue,0
felinecanis,1
bigbeanpole,1
diggy,4
photobomb,0
eyrie_(neopets),5
internal_frottage,0
kahvi_(coffeesoda),4
0ne_(only0neman),4
throwing_clothing,0
ginzake_(mizuumi),1
hokolee32,1
janong,1
clasp,0
edric_blight,4
yellow_armor,0
cum_on_mask,0
breeze_(breeze_in_the_clouds),4
sharkinn,1
pigmyhippotam,1
wolfen,4
rashii,4
silverane,1
touching_chin,0
lastik,1
kirby_triple_deluxe,3
maenomeri,1
zilya-lya,1
pale-skinned_female,0
nighdruth_(character),4
voui,1
capte,1
killerdragn,3
hosemistress,4
looking_seductive,0
slapstick70,1
11:12,7
zuri_(the-shadow-of-light),4
citrus_(character),4
cinnamon_(yoko_darkpaw),4
award_ribbon,0
murzikteijiro,1
sketch-mantel,1
kumaneko,1
charlie_(wbb),4
sadleen,1
hand_on_own_hand,0
sheep_demon_(hazbin_hotel),4
flint_westwood,4
touhoku_itako,4
obscured_pussy,0
zurezuredesigns,1
kael_(diablo_the_rex),4
fur_cape,0
ruth_failla_thomas_(the_dogsmith),4
olia,4
eiscue,5
io_(sepiruth),4
honezika427,1
youzora_samo18,1
quickietime,0
hehky,1
chikachi,4
mel_lancia,4
julius_(hugetime),4
oscar_(snivybatt),4
chymlaxi,5
saliva_puddle,0
monotone_towel,0
looking_at_mouth,0
annie_hill,4
kasusei,1
chizu_(rinnyrawr),4
chevrolet_camaro,3
inside_throat,0
fourarms,4
c-section_scar,0
jay_(fizzyjay),4
spiffydragon,1
kawa-v,1
alyss,4
big_shoes,0
chosen_undead,4
hilda_(neurodyne),4
bimm,4
solatok_shadowscale,4
tediousramen,1
kii_catano,4
options,0
blackfrost,1
officer_delgato,4
zen_(character),4
adam's_apple,0
graedius_(linoone),4
lihzahrd,5
doublade,5
ban,1
sylvia_sobaka,4
steelwings,4
mike_the_charmander,4
shishi_dog,5
ichigo_kurosaki,4
kinkajou,5
keine_kamishirasawa,4
the_barrens,0
lupanarium,3
yashendwirh,1
past_meets_present,0
blinded,0
pink_slime,0
roy_(fire_emblem),4
artrockt,1
clara_bacou,1
chiffon,4
quanno3,1
lalee,4
apple_juice,0
gio_madfang,4
herm_on_anthro,0
infected_heart,1
mario_golf,3
glowhorn,1
dubsthefox,4
boxy_briar_(lil-potion-shop),4
cyancoyote,1
14:15,7
keyshop_miss,1
puppet_(fnaf),4
khonorik,5
rating_scale,0
pwslz,1
maurick,4
ruby-milk,1
titandraugen,1
ovipositor_penetration,0
inazuma_eleven_(series),3
jeremy_(topazknight),4
st3ph3n,1
strobe-totem_(artist),1
back_mane,0
sitting_on_car,0
holding_key,0
deck_(structure),0
fhtng_the_unspeakable,4
portal_mask,0
under_bed,0
lunar_new_year,3
mafty_(artist),1
pentagram_necklace,0
dervid_(taktian),4
sulfur_snail_(artist),1
loggerhead_sea_turtle,5
borotamago,1
wide_dicephalous,0
lab_(disambiguation),-1
wired_phone,0
vargas_(fours),4
queen_vinyl_da.i'gyu-kazotetsu,4
digit_(chalo),4
fish_(thecatnamedfish),4
spiked_tailband,0
467adv,1
ulvinne,4
shackles_only,0
hellcat120,1
vortiganshine,4
luna_(lunaris_parukia),4
amazon_(company),3
gekishiro,1
annoying,0
kukurikoko,1
mebh_og_mactire,4
touching_panties,0
jax_(mels_monster),4
smol_boss_(penlink),4
shared_reaction,0
hollyleaf_(warriors),4
4622j,1
gearfox98,1
caflec,4
unbakable,1
tuli_(metrohunter),4
strip_game,0
zinny_(duase),4
windows_(saku1saya),4
mew_tuely_(fan_character),4
sheera_castellar,4
toasty_toast,4
mogy64,1
petunia_(gabbah),4
kawara_gawara,1
two_tone_pawpads,0
momma_jerbear,4
chiyosuke_masin,1
ela_(angryelanoises),4
island_kahuna_olivia,4
shikapandakuma,1
kascz,1
kelda,4
jeanne_droue,1
dolly_(nana_gel),4
evelyn_(sff),4
jericko_(germanshepherd69),4
ennfet,1
youmadsin,1
goober_(cobat),4
splishsplat,1
comparison_chart,0
humanoid_to_inanimate,0
acechan_f,1
blood_on_tongue,0
kings-gz,1
jiggling_butt,0
bitfang,1
crocodilchik,1
anon_1515,1
stormer_swiftpaws_(character),4
standing_on_object,0
chat_message,0
amelia_(sandwich-anomaly),4
sammy_(ssammyg),4
benji_(benjipaws),4
monsterverse,3
metal_mask,0
cadblank,1
shreddy_(shreddyfox),4
sauwastika,0
exv508,1
john002021,1
alister_(sangreroja),4
dumpling_(tricktrashing),4
douglas_(guchiyama),4
troy_(rudragon),4
awa_awa,1
andr0ch,1
pink_tail_tuft,0
ardy,4
eva_(reygunpocket),4
submissive_focus,0
lunchbox,0
fenrir's_revenge,1
holding_menu,0
zig_and_sharko,3
nirvanilla,1
shadowwolfhunter,1
cynicallysly,1
calzone_(artist),1
bleeding-heart_monkey,5
lupi_(playkids),4
huge_obliques,0
carrying_character,0
living_pool_toy,0
cathood,0
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natasha_(gasaraki2007),4
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thatguynamedjoe,1
penis_on_pussy,0
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eris_(cat_planet_cuties),4
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psy,4
robert_hill,1
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rick_(rukifox),4
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cyndiquill200,1
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e!,1
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aimkid_(copyright),3
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zack_(fuze),4
vergo_(darkfox49),4
michael_tanuyama,4
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jackie's_story,3
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minior,5
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luxordtimet,3
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duffy,4
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vic_(delirost),4
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cho'gath_(lol),4
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toxicempress3,1
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cynfall,4
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pacelic,1
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fluke_the_husky,4
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pewt_(synpentane),4
paul_(majin764),4
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pen_(pd),4
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virus_(kittikary),5
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leonel_(danellz),4
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reptil,5
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7624cq,1
peephole,0
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tkdchamp02,1
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ocerydia_zoona,4
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dualshock_2,3
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half-life_2,3
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jay_fortissimo_(jforte91),4
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abby_(jwinkz),4
dez-fm,1
15:16,7
anon_fox,4
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jhoneilerld,1
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kyoumoto_takayuki,1
dance2forget,1
melchior_leonall,4
lovelitchi,4
art_deco,0
veska,4
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vicuña,5
talia_(tasticstarlight),4
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asriren,1
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stcaipirinha,1
psydoux,1
dirty-canvas,1
triplecorne,1
lina_(prsmrti),4
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keaton_(animal_crossing),4
mimicry_(fluffobuggo),4
black_pseudo_hair,0
thrumbo,5
aukives,1
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sweating_towel_guy,3
mo23,1
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painted_penis,0
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tube_dress,0
glowing_hooves,0
unsafescapewolf,1
kelly_(ruribec),4
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vivi_(inline),4
sush1d0g3,1
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gelato24,1
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kathu_thal,4
cashmerekangaroo,1
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sasha_(digitalpelican),4
red_dead_(series),3
karen_(redacted),4
azenzeph,1
kashe,1
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shinto,0
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digbox,1
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neko_works,3
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agony,0
morph,5
veyll_(centum),4
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caine_(cainesart),4
maya,4
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yamcha_death_pose,3
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ryouta_sumeragi,1
josilver,1
grymmbadger,1
champagne_bottle,0
negigi,1
apoptosis,1
kotorahime_neneko,4
anidra,1
cookiekangaroo,1
akudrache,1
police_lineup,0
tanabata,3
fluffy_(artist),1
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barghest,5
latex_pants,0
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wispowillo,1
cookie_monster,4
fabinella,4
toxictigerex,1
the_oblongs,3
pinguinolog,1
dirk,4
toragami_gaou,4
tundra_dragon,5
wylderottie,4
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kernelewdies,1
xurkitree,5
cellina,4
lol_religion,0
namelessimp,1
kopa,4
ball_hug,0
rampie,1
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internet_explorer,3
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toilet_man,1
pig_(angry_birds),4
curtain_doggo_(photonoko),4
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roxadelic,4
magic_sword,0
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groenveld,1
gaymingwolfy,4
starbearie,1
chowie_tanuki,4
altagrin,1
standby,1
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zephyr_(a12),4
theyeager,1
bengal_tiger_(kemono_friends),4
rhys_wysios,4
virusotaku,1
ferolepo,1
nivawa_and_saitou,3
toroitimu,1
diamond_hakamo-o,4
valiance22,1
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laure_(thekite),4
tristen,4
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rain_(rain420),4
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aevyn,4
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c.j._(cjtheotter),4
addy_otter,4
filing_cabinet,0
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sei_kuroneko_kishidan,3
skunny,5
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shu_20625,1
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oumagadoki_zoo,3
mega_pidgeot,5
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furrycandyshop,1
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inkersod,1
marquis_the_eevee,4
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anna_(feuerfrei),4
mimizu,1
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kate_(morpheuskibbe),4
hextaur,5
yad,1
noble,0
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eridan_ampora,4
oliver_cotter,4
violet_(zootopia),4
emlan,1
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zephyrus_orgy,1
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ana_(overwatch),4
me_(master_elrest),4
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kururu,4
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chocolate_bunny,0
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colin-tox,4
lord_kass,4
spas-12,0
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stripes-the-raccoon,1
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bud_compson,4
sapphire_(disambiguation),-1
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nausicaä_of_the_valley_of_the_wind,3
the_valet_and_the_vixen,3
nanasun,1
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sion_allona,4
mikey_(scataraxia),4
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swomswom,1
laser_wisp,5
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ryouta_(leobo),4
bec_(lobokosmico),4
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vega_(stargazer),4
xer'sai,5
wenqu0304,1
hansel_(101_dalmatians),4
tora-chan_(horokusa),4
scarlena4,1
canid_kaiba,1
damon_fallow,4
tan_head,0
partially_submerged_arms,0
wrapping_paper,0
hands_on_neck,0
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baby_mario,4
ash,4
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lizzyglizzy,1
azi,0
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gou_(lagoon_lounge),4
hugoriolu,1
bringeall,1
hael,1
ihoundr,1
ember_(chillypepperstash),4
omo_kemo,1
swimwear_only,0
stoven_(xdarkspace),4
lot_par,1
phoenix_vorster,4
spookybooty,1
owen_(mels_monster),4
pengy_(pengi_nsfw),4
shlap,0
steamedvegetables,1
shaidan_(amocin),4
korfiorano,1
suckmycoccyx,1
zowslli,1
zat_virgil,1
werenimal,0
kora_brandis,4
cleaning_cock,0
nigirizushi,0
restrained_feet,0
todding,1
andee_(rvb_revolution),4
dobie-ace,1
shun_gonfano,4
peanut_(peanutham),4
joosiart,1
cocoa,4
looking_confused,0
moni_(monidraws),4
deep_rock_galactic,3
itwasscatters,1
x-s_(lumen_fox),4
vulkalu,1
rib_lines,0
obese_taur,0
jaythefox333,1
dragonsica,1
random_doom,3
omega_(yitexity),4
mirrorreach,1
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biological_illustration,0
duality448,3
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cubcam,3
darkgrim2,4
kundofox,1
setup1337,1
tricycle,0
ziyana,4
jack_(david_hopkins),4
natsume_yuujinchou,3
kasia88,1
live_on_cardliver_kakeru,3
kure,1
smog,0
shibeari,1
mirry92,1
lynraccoon,0
wanpaku,4
belly_pinch,0
yuio_maid_dress,3
mortimer_todd,1
clipped_wings,0
cum_on_car,0
tiger_blueberry,1
elek-tronikz,1
japanda,1
beetlejuice,3
sophia,4
yumiki,4
blacklight_(character),4
life_is_strange,3
roo_(valtik),4
jugend,1
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pelvis,0
meera,4
xenium,1
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javik,4
angello_cauich,1
bollard,0
mozu,1
artica_lagopus,4
brushie_brushie_brushie,3
mega_latias,5
mint_(bittenhard),4
pinkie_pie_out_of_fucking_nowhere,0
shiretsuna_(character),4
ursa_minor_(mlp),4
lilyness,1
krona,1
rift_(wolfywetfurr),4
amara,4
totem.v,1
penelope_(hamtaro),4
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andreia-chan,1
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princess_tempora,4
tanuki_costume,0
kida_howlette_(character),4
wes_(dominus),4
nookprint,1
looking_at_book,0
mewichie,1
monotone_pillow,0
bird_(petruz),4
hisuian_braviary,5
niku_(gym_pals),4
pizzacolt_(zhanbow),4
succubusbnny,1
captain_style_cinderace,4
carl_(misterstallion),4
william_(tinypigdraws),4
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maseru,1
arjinmoon,1
fenris_(zephyxus),4
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holding_flashlight,0
nobushito,1
wendover_(halcyon),4
panties_around_thighs,0
nemo_nutkin_(character),4
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disco_bear_(htf),4
gulk_(sound_effect),0
stephen_inesty,4
may_(kojondian),4
cassandra_(momiji),4
jelly_bean,0
officer_munroe,4
scientific_name,0
danganronpa_v3,3
setonanaki13,1
abby_(polyle),4
ero_jasper_(artist),1
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leydon,4
belle_the_tinkerer,4
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pixie_(brushfire),4
oopsynsfw,1
olexa_vrane,4
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teal_underwear,0
skwovet,5
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uglyshadow,1
fringe_trim,0
fluitaire,4
renaissanceofchaos,1
nixx,1
seiren_(artist),1
kitsuneymg,1
jackheretherealone,1
foot_insertion,0
eddie_brock,4
wedding_night,0
toots_(character),4
bruce_(animal_crossing),4
gawein-dragon,1
stoney-pony,1
xplaysx,1
darkestmirrored,1
open_toe_shoes,0
monkeysuit,1
minute,4
shin_maple_town_monogatari,3
death_grips,3
bandit_hermit_(gunfire_reborn),4
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tulip_(hyeing02),4
kejzfox,1
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peachiekeenie,1
cidius,1
mewscaper,1
amethese,4
zekra_(ramul),4
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derpybelle,4
pelagice_(insomniacovrlrd),5
anu,4
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bess_project,3
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this_is_fine,3
spectrobes,3
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yellow_helmet,0
sara_(nekuzx),4
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kangy_(tegerio),4
virtue's_last_reward,3
bundroid,5
wereshiba,1
holding_skull,0
kwskkaruta,1
yellow_pseudo_hair,0
cheri_(lord_magicpants),4
foxialewd,1
lilly_(inkit89),4
white_choker,0
roast_chicken_leg,0
reclining_pose,0
tylerayrton,1
smush,0
sparklyblanket,1
doggydog,1
flummery,4
catrina_(mlp),4
twin_brothers,0
fjnm000,1
kumahachi0925,1
luna_(buizanine),4
sumiwohakuneko,1
tongue_in_throat,0
harry_campbell,4
sans_(sanssouci),4
danganronpa_2,3
vixieblues,1
kisaragi_(azur_lane),4
james_flynn,4
puppycorn,4
apex_(artist),1
party_time,0
matz,4
matelk,1
minecart,0
pinki-husky,4
trapmagius,1
felicario,4
countershade_anus,0
jesse_(ciderward),4
mile_high_club,0
greg_universe,4
whitty_(fnf),4
jessica_young_melis,4
rayda_(how-about),4
koopagirl,4
super_famicom,3
traashmann,1
fuck_request,0
sensiive,1
krimble,1
sair,5
denny_(dracojeff),4
yzyhet,1
holding_feather_duster,0
harufeng,1
gemstones,0
colson,4
d.j._(101_dalmatians),4
bunny_maloney,3
bogrim,1
kidde_jukes,1
sevenserenity,1
semajsttseb2,1
madrigal_(aquest),4
yaco_puma,4
azuli,4
small_anus,0
purple_slime,0
malgam_(fidchellvore),4
amber_(scooby-doo),4
torn_bra,0
tea_(teabunni),4
floral_dress,0
skirt_around_one_leg,0
chastity_key,0
soot,0
gardemom,4
oral_pretzel,0
amber_wing,4
badger_humanoid,5
kyle_kendricks_(forestdale),4
s-q,1
venusfalls,1
comic_(under(her)tail),4
mobian_fox,5
red_sneakers,0
randoonest,1
lady_wolf_(arbuzbudesh),4
aranea_serket,4
farran_height_(artist),1
miw_(darkdoomer),4
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darkened_foreskin,0
pepper0,1
kaisgaru,5
richter,4
ionai,1
tempura_puppy,1
jelli_(jellithepanda),4
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monotone_eyelids,0
vito,1
fainting_goat,5
carne_asada,4
yabby,1
geronimo_stilton,4
venvatio,1
kojote,4
kix,4
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pashmina,4
trix_the_cat,1
diesel_(komatose),4
ammon,4
maximillianveers,1
stick_(satel),4
amaya_(bcb),4
fancy-fancy_(top_cat),4
1960s,0
mimi-fox,1
wittle_digi,4
hadalmartian,1
jaws_(film),3
baby_creeper,4
landreu,4
king_(kingbeast),4
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nilania,4
zelen,1
stasis,0
vojt,4
synad,1
mettaton_neo,4
satin,0
zuphyx,1
melory,4
jaz_(nanimoose),4
salrith,4
lindwurm,5
chamber,0
bodalack,1
mr-dna,1
rexlabs,3
roora,4
gluttony,0
big-red_(character),4
1990,7
sleeping_cap,0
seath_the_scaleless,4
lenika,1
novaberry,1
69_breast_suck,0
chikiko,1
shadowzero20,1
momo_(sy_noon),4
void_lizard,1
rey_(star_wars),4
duality_(character),4
chisai,4
fake_mustache,0
scootiebloom,1
aku_tojyo,1
ganbanman,1
obstructed_eyes,0
classic_knuckles,4
bunny_(averyshadydolphin),4
lesdias,1
xsissa,1
seturou1010,1
sierra_(ashcozy),4
dripping_milk,0
altair_(patto),4
workbench,0
pixie_and_brutus,3
laco,4
dognextdoor,1
creed,4
afika,5
randagio_(fairy_musketeers),4
spiked_shoulderpads,0
rainbow_bracelet,0
vertical,0
sheecktor,1
venisin,1
kiyo_mae,4
tawa_1908,1
drippy_dragon,4
chocolate_egg,0
fishnet_bodysuit,0
gontadanuki,1
special_week_(pretty_derby),4
aipiepo,1
leaf_crown,0
beaver_and_steve,3
innocent_expression,0
cinny_the_incineroar,4
wheat_in_mouth,0
chessie_(shycyborg),4
tentacle_growth,0
iavan,1
tiburia,4
squeakie,1
touching_own_thighs,0
driiadi,1
charlieleobo,1
forestwalker,4
shoppaaaa,1
baguette_(artist),1
lobs,1
temrin_sanjem,4
ransom,4
fox-die,1
demidevimon,5
step,0
tami_(poonani),4
quickie,0
bombette,4
armiger_(bxrnstellar),4
beast_sergal,5
purple_eyeliner,0
egyptian_god,5
spelunker_sal_(character),4
bull_sharkowski,4
combine,5
rutting,0
noel,4
bitty,4
minime_jones,4
luketh,4
taiikodon,1
buzz_lightyear_of_star_command,3
balance,0
torture_rack,0
piston,0
phantasy_star,3
ponyhidden,4
miss_moosie,1
hasani,4
kane_ridgescale,4
gothita,5
milo_(shortwings),4
test_pattern,1
jayne_doe,4
bill_(dreamkeepers),4
muscleartguy,1
ginger_snap_(mlp),4
munna,5
exposed_crotch,0
caim_(evilfawx),4
neroj,4
nikitak,5
bloominrose,1
plini,4
notveryathletic,1
egg_transfer,0
m-p-l,1
moffle_(species),5
princess_terria,4
alisa-walker,1
zankytime,1
lightz_raela,4
fr,1
amanda_(mastergodai),4
victoriano_the_chief,1
david_(anakuro),4
madeleine_(caninelove),4
in_abyss,1
hoshizora_miyuki,4
x-zudomon,1
maneframe,1
destiny_(character),4
xpfoddball,1
teleport,0
racthetmechanic,1
macbook,3
kivuru,1
hooki,1
instant_transformation,0
sleepywozzy,1
princess_romy,4
pointed_nose,0
looking_down_at_penis,0
td_(artist),1
chip_(animal_crossing),4
the_creacher,1
bone_collar_tag,0
svevato,1
transgender_symbol,0
multicolored_arm_warmers,0
cryska_wintergaze,4
flower_bow,0
crystal_sprinkles,4
lewdshark,1
clark_(smoothlabs),4
pearl_(al_gx),4
anatake,1
kapri_(kapri),4
foxibiki,1
nekomellow,1
wolftapioca,1
cat_suit_(mario),0
spy_x_family,3
curtis_(awarebear),4
drewby_(drewby),4
jseph,1
sat_v12,1
teot,1
cupped_hands,0
komokuu,4
tiago_(rio),4
orange_ringtail_thief,4
kubota_keita,1
planescape_(franchise),3
not_pulling_out,0
knotalot,1
honey_dipper,0
chloe,4
chikurako_kanoeki,4
shira_snep,4
junkrat_(overwatch),4
marco,4
star_(balto),4
sokolas,1
deannart,1
red-eyed_crocodile_skink,5
esartist,1
dominik_(lipton),4
rosemary_wells,3
violet_(thea_sisters),4
flakes,1
night_vision,0
zaya_(ajdurai),4
jidane,1
flockdraw,3
wisp_(dreamkeepers),4
steamy_feet,0
luna_(dota),4
sambers,1
amber_(armello),4
cupid_(reindeer),4
glazed_(artist),1
shaneinvasion,1
sugahbite,1
alarmed,0
dream--chan,1
suplolnope,1
minoru_mineta,4
popi,4
minish_cap,3
clockwork,0
shockwave_(transformer),4
ignis_rana,1
dashiathebunny,1
heads_together,0
steelhead,5
lenore_(lamezone),4
chatot_(eotds),4
inspector_gadget,4
joey_(alfa995),4
dinner_table,0
tsathoggua_(cthulhu),4
masked_singer,3
sirevisconde,1
black_diaper,0
gloved_hands,0
pulling_down_shorts,0
sin-chan,1
trial_captain_acerola,4
heather_moore,4
daz1319,1
akimi_(merunyaa),4
lemonpuffs,4
veronica_(natani),4
monroe_(winterspell),4
haiku_(haikufox),4
dashiesparkle,1
nardo,4
arq_(ramul),4
foxy_(nekojita),4
birch,0
profannytea,1
brown_ear_tips,0
pink_harness,0
zerika_(artist),1
scale_knight_(medicinalsucc),4
shirokyure_(watte),4
jenevive_(chimangetsu),4
kneeling_over_dildo,0
belly_punching,0
reverse_mouth_shot,0
ahcocqui,1
grabbing_own_breast,0
occhan,4
eljelicsnsfw,1
oswin_(password),4
black_text_box,0
immoveableguide,1
strapped_in_vibrator,0
caelan_(far_beyond_the_world),4
mike_(sing),4
maple_(cobat),4
magma_wolf,4
foot_on_belly,0
apollo_(dirtypaws),4
jessica,4
wonderbunny,4
kitchen_cabinet,0
araizumi_funaka,4
sugekuma,1
wg_suru,1
eve_(wolfpack67),4
fur_cloak,0
iorite_(doesnotexist),4
robin_(twoscale),4
keira_(jak_and_daxter),4
rain_(vanillayote),4
kenta_(afuckingcamel),4
lisa_(tiger),4
tentacle_eye,0
monica_raybrandt,4
hunter_(destiny_2),0
9x8bit,1
thebluebear27,1
drunkarcher,1
kusanali_(genshin_impact),4
steepled_fingers,0
cryptocrisis,1
kyoko_komurasaki,4
kisaragi_syoo,1
flame_bikini,0
ravenpen15,1
vitamin_unknown,1
avy_(syvaron),4
wolfie,4
gantan,1
blueblaze95,1
yellow_goggles,0
meowolf222,1
white-kano,1
azure_rathalos,5
collar_grab,0
hard_love,4
liona_mcgraw,4
potoroo,5
blinky_(pac-man),4
ranma_saotome,4
mark_haynes,1
dynx,5
candy_(artist),1
broly,4
swordfight,0
cindy_(fnac),4
involved_expression,0
spunky_(spunky_mutt),4
tower_of_god,3
bastiodon,5
aquariusfox,1
lobo,4
razul,4
unhinged_jaw,0
plum_(artist),1
hercules_(disney),4
jon_jafari,4
pplpvtr,1
kate_summers,4
mutual_knotting,0
tsukasa,4
geno_(geckonori),4
blue_mood,0
shared_gag,0
sonnano_ari,1
socket,0
flash_animation,0
qurupeco,5
orange_vest,0
kuglu,1
tao_2carat,1
twin_tail_nojaloli_fox,4
tail_around_pole,0
pattern_apron,0
backstory,0
hypno_goggles,0
pouch_piercing,0
kyle_walden,4
bromeister2,1
larger_sleeping,0
kompakt,1
cake_slice,0
lupinossai,5
servius_(vestina),4
russell_(adondis),4
moduckten,1
short_eyebrows,0
undressing_between_scenes,0
dante_(dreiker),4
empress_(ahit),4
shepherd_(torata3220),4
clara_(bussaca),4
mike,4
jett_(draco),4
sonic_frontiers,3
skull_print,0
komena_akamori,4
shabi96522,1
ivalyth,4
zen_allen,1
lottie_cummins_(thechavicgerman),4
underwear_wetting,0
magia_(kyuuri),4
sketchy_knight,1
achievement_unlocked,0
echo_flower,0
mehawk,1
heineken,3
bowletta,4
engraving,0
halley,4
egging_(artist),1
redbeanviolin,1
12_breasts,0
rotton,1
verdilacs,1
zoids,3
mab,1
maniani0122,1
essentialryu,1
tongue_sex,0
ponzu_tas,1
logan,4
blue_neckwear,0
spider-ham_(character),4
dorak_(artist),1
sekhmet_(egyptianexo),4
tanna_nokka,4
mrs._mayberry_(vivzmind),4
rin_(kittyrin),4
wrastor,4
kerestan,1
ear_over_eye,0
discarded_footwear,0
azzydrawsstuff,1
belt_unbuckled,0
amp,0
black_mustache,0
snowberry,1
vixycore,1
grizzlygus,3
hands_on_tail,0
distortion,0
alfred_j._kwak_(series),3
garr,4
vaginal_blood,0
art_progression,0
joy_ravenhurst,4
ferric_(chirmaya),4
sheryaugust,1
onehundredandone,1
wood_container,0
collaborative_hand_heart,0
cafet'zin,4
orenjipiiru,1
joelbearb,1
minttu_(loimu),4
suinmsg,3
un0sk,1
erdno_(commissar_prick),4
aquasixio,1
stuck_insertion,0
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teabro,1
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marisa_the_vaporeon,4
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legendary_titans,5
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yaquis_de_obregon,3
shenzel_(shenzel),4
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rio_(botter_dork),4
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clementine_(bearlylegal),4
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red_(jay_naylor),4
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sasha_(jay_wolfe),4
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sofu_(pyritie),4
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kine_(absol),4
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cbl_art,1
tamlin123,1
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kyoob_(thegentlebro),4
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nearu_nikos,4
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sade_(firestorm3),4
rune4,1
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ray_the_buck,4
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ailu_h0,1
pol6473,1
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nick_(kinaj),4
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ashleyloob,1
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rai,4
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neesah,4
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eel_humanoid,5
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pokémon_uranium,3
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ooo_(^_^),1
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coffuu,1
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1980,7
miles_(mascot),4
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phreigya_pryntounia,4
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ceobe_(arknights),4
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baji_(morobox),4
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ico_(fatzoimp),4
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mortalis_the_grim,4
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lily,4
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spiral_(thespiralaim),4
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elmer_fudd,4
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keel_(keeltheequine),4
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edis_(edis_krad),4
max_(the_secret_life_of_pets),4
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an-jun,4
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alexios_(adastra),4
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aureldrawsstuff,1
kita_(saku1saya),4
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red_magma_(oc),4
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petting_pov,0
camshow,0
mosquito_humanoid,5
kaliancia,4
blanca_(animal_crossing),4
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tour_(jewelpet),4
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may_(dewwydarts),4
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thehuskyk9,4
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alan_(alanisawolf777),4
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azerta56,1
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rousso,4
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1107263,1
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aeiou_(yoako),4
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gomadare_papi,1
cimche,1
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ave,4
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the_tale_of_jasper_gold,3
camille846,4
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19:21,7
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artist-chan,1
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kaixo,4
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coke,0
stitches_(animal_crossing),4
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massak_(fluff-kevlar),4
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metal_tongue,0
vix_(quin-nsfw),4
ampersand_ad,1
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rango,4
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kacey_(kacey),4
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two_tone_pupils,0
ursk,1
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donkey_kong_country_tropical_freeze,3
takkju,4
glistening_fingers,0
matt_(silverfox442),4
rey_(furlana),4
tentacles_in_ass,0
anise_(wonderslug),4
tony_tony_chopper_(walk_point_form),4
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light_eyelids,0
holding_trident,0
jordan,4
sharko,4
tiger_lily_cookie,4
penis_in_pseudo-penis,0
sweetwater,4
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arsenth_neodal,4
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trafalgar_law,4
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vgbutts,3
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raven_(psy101),4
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anythinggoes,1
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thorax,0
nitefang,1
370ml,1
wii_u_gamepad,0
tyruas,1
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in_the_new_age,3
siberian_cat,5
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falseknees,1
kaiuchiha15,1
infected,0
orion_(pheel),4
shado_(shadowolf17),4
quirkilicious,1
threadwing_antlion,5
fire_emblem_heroes,3
pirates_of_the_caribbean,3
moonshine_(artist),1
helena_soderberg,1
dare_(artist),1
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slenderdragon,4
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qarrezel,1
mudokon,5
pimples,0
squatlord,1
sahara_(movie),3
pog,3
college_football,3
mystic,4
dorrex,1
sammie_(spyingredfox),4
avatar_(disambiguation),-1
otherthatguy,1
roomi,4
billy_fore,1
scampi,4
tweety_bird,4
sasanoha6011,1
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cream_belly,0
dobie,4
great_izuchi,5
joxdkauss,1
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patecko,1
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sandnite,4
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helga_(cosmiclife),4
socks_million,4
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serena_(lemondude),4
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marvellous_spatuletail,5
project_diva,3
goallonely,1
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annabee_(woebeeme),4
bun_jay_(synpentane),5
lucy_fischer,4
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t.d._kennelly,4
friedrich_(poppy_opossum),4
misery,0
sparkly_(sparklyblanket),4
xinnainai,1
roachtemi,4
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scaty,1
imp-tan_(hdoom),4
kiku_(sango-kaku),4
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strabimon,5
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shyning_fingah,1
veiny_nipples,0
haxxyramdhan,1
odalia_blight,4
thyro,1
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sambun_(tim),4
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ryley_(arbiter_000),4
esrb,3
mando,1
oh_these?,3
luke_delfino_(character),4
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hoof_piercing,0
dead_stray_bear,1
yato_(yatofox),4
haruaneki,1
katkichi,1
pink_tuft,0
kobaltoco,1
sorcerland,3
finimun,1
pride_color_necklace,0
profdatdrawin1556,1
moesouna_gomi,1
bow_garter,0
izanagi_creations,1
dynotaku_(character),4
rhylith,4
noir_(jennifer),4
furrlough,3
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starri,1
ekihnox,1
blue-the-unknown,1
zhiibe,1
sourspot,1
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kogiinu,1
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germaine,4
tuton,4
skycroc,1
askar,4
cheesepuffs,1
catfiddle,1
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colored_eyes,0
furii,1
midnite,1
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pixell,4
reaper_(disambiguation),-1
thecynicalhound,1
sebastian_king,4
helloanonmyoldfriend,1
wynterhorn,1
matthew_(articwuff),4
shinju_(bittenhard),4
two_tone_scarf,0
tarou_(taroumyaki),4
mirai_(hyperion),4
henry_vargas,1
bunny_(courage_the_cowardly_dog),4
grum_(grumbbuck),4
shima_laqi,1
fufucatu,1
erin_tannis,4
maple_(cyancapsule),4
niwani_watori,1
allysia_(killy),4
who_(species),5
yùmǐ,4
toothed_whale_humanoid,5
saltyserpent,1
maxine_dragon,4
kenjilokung,1
lumi_(felino),4
maleherm/gynomorph,0
levaligress,1
abraxocleaner,1
insperatus,4
elle,4
marcus_(marcus_of_the_cloud_forest),4
zelda_(fuel),4
neebaridge,1
bolt-s_(artist),1
stardragonhorse,1
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izzy_(oc),4
smiyao,1
avery_(itsmythos),4
jacklantern_11,1
hayati,4
zale_(purplebird),4
ig1119,1
gabz,4
pixyl_(pixylbyte),4
sideways,0
efe,1
swift_(tenebscuro),4
wave_(purplebird),4
dream_maker_watanabe,1
tongue_on_balls,0
catin-milk,1
atalanta_(fate),4
mr._chazokov,4
eerieeden,1
gus_(peculiart),4
flatwoods_monster,4
bloody_knife,0
plug_(jewelry),0
sherlock_(series),3
enzeru,4
ship-wreck,1
pathetic_(meme),3
anubis_rose_(character),4
commander_braithor,1
wintech3112,1
katrosh,1
>:<,0
zennyc,1
zukaro,4
green_tail_tip,0
curiouscat96,4
istaryl,4
leaves_on_ground,0
kosaa4,1
bluejr,1
hands_over_breasts,0
chruscik_(dudelinooo),4
scops_owl,5
striped_dildo,0
shibuya_2810,1
skorupi,5
oozing_cum,0
gfox404,1
ring_eyes,0
leg_out_of_water,0
stoat_(inscryption),4
mole_(disambiguation),-1
altelier_t,1
kyle_(kaikaikyro),4
speckled_fur,0
two_tone_apron,0
ear_on_shoulder,0
unusual_sex_toy,0
red_chair,0
jet_(briefgarde),4
iida_(skweekers),4
jaye_(arknights),4
mola_(zerofox),4
stellarhusky,4
living_statue,5
khaki_fur,0
yellow_outline,0
leg_accessory,0
kimako-desu,1
diver_(artist),1
02_(kirby),4
noxy_(noxy),4
gigachad,4
lynxrush,1
vivian_boo,4
aki_the_reindeer,4
unown_text,7
english_lop,5
legos_(legos09),4
zachseligson,1
skyebold,1
ryuu-neko,5
bearing_fangs,0
vayron,5
sammy_(9tales),4
pastelpastel,1
rabbit_(parasitedeath),4
broken_key,0
shupamikey,1
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dusknoir_(eotds),4
hetomy,1
javigameboy,1
gryx,1
buzz_brambles,4
indian_giant_squirrel,5
gnull,1
junketsu,4
denver_broncos,3
cum_tank,0
kuiskata_schwarzpfote,4
joly_(kitty_pride),4
heroesheaven,1
bheni,4
nasido,4
gujira,1
emerging,0
bunnylace,1
rydian,4
ryanshinyzard,4
primer_(artist),1
kaku_renbo,4
dreiko94,1
waffle_(megawaffle),4
nameplate,0
lene_nezumi,4
sixshades,1
templar,0
nahami,1
ev133,1
heraldry,0
coren_(desertwandererr),4
astralynx,1
amorousartist,1
sakana_maru_(sakana_maru),4
help!_my_dad_fucked_my_boyfriend,3
mighty_eagle,4
kyla_(nawka),4
zephy,4
spinal_plates,0
pointy_hair,0
justice_league,3
panteon013,1
one_tone_fur,0
hillmyna,1
nergal.bragi,1
dividedwreck,1
luna_(hunterx90),4
red_rug,0
00makumakuma00,1
west_highland_white_terrier,5
addison_(arh),4
reaching_towards_another,0
lupin,5
kalaztuz,4
katauni_(ghost_forger),4
sad_cat_dance,3
mohalic,1
penis_size_comparison,0
eggseru,1
monstrifex,1
yasaikakiage,1
hands_under_breasts,0
phoebe_(felino),4
dota_underlords,3
mituni_(artist),1
agent_york,4
m1911_pistol,0
fa_mulan_(disney),4
john_burroughs_(akiric),4
numbered_anal_beads,0
hellox2,1
red_boxer_briefs,0
performance_art,0
alekseyvvcx,1
open-butt_dress,0
t-boy,1
fhyrrain,3
samantha_snakes_(unknown6505),4
paizli,4
luke_skywalker,4
draca,4
vikavolt,5
i.v._(ivycomb),4
android_21,4
trashtail,1
zero_in_panel,0
polly_polaris,4
colorfulhoovednights,1
nav,4
masonparker,1
trigger_(wordcaster),4
valeera_sanguinar,4
runsammya,1
rifel,1
gleipnir,3
ramzi,1
silverfish_(artist),1
achievement,0
jax_bline,4
bub_(bubble_bobble),4
tyrse,4
baramx,1
wotan888,1
underneath,0
xration,1
sairine,1
clamperl,5
doggie_daddy,4
kaijusamurai,1
mary_moonshine,4
eyazen,4
tempest,4
lobodelamuerte_(character),4
luckywhispersjellyfish,1
morgenergy,1
songgoishtar,1
c-string,0
dynexia,1
spraying,0
arlen_tawny,4
anthro_to_taur,0
kazami_karasu,1
dawkz,1
alaitallon,1
shadarrius_(character),4
opaque_glasses,0
rick_(fuze),4
jeanette_(xelaaredn),4
santiago_(o_im_soniic),4
soay_sheep,5
pofuilly,1
wood_sign,0
devellix,1
kella,4
xazariel_(queen-of-sin),4
stealth_the_series,3
slugger_(lilo_and_stitch),4
hood_husky,4
marilyn_e,4
roo_boy,1
misby,4
meertak,1
dot_(mcnasty),4
yokappa,1
stimia_(rampage0118),4
otterboxed,1
luci_(disenchantment),4
apin59,1
ghostli,1
tartii_(character),4
dodgercr,4
city_skyline,0
leatificat,1
pulled_to_side,0
penelopisspiss,1
poking_penis,0
kirlan,4
alexa_(10ne-v),4
jagonda,1
brown_swimwear,0
hemuchang,1
scp-1471-a-37,4
furry_rome,3
pride_color_tail,0
jason_(greyknight),4
piercing_outline,0
flying_broomstick,0
sarquiat,1
fap_(sound_effect),0
speech_scroll,0
smaggthesmug,1
mikko,4
lazorchef,1
divine,0
england,0
myra_foxworthy,4
hawkthorn,4
super_mario_land,3
broken_bottle,0
narse_(character),4
shaza_(artist),1
poppy_mouse,4
sorako,1
jen_(convel),4
nimtai,1
kedamono_(popee_the_performer),4
yuuki_yuma,1
darylith,4
miranda_doug,4
thatpeon,1
ukulilia,1
kit_(powfooo),4
panty_wetting,0
kaga_(azur_lane),4
trapped_in_underwear,0
drakons,4
toren,4
angelthegamer,1
draquarzi,1
weebl's_stuff,3
segainfinity,1
construct,0
iradium_piros,4
frickabee,1
my_sweet_piano,4
ru_(final_fantasy_unlimited),4
garrek,4
c-jen,4
haku_(maneater),4
antifa,0
horridus,4
internal_sheath,0
coronamon,5
fred,4
mocha-wing,1
rebeccacablah,1
radiance_(hollow_knight),4
foxielove,1
she-ra:_princess_of_power,3
2b_(artist),1
winged_lemur,5
white_flag,0
unauthorized_edit,0
bra_straps,0
unknown_editor,1
bell_earrings,0
belly_hump,0
wendy_(wolfy-nail),4
talbuk,5
meesh_(character),4
kwikdog,4
pokewanko,1
skeksis,5
woodlouse,5
alien_hominid,3
harbor,0
foxy_harris,1
thor_(dildo),0
black_tears,0
claw_machine,0
dark_queen,4
scruff,0
francine_(karps),4
pcz,1
punipaws,1
yamiji,1
sand_dune,0
suggestive_eating,0
cindy_(nekocrispy),4
chimera_sui_generis,5
gavin_alvarez,4
horton_hears_a_who!_(film),3
greentapok,1
lynx_(fortnite),4
tibetan_sand_fox,5
counter-strike:_global_offensive,3
babayana,1
velocirection,1
cheese.crisp,1
omelette,0
ghetto_delphox,4
mona_(genshin_impact),4
krazyivan,1
kingdom_hearts_3,3
iagu_viron,4
tiban,5
micro_focus,0
blue_(bluethegryphon),4
toaster_aaa,1
jero,4
zulu_(sleepysheepy17),4
yinhu885,1
matija,4
blood_stains,0
naschethelion,1
nklove3104,1
devy_(devil-vox),4
print_crop_top,0
jinx_(dc),4
aykos,4
caveira,4
mulberrytarthorse,1
nipple_size_difference,0
owlletta,1
goldendoqqs,1
houdini_(lilo_and_stitch),4
beast_mode,0
sheryl_(artist),1
jyrki,4
bonnibel_(roommates),4
masa_(masamune),4
ppmp,1
corvus_humanoid,5
shining_(arknights),4
amocin_(amocin),4
dirty_anus,0
bone_tail,0
just-another-vore-artist,1
trixy_the_spiderfox_(character),4
alvedo_vhiich,1
applin,5
jacksleight,1
garamando,1
penis_over_shoulder,0
kadrion,1
rope_pull,0
grey_tentacles,0
abby_(cobalt_snow),4
carnecalary,1
en_1_9,1
circumjacentity,1
zombot,5
kinky-roo,0
wiggling_hips,0
hoshino_kedama,1
cyberblade,1
rexxar,4
vallie_(ivy_trellis),4
meower_(skweekers),4
detached_arm,0
excelsus,4
face_punch,0
simple_anus,0
untied_panties,0
vanilla_(coffekitten),4
bloudin_(whatisdamatter),4
greying_hair,0
oddbuffet,1
milkysodaa,1
gao_(kusosensei),4
puyo_puyo_tetris,3
shin_mare,1
mindy_blaese,4
monotone_pseudo_hair,0
nomai,5
rambler_(hhc),4
louvely,1
lightening,0
foam_finger,0
frock,0
butterfly_knife,0
blue_toad,4
beth_callaway,4
exhaustion,0
orange_mouth,0
helena_sif_(elfox),4
gagmanzx,1
leading,0
eric_(silentiron),4
gremlins,3
mint_(deessel),4
spiritpaw,4
honey_hunter,4
ripped_tights,0
recorder_(musical_instrument),0
two_tone_eyewear,0
caramel_(pawfeather),4
pixiv_id_1806579,1
peer_pressure_insemination,0
flash_effect,0
chicken_costume,0
electric_plug,0
plastic_container,0
houkaisp,1
ichigodawashi,1
dunkin_kk,4
stann_co,1
peppa_pig,3
demenmenabung,1
giras,5
bent_over_surface,0
smonia,4
adelena_(thatonevocals),4
rollerlane,1
orange_tail_tip,0
carmine_(foxovh),4
apofiss,1
partial_69_position,0
hoka,1
subscribestar_link,0
clip_(lilo_and_stitch),4
dividing_ovum,0
zach_(dj50),4
masanori,4
game_boy_logo,0
broderick_longshanks,4
stirrup_leggings,0
bruhboi69,1
purple_bodysuit,0
nio_(patto),4
canndy_(neceet),4
peekeroro,1
neginoheya,1
kitsune_nyx,1
frist_(character),4
ktn_kuta,1
evian,1
abe_(oddworld),4
aaron_(bino668),4
boof,4
h-picaso,1
dome,0
euchre,4
ace_the_bat-hound,4
hotaru-mia,1
rika_ibori,4
bulletsoup,1
attract,0
lagomorph_pussy,0
laquadia_(legend_of_queen_opala),4
riche,4
liomynx,1
vaping,0
willow_tree,0
zokoira,1
emiridian,1
bonnie_(pokémon),4
world_war,0
minerva_(spinnerette),4
spengbab,4
haison,1
oddciders,1
omericka,4
robin_(rml),4
lucian,4
stinger_(bzeh),4
babrukus,4
croc:_legend_of_the_gobbos,3
year_of_the_monkey,3
furaiya,4
autumn_sawsbuck,5
soldier_76_(overwatch),4
viagra,3
passing_out,0
inubito,1
chigico_u,1
kaito_oji_churippa!,3
rose_(scalesindark),4
canopy,0
akrennian,5
tekno_the_canary,4
poppy_(mindnomad),4
glee,0
heroes_of_might_and_magic_iii,3
viktor_vasko,4
koopatrol,5
public_indecency,0
ghstkatt,1
rita_malone,4
allymoodyneko,1
dangaroo,4
isabella_mendez,4
chelsie_(kawa),4
kuiskata_svahrem,4
kartana,5
scp-999,4
blitzwolfer,4
thumb_in_pocket,0
dark_chest,0
cerinian,5
vera_noyce,4
leaning_on_railing,0
projekt_red_(arknights),4
kurtcbrox,1
heart_keyhole_clothing,0
otusian,5
wizaria,1
gopnik,0
diablo_(character),4
blackgatomon_uver.,5
dysart,1
hebokun,1
antelope_humanoid,5
assassyart,1
hands_free_penetration,0
xbi,1
fliwirrel_(insomniacovrlrd),5
keyring,0
after_oral_penetration,0
kale_(critterclaws),4
lee_the_meowth_(asderzx),4
marching_band_uniform,0
fleurfurr,1
belly_fluff,0
shiza_(deckerws),4
jelly_(jacobthebobcat),4
anno_dorna,4
visorelle,1
bunny_(delta.dynamics),4
lip_gloss,0
prywinko,1
hythrojin,1
sonar_caspersky,4
domoipetti,1
kiartia,1
skylar_(incorgnito),4
chibi_panda_(buddyfight),4
vasmeth,4
kayla_(rysonanthrodog),4
unrealfox,1
boiling,0
cum_on_socks,0
quantum_shift,4
service_dog,0
ash_(rksparkster),4
turz,4
hoosier,4
bestiality_girlfriend_catalog,3
bastianmage,1
dragoon-rekka,1
acala,4
performer,0
hidden_camera,0
aestheticmachine,1
acidrenamon,4
blacksnake,1
hopper_(tnt),4
foxteru,1
boman100,1
tess_(wolfyne),4
tangle,4
irae,1
cheezypb,1
karma,4
lilith_(tboi),4
talking_tom,4
killian,4
interstellarmachine,1
sereos_(character),4
ahriman_harken,4
daskingu,1
cobra_mcjingleballs,1
on_balloon,0
xanthe,4
mcpippypants,1
kenikenikeke,1
blue-ringed_octopus,5
furemani,1
glowing_background,0
speeddrop,1
stangun,1
sweet_potato,0
beretta_92,0
liz_(mykegreywolf),4
riedel_scallion,4
brisky~,1
quarter_horse,5
kirby_superstar,3
saliva_on_chest,0
cynnamin_(cynnamin),4
heelpop,0
duke_rocheister,4
dreamsthefox,1
martin_(dosent),4
gravity,0
nirik,5
booby_trap,0
vogelrove,1
chatai,1
banjo_(kihu),4
tamberlane_(comic),3
decaf,1
floppy,0
sorrel-wolf,1
tina_(jaeh),4
kiki-uma,1
under_sheets,0
yaise,1
zepht7,1
fresnel_(buyobutt),4
yosshi,1
heidi_geathers,4
timmy_turner,4
mitsuko,4
precum_stain,0
kuroba,4
mike_schmidt_(fnaf),4
mattiyyah_lion,1
smartwatch,0
teasing_with_tail,0
jake_(jakewolf00),4
nightguard,0
omni-tool,0
skxx_elliot,1
breath_fog,0
spiked_helmet,0
kabula_(artist),1
lilith,4
lopearedquentin,1
cat_dragon,5
chara_(coalava),4
shieltar,1
single_strap_swimsuit,0
forehead,0
nikki_duma,4
kizu_the_wolf,4
bella,4
colanukl,1
server_dingo,4
outline_star,0
pouncer_(httyd),4
silver_ring,0
gold_spikes,0
nonbinary_bunny,1
bran_(beez),4
pussy_stacking,0
cora_(cora),4
syakomon,5
heart_swimwear,0
single_strap_swimwear,0
zilvanv,1
multicolored_cape,0
mystgoose,5
radishflavor,1
wanda_(fop),4
cum_funnel,0
ted_(mapdark),4
dynafurryart,1
chomper_(pvz),5
holding_magazine,0
opa_wulfen,4
aura_(lazysnout),4
corrine_(naughty_skeleton),4
milkdeliveryguy,1
medieval_fantasy,0
monster_sora_(character),4
perrserker,5
lonnie_(she-ra),4
ed_(eene),4
hissoriing,1
zed_(beef_supreme),4
sparks_99,1
stone_carving,0
orange_chest,0
oiruse,1
killergrizzly,1
foxlove,4
princess_alicia_acorn,4
female_rimming_gynomorph,0
artybear,1
cilia_whitewater,4
usb_cable,0
rezzybat,4
gav_(ruddrbtt),4
lucidlemon_(artist),1
leynadtsio,1
payphone,0
chestnut_hair,0
raytig12,1
black_eyepatch,0
veris,4
alaynakgray,1
ash_(st4rsbucks),4
scrat_(ice_age),4
jason_(joushi),4
ducktales_(1987),3
note_raygun,4
darksly-z,1
nico_(sensopi),4
chompers_(fuze),4
gular_sac,0
orange_spikes,0
zyria_the_dragon,4
arctic_ikume,4
fancy_pigeon,5
trish_(hocolatemilk),4
penis_through_neckhole,0
marsupial_pussy,0
zabrak,5
yellow_blush,0
vivian_plums,4
kali_(ashnurazg),4
kitsuna_tyakoto,4
trinity_night,1
bjorn_(sturattyfur),4
shikaruneko,4
filburt_shellbach,4
horton_hears_a_who!,3
chocolate_chip_cookie,0
doctor_fauna_(mlp),4
kotalis,1
samudra_aquarium,3
delightful_shiny_pie,1
docks,0
mootcookie_(artist),1
playful_face,0
innocent_smile,0
nick_bondra,1
daisy_train,0
n647,1
jojo_(rainbowscreen),4
omega_symbol,0
graphic_digestion,0
jaxxy_fur,4
neckroo,1
vira_and_viana,4
suggestive_clothing,0
keki_(rukifox),4
ibukyu,1
nikanuar,1
luca_(chisara),4
curlers,0
the_nameless_guy,1
maru_show,1
sigil_(fursona),4
vyxx_(rimba_racer),4
skipping,0
bakeware,0
sheath_ring,0
ripperelite,1
obleacream,1
shibori_kasu,1
anvel,0
xensilver,1
orange_necktie,0
80s_hair,0
stahl_(stahlz),4
outerwear,0
glow_bracelet,0
geowaffle,1
clyde_(lilo_and_stitch),4
siraviena,1
phrisco,4
simon_(braford),4
kerespup,1
nelly,1
boo,0
gulliver_(animal_crossing),4
nachiwanko,1
body_cast,0
lockerobster,1
sowia,1
lightningjolt,4
ultrasound,0
mrfarrow,1
hobbit,5
chauvels,1
dox_drakes,4
ronald_mcdonald,4
yuuma_fujimori_(tsunderepanda),4
wolfskin,5
lockheart,1
dalek,5
sun_(smu),4
slayer,4
rusty_(bluey),4
green_helmet,0
pantaloons,0
ayasakiace,1
white_curtains,0
kc_(killedbycuriosity),4
twentle,4
yumi_akiyama_(character),4
becoming_flaccid,0
allister_carver,4
bear_and_breakfast,3
suchmim,1
black_headset,0
kitty_jo,4
nns,1
held_by_tail,0
warthog_vehicle_(halo),0
puppyemonade,1
comicstormcreations,1
barnyard,0
jackie_(jackie's_story),4
mai_(kitty_pride),4
8989to,1
penis_lift,0
out_of_focus,0
vhs_tape,0
yuuya_habu,4
the_neon_children,3
bow_lingerie,0
richie_(yinller),4
smurple_(grumbbuck),4
nikoyosan,1
mike_cubes,1
lakalando,1
smutbase,1
gav,1
lei_luo,4
seeds_of_chaos,3
zeke_white,4
lithiumred,1
nyx_(klifflod),4
showing_leg,0
gwen_(sirphilliam),4
tahomich0,1
anchor_symbol,0
ashe_(overwatch),4
hourglass_(disambiguation),0
meka_(overwatch),4
werewolferos,1
percy_lynxoln_(callmewritefag),4
ash_(abz),4
pattern_t-shirt,0
grey_seam_underwear,0
tomo_(tmtm_mf_mf),1
daniels_(twokinds),4
blossom_(thousandfoldfeathers),4
paddle-boat,1
gun_to_head,0
fennelseed,1
antennae_piercing,0
flynn_moore,4
motherkarma69,1
pb_and_j_otter,3
leaning_on_arm,0
synapsid,5
gary_(furry_force),4
procreate,3
foxlett,1
nick_(funkybun),4
dowsing_rod,0
finn_(funkybun),4
ultrahand,1
sabah_(character),4
hobkin_redux,5
sander_(dislyte),4
cappuchino,1
furiquarter,1
zakurei,3
white_dragon,5
brown_scarf,0
bottomless_andromorph,0
juncos_(jayfeath3r),4
sherry_birkin_(resident_evil),4
felix_cattelli,4
chastity_seal,0
postal_(artist),1
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15:13,7
dr._mario_(character),4
zoe_(awr_hey),4
petunia_pig,4
tailsoft,1
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puflitos,1
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caucasian,0
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trist,4
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dirty_blonde_hair,0
captain_sale,4
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joel_the_lemur,4
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caucasian_mountain_dog,5
konkitty,1
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tasha_voron,4
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asme,1
ne0fox,4
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8598:6071,0
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aurora_(spacecamper),4
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nea_(dewwydarts),4
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kiwanoni,1
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pride_color_tailband,0
purple_armor,0
13:19,7
kammuraqi,1
mira_(silent_hill),4
bu2ma,1
tentacle_plant,5
spectre_(titanfall),5
deme_0816,1
static_spark,4
dirkstridoritos,1
naoto,1
vern_(vernacularshark),4
munrei,1
poofy_dragon,1
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dracmon_(survive),4
rionquosue,4
dreyol,4
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zapper:_one_wicked_cricket,3
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oga_(plus-sized_elf),4
raiden_(nuree_art),4
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jon_(redtwin),4
marumori0619,1
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x'sara,4
xenahasaclit,1
fabi_(fabifox),4
l077,1
shilly_(sachidog),4
succubus_(book_of_lust),4
cayde-6,4
catalyst_(fortnite),4
berserker_ibaraki-douji,4
incest_marriage,0
teej_(sigma_x),4
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huger_smot,1
bunnie_(bunnielovesyou),4
church_(reccasenli),4
riukykappa,1
titty_sprinkles_(oc),4
azalea_(insomniacovrlrd),4
iyako,1
danielasdoodles,1
cyan_cube,4
minakaka_(spinneborg),4
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ryo_yambe,1
kale_(covertcanine),4
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iro_gomasio,1
kiwizerf,1
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reho,1
lizard_queen,4
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/trash/,3
slapstick,0
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cameroo,1
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dana,4
i_am_nude,1
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kilian_alexander_barker,4
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mori,4
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gatchmon,5
kaeku,1
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liber_souvlakia,4
les_pooche,3
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oinu-chan_(hanadaiteol),4
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ruffrunner_(httyd),4
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jschlatt,3
dirt_(st4rsbucks),4
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five_nights_in_anime,3
kory_bing,1
dragon_quest_xi,3
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diana_(daredemon7000),4
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shuma-gorath,4
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anakin_skywalker,4
pshyzo,1
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c0sm0,1
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captain_carrot_and_his_amazing_zoo_crew,3
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ruby_pinch_(mlp),4
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madnessandgiovanni0595,1
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bleeding_dragon,1
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lyeni,1
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xen_(kirbot12),4
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pokémon_art_academy,3
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:/,0
nonny18675309,1
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the_servant_omen,3
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bros_being_bros,0
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melly_(itsmemtfo4),4
caster_nitocris,4
rose_(disambiguation),-1
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celia_(animal_crossing),4
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neri_(azura_inalis),4
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lucareon,4
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daniela_shepard,4
houndgrey,1
mmoboys_(artist),1
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keith,4
ember_(blazikem),4
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pokemon_plushie,0
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tkraccoon,1
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borg,5
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mystia_lorelei,4
dvd_cover,7
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peter_potamus,4
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sleeve,0
salamon,5
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seraphim,1
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sek,4
remy,4
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myfi,4
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thewanderingfox,4
tygre_(artist),1
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nicki_minaj,4
stupid,0
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rio_(dangpa),4
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zuppu11,1
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elicazzz,1
malice_(pinkmoth),4
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tori_(elvche),4
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muddy_maestro,4
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lauri,4
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alyx_(alyxalyx),4
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cheeky_(roommates),4
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halabaluu,1
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k1ko,1
pulsemon,5
du_amae,1
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eliot_fanghanel,4
ayah_(prince_vaxis),4
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star_(fvt),4
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musabo3neo,1
zekken,4
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rico_(sylvanedadeer),4
momo_yaoyorozu,4
irda,4
novah_ikaro_(character),4
otter-casey,4
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shirojackote,1
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sol_doliodos_(character),4
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gurobase,1
td_coltraine,3
victor_9326,1
boundlightning,1
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discordriderr34,1
vincent_(foxmcc),4
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bree_(junk_planet),4
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mega_man_legends,3
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tolendare,4
giant_candy,0
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mith_(mith),4
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lamoz571,1
juggermelon,1
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icedragondee_(artist),1
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magyo6,1
joeycwolf,4
maleherm_on_bottom,0
onene,5
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mia_woods,4
fuzzwolf,4
feliccia,4
sakiiti,1
mod_lemon,1
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higurashi_no_naku_koro_ni,3
mosasaurus,5
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chapu,4
bonsly,5
januspeldragon,1
spacekitten,1
zodiacnicola,1
training_dummy,0
knockabiller,1
dial_m_for_monkey,3
megan_fawkes,4
lukiro,1
james_silvani,1
mizusawa_nino,1
limety,4
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charmin_bear,4
cyberspace,0
kynmedia,1
holmy_(show_by_rock!!),4
kriss_vector,0
multicolored_dildo,0
socks_with_sandals,0
confessional,0
tabby_slime,5
faeyyaa,1
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priapup,1
milkydraws8,1
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the_suicider_rat,3
reliable_excavation_demolition,3
new_world_suboscine,5
hato_the_collie,4
espro,4
saltyseaskunk,1
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haumi,4
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suzi_(susaroo),4
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shadow_chao,4
hiding_pussy,0
video_game_character,0
elseirius,1
eve_(joaoppereiraus),4
spotted_salamander,5
promotional_art,0
nicashee,4
sadasaka_usui,4
forbiddenlight,1
kylefrommicrosoft,1
pantsu_smiley,1
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askalin,1
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melody_(sakuradlyall),4
feeling,0
layla_(legend_of_queen_opala),4
canoweissmon,5
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vappy,4
dart_(httyd),4
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duly_(character),4
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kyubimaru,1
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fuzzybear,1
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mao.j,1
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joylewds,1
p-body_(portal),4
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its_cerverus,1
blake_(xenozaiko),4
cyan_glans,0
mackenzie_(theredhare),4
dr.francat,1
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bumbleborb,1
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fleety_(character),4
drawmeaponynamedbob,1
a_cat_is_fine_too,3
raum_(paladins),4
diji_(masterj291),4
human_penetrating_machine,0
april_marchand_may_(roommates),4
dwzaafi,1
aubry,4
flappydog_(character),4
tojo,4
jaggiekant,1
aero_novara,4
shawd_kaleb,4
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pussy_tongue,0
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day_with_dna,0
blast,0
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bloocarrot,1
delta_(mating_season),4
kylani,4
legend_of_kay,3
sally_(doomthewolf),4
vaerinn,4
buttdawg,1
meanmotorscooter,1
sammfeatblueheart,1
junkersquawker,1
zarana,4
mash_(artist),1
woola,4
tar0,1
tammy_(study_partners),4
dan7821,1
sally_cooper,4
toralei_stripe,4
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pop_out,0
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angus_(adios),4
beartp,4
spacebat,5
vulpes_pawpad,4
kumakuma,1
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zer0ember,1
vanellope_von_schweetz,4
jake_(jake-dragon),4
assistant_(nobody147),4
tt-n,1
cappuccino,1
chico110,1
umbra-b3ar,1
skylight_(artist),1
prdarkfox,1
zs99,1
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gummi,4
tonywolf,1
faith_(daq),4
dim333,4
sapphicneko,4
rangiku_matsumoto,4
viper_king,4
shea,4
nogard,4
amoonguss,5
keebles,1
rovoska,4
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doniroux,4
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regsmutt,1
tasha_(nightfaux),4
apimpnamedcheerilee,1
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truttle_(character),4
roza_barboskina,4
luck_of_the_draw_1,3
dusky_(oc),4
kori_(potoobrigham),4
darling,4
aang,4
dog_city,3
daragh,4
tanya_quille,4
riptor,4
arrogance,4
minnie_shoof,4
helia_(coc),4
polygamy,0
jayce_(jayce0rangetail),4
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sam_(braford),4
silverwing_(series),3
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phietto,1
volkcreed,1
chezza,4
cardinal_screen,1
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mc_(kayla-na),4
heartbreak,0
hall,0
kayiko,1
dusty_tomes,4
darkartist,1
lincoln_loud,4
melee_bishop,4
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ami,4
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alistair_(kadath),4
dr._welps,1
takenokogohan,1
turkish_angora,5
bolt_(fastener),0
cerberus_the_demogorgon,4
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purple_tail_tip,0
thunk_(gyro),4
ferdinand_the_bull,4
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throwing_pokeball,0
vhyena,1
mugicaan1,1
marcyandmathy,1
nude_outside,0
snaphappy,1
erma_felna:_edf,3
dara_(kasdaq),4
task_(the-minuscule-task),4
short_loop,0
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cooney_(wherewolf),4
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nach,1
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orgasm_from_frottage,0
hank_(bear_and_breakfast),4
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eurasian_wolf,5
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keatonnoir,1
barman,0
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clyde_(pac-man),4
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kalkaph,4
zamtrios,5
bremen_avenue_experience,3
crashwolf,1
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seikox,1
tokyo_yakult_swallows,3
trish_(4chan),4
jin_(mugenjin),1
box_art,0
syri_(tits),4
sunbetch,1
olonia,4
alice_angel,4
teeterglance,1
klace_(pokefound),4
temetnosce88,1
gamzee_makara,4
genital_fluids_drip,0
wilhamena,4
toned_body,0
catscratch08th,1
nila_(purplealacran),4
light_lips,0
striped_hat,0
donburikazoku,1
yvette_(vikbys),4
sirokitten,1
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gabriel_(robinstic),4
dustin_(sfd),4
the_last_of_us_part_ii,3
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janet_(sailoranna),4
aisuroma,1
esperanza_(cimarron),4
nozoku,1
tia_(nastypasty),4
khaki_shorts,0
gui_(smuttysquid),4
kai'sa_(lol),4
penis_silhouette,0
toby_(animal_crossing),4
aife,4
salugia,5
body_inside_body,0
husko,4
gold_lipstick,0
yev-san,1
iggy_bomb,1
herm_pred,0
bonfirefox,1
kaenidi,1
ashley_(igph),4
vera_(shinori),4
fe'lis_(character),4
nerdy_(sirphilliam),4
chocobo_taur,5
miu_vamcat_(character),4
sprout_(plant),0
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marlowws,1
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holding_club,0
blau_draconis,4
chameli_muthupalaniappan,4
impmon_x,5
oberon_(karnal),4
raiyk,1
shadownight456,1
andromorph_on_bottom,0
drug_paraphernalia,0
beachwear,0
celine_(cuddlesong),4
lawychan,1
hayhorse,1
rakeesh,4
alterise,4
dumari,4
sibling_swap,0
lockheedskunk,1
torionion,1
liea,1
words_worth,3
beau_555,1
caelacanthe,1
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virdi_(elberik),4
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tak_(aeolus06),4
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carl_(thecosmicwolf33),4
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george_heartfields,4
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mr_baton,1
palisman_(the_owl_house),5
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tammy_(disambiguation),4
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uther_(red-izak),4
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liina_(aruurara),4
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atticus_(treebarkdragon),4
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leo's_mother_(vg_cats),4
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stated_price,0
jack_mauer,4
the_forest_of_love,3
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onion_(powfooo),4
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askareth,4
sven_the_rogue_knight,4
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charlotte_(nox),4
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karen_star,4
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metaletemon,5
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andre_(dosent),4
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pac-man_and_the_ghostly_adventures,3
allen_myriad,4
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soulstealer666,1
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sasha_(jeremy_bernal),4
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yans_the_furret,4
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poi_(last_origin),4
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suite,0
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velvetdelusion,1
sharkieee,1
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gargoyle's_quest,3
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nikovi_(chasm),4
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cats_(movie),3
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kitty_(averyshadydolphin),4
golden_lion_tamarin,5
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justausernamesfm,1
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laluka,4
rura,1
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stella_(cornchip21),4
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taurus_(fairy_tail),4
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suzumaru,1
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ela,4
sir_kavalier,4
massage_oil,0
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cleo_(between_the_lions),4
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atlas,4
6/26,3
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the_wonderful_101,3
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altmer,5
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oot,1
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kayla,4
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tweak,4
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afterglow_the_ampharos,4
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lem_(latiodile),4
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mauricio,4
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y5_(adventure_time),4
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marshall_(dangpa),4
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lewxen,5
fire_axe,0
pride_color_lineup,0
squid_(minecraft),4
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werewolf_wednesday,4
momo_(kiichi),4
vilka,4
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winston_(vaanxdragon),4
samoyed-chan_(kishibe),4
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rose_(h2o2),4
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momu9172,1
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chess_(endernia),4
blackholedog1,1
natchan,1
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cyancouatl,4
sakatagintoki117,1
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blond_fur,0
isabel_(doctorartemis),4
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spoony_(spoonyfox),4
elaismurnhem_afarman,4
monta_(beartouma_0517),4
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kat_(iseenudepeople),4
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shota_equine_(marimo),4
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bxb777,1
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overlord_(game),3
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elwysprigg,1
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tdph2dubnoxxthu,1
rib_(ribera),4
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devil_horns_(disambiguation),-1
roy_(fuze),4
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shirsha,1
melon22,1
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joao_(joaoppereiraus),4
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ivory_(derideal),4
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cello2424,1
shiiorina,1
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callie_barker,4
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justwusky,4
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xxxbiliwinxxx,1
fizwack,1
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demi,4
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phessian,5
veil_heartwood,4
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famous_comics,1
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root,4
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ailah,1
thepainfultruth,1
jklind,1
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blue_one-piece_swimsuit,0
shan_chui_cha_(ffjjfjci),4
lactating_in_cup,0
1959,7
balljob,0
food_tray,0
justiceposting,1
xbox_one_console,0
fairy_tail-rella,4
jairou,1
dallas_burnside_(forestdale),4
big_boo,4
spiked_ring,0
slug_(wattchewant),4
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nevan_nedall_(colorist),1
katrina_(animal_crossing),4
notus_(character),4
shira_stargazer,1
mametchi,5
vakama,4
mrhart,1
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juanp1arts,1
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pixyfox23,1
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sen_(mewgle),4
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0mr-wolf0,1
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xanaduxxx,1
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degu,5
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marcy_wu,4
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the_legend_of_pipi,3
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su_(drawholic),4
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lando_mcflurry,4
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felicia_(flafty),4
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xbc9,1
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uitinla,1
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red_(reddeadfox),4
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hueroc,4
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love_live!,3
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alyx_vance,4
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toba_hiyoko,1
psi,4
necromuncher,1
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55level,1
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nicroxes_(thony_dog),4
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yannit,4
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strawberry_cheesecake_(glacierclear),4
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catherine_hopps_(siroc),4
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shybun,4
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patxi_(fate),4
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blue_moon_(glacierclear),4
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l_(l2876),4
maya_(ironkongstudios),4
libby_(tasteofchoklit),4
polaris_(mousguy),4
asella_(nelly63),4
kyoriichi,1
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tmgt26,1
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auro_(auropaw),4
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shira_frozenmoon,4
luraknot,1
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yule_(tas),4
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oneflymagpie,1
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pango_(klonoa),4
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devin_d_parlett,4
grow_(sound_effect),0
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wax_seal,0
magma_admin_courtney,4
butterflysneeze,1
artisyone,1
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keiko_matsuhani,4
serpentine_(ninjago),5
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queen_scarlet_(mlp),4
fenton_crackshell,4
bungo_(beartouma_0517),4
noah_(ghost_forger),4
hands_on_own_feet,0
13:10,7
flying_machine,0
jyuri_(ziroro326),4
mrwhiskerz,1
milo_(gioven),4
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ami_(character),4
merry_(ken_singshow),4
bokomon,5
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azwraith_the_phantom_lancer,4
styx_(of_orcs_and_men),4
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5j4c720,1
dreamer69,1
janine_(bad_dragon),4
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gaia_online,3
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rogue_(marvel),4
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toya_pup,4
messerschmitt,3
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sushi_(sootsprite),4
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nano_(the_gamercat),4
arcticsnow1,4
toro29,1
metro_2033,3
gen_(baconking),4
la_brea,4
cryogonal,5
hana_uzaki,4
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dakota_(wiprogress),4
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angrycaboose,1
val,4
rekkit,4
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alexandra_reznova,4
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shelgon,5
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eyvind_earle,1
everyone_is_so_mean_to_me,3
travis_(sebafox),4
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echinoderm_humanoid,5
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sarah_(disambiguation),4
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mauve_(artist),1
diandre,4
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tochinoko,1
ghost_(nateac),4
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pentagram_tattoo,0
peach88,1
silverash_(arknights),4
yak_(kemono_friends),4
mrkatman,1
zardoseus,1
25:18,7
yellow_diaper,0
freddy_lupin_(100_percent_wolf),4
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the_gray_garden,3
pride_color_highlights,0
blue_thigh_socks,0
mehajiki,0
joeyzliaotang,1
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food_clothing,0
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mirothefox,1
carduelis,1
shnuzzlez,1
617foreverwithy,1
khemia,3
koron-dash,1
poi,0
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sto-chan_(m-da_s-tarou),4
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elias_larranaga_(pluvioskunk),4
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eva_(kleiny),4
saffronic,1
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maximus_(smartydragon),4
lizzard,5
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kinkivas,1
tiger_claw,4
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quiver_(arrows),0
miazuma_shouki,1
pyrope_(genericdef),4
thawb,0
chuck_(captain_nikko),4
nameless_lucario,4
tempest_(xsomeonex),4
rudy_(kayla-na),4
little_man_(sloss),4
oliversart,1
text_on_sign,0
thalia_grace,4
belly_rubs,0
grizzlesbear,1
amarissa_(abluedeer),4
nazu'lah_(yuricrabking),4
doguma-sin,1
alice_yagami,1
furjoe0,1
lucas_(sylvanedadeer),4
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xoc_onda,4
obsius_(paledrake),4
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dehaka,4
kirsten_odessa,4
sally_pride,4
lìzhī,4
neve_(plattyneko),4
stockingshot56,1
b._wolf,4
emarosa,4
samael_(nicoya),4
panty_peeing,0
timid_cookie,4
luluangel,1
yaku_(yakushishi),4
weers,1
blvckmagic,1
no_dialogue,0
chase_(pokémon),4
tygriis,1
ulven-f,1
alakayne_alembine,4
drake_terrys,4
white_beleth_(floraverse),4
later_alligator,3
ashley_(themuffinly),4
hyper_pubes,0
eracin,1
xarr,1
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bull_(torquewintress),4
ytrall,1
nanntaro,1
amaruu,1
belo4ka,1
lazy_eye,0
popo_(telemonster),4
spit_in_mouth,0
furffles_paloosa,4
gidora_(artist),1
leonberger,5
streamers,0
blau,4
black_tights,0
gakuseigitsune,1
crystalshards,1
tommy_(jay_naylor),4
bert,4
drion,4
treasure_map,0
terri_and_terry_perry,4
shammy,4
aura_(moomanibe),4
ape_escape,3
vampirella,4
d.a.r.e.,3
ze,1
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cherrie,4
sex_show,0
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mel_(character),4
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kyoujin_(character),4
clip_studio_paint,3
nanomachines,5
pupitar,5
invictus_(caticus),4
chloe_park,4
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chakram,0
leg_bar,0
lunar_(nedoiko),4
comparing_butts,0
burmy,5
radial_(artist),1
willie_munk,4
glenn_(chrono_trigger),4
nasu,1
colette_(thea_sisters),4
graybluestudios,1
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fuel,0
improvised_lube,0
phanto,5
raine_(raine1082),4
shadow_ieyasu,4
kehto,4
graves_(lol),4
orange_flower,0
dahlia_(blen_bodega),4
silver_rain_(sergeantbuck),4
wind_sprint_(mlp),4
eiji_kakihana_(odd_taxi),4
shiny_(petokikka),4
yuuki_(princess_connect!),4
two_tone_pillow,0
crane_game,0
holding_heart,0
klempner,1
holding_cookie,0
gauged_labia,0
wiring,0
layered_heart_eyes,0
maggie_(zapper:_one_wicked_cricket),4
rekkit_the_rabbit,3
amber_(zaush),4
ose_(megami_tensei),4
fluffydave,1
drawing_on_body,0
black_frill,0
deerest_stag,1
speech_emanata,0
hazel_dormouse,5
kamina_(gurren_lagann),4
shiro_lolita,0
dodo_(dododragon),4
tail_around_body,0
pocket_square,0
knife_in_mouth,0
christina_mort,4
partial_fur,0
papriko,1
huge_size_difference,0
mistress-honey,1
holding_sack,0
<3_eyebrows,0
green_leash,0
trapeze,0
being_gagged,0
maleabel,4
this_big,0
lucidpariah,1
bear-paws,3
slang,0
nosantee,1
glowing_breasts,0
many_eyes,0
iguini,5
charge,0
mirotic,1
krispup,4
near_death,0
nekomonogatari,3
pimp_hat,0
bass_(artist),1
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nickolas,4
dictator,0
saba,4
k-on!,3
epulson,1
nightseeker_(oc),4
lacertius,4
valencia_(jmh),4
azazel_(character),4
petal_spark,4
jonesy,4
edude,1
the_jetsons,3
cayleth,1
temeraire_(series),3
scott_visnjic,4
sakazuki,0
326,1
mr2cats,1
fayleh,1
wii_nunchuk,0
jaggard,5
ximen99_(artist),1
mohg_lord_of_blood,4
jotun22,1
desdemona_(gargoyles),4
wolfeenix,1
yellow_apron,0
tripartite_jaw,0
sheebu,1
megurine_luka,4
shedding_skin,0
baelz_hakos,4
food_on_penis,0
artdemonslair,1
breast_poke,0
yuki_(caribbean_blue),4
distorted_text,0
lust_(kuroodod),4
nangnam,1
lentocn,1
assisted_autofellatio,0
tail_on_bed,0
vehicle_humanoid,5
starston,1
long_toenails,0
kally_(tits),4
kaysa,1
red_felyne,4
lena_the_kobold,4
leg_warmers_only,0
glowing_collar,0
sallos_(character),4
unoryoku,1
ambrose_the_kobold,4
archaeotter,1
denaturedalbumin,1
jawsfm,1
lawnmower333,1
black_outfit,0
madmanransom,1
simon_(zourik),4
syrrik,4
rammie_(jschlatt),4
glacey_(glaceonthepet),4
libra_bethelon_(libra-11),4
game_piece,0
wolftea42,1
snaildoki,1
junkbag,1
leg_on_furniture,0
hypnotherapy,0
legomorph,5
holding_hose,0
trivial_pursuit_(oc),4
devious_grin,0
pastries,0
incubus_(project_x),4
betty_boom_(character),4
justirri,1
noa_(lunaris_parukia),4
slimecatt,1
naze,1
gabrielgryphon_(character),4
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sweetroll,0
pluto_(woebeeme),4
girly/girly,0
andrew_(dosent),4
wes_(ultilix),4
squishheart,1
han_wenqing_(tka),4
etcexera,1
roquemapache,4
tail_ring_(piercing),0
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discarded_shoes,0
studded_ring,0
kelv_(vdisco),4
nipple_tag,0
in_your_best_interests,3
flaming_eyebrows,0
alperion,4
powerinfinity,1
anal_impalement,0
saberleo_(character),4
koraku_(baroque),4
hand_tattoo,0
penis_on_leg,0
nt6969,1
dalrus_plaguefang_(character),4
spaughtyena,3
tiffany_turlington,4
aphid,5
kayadult,1
blackberry_dragon,1
badminton_racket,0
kochimel,1
chewing_gum,0
asadama,1
inkedfur,3
heart_locket,0
emboss0320,1
blu,4
4ofswords,1
baby_bop,4
x-mas,3
open_underwear,0
stephanie_lynn,1
fire_belly_newt,5
farellemoon,1
saberuneko,1
scotty_panthertaur,4
veis,4
raivenderouthelion_(artist),1
cat_face,4
dorian_zibowski,4
cro_funky,4
virtigogun,1
decepticon_insignia,0
fgs,4
broken_mirror,0
miniature,0
mxgurl89,1
jouigidragon,1
bending_down,0
ballistic-cottontail,1
chip_n'death,1
metal_tentacles,0
arakupa,1
tina_(digitalpelican),4
sini_(auriok),4
reitounek,1
stella_(gasaraki2007),4
zorzero,1
dima_(artist),1
frozentrovador,1
filo_(the_rising_of_the_shield_hero),4
syderyvuris,1
headgrab,0
operculum,1
onojeff,1
lucky_beast,5
honey_x_scars,3
bitters,4
mortar_and_pestle,0
picco_(artist),1
cum_blending,0
kirone,4
jorie_(thirteeenth),4
todd_(vir-no-vigoratus),4
faris_(crackers),4
zero_two_(ditf),4
irkingir,1
araceli_(bzeh),4
tom_(fuze),4
mags_(hambor12),4
artemis_(nobby),4
foxinjacket,1
fenriz278,1
herald_(cobat),4
hand_on_own_waist,0
gudao_(fate),4
rina_(klr-rio),4
vic_senpai,1
18:25,7
chainedbirds,1
the_fox_(falvie),4
harkin_theflyinghog,1
tash0,1
kyra_(invasormkiv),4
filth_hound,4
shoes_on,0
stepdaughter,0
kya_(palkitos),4
licking_belly,0
anothergamer,4
huge_eyes,0
maiya_(gundam_build_divers_re:rise),4
dyani-yahto,1
crazy_company,1
arinadler,1
cloverleaf_tarragon,4
otorigin,1
bilebubble,1
doodledaeng,1
this_egg_got_me_acting_unwise,3
calendar_pinup,0
flaviomodezto,1
mystery_(twisted-doctor),4
mikojin,1
red_headkerchief,0
crouching_over_dildo,0
shrunken_city,0
iiimirai,1
fluffyglitch,1
golub_lol,1
great_kilt,0
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aluxander01,1
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hornynym,1
the_white_rabbit_(disney),4
pinereese,4
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des_(falcon_mccooper),4
furry_specific_accessory,0
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reines_(doneru),4
galvanic_mechamorph,5
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jedi_tunic,0
akane_(akanisen),4
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keke_(hotkeke1),4
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levan_zan,1
tonythetiger16,1
breanna_(9tales),4
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spirit_blossom_teemo,4
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milo_(mrtweek),4
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artgerm,1
otama,1
david_crown,4
eve,4
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rugia1992,1
sinclare,4
joshua_(longblueclaw),4
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ekubo,1
smallsavant,1
prosthetic_ear,0
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ackie,4
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lucariolover448,1
basilisk_(disambiguation),-1
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sunny_(wereshiba),4
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thex-plotion,1
twilightsaint,1
pikachu_rock_star,4
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aman,4
nelya,4
nekra,4
keno9988,1
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zwolf,1
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fyrien,4
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moa_(warframe),5
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auro_(human),4
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robin_(rusheloc),4
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capydiem,1
indarkwaters,1
reyriders,1
tuesday_(kyaramerucocoa),4
yoyofreakcj,1
captain_marvel,4
noelle_(oc),4
jezzlen,1
zootopia:_crime_files,3
steve_(smsfoxx),4
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sheldon_lee,4
mikalovesyou,1
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tetsu_hayami,4
tan_po_po_tan,1
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stan_luckbun,4
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spring_bane,1
tinky_winky_(teletubbies),4
muzzle_(disambiguation),-1
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psychonauts,3
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snarf,4
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monster_of_the_day,0
razeth_(razeth),4
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dragon_soul_e,1
red_winds,4
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nicole_(jmh),4
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tyson_hesse,1
willdrawforfood1,1
luu_(lonbluewolf),4
mami_tomoe,4
efa,4
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theonyn,1
the_red_angel,1
shira_schattenklaue,4
fliegen80s,1
maude_o'dell,4
baralust,1
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al_(littlerager),4
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aloejuice0917,1
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granite,4
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evdokiya,4
james_fox,4
majestic,0
tsukitate,4
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faust,4
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shikokubo,1
beaver_humanoid,5
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pinkyhemmit,4
ruby_sahira_(haybuck),4
samsung_sam,4
we_can_do_it!,3
ere-yandara,1
zerokun135,1
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dandy_(legendz),4
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casey_(moot_point),4
pride_color_bikini,0
small_eyebrows,0
melanie_(diddlier),4
teo_(world_flipper),4
jasmine_(draugr),4
ohayou_milk,1
chloe_(jolo),4
yugyny_white,4
shelly_(wirelessshiba),4
ps5_console,0
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neve_(naneve),4
chill,0
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kuramafurrypaw,1
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pussy_sweat,0
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maverick_chocobo,4
herex,4
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bboyhunter_(character),4
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bon_bon_(ocaritna),4
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samsti,1
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felia,4
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joe_(physicswolf),4
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macadam,1
simplepossum,1
dani_(lysergide),4
carbene_(viwrastupr),4
shieldon,5
koro_(koro_kiama),4
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zagredos_(spirit_bathouse),4
wander_(shadow_of_the_colossus),4
osiimi,1
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yssalia_(sarybomb),4
dandelion_(character),4
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soul_(undertale),4
marle_talbender_(darkflamewolf),4
jak_(dominus),4
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on_sand,0
anzox,4
lotulist,1
violetghost,1
ghrom,4
hop_(pokémon),4
sage_(kaerfflow),4
lynn_badger,4
kijimaru,1
einsamkeitus,1
luriga_freefox,4
swift_wind_(she-ra),4
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jaguatiric4,1
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drew_the_mountain,1
oscar_(pickles-hyena),4
portal_autocunnilingus,0
panquequecin,1
matsuo_kunikuni,4
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dialuca01,4
grantly,4
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arm_hold,0
pearl_aurora,4
mick,4
rainstar,4
jinx,4
webvampi,1
inosagi,1
chicken_(nuclear_throne),4
blackmoon1504,1
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rook_kawa,1
rhysio,4
narcoticdream,1
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jeancito93jean,1
keino_(firestarterwolf),4
rockblackhorn,4
fantail_pigeon,5
khris_(shining),4
zephyriwolf,1
notcuti,1
grey_ring_piercing,0
fugashi4090,1
jack_(el_tigre),4
female_teacher,0
crymini_(hazbin_hotel),4
dragonboii78,1
katy_(piporete),4
pinkdiablo,1
squid_girl_(squid_girl),4
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tommy_(kiwabiscuitcat),4
bro_aniki,1
deus_(the_suicider_rat),4
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wector,1
irishwolf_lythi,4
room_number,0
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kiyana_(marcodile),4
primma_(windborn),4
himitsu,1
n'cathe,4
anselme,1
noah-the-husker,1
andrew_(yiffyhusky),4
hainequem,1
jason_(yiffyhusky),4
jessica_(bcb),4
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ccruelangel,1
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heathenpixel,1
delmin_(show_by_rock!!),4
whisker_growth,0
penni-chan69,1
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purple_hairband,0
toyokuni,4
liquid_(negieggy),4
dearhotglues,1
snub-nosed_monkey,5
rogerbacon,1
firestrot,1
paws_(cyanroll),4
verna_(milkscrew),4
krista,4
uxdragon,1
felixf,1
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kimono_kemono,1
wyverness,1
pentagram_lingerie,0
bunnynha,1
brandon_(sloss),4
futuristic_clothing,0
i_was_banished_from_the_party_and_became_the_leader_of_the_strongest_female_dragon_party!,3
black_one-piece_swimsuit,0
sos_or_loss,1
cocoa_(cheesecaker),4
spicyteggy,1
ottiro,1
thiccc,1
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yamik64,1
sa1ntmax,1
ace_(the_human_heart),4
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lex_sergal,4
nico_(screamoshaymin),4
fuzz_fizz,4
mike_(brownieclop),4
guacamolesenpai,1
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cinicalvoice,1
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lush,4
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pokey_(mario),5
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theartofvero,1
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moji,4
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cia_the_dark_sorceress,4
:3c,0
mr-haitch,1
carrotcaramel,1
kaizen2582,1
sylvanos,4
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azlech,1
fwomp,0
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kaya_the_silverdragon_(character),4
xinnai0485,1
hirame42,1
anima_(lord_salt),4
ruby_(nope797),4
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ravirus,4
mostly_nude_humanoid,0
sentient_ooze_(roanoak),4
mime_(htf),4
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omen_(blaidd),4
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dust_(dustwolf13),4
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eastern_dragon_humanoid,5
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sunshine_lolita,4
felinid,5
zala,4
siwo_(artist),1
adriana_(usuario2),4
black_buttplug,0
shima_(lucaraixen),4
pomsky,5
tomu,1
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the_darwin_chronicles,3
arm_flailing,0
shub-niggurath_(h.p._lovecraft),4
stead_(connivingrat),4
twitching_tail,0
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sindaraye,5
fishnet_pantyhose,0
game_boy_advance_sp,3
andy_(heyitscousindave),4
934,1
hair_in_mouth,0
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gardener,0
blunderbuss,0
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hmph,0
celtic_cross,0
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rock-g,4
kfutaba,1
cosmic_(cosmicmewtwo),4
bobert_(character),4
james_(videah),4
robbie_the_rabbit_(silent_hill),4
juke_rivers,4
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beers,4
steve_(beaver_and_steve),4
colin_(moodyferret),4
shallow_rimming,0
norn,5
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robbie_(mangeybones),4
hearts_around_penis,0
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viana_(foxxd),4
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texas_toast,1
vira_(foxxd),4
moxiepawler_(artist),1
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millie_(jigglephysics),4
two_tone_back,0
kaz_(foxialewd),4
zan_(citrinelle),4
vore_sex,0
milkydreams,1
jay_(jackthespartan),4
band-aid_on_knee,0
jericho_(ulfhednar),4
alternate_timeline,0
natyss,4
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wild_knights_gulkeeva,3
dannydemon,1
watersucc,1
lancer:_the_knights_of_fenris,3
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brown_neckerchief,0
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kitunelovealt,1
aevery,4
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claire_(batartcave),4
ryan_(zourik),4
ivy_(sukebepanda),4
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rodecchi,1
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kanie_(kanel),4
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rollriver,1
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raising_arm,0
potatobud3,1
emerald_tree_boa,5
argent_cookie,1
bearhybrid,1
mr._pickles,4
gikowinko,1
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kyameta,1
glowing_abdomen,0
lilac_fur,0
puck_(re:zero),4
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nom-sympony,1
aaa-its-spook,1
ollie_rabbit,1
chikuwabu,1
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65wata,1
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ben_(rollingstoneeeeee),4
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ammy_fluff_(character),4
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fred_jones,4
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fnaf_the_alternative_story,3
tankana,4
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polly_(sherlock_hound),4
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me_gusta,0
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bhloopy,1
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dragoooon1223,1
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sanji_(one_piece),4
gwen_fawkes,4
spice_(powfooo),4
joon,1
mwako,4
aging,0
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xerlexer,1
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inky_(pac-man),4
st637,1
meowz,4
platinum_fox,0
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otakuwolf,1
pixel_flare,1
teriyaki,1
duncan_(k-9),4
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lyra_(pop'n_music),4
rick_taylor,4
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yaking,1
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melody_(zhanbow),4
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ignis,4
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thandor,1
eztli_(user_55),4
aurelia_(unbeholden),4
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rainfallcat,1
mroleoso,1
ruby_(comfycreations),4
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kazemon,5
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gio-joe,1
vivian_(bloodline),4
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onzeno,1
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matthias,4
andvari,4
salacious3d,1
gemwist,1
pearlcatcher_dragon,5
weasely,1
ziggyhusky,4
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seitora,1
stingmon,5
kamikaze_otium,4
hudson_(powfooo),4
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nes_zapper,0
dwebble,5
tsunotori_pony,4
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dekitama_(artist),1
slime_princess_(towergirls),4
black_kyurem,5
wonderduck,1
legacy_of_kain,3
spicy,0
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gabriel_gatto,4
monoglove,0
xxomgx,1
catastrophy,1
kay_(legend_of_kay),4
cavity_search,0
cyberdramon,5
halgalaz,1
cardfight!!_vanguard,3
poofy,0
chuchu_(show_by_rock!!),4
iggy,4
themirth,1
jade_faircrest,4
rusty_wollef,4
rhubella_rat,4
raphiel,1
iris-icecry,1
white_spottytail_mage,4
rebornica,1
lowrider-girl,1
kampferwolf,1
forsaken,4
contessa_(animalympics),4
kirillk,1
nedzu,4
mukucookie,1
klystron,4
sickyicky,1
thefluffyrey69,1
precum_on_floor,0
peeing_underwater,0
experiment_pod,0
tych0,4
meirune,1
hazukashii_team,3
damien_hayabusa,4
emoti,1
corsack,1
plink,4
vaaan,1
yuna_(ffx),4
golden_snub-nosed_monkey,5
larathelabrat,1
ivona_(vrabo),4
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lumi_(mrnutty12),4
pattern_hoodie,0
fransi,1
highland_psycrhen,5
plasmoid_(lilo_and_stitch),4
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workout_clothes,0
peregrine_pegs,1
undone_belt,0
akishycat,1
julie_ann_irons,4
nova-umbreon,1
tourist,0
knob,0
yuzuki_(kame_3),4
seaandsunshine,1
glass_wall,0
pouting_lips,0
clitoris_lick,0
belly_press,0
kage,4
katherine_(reindeer),4
leanfide,1
udontarai,1
noah,4
roswell_(password),4
brandy_(avogadrotoast),4
shibadoodles,1
thecrowartist,1
weiss_(darkfox49),4
growth_serum,0
katie_(fish_birb),4
notebook_paper_background,0
guadr,1
jdanieloart,1
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fire_performance,0
michisamael,1
holding_swim_ring,0
alec's_meerkat,4
branded_hem,0
robby_(beez),4
pizzacat_(zhanbow),4
felicia_lake,4
bolf_(bo-lf),4
ashentail,1
tarquon,4
rest,0
trang_(furry_force),4
shalulu_(enen666),4
commander_ryun,4
ringing_bell,0
angela_flux,4
shark_plush,0
lin_elamura_(novaduskpaw),4
nightwing_(shiro-neko),4
id_badge,0
sleet_(sonic_underground),4
nipple_penetrating,0
hellap,1
israel_(artesderatzyu),4
amarynceus,1
taped_hands,0
shamanbr96,1
girros,5
dis_the_fox,4
laura_(lddraws),4
salix_the_mossdog,4
unluckypaw,1
tail_burst,0
confusedewe,1
pickles_aplenty,4
holding_cross,0
busybunny,1
static_electricity,0
lecerf,1
yuzuki_yukari,4
holding_art,0
horn_lick,0
nikki_kofi,4
no.1_machine,1
posduroo,1
my_chemical_romance,3
bdsm_room,0
ka92,1
pattern_pajamas,0
theawesomefoxguy,1
chevka,4
glowing_object,0
pampering,0
piczel,3
butt_touch,0
domi_(altowovurr),4
orio_(character),4
zukaro_travon,1
cum_on_diaper,0
vaguecreature,1
hand_on_chair,0
bells_(animal_crossing),0
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diane_henderson,4
gene_kruger,4
godseeker,4
forced_spreading,0
sketchywolf-13,1
chikin,5
amanda_payne,1
krinn,4
deathlyinnocent,1
karishad_(housepets!),4
decko,1
uchider,1
animal_bride_4,3
vertigo_(primal_rage),4
naomi_(ajdurai),4
breast_massage,0
birdpawss,1
leinad56,1
yuri_(xdyuxd),4
african_wild_ass,5
okamiden,3
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cutesauce,1
izzy_izumi,4
sleepy_(character),4
gundam_build_fighters,3
gina_(darkspot),4
drakoilex,4
artimas,1
ondine,1
nessus,4
1984,7
olla,1
agious,1
androx,4
glowing_sword,0
merrymaladies,1
pj_saber,4
boston_crab,0
drakvir,4
genesis_controller,0
snow-wolf,1
connie_(big_mouth),4
gamukami,1
filthy_frank,3
riask,4
fangame,0
kaito_(vocaloid),4
detroit:_become_human,3
neckfluff,0
amber_(spicyocean),4
black_backpack,0
betsuto,1
beau_(starshippizza),4
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chapiduh,1
erk_gloom,4
g1,3
russell_(htf),4
hanging_upside_down,0
catscratch,3
mitsuhakai,1
yamsgarden,1
group_in_panel,0
kagami_hiiragi,4
brainsucks,1
jenel_silvermane,4
opal,4
joeycwolf_(copyright),3
corpus_(warframe),4
mila_(president_alexander),4
purple_heels,0
weight_loss,0
keglunek,1
infinumf_(artist),1
asderat,1
yukito_sinion,4
konazarame,1
slow_transformation,0
polarfoxpat,1
exedrus,1
alex_(extremexworld),4
zoey_v_(character),4
stole,0
mangoicy_(character),4
merle,4
annie_(jeso),4
avantaberrant,1
shootingstar,1
forearm_tuft,0
spitbug,5
hilary_locke,4
jmy_(character),4
legionaire,4
hanybe,1
neemon,5
beavis_and_butt-head,3
skyffan,1
butthurt,0
khanco,4
copper_top_(mlp),4
yaxerins,4
tweaker_(artist),1
willa_dragonfly,4
licking_sheath,0
vimz,1
shoresan,4
drchops,1
aarifleman,1
kosyo,1
amagaeru_(hylathewet),1
butch_(animal_crossing),4
pandadox,1
belzeber,4
dhelmise,5
olive_the_other_reindeer,3
grineer,5
moaina,4
jaryl_(character),4
barabird,3
ladyjessica,4
wildlifers,3
araphre,4
jc_(lafontaine),4
jhusky,4
mind_reading,0
skeeve,1
milftails_(herro),4
darkpatito,1
blossom_(solarbear954),4
demidelerious,1
the_fuzzy_princess,3
mysterycrab,1
katey_rune-fang,4
laurel_lightfoot,4
roden_(rodenbeard),4
mablevi_eto,4
bird-chan_(bebebebebe),4
homo_the_domo,1
obamicon,3
fellock,4
lucemon,5
pandaischub,1
sunnyleafeon_(mintyspirit),4
skull_kid_(character),4
bayernsfm,1
fenghuang_(kung_fu_panda),4
killing_bites,3
fratz,1
red_goo,0
eyewear_around_neck,0
lobotomy_corporation,3
tmnt_2014,3
pikanjo,1
aria_calliope,4
danielsung,1
sigrid_(oblivion),4
prunuscerasus_(artist),1
number_3,4
codras,1
taur_penetrating_feral,0
welsh_sheepdog,5
glowing_feathers,0
innunammi,1
lucy_the_comrade,1
eve_softpaw,4
olivia_dusset,4
duraludon,5
kiro_(kiroyal),4
gooeykazoo,1
rex_(fours),4
scaled_belly,0
masturbating_under_clothing,0
monarquis,1
merry_(animal_crossing),4
dark_elbow_gloves,0
samantha_(samantha-dragon),4
hands_around_waist,0
beastmon,5
striped_kerchief,0
miss_jenine,4
scratching_self,0
magpie_igor,1
saranya_deerwalker,4
pawkaf,4
toxic_soda,1
liuke448,1
rez,4
boyfriend_to_death,3
marshal_(serbiansockfight),4
yellow_antlers,0
blitzit,1
diboci,1
hula_dancer,0
grumpy_feathers,1
zephyri_q_wolf,4
kolt,1
enter_the_gungeon,3
wxyjs1997,1
whored_out,0
courtyard,0
dront,1
logan_(geckoguy123456789),4
theo_nyx,4
footpaw_hands,0
regreto2,1
paige_(snapshotstami),4
holly_(lammynatrix),4
art_mercenaire,1
laying_on_breasts,0
phat_smash,1
loot,0
meturem,1
zapphira,4
true_finch,5
ps4_controller,0
freja_vann,4
femboi_(lewd_can),4
roxie_roux,4
penis_squeeze,0
moria_parrell,4
pointing_back,0
calvin_(disambiguation),4
urbanator,1
fade_(koboldwrangler),4
ranlunghsu,1
nurse_giraffe_(toybox_pals),4
lazy_work_horse,1
lewa,4
oculus_(brand),3
drin_(character),4
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tetae,1
black_accessory,0
merellyn,1
bodily_fluids_from_penis,0
yvette_(jigglephysics),4
ooe_senbee,4
detectivebubble,1
ken_dalton,4
krona999,1
rhino_guard,4
breast_suppress,0
mikeyama,1
madana9622,1
hands_in_water,0
hierozaki,1
holding_letter,0
telkie_(patchkatz),4
rain_flower_(cafe_plaisir),4
stabbed_heart,0
lamekun,1
heart_sticker,0
paradim,4
oscar_(olimacdoodles),4
vales_(fvt),4
emanuel_(ritts),4
chick+a_boom,3
hisseefit,1
tan_jacket,0
matokiro,1
fev_mutant_(fallout),5
blue_curtains,0
shit-tier-skunk,1
davenachaffinch,1
brown_chitin,0
boyaka,4
s_gringo,1
gralo,4
pilot_(shepbutt),4
brother_fingering_sister,0
missmccloud,1
beach_volleyball,0
jerry_5779,1
honeyslushi,1
varra_(dragon-tear),4
snowcario_(artist),1
cow_(petruz),4
pink_wall,0
nessie_(submissivetrout),4
100_variations,0
bathroom_floor,0
spicy_wahtte,1
vm94,1
1978_rainbow_pride_colors,0
ninjin_(ninjin_clash_of_carrots),4
elysabith_(elysabith_maxima),4
tommy_(tommybunz),4
chitgor_daimei,1
langjingshen,1
donation_alert,0
solar_comet,4
touching_back,0
wisespeak,1
two_tone_bow,0
dizzyt,1
privy,1
cashmere_(cashmerix),4
pup_(puppyluxe),4
shamenando,1
gerovort,1
haylapick,1
x0009000x,1
jasmine_(loneclaw),4
shaded_sketch,0
umbralant,4
water_breaking,0
emynsfw06,1
lady_watamaraka,4
baltoy,5
cody_(bastion),4
beam_rifle,0
devin_(onta),4
gingie,4
malika,4
18th_century,7
yvonne_(lolzguy),4
basculin,5
diaper_inflation,0
batman_beyond,3
fur_boots,0
plebbles,1
throat_transfer,0
sierra_the_eevee,4
mio_(zaggatar),4
gen_the_calico,4
leviathan-29,4
buttertoast,4
rockarboom,1
contingency,1
arvie,1
xalda_(future_ver.),4
echoeternal,1
skyevixensartwork,1
cum_peril,0
rikyuu_quartz,4
warforged,5
smudgedcat,4
starfig,1
captain-cheto,1
bearpatrol,1
mintgears,1
blowup_doll,0
darzycat,1
crustle,5
belle_(lilo_and_stitch),4
jeff,4
endless_cum,0
testing,0
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kalika-tybera,1
jake_spidermonkey,4
anonymoo,4
love_train,0
containment,0
septicemic_(artist),1
grace_(chalo),4
pixels_bunni,4
abraxas,4
kaelith,4
queencold,1
barachaser,1
without_panties,0
aidan_(koyote),4
flookz_(character),4
yotsubato!,3
hiddenmode,1
cassie,4
cian_yo,1
bongo_drum,0
zant,4
ghouliegrim,1
mr-punctual,1
negaren,4
deku_baba,5
tess-27,1
lorddarke,1
zach_(gamerfox),4
arkbeast_(artist),1
wolfenstein,3
taj_(kman),4
koishi_komeiji,4
vacuum_pumping,0
koutou,1
noxor,4
aerusan,5
dinamitecupcake,1
kaioshin,5
justice_(artist),1
cum_in_toilet,0
darkyamatoman,1
ash_(fantastic_mr._fox),4
izumi_mahiru,1
spookysushi,1
chinstrap_penguin,5
umbra_(k3ta),4
gucci,3
popgoes,3
dakota_thunders,4
forest_of_pixiv,3
yotomoe,1
rudolph_the_red-nosed_reindeer:_the_movie,3
king_of_red_lions,4
nina_(breath_of_fire),4
tetsuro,4
rufus_(-rufus-),4
chairman_meow,4
pull-up,0
liminalbean,1
kemonngo,1
sibsy,1
kumbhker,1
strelka,4
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nerevar,1
enaeric,1
clogged_toilet,0
skunket,1
tsarin,4
maine,0
kryptonian,5
cracked_screen,0
audria,4
defiant,0
nudging,0
jolan_joestar,4
defending,0
bindle,0
technijui,1
tellico,1
zuki,4
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faith_(8chan),4
smokepaw,4
black_rose,0
crossed_fingers,0
nose_touching,0
shido_ya,1
rabbit_vibrator,0
equine_ears,0
mind_alteration,0
blacktip_shark,5
ps3_console,0
veiny_pussy,0
thatgrouchybadger,1
trilobite,5
ladyjekyll,1
rilea,4
wynnoa_(tolerain),4
renoit,1
red_wall,0
gary_the_snail,4
1deathpony1,1
kariga,4
gavit,4
kda,3
alex_jager,4
rooster_(the_secret_life_of_pets),4
foxidro,1
triangular_ears,0
m9kndi,1
mrs._wilde_(weaver),4
zoosadism,0
icarus_(darkgem),4
scp-3887-b,4
nekomata_ftnr,1
suikoden,3
micro_panties,0
salvia_carhina,4
adam_(spottyadam),4
vyrenn,4
aries_(zodiac),4
floaff,1
lanie_bannister_(lildredre),4
ashwyn_natharadas,4
andybear,4
marina_marshmallow,4
yen_sign,0
paper_(mlp),4
kind,0
rotodisk,1
wunderhase,1
chandra,4
copier,0
subaryun,1
pineco,5
moped,0
kemonosuki,1
catapult,0
underarm_carry,0
tygrus_(character),4
swooning,0
overgrown,0
skycrown,1
bronze_(metal),0
izuna,4
blowtorch,0
tisha,4
vdru7,1
onagi,1
metalgreymon,5
schutzstaffel,0
catherine_(alpha-wolf),4
the_scream,3
terra_the_troodon_(character),4
everette,4
rap,0
sandybelldf,1
imposing,0
alura,4
greatredfoxwolf,1
aunt_polly,4
clenching_toes,0
martin_bell,4
nomozuni,4
bloomth,1
tentacle_cum,0
kami_cheetah_(aja_williams),4
feran_blackridge,4
gorn_(species),5
reese's,3
zerpes,4
tail_whip,0
denise_(meesh),4
gnarlton66,1
king_louie,4
appmon,5
kludge,4
illaoi_(lol),4
fenrir_(rrowdybeast),4
necronomicon,4
norijaga,1
timothy_fay,1
dotaluit,1
seraphimkitsune,4
shiinotic,5
colesutra,1
san_(san_renard),4
herculesrazor,1
scrap,0
billieboypup,1
kung_fu_cat,3
lightly_breeze_(pony-way),4
pepperpkmon88,1
clyde_(blackbear),4
clovishusky,1
clothing_gain,0
elbow_on_table,0
trash_bun_(itsded),4
dragon_knight_leon,4
syu,4
wilm_(drawholic),4
monika_(klausd),4
she_scale,1
mandu_kuma,1
dj_booth,0
notafurrytho,1
balls_on_tail,0
after_cloacal,0
windstraw_kidwell,4
joey_(digitalpelican),4
fenrih,1
amai_puwai_(character),4
rulean,1
lizzy_(fish_birb),4
kiday_(kiday),4
bachri,1
spotty.cat,1
kuro_(tzug-12345),4
politepuppet,1
anther,0
may_harper,4
dirtyt's,1
cadenza_vivace,4
illfang_the_kobold_lord,4
annyglaceon_(mintyspirit),4
thundurus_(incarnate_form),5
termiteterror,1
luffsas,1
takeshi1000,1
pants_pee,0
questioning_heart,0
on_hands,0
yellow_mongoose,5
zoo_52,3
curved_arms,0
black_ring,0
goatboydraws,1
ribbonne,1
nyanners,4
1920,7
meadow_(meadow.dragon),4
green_necklace,0
sydney_van_der_boor,4
verde_okuro,4
heroin-burn,1
baraqueen,1
perfect_cell,4
insides,0
shoxxe,1
terra_vi,4
dis,4
han_drake,4
narric_techna,4
ken_(littlerager),4
leiks,4
warumono_tomii,1
alex_(lundi),4
elsa_(brand_new_animal),4
gay_bear,4
shukinhekon,1
water_tentacles,0
killystein,1
pepperidge,4
blushy,0
electric_toothbrush,0
u.s._air_force,3
toumal,3
tail_eye,0
nanney,4
white-beaked_dolphin,5
glasgow_smile,0
natasha_(spotty_the_cheetah),4
noah_mcmurphy,4
lex_(servalex),4
arflin_(species),5
fulguris,4
melira_(pibby),4
on_plate,0
eliza,4
barbed_tail,0
blood_on_stomach,0
electronic_musical_instrument,0
herm_on_humanoid,0
alyssa_kintana,4
hal_(artist),1
sawyer_snax,4
azuki_(fortnite),4
against_railing,0
numbered_heart,0
mal,4
dot_(dalmationcadet),4
myrotiggy,1
larissa_(lowrider-girl),4
moogie_(twistcmyk),4
mako_(ketoarticwolf),4
tribal_hunter,3
gunzo,4
resting_legs,0
scuty_(artist),1
sorin_(cabura),4
atta_catto,1
boke_(bokensfw),4
jawful,1
passenger_railcar,0
yovi_thing,1
zurianima_(yagdrassyl),4
farengar_dazus,4
sam_(cobalt_snow),4
mika,4
nomax_(character),4
cain_(superiorfoxdafox),4
gimuza,1
wisteria_bell,4
basdl2003,1
chana_kim,1
vanilla_(tirashanks),4
prince_(gym_pals),4
gem_(gemkin),4
reallycoykoifish,1
business_card,0
hair_heart,0
toru_(koalcleaver),4
brown_yoshi,5
tied_to_tree,0
nioh,3
yukibuster-z,1
viceviev,1
bithurga,4
box-of-chalk,1
cherry_feyre,4
detective_gammei,4
penis_in_tentacle,0
walkway,0
tek-fox,4
noruu,1
hero_(snofu),4
small_cabbage_white,5
cookie-pone,1
sadwhale,0
nico_fluff,4
laiche,4
kinfolk_(species),5
white_cape,0
petal_(kilinah),4
lightningkimba,3
ellie_(zp92),4
nekoeko,4
bileshroom,1
somnacanth,5
grabbing_own_ass,0
torn_jacket,0
bonkers_(vimhomeless),4
zoey_(zoeyleafy),4
o0aquadragon0o,1
galvan_(ben_10),5
beastcub,1
komorebihebiart,1
dark_sheath,0
orange_fingernails,0
rick_griffin_(character),4
bless_online,3
james_cabello,1
sphinx_taur,5
marya,4
kira_(kira),4
eyes_rolled_up,0
aries_(symbol),0
val_(valthejean),4
0w0wer,1
power_line,0
bee_movie,3
twinkletwilight,1
sleight_(jacksleight),4
multi_nostril,0
dustbiter_(hurricane),4
tinto_(artist),1
charonnix,1
amayasenpai,1
faith_miller,4
after_cunnilingus,0
detective_hat,0
celestiawept,1
pig_p0318,1
wanderlustzing,1
grey_apron,0
tedd77,1
ma-xx,4
zawn_(character),4
skirt_only,0
coronavirus_(organism),5
hanging_by_legs,0
tarnb,4
the_mist_walker,1
dusty_(dustwolf),4
the_sea_beast,3
blackgriffin,1
magic_item,0
holding_package,0
lewd_die,1
vanilla_(glacierclear),4
argiopa,4
mgangalion,1
aban_wrench,1
enoughinnocent,3
sleepysous,1
campfire_donut,1
piecing,0
big_hipped,0
rymogrime,1
acara_(neopets),5
suspension_bridge,0
harrison_maker,4
leg_on_side,0
chip_(pickles-hyena),4
izen_puppyfox,3
yaoifairyxxx,1
oneko_takasuke,1
ana_margarita_(leviantan581re),4
harima,4
sandbox,0
raihan_(pokemon),4
fenderquil,4
noboru_(sakamata),4
ne_(nefuraito336),1
snack_food,0
koviell,4
holding_switch,0
demi_human,5
n64_logo,0
igxxiii,1
torausimuma7,1
efilon_draghi_(nolife05),4
huge_arthropod_abdomen,0
bebe_bernarde,1
lonich,4
chowba,5
aries_(cheetahmen),4
biers,4
minato_yuu,1
naviwavi,1
hazel_(lewdshiba),4
elias_bigby,4
ankie.,1
ghost_fox,4
pencil_holder,0
choco-floof,1
turning_the_tables,0
crystal_dynamics,3
echo_(echoic),4
cut_(wound),0
pandaking757,1
calie_(s2-freak),4
flourish_glade_(pananovich),4
orange_boots,0
liquor_bottle,0
ragnir_(brawlhalla),4
mottled_balls,0
shion_(that_time_i_got_reincarnated_as_a_slime),4
blaze_monstrosity,4
white_knee_socks,0
kancho,0
tettyamansp,1
meadowbrook_(mlp),4
chase_(character),4
alfred_krastgon,4
oculus_rift,3
eddie_(atlas_rysios),4
cherry_(ztwidashz),4
caww,1
element_(zapcatelement),4
hi-rez_studios,3
marius_(fursona),4
back_horn,0
phoenix_adverdale,1
kobe_(luka),4
oreo_(kilinah),4
kinkybird,1
soendapuss,1
dizzi_(morhlis),4
bikini_shorts,0
medusabold,5
ram_(deeroni),4
blackstarv,1
spicy-eva,1
kasumi_(garasaki),4
alfred0sauce,1
ticklegasm,0
sleeveless_vest,0
union_jack_bikini,0
ethernsfw,1
halfbaked-hope,1
ellie_rinette,4
monotone_leotard,0
okuri_yamainu,4
tina_(nexus),4
taylorillustrated,1
g41_(girls_frontline),4
tidbit,4
nin_(kimacats),4
mako_mankanshoku,4
handles_on_back,0
mori_(wittyusername),4
miko_(wolf-skoll),4
fox_(figgylicious),4
ear_flick,0
xanadus,1
finger_on_face,0
white_goo,0
oto,4
ao32686845,1
tylor,4
evan,4
hydrowing,1
golfinho,1
nera_abey,4
brad_carbunkle,4
dumas,4
chaos_star,0
koutanagamori,1
laguna_silverjaw,4
spider_(minecraft),5
strangelymediocre,1
varlastreak,1
emiechii,1
fio,1
meal,0
rickenbacker,1
lykus,4
phantom_chica_(fnaf),4
dasaod,1
xaniya,4
tani_(tani_da_real),4
the_legendary_starfy_(series),3
atiratael,4
violet_(pnkng),4
escoria,1
3trees,1
dawn_(decollie),4
trinity_(warframe),4
ava's_demon,3
kirisha,4
syynx_(character),4
icarus_the_phoenix,4
goagon,5
freedom,0
embriel,1
terraraptor,1
percytheplatypus,4
astr4ltr4in,1
tiedup,0
standing_behind,0
entoxia,1
danny_(anakuro),4
wolky,4
naughtycactus,1
painted_hooves,0
kazzypoof,1
kiyttani,3
nanase,4
sundown,0
chutkat,1
canal,0
nekrall,4
v-sarg,1
arin_(letodoesart),4
beam,0
nogitsune,5
semi-colon,1
7-car-pileup,1
roaringmoon,1
malice,4
wolfsrain88,1
hybrid-no.1,1
aleutia,4
obelisk_the_tormentor,4
mega_mushroom,0
stormdragonblue_(character),4
lamneus,4
bxblazexd,1
crusader,0
dogfight,0
synkardis,4
headlamp,0
hooked_penis,0
abyssal,4
holly_(monster_rancher),4
tsutsun,1
gluttony_(kuroodod),4
boa_hancock,4
long_day_at_work,0
alchemy_stars,3
cold_yellowtail,1
denatri,4
shinozaki,4
aaron_(arlon3),4
palace_pets,3
mindless,0
wakasagihime,4
james_(kinojaggernov),4
beak_fuck,0
isaac_(colonel_klink),4
angry_koala,1
horse_lover,0
siggurd_bjornson_(character),4
kubora,1
eelektrik,5
skib,4
nextime,1
zara_(katnay),4
zerbert,0
blue-tailed_skink,5
ziggy_(wildering),4
canime,1
ponilove,1
ronindude,1
gbrushandpaint,1
fenikusu,1
1uped-art,1
pirill-poveniy,1
once_upon_a_forest,3
tyrannomon,5
the_batter_(off),4
bayzan_(artist),1
john_wilde,4
naneki,4
butt_envy,0
thexiiilightning,1
kushishekku,1
portia_(animal_crossing),4
ciel_(nicobay),4
alice_madness_returns,3
ming_(shadowzero20),4
kitti_belle,4
sailor_jupiter,4
rocking,0
alyn_gryphon,1
anal_beads_in_mouth,0
blue_sphere,0
bamumu10,1
toe_ties,0
animal_strife,3
barelynsfw_(artist),1
mallowchu,1
nox_(nox),4
pete's_dragon,3
lukinda,4
tan_cheeks,0
troglodyt-3d,1
hiding_object,0
nadeah_fur,4
justdrox,4
ag-wolf,4
philippe_(oooinfin),4
raenix_(species),5
undermafia,3
katie_killjoy_(hazbin_hotel),4
kumbomb,3
abtp,1
dekto_cat(oc),4
lolicept,1
linklynx,3
pongotora,1
yuki_(enotou_moi),4
katherine_(sem-l-grim),4
kirinit,1
iideek,1
green_towel,0
caillie-ann,4
ledge1606,1
aiko_makura,4
sheep_costume,0
ace_(lilo_and_stitch),4
duncan_the_aphid,4
henry_(henrycharizardboi),4
raidak5,1
mythological_nian,5
gummibar,4
kyssick,1
cosmic_mane,0
buttplug_pull_out,0
get_stickbugged_lol,3
manzanita,4
icarus_(tjin),4
don_(slipco),4
pokéball_necklace,0
trashbinpanda,1
rythmyr,4
xexue,1
severed_testicle,0
siegerabbit,1
lego_monkie_kid,3
polka_dot_bow,0
purple_(among_us),4
k-vrc,4
ancient_dragon_lansseax,4
gracidea_flower,0
dusk_(duskdgn),4
sexual_exploration,0
dragon-storm,1
shedon,4
black_powder_weapon,0
strigopoid,5
leather_hat,0
remi_(retrogear),4
kaen_nakayoshi,4
valkaveri,1
silviaxrk,1
skull_hair_accessory,0
digitalfurbelow,1
orez_art,1
lip_sync,0
katja_(chimangetsu),4
lime_(kemari),4
sibithia_the_cat,4
fox_girl_(ehs),4
big_mustache,0
olaf_(animal_crossing),4
green_storm_(oc),4
mega_man_battle_network,3
sourcherry,1
dimsun,1
will_(wolfpack67),4
levi_(betaetadelota),4
morelia_(snake),5
askknight,1
zyro_(zyrothedragon),4
mishark,4
eliatrope,5
falling_object,0
gaze_indicator,0
squibble_(rabblet),4
karou_(thekbear),4
mitzi_may,4
controller_on_sofa,0
shio_(quilava),4
hair_on_floor,0
elm_(glue),4
monokosenpai,1
mia_rose,4
kuroamekemo,1
laila_zephyrine,4
emerald_(viroveteruscy),4
iamoddabellatay,1
z_tsuruo,1
tom_(1ceberg),4
artians,1
debbie_(lewd_dorky),4
highware,1
20_(skushi),4
karol_(lombaxtail),4
tabunie,4
phanterbull,4
celestian_(ashnurazg),4
one_horn,0
zwishi,1
supported_leg,0
max_(maxpany),4
celestial_wolf,5
amamizu_sl,1
nyxie,5
uneven_legs,0
crystal_lizard,5
gina_(satsumalord),4
air_puff,0
throat_slitting,0
glamrock,0
fellatio_while_penetrated,0
scream_(movie),3
manebaloart,1
kuroshiba_nagomi,4
kumari,4
panne,4
iron_kodiak,4
kazusa,4
feral_noises,0
mtmcats,1
tom_(isolatedartest),4
ayumu_gouriki_(odd_taxi),4
leaf_arms,0
neonpeppermint,1
toriel_(altertale),4
kingdragyn,1
holding_both_shins,0
bluelikestea,1
frost_(gyrodoodle),4
sollarian,1
paskanaakka,1
cat-sith_(sao),5
cooking_tongs,0
elechtronshock,1
olli_(sharkrags),4
bunbijou,1
discarded_swimwear,0
dark_mask,0
chillyspicky,1
lilythekitsune,1
cum_burp,0
plug_when_not_in_use,0
xenphira_hollyvine,4
kushinadahime_(full_bokko_heroes),4
ggponk,1
clara_(meesh),4
baiying,1
skullbird,1
azerith,4
orcane,4
soulwing98,1
candy_cane_in_mouth,0
dress_bow,0
skye_rackham,4
mimicry,0
anne_(arcnod),4
logo_censor,0
gail_redmane,4
stockholm_syndrome,0
choki,1
bahamut_(dnd),4
orange_leaves,0
heart_eyewear,0
tight_bikini,0
freekiller,1
pink_liqueur,1
reminnoir,1
ahdrii,4
holding_jar,0
endra,4
woods_(dreamkeepers),4
jacqueline_renier,4
hypercawk,1
on_another,0
mother_goat,4
imminent_cock_vore,0
seamonsterping,1
0.05,1
white_cat_(taku),4
april_strongarm,4
mandy_koopa_(slb),4
rumble_randolph_(character),4
the_intern,4
golub1ka,1
eradossfm,1
swimming_pool_ladder,0
acanthurid,5
knot_frottage,0
ingrid_(harry_amoros),4
brinstar,1
kebi_(character),4
kororoman,1
paul_hayden,4
neck_pussy,0
holding_sandwich,0
tree_humanoid,5
arakune,4
pooh_(kaboozey),4
monodie,1
diapered-buns,1
pinky_out,0
maximilian_acorn,4
anges,1
thespanker_(artist),1
black_pillow,0
alexi_(alexithewerewolf),4
marlin,5
thb886,1
greg_shepherd,4
lucy_(cooliehigh),4
rithe,4
heart_belt,0
aegis_aurora,4
eli_(fleetwoodbrak),4
marshall_(shewiff),4
sideless_dress,0
phia_(impishhyena),4
llama_(parasitedeath),4
ms._tiger_(toybox_pals),4
flower_clip,0
tight_speedo,0
scarneck_(artist),1
shelby_(chimangetsu),4
violeta_herrera,4
nikki_(nikki_forever),4
harpsichord_(artist),1
kosame,1
erica_(igph),4
ruzne,4
sydney_(nobby),4
minmiki,1
admiral_brickell,4
dodogama,5
icecreamwolf,1
charlie_mandango,1
one_panel_comic,7
escape_from_tarkov,3
winged_yoshi,5
oliver_(striped_sins),4
big_outie_navel,0
primeleap,3
penny_prickles,1
anaeya_elizabeth,4
luka_(lucario),4
roc_(xenoblade),4
registereduser,1
sarah_(simplifypm),4
joid_(itisjoidok),4
draxial,4
squaliform,5
nebulilac,1
dickarmdaryl_(artist),1
felris,1
bitesize_art,1
handlebarsprites,1
kissing_forehead,0
elder_lemurian,5
nanbuskhan,1
mtcerber,1
magmarift,1
prowler,5
weso,1
hannah_artaje,4
pantyhose_down,0
janitor_closet,0
goldlightyou,1
azzy184,1
wedding_garter,0
needle_rifle,0
noodle_(jolteon),4
thedax_(character),4
wuffeet,1
karma_(lol),4
nocimon,1
hotchkisstank,1
eris,4
lucky_the_leprechaun,4
ava_seer,4
bosomancer,1
tentacle_dog,5
joshii,1
amia_(backlash91),4
retweet,0
kae_goldeye,4
tirantherex,3
kenfoxx,1
squellac,5
pink_pacifier,0
summoner_(doom),5
bloodletter,5
the_almighty_tallest,4
cum_stains,0
jackiry,1
omnitrix,0
larrydraws,1
krazen,4
nekazzy,1
imminent_suicide,0
yasumitsu,1
zoe_zamora,4
yukikiba,4
tanjil_skooma,4
sashimi,0
paintfox_(character),4
sailor_venus,4
girtablilu,5
avery_(animal_crossing),4
mircelio_(character),4
cough,0
underage_drinking,0
spitted,0
jaehnne,4
liliel,4
fidgit_(character),4
gelatinous_cube,5
zeroslash,1
barbed_girls,4
periodic_table,0
death_(tarot),0
red_button,0
f-thefirst,1
onion_rings,0
arakkoa,5
feeding_bottle,0
2=8,1
barrzalex,1
celestiathegreatest,1
anna,4
joaobear,1
hobgoblin,5
city_scape,0
kubikitsune_(character),4
baby_carrier,0
pamela_(thea_sisters),4
bun-bun_(aquabunny),4
illeroi,4
azure_(lemonynade),4
judy_hopps_allegation_challenge,3
devilstophat,1
sammy_(murrmomi),4
hyundai,3
loki_(cheatnow),4
charlie,4
squidt10tickles,1
primaldialga1992,1
yzmuya,1
shaving_cream,0
matthew_(oop1oop2),4
nue_stargazer,4
ryver,4
trapped_in_socks,0
darama,1
blizzie_(blizziethelion),4
karakusa.p,1
gopher_chucks,1
delilah_aurelian_(fiftyfifthfleet),4
purple_coat,0
wolf_of_dreams,3
japanese_armor,0
bellylliumu,1
slime_string,0
nanozapz,1
peppermint_(disambiguation),0
pacho_(olimacdoodles),4
ben_thompson,4
shino_inuzuka_(full_bokko_heroes),4
maasdam_(zentagas),4
cringecorecat,1
multi_tone_headwear,0
scratchmarks,1
notamo,1
zoom_out,0
valentine_(skullgirls),4
diantha_(zaviel),4
selin,4
misentes,1
red_cap,0
artemis_bloodfang,4
living_scarecrow,5
wia,1
milo_khembre,4
hands_on_knee,0
fuckallofit,1
pattern_wall,0
critter_catcher,1
doctor_noc,4
horn_point,5
deetr,1
humanoid_penis_with_medial_ring,0
kazyan_(kazy0008),4
janet_(vhsdaii),4
himerosy,1
cassandra_(tabra),4
traitmill,1
hau_(pokémon),4
vtalfluffy,3
sukarabe_ren,4
trace_(my_life_with_fel),4
elfein_(character),4
moziqiao,3
morrigan_(morrigan_the_marwari),4
enigma_(daltenigma),4
nignogs,1
genital_theft,0
clover_(1-upclock),4
14:19,7
general_cataloupe,4
kiwi_(mutantnight),4
nightmarionne_(fnaf),4
pepperoach,1
princess_syalis,4
public_aquarium,0
felicia_cat_(character),4
whiskey_(redwhiskey),4
maraart,1
amayakasuneko,4
bluescr33m,1
moth_lamp_(meme),3
audition,0
garro_facebreaker,4
pikachu_ph._d,4
fushigiboshi_no_futagohime,3
gelus,4
objection!,3
gravity_rush,3
torri,4
faymantra,1
james_t._kirk,4
caltro,1
murskme,1
aulann,4
compsognathus,5
karr'serath,4
capital-h,1
mocca_(character),4
lunamon,5
fleetingember,1
tail_flick,0
soulofmusashi,1
queen_yamul,4
kodalynx,4
kyala,4
in_memoriam,0
neckbeard,0
norphen_(character),4
raus_(recklessarts),4
lani_(bcb),4
flintheart_glomgold,4
mercury_light_carbuncle,4
rentham,1
covering_up,0
norwegian_elkhound,5
alpha_(character),4
cybersix_(copyright),3
guardian_dragon,5
doggycoffee,1
classical_unicorn,0
unwrapping,0
milo_(furfit),4
pahanrus2,1
danterain,1
ian_(drawdroid),4
trisha_(desmondpony),4
ordos,4
ptsd,0
mizu,4
mr._herriman,4
feather_mane,0
foxtrotarts,1
fingers_spread,0
akumademo,0
exhaling,0
frazzle_rock_(mlp),4
kahix,1
ollie_(spirit_bathouse),4
pholus_reborn,0
kaku_(kluclew),4
traz,4
cum_on_gloves,0
collection_tank,0
pink_marky,1
mino,4
scissor_blade_(kill_la_kill),0
grovyle_(asbel_lhant),4
golf_course,0
medigun,0
lady_lo,4
comparison_bet,0
yaktan,4
agumon_(survive),4
doggiemonkey,1
rickroll,3
puss_in_boots_2,3
yipkin,1
midriff_jacket,0
ms._green_(m_m),4
fantasy000,1
skillet91,1
catree,1
ryuma210,1
dalmatian_father_(pickles-hyena),4
biscuit_(disambiguation),4
white_freckles,0
thurkitten,1
mikosake,1
bobby_(fluffy_gf),4
demichan,1
thigh_scar,0
stadium_lighting,0
giraffe_print,0
wet_tongue,0
raechel_jagger,4
duda_(moki),4
bexley_(scappo),4
p.r.e.g.,3
rachellebun,1
awfuldendart_(artist),1
filtrandez,1
bicolored_tail,0
antzy,4
kitrosun,1
leif_(helvetia),4
saltypantz,1
athena_(megustalikey),4
black_sandals,0
lia_(fluff-kevlar),4
gosounokitsune_sonhaku,4
tundra_(stellar_tundra),4
professor_fluff,1
eifiemoth,1
kiwikltten,1
star_tail,0
anus_behind_g-string,0
tdxlabs,3
deadboltreturns,1
krow_(miso_souperstar),4
hibiscus_(thecon),4
side-tie_bottomwear,0
chris_(bluesh),4
blender_(disambiguation),-1
zol_(naniwacomteg),1
thehashbaron,1
finikart,1
bunta_(sisiwaka_3),1
vakari,5
monsters_at_work,3
acidic_saliva,0
fluffy_feet,0
futon_(artist),1
dei,1
dante's_inferno,3
jesse_(neus),4
measuring_cup,0
kroxy,4
unwanted_attention,0
grouse,5
aleksai,4
jessica_albert,4
konqi,4
tjdolphin,1
longboard,0
detachable_arms,0
marineangemon,5
frankie_(modjo),4
ryker_(wildering),4
lagoon,0
ryelletibun,1
lesson,0
malfestio,5
shadowfangz,1
james_bond,4
tassle,0
chaos99,1
safira,4
taylor_(onta),4
barbariank,1
orange_blush,0
blink_(artist),1
helen_(johnbrittish),4
bobskunk,4
pyrite_(oc),4
syl_(psykrow),4
mato,4
tesla_coil,0
tzarious,4
planet_of_the_apes,3
ymskk,1
velenor,1
nightwind005,1
kikivuli,4
aiza_(flashw),4
para-dice,4
bane_(species),5
zhiral,3
roland,4
paulgq,1
z-crystal,0
aron,5
zooshi_(character),4
ahzi,1
penis_harness,0
metal_skin,0
shooting_range,0
king_the_gengar_(character),4
ayelet_ripley,1
ageinu,1
corwyn,1
energizer_bunny,4
flayra,4
thaine_(character),4
malroth,1
head_kiss,0
muzzle_feet,0
extra_fingers,0
the_wuzzles,3
protatypo,1
light_machine_gun,0
rayne,4
prime-l,4
baraaya,4
zetaxen,1
dasaki,4
storm_hawks,3
porforever,1
staples,0
easkull,5
gou,1
fang_out,0
iris_(twokinds),4
mobile_suit_gundam,3
alinu_(roadiesky),4
dagger_leonelli_(character),4
artbeta,1
kvnpoulsen,1
inakagurashi,1
karen_the_fox_(mexicommie),4
forced_labor,0
throatpie,0
mawarisaka_(character),4
dominion_tank_police,3
shizumoh,1
koge_(kogenta_blackfang),4
dorothy_(artist),1
sleeping_top,0
yuriyuri_(ccc),1
tentacles_in_pussy,0
amimalcrossin,1
latex_crop_top,0
chelostracks,1
carmine_acciai,4
hobbled,0
silicas,4
breath_control,0
brown_socks,0
mummy_dragon,4
geoff_(yoshi),4
jennifer_bourne,4
nighthead,1
penta_(cum.cat),4
traxex_the_drow_ranger,4
reina,4
gatten,1
3pac,1
sonique,1
sarah_n._dippity,4
digiknight,3
lizette,4
patron,0
kitsyfluff,4
emi_(character),4
esdeath,4
dunn,4
var'kel,5
karu,4
trolley,0
pentabins,1
notfornothin,1
hicktown,4
artomis,4
tilo,4
oglaf,1
wild_eevee,1
sonicaimblu19,1
pedals_(pedalspony),4
oqtozille,1
desmond_sutherland,4
corgimarine,1
zaiel,1
fringedog,1
syria_(tid),4
purrgis,4
violet_(limebreaker),4
miguel_(bgn),4
nappana,1
loopy_tuberose,4
sinful_ink_(artist),1
penna,1
checkhoff,1
detailed_penis,0
musouduki,1
pound_(space_jam),4
foxgirl83,4
wilfred_butterton,4
foxosexual_(lane),4
queek_headtaker,4
trinanya,1
yapo,1
imminent_group_sex,0
parfait_(dessert),0
dormant_otter,1
derpsickle,4
jabot,0
through_window,0
daisuke_amaya,1
electronic_device,0
kokoha_is_here,1
hackel,4
lux_(character),4
brother_fingering_brother,0
novaray,1
deegee,1
christine_bayle,4
brown_chair,0
honeyson,1
nikkora,1
satoshi_(nakagami_takashi),4
atelierlily,1
typhoonteaser,1
krahs_tsai,1
nessova_(only0neman),4
shani_uzuri,4
wired_headphones,0
yaco_puma_striptease,3
aya_(thekite),4
loki_roland,4
nefitail,1
crunchyspoon,1
tinder_skitty,4
gurgling_balls,0
shy_worgen_lady,4
gris_vala,4
zorro,4
yamausagi0619,1
dhole_(kemono_friends),4
pickup_(disambiguation),-1
monkey_wrench_(series),3
starling_(bird),5
nagawa_tomo,1
face_on_butt,0
cutting_chipset,4
13:20,7
dasoupguy,1
shugariosa,1
mr_dark_and,1
joe_(disambiguation),-1
celebrity_paradox,0
emoji_censorship,0
chichu_littlepaw,4
penis_theft,0
unsigned_nez,1
aviators,0
booster_(fuze),4
knife_sheath,0
professor_marmalade,4
clutter,0
sangguri_32,1
floor_lamp,0
dmin,1
lhacedor,1
blue_makeup,0
yamwamm,1
dragon_quest_ii,3
darkraifu,4
klavinova,1
cheek_pull,0
princess_rei,1
the_flash,4
kairiyu,4
kenny_mccormick,4
aevsivs,4
tora_(toradoshi),4
cmaggot,1
utx,4
bad_friends,0
ajumia,4
cunny,4
ricky_(bleats),4
endium_(character),4
uneven_eyes,0
south_africa,0
fatality,0
banana_split,0
slumber_party,0
tsuki,4
xenosaga,3
darkness_(konosuba),4
chaosrruruus,1
rufen,4
moe,0
ryosuke_ishigami,4
ezzie,4
sasisage,1
joducus,4
ember_(deathhydra),4
cloud_quake,4
wimbocs,1
living_ship,5
static_(animal_crossing),4
gilian_(character),4
ningenzoo,1
gruntilda_winkybunion,4
roadkill,0
raskvel,5
the_cat_in_the_hat,4
mysterious,0
elwinne,1
lotosu,1
betsythebeaver,1
crimson,4
kusuda_korone,4
arios,4
snarbolax,5
aaros_(character),4
pahpreeka,4
cassie_the_rock_vixen,4
johannes_cuthbert,4
distention,0
nekan,1
vividred_operation,3
fitz,4
santa_coat,0
feligris,4
berserker,0
hunter_(left_4_dead),4
ghost_rider,4
rosa_pyle,4
stella-chan,4
latex_socks,0
natsuco_7star,1
ada_carlia,4
wolf_nanaki_(artist),1
ohfour,4
skidd_(character),4
jelly_otter,4
passpartou,1
zennithm,1
multum,4
cane_toad,5
banryu,4
twifoxy,1
misterbunny56,1
exuli,1
tapering_dildo,0
cleyran_maiden,4
telim,4
hyakkin,1
cornish_rex,5
saber_(raevocrei),4
drayce,4
chi's_sweet_home,3
riorix,1
hindu,0
mistergre,1
allegro,4
kinglikeme,1
agentmoose,1
ray_cyber_tech_(character),4
american_kestrel,5
jankingen,1
drayl_(character),4
rear_pussy,0
drowtales,3
asimos,1
obix,4
scylla,5
b_j_bear,1
nife_(character),4
ikkakumon,5
sahak_darkcloud,4
alternate_ending,0
nekrozs_(character),4
keyhole_lingerie,0
zika,4
byser,1
tibolf,4
venus_the_hedgehog,4
dr._heinz_doofenshmirtz,4
malani,4
lucy_loud,4
cara_black,4
erro,4
kaydee_ackland,4
valeria,4
flossy,4
vampony,4
mouse_princess,4
the_feels,0
himawari_shinomiya,4
sachiko,4
zima_(arknights),4
breezy_(lotp),4
puni_101010,1
i_am_kat95,1
ourboiroy,1
trick_moon,3
magenta_(character),4
cassandra_pines,4
kuromu_(nekojita),4
jay_(jaythefox333),4
derkeethus,4
sweet_biscuit_(mlp),4
onxel,1
stereographic_panorama,0
saren_(saren662),4
fingers_in_hair,0
big_shot_(splatoon),5
flare_phoenix,4
gz_draws,1
chargin'_chuck,5
deren_(kagami_valgus),4
fae_(fire_emblem),4
echto,1
gold_ear_piercing,0
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licking_abs,0
carne_(inkplasm),4
peach-,1
alexis_(ketzio_and_gbb),4
black_slime,0
niwa0w0,1
fizzarolli,4
natasha_(mammaawd),4
leaning_on_desk,0
turned_around,0
urbanica,1
the-crowfox,1
okamical,1
happyraccoons,1
ayame_(zillford),4
aviesergal,1
no_shirt,0
dendollae,1
white_outerwear,0
archibald_thorburn,1
ren_(aceren),4
starwarriors,3
uthalla,4
igazella_(oc),4
micaxiii,1
salisha,4
wet_arms,0
spotted_swimwear,0
jerome_(ginga),4
fenwolf,4
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mitri_rukin,4
spike_(the_land_before_time),4
freezietype,1
sweaty_neck,0
porn_cover,0
kyler_underwood_(avok),4
pine_squirrel,5
veiny_hands,0
bullbluedog,1
lance's_dad_(kloogshicer),4
bethany_blacksboro,4
emsevell,1
roop_(the_roop),4
ram_satinga,1
male_protagonist_(pokemon_sv),4
holly_(aygee),4
kassiradragon,4
fan_tail,0
weedymeadowfawn,1
hoisxi,1
therapsid,5
priscillia_(xilrayne),4
mosh_(oc),4
linhe_(mklancer00),4
drinking_from_condom,0
fish_boy_(elranno),4
vicvance,1
blackrabbitshone,1
serenity_(leafysnivy),4
1c_company,3
dan_(paintfox),4
live_action,0
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orlena,4
versus,0
electrical_outlet,0
calot,5
velux,4
saina,1
mastersounds,1
futalova1,1
studded_anklet,0
hajinn,3
fattydragonite,1
spot_pig,0
torn_swimwear,0
croc_(petruz),4
geoffrey_the_giraffe,4
maicha_(briefgarde),4
smallking,1
linda_(james_howard),4
orenjimaru,1
re_jin37,1
defense_forme_deoxys,5
new_world_vulture,5
elex_the_smeargle,4
stargrave,1
cam,4
milkcat_(hataraki),4
microshorts,0
srfkkk,1
sergeres,4
gardening_tools,0
frankensteinsmonsterfucker,1
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kevin_(sff),4
blossomblaze,1
touching_ears,0
dawn_wings_necrozma,5
pudding_(jakkid13),4
thokk's_daughter,4
selen_tatsuki,4
oskar_(coffee.png),4
r4t1cky,1
white_sports_bra,0
discarded_dildo,0
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zannos,4
dharma_(zeromccall),4
digimon_taur,5
gao_53937056,1
gecko_(fallout),5
cum_in_tube,0
multicolored_pupils,0
double_cervical_penetration,0
inkoart,1
elaine_budderbup,4
goblintown,1
27:22,7
matt_(firekit),4
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rudolph_trnd,1
green_tree_python,5
tent_through_fly,0
male_receiving,0
inkgelato,1
themadrabbit,4
alpine_ibex,5
lutz_(lutz-spark),4
zamuzaza2,1
herm/andromorph,0
may_dog,1
wolfen_(wolfracer559),4
evan_(cloud_meadow),4
ren_hana,4
bunonii,1
trixie_(eg),4
yuka_(animal_crossing),4
gajo,1
nerts,4
kabramari,1
treehouse_of_horror,3
marshal_moo_montana,4
linuell_s'eleross,4
hang_in_there,3
devicre,1
samanya_mohatu,4
d8,0
brooke_reed,4
gamera_(series),3
ruby_(salkitten),4
chazore,4
hotlegmeme,1
pokeality_love,1
julia_(ungulatr),4
araiguma,4
onigrift_(character),4
adrian_alexander,4
dark_spyro,4
kanokon,3
darkria,4
otachi,4
crablouse,1
rpadi,3
lucky_and_chocolate_charms,3
arzuros,5
modestyshorts,1
gondola_(spurdo),4
lemlem97,1
tina_rex,4
meoxie,1
owen_(adam_wan),4
spitroast_(object),0
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the_mole_(htf),4
quagga,5
erra_(tits),4
taihab,1
ceiling_cat,3
davide76,1
glenda_(trias),4
boxice,1
ice_cooler,0
mindora,4
lena_lardoon,4
lazy_amp,1
gamer2,1
tension,0
ookamicasha,1
milk_bath,0
the_mute_one,3
starry,4
richard_katellis,1
aiming_at_another,0
team_instinct,3
kelly_(tits),4
sashido_zerako,1
shuna,5
baba_looey,4
serandite,4
twinfox,1
truth_or_dare,0
naotohirai,1
danilo_correa,1
blushing_penis,0
flame_(pkmndiamond493),4
krideg,1
ti_(spazzyhusky),4
b2u5n,1
kinomikinobori,1
cylynix,1
suzumusi114,1
unicornblue,1
constipation,0
archer_gilgamesh,4
kawiku,1
kitsune_drifty,1
leno_woof,1
mining_helmet,0
natalie_kintana,4
tarsier,5
spread_thighs,0
verusha_(tits),4
scarfshepherd,1
steller's_sea_eagle,5
jaynator1,1
sonya_(averyshadydolphin),4
schnuffelienchen,4
lewd_lotl,1
daigo_kumano,4
on_heel,0
aki_(rilex_lenov),4
tacoma,1
aria_stormwood_(frooby),4
okochira,3
felicity_(rainbow_butterfly_unicorn_kitty),4
lynxwolf2,1
herm_focus,0
anahi_viron,4
tsubasagahoshi,1
valvi369,1
erovsaaaka,1
tacoma_(character),4
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dkdevil,1
sexyspots,1
elite_the_espeon,4
urethral_all_the_way_through,0
hong_kong_phooey_(character),4
kalta_(blokfort),4
underview,0
moisesgrafic,1
bottom_with_big_penis,0
lobi_top,1
walmart,3
jude_williams,4
iwano.kal_(artist),1
dale_(mykiio),4
gū,4
segufix,0
metal_virus,0
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maleherm_penetrating_female,0
inside_shoe,0
angel_lazzu,1
sora_(warsgrasp),4
raxel,1
o'hare_(animal_crossing),4
sammy_(sammy73),4
club3,1
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cum_on_soles,0
vlad_von_knottenburg,4
anatomical_diagram,0
digimaru,1
nox_(yajuu),4
disbelief,0
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restraint_device,0
ciderbunart,1
skylar_(terq),4
spacedog,1
zipperhyena,1
kobuchi_(524koki),1
green_bikini_bottom,0
hellfox,5
zangtober_2020,0
emadbz,1
holding_shoes,0
littlethieflyra,1
skaith,4
kamalia_mingan,4
softhanten,1
demon_eyes,0
oh.chert,1
"meibelle_""belle""_renee_rougeux",4
victoria_(arrkhal),4
caramelthegoat,1
ace_(ayukawataur),4
bag_of_lewds,1
cubism,0
kansen,0
two_against_one,0
panda_(catkitte),4
bunny_boy_(artist),1
chance_(experiment),4
striped_beak,0
heart_gem,0
cedrato,1
crosner_code,1
izuchi,5
skoll_wernersson,4
kisa_(donshadowwolf),4
squash_(fruit),0
litsi,1
howell_(ventkazemaru),4
mousey_love_girls,3
yopo9810,1
fortune_(fortunefox),4
weaponized_breasts,0
yellow_heart,0
babyfier,4
siakstuff,1
caphound,1
multicolored_shell,0
andro'neesa,4
gaogamon,5
miitara,1
mednia_vahl,4
steeb_the_boar,4
sippy_cup,0
asphalt,0
taiyaki,0
kuro_lolita,0
text_outside_panel,0
jenokamui,1
megan_bryar,4
gwen_(spiritfarer),4
louie_(jishwalob),4
shota_fong_(character),4
basketball_jersey,0
puko_(aggretsuko),4
chest_plates,0
tatugon,1
isaaclou,1
attached,0
flaming_head,0
breast_sway,0
holimount,1
kraken_d'waggin,4
heathcliff_rogers,4
fruit_bikini,0
6strife9,1
flamey,4
cuirass,0
shyny,3
ying_(paladins),4
sheath_pull,0
andrew_swiftwing,4
austin_abel,4
volleyball_court,0
puppyloveimani,1
goldi,4
ko-shu,1
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cocoa_puffs,3
nanodirty,1
bloodline,3
bloodhound_(artist),1
patty_dangao,4
futalova,4
designjh,1
byokiboy,1
splooge,1
monstrous_nightmare,5
disembodied_breasts,0
implied_prostitution,0
lionheartcartoon,1
zonuche,1
malboro,5
togenanafushi,1
fireman's_carry,0
emukon,1
kanon,4
patch_(101_dalmatians),4
rod,0
succ,0
jking1200,1
furloid,3
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sayo,1
ailin_gardevoir,0
madoc,1
ric-m,1
adar_nasiykh,4
nottesan,4
shelmet,5
goro_majima,4
drega,4
serendipity,4
octopussy,0
serendipity_the_pink_dragon,3
holmssie,1
tooca,4
phenique,4
booou,1
mysterymanbob,1
sky_(xenonwolf),4
dakr0819,1
mozeeeeeeeee,1
perokisu,1
domestication,0
dier_von_dan,4
zuri,4
raindancer,1
arius,1
zafo,1
katherine,4
corrin_(fire_emblem),4
waiting_in_line,0
koromon,5
kayin,4
tail_scarf,0
innards,0
jerking_off_hand_motion,0
w.d_gaster,4
bedpost,0
flats,0
deviliveb,1
ricochet,0
frigidmidnight,1
dominic_(redrusker),4
chateau_de_monster,3
janeen_brotherthat,4
green_mask,0
dubcub,4
amelia_(twokinds),4
irotha,4
anal_pull_out,0
room_701,0
ne_sun,1
littlemomoko,1
shine_ali,1
zenobia,4
viga_(character),4
pepper_(lord_salt),4
group17,3
riven_of_a_thousand_voices,4
kneeling_on_ground,0
hands_on_another's_chest,0
sillyfoxart,1
sprinklepoo,1
mcarspec,1
altered_shadow,0
lindy,4
yumyumyeen,1
kotya,1
suspender_skirt,0
msdeadsoul,1
cyberjoel,4
kamikazetiger,3
sasha_(sashaviel),4
bit_(bit-small),4
vorgy,0
euclid,0
marko_comics,1
twig_(umbra),4
thiccrobots,1
roropull,1
bec_the_rabbit,4
grounder_(adventures_of_sonic_the_hedgehog),4
nangi,4
shambles,1
cylus_karamele,4
saltwater_crocodile,5
thefishe77,1
vegetoid,4
alice_the_vixen,4
vaskurknsfw,1
bushy,0
rayna,4
bbmbbf,1
dr.p,1
aeris_(foxxd),4
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calliecho,1
selva,4
wolf_pack,0
med121,1
slithice_the_naga_siren,4
superman_(series),3
scribe_the_grey,4
sailor_suit,0
lora_northstar,4
redboi,1
wolfeus,4
mustang-blaze,1
heresy-darling,1
dodge_charger,3
andgi,4
lord_ocean,1
ratchet_zufreur,4
chris-sowden,1
lotta_lamour,4
it's_a_trap!,1
vita,4
daydream_kinamoto,4
thewildwolfy,1
bad_breath,0
bee_(mykiio),4
snakegood,1
ravenpaw_(warriors),4
makkon,1
zephir,1
cum_on_tree,0
tiare_mehran,4
imiak,1
yennsee_miller,4
rielle,1
ezreal_(lol),4
haefen,4
dieselblue,1
1989,7
toe_cleavage,0
emma_(emma),4
officer_pai_(miso_souperstar),4
mercury_gray,4
light_headwear,0
gabe_(onom),4
taint_lick,0
raised_surface_support,0
meicrackmon_vicious_mode,4
kongthegrain,1
francis_(lost-paw),4
dildo_in_slit,0
hermaeus_xerxes,1
d2_(marsminer),4
maku_(burrserk),4
birshy,1
takosen,1
korwin,4
urine_on_fur,0
bloody_bunny_(series),3
tucked_shirt,0
hornyartzsucks,1
threatening_aura,0
ashley_(shaymin),4
superdry,3
ta777371,1
shoolmail,1
jasienorko,1
momo_(mymyamoo),4
commander_d,1
beckerdobermans,1
firecracker,0
zigs_(infinitydoom),4
vore_transformation,0
penis_fingers,0
keith_(kitfox-crimson),4
lykan_(endertwinks),4
hangetsu_(ko-gami),4
grex,4
courtney_gears,4
bydena,1
parkwillbark,1
landomcflurry,1
ogami_aratsuka,4
orio_(orioles03),4
olive_(conker's_bad_fur_day),4
tan_kerchief,0
tail_through_keyhole,0
zippy_(lunaris_parukia),4
too_fast,0
princess_platinum_(mlp),4
switch_pro_controller,0
logan_(disambiguation),4
kami_the_cat,4
folded_clothing,0
doomlard,1
interactive_porn_game,0
black_forest_cake,0
landlord_(wick),4
pan_red,1
kalla_(snarlin),4
komugi_(lee),4
lotus_(lotusgoatess),4
solopipb,1
hungover,0
water_drip,0
kimber_di_furetti,4
muroku,1
kirk,4
morghus,4
toongrowner,1
jennifer_l_anderson,1
bambi's_mother,4
bizarre,0
jimmy_the_fox,4
chaor,4
clcoon,1
shimajiro,4
suea_sowwet,4
redevan_tauron,4
soloid,1
sechs_fuckheaven,4
skyress,4
byere,4
bojiku,1
jake_(the_rescuers),4
fumei,4
blue_leg_warmers,0
pop_glace,1
mana_(manahallowhound),4
wingzord,4
lexi_redd,4
ozone_griffox,4
fynncat,4
gku,1
sebastian_(lemon_smoothie),4
grumpmuzzle,1
paimon_(genshin_impact),4
childhood,0
noctobi,1
cetancodontamorph,5
fluffy_arms,0
weon1119,1
dt-b,4
touching_tail,0
mikeyex,1
squchan,1
kouhaku773,1
irbeth,4
andromorph_on_human,0
adamayster,1
hitting_head,0
lizzard_ych,1
mophingu,1
n0b0dy,1
red_gag,0
crooked_glasses,0
blairtheglaceon,4
beaked_toad,5
hiro_(toa),4
the_vermander_curse,3
jax_(yeen.queen),4
kana_(ironkongstudios),4
ishan,1
radar_(thetwfz),4
parsujera,1
alex_(ticktockfox),4
nikki_(pogonip),4
ondatra,1
andres_(elfein),4
catherine_(wolfpack67),4
veiny_neck,0
clara_(funkybun),4
rupira,4
priscilla_(slimshod),4
chase_(chasedatotter),4
sicklizardman,1
schrodinger_(hellsing),4
stretch_(alluringcryptid),4
black_wolf57,4
kuon_(telson),4
azimoert,4
aziran_(fluffybastion),4
buoy,0
lopht_desarls,4
light_blush,0
whirlott_(insomniacovrlrd),5
chubbyfurries,1
ashley_(warioware),4
evil_hare_(monster_rancher),5
boxofmonsters,1
ser_ori_tuft,4
cum_in_urinal,0
stardust_(vulpesfoxnik),4
sangerla_(anselme),4
mary_muffin,4
bogard_(fate),4
dizzy_(animal_crossing),4
qoppa,1
2tipsie4me,1
daria,4
elly,4
coshi-dragonite,1
snuggly,4
greed_(kuroodod),4
mstuarn,1
argyle,4
makare,4
symbolhero,1
dusty-pixels,1
bearlylegal,1
yougrover,1
ken_ichijouji,4
urethral_vore,0
mackeymike,1
robin_savage,4
drayjay,4
elliot_(pete's_dragon),4
legionoftoast,1
wazzart,1
skewer,0
fake_blood,0
raptor_jesus,4
ballroom,0
jyaguti,1
diamondback_rattlesnake,5
brett_booth,1
fox_gungrave,1
leelee,1
tynx,5
aydan,4
bro_bun_(hladilnik),4
vixult,5
raella_(tluuvyen),4
toynuke,1
lpawz,1
marcelo_canario,4
sheila_(rocko's_modern_life),4
the_dakota_dude,4
zett,4
flattered,0
brummington_the_chipmunk,4
springtail,4
ben_saint_james,4
bootz,4
unown_d,5
colored_eyelashes,0
richard_vulpes,4
red_outfit,0
quirrel_(hollow_knight),4
ryotsuke,1
vincent_(luvbites),4
dark_jacket,0
nasuno_(yosuke),4
locke_(spoongod),4
alpha_(mating_season),4
kabu_(pokemon),4
flashing_penis,0
palm_frond,0
tan_legwear,0
utamu_d28,1
skal_maneater,4
lantu_(artist),1
emilios,1
delta_(hardtones),4
doughroom,1
fully_submerged_tentacles,0
alex_delobo_(character),4
emufur,1
jasy,4
anthony_caligula,4
ezra_(dookfiend),4
saliva_on_tentacle,0
mermaid_position,0
polygenerational_incest,0
sitting_on_character,0
pinky_(doom),5
hands_on_own_ears,0
rocket_wisp,5
sinatzeek,1
spinel_(steven_universe),4
hachimaki,0
enia,1
foot_on_furniture,0
mawfulme,1
sunelchikito,1
indigo_(useful_bear),4
jack_sunshine_(oc),4
cube_wisp,5
juicestandmango,1
katauri_interactive,3
slasher_(dead_space),5
the_legend_of_luo_xiaohei,3
kaiden,4
wicpers,1
shelly_(skullman_777),4
downward_dog_pose,0
devilbeing_(artist),1
commodore_international,3
black_miniskirt,0
back_door_bat_(nokemop),4
slavekini,0
colored_seam_briefs,0
mackerel_(fish),5
leila_(playkids),4
hozunomi,1
falling_asleep,0
photo_album,0
hop_pop,4
howl_(grimart),4
ffloofball,1
cuckoo_clock,0
ddguy,1
20daysapple,1
orioz,1
tanning_lotion,0
rubber_mask,0
vol'jin_(warcraft),4
stack's_womb_marking,3
balancing_object,0
holding_snout,0
red_headphones,0
light_jewelry,0
thorinblender,1
nadja_(borba),4
cardueline_finch,5
varzek_(character),4
spek,4
katia_(demicoeur),4
light_censorship,0
"melinda_lou_""wendy""_thomas",4
hearth,0
sweet_tomato,1
arte_(r-mk),4
millie_(satsumalord),4
kalibus,4
welling_tears,0
body_oil,0
holding_feather,0
cardueline,5
thanuki,1
radar_chart,0
orphira,1
bun-e,1
red_boxing_gloves,0
snowleandre_(artist),1
mrkeypoo,1
russian_hippie_vixen,4
blood_on_fur,0
snowpaw2927,4
stephan_(spacebunz),4
traditional_moogle,5
koukysato,1
xama_pro,1
zillionaire,1
inkohaulyc-1,1
sollux_captor,4
raytee_lee,4
chase_cartwheel_(oc),4
super_robot_wars,3
nova_grimm,4
vyriss,1
islamic,0
olivier_oswald,4
this_isn't_my_horn,0
wayward_vagabond,4
insecure,0
jean_jacket,0
braindps,1
dragon-chan,1
paige_forsyth,4
rachel,4
tritus,4
don't_tread_on_me,3
leg_cross,0
jake_(scorpdk),4
uba,1
daddy-o,4
renu,4
gindo,5
hyper_mess,0
incendramon,4
etezarumonkey,1
fucktoy,0
mao_(chirenbo),4
allan_mcdougal,1
terrykurosu,1
cat_gloves,0
lillablu-e,4
zygarde_complete_forme,5
sashikari,4
coraline_loret,4
weredragon,5
jolaris,4
isithael,4
demiveemon,5
up_(film),3
malith_volskov,4
wormadam,5
iahfy,1
hammer_bro,5
makochin,1
elektro_(maxwell1394),4
tsukunertov,4
paddy_(artist),1
wall-e_(character),4
fred_flintstone,4
puffy_short_sleeves,0
darth_the_fox,4
hashire_(character),4
theman,1
khraym,4
arlo,4
the_melancholy_of_haruhi_suzumiya,3
mech_suit,0
hyruzon,1
reddyeno5,1
gingerm,1
nada,4
creature_from_the_black_lagoon,4
tiny_bunny,3
daloon,4
freya,4
lumi_the_lizard,4
ashi,1
hitman_(series),3
spritztheskusky,4
darkfire,4
ryoji,1
lass_(pokemon),4
robin_(silkysworld),4
badmanbastich,1
jahgo,1
gene_starwind,4
stormwolff,4
robot_chicken,3
tongueplay,0
minamoto_chizuru,4
wolfoxokamichan,1
karakina,1
hanekumo123,1
smogyday,1
danae,4
backalleyfallz,1
compound_bow,0
zeek,4
clothing_store,0
inkasylum,1
rad_(notsorad),4
shiki,4
ace_(kacey),4
tux_the_penguin,4
rainbowscreen_(character),4
thoran,4
stop_motion,0
hand_on_own_wrist,0
foxmon_(dgfox),4
whiplash_(game),3
purple_line_art,0
blaze_(blazethefox),4
ultrakill,3
otn,0
nyupu,1
glaze_(thepianofurry),4
mother:_cognitive_dissonance,3
lyla_the_honey_buzzard,4
mercy_leaf,4
psycheapple,1
maggie_(justkindofhere),4
mikeknight19,1
manbun,0
pith,4
seashell_panties,0
daddy_pone,1
pearbottom_(oc),4
lifting_object,0
dezi,1
almighty_tallest_purple,4
seshafi,4
everruler,1
rawr_(artist),1
helena_douglas,4
knighthoof_(oc),4
lipstick_smear,0
super_soaker,3
demarticusstone,1
gizee,4
celierra,4
odahviing,4
hoser,4
neifaren,1
koku_(kokurou),4
maiden,0
jollysaunter,1
umigarasu,1
justinfox,1
cold_penetration,0
mattdarey91sfm,1
jones_tsunamidusher,4
yaku_zorah,4
mumm-ra,4
kirianna,4
badfidhell,1
fleurdeiris,1
audience!,3
jupiter_(planet),0
metal_bar,0
hyper_lactation,0
zozia,4
ekg,0
muffin_top_(underwear),0
tamarik,4
baiken,4
lillium,1
g-spot,0
carbinecat_(character),4
nyrumx,1
ruben_(zaviel),4
king_richard,4
jamari,1
candle_holder,0
national_geographic,3
starving,0
kenox,4
tail_in_pants,0
koume_keito,1
door_knob,0
tweaker,4
kuritama,1
alvia,4
ecchi_graffiti,1
absolutely_everything,0
kishin,4
northern,0
saebira,1
sucy_manbavaran,4
kristofferson,4
paloma,4
acephlosion,1
sredrahs,1
floccinaucinihilipilification,1
neko_musume_michikusa_nikki,3
yuki_nexus,4
fraggle_rock,3
nora_wakeman,4
jamie_lamont,4
cinnamoncookies,1
andean_mountain_cat,5
delta,4
silber,1
drugbat,4
kori,4
pyxaron,4
lou_(stripes),4
fitz_(xandispin),4
largehadronkalidah,1
trancesequence,1
mott_(animal_crossing),4
fetish_gear,0
grunt,5
naote,4
phyore,4
ltl,1
masterxxxl,1
ancientbot,1
wimpod,5
kthanid,1
geckomania,1
thepurplemaniac,1
102_dalmatians,3
roxie_raccoon,4
rain-fox,1
allene,4
so_(artist),1
todd_(ttrf),4
theboundraccoon,1
frigadae,1
cot,0
cloud_(amaterasu1),4
kitsoone,1
andrew_(dragondrawer),4
jenni,4
noise,0
off,3
vivi_(malis),4
iron-dullahan,1
funhentai,3
aries_passadar,4
the_cleveland_show,3
scath_schattenklaue,4
schwertwal,1
fuvi,1
ryn_iiskra,4
jaime_sidor,1
ajie-g-sketchbook,1
rumiko_nonaka,4
conicer,1
white_lipstick,0
chevre_(animal_crossing),4
patreon_censored,0
thevixenmagazine,1
fajeh,1
mega_latios,5
banzai_bill,5
cardboard_tube,0
mila_(powfooo),4
nokino_k,1
schopenhauer,1
marlo,4
chobi-pho,1
lilith_(sefeiren),4
clexyoshi,1
shanet,4
taz_(facebook),4
e-102_gamma,4
geogreymon,5
nonchalant,0
elva,4
zalsfm,1
cattleya,4
grilled_cheese,0
juice87,1
darumaka,5
nekole,4
misterdavey,1
lacgl,1
fiat,3
gurdurr,5
butakoma_300g,1
zakoryu,1
nikkisheep,1
ritwell,1
kakashi_hatake,4
arunax,4
mrs._squirrel,4
da_boz,1
ravica,1
rika_(recklessarts),4
phantom_thief,0
averious,4
gdane,1
elektra,4
pugna,4
runei_worth,4
bonbonboy,1
lawlspasm,1
clifford_tibbits,4
candide_(amazinky),4
stepsister,0
midnight_rampager,1
cumulus_(character),4
silverferak,1
standard_mode_darmanitan,5
chainilla_(artist),1
sayunu,1
eleven_frame_image,0
potion_name,0
hands_on_shin,0
webb_(neko3240),4
coombrain15,1
extremely_distracting_watermark,0
chibiemiko_(artist),1
batboybasil,1
crying_sound_effect,0
world_trade_center,0
freya_(zavan),4
voogaroo,5
lap_pillow,0
pretztail,5
ava_(azura_inalis),4
pink_(among_us),4
kagekave,1
rye-beer,1
natasha_(copperback01),4
windreg1,1
master_panda_(comic),3
atom,0
dirtytabs,1
dark_grey_hair,0
qurnix,1
ongi,1
iynize,4
macroceli,4
bailee_(nikoyishi),4
tori_cro_(bistup),4
abdominal_piercing,0
jovejun,1
skriddha,4
kirby's_return_to_dream_land,3
white_lab_coat,0
semi-trailer_truck,0
bunnydewart,1
akkoarcade,1
wiskar,4
dobermann_(arknights),4
dangling_leg,0
edan_(irishkofi),4
caffeinatedsins,1
taiyo_no_hana,4
ninn,4
fatkylefat,1
doriangolovka,1
flower_bed,0
latiospegasus,1
jakiel_blacktusk,4
kai_(disambiguation),4
cryowolf9,1
sprrigs-,1
gwendolyn_ruby,4
ayla_(saphe),4
peresum,1
sheepish_grin,0
tltechelon,1
5_horns,0
mothersatana,1
black_lagoon,3
dreamtanker,1
hello_kitty_panties,0
apolar_(apolar),4
tesshin_(ginga),4
alysha_silverwing,4
bladed_tail,0
metaphor,0
queentexx,1
hunter_(the_owl_house),4
fun_bun,4
peru_fur,0
kelly_(delta.dynamics),4
fanny_mcphee,4
blake_dinewel,4
quinn_(behniis),4
hanzou_(tetsunoshin),4
erebus_(giygaswar),4
sexy_robot_(hajime_sorayama),4
celeste_(roughfluff),4
full_body_view,0
mr.woodsman_(dalwart),4
the_maid_(petokikka),4
double_entendre_(character),4
kaila_(scappo),4
aaden_(angellove44),4
timet,4
guitar_hero,3
bondage_straps,0
drimogemon,5
grind,0
guts_(berserk),4
ginga,4
daikikaitou,1
eli_(captain_nikko),4
luniquekero,1
kiska_romanov,4
juba,1
ahkahna,1
adria_(adleisio),4
sonson,4
acacia_prium,4
leo_altum,4
lewdbyaj,1
unik,1
marie_(funkybun),4
laikacat,4
licking_back,0
milk_tank,0
ensafir,5
renoahemiru,1
marwari,5
bunnyadmirer,4
anu_and_syl,4
the_lizard_(marvel),4
al-x,4
linsaya,1
chubbypandabastard,1
thekaimaster07,1
yiffmachine,1
jessi_(slither),4
rickwulfk95,1
xavier_jascoe,4
jeffron_purescale,4
heart_lock,0
skye_(acetheeevee),4
charlie_(jurassic_world),4
ktdz,1
fan_dance,0
anubis_(mge),4
polaris_(crackers),4
sadako_(leokingdom),4
illbarks,1
kastronome,1
hands_behind,0
petra_(hotkeke1),4
zenonclaw,1
manifest,1
log_(dog),4
meus,4
vita_console,0
grani_(arknights),4
flammie_(furamit),4
kaosu0195,1
lancer_cú_chulainn_child_of_light,4
yuel_(granblue_fantasy),4
stripes_(copyright),3
pink_blanket,0
milky_bell_(slb),4
omicronian,5
sp-rings,1
jag_(artist),1
cao_haze,4
ellen_(beastars),4
junthebun,1
tavit_khalil_(spiff),4
ikomochi,1
tachaal,1
spakka5,1
puredarkness500,1
tensazangitsu,1
jerome_(kokuhane),4
boss_hosstrich,4
rebe-it,1
nil_sunna,1
supporting_head,0
suzanne_hayabusa,4
elf_hat,0
furfurric,1
flint_(vir-no-vigoratus),4
pianitchi,5
death_threat,0
gaf1117,1
veiny_dick,0
vintem,1
unown_i,5
metal_shield,0
hypnotic_screen,0
piggy_bank,0
power_up,0
scrub_brush,0
star_catcher_(pre-g4),4
world_of_warcraft,3
kerock-ketu,1
smsq,0
peewee_(mlp),4
sunny_bear,4
sex_talk,0
rockman_(species),5
elemental_games,3
tundra_wolf,5
pushed_down,0
clothing_bulge,0
superfilly,0
inayori,4
long_tails,0
boys_taken,3
clothes_falling_off,0
morose_ambrose,1
under_view,0
coffeelsb,1
wetness,0
pants_aside,0
green_chair,0
chakat_blacktail,4
splattershot,0
silly_straw,0
rosie_(ionic44),4
thenekoboi,1
helen_lorraine,4
title_drop,0
feic,4
interest,0
flounder_(disney),4
bladecham,1
prawn,5
earthworm_jim,4
breezy,4
karishad,4
tabby_(animal_crossing),4
flehmen_response,0
yasha_greenpaw,4
randolfsparks,1
branch_(artist),1
abhishri,1
fuzzy_lumpkins,4
kaylee_(xenium),4
school_daze,3
mirror's_edge,3
ironclaw_(rpg),3
frogger,3
xanthus,4
shiv,0
camilla_(fire_emblem),4
chelsea_myriad,4
ninjatertel,1
eclipse_(gabe),4
fujiwara_no_mokou,4
paparazzi,0
sagwa_the_chinese_siamese_cat,3
christopherlee,1
donburi,1
tess_(frisky_ferals),4
biotari,1
kesper,4
ohlordyit'sjordie,1
star_font,0
ponytail_over_shoulder,0
purps,4
hex_sinstolus,1
recreational_vehicle,0
dirt_(mrdirt),4
omi_slidefur,4
cavasomnia,1
son-in-law,0
viximon,5
bronx_(gargoyles),4
kaittiolu,4
tyler_mcrae,4
thumb_in_waistband,0
edo_(rabblet),4
bfct,4
documentary,0
valorant,3
lisa_(feline_humanoid),4
cancer_(symbol),0
koharu_(blue_archive),4
baniraad,1
sledge_bro,5
gayrider8,1
momhopantsu,1
sonic_wolfe,4
joe_(nanoff),4
463_jun,1
malawrist,1
angora_rabbit,5
braum_(lol),4
salatranir,4
sharp_tail,0
cow_print_thighhighs,0
jayri_(jayri-veradian-avieock),4
huge_glans,0
glitch_techs,3
nico26b,1
apulaz,1
muscular_arm,0
maldoando,1
johnny_test,4
dinah_(vdisco),4
triste_miedo,4
archmage-rykarr,1
hand_on_moob,0
biowolfmlya,1
dark_lighting,0
teriyaki_(character),4
backbone_(game),3
marie_(gaturo),4
xander_hewett,4
galutine,1
walking_on_water,0
luong_lao_shi,4
vape_pen,0
strangersfm,1
aquarius_(scalier),4
kara_(chelodoy),4
shotbun_(anaugi),4
crapemyrtlemarket,1
great_ones_(bloodborne),5
nikole_(darkwolf),4
bigjoppa,1
tan_(character),4
duemeng,1
starfleet,0
fundy,4
droogiesparrow,1
fluffy_the_werewolf,4
grey_seam_briefs,0
frilly_bow,0
smenco_(artist),1
marth_wait,1
fin_scar,0
morikoke,1
mamoru's_mother_(mamoru-kun),4
marine_iguana,5
stephen_(agidyne),4
vorix,1
destruction_of_property,0
2_phut_hon,3
penis_across_breasts,0
rutilix_(live-a-hero),4
jcfireescape,1
furbeyond,1
multi_tone_hat,0
blorsh,0
volcasaur,4
heart_penis,0
kaomoji,0
lamp_shade,0
oliver,4
beastman_(gurren_lagann),5
cameron,4
levin_(dinglederp),4
pecs_vore,0
lykostherium,1
shield_moblin,5
bibbidy,1
balls_theft,0
kvie_cloverhoof_(kvie),4
uncensored_version_at_paywall,0
galia_(sandwich-anomaly),4
hand_crush,0
tensor,1
oliver_(ventkazemaru),4
ruhuyu_(show_by_rock!!),4
hockey_jersey,0
mesh_clothing,0
jane_(wolfpack67),4
hand_under_clothing,0
if_it_fits_it_sits,3
space_rangers,3
rotting_flesh,0
celery,0
sitting_together,0
leona_pryde,4
ribby_(character),4
runelocks,1
karmal,4
critical_hit,0
pushed,0
len4ik555,1
tan_dress,0
fluid_tap,0
sarah_maple,4
submissive_predator,0
miko_(snowweaver),4
lightnymfa,1
okureya,4
shadiel,4
seren_duskflare,4
blue_belt,0
rei_(guilty_gear),4
tahnee_monroe,4
sweaty_ears,0
adoptive_father,0
nyxt,4
ferris_(ferrissimulator),4
wheat_body,0
nylonheretic,1
camchao,1
rob_(daisy-marshmallow),4
monotone_bow,0
brutus_(amazingawesomesauce),4
shear_(disambiguation),-1
redvel_fett,1
precum_from_mouth,0
lexington_ulfric_izunia,4
johann_(t0l0k),4
marcus_caelesti,4
ozone_(antoreakk),4
someshittysketches,1
parrot_humanoid,5
suane_(suane_lightfurr),4
adsouto,1
kami_(queenkami),4
tani_d,1
marsh,0
camo_shorts,0
uri_bennet_(ryltar),4
s1_luna_(mlp),4
lenalia,4
charlotte_(redflare500),4
foule,0
rangstrom,4
amber_(pwnycubed),4
elastico,4
feliraichu,4
nude_to_clothed,0
kitana_(unhappy_raccoon),4
arkkusx,1
deerfox_(hilda),5
saitudon_(character),4
kaze_and_the_wild_masks,3
monotone_whiskers,0
banshee,5
sherman,4
dark_elven_forest_ranger,4
bucketoflewds,1
z0mbieraptor,1
thatweirdguy,1
fuckplant,5
chris_(silveredge),4
kon_shearo,4
colar,0
anna_(kayla-na),4
teacher_clothing,0
doggy_(disambiguation),-1
riley_bunny,4
brother_in_law,0
exposed_underwear,0
blue_condom,0
seatohao,1
kouga0225,1
dinedon,4
leroy_(joaoppereiraus),4
lapis_(endertau),4
cigarette_box,0
blue_shark,5
spike_piercing,0
xoven,4
bellyburster_(species),5
logan_thackeray,4
obakedazo,1
cat_nose,0
nightmare_at_freddy's,0
oink,0
paradisebear,3
twentle_olivica,4
around_drum,1
cum_on_hood,0
tan_spikes,0
cornfield,0
amber_(ping-ether),4
yellow_snow,0
brachiosass,1
gakor,4
sobakistan,3
tkckid,1
renaa_railos,4
collaborative_autofellatio,0
kafrizzzle,1
holding_sponge,0
domina_(friskyfoxie),4
mikachu_tuhonen,1
joshua_(ghostbane),4
h_the_raptor,4
sparky_sparkeroni_(mlp),4
aurora_(softestpuffss),4
nitroneato,1
cereal_mascot,0
jacket_around_waist,0
kantachibli,1
short_horns,0
discarded_hat,0
bunbunmuffins,1
lost_out_gi,1
briar_(squishymare),4
dancingchar-fetish,1
cigarillo,0
sweet_(deltarune),4
orange_back,0
kamiyu_saeki,4
vaginal_grip,0
pawpadpup_(character),4
calderian,5
raised_shoulder,0
mister_handy_(fallout),5
natey_(ritts),4
seago,4
egg_from_penis,0
pulp_(artist),1
iron_artist,0
suspended_by_belly,0
belly_noises,0
garasaki_(copyright),3
wisemans,1
flbl,1
thr33y3dg0at,1
wirberlwind,1
minoru_hyuga,4
silver_markings,0
purple_wall,0
caylleen,1
benjithatfoxguy,1
smg4,3
flexing_pec,0
looking_happy,0
rastacakez,1
amber_ladoe_(sabre_dacloud),4
white_piercing,0
animatedmau,1
firesalts,1
2n5,1
server_room,0
silverware,0
welcome_to_doozy,3
wistfane,4
arno_(terraapple),4
irollyouover,1
aerosouthpaws,1
lipstick_fetish,0
roxanne_(frostfur101),4
imminent_scat,0
eliza_(averyshadydolphin),4
night_dress,0
taurus_(zodiac),4
joeadok,1
demontower,3
aubrin,1
waffle_(artist),1
rina_(runei),4
shameful-display,1
echinoderma,1
qin_(character),4
gradient_inner_ear_fluff,0
anal_fluids,0
narkoto_the_water_dragon,4
icysage,1
popy,4
hex,4
jason_(mestiso),4
lily_(sefeiren),4
aazhie,1
gdhusali,4
roasted,0
bandit_(mario),5
masahiro,4
devlin_miski,4
wom-bat,1
0ishi,1
urii,1
the_cake,0
wckd,1
mbvgfw1108,1
sealeen_(twokinds),4
angry_sun,4
grace_(spotty_the_cheetah),4
sam_starfall,4
chaud_magma,1
fetalstar,1
bahm-bu,4
alebrije,5
regeneration,0
possumfox,4
foamy_(artist),1
patrickgainher,1
tokyo,0
mirage_(warframe),4
astigmatism,4
sabbath,4
limayra,4
sdeekkk,1
alleros_(howlingampharos),4
brown_sofa,0
cum_release,0
stacy_(satsumalord),4
2d,0
pickynickit,1
interior_background,0
vaginal_pull_out,0
ramenshopkenz,1
aileas_(fossi3),4
taffyy_(character),4
off_screen,0
nana_(peter_pan),4
realistic_lighting,0
dragonium_(artist),1
kusama,1
allie_(rimentus),4
long_fall_boots,0
francis_(frenky_hw),4
zerrex_(zerrexnarrius),4
torathi,1
catinatrash1,1
kurasuto_(cottontail),4
glistening_hat,0
bluechubbywolf,1
theo_(disambiguation),4
chair_breaking,0
a,0
warning_message,0
sars-cov-2,5
ruhh-roh,1
austin_(thomasthesharko),4
astralantipode,1
hail-fanged_dragon,5
nyctilian,5
exposed_ribcage,0
gazing,0
wooferwoof,1
mondegreen,1
surrendering,0
crest_(heraldry),0
fin_ring,0
delphox_waitress,4
diskofox,3
agent_washington,4
yellow_elephant,1
jewel_collar,0
alexander_the_fox,4
spreader_gag,0
paws_on_hips,0
tooeks,1
starble,5
tamsey_(vdisco),4
minekoo2,1
angie_(magolobo),4
ed_(disambiguation),4
unwanted_orgasm,0
vertical_stereogram,7
sejuani_(lol),4
brotha's,1
skiffkhariben,1
yoshu_makato,1
idiot_sandwich,3
asha,4
soul_reaver,3
hebrew_text,7
mark_(stripes),4
admiration,0
dink_the_little_dinosaur,3
silverzar,1
operator_(warframe),4
badendxxx,1
vortex,0
sandy_carington,4
shard_the_metal_sonic,4
marcus_techkat,4
camille_(scalie_schoolie),4
ps2_console,0
skybrigidrain,1
zeus_female,4
postalroo,1
rori,4
foxhole,3
over_9000,3
danny_(monstar),4
tap_dancing,0
strobe_lights,0
rachelle_verity,4
vijounne,4
arashi_maeda,4
tomoe,4
eddie_holly,1
shattered,0
lobstersnail,1
jasmine_(hexxia),4
mion_orz,1
taran_zhu,4
t.g.r.s.l,1
ajar_(sahara),4
figmandor,4
jude_(shmagaman),4
xander_moraine,4
cyril_(modjo),4
fearghus,4
lakebound,4
aatrox_(lol),4
the_eric_andre_show,3
princecoffeecakes,1
vinnie_terrio,4
wolfox09,1
grayson,4
sketchfox,4
alika,4
rooth,4
northernironbelly,1
alyssia_byrne,4
drakerogers,1
geena_gonorah,4
san_francisco,0
geworin,1
porrim_maryam,4
cabbit_(tenchi_muyo),5
saalim,4
battle_tiger,1
momo_(nekowife),4
plaguerat_(character),4
cum_on_window,0
aqui,1
kabaoum,4
agent_orange,1
dechroma,4
isuckusuck,1
dkqksms1004,1
eric,4
devinian,1
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ira,4
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cacuu,1
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kaala,4
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nora_(tasanko),4
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izzy_skye,4
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ooshio7734,1
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mr_chocolate_eclaire,4
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asena_(full_bokko_heroes),4
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kathy_(yajuu),4
hanryo,4
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19:18,7
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electricfox777,3
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tyltyl_and_mytyl's_adventurous_journey,3
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momiji_the_dolf_(artist),1
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hy_thegreykatt,1
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angie_(disambiguation),4
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muzy_frile,4
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rezzy,1
nowax,1
nekochan90,1
duosion,5
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ironpotato,1
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scipio_(khemia),4
scarlet_(doctorpurple2000),4
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stanley_(zer0rebel4),4
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kel_(capracreep),4
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sagezilla,4
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0v00w0paws_(artist),1
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rai_(nikuzi),4
arihyena,4
loot_llama_(fortnite),4
fern_plant,1
mossy,4
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<vampirefuuucker>,1
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ransom_(jijix),4
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keenei_thunderhoof,4
yagaru,4
genderfluid,0
monty_python_and_the_holy_grail,3
looking_like_about_to_cum,0
cattail_(pvz),5
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raymond_boxman,4
karo-amu,5
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chloe_the_copperhead,4
darktree,1
jennifer_(longblueclaw),4
alli_(animal_crossing),4
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maka_drake,4
noisemaker21,1
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max_mcconnor,4
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leo_(dragonslash),4
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amber_(kanel),4
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chiko_(dotkwa),4
amelia,4
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hyacinth_macaw,5
final_fantasy_fables,3
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maria_calaveras,4
fondue_(flowerimh),4
pitt_(organization1337),4
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cosmicpussycat,1
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tako_(takopupper),4
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magicorn,5
colton_(sparklepupfaeri),4
nephtys-ma'at,4
elana_(altrue),4
the_coalition,0
foxy_carter,4
eri_(mha),4
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charlene_palmer,4
=w=,0
glistening_elbow_gloves,0
lubanart,1
dr._nefarious,4
coccui,1
u_rakaz,1
auntie_lofty_(mlp),4
freyja_(merlin),4
casidhe,4
simon_tesla,4
nynn,4
za_barro,1
liz_(funkybun),4
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hinget,1
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g-milk,0
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chase_(frostedchase),4
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affax,4
zorgy_cuddles_(oc),4
danie_(moonski),4
petra_(carrot_kiss),4
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rezzit,4
seara_(fidchellvore),4
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mr._plagait,4
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myof,1
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regina_(kadath),4
bc4life,1
kain_(calamities156),4
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ask-elyse,0
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cogma,4
ahirulion,1
patiroku,1
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codename:_kids_next_door,3
bloodymascarade,1
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vii_rii_(vrart1),4
nafan,1
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wolf_maid,4
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koneko_(spazzykoneko),4
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ladybugk,1
jobbold,3
dorian_(furdo),4
skeley,4
cadaver_(skulldog),4
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devilukez,1
ahab_(ahugebird),4
mixplin,1
will_smith_slapping_chris_rock,3
inside_sex_toy,0
tarkir_(character),4
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quest_for_camelot,3
featureless_(disambiguation),-1
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meteorreb0rn,1
mow,1
lalieri,1
koki_(skunkhotel),4
voicelikecandy,1
skyverchase,1
wisp_(partran),4
tallula,4
kimmy77,1
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looking_into_eyes,0
funkydeer00,1
shadowkan666,1
axis_powers_hetalia,3
eren,4
scar_(predator),4
ellowas,1
nightfire,1
davo_(katnay),4
suspender_belt,0
blue_mage,4
warmth,0
manhandling,0
michael_williamson,4
greasy_(character),4
mic_check,4
avalath_tiger,1
genevieve_quicksilver,4
update,0
lomo,4
energy_shield,0
mystery_science_theater_3000,3
spunk3y_monk3y,1
leo286,1
ramona_(the_prey),4
aoi_(ittla),4
cerise_(delrar),4
tongue_clamp,0
boonedog,1
saphistar-tries-to-art,1
guepardoxart,1
laon8130,1
veiny_eye,0
sunnybakedham,1
vird-gi,1
hbo,3
bastoro,4
kitty_(plankboy),4
to'pkek,4
freia_(frei22),4
guratan_wa,1
nora_leslie,4
inkbuns,1
starpeace,1
littletrixter,1
elpis_dugan,4
tera_(tera_tyrant),4
horse-loving_dog_(marimo),4
lid,0
striped_mane,0
yournightlydesires,1
sykez,4
human_skull,0
arulouprouge,1
jamie_sharp,4
tennis_uniform,0
adam_lyon,4
bell_necklace,0
stroking_head,0
ariesartist,1
drax_the_destroyer,4
flinch,0
extradan,1
blue_roan_(marking),0
schwarzfox_(character),4
pussy_dripping,0
turquoise_background,0
arm_under_breast,0
pillow_fort,0
uzziah,4
haganef,1
zappy,1
unown_a,5
jackalopedewey,1
strawberry_print,0
john_fell,1
untier,1
ludis-luteo,1
white_chocolate_(glacierclear),4
xabelha,1
arthropod_abdomen_fingering,0
koriyama_meiko,4
kuma_(pixiv_881259),1
grakerrbraconc,1
ketsueki_(character),4
vynn_(icy-marth),4
orange11803164,1
two_tone_frill,0
moyoki,1
purple_bikini_bottom,0
brown_speech_bubble,0
animal_(muppets),4
squirrelfromthesouth,1
fantomartz,1
ghost_thewolf,1
leksi_(artist),1
white_(among_us),4
weight_gain_drive,0
sam_eeveelutions,1
rashouru,4
pink_curtains,0
itzal_eguzkia,4
defenestration,0
sarah_g_lato,4
bronzebanana,1
night_howlers,0
olivershiny,1
koro_(kokoro-doll),4
yoru_(xienoan),4
miijiu,1
jack-in-the-box-sfm,1
mxnster_fxcker,1
pride_color_scarf,0
werther,4
lino_the_lion,1
elastic,0
siekatar_(karmasrealm),4
quadriceps,0
body_of_water,0
wetwoof,1
feet_on_breasts,0
zilkas,4
wigmania,1
rainbow_pride_penis,0
monchichiwa,1
overall_dress,0
feet_paws,0
kat_bishop_(bishopsquared),4
blaze_(blaze125),4
nine-volt,4
dispenser_bottle,0
pressing_breasts_together,0
black_saliva,0
ike_(ironkongstudios),4
basilic_(character),4
gorimondo_(kirby),4
8-pus,1
tokonuri,1
tkup_(tkupbook),4
twelve_steps,1
the_pink_flamingos_(brand_new_animal),4
baby_(svtfoe),4
tinder_wailord,4
holding_doll,0
radanon,1
seepingooze,1
purple_egg,0
bloodscale_(imperatorcaesar),5
clown_shoes,0
muli,4
furvilous,1
richard_pinkman,1
ice_chip,4
kamesu_micchacara,1
gatomon_x,5
m1_garand,0
krendius,4
codytiger,4
virmir_(character),4
hand_bra,0
crankydove,1
kalashnikov,3
memory_loss,0
foxxo,4
cresce,4
jigokuyouchien,3
latiar_(character),4
circular_saw,0
mo-hiro,1
procene,4
claire_farron,4
digipixelfluff,1
lum,4
evil_bear,4
jomblluc,1
nabooru,4
nenenero,1
dennis_clark,1
ki_(kitsunewaffles-chan),4
lanoro,0
lq_(character),4
daniel_coppren,4
doug_ramses,4
mineva,4
kotaotake,1
bing,4
matt,4
shiarah_(character),4
commander_feral,4
rikoshi,4
silverblaze,4
ingo,4
rubonku,1
ashley_(strider_orion),4
eschiver-monty,4
theomegaridley,1
ruran,1
xena,4
coconut_crab,5
mgx0,1
restraining,0
silverbolt,4
agent,0
golden_snub-nosed_monkey_(kemono_friends),4
bino_(housepets!),4
blood_bowl,3
kiiro_the_scrafty,4
put,1
petkitten,1
pac-man_party,3
falkor,4
greening,1
ashelin,4
cadmium,4
shovel_knight_(character),4
ostuffles,1
sha'ra_(fariday),4
rute_(driftlock),4
dairuga,4
thel,5
fossa_(artist),1
nixi,4
kimen,4
yori,4
winona_nelson,1
balls_in_clothing,0
hood_down,0
boobart,1
10_legs,0
cum_on_photo,0
fiarel,1
axel_(animal_crossing),4
storm_devil,1
bf_109,0
moominpappa,4
sunset_nivaris,1
treecky,4
constance_johnson,4
kota_(hattonslayden),4
kyrathehyena,1
kagekitsoon,1
karmasi,4
morto,1
norikowolf,1
princess_luna_(eg),4
gravity_(character),4
snistrtaz,3
huffzie,4
tgtf234,1
riosha,1
oatmeal,0
peter_rabbit,4
salessi,4
hackman23,1
belfry_(tamberlane),4
gold_dildo,0
ethan_(bastion),4
jaron,4
sponsz,1
das_(character),4
pontiac_firebird,3
lifesaver,0
winged_pikmin,5
uniformvixen,1
joveehusky,1
laa-laa_(teletubbies),4
gthusky,1
ajvulpes,1
jackoffrabbit,1
kakithefox,1
selena_hala_(oc),4
burl_(connivingrat),4
wuzzle,5
olkl1234,1
nerfocalypse,1
okami_(artist),1
psychomittens,1
exposed_skull,0
seamus_o_tuathail,4
cinnamon_(dashboom),4
cleo_(twin_dragons),4
striped_antennae,0
bugbytes_(artist),1
toga_himiko,4
tenga_egg,3
xiao_ye,4
nicole_(yourfavoritelemonade),4
firra,4
female/tentacles,0
technoblade,4
little_moth_bro,4
ottlyoo,1
ha11nu,1
kai_the_dewott,4
dacsy,1
abyss_(shirakaisuri),4
volkswagen_beetle,3
mike_griffon,1
shait,1
monster_love_hotel,3
qigiu,1
17:19,7
european_robin,5
rx1213,1
sarin_(saerro),4
vulpevex,1
unknown_language,7
zeffin_(zefaa),4
nyaur,1
lonelytear,1
dualshock_1,3
tani_rinkan,4
k_pring,1
amika_zhona_(kimmikammi),4
noodlerain,1
kuraon,1
afelinesguiltypleasure,1
gradient_wings,0
laundry_machine,0
layla_s.,4
amber_(raphael),4
irbeus,1
kotaro_kou_(kotarokou),4
cawaden,4
joker_(2019_film),3
glacius_draconian,4
taffetta,4
thestinkywolf,1
two_tone_cape,0
bonk_town,1
ketlimur,1
mara_(natura),4
magra_(rezisa),4
ppt_(ppt),4
25:16,7
amelie_(lf),4
language_barrier,0
cheezar,1
ashalle,1
yusa_(polygonheart),4
queen_(band),3
kidney,0
nunu_(lol),4
light_yagami,4
deaf,0
broken_neck,0
squadrick,4
runnerman360,1
shina_(bloody_roar),4
flint_(bad_dragon),4
onedirtydog,1
banishment,0
fire_fart,0
elastrator,0
azalea,4
stella_(balto),4
obscurabuns,1
meowing,0
penis_sitting,0
miekyll,1
shibatheinu,1
dnaxcat,3
natalie_grayswift,4
guff_(fortnite),4
mothux,1
kessler,4
ball_penetration,0
super_soldier,0
boss_battle,0
dc_super_hero_girls,3
inner_mouth,0
tears_of_rage,0
raven_fields,4
jordan_wolf,1
levxrus,1
futaba_sakura,4
carrots_(starman),4
natasha_(kitfox-crimson),4
8haon8,1
aoki_kanji,1
care_bear_cousins,3
ventral_scales,0
removing_condom,0
roadkill_(sufficient),4
rockstar_chica_(fnaf),4
bardis,4
thatdirgedude,1
paprika_(miso_souperstar),4
lightsongfox,4
cartoonsaur,1
nyx_(quin-nsfw),4
taithefox,4
qaoxia,1
zipper_(character),4
tavix,4
orange_tail_feathers,0
yellow_ear_frill,0
box_tie,0
cheeki,1
hilda_(hilda),4
barnaby_(overzen),4
qīngjiǔ,4
gandalf_the_grey,4
erial_kathranis,4
holding_belt,0
orbulon,4
saniko,4
aar0njay,1
alicefrainer,1
lapis_(chowdie),4
weblure,1
vancefluff,1
public_park,0
cocooned,0
pumadime,1
nova_(ketzio_and_gbb),4
owlflame,4
maxwell_(hamtaro),4
isis_(mythology),4
sandy_(kemono_friends),4
flou,1
chi_(cat),4
maria_pappas,4
sollace,1
iti_crossgender,0
khamira,4
kuavera,1
windzurartz,1
lumia_(raven_wolf),4
numbertwos,1
shadowhulk,1
corruption_(species),5
pan_flute,0
vergence,4
short_mane,0
clippers,0
aircraft_carrier,0
tizzle_bizzle,1
spike_spiegel,4
trying_not_to_laugh,0
fat_grab,0
chiger,4
motosuwa,1
scaffolding,0
checker_pattern,0
demon_on_angel,0
guana,4
neji_neji,1
the_king_(armello),4
shimera,4
slicing,0
sprinting,0
boneless_(mlp),4
glowing_hand,0
plotcore,1
soap_dispenser,0
shoe_laces,0
bloodshot23,1
nazi_salute,0
bobbycheez,1
sixfoot,1
noodle_arms,0
shakumi,1
mana_(skimike),4
yarn_in_the_closet,3
akiwarumono,1
sammi,4
pticenoga_(natalie_de_corsair),4
doragia,4
translucent_thong,0
spitz_(princelykaden),4
erogenous_change,0
celestbrook,1
looking_at_own_genitalia,0
gold_armwear,0
13titoazazel,1
caramelhooves,1
zahra_(dragonlordfluffyxd),4
zady,1
marci_avilov,4
hermit_moth,1
pierreperifel,1
paz_(kkoart),4
sapphire_(flafty),4
metal_barrel,0
mitts,0
klaushy,1
shuichi_kagaya,4
nihilistdigby,1
marco_(marcofox),4
tape_recorder,0
yoke,0
sandra's_day,3
jasmine_(rowanakita),4
sea_grass,0
improvised_buttplug,0
maddie_(back_to_the_outback),4
led_(ironkongstudios),4
puffy_(ayaka),4
neff_(smneff),4
kara_(picti),4
gaia_(charityssongbird),4
gyi.,1
puffkiss,0
hyper_flare,0
cat_cutout,0
shaver,0
father_fingering_son,0
shared_thought_bubble,0
light_whiskers,0
hot_dog_costume,0
apologetic,0
serbaka,5
angela_(disambiguation),4
scruffydraws,1
tiny_arms,0
raven30_(artist),1
full_exadian,5
mia_(marbearis),4
potara_earrings,0
kinaisan22511,1
cat_stockings,0
kuso_800t,1
funkybun_(character),4
ophelia_(critterdome),4
maloo,4
genital_fluids_on_body,0
twokindswierdo,1
mirig_(aygee),4
leongar,4
ivy_(eerieviolet),4
raythefox,1
murenase!_seton_gakuen,3
bigok,1
penetrated_by_pumpkin,0
spencer_(spencer),4
mikes,4
kobold_(5e),5
quartz_xtal,4
jessicanyuchi,1
bellamy_(gasaraki2007),4
shilensis,4
scarlet_(oofrowdy),4
wing_ring,0
arm_length_gloves,0
rfta,1
aurora-chiaro,1
khaybat,4
squeak,0
dem,4
tsuna0,1
baxxid,5
redmiles_the_shark,4
solita,4
plasdurock,4
nommz_(character),4
koobi,4
mythcomplex,1
texy,4
whyteyote,3
miko_(character),4
dickpenis,4
hoshi_kubi,1
nightlinez,1
haripo_(artist),1
noons_(character),4
tyelle_(character),4
kyra_(cadray),4
feminist,0
azure_velour_(mlp),4
professor_birch,4
backwards,0
veedramon,5
gkg,1
nycket,1
neon_fur,0
scamwich,1
xxmioxx,1
luminari,5
humilliation,0
val_raptyress,4
drake-van-howler,4
ozyosa,1
nobody_(kingdom_hearts),5
laish,4
kiki,4
craid,4
drake_(tasuric),4
papers_please,3
pentoolqueen,1
clairissa,4
melanie_(cainesart),4
tekuho,1
tyni,4
rebel_(nuclear_throne),4
kawacy,1
kael,4
wolfcat95,1
resurrection,0
theodore_cooper,4
madoka_kaname,4
ponyecho,1
penny_darwin,4
kilowolff,4
frostibunni,4
half-orc,5
flicker_show_(oc),4
ono,4
kagesan,1
nut-bar,1
bruno-dz,1
coren,4
yurii,4
cloakedthief,3
rosa_(gvh),4
la_pockis,1
valiamoonseer,4
blackrose_(.hack),4
tanpopo_hayabusa-maru,1
mistic_hobo_(artist),1
wyla_(furball),4
murphy_(ajmarekart),4
dirtydooddoodlez,1
alex_(my_life_with_fel),4
buzz_the_bee,4
darkarlett_(character),4
chien,4
polarwave,1
luca_(nouyorus),4
rams_(world_flipper),4
zem_(artist),1
ren_(vee4eva),4
dynamictrigger22,1
angrytotemwolf,1
zylk0r,4
stella_(gvh),4
boypretties,1
verelin,1
lavin,4
goatonomous,1
dirtyhorror,1
pokéball_censor,0
yumich,1
couvert_(kishibe),4
kirby's_dream_land_3,3
colorwrath,1
schmuccubus,1
myrian_(veo),4
suction_cup_dildo,0
nsfwbunniii,1
padjet_harrington,4
african_golden_wolf,5
mikhael_rosenburg_(oc),4
leofel_barraund,1
chin_balls,0
leo_simensen_(s0uthw3st),4
cuadraws,1
joze_osaka,1
vashja,4
multicolored_sex_toy,0
hestis_(fidchellvore),4
butt_window,0
hufnaar_(character),4
warden_(minecraft),5
nobinolife,1
darkadibuja,1
butt_rubbing,0
the_boy_(the_last_guardian),4
bodskih,1
augen_sinum_(character),4
rexton_industries,1
grey_antennae,0
timber_(oc),4
fansl,1
liviuh,0
mooexe,1
zeriie,1
cash_(character),4
tighs,0
abstractunitorn,1
carnivore_cafe,3
perineal_raphe,0
skapu,1
iron_collar,0
outtabellets,1
mizuchi_(beastars),4
purple_loincloth,0
verobunnsx,1
zashi_(ashes),4
thick_feathers,0
ghostface_(scream),4
one-piece_suit,0
number_tattoo,0
chris_redfield_(resident_evil),4
veloceryx,1
glowing_head,0
screen_light,0
zakurujay,1
dekamaster,4
brightosaur,5
darius_koopa_(character),4
xxxbattery,1
bucklesandleather96,1
mirror_universe,0
artistorphy,1
mutants,0
maelice,1
skerpent,5
ohyuki,1
kecha_wacha,5
themanesex,1
kennel,0
philosophy,0
moving_vehicle,0
oldmusic_(artist),1
rabbit_hole_brothel,3
highschool_of_the_dead,3
oral_fisting,0
yagi_mutsuki,1
cielo_rey,4
felix_(kevintheradioguy),4
super_mario_3d_land,3
lag,1
epic93,1
whimsun,4
dodgeball,0
tygger,1
mendo_(bgn),4
hand_on_lap,0
bandi_(character),4
grizard,4
grawlee,4
ming,4
arrow_(scuzzyfox),4
z2727,1
bbm,0
rookiex,1
knick_knack,3
barraki,5
dark_skirt,0
kingjnar,1
kumakai,1
wolfdan86,1
pencethehence,1
green_bikini_top,0
loy_miyazaki,4
ennis,4
hally_(hally),4
stamin-up,1
renny_(darkwheel1),4
n_gata21,1
llydia_the_fluff_dragon_(darkflamewolf),4
marina_(misterz100),4
boss_(kamui_shirow),4
lisse_(leah),4
sintastein,1
lily_(lagotrope),4
treyer_(character),4
the_courier_(fallout),4
rodrick_(colelucario),4
ammy_rootpaws,1
athkore,4
umbris_(character),4
saph_(izzy223),4
zebra10045,1
kinuli,1
kocia_pierdocia,4
jack_(jackwolf),4
electrode_pad,0
burgersnshakes,1
spawnofchernobog,1
paul_robertson,1
c1a0,1
edwiener,1
blue_exorcist,3
storm_(minotaur_hotel),4
eric_the_dragon,4
wereshark,5
xanthippos,1
velvetqueenh,1
nitorou,1
tank_(mykiio),4
sesquin,4
sakitastar,1
superlolian,1
syrusdragon,4
kane780302,1
steve_prescott,1
poptop,5
disembodied_voice,0
liglig,3
dirtiran,1
rum_chaser,4
komiger,4
shy001,3
raphian,4
akaname,4
player,4
tetsuya_inoue,4
captainhoers,1
gekko_(zelda),5
burlywood_ears,0
randomdrawpony,1
erlik_(tfzn),4
katja,1
welcome_mat,0
ksenik,1
tapering_glans,0
precum_on_own_balls,0
aimee,4
teres_major,0
itazura_igen,1
plushie_pred,0
touching_own_head,0
syndra_(lol),4
clay_(enginetrap),4
pyroshay_(pyrojey),4
virgil_(virgil_deer),4
ellie_(elicitie),4
nicovault,1
james_(masterjames),4
tiamat_(smite),4
mulipios,4
gael_(windborn),4
rawhell,1
torn_coat,0
redpandawaifu,1
tachidomatte,1
sirius_hoshi_skaarsgard,4
pecking,0
chisana,1
open_flap,0
guppy_(boi),4
patra,4
place_mat,0
e.m.m.i.,4
ztar4,4
sailor_mercury,4
sitting_on_shoulders,0
axey,4
redheadpika,1
disk,0
contra,3
margay_(kemono_friends),4
suk0tto,1
syberfox,1
3dconfursion_(editor),1
chuck_(rocko's_modern_life),4
rufus_b._cobber,4
criticalhit64,3
millie_p_geot,4
heriyama,1
khyn,5
moneyshot_(artist),1
self_hypnosis,0
glytch_koore,4
vaya,4
werefish,5
akane-ichii-kitsune,1
fancymisslady,1
aiya,4
snufkin,4
cum_on_pubes,0
fleetway,3
craftyandy,1
k-ho,1
penis_rubbing,0
alex_kl5,1
abyss_(scorp29),4
aeroingo,1
gwynevere,4
tron_bonne,4
reiva_the_rabbit,4
hellsing_ultimate,3
artsy,1
haley_maruti,4
mantis_shrimp,5
cat_noir,4
gabe_(character),4
raksha_silvermoon,4
seth'cor,4
rednight,1
target_corporation,3
luckytama,4
cashew_(sif),4
anal_training,0
sketchyknight,1
dawn_crumhorn,4
gitani,4
tutu_(animal_crossing),4
shayde,4
lustrous-dreams,1
popon13,1
selena_shadowpaw,4
rikkoshaye,1
waismuth,1
syrinox,4
boog_(open_season),4
assassination_classroom,3
cleopatra_jazz_(mlp),4
sheanimale,1
staghart,4
roarak,4
seif,4
tony_(katnay),4
fernando_vargas,1
pokefusionman,1
carbonoid,1
spottedchai,1
oral_slit_play,0
mangus0723,1
orion_schilt,4
dizzy_(guilty_gear),4
vibrato,4
nurro_denthyme,4
kieren,4
yayster,1
ogre_(dragon_quest_x),5
audlin,4
holographic_fur,0
spear_(primal),4
glowing_antennae,0
reynard_foxglove,4
camilla_(101_dalmatians),4
caroline_(animal_crossing),4
guard_(squid_game),4
mutlicolored_skin,0
takoros_(character),4
thatblackcopfromdawnofthedead,1
reed_thomas_(the_dogsmith),4
nilghais,4
docu_(divide),4
pan_(sxfpantera),4
pandarita,1
alex_nerpy_fox,4
arten_reichtum,4
rupey,4
piper_(animal_crossing),4
aerial_silk,0
suzune_(acky05),4
arexualex,1
runeechan,1
draco_(zephyrthedrake),4
kulplant,1
lucid_rose,4
yonaki,1
zrin,4
ballon,0
gudako_(fate),4
osopolar-kun,1
youngtani,4
daystone,3
owlel,1
gem_(naughtymorg),4
terri_the_dragon,1
decky_(pongodecky),4
ines_(eleode),4
tyrcola,1
msdk_max,1
zombiesunlight,1
furia_(zuboko),4
shetland_pony,5
titanian,5
robotic_legs,0
trigger_word,0
kyou_(ittla),4
venasii,1
kengo_takabushi,4
berurun,5
cropped_tank_top,0
madoka_tagokoro,4
robinstic,1
angel_(dragon_ball),5
thirsthound,1
katie_(study_partners),4
sabbasarts,1
titezi-pyrump-art,1
battle_beast,3
pseudo_pussy,0
rodraccoon,1
thedemonfoxy,1
jazz_hands,0
arrjaysketch,1
artsu_(artsu),4
wrist_tattoo,0
backstripe,0
planetary_macro,0
baggy,0
fingering_mouth,0
antoinette_(el_senor_erizo),4
rope_around_penis,0
4rchf1end,1
bolo_(shantae),4
cum_unplugged,0
mstdndkkkkk,1
ember_the_firesmart_fox,4
venus_(smashmat),4
holding_feces,0
ken_(sush1d0g3),4
gummy_(disambiguation),4
holding_ladle,0
huwon,1
holding_scissors,0
domehead_(commissioner),3
phosbara,1
beepsweets,1
mashup,0
steamedeggz,1
gavrok,4
samantha_(disambiguation),4
tleatlnox59,1
exodite,4
melia_(indynd),4
heart_thigh_highs,0
megusuri,1
glistening_food,0
claw_gloves,0
arkeus,1
karin_kinako,1
multicolored_robe,0
keenadraws,1
waldo_(where's_waldo?),4
astralis_(bzeh),4
sigil_of_baphomet,0
effie_(bypbap),4
nathan_(aogami),4
wolzard_sternheld,4
chyme,0
lemonbastards,1
dimitri_2_(101_dalmatians),4
tiffany_lyall,4
tanzanite,1
holding_parasol,0
detective_button,4
red_hair_tips,0
lynxie_(subtiltycypress),4
kemonofluffy,1
riller,4
keetahspacecat,1
tony_tony_chopper_(heavy_point_form),4
canadian_flag_bikini,0
embarrassed_nude_anthro,0
canine_skull,0
musachan,1
zonkeyshow,1
curesnow,1
mat_(mbh98),4
height_reduction,0
rain_the_vaporeon,4
carys_(nebula1701),4
socarter,1
tentacles_in_water,0
aethis_stormlight,4
oro_(danji-isthmus),4
lusus_naturae,5
yugklabs,1
1_finger,0
allanor_(carenath),4
missing_finger,0
aluminum,0
cocoa_(trout),4
leaning_on_elbows,0
inking,5
carmen_hardon_(rampage0118),4
lindsay_cibos,1
gauze,0
stilts,0
mistressbloodershy,1
cloaca_juice_drip,0
tan_underbelly,0
annmaren,1
colplasticat,1
guppy_(thatpuggy),4
sorvete,4
stardust_kids,3
ahastar_(artist),1
terry_(slashysmiley),4
nobby,1
gamepony-verysecret,1
rayn_luxray,4
bryanshoot,1
kaitoharo99,1
bagelbytes,1
pointing_gun_at_viewer,0
nihaku,1
faleen,4
cropped_topwear,0
pee_on_face,0
salun_(pjt),4
animalk,1
raize_d'herian,4
pheromosa1111,1
metallic_wings,0
6:13,7
lycus_(ayx),4
straining_clothing,0
diesel_(101_dalmatians),4
toko,4
livestock,0
sylus-foxdragon,1
vulcan,5
earthia_the_seedrian,4
potchi,1
labyrinth,0
pochiro,4
reykay,1
prince_of_peent,4
gurimjang,1
nubbed_tentacles,0
chestopher,4
thoron,4
baojun,4
zephere,4
garoto_guloso,1
siv_(urw),4
scarlett,4
koyo_whitepaw,4
clancy_(tokifuji),4
six_(monferno),4
nalesia,1
openlor,1
absinthy,4
sweet_shalquoir,4
keira_(sparrow),4
goldyne,4
eliza_(mastergodai),4
trahern,4
valtykeaton,1
blufeather,1
viktor_balthis,4
pixelannex,1
l_cut,4
silentpon3,1
zora_link,4
kathiras,4
kirron,1
part,0
kikisinkspray,1
aimee_major,1
werewolf_calendar,3
kidd_(animal_crossing),4
jess,4
daniela_malfor_grey,4
juniper_(dahwchooa),4
sleepysuccubusstudios,1
print_mug,0
doregami,1
zephix_(aimbot-jones),4
vanny_(echa4797),4
spiral_tail,0
patriot_(presto),4
rilum_(kuromu),4
spooks_(agrievousspooks),4
makupatoo,1
eat_and_run,0
catsudon_(character),4
calamath_(himynameisnobody),4
inflated_breasts,0
bloodred739,1
urine_tube,0
red_towel,0
brown_glasses,0
camo_shirt,0
tsum_tsum,3
mika_(mikanishi),4
pompompurin,3
transformative_clothing,0
latex_footwear,0
vixx_(character),4
bia_(slipperyt),4
kcstando,1
1cassius1,1
kasugai,1
alfa_(alfa995),4
riceserpent,1
caim_(chu-tora),4
yellow_neckwear,0
krista_van_hoorn,4
grey_cape,0
docked_ears,0
taoz,1
fun_paste,0
anomalynexus,1
melffy_mommy,4
arume_lux,1
nutria_(rodent),5
pseudo-penis_penetration,0
chains_(zoroark),4
kira_kitira,1
catsuit_(disambiguation),-1
egg_in_nipples,0
hairless_butt,0
therianthrope,5
foxavril,1
dathomirian,5
oil-paper_umbrella,0
benji,4
leviathan_(fortnite),4
peeing_on_belly,0
classical_elements,0
prismwind,4
pinkboyjay,1
lipezkaya,1
cum_in_own_hair,0
kevin_(disambiguation),4
dat,4
dark_eyeshadow,0
umbra_(sagestrike2),4
unusual_urine,0
havoc_(tatsuchan18),4
thinking_about_another,0
flower_bracelet,0
pon_(orionop),4
shizuko_izumi,4
precum_through_jockstrap,0
bodily_fluids_string,0
unsheathing,0
gold_stripes,0
glowing_insides,0
luxiger,1
multicolored_knee_highs,0
shazetta,4
genital_swap,0
eostex,1
blue_text_border,0
goomanagarcha,1
genital_fluids_on_ground,0
5keys_x86,1
juanito_medina,1
quincy_(felino),4
azon_azimuth,4
quasi-ghost,5
fuya_(tempupupu),1
arkanumzilong,1
bottom_swap,0
cid_(vg_cats),4
sirah,4
ek_(artist),1
assistant,4
kate_hanami,4
swedish_vallhund,5
sai_(lovelymiyu),4
trix_(matetrix),4
eddie_(ice_age),4
defecating,0
ailus_tochar,4
branding_mark,0
therealnaffy,1
safi,4
fire_dancing,0
brann,4
paul_defenbaugh,1
flaich,1
urban-coyote,1
volga_(thievery),4
flame_atronach,5
karabela,4
mika_(skimike),4
fightbite,4
snow_drift_(oc),4
lovesick,0
xial,4
kio_(keovi),3
senrai,4
sakkan,1
undercover,0
aegis_tunesmith,4
lil_(katnay),4
colorlesscupcake,1
barb_castor,4
sharingan,0
shego,4
blues27xx,1
caprino_bencivenni,4
jasmine,4
joellethenose,1
herd,0
nantaly,1
luxianne,1
souzousha,1
snow_taradien,4
cockatrice_(mlp),4
helg,1
epcot,3
sir-prince,1
balrog_(cave_story),4
foreskin_worship,0
cremia,4
triprat,4
krall,5
mary_ann,4
arkblon,4
c4th,1
einar,4
visual_commodity,1
instant_orgasm,0
legion_(character),4
jorlus,4
buurgerboy,1
pussy_creature,5
x1x1,1
d'arcy,4
anesthesia,0
clementine_(plantpenetrator),4
albeon,4
knullmannen_(artist),1
vethuleion,4
rastafarian,0
rtas_'vadum,4
tophatharry,1
contessa_skunk,4
carligercarl,1
solomonfletcher,1
mutton_chops,0
bossman1969,1
issah_wywin,4
ninggeez,4
scribbles,4
elowyn,4
eclipser,1
mohawkrex,1
avimedes,1
krispy_(character),4
miss_dagger,4
nobuyuki,1
eamze,1
libra_(lazyhowl),4
pyronica,4
taeko,4
kell_dewclaw,4
heartz,1
da1k1ra,1
rena_tamer,1
evil_eyes,0
akari,1
dorian_pavus,4
karura_(artist),1
honey_nut_cheerios,3
ruun,4
taski_(character),4
stoking,0
vanir,4
jiji_(kds),4
nidopug,1
bunniehkins,1
kinkangel,1
chemicalcrux,1
toumal_(character),4
paw_holding,0
kuro_(darkumbreon90),4
quo_(rubbishdragon),4
furbulous,0
manazlash,1
ivalician_moogle,5
kartoffelowo,4
game_cover,0
fiworo,1
tammy_(tammycat),4
sz,1
poge_jirushi,1
ready_player_one,3
327b_(artist),1
chiclet_(roommates),4
faye_valentine,4
heart-of-a-dragoness,1
amber_(nebulilac),4
repent,4
asta_windsong,4
lunamuenster,1
koebi,4
lady-t_(artist),1
prototype_shadow_blitz,4
vethehex,4
ezra_(torgore),4
boxxmore,1
stygian_(mlp),4
taliamirai,1
twin_drills,0
muscle_man_(regular_show),4
feather_stride_(character),4
folds,0
grizzlyear34,1
bandi_(xen),4
snow_goose,5
aro_rouon,4
yokogami,1
farfalle_(ehnu),4
coffee_bean,0
exercise_ball_dildo,0
inbis,4
bonnie_(dirkstridoritos),4
amiga_family,3
tserera,1
cottontail_fizz,4
inflation_fetish,0
hakashe_(inukon_geek),4
larger_semi-anthro,0
darius_aurelius,4
andy_borrego_(zaush),4
regunya,1
random-bard,1
beverage_bottle,0
maxiinne_sylver,4
two_tone_flesh,0
small_glans,0
nexiv_foxclaw,1
marlena_(empskit),4
ren_(remanedur),4
nemi_(dinodoggo),4
dh29pcr,1
rin_i9,1
baccus_loka,4
silas_(ratte),4
leather_legband,0
hoverbike,0
thekitsunegamer,1
spicy_triangles,1
pngtuber,3
feo_ul,4
cussing,0
kyoka_(princess_connect),4
sinscaliecringe_(artistdragon),1
wavebird_controller,0
egg_creature,5
andre_taslim,4
shoop_da_whoop,3
daisy_(daisypayne),4
pink_text_box,0
syd_(useful_bear),4
artin_(bogexboog),4
back_fur,0
solt,1
cattle_(petruz),4
racist_slur,0
barbie_(doll),3
snowcone_(awintermoose),4
fox4,1
apollo_(cheetahmen),4
marumaru_hh,1
squirm_(sound_effect),0
virginia_wolfe,4
precum_on_belly,0
voregence,1
carter_(major_carter),4
yin_(lilo_and_stitch),4
ocelotlrama,1
albert_(moordred),4
wedge_sneakers,0
dtfrisk345,1
tori_saito,4
slipped,0
smoke_from_belly,0
sinthetic,1
chubbybunns,1
pride_color_towel,0
leaning_on,0
abstract_text,0
atomic_bomb,1
dixydelightful,1
dannydregorak,4
scrunched_nose,0
dakota_(rayliicious),4
jagg_(thatgryphonguy),4
l0ad1ng,1
pourwatter,1
zipper_underwear,0
nidinen,1
mkogwheel,1
malocke_(himeros),4
inuarashi,1
red_(aurawing),4
iris_ar-cerra,4
willian_shion,1
plague_doctor_(darkest_dungeon),4
plasma_grunt,4
sarah_(dog_fox),4
caffeine,0
quinny,4
cogsworth,4
snake_pony,5
monkey_bars,0
acidskunkwolf,1
ligynkey,1
dovecoon,1
chyo,0
sojin_advance,1
chris_bunny,4
pectoral_fin,0
sol_(luvdiz),4
leech_(kostos_art),4
amarthgul,1
surcoat,0
cory_(hevymin),4
visitor,4
sarah,4
shoronpo,4
spur_(thepatchedragon),4
watermelon_print,0
buffy-sugo,4
quick_bourne,4
ellkir_fox,1
uhokue,1
kineris_(artist),1
spudtagus,4
eleutherodactylus,5
diana_(idontknow2),4
mia_helsinki,4
dignity_or_debt,3
yasano,1
helios_(talarath),4
slightly_artistic,3
slim_humanoid,0
fake_tiger_ears,0
flickering_heart,0
dkase,1
stretched_mouth,0
holding_melon,0
light_genitals,0
sebastian_(askim_shepherd),4
marielle_oakem,4
bandits,0
wedge_sandals,0
ruttinren,1
pche,1
bjorn_jr,4
izanagi,4
metal_fence,0
stan_paprika,4
andrewgtrez,1
bular,4
jahlee_lebeau,4
hamham_sexy_dragon,4
rykliss_(tolerain),4
skunkinsly,1
checkered_bandanna,0
chunks_(bestdoggo),4
sareis_(manene),4
sharon_(naughtymorg),4
foxero,1
holding_object_with_feet,0
pekopekokuma,1
arms_on_breasts,0
between_mouths,0
sandy_marton,3
chuffo,4
thebigblackcod,1
boot_licking,0
aika_(tomlloyd),4
blue_text_box,0
soddo_(ironkongstudios),4
pawprint_print,0
florence_(deer),4
blepishepi,1
barboros_(anothereidos_r),4
caleb_(fuze),4
deadpotato,1
mercury_ruslanovich,4
rylai_the_pokegirl,4
greepurl,1
pixwell,1
chromie_(warcraft),4
the_doll_(bloodborne),4
felixgryphon,4
dendrophilia,0
flare_(flarevrc),4
pawprint_in_signature,0
mize_(ironkongstudios),4
ryke,4
gusset,0
nyabeyo,1
aarakocra_(dnd),5
key_ring,0
lyka_(gau),4
jessica_whiteclawz,4
satanick,4
beast_(pikmin),5
enna_alouette,4
jadeart9,1
yaph_(amodestmouse),4
fringe_clothing,0
sips_n_scales,3
tired_look,0
rotsw,1
light_(world_flipper),4
submissive_taur,0
college_tem_(jyto),4
captain_dragon,4
jeryl,4
lucky_(disambiguation),-1
i4_kan,1
autumn_(kumacat),4
odile_nightingale,4
sheep_witch,4
latte_(vonnir),4
myra_(glacierclear),4
gosannana,1
larikane,1
grand_theft_auto:_san_andreas,3
caribe,1
fennecsilvestre,1
berry_wabeet_(slb),4
beth_(masterj291),4
kamrose_(stinger),4
usagine_(tenshoku_safari),4
bonnie_valentine,4
djako_hyena,1
whiteraff,1
exodia_the_forbidden_one,4
zero_chan,4
julie_delacroix,4
theta_iota_kappa,3
torartlol,1
eugene_(animal_crossing),4
dekxer,1
shirakami,4
satomi_renshu,4
ice_face_eiscue,5
lazarus_(lazarus13),4
wakamolez,1
kylie_(alphafox1234),4
penis_in_stocking,0
collie_(mal-and-collie),4
revy_(terrythetazzytiger),4
lolscarletxi,1
noname_slow,1
laser_sword,0
evy,4
pawberry,1
mistmane_(mlp),4
roberto_(twokinds),4
velvet_(coldfrontvelvet),4
falchion,0
chikara,4
hylogon,1
jo_(poonani),4
nedd_bear_(fnaf),4
wawo,1
h_rt2,1
kitty_is_not_a_cat,3
reduncine,5
plateon_blueneon_(character),4
daroondar,4
ursa_(gummi_bears),4
chess_(chesshire),4
allyson,4
humorous_sound_effects,0
dracaris,4
kuon_(cyanroll),4
ruck_the_rockruff,4
kinar_(kinarofficial),4
kimbies214,1
eyebrows_off_head,0
egk513,1
norodoggo,1
drug_addict,0
qalcove_(character),4
print_sweater,0
briznaburr,1
hyper_extensor_carpi,0
whip_collar,1
sephie_(sephieredzone),4
hadva_(wub),4
johnnyzzart,1
hikoero777,1
naughtyxerigart,1
aix_(andreariel),4
coinn8,1
quaint_and_crazy_ventures,3
alex_carter,4
earth_manipulation,0
taweret_(moon_knight),4
artrogero_(artist),1
horus_(tas),4
red_backpack,0
sileynus,1
foglia_(wanderlust),4
fezateru_dragon,5
aaron_(avali),4
submissive_maleherm,0
dichromatic_eyes,0
kinktober2022,0
bestiality_pregnancy,0
32:27,0
mexicanjoe,1
louiz,1
tristan_(bluebunboi),4
sasha_(imbeethebunny),4
wilma_the_sheep,4
mune_(guardian_of_the_moon),4
akaighoul,1
spraying_water,0
chip_(rawpotatochip),4
nightcrawler_(kaiju_paradise),4
ash_greytree,4
just_woke_up,0
floral_markings,0
pfp,0
evan_(sharkrags),4
zubuzz,1
slurp_juice,0
theo_marks,4
healin'_good_precure,3
umbrella_soldier_(resident_evil),4
escher_drxii,4
raccoonbro,1
amphithere,5
wink_(mt),4
mochi_(rainbowscreen),4
mono_(artist),1
cantor_(hextra),4
thundurus_(therian_form),5
justin_turner,4
hog-nosed_snake,5
j_flores_draws,1
topo_(musashi),4
foolgirl,1
kuumar_(artca9),4
print_handwear,0
looking_at_tail,0
elisa_(teer),4
wethamster1,1
raaf_helder_(character),4
chewybun,1
stormbreeze,4
pink_condom,0
rige21,1
yiffnotgif,1
eliza_(pwcsponson),4
xandria,4
beakbum,1
christopherfoxman,4
shawl_only,0
mayumochi,1
boss_fight,0
equine_satyr,5
maximirusupauaa,1
chizitx,1
laure_(alexthecatte),4
balter_(connivingrat),4
tal_(gmeen),4
rockin_candies,1
aaron_d'verse,4
yakinikuinu,1
griff_(rtzero),4
crowne_(live-a-hero),4
alx_(lousy7),4
lactation_denial,0
pmd-unity,3
bedtime_story,0
red_eyelashes,0
shelty,4
keke_(kirby),4
fang_matos,4
owl_(winnie_the_pooh),4
rukasu,1
small_body,0
myst_(wyldfire),4
gamba,4
kacheek,5
perci,4
malorne,1
xeánica,4
peggy_(nanimoose),4
becka,4
nalani,4
persegan,1
beowulf_kennedy,1
andrew_reynart,4
melba_(animal_crossing),4
tailbiter,1
nic_(dewott),4
chilon,1
mixels,3
natasha_vladislaus,4
triple-shot,1
cruelpix,1
dazed-and-wandering,1
jack_hunter,4
rockytheprocy,1
gamorrean,5
ranshin,4
sweat_stains,0
najarala,5
kirita,1
nagano_tenzen,1
attempted_autofellatio,0
alamode,4
doctorconnie,1
lapino,4
kaji_aldewolf,4
zambuka_(character),4
dispari,4
whoa_nelly_(mlp),4
bune,4
dave_rapoza,1
teeka_(karno),4
pinkthehedgehog,1
flossing,0
omega,4
tiercel,4
serengeti_(gideon),4
jinxy_falina,4
naughtyimp,1
yotiecoyote,4
jesse_(onta),4
eddie_(guffaw),4
stigmata,1
ki_ko,4
jayce_(cursedmarked),4
aliss_(tits),4
sorika_mai_selena,4
babbuful,1
streaky_the_supercat,4
wario_land,3
ponyclopsasaurus,1
eris_(legends_of_chima),4
what_if,0
painted_underwear,0
zahira,4
tommy_the_buizel,4
zigzagziggy,4
mimic_princess,4
cheese_(modeseven),4
waddle_doo,5
wick,4
danny_(nitw),4
cyrus,4
nessie,4
granoa,4
smoothie_(artist),1
lesson_zero,0
dentist_(artist),1
wangkingfun,1
team_flare,3
the_last_supper,3
sands_of_destruction,3
daxxe_(character),4
tianshi,4
forneus,4
mistress_ruby,4
catherine_(video_game),3
tattletail,3
shnitzel,4
shiro_kamaitachi,4
viperious,4
plaguelizard,1
auto_(artist),1
werew0lfyiff,1
the_dragon_next_door,3
scylez,4
sweetvixsin,1
fractal,0
yung_yagili,4
eloriya,4
pandy_panda,4
monique_(animal_crossing),4
hahli,4
rexam-1,1
rex_(bad_dragon),4
arepo,4
rabies,0
nurse_sweetheart_(mlp),4
morrogh,4
valnos,4
peterandwhitney,1
zodd,4
mythril_blackpaw,4
kiisa,4
lukemgh,1
saasmimz,1
roy_(9tales),4
taiko-sihori,1
brennan_d_vries,1
thenameisradio,1
marinemarines2,1
khaz_(artist),1
green_belt,0
bartlebyjones,1
wally_(mhdrawin),4
vibe_(hoodielazer),4
ortwin_rd,1
infinite_anal_beads,0
pink_sandals,0
kujo,1
netcrow,1
blowing_smoke,0
ketei_(character),4
mike_(freehugz),4
pseudofaun,1
kenshin_midori,4
ninna_(ninnasaurio),4
bart_addams,4
tokaya,5
big_collar,0
arms_on_knees,0
xiztit,4
black_lives_matter,3
land_of_the_lustrous,3
two_tone_tank_top,0
chanceyb,1
ruffythelion,1
pink_kimono,0
deltaswap,0
bluetick_coonhound,5
flybeeth,1
shiaz,1
evy_(fish_birb),4
banan_zielony_(dudelinooo),4
kathrina_prowler,4
bow_kori,4
marble_(kitfox-crimson),4
showcase,0
athletic_taur,0
ears_on_shoulders,0
nail_(evilthabad),4
fluffy_clothing,0
paracanthurus,5
scarab_locke,4
crimson_night,4
belfryluna,1
gold_mask,0
harper_(harperpibble),4
lock444,1
hymin_(hybrid-mind),4
itzkoaro,1
hand_fetish,0
inbetweenie_navel,0
wallross,1
leo_zeke,1
single_strap_topwear,0
thrisker,1
sniffling,0
on_top_of_penis,0
dark_neckwear,0
aniko_rose,4
echolocaution,3
raithvaneal,1
etherwuther,1
nft_monkey,4
kelbi,5
deerie_(helluva_boss),4
zeus_(bitterstrawberries),4
joecat,1
vlw4zegrsgtsi6e,1
ferelay,1
alyssa_(lizet),4
glans_in_mouth,0
cameron_(azaleesh),4
ligia,4
a_stranger12,1
challenging,0
olivia_(disambiguation),4
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puss_in_boots,4
flat_top,0
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rectangle_rug,0
super_mutant_(fallout),5
hellfurred,1
piker,0
harmony_inkwell,4
pyry_(jzbthx),4
leg_tucked_under,0
birbit,5
room_divider,0
philoctetes,4
good_and_bad_ones,0
leah_(leo_llama),4
amper,1
possat,5
trip_(artist),1
fluffy_the_bringer_of_darkness,4
owler,1
lily_(theycalmehavoc),4
gokai-chibi,1
jellse,1
team_charm,4
tap,0
slylemur,1
ruberoidart,1
resin,0
darkhatboy,1
lex_cypher,1
hoshi_kitsunuki,1
nina_snorlax,4
alan_powers,4
fillyphalanx,1
polecat_(artist),1
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zpd,0
phantom_mangle_(fnaf),4
orchard_blossom_(mlp),4
lasterk,1
cinderella_(copyright),3
helvetica_(artist),1
jumblehorse,1
speedy_gonzales,4
wmruckwr,1
chad_(a_goofy_movie),4
alaer,1
fio_germi,4
mimi_lafloo,4
rinku,1
party_hard,0
dtmech,1
derekireba,1
prince_naveen,4
leon_(rocko's_modern_life),4
gingertail,1
professor_genki,4
mr._turnip_(mlp),4
slipe,1
nikki,4
cleasach,4
rainbow_panties,0
unown_g,5
lily_opossum,4
pickra,4
bullet_casing,0
danger_duck,4
balderdash999,1
dragonmassiel,4
zigzagg,4
true_love,0
fygar,5
mu_surai,4
bloody_bunny,4
allesey,1
knox,4
azugarlic,1
unusual_dildo,0
miia's_mother_(monster_musume),4
hyperion_(bluepanda115),4
angela,4
jacqueline_crowlie_(feldspartan),4
shinnycoyote,1
gate_(series),3
zahra_(garal),4
beatrice,4
rabbitholes,1
kinipshun,1
avi_(avibigshark),4
valentina,4
twentle_actin,4
fighter_of_the_stars,1
kadzukai,1
doncogneetoe,1
tachitamachi,1
aaron_(cyphernova),4
daigo-48,1
starlight_(disambiguation),4
jungle_aleu,3
kajy,1
pagrynga,1
paldean_wooper,5
simargl,1
cyan_(among_us),4
mrs._pig_(toybox_pals),4
cumbreon,4
spiral_glasses,0
rena_(disambiguation),4
black_sheep,4
marie_(cally3d),4
luna_giraffe,4
luxioboi22,1
oral_and_nasal_mask,0
drowsy_(lilo_and_stitch),4
aurora_(walurs),4
nahura,1
muzz_(muzz),4
snow_wave,4
dialated_pupils,0
rieko,1
cheers,0
thankfullorris,1
flat_shading,0
darkness789,1
racheltheseeker,4
martian_(duck_dodgers),5
zack_spades,4
snakehunter,1
esther_winchester_(cuphead),4
summer_(101_dalmatians),4
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purple_makeup,0
insignium,4
danish_flag,0
charcoal_(artwork),7
kimber_(devin_arts),4
valve_(mechanical),0
leo_(symbol),0
precum_on_finger,0
alexandra_(baronvondrachen),4
dane_(character),4
perfectblue97,1
sahagin,5
dusk_sarsis,4
chikokuma,1
amouge,4
sixfour_(artist),1
uni_puma,4
tempus_(under(her)tail),4
romeo_ellis,4
2_stupid_dogs,3
nickie_(dajinn),4
shantika,4
bill_katt_(8chan),4
catboy_(character),4
albana-the-dragoness,1
temeraire,4
footband,0
super_mario_bros_super_show,3
asuna_yuuki,4
xenogears,3
ailees,4
duel_masters,3
tsunomon,5
lizardking,1
princesssilverglow,1
firestormsix,1
locked_up,0
metadragonart,1
humping_leg,0
elisawind,1
relydazed,1
pilgrim,0
jonah_(alex.fetter),4
summer_sawsbuck,5
white_fangs,0
zymonasyh,1
kemomimi!,3
ucweb,3
alexalan,1
protein_shake,0
barechested,0
geode,0
matched_pair,0
specky-arts,1
smirgel,4
wedding_gloves,0
braided,0
interlacing,7
gendoempertags,1
gas_pump,0
callie_(wrinklynewt),4
pandadough,1
carousel-cat,1
rionkion,1
gix_nightstalker,4
danny_(mptm),4
boston_dynamics,3
african_wildcat,5
blackbird_(artist),1
kevsky_draws,1
dasoowolf,1
sonya_mayers,4
phusion,4
rancid_horace,4
f_is_for_family,3
danchundemei,1
cenegan's_tentacles,4
celsian,1
holding_newspaper,0
indy_(vdisco),4
hige_(wolf's_rain),4
kinaya_(dbd),4
schesta,1
froylan,4
tree_root,0
broken_object,0
nanten,4
flat_stomach,0
infinite_urine,0
pink_accessory,0
hoshiguma_(arknights),4
yoshi-eats-your-pie,1
harla,4
muscat_(fuga),4
eddy_dusty,1
boney_(earthbound),4
victory_clint,1
hoccult,1
daniel_cooper_(blair93),4
vamcat,5
akane_futaba,4
half-beak,0
nikku_lunatique_(character),4
nikki_(redeye),4
chubby_face,0
snowycrystalangel,1
unbreakablebond,1
snak3,1
yellow_loincloth,0
wolfeddown,1
tengridus,1
common_coquí,5
kugi,1
enigma_(artist),1
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piker_(thepatchedragon),4
syrra,4
daisy_(haven_insomniacovrlrd),4
experimental,0
felix_(buckfelix47),4
fantharubi,1
pov_hands,0
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tako_(character),4
piper,4
tawny_fur,0
multicolored_helmet,0
seth_raymond,4
viper_rat,4
jingu_sayoko,4
fanakfurry,1
kairy_draws,1
cooper_(ritzcat),4
sarammat,1
abryssle,4
notenoughpink,1
ezequiel,4
resin_drake,4
spook_(oc),4
being_photographed,0
pogoo,1
brannahgirl,1
bottomless_bar,3
pussy_juice_on_paw,0
a_walk_home,3
borophagine,5
tatl_(tloz),4
sarah_(caninelove),4
mordaine,4
lionbear,5
shorty_(breeze_in_the_clouds),4
lino_(manene),4
jason_steiner,4
striped_hoodie,0
bibit_(cocotama),4
sam_(jailbird),4
elinnayt,1
14:11,0
15:17,7
kaufycream,1
ascended,5
pincerpencil,1
tablet_computer,0
enigma_(psychosocial),4
atehrea,1
sexyhalofan,1
kelly(photolol.03),4
fervent_entropy,3
samantha_(jay_naylor),4
rettriverr,1
rayleigh,4
doom12,1
the_circle_game,0
brett_(bluefoxyboi),4
cupofcoco,1
jmf32,1
light_outline,0
arachnymph,1
chrry_(character),4
mama_llama,4
kyari_(adversarii),4
xiaoshun,1
sniffles_(htf),4
hand_on_own_balls,0
unicronian,5
genevieve_(micatra),4
olivia_(zer0rebel),4
curtis_(sobeloart),4
minty_(kurus),4
yuriguba,1
ta'lesy,4
cake_(deltarune),4
jars,0
shon_(shonarts),4
alulu_(paperman),4
pheeni,4
abyssalchimo,1
megan_thorne,4
spanx,4
koda_kattt,1
mal_lock,1
yamamura_sadako,4
pipe_wrench,0
reane,4
limp_wrist,0
blush_face,0
konno_tohiro,1
self_milking,0
asheru_(setting),3
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unafkennyart,1
balantes_tueri,3
rainbow_claws,0
behind_tree,0
chrono_bluewing,4
marci_mcadam,1
mspaintponies,1
hawkward,4
genesis,4
sqjgg693,1
great_blue_heron,5
fangmon,5
treads,0
thatoneweirdo,3
undyne_(underfell),4
mai_karmel,4
reading_glasses,0
promenthefus,1
xranzem,1
343_industries,3
striped_wall,0
medium_length_hair,0
mushi,4
polo_jasso,1
rikku_(final_fantasy),4
brynja_(coc),4
hair_on_breasts,0
great_nix_fox,1
nhibao,1
stomach_wraps,0
ratchet_lomu,4
awkward_pose,0
silvex,5
wakizashi,0
hoyeechun,1
merrie_melodies,3
griffon_vulture,5
wrestling_belt,0
aside_glance,0
chakisuu,1
flashbang,0
tengamanzero,1
reiser,1
devourer,5
furcon,0
mashed_potatoes,0
blackm3sh,1
akr,1
dipping,0
alma_(elfdrago),4
tkil,0
steven,4
knives_(yovi),4
flower_tail,0
2_talons,0
lance_(lancefoxcia),4
brent_abbott,4
aarondrawsarts,1
leslie_(experiment626),4
aeryn_(thehelmetguy),4
brubearbrown_(character),4
3000vnd,1
byakkotig,1
gabbslines,1
fibbelous,1
tv_lighting,0
nopantsdog,1
tausen_lion,1
green_eyelashes,0
ice_hair,0
skarneti,4
cai_(notsafeforweh),4
mudraptor,5
kate_(disambiguation),4
threaded_by_dildo,0
zixh,1
marshall123x_(artist),1
tittybat,1
cryena,1
pepper_mynt_(togothehusker),4
gloria,4
hazel_luvsol,4
pom_(suger_phox),4
wet_hands,0
resting_on_arm,0
beewomanlol,1
friday_night_funkin'_minus,3
hissmiss,1
ninanidorina,1
ash_(amazinggwen),4
booki_(character),4
sergld,1
doug_(ews),4
out_of_character,0
disgruntled_(artist),1
dry_himbones,4
chase_shinien,1
brittany_(monarquis),4
denim_(hirurux),4
shen_(sihai's_legacy),4
freckles_on_face,0
pidge_(hoot),4
cinnamonrug,1
draccy,4
toxicmilkyx,1
casey_(clementyne),4
sarah_(vulpisshadowpaws),4
coontail_v2,4
gymnasium,0
newt_wolfbuck,1
black_swimming_trunks,0
amber_(orgunis),4
achak-claw,4
bechamel_(puffquff),4
helm_(thehelmetguy),4
pearypanda,1
hands_above_breasts,0
ariana_'ari'_(darkflamewolf),4
nerf_(toy),3
razz,4
nathy_(arbuzbudesh),4
eddy_hare,4
torajiro_(taimanin_asagi),4
rubber_panties,0
fletcher_collins,4
damian_weir,4
absurd_dumbbell,0
hyaenodontid,5
nix_rayne,4
albinodragon,1
eth_(artist),1
cinnkin,1
senip,4
chataya,4
oceanus_shenron,4
aquarius_(symbol),0
cum_blockage,0
alacorna,0
chiquita_(lexoastonov),4
happstablook,4
marco_(imperatorcaesar),4
matt_(two_best_friends_play),4
glowing_bracelet,0
rodd_(fuze),4
navel_jewelry,0
scotty_(ghastlyscotty),4
present_box,0
hnav,1
bored_ape_yacht_club,3
militia_(thefuckingdevil),4
hands_on_foot,0
chompa_(character),4
charr_starz,1
shadeofshinon,1
mamaito,1
on_crate,0
maleherm_penetrating_maleherm,0
hurt_expression,0
boonieboo0,1
hero:_108,3
biscus,4
mimechan,1
slick,0
crosshair_eyes,0
novery,1
tyrrain,4
falrissa_lothe,4
bane_(identity_v),4
jurij_bajer,1
adalore,1
19:20,7
onicristice,1
hair_decoration,0
raccoonuki,1
bxhrd,1
davias00,1
grunt_(madness_combat),5
voidyy,1
dolores_(apoetofthefall),4
hemo_the_alchemist,4
londys,4
purple_cloaca,0
ante_flan,1
muffin_(bluey),4
knight_(deepest_sword),4
wizardjpeg,1
ruste_(w4g4),4
mellojellowo,1
froen_(zi_ran),4
pestilence_(plaga),4
shanegdraco,1
white_kimono,0
nekosuna5115,1
chad_(black-kitten),4
multicolored_fire,0
tan_panties,0
kathy_(felino),4
pororikin,1
all-starwarrior,1
hikerumin,1
hibernotion,1
rey_(makumo),4
licking_candy,0
draconic_burners,1
beringel_(rwby),5
pokemon_egg,0
aurel_(majp),4
tommy_(tornato33),4
cronum,1
ryukiro,1
subway_station,0
qaz,4
molasteak,1
blood_on_floor,0
rope_bridge,0
constance_(glopossum),4
male_on_back,0
akitakacbr,1
apparatus,0
laura_(fvt),4
kai_(beastars),4
crosswind,4
burmese_python,5
spyzslair,1
anjogatobr,1
red_car,0
sitting_on_fence,0
ellisarts,1
bellna,4
yude_pea,1
crown_of_thorns,0
plus_sign,0
sachat,4
hot_water,0
sega_logo,0
zandii_(thefreckleden),4
anaconda_(song),3
master_roshi,4
carrot_cake,0
tatosi_wyrdghost_(character),4
mega_tyranitar,5
kusarigama,0
ppsh-41,0
buck_(catastrophe),4
bix707,1
vik,4
snifit,5
aisukuriimu,4
phone_wallpaper,0
dyani,4
ms._endive,4
resplendence,4
gruntchovski,1
peanertgallery,1
mettaton_ex_(underfell),4
vandringar,1
gnorc,5
unleashedbrony,1
winddragon_(character),4
llewelyn,4
icefoxx,4
natali,4
lily_(sssonic2),4
ghost_(character),4
terry_(masterful),4
spikeheila,1
glenn_(character),4
eskaria145,1
stardarkfurr,4
jessica_(aj_the_flygon),4
b0rn_t0_die,1
itoril,1
ruby_gloom_(series),3
patapon,3
lambda,0
werepyre-warrior,1
wicklesmack,1
kitty_tush,4
samuraidemon,3
lightning_chaser,4
vance_(istricer),4
xiixxii,4
human_princess,4
stella_(disambiguation),-1
amy_sharkiri_(character),4
hollyk,1
"mimi_""godiva""_dulcifer",4
white-snow-wolf,1
evil_counterpart,0
obsidian_(dragon),4
uniqua,4
fwuffyfurry,1
muffy_vanderschmere,4
weirdoxs,1
xaie,4
snoop_(character),4
anna_puma,4
hound_wolf,1
nurse_lingerie,0
urine_in_uterus,0
pern,3
nan_(nightfaux),4
lokya,1
superkeen,1
mindwipe,1
monado,0
heilos,1
xylex,4
anisava,3
rudolph_(blue_dragon),4
offspring,0
caerulus,4
makucha,4
elemental_hero_neos,4
hadriae,1
salt_pepper,1
emma_fletcher,4
wide_waist,0
les_paul,3
ahmenset,4
the_devil_(tarot),0
kanta_the_invincible,1
jen_(shutupjen),4
vercursar,3
hobbesdawg,4
asotil,4
meet_and_fuck,3
cephalopussy,0
amorecadenza,1
nutshot,0
necrothant,1
akemi_(character),4
lythrion,5
lily_lace_(mlp),4
bouncing_bed,0
hoof34,1
masking,0
awoken,5
cocoa_(toba),4
afoxinabox,1
callistofox,1
klara_(monster_girl_gamu),4
tristian,4
villdyr,4
aonik,1
kakapo,5
moonlight_carbuncle,4
quadruple_handjob,0
mercurio_(armello),4
fearsfortears,1
power_suit,0
pussy_freckles,0
chuunibyou_demo_koi_ga_shitai!,3
muttninja,1
celeste_(s0c0m3),4
noodle_(gorillaz),4
adam_(gvh),4
furniture_noises,0
melanth_(artist),1
neck_penetration,0
tophire_gemhorn,4
merlin_(lllmaddy),4
group_bondage,0
barley_(wonderslug),4
cottontail_rabbit,5
artorias_(artist),1
captain_qwark,4
edioretysa,1
aspect_of_lust,4
ena_(achakura),4
sipuha,1
leaf_(animal_crossing),0
claire_(akitokit),4
28gooddays,1
multicolored_earbuds,0
noki_(hexteknik),4
open_towel,0
morethreedee,1
earth_(plaguedogs123),4
fox_ear,0
cheaty_(opqhlak),4
riddleheart,1
reverse_(reversecall),4
oumi,1
lobe-finned_fish,5
there_once_was_a_dog,3
lolita_channel,1
hazed,1
dark_cloud,3
gaspar_(asderzx),4
martuguma,1
citrus_fruit,0
neopets:_the_darkest_faerie,3
peeposleepr,1
grim-kun,1
miko_(abz),4
larger_fingered,0
dimly_lit,0
captain_kobold,1
laying_on_grass,0
porncooby,1
burhuru,1
yohei,4
canis_(canisfidelis),4
beta_morgana_(persona),4
ollie_the_otter,4
thorn_paw,1
primal_(aquilak),4
tsuda_delcat,4
precum_on_viewer,0
lura_dura,1
sudz,4
laura_(ironkongstudios),4
luiz_(mr_fuwa),4
mary_(dnapalmhead),4
antelope_penis,0
vixen_mighty_(itsjojo),4
fyonna_(twinkle-sez),4
cheer_bear,4
6ironknight9,1
rumpy_apode,0
kaia_(truekaia),4
machine_penetrated,0
jocasta_(dogsmith),4
pressing_against,0
spark:_a_space_tail,3
mud_mask,0
holly_(juicydemon),4
danny_(bluewulv),4
suhomei,5
star_guardian_poppy_(lol),4
lea_(whisperingfornothing),4
frostypuppy,1
charliemcarthy,1
jat_(thepatchedragon),4
humanoid_genetalia,0
green_hairband,0
star_topwear,0
chest_bow,0
squish_vaporeon,4
partial_fursuit,0
reallyhighriolu,1
standing_over_dildo,0
nikolai_zaccarin,4
amarok_black_(character),4
mayku_(character),4
esacosaverde,1
lani_aliikai,4
picaipii,1
mega_man_x_(character),4
luvalind_blue,4
lukeskytera,1
yiffler,1
electrode_on_breasts,0
sextuple_penetration,0
tamaki_(warriors),4
bridge_(the_man),4
closetcanibal,1
yellow_headgear,0
michael_afton,4
thigh_thighs,0
shiro_shiba,4
cyberrodrigo,1
jun_(scj),4
felibot,4
darell-sama,1
crasherchroma,1
cait_sith_(tas),4
chlamydoselachid,5
xbox_series_x,3
bulkybun,1
zoey_(mastergodai),4
kutcher,4
pyro_koshka,4
hazelnut_yui_(character),4
landylyn_(yitexity),4
inimus,1
christie_(doa),4
smappa,1
polinagribova,1
yuckydizzy,1
rory_(aquasnug),4
abby_(caldariequine),4
rockgods,3
goldfur's_cogsverse,3
k/da,3
kielah_(altrue),4
curvaceous_female,0
goldenbuckles,1
pink_smoke,0
spicykiwki,1
cyberpunk_edgerunners,3
canine_tail,0
abathur_(starcraft),4
rebecca_pawlson,4
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cells_at_work!,3
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jessica_(housepets!),4
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molly_(animal_crossing),4
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noebert_laimable,4
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uni_(wazzaldorp),4
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nick_(left_4_dead),4
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"jade_""cascade""",4
thor_volt_(swordfox),4
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ms._battleberry,4
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rubykila,1
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postiche_beard,0
middy_(midrushnic),4
karissa_(character),4
chen_shi_(white_cat_legend),4
mandela_catalogue,3
crocwife,1
two_tone_swimming_trunks,0
sugarsaturn,1
tempting_moonlight,4
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furball_shirokm,1
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emily_(10livesleft),4
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7_heads,0
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rayray72,1
buster_whelp_of_the_destruction_swordsman,4
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donkey_kong_64,3
roden,5
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wynne_patton,4
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desertyrain_minrok,4
moon_knight,4
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sweet_temptation,3
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ni-pul,4
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lunatik,4
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kaleb_(copperback01),4
marci,1
ano,4
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ravyn,4
baiko,4
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maachi,1
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izora,4
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toxi,4
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jaguar_(zootopia),4
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torute,1
pioky,4
alexandros,4
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ona,4
glas,4
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ariadne,4
melanie_summers,4
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a_(livinlovindude),4
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dante_(thelaststarfighter),4
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literallyjumble,1
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himeka_(ro),4
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kitsune_(tee_k.o.),4
skyfire_(arknights),4
lady_lovegreen,4
tex,1
gloria_(heartermobs),4
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rocky_(pocky_and_rocky),4
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sheyza,4
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maria_(alfa995),4
hanna_(sketchmantel),4
raylor_7,1
ssalbulre,1
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castle_crashers,3
privon,1
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sam_(desidobie),4
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jaana_kettu,4
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zinny_(scalesindark),4
bb-series_astromech_droid,5
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fuchi,4
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kat_(mazume),4
yigyuki,1
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sole_(saltyman66),4
lana_(bonifasko),4
tank_(animal_crossing),4
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riptide_(disambiguation),4
sr_panda_86,1
floor_bondage,0
gradient_legs,0
osahar_(securipun),4
kitty-face,4
jungle_menace,4
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cinnameana,1
citrine1105,1
yellow_tail_tip,0
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unnamed_old_ram_(furfragged),4
ashley_(ashleypikachu),4
holding_mistletoe,0
pembroke,1
atomic_corenthal,4
wovstah,1
shrike_(shaymin),4
snow_(kobu_art),4
anyyuum,1
whit3salmon_23,1
wendy_carter,4
elcnusmedy,1
bandoned,1
sh-4rk,1
mia_(disambiguation),4
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kel_(028-mendicantspire),4
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victoria_(ibengmainee),4
cyborg_taur,5
cum_on_own_sheath,0
liamhowardfluffyboy,1
fast_food_(food),0
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claragonza3,1
amishrakk_(rule63),4
chester_(extracurricular_activities),4
appule,4
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silver_penis,0
european_rabbit,5
umbreonthewhovian,1
spider_bones,1
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o_0@isogashitsu,1
furrymang453,1
kira_dra_h,1
svetlin_velinov,1
creepy_castle,3
berndem-bones,1
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digeri_dingo,4
mallory_mcmallard,4
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royal-pain-in-the-ass,1
thomas_carter,4
lunamoth,1
laramee_(aj_the_flygon),4
smelly_paws,0
romanthony,3
abc_insects,3
demfox19240,1
amari,4
deaththehusky,4
thad,4
sayori,1
zero_(character),4
jocelyn,4
tonya,4
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malwolf,4
hansi_bello,4
kago_shintaro,1
sergaelic,1
kitigaru,1
kumagoro,4
nanokyuu,1
niv-mizzet,4
jalak,4
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maria_lys,4
tempest_streamrider_(oc),4
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crocodile_humanoid,5
vendant,1
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uncle_chuck,4
playing_with_yarn,0
makisy,1
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keco,4
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akacia,1
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marble_(kiwa_flowcat),4
griff_(gargoyles),4
niki2ki884,1
suutooroo,1
robin_tinderfox,4
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joshua_(fezmangaka),4
varagod,1
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i_can't_fap_to_this,3
riley_(kadath),4
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wolfgang_von_kraftman,4
amerok,4
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mother_penetrating_daughter,0
shiro_(akishycat),4
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sasha_(trunchbull),4
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allie_(tajem),4
molly_(oc),4
renshi_vivieh_(lowkey_nottoast),4
kirya_gilbert,4
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elle_andrews,4
wiggler_(mario),5
gallonegro,1
noel_vermillion,4
bumped_tentacles,0
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nadia,4
asahina_yasuyoshi,4
duamutef,4
pencil_point,4
rufus,4
sniff_(moomin),4
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killercod,4
diasfox,1
kaijumi,4
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serpy,4
jacklyn_grayman,4
amaki_tsukishiro,4
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niki_(character),4
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fauna_fox,4
reia,4
jade-sapphire,4
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brostone_brohoof,1
lillayfran,1
candor,4
foriko,4
jujubeeluv,1
wolfi-hitory,1
tentaclemonsterchu,1
schwarz_(mattman),4
pip,4
doctor's_coat,0
lolli,4
arc_nova,4
stoop,4
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mixer1981,4
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lap_of_luxury,3
toronn,1
denise_hyena,1
heletamera,4
cupcake_(character),4
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starfy,4
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armeline_zobelpelz,4
dragonbros,3
mogli,4
jade_(whitekitten),4
ash_louren,1
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glowing_frenulum,1
big_teres_major,0
hole_in_chest,0
blacksmith_hammer,0
joanne_maple_(conadolpomp),4
olivia_(screwroot),4
pussy_juice_on_spreader_bar,0
chloe_(zaush),4
bullstorm,1
fluffybunz's_fursona,4
pandear,1
illiyanora_(himynameisnobody),4
alexdraws,1
mia_miller_(guak),4
grey_talons,0
raffle_prize,0
forced_69,0
roolthelion,1
saintbullart,1
kao_the_kangaroo,3
semirulalmite,1
risque,0
r_anon_4,1
mlinzi_(series),3
spotify,3
body_stack,0
killbeat,4
weeeeps,1
fatale,4
likeablemouse,1
procyonid_taur,5
hitomi_uzaki,4
itachi_guma,1
oleanderin,1
seminophagia_(copyright),3
pokemon_rgby,3
chief_gilbert,1
smelling_balls,0
hotchkiss,1
funnel_in_mouth,0
ordering_food,0
nijtus,1
olexafox,1
green_(among_us),4
saltorii,1
sophie_(daniel156161),4
syn_club,0
xartknight,1
cockroach_humanoid,5
ear_jewelry,0
valcyrie,4
nova_(okochira),4
mistletoe_bow,0
katzu,4
jk-kino,1
tenna1869,1
chubby_(chunknudies),4
yodi_(oc),4
koan_mascus_(rithnok),4
merry_(inline),4
zarra_(artist),1
ahava,4
ebnet,1
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glistening_pecs,0
ripushko,1
forzenta,5
alina_volkova_(renthedragon),4
leon_the_cat,4
mike_(sleepysheepy17),4
kingrusty,4
ekko1939,1
ren_(rabbity),4
fumufumu_(character),4
norna,4
valence,4
tae_(pkuai),4
alotie,4
shepy_vilkavos,4
hoookerii,1
miragoldra_(character),4
sarah_handlebach_(spiff),4
wally_wallechinsky,4
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black_crown,0
calciumchloride,1
pedal,4
chef_sigmund_bautz,4
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tender_(diadorin),4
ricebunny,1
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nameless_(venustiano),4
dakini_(flower_knight_dakini),4
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the_dark_one,4
thalia_(milkriot),4
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terra_kane,4
shrimp_humanoid,5
raikuma,1
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soleo_the_solgaleo,4
holding_necklace,0
sinker_(lilo_and_stitch),4
pijay,4
haika,1
char,4
darcknessart,1
molly_(cyancapsule),4
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kirionek,1
weezer,3
sephra,1
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brucks_(steamedvegetables),4
sph,0
lsd_(vrabo),4
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berry_(strawberrytfs),4
harvey_(stiban),4
bass_(fish),5
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pawronica_(fursona),4
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defago,1
thal_verscholen,4
vanga,4
lvl,1
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seras_(ratherdevious),4
reigan,1
menzo,4
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squall_leonhart,4
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kaneoya_sachiko,1
ami_(ellistia),4
vista,4
drash,1
r.j._(katnay),4
nod,0
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magnus_(zerofox),4
tammy_(starfighter),4
quinton_(maxl8),4
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karma_(character),4
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melrose,4
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mel_(polar),4
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josie_(spacepoptart),4
lemonscentedbiscut,1
shivah,4
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myre,4
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e1m8,1
little_sister,4
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ginger_(bittenhard),4
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nue_houjuu,4
wikiwarrior,4
thirty8ght,1
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impending_anal,0
bridget_(latchkey_kingdom),4
frazzledragon,4
rainbow_factory_(mlp_fanfic),3
dragonlord,4
lizaosan,1
ashinowen,4
wangsworth,4
alex_maxwell,4
stress,0
spottedleaf_(warriors),4
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szaeravar,4
sandal,0
daphne_maer,4
leylahshan,1
nemnth,4
makwa,4
dior,1
seregir,4
komekko_okomeya,1
girder,0
trophy_necklace,0
cathy_kitana,4
nyuuchandiannepie,1
seleccion_canina,3
ukrainian_text,7
keiko_ayano,4
las_vegas,0
shabby_blue,1
alex_young,4
hunter_(redfeatherstorm),4
sohmder,5
roksanath,1
nelizar,4
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pseudonymous,1
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gio_(electroporn),4
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vixlries,1
mattings,1
elalvaradog,1
chestfur,0
shiverfox,1
kbo_league,3
neba,1
nyarengadget,1
nightlight,0
aiden_(gekko),4
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mkb_56,1
umbu_yvynya,4
d12,0
pooky_(raichupuppy),4
lamborghini_countach,3
bonybones,1
legs_over_shoulders,0
silk_lace_(oc),4
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human_to_monster,0
journey_into_imagination,3
monotone_fin,0
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kissing_tip,0
nintendo_3ds_console,0
lady_luna,1
faceless_herm,0
usagina_(sergiroth),4
tony_keys,4
pao_pao_(crimsoncolored),4
arttmadness,1
matt_draggot,4
dismembered,0
yeeta_the_puppy,4
flow_(splatoon),4
612,4
artha-amberose,1
drxeno,4
kai_(x13),4
bianca_(animal_crossing),4
dj_mixer,1
paintball,0
prey_for_me_(comic),3
kutan,1
allo_(dinosaucers),4
kuro_(kuronell),1
vampi,1
ortensia_(artist),1
skrap,4
claire_dearing,4
fyrefly,1
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maneater,1
kyuu,4
dailevy,1
plug_suit,0
kouya_(kekkaishi),4
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arizona_iced_tea,3
silverwolf_(character),4
maria_notte,4
zayne_kingsley,4
jolie_sparks,4
darkuangel,1
jetta,4
ultron,4
keith2002,1
vanguard,4
kindle_wolf,4
vicar,1
charlene_(mousey_love),4
azureblues,4
carrot_kingdom,3
the_get_along_gang,3
misskin,1
aleksi,4
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themauvestorm,1
kizzneth,4
tokiya_sakuba,1
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genma_saotome,4
ptolemy,4
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shalinka_(character),4
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vergewemyasb,1
ventilation,0
mieu,4
sparxus,4
sparra,4
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lapis_(jewelpet),4
shampoo_(ranma_1/2),4
shutupjen,1
blackagumon,5
dragon_quest_iii,3
zoorama_comics,3
bill_(left_4_dead),4
fluffylai,1
sinister_taz,4
shinobu,4
kazza,4
din,4
thweatted,1
twi,4
horse_tack,0
dafnier,4
zanji_zamamura,4
anoroth,1
spitshy,1
black_doom,4
sheriff_(artist),1
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makoh,4
kanerudo,0
po_ni,1
princess_celestia_(eg),4
karate_belt,0
nezudomo,1
itou_yuuji,1
fluffy_(metal-renamon),4
dolf,4
tobias_(fox),4
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phalloi,0
black_mesa,3
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kezmmar,1
greypolf,4
total-krazen,1
ziude_(character),4
morpha,4
seto_kaiba,4
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kizi0n,1
janemba,4
bruin,4
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sy_freedom,4
trinity,4
fazar,1
sonitweek,1
pyrexia,4
koriah,4
lovelybonez,1
nectarine_wynne,4
john_bull_(mlp),4
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robotic_arms,0
ry-u,1
jesie,1
rivus,1
when_you_see_it,0
beretta,3
funnymouth,1
multi_urethra,0
loba,4
imactuallygarbage,1
lupe_(ferdinand),4
remadi,4
hawthorne_foxington,4
horus_(puzzle_and_dragons),4
baron_(zerofox1000),4
nikki23,4
nevermown,1
nacked,0
stripe-faced_fruit_bat,5
nifela,4
side_table,0
shellvi,1
zoomer_(nanoff),4
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shuttlecock,0
shoulder_holster,0
silk_(dreamkeepers),4
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misty_(oc),4
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pussy_fins,0
kumiho_(purplelemons),4
jarlarild,3
mfus,1
si_(lady_and_the_tramp),4
ma'ara,4
harvey_(lysergide),4
lewdkitten,1
kati,4
sam_(tiquana),4
street_corner,0
azura_(loneguardian),4
simoon,1
vitexen,1
dendy_(ok_k.o.!_lbh),4
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jyueng,1
ciril_kastion,4
nikkimory,1
allmate,5
midnightmuser,1
rockosedits,1
sucked_and_plowed,0
brigitte_(overwatch),4
kari_(0laffson),4
yuki_(tooboe_bookmark),4
kira_(staffkira2891),4
chicken_run,3
pride_(undeadkitty13),4
inklingbear_(artist),1
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chango_the_cow,4
cetacean_taur,5
gokushiki_kasuta,4
o-kemono_(character),4
aurora_(insomniacovrlrd),4
rometwin_(artist),1
exo_stranger,4
button_down_shirt,0
likri,1
willow,4
drinking_pussy_juice,0
guy-am-i,4
corona_(beer),3
felixleong,1
shaun_the_sheep,3
feha_(killergrizzly),4
svd,0
hood's_mad_animals,3
erron_buchanan,4
soraru_(sorafoxyteils),4
burnt-toast,1
northern_lights_(coca-cola),4
vicuna_dog,4
tanya_(dullpoint),4
hozure,1
haru_(cainethelongshot),4
fantasy_world,0
hay_bed,0
twig_(hilda),4
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jay_the_cat,4
crustacean_taur,5
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chum_(splatoon),5
annoy_(character),4
ringofriend,1
spotted_bikini,0
kete_wawakari,4
beat_saber,3
subjectdie_(artist),1
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lite_(character),4
butterscotch_(hoodie),4
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divan,0
riverta,4
zack_the_dragon,4
draeus,4
the-urocyon,1
aero_(aeromagazines),4
coyzoi,1
cheesy_(cheese_cake),4
captain_sharkbait,4
rananya_(vrabo),4
iggy_(ignatius_husky),4
holy_water,0
erection_under_loincloth,0
samantha_drazelle,4
amora_the_t-rex,4
cariboops,1
teporingo_(species),5
skaereth,4
chunkyoreo,1
arcade_stick,0
kaki_no_tane1,1
remmm,4
saros_(copperback01),4
green_paint,0
bob_vorsh_(jack),4
nicoguer,1
potap,4
rosetta_(jludragoon),4
60:47,7
rodin,1
tunky_(furryfight_chronicles),4
ikunsfw17,1
two_tone_headset,0
olivie_rosestar,4
shadethehellhound,4
kitkat_(ethan64),4
reindeer_(petruz),4
fuxiao_(ffjjfjci),4
mossist,1
score_(mlp),4
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pharos.e,1
mr_dog,1
gamer_girl,0
golden_axe,3
tigra_watanabe,1
chickhawk96,1
nykka,4
lucemon_(rookie),5
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alice_(cayo),4
elbestia_(character),4
elysia_(shadowkitteh123),4
ahsan,4
muffle,1
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shadowcat_(artist),1
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june_(blackknife12),4
lavenderhush,1
rafael_(rio),4
fluffabie,1
oh_joy_sex_toy,3
crimsonbugeye,1
vampire_the_masquerade,3
forced_handjob,0
zayne_camacho,4
dolan,4
backstreet,0
lilandria,4
carrion_(character),4
figment,4
siegebeast,1
congee-painting,1
beatrix,4
goodbye,0
decomposition,0
meammy,1
uktemperance,4
noisemaker,0
varric,4
ffefox,4
starling_(snowrose),4
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thebluevibrava,1
silent-fly,1
lockhart,4
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barret_wallace,4
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ny-4n,4
avimcmillan,4
shinigami,5
a_centaur's_life,3
rin_(nihilochannel),4
kena,1
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maxis_core,4
spats_(lilo_and_stitch),4
tal_rex,4
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stonedshiba,1
kine_(kirby),4
ben_morbez,1
clothes_bursting,0
dunks,1
norwegian_text,7
little_dragon,1
rhino_penis,0
fa_plastilinka,1
arms_down,0
giantmosquito,1
rommy_winterlight,4
prairie,0
yaya_han,1
nordic_runes,0
heads_up_display,0
penis_down,0
erohd,1
war_beast,5
white_fang_(tenebscuro),4
hip_hop,0
the_tower_(tarot),0
snoway,1
esmareld,4
messier_87,0
donabe,1
library_of_ruina,3
amarimo,1
archivist-kayl,1
promedio,1
nsfwkingrian,1
bluesnesscasual,1
ara_chibi,1
cactus_flower,0
gartie,1
jacksonbottestr,1
breel_(housepets!),4
ferionion,1
zetamath,1
reshimom_(thiccwithaq),4
u5rilla_ktzk,1
negativskill,1
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boa_only,0
thescorchingdragon,1
layered_heart_pupils,0
mikami49,1
galore_(flight_rising),4
unknownlewder,1
pride_color_stockings,0
bloom_(rodinsinker),4
belovedfoxx,1
maugrim,4
aero_the_acro-bat_(series),3
gjall,4
alolan_grimer,5
fendalton_sinclaire,4
todviorns,1
voidsylphid,1
kaia_(ulfhednar),4
lemonlycan,1
feral_dominating_female,0
moon_marking,0
ghost_meowscles,4
throw_net,0
andira_(granblue_fantasy),4
text_on_crop_top,0
defense_stat_(yu-gi-oh),0
arisel_uwu,1
1111122,1
mold_(fungus),0
fetal_kick,0
blblchiyori,1
camo_underwear,0
linnaea_(silviathepony),4
eleanor,4
yaris_(chimangetsu),4
patrick_(lunchmeat),4
touching_own_stomach,0
sparkly_eyes,0
ferivyr_(shift),4
krista_blonde,4
cell_(cells_at_work!),5
citrus_tree,0
kona_(fluff-kevlar),4
spitting_on_face,0
furry_in_a_hurry,1
kantus,4
mellie_(only0neman),4
edwina,4
light_foreskin,0
gym_topwear,0
furrawin,1
chostrawbie,1
ankle_ring,0
metronome_bat,4
wasim_(meesh),4
a_pony,4
mistel_(capaoculta),4
golde_(golde),4
mango_(h2o2),4
ill_fitting_clothing,0
hensa_(waitress),4
dazzle_(hazbin_hotel),4
maw-malice,1
chicken_wings,0
donovan_dmc,4
ciggybunny,1
fingers_on_face,0
bea_(toomuchdynamite),4
hinzen,1
pride_color_thong,0
flexing_flexor_carpi,0
cum_on_topwear,0
toria,4
ysabelle_kentuck,4
avery_(disambiguation),4
genesis_yuya_kono_(desertpunk06),4
riu,1
monotone_briefs,0
skunkworth,1
morgan,4
tano_(far_beyond_the_world),4
gauged_tongue,0
miriam_smyth,4
dusky_the_dusky,4
lynofdeath,1
weretober,0
guaxraccoon,1
face_closeup,0
unfoe363,1
monty_mole,5
viola_(masterofall),4
creation,0
holly_(ews),4
body_encapsulation,0
tsumeno,1
orange_jockstrap,0
single_strap_shirt,0
spindle_(mlp),4
ramuchoppu,1
transparent_fabric,0
kaijin_(thatrandomlurkingguy),4
yukon,4
jaundice_rossenfield,4
smogville,1
fleur_ladouce,4
princess_smolder_(mlp),4
pikachurin,1
polka_dot_bra,0
hyper_glans,0
game_boy_advance_console,0
slyus_(fursona),4
shiron_(jude-shyo),4
submerged_hand,0
phil,4
mr._williams_(rotten_robbie),4
spark_(sparkitty),4
cachorroamor,1
neonsheath,1
quantum_deathclaw_(fallout),5
desamistesae_fafratarello_flumendo_styx,4
ezmeralda,4
jim,1
angel_(badlandsdrws),4
mario_tennis,3
jaxon_(fuze),4
giant_salamander,5
figurines,0
deezchief,1
nick_(chana_kim),4
plapjax,1
lesley_(funkybun),4
diane_evans,4
namabito,1
club_dance_dragon,3
spitting_out,0
blood_from_eyes,0
print_gloves,0
neofi,1
dekoude,1
terri_(morkai88),4
ono_kitsune,4
marcus_poorner,1
father_fingering_daughter,0
darkling,5
jiara_jaro_(coltron20),4
reiji_(reijikitty),4
donation_message,0
booze_roo,4
pepper_ann_(las_lindas),4
nutty_(el_senor_erizo),4
touching_own_crotch,0
friends_(panken),3
armored_trico_(the_last_guardian),4
notedwolf,1
andrealphus_(helluva_boss),4
striped_neckerchief,0
siljan_clawson,4
tre_(chuki),4
lana_(heellana),4
jammyjams,1
horn_decoration,0
chaikodog,1
cat_city,3
t-ace_juice,1
waa153,1
tomoe_ame,4
lizzles,5
punky,4
ajun_(character),4
verysleepycat,1
gothpanda,1
las_plagas_parasite_(resident_evil),5
dracanine,5
grace_(shining),4
smiling_friends,3
mp4ort,1
iv_line,0
mushroom_glans,0
inert-ren,1
alexzandr_sokolov,4
black_earbuds,0
imminent_spanking,0
illian_(komodo_dragon)_(ritts),4
plant_transformation,0
urethral_candle,0
poundbandana,1
i_quillian,1
cynthia_saito,4
neksu_(pokesona),4
wrongcable,1
mini-ham,4
pakobutt,1
clove,4
xmobile,1
shadowraine,1
lucky_(blondefoxy),4
the_stare,0
rip_slyme,4
scorchingnova,1
faithandfreedom,1
shui,4
knight_princess,4
iyarin,1
heavy_ball,0
miss_kitty_mouse_(artist),1
urine_from_nose,0
begami,1
farly_(firenor),4
jko_2,1
unstableimagination,1
blueberry_(disambiguation),-1
luca_shoal,4
homealone,1
lavaar,1
shady_daze_(mlp),4
enchantment,0
holiday_dressed,0
rainier_(artist),1
irongut,4
cerberus_(houtengeki),4
hatch,0
scruff_mcgruff,4
sl,0
san_(tigerinspace),4
tyr,4
milo_nettle_(character),4
theodore_winters,4
carolina_panthers,3
hodgepodgedl,1
shira_seskai,4
digitamamon,5
n-prophet,1
jenner_(nimh),4
alternate_eye_color,0
ceri,4
cum_on_stockings,0
ukagaka,3
selo,4
satyn,4
vivamus_lican.,1
kwami,5
shaw_(arknights),4
killing_floor,3
kipper,1
pup_(grisser),4
pedals,0
alitica,4
sex_club,0
wordsworth,4
jace_beleren,4
10hmugen,1
chronoa,4
withered,0
lena,4
bras_d'honneur,0
ben_the_dragon,1
sebulba,4
jezzel_(hth),4
fighting_vipers,3
tube_socks,0
toe_grab,0
identity_disc,0
muzzzzz,1
mandakatt,4
glowing_piercing,0
lady_weavile,4
lundsfryd,1
fierywitherrose,3
the_lovers_(tarot),0
bee_(gremm),4
ratchetsly,1
hachisan5656,1
coif,0
happie_(character),4
warumonzaemon,5
link_(rito_form),4
black_tip_reefshark,5
mossy_(nirvana3),4
artolvrsmth,1
nerf_blaster,0
moka_yume,4
f-22_raptor,0
bilabee_(character),4
koriko_(kanikui),1
maite_terranova,4
sacredsun519,1
bargaining,0
tail_scales,0
valor_(eonshinato),4
victoria_(pockediginut),4
learning_the_mirrors,3
gurney,0
volcanic_eruption,0
mr._polywoo,4
prescott,4
perninae,5
serratia,4
arustuff,1
slapfuzzy,1
worship_play,0
specter_koen,4
katazaki,1
stardust_(shyvrc),4
translucent_nightgown,0
nipple_kiss,0
vance_sloan,4
josie_mouse,4
laura_(joaoppereiraus),4
luna_star,4
siela_(velannal),4
josiah_(themuffinly),4
bryta_(zummeng),4
fahrencrysis,1
ark_noir_(character),4
dea_(conrie),4
olyvia_cross_(sexyblaziken),4
great_girros,5
rogbiejoke,1
pegasus_humanoid,5
cohozuna,5
randrana,4
ma_wen,1
rex_(samanator_club),4
naz'akh,4
custom,0
hayrider,1
whan,1
just_(justkindofhere),4
shatteter,1
rouge_flare_(character),4
backdrop,0
liunkaya,4
tigerfur,1
same_size_pred,0
luke_(james_howard),4
oxocrudo,1
complex_shading,0
pink_backpack,0
starburn,4
imminent_inflation,0
hanzotitmada,1
snickersthecat,1
jnstudio,1
five_nights_at_freddy's:_fazbear_frights,3
zenberu_gugu,4
tekkymunster,1
terah,4
keyboard-draws,1
alykia_lumine,4
faraday_(fluff-kevlar),4
thatcatzoey,1
sari_sumdac,4
bobby_(maple_town),4
skellymor,1
ward,0
justwannadraw,1
bosstones22,1
cody_(rocketraccoon),4
nine,4
symbiotic,0
fallynangel,1
sami_(character),4
yiff_in_hell,0
raksha_noroi,4
kina_jardine_(kinojaggernov),4
sandy_rivers,4
tavia,4
foot_on_breasts,0
dummy_(undertale),4
sanaki_koga,4
headmaster,1
ishioto,1
emit,1
zidonuke,4
range_weapon,0
lucy_(school_days),4
twilight_(series),3
zicker,1
wiizeru3950,1
persephone,4
lyle~b,4
rienn,4
rakshani,5
sonikey0_0,1
sochi_2014,3
zephyrflash,1
streaks_skunk,4
kurona_(neko_musume_michikusa_nikki),4
kattotang,1
piraka,5
crystallos,1
stan_melgar,4
fafnir_(little_witch_academia),4
ceraph_(coc),4
dante-ml,1
tutankabron2uv,1
nintendoland,3
dawnwalkerwolf,1
eli-sin-g_(artist),1
nado_(character),4
incense_burner,0
naburus,1
rukacollie,4
chronicles_of_narnia,3
tiluta,1
furmarks,0
ahgot,1
entwined_penises,0
lemmy_(sapphinarose),4
moon_symbol,0
limb_regression,0
gumbo_(colo),4
carpeted_floor,0
crystal_panier,4
redriver24816_(artist),1
mx_ragam,1
pomegraniter,1
buiron,4
xerneas_(neutral_mode),5
sikur,5
ethan_forestfox,1
sir_dragon,4
flag_patch,0
mimi_(pop'n_music),4
vee,4
neoma_(reign-2004),4
tail_under_clothing,0
broxoles,1
fingers_on_butt,0
mysterydad,1
cinnie_(wizpig),4
k2146938,1
kogitune,1
naya_(nuree_art),4
external,0
annabelle_(madmax),4
crown_(kemokin_mania),4
doggette_(bahnbahn),4
urine_in_condom,0
smalldom,0
alternate_gender,0
khaki_face,0
arty,4
spikes_wisp,5
pulling_down_shirt,0
spoopy-abby,1
muggsart,1
hbky1101,1
visibel,1
morse_code,0
julesy,4
nintendo_ds_console,0
allaros,1
lean_body,0
levitus,4
thedrunk,1
teacup_gryphon,5
animal_body,0
nikki_(phenyanyanya),4
ben_gally_(callmewritefag),4
imightbemick,1
drakkmac,1
shikyotis,1
revna_(garal),4
connor_walker_(leviantan581re),4
vehicle_crash,0
opening_shirt,0
yuki_(yukitallorean),4
nerif_(nerif),4
ryanburr,1
tasha,4
gecko_humanoid,5
saliva_on_legs,0
maxima_(inukon_geek),4
alex_winter,4
nova_(hijinxfantasy),4
xerhom,3
sve_ulfrota,4
clint_(lelewinter),4
foot_in_ass,0
memjioof,1
rook_(nimratu),4
aniu_(balto),4
think_tank_(fallout),5
pimientanas,1
thought_bubble_outside_panel,0
transformation_transition_effect,0
mommy_(sloss),4
dusk_fonce,4
monotone_belt,0
meruka-kun,4
nablacah,1
ruddy,0
wingsandfire72,1
dr._k,4
mangrove_(mangrovefox),4
stardustpony,1
ych,0
akiidiamondheart_(artist),1
aion_(city),4
citail,3
hole_in_pants,0
hyena-kun_(manadezimon),4
cd_case,0
subject_67_(helltaker),4
napoleon_(random_doom),4
khaba,4
okapi_(kemono_friends),4
joltink,1
malladus_(char),4
bupkus_(space_jam),4
siren_(lightwolf27),4
cage_(artist),1
deesamane,1
arik_(dttart),4
kyron_farspire,4
queen_kaliendra,4
driveway,0
vavelu,1
haruki_genia,1
azentaru_(r-a-s-p),4
skypup,4
double_fine_productions,3
this_is_sparta_(meme),3
aisha_(longinius),4
amanda_(flufflewdzthings),4
zoot_suit,0
angel_in_the_forest,3
malina_(kazukio),4
hendersimp,1
zvn,1
bela_dimitrescu,4
rider117,1
zipper_skirt,0
josomez,1
daddy_mug,0
coconomi,1
genital_fluids_on_self,0
hand_on_another's_pussy,0
marqslewds,1
deer_spots,0
tyler_(ccwoah),4
phantom_balloon_boy_(fnaf),4
wing_tuft,0
phylla_(coc),4
bassfxlimit,1
penny_(mao_mao:_heroes_of_pure_heart),4
cow_horns_crown,0
theflerffyburr,1
detectivecoon,1
allay,5
yuka_(layer),4
horsecat,1
blurau,1
wheresmyale,1
ruby_(othinus),4
karmi_(pencil_bolt),4
raijū,5
voltybee,1
null_(nefariousnull),4
cube666dice,1
don't_dead_open_inside,0
uni_hkdm_gsg,1
rigbythememe_(artist),1
riocynn_ra'ak,4
pred_quest,3
myuu_(arisumeria),1
00fantasy00,1
milkpudding,1
disney's_house_of_mouse,3
druid_(feral),0
backless_swimsuit,0
sora_(arknights),4
jomasfaceisugly,1
moonbane_(artist),1
fiji_(johann),4
toilet_paper_dispenser,0
foxworks,1
44shio,1
axle,4
sairisha,4
gart,1
gnj97,1
cum_on_magazine,0
tigeraf,1
amao,4
taur_transformation,0
turner_(grafton),4
zekit,1
trickster.puppy,1
barry_torres,4
bo_(domovoi_lazaroth),4
maya_the_bee,4
monita_(nintendoland),4
damalia,1
feralmoonlight,1
synx_ryugami,1
cloe,4
creaturecandy,1
dewey_teagarden,4
sabrina:_the_animated_series,3
lenora_(specter01),4
rawrzky,4
mkonstantinov,1
oceanrider,4
squilliam_fancyson,4
water_spirit,5
kyree,4
leorajh_(aurastrasza),4
dieff,1
striped_dolphin,5
cassia_(seff),4
kinoko.kemono,1
tarasa,4
escaflowne,3
olaf_(frozen),4
kawaguchi_youhei,1
reddoshirousagi06,1
nanites,0
popcornkitten,1
silver_stiletto_fly,5
longing,0
wildkicheko,1
satoru_iwata,4
wind_turbine,0
used_tissue,0
baywatch,3
hybridmink,1
digimon_humanoid,5
mouse_lemur,5
tumblr_ask,0
toonlancer,1
meena_the_kobold,4
huang_(takataka),4
furisukiii,1
messiah_rundall,4
spooderdoodler,1
h3llvixn,1
divifilly,1
zhongli_(genshin_impact),4
mollusk_merfolk,5
sugoi_hi,1
dodoro_(zoohomme),4
terri_(lordflawn),4
uchimaru,1
mamako_oosuki,4
octopus_merfolk,5
jolbee,1
climbing_tree,0
janemoroh,1
max_(shining),4
soggygoat,1
charlyhorse,1
inflatable_chair,0
plotting,0
aeon-a302h311_(character),4
delloknon,1
foot_on_ankle,0
archer_emiya,4
experience_bar,0
kopy_k,1
holding_stack,0
bell_(overgrown_lizards),4
throatspit,1
spider_strumpet,1
ryder_(seintaur),4
light_nails,0
horn_spikes,0
icon_of_sin_(doom),4
wigglelines_(artist),1
himbocuga,4
joey_(sentharn),4
bella_(fluffyglitch),4
tama_(the_lion_king),4
cj_fauxx~,1
dorak_(character),4
tay_(tayjayee),4
lauren_aza,4
massjoinment,0
jaeger_(lewddragons),4
papilla,0
figaro_(artist),1
julian_(cadaverrdog),4
old_speckled_hen,3
touching_neck,0
omaha_the_cat_dancer_(franchise),3
vex_(destiny),5
tails_of_iron,3
camo_headwear,0
peteko,1
rafaknight-rk,1
thieu_(thieutheowl),4
perserika_(doneru),4
nipple_clamp_pull,0
chiro_(zerochill),4
on_ceiling,0
lilmothiit,5
seppuku_(artist),1
year_of_the_rabbit,3
kintaro,4
edryn_(coc),4
blindwatchman,1
puffedup,1
phallusfantasy,1
rockk,1
axel_gear,4
rick_(kirby),4
inuwanko,1
evan_(thaine),4
derago,4
dunceneygak,1
glacialpain,1
megan_(two-ts),4
lex_kempo,4
princess_(harvey_beaks),4
lunarwolf,1
artemis_(pokesona),4
ranek,4
v-v,1
balance_beam,0
pumpkinmon,5
felix_radziwill_(kinrovaldis),4
zazi,4
janet_(trias),4
sevota,4
likanen,1
gwendolyn_mai,4
zerospanda,1
associated_student_bodies,3
prices,0
raijin_(shaftboop),4
acting,0
minish,5
stalafarnahe,1
sweethellgirl,1
calli_(scalie_schoolie),4
kizzneth_(character),4
toby_(tatertots),4
tectael,4
kal_noven,4
knight_knight,4
tio,4
bessy_(back_at_the_barnyard),4
labatate,1
moro,4
mesperal,1
codelizard,4
murderwolf,4
algooriginal,1
laura_(derideal),4
wendy_(bugzilla),4
pinkpossum,1
echinozoan_humanoid,5
naughty_(bear213),4
aukai,4
sunny_miami,4
spunkdragon,1
p-balloon,0
erin-fox_(character),4
nagamo-kun,4
auri_(thehuskylord),4
kmmm,1
kehta00,1
security_puppet_(fnaf),4
loaf_(dogs_in_space),4
chinchilla010,1
clapperboard,0
travis_mayer,1
raven_baxter,4
hip_focus,0
blackshepard,4
aleph,4
aisu_kyo,4
pentarch,1
paula_(1-upclock),4
german_(altpuppy),4
takaoka_motofumi,1
6_penises,0
jade_(fvt),4
fureezy,1
shahra_genie_of_the_ring,4
riley_(jendays),4
vailet_deer,1
babie_fluff,1
action_shot,0
trace_raymes,4
andesc,1
nightmarebat,4
black_and_tan,0
flying_car,0
the_eighth_sign,3
nietzab,1
angelo_(siperianhusky),4
caribooty,4
hotdog_costume,0
aniki_faux_(character),4
lyla_no'uka,4
sylvester_merrimen,4
monikano,1
kaltt,0
cody_hedgie,4
ipetro,4
jill_bridges,1
rabbit_panties,0
eurovision_song_contest,3
maoochao,1
smuppetfan69,1
juno_(tabunnie),4
melanie_(fiercedeitylynx),4
attack_stat_(yu-gi-oh),0
guarana_(fruit),0
/co/nrad,4
surprise_oral,0
orange_frill,0
xiaoman_(yogin),4
abomination_(darkest_dungeon),4
electricjolt,1
danger_n00dle,4
realistic_hypnosis,0
dark_shorts,0
cock_corset,0
winnigrette,1
yaky_(yakyarts),4
lilmousybianca,1
feerone,1
tinbash,1
thehiggles,1
cum_on_eyes,0
diamonikon,1
vellacraptor,1
telethia_(scorpdk),4
year_of_the_snake,3
morumotto,1
synthesizer,0
turnsky,1
badminton,0
chazwolf,4
tolbi_angara,4
donitkitt,1
kizrae,1
clouded_(fa),1
fuzon-s,1
prunella_deegan,4
artamis_snowpaw,4
drena,4
fatecoyote,4
betsumon,5
tribo,1
mooshroom,5
couchcrusader,1
nisimawari,1
jack_(shadowcatcher),4
zee,4
skuell,1
grandifloru,1
rayverak,1
quokka,5
apophis,4
creeper_hoodie,0
gao_mikado,4
spanking_(artist),1
slaughter,0
hilt,0
donovan,4
twodeepony,1
kazuma_kiryu,4
ahemai_(maim),4
amy_lawrence,4
darkin,5
lyra_belle,4
professor_hubert,4
angie_(character),4
rustbust,1
starcrossing,1
duncan_(dark_nurse),4
yellowstumps,1
terru,4
ero_trap_dungeon,3
dokara,1
tight_coupling,3
sapphire_(steven_universe),4
nero_(devil_may_cry),4
yarne,4
yuri_the_lion,4
seong_mi-na,4
ivory_(blitza),4
biscuit_(carrot),4
pinopino,1
tessa,4
draculaura,4
keeby,4
ethan_alexander_(tabra),4
slavcat,4
princess_(alpha_and_omega),4
taw,4
raceplay,0
c_j,1
yazoo,4
flying_afro,1
amy_the_zangoose,4
d3monstar,1
merlock,4
danelda,4
veck,1
jira_lightstalker,4
wormhole,0
wolfplot,3
rj,4
rum_tum_tugger,4
xelophus,4
mother_mountain,4
cybersix,4
pico_(ayame_emaya),4
cassidy_(spoonyfox),4
aryan_(species),5
hand_on_nose,0
trainer_penelope,4
average_artist,1
urban_(artist),1
cupcake_(oriole),4
rivercoon,1
kino_(character),4
cowdere,4
justin_bieber,4
imaajfpstnfo,1
buns_rabbot,4
rip_kackel,4
jamesthedoc,1
tenth_doctor,4
belun,1
rydah_hazard,4
renx,1
luigiman,1
nikki_(balto),4
gachimuchi,3
pandafox_(artist),1
mareef,4
smudge,1
rex_(bgn),4
mr_zero,1
snoop_dogg,4
helping_hand,0
mega_beedrill,5
kuroki_tomoko,4
sailor_mars,4
rukia_kuchiki,4
fray_(icy_vixen),4
d-gate,1
ecf,4
fidgit,1
bennemonte,1
pear_of_anguish,0
fossil_fighters,3
monster_under_your_bed,5
ouma_tokiichi,1
alligator_snapping_turtle,5
coo_(kirby),4
blackgabumon,5
termite,5
rainbow_earring,0
tsen,4
moyses_damasceno,1
revankitty,3
yamame_kurodani,4
justaweirdogirl,1
jousterl,4
slyphin_drakil,4
osiosi,4
evil_varby,4
valeford,4
shirukawaboulevard,1
sebris,4
bushtit,5
outbreak_company,3
cut_tail,0
f-16,0
michael_myers,4
rod_(animal_crossing),4
ceo,0
richard_(rikarda),4
>:c,0
gale_and_gloria,4
thekc,1
littlenaughtypony,1
sosarin,4
adjusting_swimsuit,0
ruby_(deonwolf),4
citrine,4
kenko,4
gorgoroth,4
nausicaä,4
tute_(character),4
leash_training,3
dyluck,4
owlboy,3
silver_(oxsilverxo),4
omaha_the_cat_dancer,4
krow,4
chingilin,1
baltomutt,1
frocta,4
austiniousi,1
astra,4
zell_(animal_crossing),4
forging,0
mariel_(tch),4
keeyrow,1
evana,4
eva_(sahara),4
ray_van_siegheart,4
mox,4
catahoula_cur,5
renji_(cocotama),4
allergic_reaction,0
maomaogames,3
blood_blockade_battlefront,3
blanko_(space_jam),4
furiorid,4
whoops,0
floating_eyebrows,0
desmond_fallout_(desmondfallout),4
night_fumei,4
solar_(aspect.tribal.wolf),4
ehri_(rashchaos),4
el-recondite,1
catsithx_(character),4
oinking,0
implied_bisexual,0
demacianyordle,1
pukopop,1
ganler,1
darius_shah,4
puffinza,1
chell_(fursona),4
green_boxer_briefs,0
harasaki,1
themimicartist,1
shredder_(hocolatemilk),4
rennafestive,1
pressing_against_cheek,0
chairoi_tategami,3
gloria_(slither),4
skrekdathird,1
angelgatomon_(character),4
chaclaveloz,1
mangchi,1
tatum_(tolng),4
tongmung24,1
zelena_(thatonevocals),4
spotted_chest,0
issack_wink,1
vyncent_(omegaozone),4
elma_(sleepiness18),4
lightning_flash_(oc),4
eva_(dewwydartz),4
pop_cat,3
tinydevil,1
susan_(tunesdesu),4
bulge_under_clothes,0
amber_wind_(character),4
kasloth,1
flamberry_(artist),1
tuti_(tutifruti),4
ashra-nour,4
kon_(habitualboomer),4
aylornia,1
silverzard,4
dronis_(psyphix),4
foxy_loxy_(1943),4
diana_(huffslove),4
shadeii,1
yao_(takataka),4
collaborative_ball_licking,0
gibly,1
moitasart,1
tianzhen_xianhua,4
aurochs_humanoid,5
sprocco_(artist),1
brimstone_(zer0rebel4),4
k'joor_(artist),1
zzyzx,4
foot_on_own_penis,0
blue_wristband,0
wish_(splishsplat),4
shmny,1
peachykeen,1
knia_(devil-vox),4
yellow_head_tuft,0
ryou_(ryoudrake),4
parinton,4
futaba_(mamoru-kun),4
shibusun,1
chocky_(pop'n_music),4
ellis_(left_4_dead),4
eris_bird,4
poison_control,3
glistening_goggles,0
kaitii_(kaitii),4
wei_yenwu_(arknights),4
gabe_walker_(pawpadcomrade),4
bredlay,4
soren_kisamora,1
daku,5
fluffy_hypocrite,1
sora_(gargoyles),4
brooke_marlow,4
esatu,1
take_a_number,0
jack_hoo_(artist),1
maxa',1
rosycozy,1
ninu_kenage,4
potes,1
zerdaofdebauchery,1
sketchkat_(character),4
apollo_caelum,4
bun_cover,0
looking_at_smartphone,0
cannonball_(game),3
bluefire,4
knightsfm,1
awanata_lighthoof,4
afterlife,0
magic_show,0
kaos,4
kyn,4
dylarts,1
adolyn,4
character_creator,0
rasher_(animal_crossing),4
parasyte,5
ricky_(zerofox1000),4
hazel_(cherrybxnnie),4
platinum_hair,0
female/female_symbol,0
dom_turn_sub,0
bonznsfw,1
instinct,0
ellian,4
tight_highs,0
idate,4
jess_mcclover,4
[in]vader,1
juniper_(coffeefly),4
thehurdygurdyman,1
shennanigma,1
nomi_no_sukune,4
jenn_(jackaloo),4
head_tattoo,0
claire_(golderoug),4
afurreak,1
mascot_costume,0
gradient_scales,0
higgo,4
athyn,4
werner,4
lunarthunderstorm,1
tuuwa,4
pacificside18,1
zanakil,1
amailemon,1
khalo_(jelomaus),4
peachtree_(hoodielazer),4
chuchu_(dragoon-rekka),4
light_grey_body,0
hinatawolf,1
puck,4
amakagew,1
mhfap!,3
showers_the_vaporeon,4
edjit_(character),4
carpo,4
kazumichi_irie,4
furry_outpost,1
scratchdex,1
axtrosis,4
trina_park_(sketchybug),4
hand_on_door,0
vex_(vexlynx),4
ian_(disambiguation),-1
peony_(fvt),4
mattaku_mousuke,1
raz_von_coon,4
breaker-of-oaths,1
jiang_winters,4
nemi_(tenshoku_safari),4
kloseddoors,1
green_lizard_(rain_world),5
strongbana,1
foxyhunter,1
purple_armband,0
idlerawr,1
mufksin,1
big_pawpads,0
jersey_cattle,5
foxnose,1
bralios,4
savannah_(saxa),4
kaddo,1
rovafur,1
atlas-66,1
iguana_(genus),5
zhh,1
white_shell,0
kynosura,1
brown_hood,0
kila_ilo_(character),4
kurimi_(lunarspy),4
icarus_(dragon_ball),4
sheath_pressing,0
tay_the_bear,4
jeremiah_(seth-iova),4
ryden_dire,1
asura_(rinkai),4
graham_(tuke),4
leg_on_thigh,0
gureeookami,1
cath_palug,4
anakaris,4
aidan_(apollo13),4
wallabee,4
anon_(koorivlf),4
delta3814,1
heart_balloon,0
sitting_on_breasts,0
rex_(magpiehyena),4
kfc_bucket,0
blue_inner_pussy,0
chuck_fenmore,4
farv_(funkyknife),4
dhahabu,4
lyritha,4
apelairplane,1
roople,4
jacktheripper2008,1
shira_(furryfight_chronicles),4
rollfox,1
claws_in_mouth,0
monotone_thong,0
jackiey,1
peargor,1
madmark,1
pix_(piczel),4
foxxfire_(character),4
jacklyn_brown,1
bastet_(full_bokko_heroes),4
unguligrade_hands,0
tonycomics,3
reindeer_orisa_(overwatch),4
striped_chest,0
guardian_(zelda),5
kira_(hellshound),4
autumn_vector,4
vamptod,1
marnie_(grimart),4
aoi_futaba,4
silver_reichtum,4
tentacle_wrapped_around_leg,0
petpet_(neopets),5
ligerbull_(species),5
singarti,0
scott_calico_(scottc),4
adamai_(wakfu),4
tagumi,1
cunnilingus_through_clothing,0
shanty_(tfh),4
lesbian_correction,0
desecration_of_graves,0
white_skinsuit,0
electrical_hair,0
glowing_necklace,0
petrus_(petruslol),4
yakumodaisuki,1
umbrella_drink,0
dark_green_fur,0
bunzerk,1
rusty_(air_bound),4
cuddle_knight,4
song_sehee,4
17:12,7
techboy1423,1
disembodied_limb,0
red_one-piece_swimsuit,0
bree_(bender),4
wokky,1
gign-3208,1
snocotan,1
structure,0
othoxon,1
chris_dunn,1
orange_(among_us),4
magellan,4
alt_h34,4
thespel,4
verbrand,4
glowing_arms,0
lakritz_(quin-nsfw),4
didky,1
dump_truck_ass,0
bear_(bear_in_the_big_blue_house),4
deerdaddy13,1
daisy_moore,4
nipple_bow,0
tattered_ears,0
dawnwashere,1
cum_on_own_head,0
dusto,1
hanakusotokyo,1
shiningloardaaa,4
human_penetrating_ambiguous,0
nipple_tentacles,0
apep,4
cuchufflay,1
squeaks_(rimentus),4
antrimbull,1
on_snout,0
paigescribe,1
tai_(viskasunya),4
ren_winterfox_(character),4
maleherm_penetrating_male,0
asrael_zogendral,4
lipstick_on_pussy,0
elh_mimaiya,4
cannons,0
ifra,1
lars_(anti_dev),4
layndon,1
shroudedmouse,1
artwithmoira,1
doug_(daxhush),4
tiziri,1
wuffypie,1
loketoki,4
jeanne_(hyenahonk),4
raccoon_ears,0
garrett_the_turtle_(character),4
orange_eyelids,0
xandra_(xandertheblue),4
torusop,1
bifurcated,0
orca_taur,5
danger_rangers,3
crimson_crescent_(nolollygagging),4
dromarch,4
silksong,3
bra_removed,0
scarlet_dragoon,1
adjustable_wrench,0
srfennec,1
leo_(zodiac),4
digimonforever,1
radiation.,1
kila_ilo,3
isuku,4
katja_(baldrek),4
keiko_(artica),4
oswald_(ahnik),4
treeing_dog,5
bluizer,1
palemoonwendigo,1
adler_(feber12),4
mg_42,0
wadjet_(kiala_tiagra),4
kamochiruu,1
epsilion,4
zabumaku,1
gill_penetration,0
akima_(eddiew),4
selara,4
rubikang,5
gordian,1
dibbun_(ordosan),4
nana_(fadey),4
meguru_salmon,1
daisy_(furel),4
blanket_(character),4
vanna_(jomokin),4
margarine_(deumosden),4
panzercaptjason,1
ghen_(artist),1
frostedwarlock,1
korol,1
space_cat,5
robonyan,4
sandbag_(super_smash_bros.),4
yorunoko,1
pixel_(the_gamercat),4
xizana,1
alistair_(toothpasta),4
bullsquid,5
modifiedlast,4
grimlock,4
vernon_hunter,4
season,0
8-xenon-8,1
nishi_yoshi,4
smuttybacon,1
alicia_(ricochetcoyote),4
tachiyawolf,1
koto_(yu_yu_hakusho),4
wesleyfoxx,4
mcgruff_the_crime_dog,4
shivra,1
rockin,4
nanuk,4
kuridora,1
sheepwithcrystallicfur,1
cyberwuffy,1
aero_the_acro-bat,4
mabeelz,1
primehunterwolf,1
katanimate,1
walker,0
aoba_seragaki,4
felix_(modeseven),4
timesplitters,3
pirates,0
squishy_kitty_(artist),1
francis_(left_4_dead),4
bigdaddyrhino,4
american_psycho,3
eros_(comic),3
enf-lover,1
ask-a-deathclaw_(artist),1
makogrey,4
foxfeather,1
dental_gag,0
cassi_dupuis,4
lincoln,4
autofrottage,0
alice_reed,4
notyoursagittarius,1
rage_grenade,1
lupis_wolfe,4
keitai_denjuu_telefang,3
glace,4
joona_(colarix),4
raichamonolith,5
marilith,5
inutonotawamure,3
legs_lifted,0
invisibility,0
sin_teal,1
samiweasel,1
yellow_wall,0
zucker_(animal_crossing),4
frostleaf_(arknights),4
vibramare,4
omiya599,1
airport_security,0
freya_(tanrowolf),4
koi_(carrot_kiss),4
courtains,0
grandfather_penetrating_grandson,0
morphing,0
kittyq,1
gagged_top,0
naturalist_panther,4
cap'n_(deltarune),4
sylve0n_(user),4
oyabun_to_fushigina_kusuri,3
rerebrace,0
feedies,1
cum_on_face_mask,0
double_barrel_shotgun,0
riki_(ginga),4
wavy_eyebrows,0
heart_apron,0
aliv141,1
youzaiyouzai112,1
looking_at_console,0
ashley_(pickles-hyena),4
explosive_pone,4
barry_(eroborus),4
geriel_(iztli),4
superbusty,1
katiana_(ruaidri),4
jaze_(gau),4
pasufani,4
alarm_cock,0
makima_(chainsaw_man),4
saya26,1
caroline_(fiercedeitylynx),4
hyper_brachioradialis,0
arianna_(jackpothaze),4
tram,0
mango_the_pikachu,4
kemofujin_(comic),3
neon_dark_(destiny-yuuei),4
waterbaby_(centaurworld),4
katya_zhang,4
mammoth_humanoid,5
cinccina_(lucyfercomic),4
kinkykaida,1
cooper_(cooper),4
greiser,1
jamie_(jailbird),4
kate,4
illuminated,0
cody_(pizzakittynyan),4
xbox_original,3
multicolored_armband,0
dog_princess,4
giovanna_(guilty_gear),4
babysteps,1
vantablack_fur,0
oriental_longhair,5
zenia_wolf,1
bdrd,1
sharkmaps,1
blue_virus_(dr._mario),5
bangle_(animal_crossing),4
liam_(codymathews),4
sprocket_(oc),4
lulitadarkrose,1
blackheart_xi,1
holding_wood,0
mead_(kuiperfrog),4
redgi_(tails_of_iron),4
analog_raccoon,1
ellie_(maddeku),4
maculata,4
axel_hell,1
discarded_pants,0
tarumeow,1
melanie_mortrose,4
shaker,0
arclun,4
aladar,4
bluewoman,1
bigdogenzo,1
hu_tao_(genshin_impact),4
begging_not_to_stop,0
fumonpaw,1
kirika_(peach88),4
aiden_(kadiboudacz),4
pythor_(fan_character),4
egg_transformation,0
faxy_(pillo),4
hazama,1
do-rag,0
shuffy,1
star_necklace,0
kippz,1
adynamical,1
chi_(fumblingfoxpaws),4
suzumiya11,1
cosmic_frill,0
body_part_transformation,0
andrea_(deminothedragon),4
jacob,4
heckler_and_koch,3
onyx_kingstone,4
green_bracelet,0
micco_(grimart),4
avery_(peculiart),4
question_block,0
noct_(noctilus),4
chsim662iawh7ai,1
kobold_(warcraft),5
p812p,1
hollow-dragon,1
wasabikitten,1
jared_(lipton),4
baal_(cult_of_the_lamb),4
jusu-tengu,1
donacutie,4
safeword_(character),4
virdemis,1
accipitriform_humanoid,5
egsaku_(character),4
blood_covered,0
aldi,1
vilosa,4
drawer_(artist),1
fu-do,1
ravager_(naruever),4
that_pool,0
crystal_glaze_(oc),4
mint_(goldenbuckles),4
squid_baron,4
croiyan_(character),4
lurker_bot,1
radjin_(radjinwolf),4
emmerich_(evkenn),4
sitting_on_building,0
sela,4
ragna_(hollandworks),4
snackable,1
ostrich_feather,0
lord_darcia_iii,4
ti-suger,1
backwards_text,0
vulprofox,1
harper_del,1
wilcar92,1
evil_anaunara,1
orgasmic_birth,0
staino,1
aquarius_(bgn),4
alargewoodenbadger,1
osabakitina,1
danny_(zackdadawg),4
hades_(disney),4
smelling_penis,0
candy_the_skitty,4
amnesia_creations,1
crystal_necklace,0
dogtanian,4
itsdatskelebutt,1
rusti,4
sting_12,1
festive_winter,4
lunati,1
knot_squeeze,0
homemosaco,1
doodledan86,1
rova,4
mknrhsm,1
wolfgang_(fuze),4
raunikayle,5
ari_(kalofoxfire),4
elder_scruffy,4
zyena,4
cool_cat_(series),3
mewgia_(species),5
komahu,1
azumi_kix,1
mofuringu,1
drake_(dreyk-daro),4
shadowboxer,1
roantiger,4
kingatrandom_(kar),4
10_nipples,0
megan_(al_gx),4
kuro_(cyborg_kuro-chan),4
penis_popsicle,0
cowprintsoda,1
serenaught,1
garreth_(thedgamesd),4
spicydurassno,1
komolord,1
arm_torture,0
samson_(zentt),4
wolfie-feral,4
senphyro_(character),4
bus_stop_sign,0
megamilk_(character),4
vanthi,1
jim_(disambiguation),1
rocks_and_bugs_and_things,3
ysilla,4
johnny_(kralex),4
shay_(heatzone),4
cotton_candy_(glacierclear),4
barry_the_pizza_bear,4
orion_(jackalope),4
heart_wall,0
foreground_sparkles,0
calira_(mcfan),4
rim_lighting,0
rocketwolfy,1
silica_(forktongue),4
roxanne_(skarlett_cynder),4
autumntree,1
aureskarispriomnis,1
cunnilingus_request,0
redick,4
abzollozdol,1
blu_(bludoe),4
7_horns,0
zofia_squirrel,4
margot_mallard,4
tired_of_this_shit,0
javadog,4
american_red_squirrel,5
martin_the_warrior,4
genital_raphe,0
hunterandom,1
cinnamon_(spice),0
v1sage,1
hennin,0
asd5,1
narci_(moreuselesssource),4
cookieglory,1
ryan_(sing),4
aguila_la_serie_animada,3
multicolored_crop_top,0
carbon,4
gynomorph/male/gynomorph,0
mig_(interspecies_reviewers),4
businessman,0
lee_(disambiguation),4
verk_(presto),4
kioreii_(character),4
ddevil,1
mavy_(alicethedeer),4
rokutenachimutsu,1
low_cut_shirt,0
stickerboard_(tf2_demo_2004),4
hairpiece,0
mentalkitty,1
fay_feline,1
bonniegame31,1
hane_w_maxwell_(hane),4
swunnel_(insomniacovrlrd),5
crowsfire,1
shenshepa,1
jelly_zafara,5
josh-u4,1
painted_text,0
jorani,4
deelohial,4
mysticfluff,1
s.wolf,1
pawprint_background,0
victor_(igiveyoulemons),4
tsk03,1
precum_on_bed,0
umbreeunix,1
junibuoy_(character),4
black_boxers,0
verna_floplock,4
pecs_touching,0
sodabox,1
plasma,0
saucepit_(artist),1
puzzleplush,1
four_tone_fur,0
bastiel_(character),4
fryegg,1
flytrap_humanoid,5
rokh_(artist),1
llunablue,4
jugger_(character),4
xxsanitariumxx,1
pinniped_pussy,0
alternate_(species),5
netsuki,1
scout_(joshuamk2),4
dirty_fur,0
roland_(tehweenus),4
kizuna_ai,4
naamah_(slb),4
aysu_(roxannetheokami),4
cheefurraacc,1
quija,4
aidan_(sryer),4
ariku,1
rupee_(character),4
bluestream,1
amidasuke,1
luna_(overwatch),4
imp_(not_safe_for_reality),4
regenerator_(resident_evil),5
digimon_xros_wars,3
ollie_(overgrown_lizards),4
gosuzun,4
orca_teacher_(bebebebebe),4
nasharan,1
dimorphodon,5
miscellanea,4
aleks_(armorine),4
pink_neckwear,0
marrubi,4
galarian_weezing,5
max_(max_and_ruby),4
fluffychuck91,1
cumfall,0
nt00,1
clove_vaughn,4
salga_0310,1
ryme,1
lulucien,1
behold_my_girth,1
reizu47,1
hibakiti,1
khronosz,1
sasha_moss,4
motiyuki,1
asti_(lexoastonov),4
garlic_bread,0
akito_yagami,1
huge_aerola,0
softuftos,1
heresy,0
bushycat,1
kira_(nekogirl1257),4
envious,0
puppet_strings,0
shakuto,1
onegathos,1
loui,1
semi-anthro_penetrating,0
"""big_mama""_rosie_(blackfox85)",4
smite_(artist),1
manure,0
ryusei_iouko,4
dust_bunny,5
the_sun_(tarot),0
ellis1342,1
art_class,0
greninjohns,4
buttgrab,0
kisakarou,1
mathematics,0
raph,4
red_sun,0
chill_(dr._mario),4
wdl,1
elslowmo,1
weeds,0
lyle_(icehawk),4
dangus-llc,1
bitcoin,3
spike_(lilo_and_stitch),4
oval,0
pokemonartist,1
pillow_talk_(oc),4
rile-reptile,1
tukamos_(character),4
knee_guards,0
bucket_hat,0
pohatu,4
red-striped_basculin,5
crazyeyes,1
pesakd,1
riku_usi,1
yana_(jelomaus),4
konnestra,1
rivak,1
animal_hat,0
muffin_top_(legwear),0
ossifrago,4
riyo,4
rico_ro-ni,4
naughtywrens,1
bailey_(kirbot12),4
jggjqm522,1
octo-risotto,1
falconry,0
dsana,1
popporunga,1
pyrope,4
zazz,4
betty_rubble,4
galondria,4
cubeychou,1
apple_watch,3
graros,1
chewythewolf,4
shalone,4
rhari_(character),4
kimmy,4
sasha_(eradragon),4
jinny,4
svenno,1
phantom_marionette_(fnaf),4
urethral_piercing,0
swiftnicity,1
jean_(artist),1
digimon_tri,3
poprocks_(oc),4
jogger,0
tikki_(miraculous_ladybug),4
arkant_monanza,4
orangesoiree,1
dark_talbain,4
embryo,0
slam_dunk,0
night_akula,4
personal_training,0
watamote,3
yekkusu,4
wizbooty,1
lumina_flowlight,4
dankwart,1
hungerynano,1
idaialo,1
toraneko,1
motiif,1
braxia_(artist),1
kiyochii,1
shrunk,0
aaaamory,1
emoticon_(renard),4
hot_wax,0
taya,4
kogawa_kenji,1
rupert_bear,3
agua,4
twitchdawoof,4
psicoyote,1
cassandra_(tailsrulz),4
kagami,1
kammy_koopa,4
seb,4
tyskha,4
wdj,1
dark_vilani,4
laila_myrhe,4
anajlis,4
shark_tale,3
male_taking_female_virginity,0
amanda_caracal,4
torben,4
kit_(james_howard),4
warriors,0
flaming_weapon,0
caan_(james_howard),4
burrbito,1
monster_sam,1
atelier_sol_aer,1
supersly-mouseguy,1
wrists_to_legs,0
smogslog,1
nen_nen_koro4,1
sucking_anus,0
alpha_(aisu),4
dinobot_(species),5
andrea_(dj50),4
kuroneko_coffee,1
phoneris,5
kaizermelvon,1
mozyz,4
aphast,4
bepisfox,4
shuifi_(shuifi),4
laura_(soft_rain),4
kubwa_kalua,4
ribbon_(cocotama),4
neonxhusky,4
kathleen_rosetail,4
cheeky_smile,0
xeinzeru,1
heterogeneous_tentacles,0
grogu,4
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rinyabjorn,1
tongue_on_foot,0
cyrucrem,1
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mei_lian_(magiace),4
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zero_(vavacung),4
pat_(later_alligator),4
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scarlet_(wof),4
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ni_no_kuni_2,3
feoi,4
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flan_(puppyemonade),4
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sousuke_(lagoon_lounge),4
alabaster_(daikuhiroshiama),4
guoba_(genshin_impact),4
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lena_(thekite),4
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cheddy_(galcheddy),4
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thyz_(kouh),4
amp_(amphissal),4
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thehobbyhorse,1
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bluwolfie42,1
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rin,4
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sonic_robo_blast_2,3
the_silence_of_the_lambs,3
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nova_(nova_umbreon),4
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momo_(sirbrownbear),4
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puma_tuler,4
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rasik,1
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huan_tian_xue_lang,1
nintendo_seal,0
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urian_(nateday),4
raoni_(felino),4
zeso,1
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mr._sheep_(toybox_pals),4
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simejinameko,1
may_(disambiguation),4
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leo_(pikanodannna),4
nearie,4
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proteus_(proteusiii),4
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vizza,1
b.b._hood,4
quasar,4
kroq_gar,4
pet_store,0
opal_(steven_universe),4
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olga_discordia,4
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josia,4
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venus_impera,4
sianna,4
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tuke_(brother_bear),4
a_user_to_remember_(artist),1
alasiri_kivuli,4
elder_titan_the_worldsmith,4
lux_hearths,4
minx_lyra,4
jixer,4
zi-tei,4
soothingbunny,1
moth_(creepy_castle),4
esteban,1
slink_(character),4
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iravo_(arcanine),4
mellow_(character),4
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betting,0
cartoonmeat,1
thebestfox,4
rocky_road,1
katkhol,1
waterbot,5
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otamom,4
ball_clamp,0
dsketch3r_(artist),1
aturn,1
stepfather_and_stepchild,0
hy_eight,4
on_hay,0
betty_lu,4
esotericer,1
goldfishdog,1
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haku_hyogaki,4
scorbunny_(artist),1
drinking_beer,0
justicecaptainv,1
unnamed_rabbit,4
tamatamako,1
katerine_flores,4
nyx_(icebounde),4
deaglen,1
xena_(megustalikey),4
triforce_symbol,0
king_noble,4
kuranaga15,1
savannah_pendragon,4
caressing_belly,0
niwan,1
fenris_ragnulf,4
bukibun,1
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makuta_teridax,4
husky_inu,5
billy_(juvira),4
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fanet,1
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frilly_garter,0
seres_(seresdragon),4
cum_as_lube,0
shirothedragon123,1
unzipping_pants,0
assassin_cartuja,4
little_bits,4
cally3d,1
pupwolf,1
amethyst_aria,4
rmmlusitania,1
chenannanm,1
thatonegojimun,1
mostima_(arknights),4
zgrate,4
dabura,4
deneira_(diamondstorm),4
sammy_darkeye,4
tentacle/female,0
niqab,0
vae_(character),4
wallace_(wallace_and_gromit),4
saradrawsart,1
brimstone_(jasafarid),4
engrid,4
liskis,1
kaden_(fire_emblem),4
torsten_knot_(knotthere),4
multigenerational_incest,0
bone_piercing,0
takeyama_yu,4
benji_(archonwanderer),4
21:32,7
pelycosaur,5
cowlink_(cashier:3),4
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lynn_(orang111),4
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athosart,1
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gaku_(hhc),4
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dominohyena,1
gold_headwear,0
rathain_kuromaru,4
jebbthefoxy,1
hand_between_breasts,0
leopold_(zummeng),4
zaer_(vju79),4
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deshee,4
lorret_(infinitydoom),4
neapolitan_ice_cream_(skateryena),4
agoti,4
fiona_(phantomfin),4
bioluminescent_eyes,0
kaiza_teira,1
whitescale_sisters,4
gale_(slither),4
nora_(rain-yatsu),4
wahuku18,1
jzarko_(artist),1
marti_dumont,4
shanikafaceless,1
wendy_(beatleboy62),4
sharing_beverage,0
sans_(altertale),4
two_tone_mask,0
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talo_tsurrat_(nimratu),4
shrunken_planet,0
ora,4
mosfet_(character),4
beeprlae16,1
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akishiberian,1
julienne_(reapcreates831),4
male_non-humanoid_creature,0
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logyn,4
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luxatile,1
tiberius_(mlp),4
spittle,0
pilot_(titanfall),4
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lordfriez,1
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sandworm,5
riley_(pokémon),4
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ven,4
darkshadow,1
nobody_(oc),4
doopliss,4
mitsui-jun,1
ivy_terroir,4
raindrop,0
alf,3
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griz_sylverwing,4
myan_(artist),1
marshal_blue,4
raz_(nanimoose),4
star_rod,0
nexybutt,1
nightfox,4
felicity,4
takame,1
ghirahim,4
okamy,1
frau_streusel,4
sphenacodontid,5
twitter_bird,4
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daniel_ottmaw,4
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ctfbm,1
assassin_of_black,4
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nagoshinikake,1
scarleteyedragon,1
pd04140298,1
todd_bronson,4
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greggory,4
piper_(bralios),4
silver_ears,0
riley_(tinydeerguy),4
trip_hamston,4
monte_(worgenrouge),4
gene_(kloogshicer),4
anfani_(artist),1
lucas_lightfoot_(zaush),4
smut,0
samia_of_the_shifting_sands,4
chiwidth,1
viviana_(lord_salt),4
electricblueruby,1
dragonboy618,1
lane_(kilinah),4
ash_(yeahbeezii),4
face_kick,0
daro_silvertail,4
kayle_(ravencrafte),4
rings_on_arms,0
keep_your_hands_off_eizouken!,3
chris_deck_(character),4
film_reel,0
grey_ear_ring,0
crazyaniknowit,1
foot_on_stomach,0
canind,5
chief_thunderhooves_(mlp),4
hornedrat,1
sir_lintcelot_(mlp),4
hands_crossed,0
high_tights,0
gala_(animal_crossing),4
volkswagen_bus,3
punch_(drink),0
viven,1
pox,4
kevin_(housepets!),4
bob_trumpet,4
archangel,5
riznben_(artist),1
manicure,0
reverse_prayer,0
mutilated,0
bevor,0
brandishing,0
crenelations,0
modest,0
nabbiekitty,1
stephen_mcguffin,4
mosin_nagant,0
chocolatechilla,1
snicker,0
.,0
giant_man,4
paw_markings,0
wacky_races,3
mingi,4
long_feathers,0
quick_ball,0
rinkaru,4
crazed_look,0
gutter,0
flow_the_riolu,4
devil_children,3
tayla,4
faris_scherwiz,4
qu'eii,5
starburst_pupil,0
arcas,4
bound_leg,0
tabris,1
black_undershirt,0
colored_edit,0
spotting,0
blood_on_balls,0
phantom_spirit_battle_academy,3
finger_grab,0
head_bag,0
atv,0
ctb-36,1
virgin_mary,4
still,0
feather_antennae,0
photographed_drawing,0
kishu,4
mandolin,0
heart_on_butt,0
anitech,1
curb,0
cum_squirting,0
princess_fluttershy,0
clutterdrop,1
shadowball,1
muscle_vein,0
baitong9194,1
hanging_from_ceiling,0
shinai,0
slave_sale,0
harvesting,0
corrector_yui,3
claire_(cloudtrotter),4
hfps,1
advent,5
kumiko_(aleron),1
arnold_the_pitbull,4
senpaiworks,1
ace_(lumar),4
reddandwhyte,1
miezekatze,4
synzin_(character),4
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icewolf,1
zaiksmckraven,1
brushy,4
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poison_gas,0
cyrion100,3
rampage_(game),3
juliart15,1
koneko_toujou,4
aym_(cult_of_the_lamb),4
luminousdazzle,1
rascal_(thehades),4
saffryn,4
street-panit2015,1
indigo_(fursdd),4
glazirka,1
orinvega,1
sable_(ivory-raven),4
ignitus_(artist),1
helena_(hugetime),4
amber_(snoot_game),4
poppet_(conker),4
khopesh_aeterno,4
paprika_(sapphinarose),4
arcc_(character),4
gewahren_(austrum),4
pink_corset,0
areola_outline,0
diamond_armor,0
hirari_(hhc),4
robo_(robosylveon),4
splendidsplend8,1
stonks,3
iain_(zhanbow),4
ivorden_bar,1
cody_the_coelophysis_(character),4
tilitea,4
bike_pump,0
zikaur,1
charlotart,1
cornelia_(kosa),4
cafe_(coffeebeangoat),4
morusaart,1
dark_mouth,0
daoru-drawing,1
sinelesh,1
lateri,5
trinketsworkshop,1
bubbletea,4
lizardpuke,1
lgag006k043_(artist),1
spiffybunny,1
1nar,1
cresty_(white_crest),4
star_before_text,0
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sable_(characters),4
artejaxton,1
jules_(dutch),4
alyssa_bandicoot_(oc),4
ichorynth,5
nate_croou,4
scribblesock,1
rahel_efrayim,4
smile_(kiaren),4
hand_out_of_water,0
serenity_(arctic),4
nuitt,1
krystal_carol_(chriskwolf),4
kaito_shinonome,4
ezra_(trashy_panda),4
aurum_(lupinator),4
ru_melbourne,4
goat_(halcyon),4
pyra_(lyorenth-the-dragon),4
mitzi_(characters),4
manhole_cover,0
holding_paddle,0
oliver_(rockk),4
sashiki,4
spike_(animal_crossing),4
pog_benis,1
dogfuji15,1
kamuii_(lyreska),4
big_chin,0
apollo_(artist),1
kanak,4
foxsnacks,1
grey_(sheep_and_wolves),4
karina.gk,1
silent_refuge_(oc),4
sabre_(tabbysabby),4
pawp,4
minion_of_set,0
trinket_(character),4
gale_(lordy_dia),4
kiri_(sky),4
zeke_(chewycuticle),4
sand_cat_humanoid,5
spiked_body,0
trinity_(furbakirkie),4
fluorings,1
linkoblin,4
maschinengewehr_42,0
leaned_back,0
ceru_(eurocracy),4
clauren_(scottieman),4
jazzycard,1
thundernawr,1
carrus_olivia,4
taymankill,4
xx56su_x,1
elvian,5
krazak,1
substance_painter_(software),0
saline,0
ash_(jackaldoc),4
ivee_rose,4
ms._b_(jooshy),4
four_of_hearts,0
korone_(setonanaki13),4
certhioid,5
devon_(panken),4
ryokun,1
jakepankeke,1
sayrina_vakesen,4
ivan_(ivan9402),4
arianna_(ariannafray),4
alator,1
rashishibe,1
laranxart,1
ten_(beastars),4
red_bag,0
geometric_shapes,0
cum_drunk,0
hidden97,1
como,4
soleil_(animal_crossing),4
mogul_khan_the_axe,4
5toes,0
lolphi,4
shadow_weaver,4
kilcodo,1
kahei_yattsu,1
carmen_sandiego,4
munchy_(diskodeath),4
rock_star,0
theuseless,1
draik_(neopets),5
lus,4
dbear,4
hazel_lulamoon,4
darkglider,1
graaz,4
candlebars,1
rinko_iori,4
house_of_the_dead,3
vampire_squid,5
amberli,1
arkthewoff,4
cock_bondage,0
kao,4
guglehupf,1
kanpachi,4
akatsuki_no_guuru,1
frost_rotom,5
celesti,4
steppe_in_the_right_direction,3
vel'gahrus,4
octopus_tadashi,1
natalya_gremory,4
abbbbbbk(,4
delphine_(officialbitwave),4
dexter_(demicoeur),4
mike-,1
meteorsmash_(character),4
darkseid,4
pots,0
dirty_bristles,1
ragna_the_bloodedge,4
novah_ikaro,1
nayeli,4
owlhaus,1
feranya,1
tree_trunks,4
nose_plugs,0
nattya,1
dorri_lawless,4
ropang,1
memoriesforgotten,1
naoto_shirogane,4
suridora-chan,4
deerdigger,4
nikraria,1
anarchoshark,1
dewbon,4
goober.,1
sunnyside,4
nameless_(chaosie),4
fatcat17,1
quelyntr,1
dia,4
loopus,1
gunter_(galaxy_fight),4
ainoko,4
beatrice_horseman,4
mmtora,1
toraneko38,1
tcw_(character),4
silverslime,1
jeff_(zp92),4
vivian_(valtik),4
serith,4
mega,4
greykenny_wolfz_(artist),1
deirdre_(animal_crossing),4
yun,4
wolgensim,1
head_bob,0
parfait_(artist),1
tsavo_(slightly_damned),4
atlantis:_the_lost_empire,3
megan_williams,4
kenzie_wong,4
alpacinosbaby,1
kiddy,4
greece,0
zaun-derground,1
frostpaw,4
neybulot,4
sammy_(chalo),4
joey_(housepets!),4
fenrirwolfen,1
quintus_(handsdownhound),4
novaquinmat,1
drider_princess,4
chrome_pony,4
glek_tarssza,4
mugenillustrations,1
armlock,0
tina_(e-01),4
suezo_(monster_rancher),5
kg,4
yellow_flesh,0
mooshmouse,1
sailorsyaoran,1
zeeka_(character),4
cavewoman,0
akira_slide,3
monotone_speedo,0
quinarth_snowtide_(eastern),4
oil_cum,0
amelia_(mistpirit),4
cadence_the_goodra,4
dreadqueen_rathian,5
clothed_male_nude_ambiguous,0
honorboundnoob,1
red_genitals,0
riso_bianca,1
jazzhands,1
jandaya,5
azure_(star),1
raccoon_douglas_(character),4
urine_splatter,0
rose_(skaii-flow),4
satoss,1
billie_(billie_bust_up),4
akiione,1
pull_up,0
kamek_sans,1
zevrant_bapt_(artist),1
liz_(wolflance),4
atta_(gahowolf),4
fiishdude,1
puffer_puff,1
stepparent_and_stepson,0
rory_(kadath),4
sphaded,1
zoe_pandora_(seuchenkater),4
katlo,4
foldblank,1
berry_(skye3337),4
oldsoup,1
pencil_(disambiguation),0
taqiyah,0
meeko_(disney),4
aquarius_(zodiac),4
baliyo,4
spells,0
fluffyvulps,1
mara_(spwalo),4
hisone_to_masotan,3
donkizari,4
li'l_cheese_(mlp),4
amy_(flash_draw),4
reilion,4
diaper_on_face,0
bonbonbear,1
diaper_check,0
risew,4
gvz_7,1
night-on-fire,1
brown_backpack,0
scp-1472,4
yanni,4
jay_lurker_(ayejaykay),4
pandapower897,4
hank_j._wimbleton,4
pogo_(joaoppereiraus),4
on_barrel,0
siber_(thegoldenjackal),4
snowblind,4
swimwear_around_legs,0
chroma_(chromamancer),4
rima_(princess_connect!),4
spore_(game),3
terrance_(saberterranced),4
barnikan,1
ruined_pussy,0
panakotta,1
roman_(arbor_fox),4
conjoined_at_genitalia,0
ho-oh_(025aki),4
zabozamojo,1
mousepaw,4
buckled_boots,0
lost_soul,5
human_brain_to_dog_brain,0
unusual_ovaries,0
etya_(dmin),4
record_of_ragnarok,3
west_(westfox),4
moeflavor_cheerleader_uniform,3
intersex_anthro,0
dotty_(jovi_cap),4
votm,1
metta,4
on_heels,0
tamalesyatole,1
beach_bar,0
goodnites,3
flith,0
frottage_in_slit,0
raine_whispers,4
jabberwock_(full_bokko_heroes),4
aarqon,1
lilavi,1
feral_menace,4
flexing_brachioradialis,0
gil_softpaws,1
soul_nero,1
kongiku,4
8ud,4
yaj_leaf,1
arekusanderu,4
adjustedbicuspid,1
famale,4
vasili,4
garf_of_finland,1
maxcoyote,4
ninjato,0
lauir,4
drawesome,1
chucky_(child's_play),4
petunia_(raichupuppy),4
frothing,0
haruhi_suzumiya,4
milly_(millydeer),4
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little_shewolf,3
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teevo,1
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licks,0
the_dog's_days_of_summer,3
shades_of_elysium,3
lycos,4
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knrin,1
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mitsu_(derek_hetrick),4
cielorey,4
rose_tattoo,0
grey_kangaroo,5
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neko_majin,1
harvek,1
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inusawa,1
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wulven_game_studios,3
christmas_card,0
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eevo_(author),1
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xander_olivieri,4
rift_dragon,5
rachelle,4
dice_magica_(oc),4
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rorykenneigh,1
hesh,4
jane_(avencri),4
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pirotess,4
tl;dr,0
sasako_(shirokuma_cafe),4
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dr._fox,4
tifu,1
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pango_(animal_crossing),4
heavenly_nostrils,3
luos,4
kelkko,4
tacha_encre,4
quil_(yodishy),4
bucket_of_semen,0
tygrysiolowek,1
bryce-roo,4
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mr._wolf_(takemoto_arashi),4
replicant,5
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zajji,4
yamino,1
levy_(longlevy),4
fever_(dr._mario),4
egglaying_advise,0
kabuto0495,1
marcie_(animal_crossing),4
beer_pong,0
slipperyt,1
saoul,4
ki-software,1
ni_ki,1
miyamae_porin,1
wishful_thinking,0
rebelgandre,1
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ysa,4
rainbow_nails,0
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peleng,5
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lovemilliesm,1
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reindog_(multiversus),4
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ponehanon_(character),4
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alto,1
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nixi_(athighhighguy),4
brotherhood_of_steel_(fallout),4
monster_(fnf),4
belladonna_(prismatic_wolf),4
pokedollar_sign,0
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toofy_(character),4
akame_(ginga),4
yen_tokk,1
tara_(spacecamper),4
pinabble,1
great_sword_(monster_hunter),0
srgriko,1
mesopotamian_mythology,3
grace_persia,4
shota_cow_(cheesecrumbles),4
kal'tsit_(arknights),4
cape_fox,5
ness_(larathelabrat),4
single_shoe,0
vinlom,1
pikie_folfuwu,4
archvile_(doom),5
void_(atomic_cat),4
septimus_(smartydragon),4
kiera_southpaw,4
nabo_(olimacdoodles),4
ocellus_(dragon_form),4
avii_cinderfeather,4
dragorii,5
esmeralda_(cerebro),4
prehensile_holding,0
karri,4
orca_(pochincoff),4
shaxs,4
jack_(joaoppereiraus),4
lurdanjo_mistaro_(lurdanjo),4
jaidreamsicle,4
wind_demon,5
smozumi_(insomniacovrlrd),5
nigerian_dwarf_goat,5
kousen_(kousenzephyr),4
magic_hat,0
sakuhuu,1
simplex,0
m-preg,1
deerwalker,1
sandwich_grill,1
don_the_bear,4
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monica_(baconbasket),4
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shaundi_(darkwolf),4
amy,4
two_tone_piercing,0
jazzymongoose,1
panini_(panininha),4
suzie_(sindiewen),4
dumbification_to_dog_brain,0
jack_(jackaloo),4
cueliton,1
marytoad,1
claw_foot_bathtub,0
dragon_land,0
cat_breakdancing_meme,3
hyper_flexor_carpi,0
ricka_(wolfling),4
himuhino,1
shinkichi,1
spanki_the_kangadra,4
poju_(geir),4
yuwi-cyu,1
cavalina,4
sylver_tsuki,4
al-an_(subnautica),4
leandre_snow,4
hanging_streamer,0
bwrd,1
castell,1
pseudo_clothing_removed,0
purple_rubber,0
stuck_to_butt,0
gdn0522,1
cherri_(cherrii_pop),4
crimson_(crimsoncreaturecreations),4
ryka_(ultilix),4
devil_may_cry_5,3
erin_(dualitydot),4
atlas_(avali),4
molinatwins,1
scalenes,0
matt_the_hedgehog,4
ramen_mao,1
the_underworld,3
furyraptor23,1
srxnico97,1
trevor,4
musical_note_(marking),0
tsukamototgg,1
magic_kingdom,3
penumbraart,1
fumizuki_(arknights),4
adam_flint,4
march_hare_(disney),4
jigjig,1
nebbii_(laniii),4
clover_(luckyabsol),4
dclxvi-heaven,1
devon_the_sobble,4
watermelon_popsicle,0
skydiggitydive,1
sanmamiya,1
cung,1
alessandra_(scream),4
zaflep,1
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ms._kitty,4
kymera,5
washing_partner,0
virus_(dr._mario),4
yiri_(milkand),4
maghost,1
punishedgen,1
fizzy_pop_the_popplio,4
mc_skat_kat_and_the_stray_mob,3
shaded_color,0
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mao_lee_(sirjzau),4
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joanna_marshall,4
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panon_rabbit,5
0c95yzr015ps0j5,1
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softshikioni,1
aphelion_darkmoon,4
vox_(space_station_13),5
dualitydot,1
absinthe_(drink),0
wiwirawr,1
orikomi_(character),4
zaunis,1
crystal_(blitza),4
hannibal_lecter,4
cryptid_(call_of_duty),5
irelia_(lol),4
supercell,4
xk2x,1
tameraali,1
urethral_sex,0
elcoyote,4
graveyardshift,1
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serina_franizzi,4
husqi,4
eli,4
shaman_king,3
gibbet,0
fear_and_loathing_in_las_vegas,3
party_pat,4
mabari,5
lyren,4
kusa_(iridiumx),4
daire,4
nirai_(artist),1
shuffahlong,1
system_shock,3
leben_schnabel,4
vader_(vader-san),4
gunslinger,0
clawsofslash,1
perry_(hextra),4
jenna_(aj_the_flygon),4
spawn,4
dj_acid,4
dudley,4
maple_(modeseven),4
nekohaiku,4
hermit_(.hack),4
jax_rabbit,4
hoi~poi,1
inferno_sabercat,4
emily_valia,4
blood_sucking,0
abel_(dmfa),4
blood_on_ear,0
big_papa_(paradisebear),4
anastasia_(kalahari),4
kuufuku,4
hitotsuki_nebura,1
gooeythedragon,1
pop_candy,4
digimon_fusion,3
victoria_crowned_pigeon,5
brian_cochen,4
quiver_silvertongue,4
lolly-pup,3
toonpower,1
moo_(atryl),4
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timetime726,1
featherscale,1
gen_(inuki),4
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myanie,1
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fan_(fnaf),4
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efu,4
cormorant,5
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charcoal,4
dani_(devoid-kiss),4
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5tarex,1
selonian,5
ivy_(sweetfilthyfun),4
remyl,1
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the-star-hunter,1
nightstalker400,4
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roxley,4
gratiel,1
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jager_the_wolf,4
judgement_(tarot),0
nezuney,1
zena,4
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strawberry_milk,0
caretaker,0
fiberopticfeline,1
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yully_yullington,4
piia,1
scrunchy,0
tsavo_(artist),1
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stepladder,0
hans_faffing,4
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fluttershyspy,3
yingpingu,3
inverted_belly_button,0
kinakomochi_199,1
grove,0
clary_(creamgag),4
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felicia_fox,4
metal_armor,0
evelyn_(whygena),4
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vonce_(bgn),4
yellow_guy_(dhmis),4
drawing_program,0
kaliyah_(charr),4
azlyn_(character),4
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blue_b,1
kirf,1
software,0
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kirsenter,1
fermrviper,1
mannack,1
razuya.torazaki,1
pholidine,5
two_tone_helmet,0
hyness_(kirby),4
fenton_(dizzyvixen),4
holding_by_tail,0
pettankochan,1
volmise,1
kathy_(mrrottson),4
erise,4
kuroorcas,1
kudo_the_fox,1
josephine_(dovecoon),4
chelsea_fortuna,4
cerealkat,1
eccentricspider,1
ureos_(artist),1
leopold_(waitforrain),4
draken_sjacharytiss,4
jgbun,1
hurikata_judodo,4
sagittarius_(symbol),0
yuri_catsuki_(yitexity),4
brown_cloak,0
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veemecelli,1
leg_between_thighs,0
angelica_(ashkelling),4
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y_(doneru),4
log_draws,1
alfas,1
emarcanine,4
nur_(0laffson),4
lolu,4
green_fin,0
wartorn,3
jt_(limitedvision),4
tentacle_on_leg,0
cynosure,1
eleanor_'elly'_zhi,4
captain_emily,4
petunia_petals_(mlp),4
bjorn_(makaronnie),4
game_boy_advance_sp_console,0
shokly~,1
long_pigtails,0
neverdream,1
alex_the_dilophosaurus,4
ganymede_(overwatch),4
ntm.,1
zea_(character),4
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sphincter,0
winston_(roller-2009),4
acira_selvrica,4
shade-shark,1
jasmine_winters,4
dawnrn,1
varcey,1
bellspurgebells,1
justinblong2003,1
zoe_(chimangetsu),4
jawa,5
jedader,4
ssthisto_(artist),1
imdudrjsmi,1
kemo-chan_(8chan),4
hearto,4
tinyfawks,4
justeen,4
faolan,4
kagehara_hanzow,1
kaya,4
gallantmon,5
cowardly_lion,4
aaru_(tenrake_chaya),1
septer,1
tusk_(dota),4
aryte,4
mimic_(artist),1
matchstick,0
grimmjow_jaegerjaquez,4
pharos,4
ukiki,4
kero_(luniquekero),4
quantumdot,4
lexi_(portzebie),4
urai,4
walzrj,1
tambre_the_ferret,4
fastspeedy,1
annamaria,4
fetishes,0
kat_chipmunk,4
aeywon,4
peep_and_the_big_wide_world,3
baatlif,1
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steve_du,4
charlie-spaice-doggy,1
sukeban,0
sharna,4
chibitakumi,4
imoya,4
jason_(mobilemutt),4
kougi,4
ferretface99,1
wipes,0
echowolf800,1
jessica_hubbard,4
goldie,4
ryusuke,4
gritou,4
mixing,0
ling-ling,4
blix,4
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kris,4
qa'a,4
mushni,4
macher,4
fasha,4
quillcy,4
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zana_zamora,4
tactical_snake,1
macie_(phsuke),4
slashweilerdog,1
danaroo,4
crystalcheese,1
minoir,4
dimetrodon,5
changeling_hive,0
peg_(disney),4
tylenn,1
doki_adventures,3
a-02,1
rumsshi,4
theia,4
bran_(strangerdanger),4
meow's_father_(space_dandy),4
olympia_vale,4
maintenance,0
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igrushka_(polyle),4
laenei,5
viata,4
umbra_(disambiguation),-1
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tammy_(fukmin-dx),4
myria_(salverion),4
elleseve,4
dan_(meesh),4
zano_zevash_(runawaydanish),4
colt_bronco,4
sato_(artist),1
marsha_(marshalepochi),4
cedricktheluxray,4
rokonrico,1
batsy,4
lynxon,0
silver_(alopex),4
collette_(lady_and_the_tramp),4
dreamflowerbunny,1
pink_headphones,0
zia_(hel-ish),4
clair_sewall,4
aria_the_espeon,4
okapi_humanoid,5
starling_fox,4
kevin_murphy,4
torri_biggs,4
macchiato_fox,1
donnie_(yoko_arika),4
harper_(fairdahlia),4
bengt_(pewdiepie),4
cole_(animal_crossing),4
bird_jesus_(twitch_plays_pokémon),4
30:23,0
bombstaticz,1
foot_on_arm,0
elitedog,1
jey_caroot,4
laserflip,1
paisley_(nobby),4
neecheecoyote,1
ambrosine_(snowyfeline),4
rocket-lawnchair,1
red_leggings,0
spacestation,0
tantlysa,1
rafiq_(zsisron),4
cartoon_violence,0
shaye,4
cass_(falcon_mccooper),4
thresher_(lilo_and_stitch),4
was_scepter,0
ezili,4
i_want_you,3
dreamertooth_(character),4
flutti,1
fenrir_(kobold_adventure),4
charli_(rickgriffin),4
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quada_platt,4
butcha-u,1
citrox,4
elbow_deep_in_a_horse,0
coffeekit,1
thatnav,1
censorrottie,1
walnut_(pixiv),1
piru_(girokett),4
linden_mcconville_(character),4
ocelot_humanoid,5
marcie_roo,4
llama_mama_(thehoneybutter),4
black_bridal_gauntlets,0
the-j-man,1
nostril_penetration,0
filmon,5
cumming_dildo,0
nico_oyabin,1
niccu,4
plant_cloak_burmy,5
powerless,0
zhu,4
carbie_(atrolux),4
<:<,0
marcy_(marcy's_lewd_images),4
keenfox,1
bottom_bunk_filling_meme_(tofudumps),3
flazy,4
rime_(9tales),4
gunfire_games'_the_ashes_(franchise),3
breeding_request,0
td-4,4
their_name_(series),3
tipped_ears,0
arnold_(software),3
daiazuma,1
casimirevore,1
stargazer_(goldfur),4
plum_(shellyochunks),4
gyshka,1
exadian,5
nian_(arknights),4
fennecfuchs,1
shinonizaru,1
shallot_fogg_(artist),1
tsaotsaow,1
valion_(irishderg),4
astropandaart,1
ttrop,1
jack_(sanssketches16),4
fully_clothed_taur,0
zhenyaburnt_(character),4
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igarashi_ta,1
veerr_(orphen-sirius),4
low_tier_god,3
ophelia_(fiftyfifthfleet),4
shell_casings,0
breath_of_the_wild_2,3
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spiral_light,4
squeezing_breast,0
general_buta,1
bird_d,1
amiga_(computer),3
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midnightsultry,1
roquet,4
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mightywheelchair,1
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mssafiru,1
archilycant,1
white_bow_(anatomy),0
radic,4
shenvalor,1
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rastafariansfm,1
leo_(artist),1
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wuk_kathell_(psychofuchs),4
c4d_max,1
pattie_(pencil_bolt),4
kooky_fox,4
darkfox,4
nephthys,4
jupiter_jyohti,1
cathleen_keiser,4
aether,4
nozdraws,1
arthurleoufdu60,1
crotch_zipper,0
coelacanth,5
hyde,5
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lumi,4
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rezzic,4
hugh_(taz-mania),4
pacato,4
alphamon,5
einthebusinessdeer,1
artsyrobo,1
here's_johnny,0
octavia_(warframe),4
zato,4
starhill_magic_erotica,3
fuckemall,1
tyrana,4
acesential,1
turtonator,5
cannibal,4
karla,4
r0ckya,1
anne,4
kyaru,4
natsumi_hinata,4
mashuu,4
msi_dragon_lucky,4
claptrap,4
aralyn,4
purplegriffin,1
uchu_sentai_kyuranger,3
charmcaster,4
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platonic,0
cece,4
tiriosh,4
kanzakietc,1
starbomb,3
apefromspace3,1
cyberunique,1
kriin,5
dishonored,3
anju,4
konsumo,1
nipha,4
lycanthropy,0
desiree_(danny_phantom),4
jezekin,4
w0lfb0ne,1
smerk,0
grubbin,5
malph,4
babs,4
venomoussausage,1
arden,4
suwako_moriya,4
miss_heart,4
crescera,4
twistedskunk,1
parasoul,4
victoria_tallcos,4
rydia,4
sharpe,4
tail_holding,0
blooper_(artist),1
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aethial,4
kilroy_was_here,3
everestdragon,4
dirty_pair,3
veronicaskunkette,1
messier_87_(personification),4
hecate_(megami_tensei),4
arctovish,5
gullet_(character),4
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blastoiseanimator,1
velouria,4
monotone_mask,0
larger_maleherm,0
kygen,1
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louise_(elvche),4
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f-euphrates,1
mousesix,1
astro_(astro_ferret),4
ribbonfemale,1
dean_(libra11),4
charmin_ultra_soft_mom,4
that1greenbro,1
hork-bajir,5
fera_(disambiguation),-1
akki_cnmn,1
dynamoheart_(artist),1
twitter_username,1
gnabgnoffe,1
beat_bladesman_fur_hire,4
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setta_flamowitz,4
dark_eyewear,0
pumpkin_rabbit,4
snoot_(artist),1
wayne_(hylics),4
revan_grey,4
6kix,1
lamont_(fursona),4
shiloh_(shicho),4
andorian,5
unknown_position,0
m1_abrams,0
gruziya,4
honeymooo,1
reaper_(djijey_hellfire),4
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mana_inuyama,4
holding_mushroom,0
yamowl,1
disciplewinter,4
gentlebun,4
eagle_(petruz),4
samson_(hugetime),4
sekk'ral's_father,4
emad,4
ian_the_wolf,4
verbal_consent,0
bystander,0
kit_(fortnite),4
ssb_(artist),1
kate_sweetblood,4
hands_on_own_head,0
monkeywithaafro,1
pink_visor,0
eye_of_providence,5
taorena_(evil-sprite),4
mempha,4
ickyfig,1
pattern_jacket,0
peeing_into_river,0
bokken,0
nekokun28,1
dave,4
green_neckwear,0
jewel_(himynameisnobody),4
randytheartdog,1
jennifer_(chris94),4
swift_sketch_(character),4
cynocephaly,1
rogerwoof,1
delux,1
hickory_(hhc),4
ante_(ante_flan),4
rdnw0519,1
willow_(vdisco),4
mackmcstacks,1
somemf,1
sadnicole,1
da_shangzhi_zhi,1
thin_beak,0
sonic_characters_walking_into_stores,3
lucas_(elchilenito),4
glurch,0
rick_(disambiguation),4
kawasaki,3
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josue_pereira,1
sinuous_horn,0
atari_controller,0
mix_(ikkykrrk),4
airotf,1
alyx_(tiredfeathers),4
lemondrop_(character),4
sabrina_(disambiguation),4
rimedown,1
packaged_console,0
monica_blackwater_(wsad),4
uni0421,1
lostipants,1
blizzarian,5
yoghurt_(pazymomo),4
iron_golem_(minecraft),5
discarded_bikini,0
kins_(brok_the_investigator),4
arti_(alfredblaze),4
blinian,1
bra_up,0
mage_bun_(niking),4
toontk,1
amber_(gem),0
nina_(lavabath),4
luxro,4
multiple_colors,0
red8ean,1
hakuginnosora,1
minami_(remanedur),4
fluffies,0
kyra_(nightargen),4
double_trouble_(she-ra),4
momikacha,1
holding_chainsaw,0
bellydancing,0
amidnarasu,1
bloomz,1
winnie_(ajk),4
charlie_(charkie),4
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                "phrase": "Adolf Born",
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                "phrase": "Adolf Dietrich",
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                "phrase": "Adolf F\u00e9nyes",
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                "phrase": "Adolf Hir\u00e9my-Hirschl",
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                "phrase": "Adolf H\u00f6lzel",
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                "phrase": "Adolf Ulric Wertm\u00fcller",
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                "phrase": "Adolfo M\u00fcller-Ury",
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                "phrase": "Adolph Gottlieb",
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                "phrase": "Adolph Menzel",
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                "filter_group": "",
                "description": ""
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                "phrase": "Adolphe Willette",
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                "phrase": "Adonna Khare",
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                "phrase": "Adriaen Brouwer",
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                "description": ""
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                "phrase": "Adriaen Coorte",
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                "description": ""
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                "phrase": "Adriaen Hanneman",
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                "phrase": "Adriaen Isenbrant",
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                "description": ""
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                "phrase": "Adriaen van de Velde",
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                "description": ""
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                "phrase": "Adriaen van de Venne",
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                "description": ""
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            {
                "phrase": "Adriaen van der Werff",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Adriaen van Ostade",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Adriaen van Outrecht",
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                "filter_group": "",
                "description": ""
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                "phrase": "Adrian Ghenie",
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                "filter_group": "",
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                "phrase": "Adrian Paul Allinson",
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                "filter_group": "",
                "description": ""
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                "phrase": "Adrian Smith",
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                "filter_group": "",
                "description": ""
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                "phrase": "Adrian Tomine",
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                "filter_group": "",
                "description": ""
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                "phrase": "Adrianus Eversen",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Adrienn Henczn\u00e9 De\u00e1k",
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                "show_if": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Aelbert Cuyp",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Aert de Gelder",
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                "filter_group": "",
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            {
                "phrase": "Aert van der Neer",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Aertgen van Leyden",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Afarin Sajedi",
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                "filter_group": "",
                "description": ""
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                "phrase": "Affandi",
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                "phrase": "Agnes Cecile",
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                "phrase": "Agnes Lawrence Pelton",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Agnes Martin",
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                "description": ""
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            {
                "phrase": "Agnolo Bronzino",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Agnolo Gaddi",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Agostino Arrivabene",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Agostino Carracci",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Agostino Tassi",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Aguri Uchida",
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                "description": ""
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            {
                "phrase": "Agust\u00edn Fern\u00e1ndez",
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                "filter_group": "",
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                "phrase": "Ahmed Karahisari",
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                "filter_group": "",
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                "phrase": "Ahmed Yacoubi",
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                "phrase": "Ai Weiwei",
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                "phrase": "Ai Xuan",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Ai Yazawa",
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                "filter_group": "",
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                "phrase": "Aileen Eagleton",
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            {
                "phrase": "Aim\u00e9 Barraud",
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                "filter_group": "",
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                "phrase": "Akihiko Yoshida",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Akira Toriyama",
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                "phrase": "Akos Major",
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                "phrase": "Akseli Gallen-Kallela",
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            {
                "phrase": "Al Capp",
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            {
                "phrase": "Al Feldstein",
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                "filter_group": "",
                "description": ""
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                "phrase": "Al Williamson",
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                "filter_group": "",
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                "phrase": "Alain Laboile",
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                "phrase": "Alan Bean",
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                "filter_group": "",
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                "phrase": "Alan Davis",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Alan Lee",
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                "filter_group": "",
                "description": ""
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                "phrase": "Alan Moore",
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                "phrase": "Alan Parry",
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                "phrase": "Alasdair Grant Taylor",
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                "filter_group": "",
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                "phrase": "Alasdair Gray",
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                "description": ""
            },
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                "phrase": "Alasdair McLellan",
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                "phrase": "Alayna Lemmer",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Albert Anker",
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                "filter_group": "",
                "description": ""
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                "phrase": "Albert Benois",
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                "description": ""
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                "phrase": "Albert Bertelsen",
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                "phrase": "Albert Bierstadt",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Albert Bloch",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Albert Dorne",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Dubois-Pillet",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Eckhout",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Albert Edelfelt",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Albert Gleizes",
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                "description": ""
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            {
                "phrase": "Albert Goodwin",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Guillaume",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Albert Henry Krehbiel",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Irvin",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Albert J. Welti",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Albert Joseph Moore",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Albert Joseph P\u00e9not",
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                "filter_group": "",
                "description": ""
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                "phrase": "Albert Keller",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Albert Koetsier",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Kotin",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Lynch",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Marquet",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Namatjira",
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                "description": ""
            },
            {
                "phrase": "Albert Paris G\u00fctersloh",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Pinkham Ryder",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Robida",
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                "pattern_override": "",
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                "search_tags": "cartoon",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Servaes",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "black-white",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Albert Swinden",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Albert Tucker",
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                "pattern_override": "",
                "show_if": "",
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                "filter_group": "",
                "description": ""
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                "phrase": "Albert Watson",
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                "phrase": "Alfonse Mucha",
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                "phrase": "Alfred Charles Parker",
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                "phrase": "Alfred Cheney Johnston",
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                "phrase": "Alfred East",
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                "phrase": "Alfred Edward Chalon",
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            {
                "phrase": "Alfred Eisenstaedt",
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                "phrase": "Alfred Guillou",
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                "phrase": "Alfred Heber Hutty",
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            {
                "phrase": "Antoine Blanchard",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antoine Ignace Melling",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antoine Le Nain",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antoine Verney-Carron",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antoine Wiertz",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton A\u017ebe",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton Corbijn",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton Domenico Gabbiani",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton Fadeev",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "cartoon",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton Graff",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton Lehmden",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton Mauve",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton M\u00f6ller",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton Otto Fischer",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton Pieck",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton R\u00e4derscheidt",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton Raphael Mengs",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton Semenov",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonello da Messina",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antoni Gaudi",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antoni Pitxot",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antoni T\u00e0pies",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonin Artaud",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton\u00edn Chittussi",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Anton\u00edn Slav\u00ed\u010dek",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio Canova",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio Cavallucci",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio Ciseri",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio de la Gandara",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio Donghi",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio Galli Bibiena",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio J. Manzanedo",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio Mancini",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio Mora",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ant\u00f4nio Parreiras",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio Rotta",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio Roybal",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antonio Saura",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Antony Gormley",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Apelles",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Apollinary Vasnetsov",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Apollonia Saintclair",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "anime",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arabella Rankin",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Araceli Gilbert",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Archibald Motley",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Archibald Robertson",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Archibald Skirving",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Archibald Standish Hartrick",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Archibald Thorburn",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arcimboldo",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arent Arentsz",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arie Smit",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Aries Moross",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Aristarkh Lentulov",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Aristide Maillol",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arkhip Kuindzhi",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arkhyp Kuindzhi",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arlington Nelson Lindenmuth",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Armand Guillaumin",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Armand Point",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Armin Baumgarten",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Armin Hansen",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "anime",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arnold Blanch",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arnold Bocklin",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arnold B\u00f6cklin",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arnold Bronckhorst",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arnold Br\u00fcgger",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arnold Franz Brasz",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nudity",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arnold Mesches",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arnold Newman",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arnold Schoenberg",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Aron Demetz",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Aron Wiesenfeld",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nudity",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arshile Gorky",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Art Brenner",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Art Fitzpatrick",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Art Frahm",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Art Green",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Art of Brom",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Art Spiegelman",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Artemisia Gentileschi",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Artgerm",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Adams",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur B. Carles",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Boyd",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Burdett Frost",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Dove",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Garfield Dove",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Hacker",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Hughes",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Lismer",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Melville",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Merric Boyd",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Quartley",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Rackham",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Radebaugh",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Sarnoff",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Stanley Wilkinson",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Streeton",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Arthur Tress",
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                "phrase": "Arthur Wardle",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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                "phrase": "Arthur Webster Emerson",
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                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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                "phrase": "Artur Bordalo",
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                "filter_group": "",
                "description": ""
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                "phrase": "Arturo Souto",
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                "filter_group": "",
                "description": ""
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                "phrase": "Artus Scheiner",
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                "filter_group": "",
                "description": ""
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                "phrase": "Arvid Nyholm",
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                "phrase": "Ary Scheffer",
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                "description": ""
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                "phrase": "Asaf Hanuka",
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                "filter_group": "",
                "description": ""
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                "phrase": "Asger Jorn",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Asher Brown Durand",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Ashley Wood",
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                "filter_group": "",
                "description": ""
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                "phrase": "Atelier Olschinsky",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Atey Ghailan",
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                "filter_group": "",
                "description": ""
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                "phrase": "Aubrey Beardsley",
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                "filter_group": "",
                "description": ""
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                "phrase": "Audrey Kawasaki",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "August Friedrich Schenck",
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                "show_if": "",
                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "August Lemmer",
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                "show_if": "",
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                "filter_group": "",
                "description": ""
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                "phrase": "August Macke",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "August Querfurt",
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                "show_if": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "August Sander",
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                "trigger": "",
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                "description": ""
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                "phrase": "August von Pettenkofen",
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                "show_if": "",
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                "filter_group": "",
                "description": ""
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                "phrase": "Auguste Baud-Bovy",
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                "filter_group": "",
                "description": ""
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                "phrase": "Auguste Herbin",
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                "show_if": "",
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                "filter_group": "",
                "description": ""
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                "phrase": "Auguste Mambour",
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                "filter_group": "",
                "description": ""
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                "phrase": "Auguste Toulmouche",
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                "phrase": "Augustin Meinrad B\u00e4chtiger",
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                "filter_group": "",
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                "phrase": "Augustus Dunbier",
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                "filter_group": "",
                "description": ""
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                "phrase": "Augustus Earle",
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                "filter_group": "",
                "description": ""
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                "description": ""
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                "phrase": "Augustyn Mirys",
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                "phrase": "Aur\u00e9l Bern\u00e1th",
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                "phrase": "Austin Briggs",
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                "phrase": "Austin Osman Spare",
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                "filter_group": "",
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                "phrase": "Avigdor Arikha",
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                "phrase": "Axel T\u00f6rneman",
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                "phrase": "Ayako Rokkaku",
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                "phrase": "Ayami Kojima",
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                "phrase": "Aykut Aydogdu",
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                "phrase": "Ayshia Ta\u015fk\u0131n",
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                "phrase": "Bai\u014dken Eishun",
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                "phrase": "Bal\u00e1zs Di\u00f3szegi",
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                "phrase": "Balcomb Greene",
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                "phrase": "Bapu",
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                "phrase": "Barbara Greg",
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                "phrase": "Barbara Kruger",
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                "phrase": "Barth\u00e9lemy d'Eyck",
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                "phrase": "Bartolome Esteban Murillo",
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                "phrase": "Bartolom\u00e9 Esteban Murillo",
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                "phrase": "Bartolomeo Cesi",
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                "phrase": "Bartolomeo Vivarini",
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                "description": ""
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                "phrase": "Basil Blackshaw",
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                "description": ""
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                "phrase": "Basil Gogos",
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                "filter_group": "",
                "description": ""
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                "phrase": "Bastien L. Deharme",
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                "description": ""
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                "phrase": "Bastien Lecouffe-Deharme",
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                "filter_group": "",
                "description": ""
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                "phrase": "Basuki Abdullah",
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                "filter_group": "",
                "description": ""
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                "phrase": "Bauhaus",
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                "description": ""
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            {
                "phrase": "Bayard Wu",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Beatrice Ethel Lithiby",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Beatrice Huntington",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Beatrix Potter",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Beauford Delaney",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Becky Cloonan",
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                "show_if": "",
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                "search_tags": "cartoon",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Bedwyr Williams",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Beeple",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Beksinski",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "B\u00e9la Ap\u00e1ti Abkarovics",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "B\u00e9la Cz\u00f3bel",
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                "pattern_override": "",
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                "phrase": "Brad Kunkle",
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                "phrase": "Caroline Lucy Scott",
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                "filter_group": "",
                "description": ""
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                "phrase": "Carrie Mae Weems",
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                "description": ""
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                "phrase": "Casey Weldon",
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                "phrase": "Caspar David Friedrich",
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                "phrase": "Caspar Netscher",
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                "filter_group": "",
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            {
                "phrase": "Caspar van Wittel",
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                "filter_group": "",
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            {
                "phrase": "Cassandra Austen",
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                "description": ""
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            {
                "phrase": "Cassius Marcellus Coolidge",
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                "search_tags": "c",
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                "description": ""
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            {
                "phrase": "Catherine Hyde",
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            {
                "phrase": "Catrin G Grosse",
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                "phrase": "Catrin Welz-Stein",
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                "description": ""
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            {
                "phrase": "Cecil Beaton",
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                "phrase": "Cecile Walton",
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                "phrase": "Cecilia Beaux",
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                "description": ""
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            {
                "phrase": "Cecily Brown",
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                "description": ""
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                "phrase": "Cedric Peyravernay",
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            {
                "phrase": "Cedric Seaut",
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                "description": ""
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            {
                "phrase": "Cedric Seaut (Keos Masons)",
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                "pattern_override": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Cefer\u00ed Oliv\u00e9",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
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                "filter_group": "",
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            {
                "phrase": "Celia Fiennes",
                "priority": "",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Celia Frances Bedford",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Cerith Wyn Evans",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "C\u00e9zanne",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Chad Knight",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Chagall",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Chaim Soutine",
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                "pattern_override": "",
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                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Cha\u00efm Soutine",
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                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Chantal Joffe",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Addams",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Alphonse du Fresnoy",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Alston",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Angrand",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Bird King",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Blackman",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Camoin",
                "priority": "",
                "pattern_override": "",
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                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Codman",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Conder",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Crodel",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Cundall",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Dana Gibson",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Demuth",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles E. Burchfield",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Ellison",
                "priority": "",
                "pattern_override": "",
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                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Fremont Conner",
                "priority": "",
                "pattern_override": "",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Furneaux",
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                "pattern_override": "",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Ginner",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Gleyre",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Gwathmey",
                "priority": "",
                "pattern_override": "",
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                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles H. Woodbury",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Harold Davis",
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                "pattern_override": "",
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                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Haslewood Shannon",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Hinman",
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            {
                "phrase": "Charles Hopkinson",
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                "filter_group": "",
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            },
            {
                "phrase": "Charles Joshua Chaplin",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Le Brun",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Le Roux",
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                "description": ""
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            {
                "phrase": "Charles Liu",
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                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Mahoney",
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                "search_tags": "digipa-high-impact",
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            {
                "phrase": "Charles Marion Russell",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Martin",
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                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Maurice Detmold",
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                "pattern_override": "",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles McAuley",
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                "pattern_override": "",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Mozley",
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                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Ragland Bunnell",
                "priority": "",
                "pattern_override": "",
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                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Charles Rennie Mackintosh",
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                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Ricketts",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Roka",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Rollier",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles S. Kaelin",
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            {
                "phrase": "Charles Schridde",
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            {
                "phrase": "Charles Schulz",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Spencelayh",
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            {
                "phrase": "Charles Thomson",
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                "filter_group": "",
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            {
                "phrase": "Charles Uzzell-Edwards",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Vess",
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                "search_tags": "cartoon",
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                "description": ""
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            {
                "phrase": "Charles W. Bartlett",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Charles Williams",
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                "search_tags": "digipa-med-impact",
                "filter_group": "",
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            {
                "phrase": "Charles Willson Peale",
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            {
                "phrase": "Charles-Am\u00e9d\u00e9e-Philippe van Loo",
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            {
                "phrase": "Charles-Andr\u00e9 van Loo",
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                "phrase": "Charles-Francois Daubigny",
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            {
                "phrase": "Charles-Fran\u00e7ois Daubigny",
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            {
                "phrase": "Charlie Bowater",
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                "search_tags": "special",
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                "description": ""
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            {
                "phrase": "Charline von Heyl",
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            {
                "phrase": "Charlotte Nasmyth",
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                "filter_group": "",
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            {
                "phrase": "Charmion von Wiegand",
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                "filter_group": "",
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            {
                "phrase": "Chen Daofu",
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                "phrase": "Chen Hong",
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                "search_tags": "fareast",
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                "description": ""
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            {
                "phrase": "Chen Hongshou",
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                "search_tags": "ukioe",
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                "description": ""
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            {
                "phrase": "Chen Jiru",
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                "phrase": "Chen Lin",
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                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
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                "phrase": "Chen Rong",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Chen Yifei",
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                "filter_group": "",
                "description": ""
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                "phrase": "Chen Zhen",
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                "filter_group": "",
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            {
                "phrase": "Cheng Jiasui",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Cheng Zhengkui",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Cherryl Fountain",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Chesley Bonestell",
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                "filter_group": "",
                "description": ""
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                "phrase": "Cimabue",
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                "phrase": "David Teniers the Elder",
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                "phrase": "David Wiesner",
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                "description": ""
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                "phrase": "David Wilkie",
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                "description": ""
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                "phrase": "David Wojnarowicz",
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                "filter_group": "",
                "description": ""
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                "phrase": "David Young Cameron",
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                "filter_group": "",
                "description": ""
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                "phrase": "De Hirsh Margules",
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                "filter_group": "",
                "description": ""
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                "phrase": "Dean Cornwell",
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                "filter_group": "",
                "description": ""
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                "phrase": "Dean Ellis",
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                "filter_group": "",
                "description": ""
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                "phrase": "Dean Roger",
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                "phrase": "Debbie Criswell",
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                "phrase": "Delaunay",
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                "filter_group": "",
                "description": ""
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                "phrase": "Delmer J. Yoakum",
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                "description": ""
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                "phrase": "Delphin Enjolras",
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                "phrase": "Demetrios Farmakopoulos",
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                "phrase": "Denis Eden",
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                "phrase": "Dennis Flanders",
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                "phrase": "Dennis H. Farber",
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                "phrase": "Dennis Miller Bunker",
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                "filter_group": "",
                "description": ""
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                "phrase": "Derek Chittock",
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                "phrase": "Derek Gores",
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                "phrase": "Derek Jarman",
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                "phrase": "Desmond Morris",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Diane Arbus",
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                "phrase": "Diane Dillon",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Dick Bickenbach",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Diego Dayer",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Diego Giacometti",
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                "filter_group": "",
                "description": ""
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                "phrase": "Diego Rivera",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Diego Vel\u00e1zquez",
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                "filter_group": "",
                "description": ""
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                "phrase": "Dieric Bouts",
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                "filter_group": "",
                "description": ""
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                "phrase": "Dino Valls",
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                "phrase": "Dionisio Baixeras Verdaguer",
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                "description": ""
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                "phrase": "Dionisius",
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                "phrase": "Dirck de Bray",
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                "description": ""
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            {
                "phrase": "Dirck de Quade van Ravesteyn",
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                "filter_group": "",
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                "phrase": "Dirck Hals",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Dirck van Baburen",
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                "show_if": "",
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                "filter_group": "",
                "description": ""
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                "phrase": "Dirck van Delen",
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                "filter_group": "",
                "description": ""
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                "phrase": "Dirck van der Lisse",
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                "show_if": "",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Ditlev Blunck",
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                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Dmitry Kustanovich",
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                "filter_group": "",
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                "phrase": "Doc Hammer",
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                "filter_group": "",
                "description": ""
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                "phrase": "Dod Procter",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Domenichino",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Domenico di Pace Beccafumi",
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                "filter_group": "",
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                "phrase": "Domenico Ghirlandaio",
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                "filter_group": "",
                "description": ""
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                "phrase": "Domenico Induno",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Domenico Pozzi",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Domenico Quaglio the Younger",
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                "show_if": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Domenico Zampieri",
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                "show_if": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Don Bluth",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Don Maitz",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Donald Judd",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Donald Roller Wilson",
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                "filter_group": "",
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                "phrase": "Donald Sherwood",
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                "filter_group": "",
                "description": ""
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                "phrase": "Donato Giancola",
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                "filter_group": "",
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                "phrase": "Dong Kingman",
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                "phrase": "Dong Yuan",
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                "filter_group": "",
                "description": ""
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                "phrase": "Dora Carrington",
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                "phrase": "D\u00f3ra Keresztes",
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                "filter_group": "",
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                "phrase": "Dora Maar",
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                "phrase": "Dorina Costras",
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                "filter_group": "",
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            {
                "phrase": "Doris Blair",
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                "phrase": "Doris Boulton-Maude",
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                "phrase": "Dorning Rasbotham",
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                "filter_group": "",
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                "phrase": "Dorothea Braby",
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                "phrase": "Dorothea Lange",
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                "search_tags": "black-white",
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                "description": ""
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                "phrase": "Dorothea Tanning",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Dorothea Warren O'Hara",
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                "filter_group": "",
                "description": ""
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                "phrase": "Dorothy Bradford",
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                "phrase": "Dorothy Burroughes",
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                "phrase": "Dorothy Coke",
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            {
                "phrase": "Dorothy Elizabeth Bradford",
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                "description": ""
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                "phrase": "Dorothy Hood",
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                "phrase": "Dorothy Johnstone",
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                "description": ""
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                "phrase": "Dorothy King",
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                "phrase": "Dorothy Lathrop",
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                "description": ""
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            {
                "phrase": "Dorothy Lockwood",
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                "filter_group": "",
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            {
                "phrase": "Dosso Dossi",
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                "phrase": "Doug Chiang",
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                "description": ""
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                "phrase": "Doug Wildey",
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                "phrase": "Douglas Bourgeois",
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                "phrase": "Douglas Robertson Bisset",
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                "phrase": "Douglas Shuler",
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                "phrase": "Douglas Smith",
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                "phrase": "Dr. Atl",
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                "phrase": "Dr. Seuss",
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                "phrase": "Drew Struzan",
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                "phrase": "Du Jin",
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            {
                "phrase": "Du Qiong",
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            {
                "phrase": "Duccio",
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                "phrase": "Dugald Sutherland MacColl",
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                "filter_group": "",
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            {
                "phrase": "Dulah Marie Evans",
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                "phrase": "Duncan Grant",
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                "description": ""
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            {
                "phrase": "D\u00fcrer",
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                "filter_group": "",
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            {
                "phrase": "Dustin Nguyen",
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                "filter_group": "",
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            {
                "phrase": "Duy Huynh",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Dwight William Tryon",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "E. Charlton Fortune",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "E. H. Shepard",
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                "filter_group": "",
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            },
            {
                "phrase": "E. Simms Campbell",
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                "phrase": "Ethel Schwabacher",
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                "phrase": "Etienne Delessert",
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                "phrase": "Euan Uglow",
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                "phrase": "Eva Gonzal\u00e8s",
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                "phrase": "Everett Raymond Kinstler",
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                "phrase": "Feng Zhu",
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                "phrase": "Ferdinand Bol",
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            {
                "phrase": "Ferdinand Van Kessel",
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            {
                "phrase": "Ferenc Joachim",
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                "description": ""
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                "filter_group": "",
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                "phrase": "Fikret Muall\u00e2 Sayg\u0131",
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                "filter_group": "",
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                "phrase": "Filip Hodas",
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                "phrase": "Filippino Lippi",
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                "phrase": "Fiona Rae",
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                "description": ""
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                "phrase": "Fiona Stephenson",
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                "filter_group": "",
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            {
                "phrase": "Fitz Henry Lane",
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                "filter_group": "",
                "description": ""
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                "phrase": "Fitz Hugh Lane",
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                "filter_group": "",
                "description": ""
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                "phrase": "Flavia Blois",
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                "phrase": "Fletcher Martin",
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                "phrase": "Flora Borsi",
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                "phrase": "Flora Macdonald Reid",
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                "filter_group": "",
                "description": ""
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                "phrase": "Florence Engelbach",
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                "phrase": "Floris van Dyck",
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                "phrase": "Floris van Schooten",
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                "phrase": "Ford Madox Brown",
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                "phrase": "Fra Angelico",
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                "description": ""
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                "phrase": "Fra Bartolomeo",
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            {
                "phrase": "Fra Filippo Lippi",
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                "phrase": "Frances C. Fairman",
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                "phrase": "Frances Currey",
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                "phrase": "Frances Hodgkins",
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                "phrase": "Frances Jetter",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frances MacDonald",
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                "phrase": "Francesca Woodman",
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                "phrase": "Francesco Albani",
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                "description": ""
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                "phrase": "Francesco Bartolozzi",
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                "phrase": "Francesco Bonsignori",
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                "filter_group": "",
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                "phrase": "Francesco Clemente",
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                "phrase": "Francesco Cozza",
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                "phrase": "Francesco del Cossa",
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                "phrase": "Francesco Filippini",
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                "phrase": "Francesco Furini",
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                "phrase": "Francesco Guardi",
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                "phrase": "Francesco Hayez",
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                "phrase": "Francesco Raibolini",
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                "description": ""
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                "phrase": "Francesco Zuccarelli",
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                "phrase": "Francis Bacon",
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                "phrase": "Francis Bourgeois",
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                "phrase": "Francis Cadell",
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                "filter_group": "",
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                "phrase": "Francis Coates Jones",
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                "phrase": "Francis Davis Millet",
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                "description": ""
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                "phrase": "Francis Ernest Jackson",
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                "phrase": "Francis Focer Brown",
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                "phrase": "Francis Helps",
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                "phrase": "Francis Picabia",
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                "description": ""
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                "phrase": "Francisco de Burgos Mantilla",
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                "phrase": "Francisco De Goya",
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                "phrase": "Francisco de Holanda",
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                "phrase": "Francisco de Zurbar\u00e1n",
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            {
                "phrase": "Francisco Goya",
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                "phrase": "Francisco Jos\u00e8 de Goya",
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                "phrase": "Francisco Mart\u00edn",
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                "phrase": "Francisco Oller",
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            {
                "phrase": "Francisco Z\u00fa\u00f1iga",
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                "phrase": "Franciszek Kostrzewski",
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                "phrase": "Franciszek Smuglewicz",
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                "description": ""
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                "phrase": "Franciszek Starowieyski",
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            {
                "phrase": "Franciszek \u017bmurko",
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            {
                "phrase": "Fran\u00e7ois Barraud",
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            {
                "phrase": "Fran\u00e7ois Bocion",
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            {
                "phrase": "Fran\u00e7ois Boquet",
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            {
                "phrase": "Fran\u00e7ois Boucher",
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            {
                "phrase": "Fran\u00e7ois Clouet",
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                "phrase": "Fran\u00e7ois Girardon",
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            {
                "phrase": "Fran\u00e7ois Joseph Heim",
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            {
                "phrase": "Fran\u00e7ois Louis Thomas Francia",
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            {
                "phrase": "Fran\u00e7ois Quesnel",
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                "phrase": "Frank Auerbach",
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            {
                "phrase": "Frank Barrington Craig",
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                "phrase": "Frank Buchser",
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                "phrase": "Frank DuMond",
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                "phrase": "Frank Frazetta",
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                "phrase": "Frank Gehry",
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                "phrase": "Frank Holl",
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            {
                "phrase": "Frank J. Girardin",
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            {
                "phrase": "Frank Leonard Brooks",
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                "filter_group": "",
                "description": ""
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                "phrase": "Frank Lloyd Wright",
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                "description": ""
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                "phrase": "Frank Mason",
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                "phrase": "Frank McKelvey",
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                "filter_group": "",
                "description": ""
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                "phrase": "Frank Miller",
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                "phrase": "Frank Montague Moore",
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                "filter_group": "",
                "description": ""
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                "phrase": "Frank O'Meara",
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                "phrase": "Frank Schoonover",
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                "phrase": "Frank Stella",
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                "filter_group": "",
                "description": ""
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                "phrase": "Frank Tinsley",
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            {
                "phrase": "Frank Weston Benson",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frank Xavier Leyendecker",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Franklin Booth",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Franklin Carmichael",
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                "filter_group": "",
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                "phrase": "Frans Hals",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frans Koppelaar",
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            {
                "phrase": "Frans Masereel",
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                "description": ""
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                "phrase": "Frans van Mieris the Elder",
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                "description": ""
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                "phrase": "Frans van Mieris the Younger",
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                "description": ""
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            {
                "phrase": "Franti\u0161ek Jakub Proky\u0161",
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                "description": ""
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                "phrase": "Franti\u0161ek Kav\u00e1n",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Franti\u0161ek Kupka",
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                "show_if": "",
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                "filter_group": "",
                "description": ""
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                "phrase": "Franz Ci\u017eek",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
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                "phrase": "Franz Fedier",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
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                "phrase": "Franz Hegi",
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                "description": ""
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                "phrase": "Franz Karl Basler-Kopp",
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                "filter_group": "",
                "description": ""
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                "phrase": "Franz Kline",
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                "description": ""
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                "description": ""
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                "phrase": "Franz Sedlacek",
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                "filter_group": "",
                "description": ""
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                "phrase": "Franz Stuck",
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                "filter_group": "",
                "description": ""
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                "filter_group": "",
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                "phrase": "Franz von Lenbach",
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                "description": ""
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                "phrase": "Franz Xaver Winterhalter",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Fred A. Precht",
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                "filter_group": "",
                "description": ""
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                "phrase": "Fred Cress",
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                "description": ""
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                "phrase": "Fred Ludekens",
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                "phrase": "Fred Marcellino",
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                "phrase": "Fred Mitchell;",
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                "description": ""
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                "phrase": "Fred Williams",
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                "description": ""
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            {
                "phrase": "Fr\u00e9d\u00e9ric Bazille",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frederic Church",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frederic Edwin Church",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frederic Leighton",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frederic Remington",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frederick Carl Frieseke",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frederick Edwin Church",
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                "filter_group": "",
                "description": ""
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                "phrase": "Frederick Goodall",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frederick Hammersley",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frederick Lord Leighton",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frederick McCubbin",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frederik de Moucheron",
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                "filter_group": "",
                "description": ""
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                "phrase": "Frederik Vermehren",
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                "phrase": "Frida Kahlo",
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                "filter_group": "",
                "description": ""
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                "phrase": "Friedensreich Hundertwasser",
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                "description": ""
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                "phrase": "Friedrich Gauermann",
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                "phrase": "Friedrich Ritter von Friedl\u00e4nder-Malheim",
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                "filter_group": "",
                "description": ""
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                "phrase": "Friedrich Traffelet",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Friedrich von Amerling",
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                "filter_group": "",
                "description": ""
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                "phrase": "Frieke Janssens",
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                "phrase": "Frits Thaulow",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Frits Van den Berghe",
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                "show_if": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Fritz Baumann",
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                "show_if": "",
                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Fritz Bultman",
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                "show_if": "",
                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Fritz Glarner",
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            {
                "phrase": "Fritz Puempin",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Fritz von Dardel",
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                "phrase": "Fritz von Uhde",
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                "filter_group": "",
                "description": ""
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                "phrase": "Fujishima Takeji",
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                "phrase": "Fujiwara Nobuzane",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Fujiwara Takanobu",
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                "phrase": "Fuller Potter",
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                "phrase": "Fuyuko Matsui",
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                "phrase": "Fyodor Slavyansky",
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                "phrase": "Fyodor Vasilyev",
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                "phrase": "Gabor Szikszai",
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                "phrase": "Gabriel Ba",
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                "description": ""
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                "phrase": "Gabriel Dawe",
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                "filter_group": "",
                "description": ""
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                "phrase": "Gabriel Metsu",
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                "description": ""
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            {
                "phrase": "Gabriele M\u00fcnter",
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                "phrase": "Gaetano Pesce",
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            {
                "phrase": "Gaetano Previati",
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                "filter_group": "",
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            {
                "phrase": "Gaetano Sabatini",
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                "phrase": "Gai Qi",
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            {
                "phrase": "Gang Se-hwang",
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                "phrase": "Gao Cen",
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            {
                "phrase": "Gao Fenghan",
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                "phrase": "Gao Qipei",
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            {
                "phrase": "Gao Xiang",
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                "phrase": "Gareth Pugh",
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                "phrase": "Garry Winogrand",
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            {
                "phrase": "Gary Larson",
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            {
                "phrase": "Gary Panter",
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                "filter_group": "",
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            {
                "phrase": "Gaston Anglade",
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                "phrase": "Gaston Bussi\u00e8re",
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            {
                "phrase": "Gat\u014dken Shunshi",
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            {
                "phrase": "Gaudi",
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                "phrase": "Gaugin",
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                "phrase": "Gavin Hamilton",
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                "phrase": "Gediminas Pranckevicius",
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                "phrase": "Geertgen tot Sint Jans",
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                "phrase": "Gen Paul",
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                "description": ""
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                "phrase": "Gene Davis",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Genevieve Springston Lynch",
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                "phrase": "Genndy Tartakovsky",
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                "filter_group": "",
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            {
                "phrase": "Gentile Bellini",
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                "filter_group": "",
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            {
                "phrase": "Gentile Tondino",
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                "phrase": "Geof Darrow",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Geoffrey Dyer",
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            {
                "phrase": "Georg Arnold-Grabon\u00e9",
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                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Georg Baselitz",
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                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Georg Friedrich Kersting",
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                "show_if": "",
                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Georg Friedrich Schmidt",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Georg Jensen",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Georg Karl Pfahler",
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                "pattern_override": "",
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                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
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                "phrase": "George Aleef",
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                "phrase": "George Ault",
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                "description": ""
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                "phrase": "George B. Bridgman",
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                "phrase": "George B. Sutherland",
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                "phrase": "George Bain",
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                "phrase": "George barbier",
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                "phrase": "George Barker",
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                "phrase": "George Barret; Jr.",
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                "phrase": "George Barret; Sr.",
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                "phrase": "George Baselitz",
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                "phrase": "George Bell",
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                "phrase": "George Benjamin Luks",
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                "phrase": "George Biddle",
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                "phrase": "George Caleb Bingham",
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                "phrase": "George Dionysus Ehret",
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                "phrase": "George Earl Ortman",
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                "phrase": "George Fiddes Watt",
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                "phrase": "George Frederic Watts",
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                "phrase": "George Frederick Harris",
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                "phrase": "George French Angas",
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                "phrase": "George Gardner Symons",
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                "phrase": "George Hendrik Breitner",
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                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giorgio de Chirico",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giorgio De Vincenzi",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giorgio Giulio Clovio",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giorgio Morandi",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giorgione",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giotto",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giotto Di Bondone",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Antonio Galli",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Battista Bracelli",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Battista Cipriani",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Battista Gaulli",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Battista Innocenzo Colombo",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Battista Piazzetta",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Battista Piranesi",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Battista Tiepolo",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Battista Venanzi",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Bellini",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Bernardino Asoleni",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Bernardino Azzolini",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Bernardino Mazzolini",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Boldini",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni da Udina",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Fattori",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Francesco Barbieri",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Giacometti",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Lanfranco",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Paolo Cavagna",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Paolo Pannini",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giovanni Pelliccioli",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Girolamo Muziano",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giuseppe Antonio Petrini",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giuseppe Arcimboldo",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giuseppe Avanzi",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giuseppe Bernardino Bison",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giuseppe Camuncoli",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giuseppe de Nittis",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Giuseppe Grisoni",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gjon Mili",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gladys Dawson",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gladys Kathleen Bell",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Glen Angus",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Glen Keane",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "cartoon",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Glenn Fabry",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Glenys Cour",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gloria Stoll Karn",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Go Nagai",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "anime",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Godfrey Blow",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Godfried Schalcken",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gong Kai",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gong Xian",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gonzalo Endara Crow",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gordon Browne",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Gordon Parks",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Goro Fujita",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "cartoon",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gottfried Helnwein",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Govert Dircksz Camphuysen",
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                "show_if": "",
                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Govert Flinck",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Goy\u014d Hashiguchi",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "ukioe",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Grace Cossington Smith",
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                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Grace English",
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                "show_if": "",
                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Grace Pailthorpe",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
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                "phrase": "Graham Sutherland",
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                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
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                "phrase": "Grandma Moses",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Grant Wood",
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                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Grayson Perry",
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                "show_if": "",
                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Greg Hildebrandt",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Greg Rutkowski",
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                "pattern_override": "",
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                "search_tags": "special",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Greg Simkins",
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                "pattern_override": "",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Greg Staples",
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                "pattern_override": "",
                "show_if": "",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gregorio Lazzarini",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gregorio Prestopino",
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                "pattern_override": "",
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                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gregory Crewdson",
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                "pattern_override": "",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Gregory Manchess",
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                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Grete Stern",
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                "pattern_override": "",
                "show_if": "",
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                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Grethe J\u00fcrgens",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Grigoriy Myasoyedov",
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                "show_if": "",
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                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Grzegorz Rutkowski",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "special",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gu Hongzhong",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gu Kaizhi",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gu Zhengyi",
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                "pattern_override": "",
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                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Guan Daosheng",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Guerrilla Girls",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Guido Borelli Da Caluso",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Guido Crepax",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "cartoon",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Guido Reni",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Guillermo del Toro",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Guo Pei",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gustaf Munch-Petersen",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gustaf Tenggren",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "cartoon",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gustav Dor\u00e9",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gustav Klimt",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nudity",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gustave Baumann",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gustave Boulanger",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gustave Buchet",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gustave Caillebotte",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gustave Courbet",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gustave Dore",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Gustave Dor\u00e9",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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                "phrase": "Helio Oiticica",
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                "phrase": "Helmut Newton",
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                "phrase": "Hendrick Avercamp",
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                "phrase": "Hendrick Cornelisz Vroom",
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                "phrase": "Hendrick Cornelisz. van Vliet",
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                "phrase": "Hendrick Goltzius",
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                "phrase": "Hendrick Goudt",
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                "phrase": "Hendrick Terbrugghen",
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                "phrase": "Hendrick van Balen",
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                "phrase": "Hendrick van Streeck",
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                "phrase": "Hendrik Gerritsz Pot",
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                "phrase": "Hendrik Kerstens",
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                "phrase": "Hendrik Martenszoon Sorgh",
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                "phrase": "Hendrik van Steenwijk I",
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                "phrase": "Hendrik Willem Mesdag",
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                "phrase": "Henning Jakob Henrik Lund",
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                "phrase": "Henri Bellechose",
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                "phrase": "Henri Biva",
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                "phrase": "Henri Fantin Latour",
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                "phrase": "Henri Harpignies",
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                "phrase": "Henri Le Sidaner",
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                "phrase": "Henri Matisse",
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                "phrase": "Henri Michaux",
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                "phrase": "Henri Rousseau",
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                "phrase": "Henri-Edmond Cross",
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                "phrase": "Henri-Julien Dumont",
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                "phrase": "Henric Trenk",
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                "phrase": "Henriett Seth F.",
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                "phrase": "Henry Asencio",
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                "phrase": "Henry B. Christian",
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                "phrase": "Henry Justice Ford",
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                "phrase": "Henry Lamb",
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                "phrase": "Henry Scott Tuke",
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                "phrase": "Henryk Siemiradzki",
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                "phrase": "Herb Ritts",
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                "phrase": "Herbert Bayer",
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                "phrase": "Herbert James Gunn",
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                "phrase": "Herbert List",
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                "phrase": "Hercules Seghers",
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                "phrase": "Herman Saftleven",
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                "phrase": "Herman van Swanevelt",
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                "phrase": "Hermenegildo Anglada Camarasa",
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                "phrase": "Hermione Hammond",
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                "phrase": "Herve Groussin",
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                "phrase": "Herv\u00e9 Guibert",
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                "phrase": "Hew Lorimer",
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                "phrase": "Hidari Jingor\u014d",
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                "phrase": "Hideyuki Kikuchi",
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                "phrase": "Hieronim Bosch",
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                "phrase": "Hieronymous Bosch",
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                "search_tags": "scribbles",
                "filter_group": "",
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            {
                "phrase": "Hikari Shimoda",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
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                "filter_group": "",
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            {
                "phrase": "Hilda Annetta Walker",
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                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hilda May Gordon",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hilma af Klint",
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                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
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                "phrase": "Hirohiko Araki",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "anime",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hiromitsu Takahashi",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hiromu Arakawa",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "anime",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hiroshi Honda",
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                "trigger": "",
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                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hiroshi Nagai",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "cartoon",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hiroshi Sugimoto",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hiroshi Yoshida",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "ukioe",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hiroshige",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "ukioe",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hiroyuki Tajima",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hishida Shuns\u014d",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hishikawa Moronobu",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "ukioe",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hisui Sugiura",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hokusai",
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                "show_if": "",
                "trigger": "",
                "search_tags": "ukioe",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Honor C. Appleton",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Honor\u00e9 Daumier",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hope Gangloff",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Horace Vernet",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Horatio McCulloch",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Horatio Nelson Poole",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Horst Antes",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hovsep Pushman",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Howard Arkley",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Howard Butterworth",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Howard Chandler Christy",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nudity",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Howard Chaykin",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "cartoon",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Howard Finster",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Howard Hodgkin",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Howard Kanovitz",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Howard Knotts",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Howard Lyon",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Howard Mehring",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Howard Pyle",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Howardena Pindell",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "n",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hristofor \u017defarovi\u0107",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hristofor Zhefarovich",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hsiao-Ron Cheng",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hu Jieqing",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hu Zaobin",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Huang Binhong",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Huang Ding",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Huang Gongwang",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Huang Guangjian",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Huang Ji",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Huang Tingjian",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hubert Robert",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hubert van Eyck",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hubert van Ravesteyn",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hubert von Herkomer",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hugh Adam Crawford",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hugh Ferriss",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "black-white",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hugh Kretschmer",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hugh William Williams",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hugo Anton Fisher",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hugo Heyrman",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hugo K\u0101rlis Grotuss",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
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            {
                "phrase": "Hugo S\u00e1nchez Bonilla",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hugo Scheiber",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hugo Simberg",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hugo van der Goes",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hundertwasser",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Hyacinthe Rigaud",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "I Ketut Soki",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Iain Faulkner",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ian Fairweather",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ian Hamilton Finlay",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ian McQue",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ian Miller",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ib Eisner",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ibrahim Kodra",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ida Rentoul Outhwaite",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "scribbles",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ignacio Bazan-Lazcano",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ignacio Zuloaga",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ignacy Witkiewicz",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ignat Bednarik",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-low-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Igor Morski",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Igor Zenin",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ike no Taiga",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "anime",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ikuo Hirayama",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ilya Glazunov",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ilya Kuvshinov",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "special",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ilya Ostroukhov",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ilya Repin",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ilya Yefimovich Repin",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Incarcerated Jerkfaces",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ingrid Baars",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Inio Asano",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "anime",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Inoue Naohisa",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fareast",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Irma Stern",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Iryna Yermolova",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Isaac Cordal",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "weird",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Isaac Levitan",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "fineart",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Ismail Inceoglu",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "special",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "Istvan Banyai",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-med-impact",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "It\u014d Jakuch\u016b",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "digipa-high-impact",
                "filter_group": "",
                "description": ""
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                "phrase": "Martin Kippenberger",
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                "phrase": "Naoto Hattori",
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                "phrase": "Natalia Goncharova",
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                "phrase": "Nathan Coley",
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                "phrase": "Nathan Wirth",
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                "phrase": "NC Wyeth",
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                "phrase": "Neil Welliver",
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                "phrase": "Philip Guston",
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                "phrase": "Pieter Bruegel The Elder",
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                "phrase": "Pixar",
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                "phrase": "Pixar Concept Artists",
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                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "penis",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_mild, nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "perfect vagina",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_mild, nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "pointy ears",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "pubic hair",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "pussy",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_mild, nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "pussy juice",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_mild, nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "rabbit ears",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "saliva",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "scar",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "gore_strict",
                "description": ""
            },
            {
                "phrase": "shiny skin",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "shoulders",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "skindentation",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "small breasts",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "soles",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "stomach",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sweat",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sweatdrop",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "tail",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "tattoo",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "tattoo",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "tears",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "teeth",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "testicles",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "thick eyebrows",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "thick thighs",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "thighs",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "toes",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "tongue",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "upper body",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "upper teeth",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "v-shaped eyebrows",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "vagina",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_mild, nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "vaginal",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_mild, nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "vulva",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_mild, nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "wings",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "wolf ears",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            }
        ]
    },
    "Clothing": {
        "priority": "",
        "pattern": "[, {} {|}]",
        "trigger": "",
        "id": "99501894",
        "description": "",
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                "phrase": "apron",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "arm support",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "gore_strict",
                "description": ""
            },
            {
                "phrase": "armor",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "ascot",
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                "pattern_override": "",
                "show_if": "",
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                "search_tags": "tie",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "ascot tie",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "tie",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "backpack",
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                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "bag",
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                "pattern_override": "",
                "show_if": "",
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                "search_tags": "bag",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "bandages",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "gore_strict",
                "description": ""
            },
            {
                "phrase": "belt",
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                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "beret",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "military, headwear",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "bikini",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "swim, swimming",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "blazer",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "bodysuit",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "boots",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "shoes",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "bow",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "bowtie",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "neck, tie",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "bra",
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                "pattern_override": "",
                "show_if": "",
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                "search_tags": "underwear",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "bracelet",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "jewelry",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "bunny suit",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "buttons",
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                "pattern_override": "",
                "show_if": "",
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                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "cameltoe",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "cape",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "capelet",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "chinese clothes",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "chinese",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "choker",
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                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "jewelry, neck",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "cleavage cutout",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "clothing cutout",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "coat",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "collar",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "collared shirt",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "shirt",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "completely nude",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "cosplay",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "costume",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "costume",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "covered nipples",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "crop top",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "detached collar",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "detached sleeves",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "sleeves",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "dress",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "dress",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "dress shirt",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "earrings",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "jewelry",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "elbow gloves",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "gloves",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "eyepatch",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "headwear",
                "filter_group": "gore_strict",
                "description": ""
            },
            {
                "phrase": "fake animal ears",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "headwear",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "fingerless gloves",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "gloves",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "footwear",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "feet",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "frills",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "fur trim",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "garter straps",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "glasses",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "headwear, glasses",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "gloves",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "gloves",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "hair bow",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "hair",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "hair flower",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "hair",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "hair ribbon",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "hair",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "hair tubes",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "hairband",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "hair",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "hairclip",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "jewelry",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "halterneck",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "dress",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "hat",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "hat",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "hat ribbon",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "headwear",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "headband",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "headwear",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "headgear",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "headwear",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "headwear",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "headwear",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "high heels",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "highleg",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "hood",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "hoodie",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "headwear",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "hot pants",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "skimpy, pants",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "hotpants",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "skimpy, pants",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "jacket",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "jacket",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "japanese clothes",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "japanese",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "jewelry",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "jewelry",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "kimono",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "japanese",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "knee boots",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "kneehighs",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "underwear",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "legwear",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "legwear",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "leotard",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "lingerie",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_mild, nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "long sleeves",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "shirt",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "maid headdress",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "mask",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "micro miniskirt",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "skimpy, skirt",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "midriff",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "skimpy",
                "filter_group": "nsfw_strict",
                "description": "In fashion, the midriff is the exposed human abdomen."
            },
            {
                "phrase": "military uniform",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "miniskirt",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "skimpy, skirt",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "mobcap",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "mocap suit",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "neck ribbon",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "neck",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "neckerchief",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "neck",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "necklace",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "jewelry",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "necktie",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "neck, tie",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "no bra",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "no panties",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_mild, nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "no shoes",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "nude",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "one-piece bodysuit",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "one-piece swimsuit",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "open clothes",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "open jacket",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "open shirt",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "panties",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "underwear",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "pants",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "pants",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "pantyhose",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "underwear",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "piercing",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "jewelry",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "plaid skirt",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "playboy bunny",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "pleated skirt",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "skirt",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "puffy short sleeves",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "puffy sleeves",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "sleeves",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "ribbon",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "hair",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sailor collar",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sandals",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sash",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "scarf",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "school swimsuit",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "swim, swimming",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "school uniform",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "uniform, school",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "scrunchie",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "serafuku",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "japanese, uniform, school",
                "filter_group": "nsfw_strict",
                "description": "A Japanese school uniform."
            },
            {
                "phrase": "shirt",
                "priority": "5",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "shirt",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "shirt lift",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "shoes",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "shoes",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "short dress",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "short shorts",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "skimpy, pants",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "short sleeves",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "shirt",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "shorts",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "pants",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "side-tie bikini bottom",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "swim, swimming",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "skirt",
                "priority": "5",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "skirt",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "skirt lift",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "sleeveless",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "sleeves",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sleeveless dress",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "dress",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sleeveless shirt",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sleeves",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "sleeves",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sleeves past wrists",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "socks",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "socks",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "strapless",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sunglasses",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "headwear, glasses",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sweater",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "swimsuit",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "swim, swimming",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "thigh boots",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "thigh strap",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "thighhighs",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "underwear",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "topless",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "torn clothes",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "turtleneck",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "shirt",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "underwear",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "underwear",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "underwear only",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "underwear",
                "filter_group": "nsfw_strict",
                "description": ""
            },
            {
                "phrase": "uniform",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "vest",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "wide sleeves",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "sleeves",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "witch hat",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "headwear",
                "filter_group": "",
                "description": ""
            }
        ]
    },
    "Color and Texture": {
        "priority": "",
        "pattern": "[, {} {|}]",
        "trigger": "",
        "id": "2008843282",
        "description": "",
        "entries": [
            {
                "phrase": "floral print",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "looking back",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "shiny",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "striped",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            }
        ]
    },
    "Composition": {
        "priority": "",
        "pattern": "[, {} {|}]",
        "trigger": "",
        "id": "1606894233",
        "description": "",
        "entries": [
            {
                "phrase": "4koma",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "comic, manga",
                "filter_group": "",
                "description": "Yonkoma manga, a comic strip format, generally consists of gag comic strips within four panels of equal size ordered from top to bottom."
            },
            {
                "phrase": "ass visible through thighs",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "nsfw_basic, position, framing, shot",
                "filter_group": "nsfw_basic, nsfw_strict",
                "description": ""
            },
            {
                "phrase": "blurry background",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "background",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "close up",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "position, framing, shot",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "cowboy shot",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "position, framing, shot",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "dutch angle",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "position, framing, shot",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "feet out of frame",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "position, framing, shot",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "from above",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "position, framing, shot",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "from below",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "from side",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "position, framing, shot",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "full body",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "position, framing, shot ",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "gradient background",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "background",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "grey background",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "background",
                "filter_group": "",
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                "phrase": "hair between eyes",
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                "phrase": "hair bun",
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            },
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                "phrase": "hair ornament",
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            {
                "phrase": "hair over one eye",
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                "phrase": "multicolored hair",
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            {
                "phrase": "orange hair",
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                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "silhouetted",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sketch",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            }
        ]
    },
    "Time": {
        "priority": "",
        "pattern": "[, {} {|}]",
        "trigger": "",
        "id": "943080489",
        "description": "",
        "entries": [
            {
                "phrase": "autumn",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "dated",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "day",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "night",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "spring",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "summer",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "winter",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "",
                "filter_group": "",
                "description": ""
            }
        ]
    },
    "Tools and Weapons": {
        "priority": "",
        "pattern": "[, {} {|}]",
        "trigger": "",
        "id": "1074728780",
        "description": "",
        "entries": [
            {
                "phrase": "bow",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "weapon, hunt, bow",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "gun",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "weapon",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "holding sword",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "weapon, sword",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "holding weapon",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "weapon",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "sword",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "weapon, sword",
                "filter_group": "",
                "description": ""
            },
            {
                "phrase": "weapon",
                "priority": "",
                "pattern_override": "",
                "show_if": "",
                "trigger": "",
                "search_tags": "weapon",
                "filter_group": "",
                "description": ""
            }
        ]
    }
}


================================================
FILE: data/tags/thumbnails.json
================================================
[File too large to display: 12.2 MB]

================================================
FILE: docker-compose.yml
================================================
version: '3.9'

services:
  stable-diffusion:
    container_name: sd-webui
    build:
      context: .
      dockerfile: Dockerfile
    env_file: .env_docker
    environment:
      PIP_EXISTS_ACTION: w
    volumes:
      - .:/sd
      - ./outputs:/sd/outputs
      - ./model_cache:/sd/model_cache
      - conda_env:/opt/conda
      - root_profile:/root
    ports:
      - '7860:7860'
      - '8501:8501'
    deploy:
      resources:
        reservations:
          devices:
            - capabilities: [ gpu ]

volumes:
  conda_env:
  root_profile:


================================================
FILE: docs/1.Installation/1.one-click-installer.md
================================================
# Installer

---

### Sygil WebUI

Home Page: https://github.com/Sygil-Dev/sygil-webui

### Installation on Windows:



- Clone or download the code from the [Repository](https://github.com/Sygil-Dev/sygil-webui).

- Open the `installer` folder and copy the `install.bat` to the root folder next to the `webui.bat`

- Double-click the `install.bat` file and wait for it to handle everything for you.

### Installation on Linux:

- Clone or download the code from the [Repository](https://github.com/Sygil-Dev/sygil-webui).

- Open the `installer` folder and copy the `install.sh` to the root folder next to the `webui.sh`

- Open a terminal on the folder where the code is located and run `./install.sh` ,make sure it has the right permissions and can be executed.

- Wait for the installer to handle everything for you.

After installation, you can run the `webui.cmd` file (on Windows) or `webui.sh` file (on Linux/Mac) to start the WebUI.


================================================
FILE: docs/1.Installation/2.windows-installation.md
================================================
---
title: Manual Windows Installation
---

<!--
This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

Copyright 2022 Sygil-Dev team.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
-->

# Initial Setup

> This is a windows guide. [To install on Linux, see this page.](3.linux-installation.md)

## Pre requisites

### Install Git & Miniconda :

* https://gitforwindows.org/ Download this, and accept all of the default settings it offers except for the default editor selection. Once it asks for what the default editor is, most people who are unfamiliar with this should just choose Notepad because everyone has Notepad on Windows.

![CleanShot 2022-08-31 at 16 29 48@2x](https://user-images.githubusercontent.com/463317/187796320-e6edbb39-dff1-46a2-a1a1-c4c1875d414c.jpg)

* Download Miniconda3:
  [https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe](https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe) Get this installed so that you have access to the Miniconda3 Prompt Console.

* Open Miniconda3 Prompt from your start menu after it has been installed

* _(Optional)_ Create a new text file in your root directory `/sygil-webui/custom-conda-path.txt` that contains the path to your relevant Miniconda3, for example `C:\Users\<username>\miniconda3` (replace `<username>` with your own username). This is required if you have more than 1 miniconda installation or are using custom installation location.

## Cloning the repo

Type `git clone https://github.com/Sygil-Dev/sygil-webui.git` into the prompt.

This will create the `sygil-webui` directory in your Windows user folder.
![CleanShot 2022-08-31 at 16 31 20@2x](https://user-images.githubusercontent.com/463317/187796462-29e5bafd-bbc1-4a48-adc8-7eccc174cb62.jpg)

---

Once a repo has been cloned, updating it is as easy as typing `git pull` inside of Miniconda when in the repo’s topmost directory downloaded by the clone command. Below you can see I used the `cd` command to navigate into that folder.

![CleanShot 2022-08-31 at 16 36 34@2x](https://user-images.githubusercontent.com/463317/187796970-db94402f-717b-43a8-9c85-270c0cd256c3.jpg)

* Next you are going to want to create a Hugging Face account: [https://huggingface.co/](https://huggingface.co/)

* After you have signed up, and are signed in go to this link and click on Authorize: [https://huggingface.co/CompVis/stable-diffusion-v-1-4-original](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)

* After you have authorized your account, go to this link to download the model weights for version 1.4 of the model, future versions will be released in the same way, and updating them will be a similar process :
  [https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt)

* Download the model into this directory: `C:\Users\<username>\sygil-webui\models\ldm\stable-diffusion-v1`

* Rename `sd-v1-4.ckpt` to `model.ckpt` once it is inside the stable-diffusion-v1 folder.

* Since we are already in our sygil-webui folder in Miniconda, our next step is to create the environment Stable Diffusion needs to work.

* _(Optional)_ If you already have an environment set up for an installation of Stable Diffusion named ldm open up the `environment.yaml` file in `\sygil-webui\` change the environment name inside of it from `ldm` to `ldo`

---

## First run

* `webui.cmd` at the root folder (`\sygil-webui\`) is your main script that you'll always run. It has the functions to automatically do the followings:

  * Create conda env
  * Install and update requirements
  * Run the relauncher and webui.py script for gradio UI options

* Run `webui.cmd` by double clicking the file.

* Wait for it to process, this could take some time. Eventually it’ll look like this:

![First successful run](https://user-images.githubusercontent.com/3688500/189009827-66c5df32-be44-4851-a265-6791444f537f.JPG)

* You'll receive warning messages on **GFPGAN**, **RealESRGAN** and **LDSR** but these are optionals and will be further explained below.

* In the meantime, you can now go to your web browser and open the link to [http://localhost:7860/](http://localhost:7860/).

* Enter the text prompt required and click generate.

* You should be able to see progress in your `webui.cmd` window. The [http://localhost:7860/](http://localhost:7860/) will be automatically updated to show the final image once progress reach 100%

* Images created with the web interface will be saved to `\sygil-webui\outputs\` in their respective folders alongside `.yaml` text files with all of the details of your prompts for easy referencing later. Images will also be saved with their seed and numbered so that they can be cross referenced with their `.yaml` files easily.

---

### Optional additional models

There are three more models that we need to download in order to get the most out of the functionality offered by Sygil-Dev.

> The models are placed inside `src` folder. If you don't have `src` folder inside your root directory it means that you haven't installed the dependencies for your environment yet. [Follow this step](#first-run) before proceeding.

### GFPGAN

1. If you want to use GFPGAN to improve generated faces, you need to install it separately.
2. Download [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) and [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth) and put it into the `/sygil-webui/models/gfpgan` directory.

### RealESRGAN

1. Download [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth) and [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth).
2. Put them into the `sygil-webui/models/realesrgan` directory.

### LDSR

1. Detailed instructions [here](https://github.com/Hafiidz/latent-diffusion). Brief instruction as follows.
2. Git clone [Hafiidz/latent-diffusion](https://github.com/Hafiidz/latent-diffusion) into your `/sygil-webui/src/` folder.
3. Run `/sygil-webui/models/ldsr/download_model.bat` to automatically download and rename the models.
4. Wait until it is done and you can confirm by confirming two new files in `sygil-webui/models/ldsr/`
5. _(Optional)_ If there are no files there, you can manually download **LDSR** [project.yaml](https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1) and [model last.cpkt](https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1).
6. Rename last.ckpt to model.ckpt and place both under `sygil-webui/models/ldsr/`.
7. Refer to [here](https://github.com/Sygil-Dev/sygil-webui/issues/488) for any issue.

# Credits

> Modified by [Hafiidz](https://github.com/Hafiidz) with helps from Sygil-Dev discord and team.


================================================
FILE: docs/1.Installation/3.linux-installation.md
================================================
---
title: Manual Linux Installation
---

<!--
This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

Copyright 2022 Sygil-Dev team.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
-->

### The definitive Stable Diffusion WebUI experience ™ Now 100% Linux Compatible!

#### Created by [Joshua Kimsey](https://github.com/JoshuaKimsey)

> This is a Linux guide. [To install on Windows, see this page](2.windows-installation.md).

**Note: This guide describes how to use the automated installation script created by [Joshua Kimsey](https://github.com/JoshuaKimsey). It is my personal recommendation to use this script as it will automatically handle downloading and updating the repo for you. This is not a requirement to running the Stable Diffusion WebUI on Linux, but you will need to install your own conda environment manually and then run `webui.sh` to run the Stable Diffusion WebUI.**

## Features

- Automates the process of installing and running hlky's fork of Stable Diffusion with the WebUI for Linux-based OS users.
- Handles updating from the hlky fork automatically if the users wishes to do so.
- Allows the user to preset their configs for running their setup (Gradio version only).

## Initial Start Guide

**Note:** This guide assumes you have installed Anaconda already, and have it set up properly. If you have not, please visit the [Anaconda](https://www.anaconda.com/products/distribution) website to download the file for your system and install it.

**WARNING: Multiple Linux users have reported issues using this script, and potentially Stable Diffusion in general, with Miniconda. As such, I can not recommend using it due to these issues with unknown causes. Please use the full release of Anaconda instead.**

**Step 1:** Create a folder/directory on your system and place this [script](https://github.com/JoshuaKimsey/Linux-StableDiffusion-Script/blob/main/linux-sd.sh) in it, named `linux-sd.sh`. This directory will be where the files for Stable Diffusion will be downloaded.

**Step 2:** Download the 1.4 AI model from HuggingFace (or another location, the original guide has some links to mirrors of the model) and place it in the same directory as the script.

**Step 3:** Make the script executable by opening the directory in your Terminal and typing `chmod +x linux-sd.sh`, or whatever you named this file as.

**Step 4:** Run the script with `./linux-sd.sh`, it will begin by cloning the [WebUI Github Repo](https://github.com/Sygil-Dev/sygil-webui) to the directory the script is located in. This folder will be named `sygil-webui`.

**Step 5:** The script will pause and ask that you move/copy the downloaded 1.4 AI models to the `sygil-webui` folder. Press Enter once you have done so to continue.

**If you are running low on storage space, you can just move the 1.4 AI models file directly to this directory, it will not be deleted, simply moved and renamed. However my personal suggestion is to just **copy** it to the repo folder, in case you desire to delete and rebuild your Stable Diffusion build again.**

**Step 6:** Next, the script will ask if you wish to customize any of the launch arguments for the Gradio WebUI Interface. If yes, then a series of options will be presented to the user:
    - Use the CPU for Extra Upscaler Models to save on VRAM
    - Automatically open a new browser window or tab on first launch
    - Use Optimized mode for Ultimate Stable Diffusion, which only requires 4GB of VRAM at the cost of speed
    - Use Optimized Turbo which uses more VRAM than regular optimized, but is faster (Incompatible with regular optimized mode)
    - Open a public xxxxx.gradi.app URL to share your interface with others (Please be careful with this, it is a potential security risk)

The user will have the ability to set these to yes or no using the menu choices.

**Note: These only apply to the Gradio WebUI interface. The Streamlit Interface version has/will have the ability to set its own preferences from within the WebUI**

**Step 7:** The script will then proceed to call the `webui.sh` file within the repo folder. This will handle the creation and updating of the conda environment, named `ldm`, as well as handle the downloading of the upsclaer models used by both Streamlit and Gradio. It will also download the Concepts Library for using custom models in the Streamlit version.

**Building the Conda environment may take upwards of 15 minutes, depending on your network connection and system specs. This is normal, just leave it be and let it finish. If you are trying to update and the script hangs at `Installing PIP Dependencies` for more than 10 minutes, you will need to `Ctrl-C` to stop the script, delete your `src` folder, and rerun `linux-sd.sh` again.**

**Step 8:** Once the conda environment has been created and the upscaler models have been downloaded, then the user is presented with a choice to choose between the Streamlit or Gradio versions of the WebUI Interface.
    - Streamlit:
        - Has A More Modern UI
        - More Features Planned
        - Will Be The Main UI Going Forward
        - Currently In Active Development
        - Missing Some Gradio Features

    - Gradio:
        - Currently Feature Complete
        - Uses An Older Interface Style
        - Will Not Receive Major Updates

**Step 9:** If everything has gone successfully, either a new browser window will open with the Streamlit version, or you should see `Running on local URL:  http://localhost:7860/` in your Terminal if you launched the Gradio Interface version. Generated images will be located in the `outputs` directory inside of `sygil-webui`. Enjoy the definitive Stable Diffusion WebUI experience on Linux! :)

## Ultimate Stable Diffusion Customizations

When running the script again after the initial use, the user will be presented with a choice to run Stable Diffusion with the last used parameters used to launch it. If the user chooses `Yes`, then all customization steps will be skipped and the Stable Diffusion WebUI will launch without pulling in new updates.

If the user chooses to Customize their setup, then they will be presented with these options on how to customize their Ultimate Stable Diffusion setup:

- Update the Stable Diffusion WebUI fork from the GitHub Repo
- Customize the launch arguments for Gradio Interface version of Stable Diffusion (See Above)

### Refer back to the original [WebUI Github Repo](https://github.com/Sygil-Dev/sygil-webui) for useful tips and links to other resources that can improve your Stable Diffusion experience

## Planned Additions

- Investigate ways to handle Anaconda automatic installation on a user's system.


================================================
FILE: docs/1.Installation/4.docker-guide.md
================================================
---
title: Running Stable Diffusion WebUI Using Docker
---
<!--
This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

Copyright 2022 sd-webui team.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
-->

## Running prebuilt image

The easiest way to run Stable Diffusion WebUI is to use the prebuilt image from Docker Hub.

```bash
docker pull tukirito/sygil-webui:latest
```
This image includes the barebones environment to run the Web UI. The models will be downloaded during the installation process. You will have to take care of the volume for the `sd/models` directory.


You can run the image using the following command:
```bash
docker container run --rm -d -p 8501:8501 -e STREAMLIT_SERVER_HEADLESS=true -e "WEBUI_SCRIPT=webui_streamlit.py" -e "VALIDATE_MODELS=false" -v "${PWD}/outputs:/sd/outputs" --gpus all tukirito/sygil-webui:latest
```

> Note: if you are running it on your local machine the output directory will be created in the current directory from where you run this command.

## Building the image

This Docker environment is intended to speed up development and testing of Stable Diffusion WebUI features. Use of a container image format allows for packaging and isolation of Stable Diffusion / WebUI's dependencies separate from the Host environment.

You can use this Dockerfile to build a Docker image and run Stable Diffusion WebUI locally.


Requirements:
* Host computer is AMD64 architecture (e.g. Intel/AMD x86 64-bit CPUs)
* Host computer operating system (Linux or Windows with WSL2 enabled)
    * See [Microsoft WSL2 Installation Guide for Windows 10] (https://learn.microsoft.com/en-us/windows/wsl/) for more information on installing.
    * Ubuntu (Default) for WSL2 is recommended for Windows users
* Host computer has Docker, or compatible container runtime
    * Docker Compose (v1.29+) or later
    * See [Install Docker Engine] (https://docs.docker.com/engine/install/#supported-platforms) to learn more about installing Docker on your Linux operating system
* 10+ GB Free Disk Space (used by Docker base image, the Stable Diffusion WebUI Docker image for dependencies, model files/weights)

Additional Requirements:
* Host computer is equipped with a CUDA-compatible GPU (e.g. Nvidia RTX 2xxx, 3000x)
* NVIDIA Container Toolkit is installed
    * See [NVIDIA Container Toolkit Installation Guide] (https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#supported-platforms "Official NVIDIA Installation Guide") for more information on installing.

Other Notes:
* "Optional" packages commonly used with Stable Diffusion WebUI workflows such as, RealESRGAN, GFPGAN, will be installed by default.
* An older version of running Stable Diffusion WebUI using Docker exists here: https://github.com/Sygil-Dev/sygil-webui/discussions/922

### But what about AMD?
There is tentative support for AMD GPUs through docker which can be enabled via `docker-compose.amd.yml`,
although this is still in the early stages. Right now, this _only_ works on native linux (not WSL2) due
to issues with AMDs support of GPU passthrough. You also _must_ have ROCm drivers installed on the host.
```
    docker compose -f docker-compose.yml -f docker-compose.amd.yml ...
```
or, by setting
```
    export COMPOSE_FILE=docker-compose.yml:docker-compose.amd.yml
```
in your `.profile` or through a tool like `direnv`


---

## First-Time Startup Instructions

### Clone Repository

* Clone this repository to your host machine:
  * `git clone https://github.com/Sygil-Dev/sygil-webui.git`
* If you plan to use Docker Compose to run the image in a container (most users), create an `.env_docker` file using the example file:
  * `cp .env_docker.example .env_docker`
  * Edit `.env_docker` using the text editor of your choice.
  * Ensure `VALIDATE_MODELS` is set to `true`

    > Options available in `.env_docker` allow you to control automatic model file checking/download during startup, and to select the Stable Diffusion WebUI implementation to run (Gradio vs Streamlit). You may the set `VALIDATE_MODELS` option to `false` after the initial run (once models are downloaded) to speed up startup time.

### Create a Container Instance Using Docker Compose

The default `docker-compose.yml` file  will create a Docker container instance named `sd-webui`

* Create an instance of the Stable Diffusion WebUI image as a Docker container:
  * `docker compose up`
* During the first run, the container image will be build containing all of the dependencies necessary to run Stable Diffusion. This build process will take several minutes to complete
  * After the image build has completed, you will have a docker image for running the Stable Diffusion WebUI tagged `sygil-webui:dev`

(Optional) Daemon mode:

* You can start the container in "daemon" mode by applying the `-d` option:  `docker compose up -d`. This will run the server in the background so you can close your console window without losing your work.
* When running in daemon mode, you can view logging output from your container by running `docker logs sd-webui`

> Note: Depending on your version of Docker/Docker Compose installed, the command may be `docker-compose` (older versions) or `docker compose` (newer versions)

### Accessing your Stable Diffusion WebUI Instance

The container may take several minutes to start up if model weights/checkpoints need to be downloaded. You can view progress via `docker compose ps` to see the current status or by checking the logs using `docker compose logs`.

Depending on the WebUI implementation you selected in `.env_docker`, you can access the WebUI at the following URLs:

* Gradio:  http://localhost:7860
* Streamlit:  http://localhost:8501

You can expose and access your WebUI to/from remote hosts by the machine's IP address:
(note: This generally does not apply to Windows/WSL2 users due to WSL's implementation)

* Gradio:  http://\<host-ip-address\>:7860
* Streamlit:  http://\<host-ip-address\>:8501

### Where is ___ stored?

By default, model weights/checkpoint files will be stored at the following path:

* `./model_cache/`

Output files generated by Stable Diffusion will be stored at the following path:

* `./output/`

The above paths will be accessible directly from your Docker container's host.

### Shutting down your Docker container

You can stop your Docker container by pressing the `CTRL+C` key combination in the terminal where the container was started..

If you started the container using `docker compose`, you can stop the container with the command:

* `docker compose down`

Using the default configuration, your Stable Diffusion output, cached model weights/files, etc will persist between Docker container starts.

---

## Resetting your Docker environment

Should you need to do so, the included `docker-reset.sh` script will remove all docker images, stopped containers, and cached model weights/checkpoints.

You will need to re-download all associated model files/weights used by Stable Diffusion WebUI, which total to several gigabytes of data. This will occur automatically upon the next startup.

## Misc Related How-to

* You can obtain shell access to a running Stable Diffusion WebUI container started with Docker Compose with either of the following commands:
  * `docker exec -it st-webui /bin/bash`
  * `docker compose exec stable-diffusion bash`
* To start a container using the Stable Diffusion WebUI Docker image without Docker Compose, you can do so with the following command:
  * `docker run --rm -it --entrypoint /bin/bash sygil-webui:dev`
* To start a container, with mapped ports, GPU resource access, and a local directory bound as a container volume, you can do so with the following command:
  * `docker run --rm -it -p 8501:8501 -p 7860:7860 --gpus all -v $(pwd):/sd --entrypoint /bin/bash sygil-webui:dev`

---

## Dockerfile Implementation Notes

Compared to base Stable Diffusion distribution, Conda-based package management was removed.

The Pytorch base image with Nvidia CUDA support is used as the base Docker image to simplify dependencies.

Python dependency requirements for various packages used by Stable Diffusion WebUI have been separated into different groups. During the container image build process, requirements are installed in the following order:

1. Stable Diffusion (core) requirements (`sd_requirements.txt`)
2. General Requirements (`requirements.txt`)
3. External optional packages requirements (`ext_requirements.txt`)
4. WebUI requirements (`ui_requirements.txt`)

Python package dependencies have been version-pinned where possible.

**Developers:  When developing new features or making changes to the environment that require dependency changes, please update and make notes in the appropriate file to help us better track and manage dependencies.**

### Other Notes

* The `root_profile` Docker Volume
  * The `huggingface/transformers` package will download files to a cache located at `/root/.cache/huggingface/transformers` totalling nearly ~1.6 GB


================================================
FILE: docs/2.Streamlit/1.streamlit-interface.md
================================================
# Streamlit Web UI Interface

## Features:

- Clean and easy to use UI: A clean and simple UI with an easy to use design.

- Support for widescreen displays.

- Dynamic Image Preview: This allows you to see how your image is looking during the generation process in real time or near real time (no more waiting until the end to see your image).

- Stop Button: A stop button to stop your generation mid-way if you do not like how your result is looking based on the image preview.

- An integrated gallery to show the generations for a prompt or session (Coming soon!)

- Better optimization VRAM usage optimization, less errors for bigger generations.

- Ability to load and use almost any `ckpt` models or model from HuggingFace.

- Text2Video: Generate video clips from text prompts right from the Web UI thanks to Diffusion Walk.

- Concepts Library: Easy to browse and use textual inversion library of custom embeddings others people (or yourself) have made via textual inversion.

- Textual Inversion: Allow you to create your own embeddings from images and use them in the UI.

- Actively being developed with new features being added and planned - Stay Tuned!

- Streamlit is now the new primary UI for the project moving forward.

- *Currently in active development but still missing some of the features present in the Gradio Interface (Post-Processing tab).*

---

## Text To Image

![](../../images/streamlit/streamlit-t2i.png)

Streamlit Text2Image allows for a modern, but well known, Stable Diffusion Textual Image generation experience. Here is a quick description of some of the features of Text2Image and what they do:

- Width and Height: Control the size of the generated image (Default is 512px)
- Classifier Free Guidance (CFG): How closely the final image should follow your prompt (Default is 7.5)
- Seed: The number (or word) used to generate an image with.
- Images Per Batch: The number of images to generate consecutively (Does not affect VRAM usage)
- Number of Batches: How many images to generate at once (Very VRAM Intensive)
- Sampling Steps: The quality of the final output, higher is better (Default is 30)
- Sampling Method: Which sampler to use to generate the image (Default is `k_euler`)

---

## Image To Image

![](../../images/streamlit/streamlit-i2i.png)

Streamlit Image2Image allows for you to take an image, be it generated by Stable Diffusion or otherwise, and use it as a base for another generation. This has the potential to really enhance images and fix issues with initial Text2Image generations. It also includes some built-in drawing and masking tools to help create custom generations. Some notable features of Gradio Image2Image are:

- Image Editor Mode: Choose whether you wish to mask, crop, or uncrop the image
- Mask Mode: Allows you to decide if a drawn mask should be generated or kept
- Denoising Strength: How much of the generated image should replace the original image. (default is 75%)
- Width and Height: Control the size of the generated image (Default is 512px)
- Classifier Free Guidance (CFG): How closely the final image should follow your prompt (Default is 7.5)
- Seed: The number (or word) used to generate an image with
- Images Per Batch: The number of images to generate consecutively (Does not affect VRAM)
- Number of Batches: How many images to generate at once (Very VRAM Intensive)
- Sampling Steps: The quality of the final output, higher is better (Default is 30)
- Sampling Method: Which sampler to use to generate the image (Default is `k_euler`)

## Text To Video

---

![](../../images/streamlit/streamlit-t2v.png)

The Text2Video tab allow you to generate videos based on a text prompt, it does so by using diffusion walk which generates images using txt2img and then interpolate them in order to walk between prompts or parts of a prompt. As of right now you can only use a single prompt which is then duplicated based on the number of seeds you use.

#### Options:

- Width: The width in pixels for your output image.

- Height: The height in pixels for your output image.

- CFG: How close you want clip to guide the generation toward your prompt.

- Seed: Seed to use during generation, can be an integer, a word or several integers and words separated by comma. When multiple seeds are used the resulting video might be more consistent than when using a single seed.

- Max Duration in Seconds: How long your video should be in seconds.

- Custom Model: A list of models from the HuggingFace website to use, any model hosted on HuggingFace can be used for txt2vid. To add a new model to this list you need to edit the config file on `configs/webui/userconfig_streamlit.yaml`, find the `txt2vid` section and add your model under `custom_models_list`, use other models there for reference on how the format should looks like.

- Sampling Steps: How many steps to perform on an image, this can be seen as the amount of denoising steps performed on the image, higher values mean you get an image with less noise, it also affects the amount of repetition on the image you will get. For higher resolutions with short prompts and long prompts with low resolution you need to increase the number of samplings steps to get a better result, otherwise the amount of repetition on the image will increase exponentially if you use a lower sampling steps value.

- Inference Steps: Extra denoising steps performed between frames/images. This helps make the video more consistent and jump less around, higher values are always better but will make it so the video takes longer for generate.

- Scheduler: Scheduler to use for inference.

- Beta Schedule Type: Scheduler type to use.

## SD Concepts Library

---

![](../../images/streamlit/streamlit-concepts.png)

The Concept Library allows for the easy usage of custom textual inversion models. These models may be loaded into `models/custom/sd-concepts-library` and will appear in the Concepts Library in Streamlit. To use one of these custom models in a prompt, either copy it using the button on the model, or type `<model-name>` in the prompt where you wish to use it.

Please see the [Concepts Library](https://github.com/Sygil-Dev/sygil-webui/blob/master/docs/7.concepts-library.md) section to learn more about how to use these tools.

## Textual Inversion

---

Textual Inversion allows you to create your own embed files training the model in a few images to teach it a new concept based on what is already in the original model, this is fast and do not need too many resources to do so. You can train a new concept to use on the Concepts Library with Textual Inversion, once you finish training it you can then use it on the concepts library.

## Model Manager

---

![](../../images/streamlit/streamlit-model-manager.png)

The Model Manager page allows you to download models with the click of a button, instead of having to go and manually find the link to download a model and then finding where to put it on your disk for the app to detect it we made it so the Model Manager page does most of the heavy lifting for you.

## Tools

This section is a work in progress.

The idea behind it is to provide extra tools for advanced users to do things they need that are not possible with other normal pages. This is intended mainly for developers or technical people wanting to do more than just basic stuff with the UI and even integrating their own things into it.

## Settings

---

![](../../images/streamlit/streamlit-settings.png)

The Settings page allows you to customize how you want Streamlit to run. These changes will be saved to a personalized user setting file in `configs/webui/userconfig_streamlit.yaml`, this file is independent from the defaults file located in `configs/webui/webui_streamlit.yaml` and will not be reset after updates unless a mayor change that would break some options is released, in which case this file will be reset to the defaults values and you would need to configure your settings on the Settings page again. After editing the settings on the Settings page you need to scroll down and hit the `Save` button for it to take effect, you can also use the `Reset` button to reset your settings manually to the defaults in case you made a mistake with any of your settings and you need to reset them, so, feel free to experiment with them and test which settings work best for you and which don't.


================================================
FILE: docs/3.Gradio/2.gradio-interface.md
================================================
# Gradio Web UI Interface

:::danger Warning

The Gradio WebUI is considered legacy or deprecated, it is no longer being maintained and it lacks several features compared to new versions. Always try to use the most recent version of our UI when possible.

:::

### Gradio Web UI Features:

- Older UI design that is fully functional and feature complete.
- Has access to all upscaling models, including LSDR.
- Dynamic prompt entry automatically changes your generation settings based on `--params` in a prompt.
- Includes quick and easy ways to send generations to Image2Image or the Image Lab for upscaling.
- *Note, the Gradio interface is no longer being actively developed and is only receiving bug fixes.*

### Launching The Gradio Web UI

To launch the Gradio Web UI, you will need to do the following:

- Windows:
  - Open your command line in the repo folder and run the `webui.cmd` file.
- Linux:
  - Open your terminal to the repo folder and run `webui.sh`, then press `2` when prompted.
- Manually:
  - Open your terminal to the repo folder.
  - Activate the conda environment using `conda activate ldm`
  - Run the command `python scripts/relauncher.py`

Once the Gradio Web UI launches, a link will appear in your command line or terminal, click or copy and paste that link into your browser to access the interface.

## Text2Image

![](../../images/gradio/gradio-t2i.png)

Gradio Text2Image allows for the classic and well known Stable Diffusion Textual Image generation. Here is a quick description of some of the features of Text2Image and what they do:

- Width and Height: Control the size of the generated image (Default is 512px)
- Classifer Free Guidance (CFG): How closely the final image should follow your prompt (Default is 7.5)
- Seed: The number (or word) used to generate an image with
- Images Per Batch: The number of images to generate consecutively (Does not affect VRAM)
- Number of Batches: How many images to generate at once (Very VRAM Intensive)
- Sampling Steps: The quality of the final output, higher is better with dimiishing returns (Default is 50)
- Sampling Method: Which sampler to use to generate the image (Default is `k_lms`)
- Push to Img2Img: Send the image to the Image2Image tool to continue working with it via Stable Diffusion
- Send to Image Lab: Send the image to the Image Lab for Enhancement and Upscaling.

## Image2Image

---

![](../../images/gradio/gradio-i2i.png)

Gradio Image2Image allows for you to take an image, be it generated by Stable Diffusion or otherwise, and use it as a base for another generation. This has the potential to really enhance images and fix issues with initial Text2Image generations. It also includes some built-in drawing and masking tools to help create custom generations. Some notable features of Gradio Image2Image are:

- Image Editor Mode: Choose whether you wish to mask, crop, or uncrop the image
- Mask Mode: Allows you to decide if a drawn mask should be generated or kept
- Denoising Strength: How much of the generated image should replace the original image. (default is 70%)
- Width and Height: Control the size of the generated image (Default is 512px)
- Classifier Free Guidance (CFG): How closely the final image should follow your prompt (Default is 7.5)
- Seed: The number (or word) used to generate an image with
- Images Per Batch: The number of images to generate consecutively (Does not affect VRAM)
- Number of Batches: How many images to generate at once (Very VRAM Intensive)
- Sampling Steps: The quality of the final output, higher is better (Default is 50)
- Sampling Method: Which sampler to use to generate the image (Default is `k_lms`)

## Image Lab

---

![](../../images/gradio/gradio-upscale.png)

The Gradio Image Lab is a central location to access image enhancers and upscalers. Though some options are available in all tabs (GFPGAN and realESRGAN), the Image Lab is where all of these tools may be easily accessed. These upscalers can be used for generated images sent to the lab, or on other images uploaded to it. The tools included here are:

- GFPGAN: Fixes and enhances faces
- realESRGAN: A 4x upscaler that uses a GAN to achieve its results
- GoBig: A 2x upscaler that uses realESRGAN, but preserves more detail
- LSDR: A 4x upscaler that uses Latent Diffusion, preserving a lot more detail at the cost of speed and VRAM
- GoLatent: Uses LSDR to do a 4x upscale, then GoBig to make a final 8x upscale with great detail preservation.

Please see the [Image Enhancers](../4.post-processing.md) section to learn more about how to use these tools.

## Scene2Image

---

![](../../images/gradio/gradio-s2i.png)

Gradio Scene2Image allows you to define layers of images in a markdown-like syntax.

> Would it be possible to have a layers system where we could do have
> foreground, mid, and background objects which relate to one another and
> share the style? So we could say generate a landscape, one another layer
> generate a castle, and on another layer generate a crowd of people.

You write a a multi-line prompt that looks like markdown, where each section declares one layer.
It is hierarchical, so each layer can have their own child layers.
In the frontend you can find a brief documentation for the syntax, examples and reference for the various arguments.
Here a summary:

Markdown headings, e.g. '# layer0', define layers.
The content of sections define the arguments for image generation.
Arguments are defined by lines of the form 'arg:value' or 'arg=value'.

Layers are hierarchical, i.e. each layer can contain more layers.
The number of '#' increases in the headings of a child layers.
Child layers are blended together by their image masks, like layers in image editors.
By default alpha composition is used for blending.
Other blend modes from [ImageChops](https://pillow.readthedocs.io/en/stable/reference/ImageChops.html) can also be used.

Sections with "prompt" and child layers invoke Image2Image, without child layers they invoke Text2Image.
The result of blending child layers will be the input for Image2Image.

Without "prompt" they are just images, useful for mask selection, image composition, etc.
Images can be initialized with "color", resized with "resize" and their position specified with "pos".
Rotation and rotation center are "rotation" and "center".

Mask can automatically be selected by color, color at pixels of the image, or by estimated depth.

You can chose between two different depth estimation models, see frontend reference for name of arguments.
[Monocular depth estimation](https://huggingface.co/spaces/atsantiago/Monocular_Depth_Filter) can be selected as depth model `0`.
[MiDaS depth estimation](https://huggingface.co/spaces/pytorch/MiDaS), used by default, can be selected as depth model `1`.

Depth estimation can be used for traditional 3d reconstruction.
Using `transform3d=True` the pixels of an image can be rendered from another perspective or with a different field of view.
For this you specify pose and field of view that corresponds to the input image and a desired output pose and field of view.
The poses describe the camera position and orientation as x,y,z,rotate_x,rotate_y,rotate_z tuple with angles describing rotations around axes in degrees.
The camera coordinate system is the pinhole camera as described and pictured in [OpenCV "Camera Calibration and 3D Reconstruction" documentation](https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html).

When the camera pose `transform3d_from_pose` where the input image was taken is not specified, the camera pose `transform3d_to_pose` to which the image is to be transformed is in terms of the input camera coordinate system:
Walking forwards one depth unit in the input image corresponds to a position `0,0,1`.
Walking to the right is something like `1,0,0`.
Going downwards is then `0,1,0`.

## Gradio Optional Customizations

---

Gradio allows for a number of possible customizations via command line arguments/terminal parameters. If you are running these manually, they would need to be run like this: `python scripts/webui.py --param`. Otherwise, you may add your own parameter customizations to `scripts/relauncher.py`, the program that automatically relaunches the Gradio interface should a crash happen.

Inside of `relauncher.py` are a few preset defaults most people would likely access:

```
# Run upscaling models on the CPU
extra_models_cpu = False

# Automatically open a new browser window or tab on first launch
open_in_browser = False

# Run Stable Diffusion in Optimized Mode - Only requires 4Gb of VRAM, but is significantly slower
optimized = False

# Run in Optimized Turbo Mode - Needs more VRAM than regular optimized mode, but is faster
optimized_turbo = False

# Creates a public xxxxx.gradio.app share link to allow others to use your interface (requires properly forwarded ports to work correctly)
share = False

# Generate tiling images
tiling = False
```

Setting any of these to `True` will enable those parameters on every launch. Alternatively, if you wish to enable a `--parameter` not listed here, you can enter your own custom ones in this field inside of `scripts/relauncher.py`:

```
# Enter other `--arguments` you wish to use - Must be entered as a `--argument ` syntax
additional_arguments = ""
```

## List of command line options

---

This is a list of the full set of optional parameters you can launch the Gradio Interface with.

```
usage: webui.py [-h] [--ckpt CKPT] [--cli CLI] [--config CONFIG] [--defaults DEFAULTS] [--esrgan-cpu] [--esrgan-gpu ESRGAN_GPU] [--extra-models-cpu] [--extra-models-gpu] [--gfpgan-cpu] [--gfpgan-dir GFPGAN_DIR] [--gfpgan-gpu GFPGAN_GPU] [--gpu GPU]
                [--grid-format GRID_FORMAT] [--inbrowser] [--ldsr-dir LDSR_DIR] [--n_rows N_ROWS] [--no-half] [--no-progressbar-hiding] [--no-verify-input] [--optimized-turbo] [--optimized] [--outdir_img2img [OUTDIR_IMG2IMG]] [--outdir_imglab [OUTDIR_IMGLAB]]
                [--outdir_txt2img [OUTDIR_TXT2IMG]] [--outdir [OUTDIR]] [--filename_format [FILENAME_FORMAT]] [--port PORT] [--precision {full,autocast}] [--realesrgan-dir REALESRGAN_DIR] [--realesrgan-model REALESRGAN_MODEL] [--save-metadata]
                [--share-password SHARE_PASSWORD] [--share] [--skip-grid] [--skip-save] [--no-job-manager] [--max-jobs MAX_JOBS] [--tiling]

optional arguments:
  -h, --help            show this help message and exit
  --ckpt CKPT           path to checkpoint of model (default: models/ldm/stable-diffusion-v1/model.ckpt)
  --cli CLI             don't launch web server, take Python function kwargs from this file. (default: None)
  --config CONFIG       path to config which constructs model (default: configs/stable-diffusion/v1-inference.yaml)
  --defaults DEFAULTS   path to configuration file providing UI defaults, uses same format as cli parameter (default: configs/webui/webui.yaml)
  --esrgan-cpu          run ESRGAN on cpu (default: False)
  --esrgan-gpu ESRGAN_GPU
                        run ESRGAN on specific gpu (overrides --gpu) (default: 0)
  --extra-models-cpu    run extra models (GFGPAN/ESRGAN) on cpu (default: False)
  --extra-models-gpu    run extra models (GFGPAN/ESRGAN) on gpu (default: False)
  --gfpgan-cpu          run GFPGAN on cpu (default: False)
  --gfpgan-dir GFPGAN_DIR
                        GFPGAN directory (default: ./GFPGAN)
  --gfpgan-gpu GFPGAN_GPU
                        run GFPGAN on specific gpu (overrides --gpu) (default: 0)
  --gpu GPU             choose which GPU to use if you have multiple (default: 0)
  --grid-format GRID_FORMAT
                        png for lossless png files; jpg:quality for lossy jpeg; webp:quality for lossy webp, or webp:-compression for lossless webp (default: jpg:95)
  --inbrowser           automatically launch the interface in a new tab on the default browser (default: False)
  --ldsr-dir LDSR_DIR   LDSR directory (default: ./LDSR)
  --n_rows N_ROWS       rows in the grid; use -1 for autodetect and 0 for n_rows to be same as batch_size (default: -1) (default: -1)
  --no-half             do not switch the model to 16-bit floats (default: False)
  --no-progressbar-hiding
                        do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware accleration in browser) (default: False)
  --no-verify-input     do not verify input to check if it's too long (default: False)
  --optimized-turbo     alternative optimization mode that does not save as much VRAM but runs siginificantly faster (default: False)
  --optimized           load the model onto the device piecemeal instead of all at once to reduce VRAM usage at the cost of performance (default: False)
  --outdir_img2img [OUTDIR_IMG2IMG]
                        dir to write img2img results to (overrides --outdir) (default: None)
  --outdir_imglab [OUTDIR_IMGLAB]
                        dir to write imglab results to (overrides --outdir) (default: None)
  --outdir_txt2img [OUTDIR_TXT2IMG]
                        dir to write txt2img results to (overrides --outdir) (default: None)
  --outdir [OUTDIR]     dir to write results to (default: None)
  --filename_format [FILENAME_FORMAT]
                        filenames format (default: None)
  --port PORT           choose the port for the gradio webserver to use (default: 7860)
  --precision {full,autocast}
                        evaluate at this precision (default: autocast)
  --realesrgan-dir REALESRGAN_DIR
                        RealESRGAN directory (default: ./RealESRGAN)
  --realesrgan-model REALESRGAN_MODEL
                        Upscaling model for RealESRGAN (default: RealESRGAN_x4plus)
  --save-metadata       Store generation parameters in the output png. Drop saved png into Image Lab to read parameters (default: False)
  --share-password SHARE_PASSWORD
                        Sharing is open by default, use this to set a password. Username: webui (default: None)
  --share               Should share your server on gradio.app, this allows you to use the UI from your mobile app (default: False)
  --skip-grid           do not save a grid, only individual samples. Helpful when evaluating lots of samples (default: False)
  --skip-save           do not save indiviual samples. For speed measurements. (default: False)
  --no-job-manager      Don't use the experimental job manager on top of gradio (default: False)
  --max-jobs MAX_JOBS   Maximum number of concurrent 'generate' commands (default: 1)
  --tiling              Generate tiling images (default: False)
```


================================================
FILE: docs/4.post-processing.md
================================================
---
title: Post Processing
---
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along with this program.  If not, see <http://www.gnu.org/licenses/>.
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Included with both versions of the Web UI Interface are a series of image restorers and upscalers. They are included to help users create outputs with restored features, such as better faces, or at larger resolutions than Stable Diffusion is able to natively output.

## GFPGAN
---

![](../images/GFPGAN.png)

GFPGAN is designed to help restore faces in Stable Diffusion outputs. If you have ever tried to generate images with people in them, you know why having a face restorer can come in handy. This is where GFPGAN comes in handy. It uses it's own GAN to detect and restore the faces of subjects within an image. It greatly helps to enhance the details in human faces, while also fixing issues with asymmetry or awkward looking eyes.

If you want to use GFPGAN to improve generated faces, you need to download the models for it separately if you are on Windows or doing so manually on Linux.
Download [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) and put it
into the `/sygil-webui/models/gfpgan` directory after you have setup the conda environment for the first time.

## RealESRGAN

---

![](../images\RealESRGAN.png)

RealESRGAN is a 4x upscaler built into both versions of the Web UI interface. It uses its own GAN to upscale images while retaining details of an image. Two different versions of RealESRGAN can be used, `RealESRGAN 4X` and `RealESRGAN 4X Anime`. Despite the name, don't hesitate to try either version when upscaling an image to see which works best for a given output.

If you want to use RealESRGAN to upscale your images, you need to download the models for it separately if you are on Windows or doing so manually on Linux.
Download [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth) and [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth).
Put them into the `sygil-webui/models/realesrgan` directory after you have setup the conda environment for the first time.

## GoBig (Gradio only currently)

---

GoBig is a 2X upscaler that uses RealESRGAN to upscale the image and then slice it into small parts, each part gets diffused further by SD to create more details, great for adding and increasing details but will change the composition, might also fix issues like eyes etc. The settings are similar to Image2Image, with regards to strength and seed of the generation.

To use GoBig, you will need to download the RealESRGAN models as directed above.

## Latent Diffusion Super Resolution - LSDR (Gradio only currently)

---

LSDR is a 4X upscaler with high VRAM usage that uses a Latent Diffusion model to upscale the image. This will accentuate the details of an image, but won't change the composition. This might introduce sharpening, but it is great for textures or compositions with plenty of details. However, it is slower and will use more VRAM.

If you want to use LSDR to upscale your images, you need to download the models for it separately if you are on Windows or doing so manually on Linux.
Download the LDSR [project.yaml](https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1) and [ model last.cpkt](https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1). Rename `last.ckpt` to `model.ckpt` and place both in the `sygil-webui/models/ldsr` directory after you have setup the conda environment for the first time.

## GoLatent (Gradio only currently)

---

GoLatent is an 8X upscaler with high VRAM usage. It uses GoBig to add details and then uses a Latent Diffusion (LSDR) model to upscale the image. This will result in less artifacts and sharpening. Use the settings to feed GoBig settings that will contribute to the result. Please note, this mode is considerably slower and uses significantly more VRAM.

To use GoLatent, you will need to download the appropriate LSDR models as described above.

---

## Future Additions

Currently, these are the 4 main enhancers and upscalers used in the project, but more may be implemented in the future. Stay Tuned!


================================================
FILE: docs/5.concepts-library.md
================================================
# SD Concepts Library

## SD Concepts Library

---

![](../images/streamlit/streamlit-concepts.png)

The Concept Library allows for the easy usage of custom textual inversion models. These models may be loaded into `models/custom/sd-concepts-library` and will appear in the Concepts Library in Streamlit. To use one of these custom models in a prompt, either copy it using the button on the model, or type `<model-name>` in the prompt where you wish to use it.


================================================
FILE: docs/6.custom-models.md
================================================
# Custom Models

<!--
This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

Copyright 2022 Sygil-Dev team.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
-->

You can use other *versions* of Stable Diffusion, and *fine-tunes* of Stable Diffusion.

Any model with the `.ckpt` extension can be placed into the `models/custom` folder and used in the UI. The filename of the model will be used to show the model on the drop-down menu on the UI from which you can select and use your custom model so, make sure it has a good filename so you can recognize it from the drop-down menu.

# Official models supported:

## Stable Diffusion versions:

- ### [Stable Diffusion v1-3](https://huggingface.co/CompVis/stable-diffusion-v1-3)

- ### [Stable Diffusion v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)

- ### [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)

## Waifu Diffusion versions:

- ### Waifu Diffusion v1.2

- ### [Waifu Diffusion v1.3](https://huggingface.co/hakurei/waifu-diffusion-v1-3)

## Trinart:

- ### [Trinart Characters](https://huggingface.co/naclbit/trinart_characters_19.2m_stable_diffusion_v1)

- ### [Trinart v2](https://huggingface.co/naclbit/trinart_stable_diffusion_v2)

-

## Unofficial Model List:

- ### [Rentry Stable Diffusion Model List.](https://rentry.org/sdmodels)


================================================
FILE: docusaurus.config.js
================================================
// @ts-check
// Note: type annotations allow type checking and IDEs autocompletion

const lightCodeTheme = require('prism-react-renderer/themes/github');
const darkCodeTheme = require('prism-react-renderer/themes/dracula');

/** @type {import('@docusaurus/types').Config} */
const config = {
  title: 'Sygil WebUI',
  tagline: 'Web-based UI for Stable Diffusion',
  url: 'https://sygil-dev.github.io',
  baseUrl: '/sygil-webui',
  onBrokenLinks: 'throw',
  onBrokenMarkdownLinks: 'warn',
  //favicon: 'images/logo.svg',

  // GitHub pages deployment config.
  // If you aren't using GitHub pages, you don't need these.
  organizationName: 'Sygil-Dev', // Usually your GitHub org/user name.
  projectName: 'sygil-webui', // Usually your repo name.
  deploymentBranch: 'gh-pages',

  // Even if you don't use internalization, you can use this field to set useful
  // metadata like html lang. For example, if your site is Chinese, you may want
  // to replace "en" with "zh-Hans".
  i18n: {
    defaultLocale: 'en',
    locales: ['en'],
  },

  // ...
  plugins: [
    [
	  // enable search using docusaurus-search-local
      require.resolve("@cmfcmf/docusaurus-search-local"),
      {
	  // whether to index docs pages
	  indexDocs: true,

	  // Whether to also index the titles of the parent categories in the sidebar of a doc page.
	  // 0 disables this feature.
	  // 1 indexes the direct parent category in the sidebar of a doc page
	  // 2 indexes up to two nested parent categories of a doc page
	  // 3...
	  //
	  // Do _not_ use Infinity, the value must be a JSON-serializable integer.
	  indexDocSidebarParentCategories: 1,

	  // whether to index blog pages
	  indexBlog: true,

	  // whether to index static pages
	  // /404.html is never indexed
	  indexPages: false,

	  // language of your documentation, see next section
	  language: "en",

	  // setting this to "none" will prevent the default CSS to be included. The default CSS
	  // comes from autocomplete-theme-classic, which you can read more about here:
	  // https://www.algolia.com/doc/ui-libraries/autocomplete/api-reference/autocomplete-theme-classic/
	  // When you want to overwrite CSS variables defined by the default theme, make sure to suffix your
	  // overwrites with `!important`, because they might otherwise not be applied as expected. See the
	  // following comment for more information: https://github.com/cmfcmf/docusaurus-search-local/issues/107#issuecomment-1119831938.
	  style: undefined,

	  // The maximum number of search results shown to the user. This does _not_ affect performance of
	  // searches, but simply does not display additional search results that have been found.
	  maxSearchResults: 8,

	  // lunr.js-specific settings
	  lunr: {
		// When indexing your documents, their content is split into "tokens".
		// Text entered into the search box is also tokenized.
		// This setting configures the separator used to determine where to split the text into tokens.
		// By default, it splits the text at whitespace and dashes.
		//
		// Note: Does not work for "ja" and "th" languages, since these use a different tokenizer.
		tokenizerSeparator: /[\s\-]+/,
		// https://lunrjs.com/guides/customising.html#similarity-tuning
		//
		// This parameter controls the importance given to the length of a document and its fields. This
		// value must be between 0 and 1, and by default it has a value of 0.75. Reducing this value
		// reduces the effect of different length documents on a term’s importance to that document.
		b: 0.75,
		// This controls how quickly the boost given by a common word reaches saturation. Increasing it
		// will slow down the rate of saturation and lower values result in quicker saturation. The
		// default value is 1.2. If the collection of documents being indexed have high occurrences
		// of words that are not covered by a stop word filter, these words can quickly dominate any
		// similarity calculation. In these cases, this value can be reduced to get more balanced results.
		k1: 1.2,
		// By default, we rank pages where the search term appears in the title higher than pages where
		// the search term appears in just the text. This is done by "boosting" title matches with a
		// higher value than content matches. The concrete boosting behavior can be controlled by changing
		// the following settings.
		titleBoost: 5,
		contentBoost: 1,
		tagsBoost: 3,
		parentCategoriesBoost: 2, // Only used when indexDocSidebarParentCategories > 0
	  }
	},
    ],
  ],

  presets: [
    [
      'classic',
      /** @type {import('@docusaurus/preset-classic').Options} */
      ({
        docs: {

			sidebarCollapsed: false,
			sidebarPath: require.resolve('./sidebars.js'),
			// Please change this to your repo.
			// Remove this to remove the "edit this page" links.
			editUrl:
				'https://github.com/Sygil-Dev/sygil-webui/tree/main/',
		},
        blog: {
			showReadingTime: true,
			// Please change this to your repo.
			// Remove this to remove the "edit this page" links.
			editUrl:
				'https://github.com/Sygil-Dev/sygil-webui/tree/main/',
		},
        theme: {
			customCss: require.resolve('./frontend/css/docs_custom.css'),
        },
      }),
    ],
  ],

  themeConfig:
    /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
    ({
      navbar: {
        title: 'Sygil WebUI',
        //logo: {
        //  alt: 'Sygil Logo',
        //  src: 'images/logo.svg',
        //},
        items: [
          {
            type: 'doc',
            docId: 'Installation/one-click-installer',
            position: 'left',
            label: 'Getting Started',
          },
          {to: '/blog', label: 'Blog', position: 'left'},
          {
            href: 'https://github.com/Sygil-Dev/sygil-webui',
            label: 'GitHub',
            position: 'right',
          },
        ],
      },
      footer: {
        style: 'dark',
        links: [
          {
            title: 'Community',
            items: [
              {
                label: 'Discord',
                href: 'https://discord.gg/ttM8Tm6wge',
              },
              {
                label: 'Twitter',
                href: 'https://twitter.com/Sygil_Dev',
              },
            ],
          },
          {
            title: 'More',
            items: [
              {
                label: 'Blog',
                to: '/blog',
              },
              {
                label: 'GitHub',
                href: 'https://github.com/Sygil-Dev/sygil-webui',
              },
            ],
          },
        ],
        copyright: `Copyright © ${new Date().getFullYear()} Sygil.Dev`,
      },
      prism: {
        theme: lightCodeTheme,
        darkTheme: darkCodeTheme,
      },
    }),
};

module.exports = config;


================================================
FILE: entrypoint.sh
================================================
#!/bin/bash
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
# Starts the webserver inside the docker container
#

# set -x

SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
cd $SCRIPT_DIR
export PYTHONPATH=$SCRIPT_DIR

if [[ $PUBLIC_KEY ]]
then
    mkdir -p ~/.ssh
    chmod 700 ~/.ssh
    cd ~/.ssh
    echo $PUBLIC_KEY >> authorized_keys
    chmod 700 -R ~/.ssh
    cd /
    service ssh start
    echo "SSH Service Started"
fi


MODEL_DIR="${SCRIPT_DIR}/user_data/model_cache"
mkdir -p $MODEL_DIR
# Array of model files to pre-download
# local filename
# local path in container (no trailing slash)
# download URL
# sha256sum
MODEL_FILES=(
    'model.ckpt models/ldm/stable-diffusion-v1 https://www.googleapis.com/storage/v1/b/aai-blog-files/o/sd-v1-4.ckpt?alt=media'
    'GFPGANv1.4.pth models/gfpgan https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth'
    'detection_Resnet50_Final.pth gfpgan/weights https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth'
    'parsing_parsenet.pth gfpgan/weights https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth'
    'RealESRGAN_x4plus.pth models/realesrgan https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'
    'RealESRGAN_x4plus_anime_6B.pth models/realesrgan https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth'
    'project.yaml models/ldsr https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1'
    'model.ckpt models/ldsr https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1'
    'waifu-diffusion.ckpt models/custom https://huggingface.co/crumb/pruned-waifu-diffusion/resolve/main/model-pruned.ckpt'
    'trinart.ckpt models/custom https://huggingface.co/naclbit/trinart_stable_diffusion_v2/resolve/main/trinart2_step95000.ckpt'
    'model__base_caption.pth models/blip https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth'
    'pytorch_model.bin models/clip-vit-large-patch14 https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin'
    'config.json models/clip-vit-large-patch14 https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/config.json'
    'merges.txt models/clip-vit-large-patch14 https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/merges.txt'
    'preprocessor_config.json models/clip-vit-large-patch14 https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/preprocessor_config.json'
    'special_tokens_map.json models/clip-vit-large-patch14 https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/special_tokens_map.json'
    'tokenizer.json models/clip-vit-large-patch14 https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/tokenizer.json'
    'tokenizer_config.json models/clip-vit-large-patch14 https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/tokenizer_config.json'
    'vocab.json models/clip-vit-large-patch14 https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/vocab.json'
)

downloadModel() {
    local file=$1
    local path="${SCRIPT_DIR}/${2}"
    local path_dir="${MODEL_DIR}/$2"
    local url=$3

    if [[ ! -e "${MODEL_DIR}/$2/${file}" ]]; then
        echo "Downloading: ${url} please wait..."
        mkdir -p ${MODEL_DIR}/$2
        mkdir -p ${path}
        wget --output-document=${MODEL_DIR}/$2/${file} --no-verbose --show-progress --progress=dot:giga ${url}
        ln -sf ${MODEL_DIR}/$2/${file} ${path}/${file}
        if [[ -e "${path}/${file}" ]]; then
            echo "saved ${file}"
        else
            echo "error saving ${MODEL_DIR}/$2/${file}!"
            exit 1
        fi
    fi
}

echo "Downloading model files..."
for models in "${MODEL_FILES[@]}"; do
    model=($models)
    if [[ ! -e ${model[1]}/${model[0]} || ! -L ${model[1]}/${model[0]} ]]; then
        downloadModel ${model[0]} ${model[1]} ${model[2]}
    fi
done

# Create directory for diffusers models
mkdir -p ${MODEL_DIR}/diffusers/stable-diffusion-v1-4
mkdir -p ${MODEL_DIR}/diffusers/waifu-diffusion
mkdir -p ${SCRIPT_DIR}/diffusers/stable-diffusion-v1-4
mkdir -p ${SCRIPT_DIR}/diffusers/waifu-diffusion
# Link tokenizer to diffusers models
ln -fs ${SCRIPT_DIR}/models/clip-vit-large-patch14/ ${SCRIPT_DIR}/diffusers/stable-diffusion-v1-4/tokenizer
ln -fs ${SCRIPT_DIR}/models/clip-vit-large-patch14/ ${SCRIPT_DIR}/diffusers/waifu-diffusion/tokenizer

if [[ -e "${MODEL_DIR}/sd-concepts-library" ]]; then
    # concept library exists, update
    cd ${MODEL_DIR}/sd-concepts-library
    git pull
else
    # concept library does not exist, clone
    cd ${MODEL_DIR}
    git clone https://github.com/Sygil-Dev/sd-concepts-library.git
fi
# create directory and link concepts library
mkdir -p ${SCRIPT_DIR}/models/custom
ln -fs ${MODEL_DIR}/sd-concepts-library/sd-concepts-library/ ${SCRIPT_DIR}/models/custom/sd-concepts-library

mkdir -p ${SCRIPT_DIR}/user_data/outputs
ln -fs ${SCRIPT_DIR}/user_data/outputs/ ${SCRIPT_DIR}/outputs

echo "export HF_HOME=${MODEL_DIR}" >> ~/.bashrc
echo "export XDG_CACHE_HOME=${MODEL_DIR}" >> ~/.bashrc
echo "export TRANSFORMERS_CACHE=${MODEL_DIR}" >> ~/.bashrc
source ~/.bashrc
cd $SCRIPT_DIR
launch_command="streamlit run ${SCRIPT_DIR}/scripts/webui_streamlit.py"

$launch_command

sleep infinity


================================================
FILE: environment.yaml
================================================
name: ldm
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
channels:
  - pytorch
  - defaults
  - nvidia
# Psst. If you change a dependency, make sure it's mirrored in the docker requirement
# files as well.
dependencies:
  - conda-forge::nodejs=18.11.0
  - yarn=1.22.19
  - cudatoolkit=11.7
  - git
  - numpy=1.23.3
  - pip=20.3
  - python=3.8.5
  - pytorch=1.13.0
  - scikit-image=0.19.2
  - torchvision=0.14.0
  - pip:
    - -r requirements.txt


================================================
FILE: frontend/.eslintrc.js
================================================
module.exports = {
  env: {
    node: true
  },
  'extends': [
    'plugin:vue/vue3-essential',
    "plugin:@typescript-eslint/eslint-recommended",
    "plugin:@typescript-eslint/recommended",
    'eslint:recommended',
    '@vue/typescript/recommended'
  ],
  ignorePatterns: [
    'node_modules/',
    'dist/',
    'coverage/',
    'pnpm-lock.yaml',
    '*.js'
  ],
  rules: {
    'no-console': process.env.NODE_ENV === 'production' ? 'warn' : 'off',
    'no-debugger': process.env.NODE_ENV === 'production' ? 'warn' : 'off'
  }
}


================================================
FILE: frontend/__init__.py
================================================


================================================
FILE: frontend/css/custom.css
================================================
/* ----------------------------------------------
 * Generated by Animista on 2022-9-3 12:0:51
 * Licensed under FreeBSD License.
 * See http://animista.net/license for more info.
 * w: http://animista.net, t: @cssanimista
 * ---------------------------------------------- */

/**
 * ----------------------------------------
 * animation fade-in
 * ----------------------------------------
 */
 @-webkit-keyframes fade-in {
    0% {
      opacity: 0;
    }
    100% {
      opacity: 1;
    }
  }
  @keyframes fade-in {
    0% {
      opacity: 0;
    }
    100% {
      opacity: 1;
    }
  }


/* CSS HEX */
:root {
--eerie-black: #141414ff;
--jet: #373737ff;
--white: #ffffffff;
--rajah: #faa549ff;
--tart-orange: #9c85fb;
--light-steel-blue: #b7d0f1ff;
}
/* Gallery items (not working) */
.gallery-item.svelte-1g9btlg.svelte-1g9btlg{
    border: none!important;
}
/* Loading background */
.dark .wrap.svelte-2fvq7v{
    background-color: #373737ff;
}

/* generate button */
#generate, #img2img_mask_btn, #img2img_edit_btn{
    transition: 0.3s;
    color: #9c85fb!important;
    border-color: #9c85fb!important;
}

#generate:hover, #img2img_mask_btn:hover, #img2img_edit_btn:hover{
    color: #141414ff!important;
    background-color: #9c85fb!important;
}


/* Generation paramters */
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(2){
    background: none!important;
    color: white!important;
}
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(4){
    background: none!important;
    color: white!important;
}
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(6){
    background: none!important;
    color: white!important;
}
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(8){
    background: none!important;
    color: white!important;
}
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(10){
    background: none!important;
    color: white!important;
}
#highlight > div.textfield.bg-white.dark\:bg-transparent.rounded-sm.text-sm.box-border.max-w-full.break-word.leading-7.mt-7 > span:nth-child(12){
    background: none!important;
    color: white!important;
}

/* Mask background */
.dark .bg-gray-200{
    background-color:rgba(55, 55, 55, 0.105)!important;
}
.cropper-wrap-box, .cropper-canvas{
    background-color:rgba(55, 55, 55, 0.105)!important;
}
.cropper-bg {
    background: none!important;
}

select {
    background:#000;
    color:#fff;
}

select * {
    background:#373737ff;
    color:#9c85fb;
}

/* General Background */
.gradio-container {background:#141414ff;}

/*General Text color on hover */
.dark .hover\:text-gray-700:hover{
    color: #9d85fb8a!important;
}
/*General Text color */
.text-gray-400{
    color:rgba(255, 255, 255, 0.504);
    transition: 0.3s;
}

/* General container of everything */
.dark .dark\:bg-gray-950 {background-color: #141414ff!important;
    -webkit-animation: fade-in 1s ease-in both;
	        animation: fade-in 1s ease-in both;
}
/* labels in frames of gallery */
.dark .dark\:bg-gray-900 {
background-color:#9d85fbdf!important;
border: none!important;}
/* Background for Gradio stuff along with colors for text */
.dark .gr-box {


    background-color:rgba(55, 55, 55, 0.105)!important;
    border: solid 0.5px!important;
    border-color: rgba(55, 55, 55, 0.055)!important;
    /* sampler selector color */
    color: #9c85fb!important;}

/* Secondary Buttons color */
.dark .gr-button-secondary{
    color: #9c85fb;
    border-color: #9d85fb5c;
    transition: 0.3s;}

.dark .gr-button-secondary:hover{
    color: #141414ff!important;
    background-color: #9c85fb!important;}

.dark .gr-button-primary{
    color: #9c85fb;
    border-color: #9d85fb5c;
    transition: 0.3s;}

.dark .gr-button-primary:hover{
    color: #141414ff!important;
    background-color: #9c85fb!important;}

/* image lab process button */
div[id*="111"]{
    width: 50% !important;
    align-self: center !important;
}
/* Selected tabs color */
button, input, optgroup, select, textarea {color: #9c85fb!important}

/* -or- text color wtf */
.text-gray-300{
    color:rgba(255, 255, 255, 0.504);
}

/* Sliders colors */
button, input, optgroup, select, textarea{
accent-color: #9c85fb!important;
}
/* Text color for placeholder in prompt */
input.scroll-hide.block.gr-box.gr-input.w-full.gr-text-input::placeholder{
    color: #9d85fb5c;
    transition: 0.3s;
}
/* disabling borders for stuff */
.border-gray-200{
    /* no border */
    border: none;
}

.border-b-2{
    /* no border */
    border: none;
}
/* disabling backgrounds for labels and buttons */
button, select, textarea {
    background: none!important;
}
/* radio selection border and background */
.dark .gr-input-label{
    background: none!important;
    border: none!important;
}
/* checkbox and radio buttons color when checked */
.dark .gr-check-radio:checked{
    background-color: #9c85fb!important;
}
/* checkbox and radio buttons color when unchecked */
.dark .gr-check-radio{
    background-color: #373737ff!important;
}


================================================
FILE: frontend/css/docs_custom.css
================================================
/**
 * Any CSS included here will be global. The classic template
 * bundles Infima by default. Infima is a CSS framework designed to
 * work well for content-centric websites.
 */

/* You can override the default Infima variables here. */
:root {
  --ifm-color-primary: #2e8555;
  --ifm-color-primary-dark: #29784c;
  --ifm-color-primary-darker: #277148;
  --ifm-color-primary-darkest: #205d3b;
  --ifm-color-primary-light: #33925d;
  --ifm-color-primary-lighter: #359962;
  --ifm-color-primary-lightest: #3cad6e;
  --ifm-code-font-size: 95%;
  --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.1);
}

/* For readability concerns, you should choose a lighter palette in dark mode. */
[data-theme='dark'] {
  --ifm-color-primary: #25c2a0;
  --ifm-color-primary-dark: #21af90;
  --ifm-color-primary-darker: #1fa588;
  --ifm-color-primary-darkest: #1a8870;
  --ifm-color-primary-light: #29d5b0;
  --ifm-color-primary-lighter: #32d8b4;
  --ifm-color-primary-lightest: #4fddbf;
  --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.3);
}


================================================
FILE: frontend/css/no_progress_bar.css
================================================
/*
This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

Copyright 2022 Sygil-Dev team.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
*/
.wrap .m-12 svg { display:none!important; }
.wrap .m-12::before { content:"Loading..." }
.progress-bar { display:none!important; }
.meta-text { display:none!important; }


================================================
FILE: frontend/css/streamlit.main.css
================================================
/*
This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

Copyright 2022 Sygil-Dev team.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
*/

/***********************************************************
* Additional CSS for streamlit builtin components          *
************************************************************/

/* Tab name (e.g. Text-to-Image) //improve legibility*/
button[data-baseweb="tab"] {
  font-size: 25px;
}

/* Image Container (only appear after run finished)//center the image, especially better looks in wide screen */
.css-1kyxreq{
	justify-content: center;
}


/* Streamlit header */
.css-1avcm0n {
  background-color: transparent;
}

/* Main streamlit container (below header) //reduce the empty spaces*/
.css-18e3th9 {
  padding-top: 1rem;
}



/***********************************************************
* Additional CSS for streamlit custom/3rd party components *
************************************************************/
/* For stream_on_hover */
section[data-testid="stSidebar"] > div:nth-of-type(1) {
  background-color: #111;
}

button[kind="header"] {
  background-color: transparent;
  color: rgb(180, 167, 141);
}

@media (hover) {
  /* header element */
  header[data-testid="stHeader"] {
    /* display: none;*/ /*suggested behavior by streamlit hover components*/
    pointer-events: none; /* disable interaction of the transparent background */
  }

  /* The button on the streamlit navigation menu  */
  button[kind="header"] {
    /* display: none;*/ /*suggested behavior by streamlit hover components*/
    pointer-events: auto; /* enable interaction of the button even if parents intereaction disabled */
  }

  /* added to avoid main sectors (all element to the right of sidebar from) moving */
  section[data-testid="stSidebar"] {
    width: 3.5% !important;
    min-width: 3.5% !important;
  }

  /* The navigation menu specs and size */
  section[data-testid="stSidebar"] > div {
    height: 100%;
    width: 2% !important;
    min-width: 100% !important;
    position: relative;
    z-index: 1;
    top: 0;
    left: 0;
    background-color: #111;
    overflow-x: hidden;
    transition: 0.5s ease-in-out;
    padding-top: 0px;
    white-space: nowrap;
  }

  /* The navigation menu open and close on hover and size */
  section[data-testid="stSidebar"] > div:hover {
    width: 300px !important;
  }
}

@media (max-width: 272px) {
  section[data-testid="stSidebar"] > div {
    width: 15rem;
  }
}

/***********************************************************
*  Additional CSS for other elements
************************************************************/
button[data-baseweb="tab"] {
  font-size: 20px;
}

@media (min-width: 1200px){
h1 {
    font-size: 1.75rem;
}
}
#tabs-1-tabpanel-0 > div:nth-child(1) > div > div.stTabs.css-0.exp6ofz0 {
  width: 50rem;
  align-self: center;
}
div.gallery:hover {
  border: 1px solid #777;
}
.css-dg4u6x p {
  font-size: 0.8rem;
  text-align: center;
  position: relative;
  top: 6px;
}

.row-widget.stButton {
  text-align: center;
}

/********************************************************************
    Hide anchor links on titles
*********************************************************************/
/*
.css-15zrgzn {
	display: none
	}
.css-eczf16 {
	display: none
	}
.css-jn99sy {
	display: none
	}

/* Make the text area widget have a similar height as the text input field */
.st-dy{
	height: 54px;
	min-height: 25px;
}
.css-17useex{
	gap: 3px;

}

/* Remove some empty spaces to make the UI more compact. */
.css-18e3th9{
	padding-left: 10px;
	padding-right: 30px;
	position: unset !important; /* Fixes the layout/page going up when an expander or another item is expanded and then collapsed */
}
.css-k1vhr4{
	padding-top: initial;
}
.css-ret2ud{
	padding-left: 10px;
	padding-right: 30px;
	gap: initial;
	display: initial;
}

.css-w5z5an{
	gap: 1px;
}


================================================
FILE: frontend/css/styles.css
================================================
/*
This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

Copyright 2022 Sygil-Dev team.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
*/
[data-testid="image"] {min-height: 512px !important}
* #body>.col:nth-child(2){
    width:250%;
    max-width:89vw
}

#increment_btn_minus {
    align-self: center;
    background: none;
    border: none;
}
#increment_btn_plus {
    align-self: center;
    background: none;
    border: none;
}
#prompt_input, #img2img_prompt_input {
    padding: 0px;
    border: none;
}

#prompt_row input,
#prompt_row textarea {
    font-size: 1.2rem;
    line-height: 1.6rem;
}

input[type=number]:disabled { -moz-appearance: textfield; }

#generate, #img2img_mask_btn, #img2img_edit_btn {
    align-self: stretch;
}

#gan_image {
    width: 512px;
    height: auto;
}

[id$="highlight"]{
 font-size: 1.2rem
 }
[id$="highlight"] .uppercase{
    text-transform: initial;

}
[id$="highlight"] .textfield .textspan:nth-child(1){
    font-size: 1.2rem
}

/* Mobile fixes and styles */
@media only screen and (max-width: 768px) {
    /* rearrange the columns on mobile */

    #body>.col:nth-child(2){
        width:100%;
        max-width:100vw;
        order: 1;
    }
    #body>.col:nth-child(1){
        order: 2;
    }
    #body>.col:nth-child(3){
        order: 3;
    }

    #txt2img_actions_row,
    #txt2img_output_row{
        flex-direction: row;
        flex-wrap: wrap-reverse;
    }
    /* fix buttons layouts */

}

/* Gradio 3.4 FIXES */
#prompt_row button {
    max-width: 20ch;
}
#text2img_col2 {
    flex-grow: 2 !important;
}


================================================
FILE: frontend/css_and_js.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
from os import path
import json


def readTextFile(*args):
    dir = path.dirname(__file__)
    entry = path.join(dir, *args)
    with open(entry, "r", encoding="utf8") as f:
        data = f.read()
    return data


def css(opt):
    styling = readTextFile("css", "styles.css")
    if not opt.no_progressbar_hiding:
        styling += readTextFile("css", "no_progress_bar.css")
    return styling


def js(opt):
    data = readTextFile("js", "index.js")
    data = "(z) => {" + data + "; return z ?? [] }"
    return data


# Wrap the typical SD method call into async closure for ease of use
# Supplies the js function with a params object
# That includes all the passed arguments and input from Gradio: x
# ATTENTION: x is an array of values of all components passed to your
# python event handler
# Example call in Gradio component's event handler (pass the result to _js arg):
# _js=call_JS("myJsMethod", arg1="string", arg2=100, arg3=[])
def call_JS(sd_method, **kwargs):
    param_str = json.dumps(kwargs)
    return f"async (...x) => {{ return await SD.{sd_method}({{ x, ...{param_str} }}) ?? []; }}"


================================================
FILE: frontend/dists/concept-browser/dist/assets/index.3ab9729b.css
================================================
svg.no-preview-icon{fill:var(--text-color);width:72px;height:72px;opacity:.2}.no-preview{align-self:center;text-align:center;color:var(--text-color)}.concept-card{background-color:var(--secondary-background-color);border-radius:5px;margin-bottom:20px}.concept-card-content-wrapper{flex-direction:column!important;display:flex!important;height:360px}.concept-title{margin-top:0;margin-bottom:24px;font-size:1em;color:var(--text-color)}.concept-img-wrapper{flex-grow:1!important}.card-favorite{text-align:end}.concept-img{max-height:100%;height:100%}.concept-img img{border-radius:8px;object-fit:cover}.img-bg{background-size:cover;background-position:center;background-origin:content-box;background-repeat:no-repeat;height:100%;width:100%;border-radius:8px}.icon-star{cursor:pointer;position:relative;top:-3px}.token-char{color:#939393;font-weight:700;position:relative;top:1px}.concept-card-footer{align-items:center}.concept-type-tag{background-color:#898989;border-radius:16px;padding:5px 16px;font-size:.7em;color:#fff;display:inline-block;font-weight:700}.concept-type-style{background-color:#0095ff}.concept-type-object{background-color:#ff9031}.button{height:35px;cursor:pointer;display:inline-flex;-webkit-box-align:center;align-items:center;-webkit-box-pack:center;justify-content:center;font-weight:400;padding:.25rem .75rem;border-radius:.25rem;margin:0;line-height:1.6;color:inherit;width:auto;user-select:none;background-color:var(--background-color);border:1px solid rgba(128,128,128,.8)}.button:hover{color:var(--primary-color);border-color:var(--primary-color)}.err__title[data-v-4d5bb78e],.err__msg[data-v-4d5bb78e]{margin:0}body,html{margin:0;font-family:Source Sans Pro,sans-serif}/*!
 * Bootstrap Grid Only (grid and responsive utilities extracted from Bootstrap 4.1.3)
 * GitHub: https://github.com/dmhendricks/bootstrap-grid-css
 * Bootstrap License: MIT (https://github.com/twbs/bootstrap/blob/v4-dev/LICENSE)
 * Credits: Twitter, Inc. & The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
 */@-ms-viewport{width:device-width}html{-webkit-box-sizing:border-box;box-sizing:border-box;-ms-overflow-style:scrollbar}*,*:before,*:after{-webkit-box-sizing:inherit;box-sizing:inherit}.bootstrap-wrapper .clearfix:after{display:block;clear:both;content:""}.bootstrap-wrapper .visible{visibility:visible!important}.bootstrap-wrapper .invisible{visibility:hidden!important}.bootstrap-wrapper .hidden-xs-up{display:none!important}@media (max-width:575.98px){.bootstrap-wrapper .hidden-xs-down{display:none!important}}@media (min-width:576px){.bootstrap-wrapper .hidden-sm-up{display:none!important}}@media (max-width:767.98px){.bootstrap-wrapper .hidden-sm-down{display:none!important}}@media (min-width:768px){.bootstrap-wrapper .hidden-md-up{display:none!important}}@media (max-width:991.98px){.bootstrap-wrapper .hidden-md-down{display:none!important}}@media (min-width:992px){.bootstrap-wrapper .hidden-lg-up{display:none!important}}@media (max-width:1199.98px){.bootstrap-wrapper .hidden-lg-down{display:none!important}}@media (min-width:1200px){.bootstrap-wrapper .hidden-xl-up{display:none!important}}.bootstrap-wrapper .hidden-xl-down,.bootstrap-wrapper .visible-print-block{display:none!important}@media print{.bootstrap-wrapper .visible-print-block{display:block!important}}.bootstrap-wrapper .visible-print-inline{display:none!important}@media print{.bootstrap-wrapper .visible-print-inline{display:inline!important}}.bootstrap-wrapper .visible-print-inline-block{display:none!important}@media print{.bootstrap-wrapper .visible-print-inline-block{display:inline-block!important}}@media print{.bootstrap-wrapper .hidden-print{display:none!important}}.bootstrap-wrapper .container{width:100%;padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:576px){.bootstrap-wrapper .container{max-width:540px}}@media (min-width:768px){.bootstrap-wrapper .container{max-width:720px}}@media (min-width:992px){.bootstrap-wrapper .container{max-width:960px}}@media (min-width:1200px){.bootstrap-wrapper .container{max-width:1140px}}.bootstrap-wrapper .container-fluid{width:100%;padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.bootstrap-wrapper .row{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;margin-right:-15px;margin-left:-15px}.bootstrap-wrapper .no-gutters{margin-right:0;margin-left:0}.bootstrap-wrapper .no-gutters>.col,.bootstrap-wrapper .no-gutters>[class*=col-]{padding-right:0;padding-left:0}.bootstrap-wrapper .col-1,.bootstrap-wrapper .col-2,.bootstrap-wrapper .col-3,.bootstrap-wrapper .col-4,.bootstrap-wrapper .col-5,.bootstrap-wrapper .col-6,.bootstrap-wrapper .col-7,.bootstrap-wrapper .col-8,.bootstrap-wrapper .col-9,.bootstrap-wrapper .col-10,.bootstrap-wrapper .col-11,.bootstrap-wrapper .col-12,.bootstrap-wrapper .col,.bootstrap-wrapper .col-auto,.bootstrap-wrapper .col-sm-1,.bootstrap-wrapper .col-sm-2,.bootstrap-wrapper .col-sm-3,.bootstrap-wrapper .col-sm-4,.bootstrap-wrapper .col-sm-5,.bootstrap-wrapper .col-sm-6,.bootstrap-wrapper .col-sm-7,.bootstrap-wrapper .col-sm-8,.bootstrap-wrapper .col-sm-9,.bootstrap-wrapper .col-sm-10,.bootstrap-wrapper .col-sm-11,.bootstrap-wrapper .col-sm-12,.bootstrap-wrapper .col-sm,.bootstrap-wrapper .col-sm-auto,.bootstrap-wrapper .col-md-1,.bootstrap-wrapper .col-md-2,.bootstrap-wrapper .col-md-3,.bootstrap-wrapper .col-md-4,.bootstrap-wrapper .col-md-5,.bootstrap-wrapper .col-md-6,.bootstrap-wrapper .col-md-7,.bootstrap-wrapper .col-md-8,.bootstrap-wrapper .col-md-9,.bootstrap-wrapper .col-md-10,.bootstrap-wrapper .col-md-11,.bootstrap-wrapper .col-md-12,.bootstrap-wrapper .col-md,.bootstrap-wrapper .col-md-auto,.bootstrap-wrapper .col-lg-1,.bootstrap-wrapper .col-lg-2,.bootstrap-wrapper .col-lg-3,.bootstrap-wrapper .col-lg-4,.bootstrap-wrapper .col-lg-5,.bootstrap-wrapper .col-lg-6,.bootstrap-wrapper .col-lg-7,.bootstrap-wrapper .col-lg-8,.bootstrap-wrapper .col-lg-9,.bootstrap-wrapper .col-lg-10,.bootstrap-wrapper .col-lg-11,.bootstrap-wrapper .col-lg-12,.bootstrap-wrapper .col-lg,.bootstrap-wrapper .col-lg-auto,.bootstrap-wrapper .col-xl-1,.bootstrap-wrapper .col-xl-2,.bootstrap-wrapper .col-xl-3,.bootstrap-wrapper .col-xl-4,.bootstrap-wrapper .col-xl-5,.bootstrap-wrapper .col-xl-6,.bootstrap-wrapper .col-xl-7,.bootstrap-wrapper .col-xl-8,.bootstrap-wrapper .col-xl-9,.bootstrap-wrapper .col-xl-10,.bootstrap-wrapper .col-xl-11,.bootstrap-wrapper .col-xl-12,.bootstrap-wrapper .col-xl,.bootstrap-wrapper .col-xl-auto{position:relative;width:100%;padding-right:15px;padding-left:15px}.bootstrap-wrapper .col{-webkit-flex-basis:0;-ms-flex-preferred-size:0;flex-basis:0;-webkit-box-flex:1;-webkit-flex-grow:1;-ms-flex-positive:1;flex-grow:1;max-width:100%}.bootstrap-wrapper .col-auto{-webkit-box-flex:0;-webkit-flex:0 0 auto;-ms-flex:0 0 auto;flex:0 0 auto;width:auto;max-width:100%}.bootstrap-wrapper .col-1{-webkit-box-flex:0;-webkit-flex:0 0 8.3333333333%;-ms-flex:0 0 8.3333333333%;flex:0 0 8.3333333333%;max-width:8.3333333333%}.bootstrap-wrapper .col-2{-webkit-box-flex:0;-webkit-flex:0 0 16.6666666667%;-ms-flex:0 0 16.6666666667%;flex:0 0 16.6666666667%;max-width:16.6666666667%}.bootstrap-wrapper .col-3{-webkit-box-flex:0;-webkit-flex:0 0 25%;-ms-flex:0 0 25%;flex:0 0 25%;max-width:25%}.bootstrap-wrapper .col-4{-webkit-box-flex:0;-webkit-flex:0 0 33.3333333333%;-ms-flex:0 0 33.3333333333%;flex:0 0 33.3333333333%;max-width:33.3333333333%}.bootstrap-wrapper .col-5{-webkit-box-flex:0;-webkit-flex:0 0 41.6666666667%;-ms-flex:0 0 41.6666666667%;flex:0 0 41.6666666667%;max-width:41.6666666667%}.bootstrap-wrapper .col-6{-webkit-box-flex:0;-webkit-flex:0 0 50%;-ms-flex:0 0 50%;flex:0 0 50%;max-width:50%}.bootstrap-wrapper .col-7{-webkit-box-flex:0;-webkit-flex:0 0 58.3333333333%;-ms-flex:0 0 58.3333333333%;flex:0 0 58.3333333333%;max-width:58.3333333333%}.bootstrap-wrapper .col-8{-webkit-box-flex:0;-webkit-flex:0 0 66.6666666667%;-ms-flex:0 0 66.6666666667%;flex:0 0 66.6666666667%;max-width:66.6666666667%}.bootstrap-wrapper .col-9{-webkit-box-flex:0;-webkit-flex:0 0 75%;-ms-flex:0 0 75%;flex:0 0 75%;max-width:75%}.bootstrap-wrapper .col-10{-webkit-box-flex:0;-webkit-flex:0 0 83.3333333333%;-ms-flex:0 0 83.3333333333%;flex:0 0 83.3333333333%;max-width:83.3333333333%}.bootstrap-wrapper .col-11{-webkit-box-flex:0;-webkit-flex:0 0 91.6666666667%;-ms-flex:0 0 91.6666666667%;flex:0 0 91.6666666667%;max-width:91.6666666667%}.bootstrap-wrapper .col-12{-webkit-box-flex:0;-webkit-flex:0 0 100%;-ms-flex:0 0 100%;flex:0 0 100%;max-width:100%}.bootstrap-wrapper .order-first{-webkit-box-ordinal-group:0;-webkit-order:-1;-ms-flex-order:-1;order:-1}.bootstrap-wrapper .order-last{-webkit-box-ordinal-group:14;-webkit-order:13;-ms-flex-order:13;order:13}.bootstrap-wrapper .order-0{-webkit-box-ordinal-group:1;-webkit-order:0;-ms-flex-order:0;order:0}.bootstrap-wrapper .order-1{-webkit-box-ordinal-group:2;-webkit-order:1;-ms-flex-order:1;order:1}.bootstrap-wrapper .order-2{-webkit-box-ordinal-group:3;-webkit-order:2;-ms-flex-order:2;order:2}.bootstrap-wrapper .order-3{-webkit-box-ordinal-group:4;-webkit-order:3;-ms-flex-order:3;order:3}.bootstrap-wrapper .order-4{-webkit-box-ordinal-group:5;-webkit-order:4;-ms-flex-order:4;order:4}.bootstrap-wrapper .order-5{-webkit-box-ordinal-group:6;-webkit-order:5;-ms-flex-order:5;order:5}.bootstrap-wrapper .order-6{-webkit-box-ordinal-group:7;-webkit-order:6;-ms-flex-order:6;order:6}.bootstrap-wrapper .order-7{-webkit-box-ordinal-group:8;-webkit-order:7;-ms-flex-order:7;order:7}.bootstrap-wrapper .order-8{-webkit-box-ordinal-group:9;-webkit-order:8;-ms-flex-order:8;order:8}.bootstrap-wrapper .order-9{-webkit-box-ordinal-group:10;-webkit-order:9;-ms-flex-order:9;order:9}.bootstrap-wrapper .order-10{-webkit-box-ordinal-group:11;-webkit-order:10;-ms-flex-order:10;order:10}.bootstrap-wrapper 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t<=Wg&&(n=this.nullBitmap)&&(this._nullCount=t=this.length-$s(n,this.offset,this.offset+this.length)),t}clone(t,n=this.offset,r=this.length,i=this._nullCount,o=this,s=this.childData){return new L(t,n,r,i,o,s,this.dictionary)}slice(t,n){const{stride:r,typeId:i,childData:o}=this,s=+(this._nullCount===0)-1,a=i===16?r:1,c=this._sliceBuffers(t,n,r,i);return this.clone(this.type,this.offset+t,n,s,c,!o.length||this.valueOffsets?o:this._sliceChildren(o,a*t,a*n))}_changeLengthAndBackfillNullBitmap(t){if(this.typeId===y.Null)return this.clone(this.type,0,t,0);const{length:n,nullCount:r}=this,i=new Uint8Array((t+63&-64)>>3).fill(255,0,n>>3);i[n>>3]=(1<<n-(n&-8))-1,r>0&&i.set(vo(this.offset,n,this.nullBitmap),0);const o=this.buffers;return o[V.VALIDITY]=i,this.clone(this.type,0,t,r+(t-n),o)}_sliceBuffers(t,n,r,i){let o,{buffers:s}=this;return(o=s[V.TYPE])&&(s[V.TYPE]=o.subarray(t,t+n)),(o=s[V.OFFSET])&&(s[V.OFFSET]=o.subarray(t,t+n+1))||(o=s[V.DATA])&&(s[V.DATA]=i===6?o:o.subarray(r*t,r*(t+n))),s}_sliceChildren(t,n,r){return t.map(i=>i.slice(n,r))}static new(t,n,r,i,o,s,a){switch(o instanceof L?o=o.buffers:o||(o=[]),t.typeId){case y.Null:return L.Null(t,n,r);case y.Int:return L.Int(t,n,r,i||0,o[V.VALIDITY],o[V.DATA]||[]);case y.Dictionary:return L.Dictionary(t,n,r,i||0,o[V.VALIDITY],o[V.DATA]||[],a);case y.Float:return L.Float(t,n,r,i||0,o[V.VALIDITY],o[V.DATA]||[]);case y.Bool:return L.Bool(t,n,r,i||0,o[V.VALIDITY],o[V.DATA]||[]);case y.Decimal:return L.Decimal(t,n,r,i||0,o[V.VALIDITY],o[V.DATA]||[]);case y.Date:return L.Date(t,n,r,i||0,o[V.VALIDITY],o[V.DATA]||[]);case y.Time:return L.Time(t,n,r,i||0,o[V.VALIDITY],o[V.DATA]||[]);case y.Timestamp:return L.Timestamp(t,n,r,i||0,o[V.VALIDITY],o[V.DATA]||[]);case y.Interval:return L.Interval(t,n,r,i||0,o[V.VALIDITY],o[V.DATA]||[]);case 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L(t,n,r,i,[Pr(s),void 0,lt(o)],[a])}static Union(t,n,r,i,o,s,a,c){const l=[void 0,void 0,lt(o),at(t.ArrayType,s)];return t.mode===xn.Sparse?new L(t,n,r,i,l,a):(l[V.OFFSET]=Pr(a),new L(t,n,r,i,l,c))}}L.prototype.childData=Object.freeze([]);const Yg=void 0;function vi(e){if(e===null)return"null";if(e===Yg)return"undefined";switch(typeof e){case"number":return`${e}`;case"bigint":return`${e}`;case"string":return`"${e}"`}return typeof e[Symbol.toPrimitive]=="function"?e[Symbol.toPrimitive]("string"):ArrayBuffer.isView(e)?`[${e}]`:JSON.stringify(e)}function Hg(e){if(!e||e.length<=0)return function(i){return!0};let t="",n=e.filter(r=>r===r);return n.length>0&&(t=`
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this.ArrayType(t),this.buffer)}}Ji.prototype.offset=0;class Gi extends Ji{last(){return this.get(this.length-1)}get(t){return this.buffer[t]}set(t,n){return this.reserve(t-this.length+1),this.buffer[t*this.stride]=n,this}}class fd extends Gi{constructor(t=new Uint8Array(0)){super(t,1/8),this.numValid=0}get numInvalid(){return this.length-this.numValid}get(t){return this.buffer[t>>3]>>t%8&1}set(t,n){const{buffer:r}=this.reserve(t-this.length+1),i=t>>3,o=t%8,s=r[i]>>o&1;return n?s===0&&(r[i]|=1<<o,++this.numValid):s===1&&(r[i]&=~(1<<o),--this.numValid),this}clear(){return this.numValid=0,super.clear()}}class dd extends Gi{constructor(t=new Int32Array(1)){super(t,1)}append(t){return this.set(this.length-1,t)}set(t,n){const r=this.length-1,i=this.reserve(t-r+1).buffer;return r<t++&&i.fill(i[r],r,t),i[t]=i[t-1]+n,this}flush(t=this.length-1){return t>this.length&&this.set(t-1,0),super.flush(t+1)}}class hd extends Ji{get ArrayType64(){return this._ArrayType64||(this._ArrayType64=this.buffer instanceof Int32Array?li:Wi)}set(t,n){switch(this.reserve(t-this.length+1),typeof n){case"bigint":this.buffer64[t]=n;break;case"number":this.buffer[t*this.stride]=n;break;default:this.buffer.set(n,t*this.stride)}return this}_resize(t){const n=super._resize(t),r=n.byteLength/(this.BYTES_PER_ELEMENT*this.stride);return _o&&(this.buffer64=new this.ArrayType64(n.buffer,n.byteOffset,r)),n}}class Ut{constructor({type:t,nullValues:n}){this.length=0,this.finished=!1,this.type=t,this.children=[],this.nullValues=n,this.stride=ud(t),this._nulls=new fd,n&&n.length>0&&(this._isValid=Hg(n))}static new(t){}static throughNode(t){throw new Error('"throughNode" not available in this environment')}static throughDOM(t){throw new Error('"throughDOM" not available in this environment')}static throughIterable(t){return Jg(t)}static throughAsyncIterable(t){return Gg(t)}toVector(){return dt.new(this.flush())}get ArrayType(){return this.type.ArrayType}get nullCount(){return this._nulls.numInvalid}get numChildren(){return this.children.length}get byteLength(){let t=0;return this._offsets&&(t+=this._offsets.byteLength),this._values&&(t+=this._values.byteLength),this._nulls&&(t+=this._nulls.byteLength),this._typeIds&&(t+=this._typeIds.byteLength),this.children.reduce((n,r)=>n+r.byteLength,t)}get reservedLength(){return this._nulls.reservedLength}get reservedByteLength(){let t=0;return this._offsets&&(t+=this._offsets.reservedByteLength),this._values&&(t+=this._values.reservedByteLength),this._nulls&&(t+=this._nulls.reservedByteLength),this._typeIds&&(t+=this._typeIds.reservedByteLength),this.children.reduce((n,r)=>n+r.reservedByteLength,t)}get valueOffsets(){return this._offsets?this._offsets.buffer:null}get values(){return this._values?this._values.buffer:null}get nullBitmap(){return this._nulls?this._nulls.buffer:null}get typeIds(){return this._typeIds?this._typeIds.buffer:null}append(t){return this.set(this.length,t)}isValid(t){return this._isValid(t)}set(t,n){return this.setValid(t,this.isValid(n))&&this.setValue(t,n),this}setValue(t,n){this._setValue(this,t,n)}setValid(t,n){return this.length=this._nulls.set(t,+n).length,n}addChild(t,n=`${this.numChildren}`){throw new Error(`Cannot append children to non-nested type "${this.type}"`)}getChildAt(t){return this.children[t]||null}flush(){const t=[],n=this._values,r=this._offsets,i=this._typeIds,{length:o,nullCount:s}=this;i?(t[V.TYPE]=i.flush(o),r&&(t[V.OFFSET]=r.flush(o))):r?(n&&(t[V.DATA]=n.flush(r.last())),t[V.OFFSET]=r.flush(o)):n&&(t[V.DATA]=n.flush(o)),s>0&&(t[V.VALIDITY]=this._nulls.flush(o));const a=L.new(this.type,0,o,s,t,this.children.map(c=>c.flush()));return this.clear(),a}finish(){return this.finished=!0,this.children.forEach(t=>t.finish()),this}clear(){return this.length=0,this._offsets&&this._offsets.clear(),this._values&&this._values.clear(),this._nulls&&this._nulls.clear(),this._typeIds&&this._typeIds.clear(),this.children.forEach(t=>t.clear()),this}}Ut.prototype.length=1;Ut.prototype.stride=1;Ut.prototype.children=null;Ut.prototype.finished=!1;Ut.prototype.nullValues=null;Ut.prototype._isValid=()=>!0;class er extends Ut{constructor(t){super(t),this._values=new Gi(new this.ArrayType(0),this.stride)}setValue(t,n){const r=this._values;return r.reserve(t-r.length+1),super.setValue(t,n)}}class Ao extends Ut{constructor(t){super(t),this._pendingLength=0,this._offsets=new dd}setValue(t,n){const r=this._pending||(this._pending=new Map),i=r.get(t);i&&(this._pendingLength-=i.length),this._pendingLength+=n.length,r.set(t,n)}setValid(t,n){return super.setValid(t,n)?!0:((this._pending||(this._pending=new Map)).set(t,void 0),!1)}clear(){return this._pendingLength=0,this._pending=void 0,super.clear()}flush(){return this._flush(),super.flush()}finish(){return this._flush(),super.finish()}_flush(){const t=this._pending,n=this._pendingLength;return this._pendingLength=0,this._pending=void 0,t&&t.size>0&&this._flushPending(t,n),this}}function Jg(e){const{["queueingStrategy"]:t="count"}=e,{["highWaterMark"]:n=t!=="bytes"?1e3:2**14}=e,r=t!=="bytes"?"length":"byteLength";return function*(i){let o=0,s=Ut.new(e);for(const a of i)s.append(a)[r]>=n&&++o&&(yield s.toVector());(s.finish().length>0||o===0)&&(yield s.toVector())}}function Gg(e){const{["queueingStrategy"]:t="count"}=e,{["highWaterMark"]:n=t!=="bytes"?1e3:2**14}=e,r=t!=="bytes"?"length":"byteLength";return async function*(i){let o=0,s=Ut.new(e);for await(const a of i)s.append(a)[r]>=n&&++o&&(yield s.toVector());(s.finish().length>0||o===0)&&(yield s.toVector())}}class Zg extends Ut{constructor(t){super(t),this._values=new fd}setValue(t,n){this._values.set(t,+n)}}class Xg extends Ut{setValue(t,n){}setValid(t,n){return 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Object.create(this.source.throw&&this.source.throw(t)||Rt)}return(t){return Object.create(this.source.return&&this.source.return(t)||Rt)}}class cr{constructor(t){this.source=t,this._closedPromise=new Promise(n=>this._closedPromiseResolve=n)}async cancel(t){await this.return(t)}get closed(){return this._closedPromise}async read(t){return(await this.next(t,"read")).value}async peek(t){return(await this.next(t,"peek")).value}async next(t,n="read"){return await this.source.next({cmd:n,size:t})}async throw(t){const n=this.source.throw&&await this.source.throw(t)||Rt;return this._closedPromiseResolve&&this._closedPromiseResolve(),this._closedPromiseResolve=void 0,Object.create(n)}async return(t){const n=this.source.return&&await this.source.return(t)||Rt;return this._closedPromiseResolve&&this._closedPromiseResolve(),this._closedPromiseResolve=void 0,Object.create(n)}}class uu extends qs{constructor(t,n){super(),this.position=0,this.buffer=lt(t),this.size=typeof n=="undefined"?this.buffer.byteLength:n}readInt32(t){const{buffer:n,byteOffset:r}=this.readAt(t,4);return new DataView(n,r).getInt32(0,!0)}seek(t){return this.position=Math.min(t,this.size),t<this.size}read(t){const{buffer:n,size:r,position:i}=this;return n&&i<r?(typeof t!="number"&&(t=1/0),this.position=Math.min(r,i+Math.min(r-i,t)),n.subarray(i,this.position)):null}readAt(t,n){const r=this.buffer,i=Math.min(this.size,t+n);return r?r.subarray(t,i):new Uint8Array(n)}close(){this.buffer&&(this.buffer=null)}throw(t){return this.close(),{done:!0,value:t}}return(t){return this.close(),{done:!0,value:t}}}class Js extends Ar{constructor(t,n){super(),this.position=0,this._handle=t,typeof n=="number"?this.size=n:this._pending=(async()=>{this.size=(await t.stat()).size,delete this._pending})()}async readInt32(t){const{buffer:n,byteOffset:r}=await this.readAt(t,4);return new DataView(n,r).getInt32(0,!0)}async seek(t){return this._pending&&await this._pending,this.position=Math.min(t,this.size),t<this.size}async read(t){this._pending&&await this._pending;const{_handle:n,size:r,position:i}=this;if(n&&i<r){typeof t!="number"&&(t=1/0);let o=i,s=0,a=0,c=Math.min(r,o+Math.min(r-o,t)),l=new Uint8Array(Math.max(0,(this.position=c)-o));for(;(o+=a)<c&&(s+=a)<l.byteLength;)({bytesRead:a}=await n.read(l,s,l.byteLength-s,o));return l}return null}async readAt(t,n){this._pending&&await this._pending;const{_handle:r,size:i}=this;if(r&&t+n<i){const o=Math.min(i,t+n),s=new Uint8Array(o-t);return(await r.read(s,0,n,t)).buffer}return new Uint8Array(n)}async close(){const t=this._handle;this._handle=null,t&&await t.close()}async throw(t){return await this.close(),{done:!0,value:t}}async return(t){return await this.close(),{done:!0,value:t}}}const Bw=1<<16;function jr(e){return e<0&&(e=4294967295+e+1),`0x${e.toString(16)}`}const ci=8,Lc=[1,10,100,1e3,1e4,1e5,1e6,1e7,1e8];class Mc{constructor(t){this.buffer=t}high(){return this.buffer[1]}low(){return this.buffer[0]}_times(t){const n=new Uint32Array([this.buffer[1]>>>16,this.buffer[1]&65535,this.buffer[0]>>>16,this.buffer[0]&65535]),r=new Uint32Array([t.buffer[1]>>>16,t.buffer[1]&65535,t.buffer[0]>>>16,t.buffer[0]&65535]);let i=n[3]*r[3];this.buffer[0]=i&65535;let o=i>>>16;return i=n[2]*r[3],o+=i,i=n[3]*r[2]>>>0,o+=i,this.buffer[0]+=o<<16,this.buffer[1]=o>>>0<i?Bw:0,this.buffer[1]+=o>>>16,this.buffer[1]+=n[1]*r[3]+n[2]*r[2]+n[3]*r[1],this.buffer[1]+=n[0]*r[3]+n[1]*r[2]+n[2]*r[1]+n[3]*r[0]<<16,this}_plus(t){const n=this.buffer[0]+t.buffer[0]>>>0;this.buffer[1]+=t.buffer[1],n<this.buffer[0]>>>0&&++this.buffer[1],this.buffer[0]=n}lessThan(t){return this.buffer[1]<t.buffer[1]||this.buffer[1]===t.buffer[1]&&this.buffer[0]<t.buffer[0]}equals(t){return this.buffer[1]===t.buffer[1]&&this.buffer[0]==t.buffer[0]}greaterThan(t){return t.lessThan(this)}hex(){return`${jr(this.buffer[1])} ${jr(this.buffer[0])}`}}class wt extends Mc{times(t){return this._times(t),this}plus(t){return this._plus(t),this}static from(t,n=new Uint32Array(2)){return wt.fromString(typeof t=="string"?t:t.toString(),n)}static fromNumber(t,n=new Uint32Array(2)){return wt.fromString(t.toString(),n)}static fromString(t,n=new Uint32Array(2)){const r=t.length;let i=new wt(n);for(let o=0;o<r;){const s=ci<r-o?ci:r-o,a=new wt(new Uint32Array([parseInt(t.substr(o,s),10),0])),c=new wt(new Uint32Array([Lc[s],0]));i.times(c),i.plus(a),o+=s}return i}static convertArray(t){const n=new Uint32Array(t.length*2);for(let r=-1,i=t.length;++r<i;)wt.from(t[r],new Uint32Array(n.buffer,n.byteOffset+2*r*4,2));return n}static multiply(t,n){return new wt(new Uint32Array(t.buffer)).times(n)}static add(t,n){return new wt(new Uint32Array(t.buffer)).plus(n)}}class ne extends Mc{negate(){return this.buffer[0]=~this.buffer[0]+1,this.buffer[1]=~this.buffer[1],this.buffer[0]==0&&++this.buffer[1],this}times(t){return this._times(t),this}plus(t){return this._plus(t),this}lessThan(t){const n=this.buffer[1]<<0,r=t.buffer[1]<<0;return n<r||n===r&&this.buffer[0]<t.buffer[0]}static from(t,n=new Uint32Array(2)){return ne.fromString(typeof t=="string"?t:t.toString(),n)}static fromNumber(t,n=new Uint32Array(2)){return ne.fromString(t.toString(),n)}static fromString(t,n=new Uint32Array(2)){const r=t.startsWith("-"),i=t.length;let o=new ne(n);for(let s=r?1:0;s<i;){const a=ci<i-s?ci:i-s,c=new ne(new Uint32Array([parseInt(t.substr(s,a),10),0])),l=new ne(new Uint32Array([Lc[a],0]));o.times(l),o.plus(c),s+=a}return r?o.negate():o}static convertArray(t){const n=new Uint32Array(t.length*2);for(let r=-1,i=t.length;++r<i;)ne.from(t[r],new Uint32Array(n.buffer,n.byteOffset+2*r*4,2));return n}static multiply(t,n){return new ne(new Uint32Array(t.buffer)).times(n)}static add(t,n){return new ne(new Uint32Array(t.buffer)).plus(n)}}class Ue{constructor(t){this.buffer=t}high(){return new ne(new Uint32Array(this.buffer.buffer,this.buffer.byteOffset+8,2))}low(){return new ne(new Uint32Array(this.buffer.buffer,this.buffer.byteOffset,2))}negate(){return this.buffer[0]=~this.buffer[0]+1,this.buffer[1]=~this.buffer[1],this.buffer[2]=~this.buffer[2],this.buffer[3]=~this.buffer[3],this.buffer[0]==0&&++this.buffer[1],this.buffer[1]==0&&++this.buffer[2],this.buffer[2]==0&&++this.buffer[3],this}times(t){const n=new wt(new Uint32Array([this.buffer[3],0])),r=new wt(new Uint32Array([this.buffer[2],0])),i=new wt(new Uint32Array([this.buffer[1],0])),o=new wt(new Uint32Array([this.buffer[0],0])),s=new wt(new Uint32Array([t.buffer[3],0])),a=new wt(new Uint32Array([t.buffer[2],0])),c=new wt(new Uint32Array([t.buffer[1],0])),l=new wt(new Uint32Array([t.buffer[0],0]));let d=wt.multiply(o,l);this.buffer[0]=d.low();let p=new wt(new Uint32Array([d.high(),0]));return d=wt.multiply(i,l),p.plus(d),d=wt.multiply(o,c),p.plus(d),this.buffer[1]=p.low(),this.buffer[3]=p.lessThan(d)?1:0,this.buffer[2]=p.high(),new wt(new Uint32Array(this.buffer.buffer,this.buffer.byteOffset+8,2)).plus(wt.multiply(r,l)).plus(wt.multiply(i,c)).plus(wt.multiply(o,a)),this.buffer[3]+=wt.multiply(n,l).plus(wt.multiply(r,c)).plus(wt.multiply(i,a)).plus(wt.multiply(o,s)).low(),this}plus(t){let n=new Uint32Array(4);return n[3]=this.buffer[3]+t.buffer[3]>>>0,n[2]=this.buffer[2]+t.buffer[2]>>>0,n[1]=this.buffer[1]+t.buffer[1]>>>0,n[0]=this.buffer[0]+t.buffer[0]>>>0,n[0]<this.buffer[0]>>>0&&++n[1],n[1]<this.buffer[1]>>>0&&++n[2],n[2]<this.buffer[2]>>>0&&++n[3],this.buffer[3]=n[3],this.buffer[2]=n[2],this.buffer[1]=n[1],this.buffer[0]=n[0],this}hex(){return`${jr(this.buffer[3])} ${jr(this.buffer[2])} ${jr(this.buffer[1])} ${jr(this.buffer[0])}`}static multiply(t,n){return new Ue(new Uint32Array(t.buffer)).times(n)}static add(t,n){return new Ue(new Uint32Array(t.buffer)).plus(n)}static from(t,n=new Uint32Array(4)){return Ue.fromString(typeof t=="string"?t:t.toString(),n)}static fromNumber(t,n=new Uint32Array(4)){return Ue.fromString(t.toString(),n)}static fromString(t,n=new Uint32Array(4)){const r=t.startsWith("-"),i=t.length;let o=new Ue(n);for(let s=r?1:0;s<i;){const a=ci<i-s?ci:i-s,c=new Ue(new Uint32Array([parseInt(t.substr(s,a),10),0,0,0])),l=new Ue(new Uint32Array([Lc[a],0,0,0]));o.times(l),o.plus(c),s+=a}return r?o.negate():o}static convertArray(t){const n=new Uint32Array(t.length*4);for(let r=-1,i=t.length;++r<i;)Ue.from(t[r],new Uint32Array(n.buffer,n.byteOffset+4*4*r,4));return n}}var Ow=Object.freeze(Object.defineProperty({__proto__:null,BaseInt64:Mc,Uint64:wt,Int64:ne,Int128:Ue},Symbol.toStringTag,{value:"Module"}));class Wd extends ct{constructor(t,n,r,i){super(),this.nodesIndex=-1,this.buffersIndex=-1,this.bytes=t,this.nodes=n,this.buffers=r,this.dictionaries=i}visit(t){return super.visit(t instanceof ht?t.type:t)}visitNull(t,{length:n}=this.nextFieldNode()){return L.Null(t,0,n)}visitBool(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Bool(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitInt(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Int(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitFloat(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Float(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitUtf8(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Utf8(t,0,n,r,this.readNullBitmap(t,r),this.readOffsets(t),this.readData(t))}visitBinary(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Binary(t,0,n,r,this.readNullBitmap(t,r),this.readOffsets(t),this.readData(t))}visitFixedSizeBinary(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.FixedSizeBinary(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitDate(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Date(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitTimestamp(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Timestamp(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitTime(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Time(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitDecimal(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Decimal(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitList(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.List(t,0,n,r,this.readNullBitmap(t,r),this.readOffsets(t),this.visit(t.children[0]))}visitStruct(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Struct(t,0,n,r,this.readNullBitmap(t,r),this.visitMany(t.children))}visitUnion(t){return t.mode===xn.Sparse?this.visitSparseUnion(t):this.visitDenseUnion(t)}visitDenseUnion(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Union(t,0,n,r,this.readNullBitmap(t,r),this.readTypeIds(t),this.readOffsets(t),this.visitMany(t.children))}visitSparseUnion(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Union(t,0,n,r,this.readNullBitmap(t,r),this.readTypeIds(t),this.visitMany(t.children))}visitDictionary(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Dictionary(t,0,n,r,this.readNullBitmap(t,r),this.readData(t.indices),this.readDictionary(t))}visitInterval(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Interval(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitFixedSizeList(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.FixedSizeList(t,0,n,r,this.readNullBitmap(t,r),this.visit(t.children[0]))}visitMap(t,{length:n,nullCount:r}=this.nextFieldNode()){return L.Map(t,0,n,r,this.readNullBitmap(t,r),this.readOffsets(t),this.visit(t.children[0]))}nextFieldNode(){return this.nodes[++this.nodesIndex]}nextBufferRange(){return this.buffers[++this.buffersIndex]}readNullBitmap(t,n,r=this.nextBufferRange()){return n>0&&this.readData(t,r)||new Uint8Array(0)}readOffsets(t,n){return this.readData(t,n)}readTypeIds(t,n){return this.readData(t,n)}readData(t,{length:n,offset:r}=this.nextBufferRange()){return this.bytes.subarray(r,r+n)}readDictionary(t){return this.dictionaries.get(t.id)}}class xw extends Wd{constructor(t,n,r,i){super(new Uint8Array(0),n,r,i),this.sources=t}readNullBitmap(t,n,{offset:r}=this.nextBufferRange()){return n<=0?new Uint8Array(0):Li(this.sources[r])}readOffsets(t,{offset:n}=this.nextBufferRange()){return at(Uint8Array,at(Int32Array,this.sources[n]))}readTypeIds(t,{offset:n}=this.nextBufferRange()){return at(Uint8Array,at(t.ArrayType,this.sources[n]))}readData(t,{offset:n}=this.nextBufferRange()){const{sources:r}=this;return nt.isTimestamp(t)||(nt.isInt(t)||nt.isTime(t))&&t.bitWidth===64||nt.isDate(t)&&t.unit===pn.MILLISECOND?at(Uint8Array,ne.convertArray(r[n])):nt.isDecimal(t)?at(Uint8Array,Ue.convertArray(r[n])):nt.isBinary(t)||nt.isFixedSizeBinary(t)?Fw(r[n]):nt.isBool(t)?Li(r[n]):nt.isUtf8(t)?go(r[n].join("")):at(Uint8Array,at(t.ArrayType,r[n].map(i=>+i)))}}function Fw(e){const t=e.join(""),n=new Uint8Array(t.length/2);for(let r=0;r<t.length;r+=2)n[r>>1]=parseInt(t.substr(r,2),16);return n}var Ew=b.Long,fu=v.apache.arrow.flatbuf.Null,ds=v.apache.arrow.flatbuf.Int,Jo=v.apache.arrow.flatbuf.FloatingPoint,du=v.apache.arrow.flatbuf.Binary,hu=v.apache.arrow.flatbuf.Bool,pu=v.apache.arrow.flatbuf.Utf8,hs=v.apache.arrow.flatbuf.Decimal,Go=v.apache.arrow.flatbuf.Date,ps=v.apache.arrow.flatbuf.Time,ys=v.apache.arrow.flatbuf.Timestamp,Zo=v.apache.arrow.flatbuf.Interval,yu=v.apache.arrow.flatbuf.List,mu=v.apache.arrow.flatbuf.Struct_,Ur=v.apache.arrow.flatbuf.Union,hi=v.apache.arrow.flatbuf.DictionaryEncoding,Xo=v.apache.arrow.flatbuf.FixedSizeBinary,Qo=v.apache.arrow.flatbuf.FixedSizeList,ta=v.apache.arrow.flatbuf.Map;class Dw extends ct{visit(t,n){return t==null||n==null?void 0:super.visit(t,n)}visitNull(t,n){return fu.startNull(n),fu.endNull(n)}visitInt(t,n){return ds.startInt(n),ds.addBitWidth(n,t.bitWidth),ds.addIsSigned(n,t.isSigned),ds.endInt(n)}visitFloat(t,n){return Jo.startFloatingPoint(n),Jo.addPrecision(n,t.precision),Jo.endFloatingPoint(n)}visitBinary(t,n){return du.startBinary(n),du.endBinary(n)}visitBool(t,n){return hu.startBool(n),hu.endBool(n)}visitUtf8(t,n){return pu.startUtf8(n),pu.endUtf8(n)}visitDecimal(t,n){return hs.startDecimal(n),hs.addScale(n,t.scale),hs.addPrecision(n,t.precision),hs.endDecimal(n)}visitDate(t,n){return Go.startDate(n),Go.addUnit(n,t.unit),Go.endDate(n)}visitTime(t,n){return ps.startTime(n),ps.addUnit(n,t.unit),ps.addBitWidth(n,t.bitWidth),ps.endTime(n)}visitTimestamp(t,n){const r=t.timezone&&n.createString(t.timezone)||void 0;return ys.startTimestamp(n),ys.addUnit(n,t.unit),r!==void 0&&ys.addTimezone(n,r),ys.endTimestamp(n)}visitInterval(t,n){return Zo.startInterval(n),Zo.addUnit(n,t.unit),Zo.endInterval(n)}visitList(t,n){return yu.startList(n),yu.endList(n)}visitStruct(t,n){return mu.startStruct_(n),mu.endStruct_(n)}visitUnion(t,n){Ur.startTypeIdsVector(n,t.typeIds.length);const r=Ur.createTypeIdsVector(n,t.typeIds);return Ur.startUnion(n),Ur.addMode(n,t.mode),Ur.addTypeIds(n,r),Ur.endUnion(n)}visitDictionary(t,n){const r=this.visit(t.indices,n);return hi.startDictionaryEncoding(n),hi.addId(n,new Ew(t.id,0)),hi.addIsOrdered(n,t.isOrdered),r!==void 0&&hi.addIndexType(n,r),hi.endDictionaryEncoding(n)}visitFixedSizeBinary(t,n){return Xo.startFixedSizeBinary(n),Xo.addByteWidth(n,t.byteWidth),Xo.endFixedSizeBinary(n)}visitFixedSizeList(t,n){return Qo.startFixedSizeList(n),Qo.addListSize(n,t.listSize),Qo.endFixedSizeList(n)}visitMap(t,n){return ta.startMap(n),ta.addKeysSorted(n,t.keysSorted),ta.endMap(n)}}const ea=new Dw;function Lw(e,t=new Map){return new Ot(Cw(e,t),Ts(e.customMetadata),t)}function Yd(e){return new Ae(e.count,Hd(e.columns),Kd(e.columns))}function Mw(e){return new yn(Yd(e.data),e.id,e.isDelta)}function Cw(e,t){return(e.fields||[]).filter(Boolean).map(n=>ht.fromJSON(n,t))}function bu(e,t){return(e.children||[]).filter(Boolean).map(n=>ht.fromJSON(n,t))}function Hd(e){return(e||[]).reduce((t,n)=>[...t,new Lr(n.count,Rw(n.VALIDITY)),...Hd(n.children)],[])}function Kd(e,t=[]){for(let n=-1,r=(e||[]).length;++n<r;){const i=e[n];i.VALIDITY&&t.push(new dn(t.length,i.VALIDITY.length)),i.TYPE&&t.push(new dn(t.length,i.TYPE.length)),i.OFFSET&&t.push(new dn(t.length,i.OFFSET.length)),i.DATA&&t.push(new dn(t.length,i.DATA.length)),t=Kd(i.children,t)}return t}function Rw(e){return(e||[]).reduce((t,n)=>t+ +(n===0),0)}function Uw(e,t){let n,r,i,o,s,a;return!t||!(o=e.dictionary)?(s=_u(e,bu(e,t)),i=new ht(e.name,s,e.nullable,Ts(e.customMetadata))):t.has(n=o.id)?(r=(r=o.indexType)?gu(r):new Ir,a=new Xn(t.get(n),r,n,o.isOrdered),i=new ht(e.name,a,e.nullable,Ts(e.customMetadata))):(r=(r=o.indexType)?gu(r):new Ir,t.set(n,s=_u(e,bu(e,t))),a=new Xn(s,r,n,o.isOrdered),i=new ht(e.name,a,e.nullable,Ts(e.customMetadata))),i||null}function Ts(e){return new Map(Object.entries(e||{}))}function gu(e){return new de(e.isSigned,e.bitWidth)}function _u(e,t){const n=e.type.name;switch(n){case"NONE":return new ei;case"null":return new ei;case"binary":return new Mi;case"utf8":return new ii;case"bool":return new Ci;case"list":return new oi((t||[])[0]);case"struct":return new Ke(t||[]);case"struct_":return new Ke(t||[])}switch(n){case"int":{const r=e.type;return new de(r.isSigned,r.bitWidth)}case"floatingpoint":{const r=e.type;return new Sr(Ee[r.precision])}case"decimal":{const r=e.type;return new js(r.scale,r.precision)}case"date":{const r=e.type;return new si(pn[r.unit])}case"time":{const r=e.type;return new zs(Bt[r.unit],r.bitWidth)}case"timestamp":{const r=e.type;return new Ws(Bt[r.unit],r.timezone)}case"interval":{const r=e.type;return new Ys(ti[r.unit])}case"union":{const r=e.type;return new Ri(xn[r.mode],r.typeIds||[],t||[])}case"fixedsizebinary":{const r=e.type;return new Hs(r.byteWidth)}case"fixedsizelist":{const r=e.type;return new Ui(r.listSize,(t||[])[0])}case"map":{const r=e.type;return new Ni((t||[])[0],r.keysSorted)}}throw new Error(`Unrecognized type: "${n}"`)}var Tr=b.Long,Nw=b.Builder,Pw=b.ByteBuffer,jt=v.apache.arrow.flatbuf.Type,Re=v.apache.arrow.flatbuf.Field,In=v.apache.arrow.flatbuf.Schema,Vw=v.apache.arrow.flatbuf.Buffer,Pn=Gt.apache.arrow.flatbuf.Message,zn=v.apache.arrow.flatbuf.KeyValue,kw=Gt.apache.arrow.flatbuf.FieldNode,wu=v.apache.arrow.flatbuf.Endianness,Vn=Gt.apache.arrow.flatbuf.RecordBatch,Vr=Gt.apache.arrow.flatbuf.DictionaryBatch;class se{constructor(t,n,r,i){this._version=n,this._headerType=r,this.body=new Uint8Array(0),i&&(this._createHeader=()=>i),this._bodyLength=typeof t=="number"?t:t.low}static fromJSON(t,n){const r=new se(0,Pe.V4,n);return r._createHeader=$w(t,n),r}static decode(t){t=new Pw(lt(t));const n=Pn.getRootAsMessage(t),r=n.bodyLength(),i=n.version(),o=n.headerType(),s=new se(r,i,o);return s._createHeader=jw(n,o),s}static encode(t){let n=new Nw,r=-1;return t.isSchema()?r=Ot.encode(n,t.header()):t.isRecordBatch()?r=Ae.encode(n,t.header()):t.isDictionaryBatch()&&(r=yn.encode(n,t.header())),Pn.startMessage(n),Pn.addVersion(n,Pe.V4),Pn.addHeader(n,r),Pn.addHeaderType(n,t.headerType),Pn.addBodyLength(n,new Tr(t.bodyLength,0)),Pn.finishMessageBuffer(n,Pn.endMessage(n)),n.asUint8Array()}static from(t,n=0){if(t instanceof Ot)return new se(0,Pe.V4,xt.Schema,t);if(t instanceof Ae)return new se(n,Pe.V4,xt.RecordBatch,t);if(t instanceof yn)return new se(n,Pe.V4,xt.DictionaryBatch,t);throw new Error(`Unrecognized Message header: ${t}`)}get type(){return this.headerType}get version(){return this._version}get headerType(){return this._headerType}get bodyLength(){return this._bodyLength}header(){return this._createHeader()}isSchema(){return this.headerType===xt.Schema}isRecordBatch(){return this.headerType===xt.RecordBatch}isDictionaryBatch(){return this.headerType===xt.DictionaryBatch}}class Ae{get nodes(){return this._nodes}get length(){return this._length}get buffers(){return this._buffers}constructor(t,n,r){this._nodes=n,this._buffers=r,this._length=typeof t=="number"?t:t.low}}class yn{get id(){return this._id}get data(){return this._data}get isDelta(){return this._isDelta}get length(){return this.data.length}get nodes(){return this.data.nodes}get buffers(){return this.data.buffers}constructor(t,n,r=!1){this._data=t,this._isDelta=r,this._id=typeof n=="number"?n:n.low}}class dn{constructor(t,n){this.offset=typeof t=="number"?t:t.low,this.length=typeof n=="number"?n:n.low}}class Lr{constructor(t,n){this.length=typeof t=="number"?t:t.low,this.nullCount=typeof n=="number"?n:n.low}}function $w(e,t){return()=>{switch(t){case xt.Schema:return Ot.fromJSON(e);case xt.RecordBatch:return Ae.fromJSON(e);case xt.DictionaryBatch:return yn.fromJSON(e)}throw new Error(`Unrecognized Message type: { name: ${xt[t]}, type: ${t} }`)}}function jw(e,t){return()=>{switch(t){case xt.Schema:return Ot.decode(e.header(new In));case xt.RecordBatch:return Ae.decode(e.header(new Vn),e.version());case xt.DictionaryBatch:return yn.decode(e.header(new Vr),e.version())}throw new Error(`Unrecognized Message type: { name: ${xt[t]}, type: ${t} }`)}}ht.encode=Qw;ht.decode=Zw;ht.fromJSON=Uw;Ot.encode=Xw;Ot.decode=zw;Ot.fromJSON=Lw;Ae.encode=tv;Ae.decode=Ww;Ae.fromJSON=Yd;yn.encode=ev;yn.decode=Yw;yn.fromJSON=Mw;Lr.encode=nv;Lr.decode=Kw;dn.encode=rv;dn.decode=Hw;function zw(e,t=new Map){const n=Gw(e,t);return new Ot(n,Bs(e),t)}function Ww(e,t=Pe.V4){return new Ae(e.length(),qw(e),Jw(e,t))}function Yw(e,t=Pe.V4){return new yn(Ae.decode(e.data(),t),e.id(),e.isDelta())}function Hw(e){return new dn(e.offset(),e.length())}function Kw(e){return new Lr(e.length(),e.nullCount())}function qw(e){const t=[];for(let n,r=-1,i=-1,o=e.nodesLength();++r<o;)(n=e.nodes(r))&&(t[++i]=Lr.decode(n));return t}function Jw(e,t){const n=[];for(let r,i=-1,o=-1,s=e.buffersLength();++i<s;)(r=e.buffers(i))&&(t<Pe.V4&&(r.bb_pos+=8*(i+1)),n[++o]=dn.decode(r));return n}function Gw(e,t){const n=[];for(let r,i=-1,o=-1,s=e.fieldsLength();++i<s;)(r=e.fields(i))&&(n[++o]=ht.decode(r,t));return n}function vu(e,t){const n=[];for(let r,i=-1,o=-1,s=e.childrenLength();++i<s;)(r=e.children(i))&&(n[++o]=ht.decode(r,t));return n}function Zw(e,t){let n,r,i,o,s,a;return!t||!(a=e.dictionary())?(i=Su(e,vu(e,t)),r=new ht(e.name(),i,e.nullable(),Bs(e))):t.has(n=a.id().low)?(o=(o=a.indexType())?Iu(o):new Ir,s=new Xn(t.get(n),o,n,a.isOrdered()),r=new ht(e.name(),s,e.nullable(),Bs(e))):(o=(o=a.indexType())?Iu(o):new Ir,t.set(n,i=Su(e,vu(e,t))),s=new Xn(i,o,n,a.isOrdered()),r=new ht(e.name(),s,e.nullable(),Bs(e))),r||null}function Bs(e){const t=new Map;if(e)for(let n,r,i=-1,o=e.customMetadataLength()|0;++i<o;)(n=e.customMetadata(i))&&(r=n.key())!=null&&t.set(r,n.value());return t}function Iu(e){return new de(e.isSigned(),e.bitWidth())}function Su(e,t){const n=e.typeType();switch(n){case jt.NONE:return new ei;case jt.Null:return new ei;case jt.Binary:return new Mi;case jt.Utf8:return new ii;case jt.Bool:return new Ci;case jt.List:return new oi((t||[])[0]);case jt.Struct_:return new Ke(t||[])}switch(n){case jt.Int:{const r=e.type(new v.apache.arrow.flatbuf.Int);return new de(r.isSigned(),r.bitWidth())}case jt.FloatingPoint:{const r=e.type(new v.apache.arrow.flatbuf.FloatingPoint);return new Sr(r.precision())}case jt.Decimal:{const r=e.type(new v.apache.arrow.flatbuf.Decimal);return new js(r.scale(),r.precision())}case jt.Date:{const r=e.type(new v.apache.arrow.flatbuf.Date);return new si(r.unit())}case jt.Time:{const r=e.type(new v.apache.arrow.flatbuf.Time);return new zs(r.unit(),r.bitWidth())}case jt.Timestamp:{const r=e.type(new v.apache.arrow.flatbuf.Timestamp);return new Ws(r.unit(),r.timezone())}case jt.Interval:{const r=e.type(new v.apache.arrow.flatbuf.Interval);return new Ys(r.unit())}case jt.Union:{const r=e.type(new v.apache.arrow.flatbuf.Union);return new Ri(r.mode(),r.typeIdsArray()||[],t||[])}case jt.FixedSizeBinary:{const r=e.type(new v.apache.arrow.flatbuf.FixedSizeBinary);return new Hs(r.byteWidth())}case jt.FixedSizeList:{const r=e.type(new v.apache.arrow.flatbuf.FixedSizeList);return new Ui(r.listSize(),(t||[])[0])}case jt.Map:{const r=e.type(new v.apache.arrow.flatbuf.Map);return new Ni((t||[])[0],r.keysSorted())}}throw new Error(`Unrecognized type: "${jt[n]}" (${n})`)}function Xw(e,t){const n=t.fields.map(o=>ht.encode(e,o));In.startFieldsVector(e,n.length);const r=In.createFieldsVector(e,n),i=t.metadata&&t.metadata.size>0?In.createCustomMetadataVector(e,[...t.metadata].map(([o,s])=>{const a=e.createString(`${o}`),c=e.createString(`${s}`);return zn.startKeyValue(e),zn.addKey(e,a),zn.addValue(e,c),zn.endKeyValue(e)})):-1;return In.startSchema(e),In.addFields(e,r),In.addEndianness(e,iv?wu.Little:wu.Big),i!==-1&&In.addCustomMetadata(e,i),In.endSchema(e)}function Qw(e,t){let n=-1,r=-1,i=-1,o=t.type,s=t.typeId;nt.isDictionary(o)?(s=o.dictionary.typeId,i=ea.visit(o,e),r=ea.visit(o.dictionary,e)):r=ea.visit(o,e);const a=(o.children||[]).map(d=>ht.encode(e,d)),c=Re.createChildrenVector(e,a),l=t.metadata&&t.metadata.size>0?Re.createCustomMetadataVector(e,[...t.metadata].map(([d,p])=>{const _=e.createString(`${d}`),O=e.createString(`${p}`);return zn.startKeyValue(e),zn.addKey(e,_),zn.addValue(e,O),zn.endKeyValue(e)})):-1;return t.name&&(n=e.createString(t.name)),Re.startField(e),Re.addType(e,r),Re.addTypeType(e,s),Re.addChildren(e,c),Re.addNullable(e,!!t.nullable),n!==-1&&Re.addName(e,n),i!==-1&&Re.addDictionary(e,i),l!==-1&&Re.addCustomMetadata(e,l),Re.endField(e)}function tv(e,t){const n=t.nodes||[],r=t.buffers||[];Vn.startNodesVector(e,n.length),n.slice().reverse().forEach(s=>Lr.encode(e,s));const i=e.endVector();Vn.startBuffersVector(e,r.length),r.slice().reverse().forEach(s=>dn.encode(e,s));const o=e.endVector();return Vn.startRecordBatch(e),Vn.addLength(e,new Tr(t.length,0)),Vn.addNodes(e,i),Vn.addBuffers(e,o),Vn.endRecordBatch(e)}function ev(e,t){const n=Ae.encode(e,t.data);return Vr.startDictionaryBatch(e),Vr.addId(e,new Tr(t.id,0)),Vr.addIsDelta(e,t.isDelta),Vr.addData(e,n),Vr.endDictionaryBatch(e)}function nv(e,t){return kw.createFieldNode(e,new Tr(t.length,0),new Tr(t.nullCount,0))}function rv(e,t){return Vw.createBuffer(e,new Tr(t.offset,0),new Tr(t.length,0))}const iv=function(){const e=new ArrayBuffer(2);return new DataView(e).setInt16(0,256,!0),new Int16Array(e)[0]===256}();var qd=b.ByteBuffer;const Cc=e=>`Expected ${xt[e]} Message in stream, but was null or length 0.`,Rc=e=>`Header pointer of flatbuffer-encoded ${xt[e]} Message is null or length 0.`,Jd=(e,t)=>`Expected to read ${e} metadata bytes, but only read ${t}.`,Gd=(e,t)=>`Expected to read ${e} bytes for message body, but only read ${t}.`;class Zd{constructor(t){this.source=t instanceof qs?t:new qs(t)}[Symbol.iterator](){return this}next(){let t;return(t=this.readMetadataLength()).done||t.value===-1&&(t=this.readMetadataLength()).done||(t=this.readMetadata(t.value)).done?Rt:t}throw(t){return this.source.throw(t)}return(t){return this.source.return(t)}readMessage(t){let n;if((n=this.next()).done)return null;if(t!=null&&n.value.headerType!==t)throw new Error(Cc(t));return n.value}readMessageBody(t){if(t<=0)return new Uint8Array(0);const n=lt(this.source.read(t));if(n.byteLength<t)throw new Error(Gd(t,n.byteLength));return n.byteOffset%8===0&&n.byteOffset+n.byteLength<=n.buffer.byteLength?n:n.slice()}readSchema(t=!1){const n=xt.Schema,r=this.readMessage(n),i=r&&r.header();if(t&&!i)throw new Error(Rc(n));return i}readMetadataLength(){const t=this.source.read(xo),n=t&&new qd(t),r=n&&n.readInt32(0)||0;return{done:r===0,value:r}}readMetadata(t){const n=this.source.read(t);if(!n)return Rt;if(n.byteLength<t)throw new Error(Jd(t,n.byteLength));return{done:!1,value:se.decode(n)}}}class sv{constructor(t,n){this.source=t instanceof Ar?t:Xf(t)?new Js(t,n):new Ar(t)}[Symbol.asyncIterator](){return this}async next(){let t;return(t=await this.readMetadataLength()).done||t.value===-1&&(t=await this.readMetadataLength()).done||(t=await this.readMetadata(t.value)).done?Rt:t}async throw(t){return await this.source.throw(t)}async return(t){return await this.source.return(t)}async readMessage(t){let n;if((n=await this.next()).done)return null;if(t!=null&&n.value.headerType!==t)throw new Error(Cc(t));return n.value}async readMessageBody(t){if(t<=0)return new Uint8Array(0);const n=lt(await this.source.read(t));if(n.byteLength<t)throw new Error(Gd(t,n.byteLength));return n.byteOffset%8===0&&n.byteOffset+n.byteLength<=n.buffer.byteLength?n:n.slice()}async readSchema(t=!1){const n=xt.Schema,r=await this.readMessage(n),i=r&&r.header();if(t&&!i)throw new Error(Rc(n));return i}async readMetadataLength(){const t=await this.source.read(xo),n=t&&new qd(t),r=n&&n.readInt32(0)||0;return{done:r===0,value:r}}async readMetadata(t){const n=await this.source.read(t);if(!n)return Rt;if(n.byteLength<t)throw new Error(Jd(t,n.byteLength));return{done:!1,value:se.decode(n)}}}class ov extends Zd{constructor(t){super(new Uint8Array(0)),this._schema=!1,this._body=[],this._batchIndex=0,this._dictionaryIndex=0,this._json=t instanceof ru?t:new ru(t)}next(){const{_json:t}=this;if(!this._schema){this._schema=!0;const n=se.fromJSON(t.schema,xt.Schema);return{done:!1,value:n}}if(this._dictionaryIndex<t.dictionaries.length){const n=t.dictionaries[this._dictionaryIndex++];this._body=n.data.columns;const r=se.fromJSON(n,xt.DictionaryBatch);return{done:!1,value:r}}if(this._batchIndex<t.batches.length){const n=t.batches[this._batchIndex++];this._body=n.columns;const r=se.fromJSON(n,xt.RecordBatch);return{done:!1,value:r}}return this._body=[],Rt}readMessageBody(t){return n(this._body);function 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d,p=-1,_=t.children.length;++p<_;)if(d=e.getChildAt(p)){const O=t.typeIds[p],P=Math.min(n,s[O]);this.visit(d.slice(a[O],P))}}}return this}function lv(e){let t;return e.nullCount>=e.length?ze.call(this,new Uint8Array(0)):(t=e.values)instanceof Uint8Array?ze.call(this,vo(e.offset,e.length,t)):ze.call(this,Li(e))}function nr(e){return ze.call(this,e.values.subarray(0,e.length*e.stride))}function Qd(e){const{length:t,values:n,valueOffsets:r}=e,i=r[0],o=r[t],s=Math.min(o-i,n.byteLength-i);return ze.call(this,wo(-r[0],t,r)),ze.call(this,n.subarray(i,i+s)),this}function Nc(e){const{length:t,valueOffsets:n}=e;return n&&ze.call(this,wo(n[0],t,n)),this.visit(e.getChildAt(0))}function Ra(e){return this.visitMany(e.type.children.map((t,n)=>e.getChildAt(n)).filter(Boolean))[0]}$t.prototype.visitBool=lv;$t.prototype.visitInt=nr;$t.prototype.visitFloat=nr;$t.prototype.visitUtf8=Qd;$t.prototype.visitBinary=Qd;$t.prototype.visitFixedSizeBinary=nr;$t.prototype.visitDate=nr;$t.prototype.visitTimestamp=nr;$t.prototype.visitTime=nr;$t.prototype.visitDecimal=nr;$t.prototype.visitList=Nc;$t.prototype.visitStruct=Ra;$t.prototype.visitUnion=cv;$t.prototype.visitInterval=nr;$t.prototype.visitFixedSizeList=Nc;$t.prototype.visitMap=Nc;class Pc extends Fr{constructor(t){super(),this._position=0,this._started=!1,this._sink=new Ii,this._schema=null,this._dictionaryBlocks=[],this._recordBatchBlocks=[],this._dictionaryDeltaOffsets=new Map,Se(t)||(t={autoDestroy:!0,writeLegacyIpcFormat:!1}),this._autoDestroy=typeof t.autoDestroy=="boolean"?t.autoDestroy:!0,this._writeLegacyIpcFormat=typeof t.writeLegacyIpcFormat=="boolean"?t.writeLegacyIpcFormat:!1}static throughNode(t){throw new Error('"throughNode" 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WritableStream({abort:()=>{this._builder.clear()},write:()=>{this._maybeFlush(this._builder,this._controller)},close:()=>{this._maybeFlush(this._builder.finish(),this._controller)}},{highWaterMark:a,size:c=>this._writeValueAndReturnChunkSize(c)})}_writeValueAndReturnChunkSize(t){const n=this._bufferedSize;return this._bufferedSize=this._getSize(this._builder.append(t)),this._bufferedSize-n}_maybeFlush(t,n){n!==null&&(this._bufferedSize>=n.desiredSize&&++this._numChunks&&this._enqueue(n,t.toVector()),t.finished&&((t.length>0||this._numChunks===0)&&++this._numChunks&&this._enqueue(n,t.toVector()),!this._finished&&(this._finished=!0)&&this._enqueue(n,null)))}_enqueue(t,n){this._bufferedSize=0,this._controller=null,n===null?t.close():t.enqueue(n)}}const xu=e=>e.length,Fu=e=>e.byteLength;function V0(e,t){const n=new Ii;let r=null;const i=new ReadableStream({async cancel(){await n.close()},async start(a){await s(a,r||(r=await o()))},async pull(a){r?await s(a,r):a.close()}});return{writable:new WritableStream(n,{highWaterMark:2**14,...e}),readable:i};async function o(){return await(await We.from(n)).open(t)}async function s(a,c){let l=a.desiredSize,d=null;for(;!(d=await c.next()).done;)if(a.enqueue(d.value),l!=null&&--l<=0)return;a.close()}}function k0(e,t){const n=new this(e),r=new Ar(n),i=new ReadableStream({type:"bytes",async cancel(){await r.cancel()},async pull(s){await o(s)},async start(s){await o(s)}},{highWaterMark:2**14,...t});return{writable:new WritableStream(n,e),readable:i};async function o(s){let a=null,c=s.desiredSize;for(;a=await r.read(c||null);)if(s.enqueue(a),c!=null&&(c-=a.byteLength)<=0)return;s.close()}}class qr{eq(t){return t instanceof qr||(t=new Jr(t)),new $0(this,t)}le(t){return t instanceof qr||(t=new Jr(t)),new j0(this,t)}ge(t){return t instanceof qr||(t=new Jr(t)),new z0(this,t)}lt(t){return new Os(this.ge(t))}gt(t){return new Os(this.le(t))}ne(t){return new Os(this.eq(t))}}class Jr extends qr{constructor(t){super(),this.v=t}}class Vh extends qr{constructor(t){super(),this.name=t}bind(t){if(!this.colidx){this.colidx=-1;const r=t.schema.fields;for(let i=-1;++i<r.length;)if(r[i].name===this.name){this.colidx=i;break}if(this.colidx<0)throw new Error(`Failed to bind Col "${this.name}"`)}const n=this.vector=t.getChildAt(this.colidx);return r=>n.get(r)}}class Zc{and(...t){return new tl(this,...t)}or(...t){return new el(this,...t)}not(){return new Os(this)}}class Xc extends Zc{constructor(t,n){super(),this.left=t,this.right=n}bind(t){return this.left instanceof Jr?this.right instanceof Jr?this._bindLitLit(t,this.left,this.right):this._bindLitCol(t,this.left,this.right):this.right instanceof Jr?this._bindColLit(t,this.left,this.right):this._bindColCol(t,this.left,this.right)}}class Qc extends Zc{constructor(...t){super(),this.children=t}}Qc.prototype.children=Object.freeze([]);class tl extends Qc{constructor(...t){t=t.reduce((n,r)=>n.concat(r instanceof tl?r.children:r),[]),super(...t)}bind(t){const n=this.children.map(r=>r.bind(t));return(r,i)=>n.every(o=>o(r,i))}}class el extends Qc{constructor(...t){t=t.reduce((n,r)=>n.concat(r instanceof el?r.children:r),[]),super(...t)}bind(t){const n=this.children.map(r=>r.bind(t));return(r,i)=>n.some(o=>o(r,i))}}class $0 extends Xc{_bindLitLit(t,n,r){const i=n.v==r.v;return()=>i}_bindColCol(t,n,r){const i=n.bind(t),o=r.bind(t);return(s,a)=>i(s,a)==o(s,a)}_bindColLit(t,n,r){const i=n.bind(t);if(n.vector instanceof Wc){let o;const s=n.vector;return s.dictionary!==this.lastDictionary?(o=s.reverseLookup(r.v),this.lastDictionary=s.dictionary,this.lastKey=o):o=this.lastKey,o===-1?()=>!1:a=>s.getKey(a)===o}else return(o,s)=>i(o,s)==r.v}_bindLitCol(t,n,r){return this._bindColLit(t,r,n)}}class j0 extends Xc{_bindLitLit(t,n,r){const i=n.v<=r.v;return()=>i}_bindColCol(t,n,r){const i=n.bind(t),o=r.bind(t);return(s,a)=>i(s,a)<=o(s,a)}_bindColLit(t,n,r){const i=n.bind(t);return(o,s)=>i(o,s)<=r.v}_bindLitCol(t,n,r){const i=r.bind(t);return(o,s)=>n.v<=i(o,s)}}class z0 extends Xc{_bindLitLit(t,n,r){const i=n.v>=r.v;return()=>i}_bindColCol(t,n,r){const i=n.bind(t),o=r.bind(t);return(s,a)=>i(s,a)>=o(s,a)}_bindColLit(t,n,r){const i=n.bind(t);return(o,s)=>i(o,s)>=r.v}_bindLitCol(t,n,r){const i=r.bind(t);return(o,s)=>n.v>=i(o,s)}}class Os extends Zc{constructor(t){super(),this.child=t}bind(t){const n=this.child.bind(t);return(r,i)=>!n(r,i)}}mt.prototype.countBy=function(e){return new ns(this.chunks).countBy(e)};mt.prototype.scan=function(e,t){return new ns(this.chunks).scan(e,t)};mt.prototype.scanReverse=function(e,t){return new ns(this.chunks).scanReverse(e,t)};mt.prototype.filter=function(e){return new ns(this.chunks).filter(e)};class ns extends mt{filter(t){return new nl(this.chunks,t)}scan(t,n){const r=this.chunks,i=r.length;for(let o=-1;++o<i;){const s=r[o];n&&n(s);for(let a=-1,c=s.length;++a<c;)t(a,s)}}scanReverse(t,n){const r=this.chunks,i=r.length;for(let o=i;--o>=0;){const s=r[o];n&&n(s);for(let a=s.length;--a>=0;)t(a,s)}}countBy(t){const n=this.chunks,r=n.length,i=typeof t=="string"?new Vh(t):t;i.bind(n[r-1]);const o=i.vector;if(!nt.isDictionary(o.type))throw new Error("countBy currently only supports dictionary-encoded columns");const s=Math.ceil(Math.log(o.length)/Math.log(256)),a=s==4?Uint32Array:s>=2?Uint16Array:Uint8Array,c=new a(o.dictionary.length);for(let l=-1;++l<r;){const d=n[l];i.bind(d);const p=i.vector.indices;for(let _=-1,O=d.length;++_<O;){let P=p.get(_);P!==null&&c[P]++}}return new kh(o.dictionary,Ge.from(c))}}class kh extends mt{constructor(t,n){const r=new Ot([new ht("values",t.type),new ht("counts",n.type)]);super(new ue(r,n.length,[t,n]))}toJSON(){const t=this.getColumnAt(0),n=this.getColumnAt(1),r={};for(let i=-1;++i<this.length;)r[t.get(i)]=n.get(i);return r}}class nl extends ns{constructor(t,n){super(t),this._predicate=n}scan(t,n){const r=this._chunks,i=r.length;for(let o=-1;++o<i;){const s=r[o],a=this._predicate.bind(s);let c=!1;for(let l=-1,d=s.length;++l<d;)a(l,s)&&(n&&!c&&(n(s),c=!0),t(l,s))}}scanReverse(t,n){const r=this._chunks,i=r.length;for(let o=i;--o>=0;){const s=r[o],a=this._predicate.bind(s);let c=!1;for(let l=s.length;--l>=0;)a(l,s)&&(n&&!c&&(n(s),c=!0),t(l,s))}}count(){let t=0;const n=this._chunks,r=n.length;for(let i=-1;++i<r;){const o=n[i],s=this._predicate.bind(o);for(let a=-1,c=o.length;++a<c;)s(a,o)&&++t}return t}*[Symbol.iterator](){const t=this._chunks,n=t.length;for(let r=-1;++r<n;){const i=t[r],o=this._predicate.bind(i);for(let s=-1,a=i.length;++s<a;)o(s,i)&&(yield i.get(s))}}filter(t){return new nl(this._chunks,this._predicate.and(t))}countBy(t){const n=this._chunks,r=n.length,i=typeof t=="string"?new Vh(t):t;i.bind(n[r-1]);const o=i.vector;if(!nt.isDictionary(o.type))throw new Error("countBy currently only supports dictionary-encoded columns");const s=Math.ceil(Math.log(o.length)/Math.log(256)),a=s==4?Uint32Array:s>=2?Uint16Array:Uint8Array,c=new a(o.dictionary.length);for(let l=-1;++l<r;){const d=n[l],p=this._predicate.bind(d);i.bind(d);const _=i.vector.indices;for(let O=-1,P=d.length;++O<P;){let X=_.get(O);X!==null&&p(O,d)&&c[X]++}}return new kh(o.dictionary,Ge.from(c))}}({...c_,...Ow,...Cg,...i_,...xg,...U_});we.toDOMStream=C0;Ut.throughDOM=N0;We.throughDOM=V0;Pc.throughDOM=k0;/**
 * @license
 * Copyright 2018-2021 Streamlit Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */var Eu=function(){function e(t,n,r,i){var o=this;this.getCell=function(s,a){var c=s<o.headerRows&&a<o.headerColumns,l=s>=o.headerRows&&a<o.headerColumns,d=s<o.headerRows&&a>=o.headerColumns;if(c){var p=["blank"];return a>0&&p.push("level"+s),{type:"blank",classNames:p.join(" "),content:""}}else if(d){var _=a-o.headerColumns,p=["col_heading","level"+s,"col"+_];return{type:"columns",classNames:p.join(" "),content:o.getContent(o.columnsTable,_,s)}}else if(l){var O=s-o.headerRows,p=["row_heading","level"+a,"row"+O];return{type:"index",id:"T_"+o.uuid+"level"+a+"_row"+O,classNames:p.join(" "),content:o.getContent(o.indexTable,O,a)}}else{var O=s-o.headerRows,_=a-o.headerColumns,p=["data","row"+O,"col"+_],P=o.styler?o.getContent(o.styler.displayValuesTable,O,_):o.getContent(o.dataTable,O,_);return{type:"data",id:"T_"+o.uuid+"row"+O+"_col"+_,classNames:p.join(" "),content:P}}},this.getContent=function(s,a,c){var l=s.getColumnAt(c);if(l===null)return"";var d=o.getColumnTypeId(s,c);switch(d){case y.Timestamp:return o.nanosToDate(l.get(a));default:return l.get(a)}},this.dataTable=mt.from(t),this.indexTable=mt.from(n),this.columnsTable=mt.from(r),this.styler=i?{caption:i.caption,displayValuesTable:mt.from(i.displayValues),styles:i.styles,uuid:i.uuid}:void 0}return Object.defineProperty(e.prototype,"rows",{get:function(){return this.indexTable.length+this.columnsTable.numCols},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"columns",{get:function(){return this.indexTable.numCols+this.columnsTable.length},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"headerRows",{get:function(){return this.rows-this.dataRows},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"headerColumns",{get:function(){return this.columns-this.dataColumns},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"dataRows",{get:function(){return this.dataTable.length},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"dataColumns",{get:function(){return this.dataTable.numCols},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"uuid",{get:function(){return this.styler&&this.styler.uuid},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"caption",{get:function(){return this.styler&&this.styler.caption},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"styles",{get:function(){return this.styler&&this.styler.styles},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"table",{get:function(){return this.dataTable},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"index",{get:function(){return this.indexTable},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"columnTable",{get:function(){return this.columnsTable},enumerable:!0,configurable:!0}),e.prototype.serialize=function(){return{data:this.dataTable.serialize(),index:this.indexTable.serialize(),columns:this.columnsTable.serialize()}},e.prototype.getColumnTypeId=function(t,n){return t.schema.fields[n].type.typeId},e.prototype.nanosToDate=function(t){return new Date(t/1e6)},e}();/**
 * @license
 * Copyright 2018-2021 Streamlit Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */var Si=globalThis&&globalThis.__assign||function(){return Si=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++){t=arguments[n];for(var i in t)Object.prototype.hasOwnProperty.call(t,i)&&(e[i]=t[i])}return e},Si.apply(this,arguments)},Ai;(function(e){e.COMPONENT_READY="streamlit:componentReady",e.SET_COMPONENT_VALUE="streamlit:setComponentValue",e.SET_FRAME_HEIGHT="streamlit:setFrameHeight"})(Ai||(Ai={}));var un=function(){function e(){}return e.API_VERSION=1,e.RENDER_EVENT="streamlit:render",e.events=new Gn,e.registeredMessageListener=!1,e.setComponentReady=function(){e.registeredMessageListener||(window.addEventListener("message",e.onMessageEvent),e.registeredMessageListener=!0),e.sendBackMsg(Ai.COMPONENT_READY,{apiVersion:e.API_VERSION})},e.setFrameHeight=function(t){t===void 0&&(t=document.body.scrollHeight),t!==e.lastFrameHeight&&(e.lastFrameHeight=t,e.sendBackMsg(Ai.SET_FRAME_HEIGHT,{height:t}))},e.setComponentValue=function(t){var n;t instanceof Eu?(n="dataframe",t=t.serialize()):Y0(t)?(n="bytes",t=new Uint8Array(t.buffer)):t instanceof ArrayBuffer?(n="bytes",t=new Uint8Array(t)):n="json",e.sendBackMsg(Ai.SET_COMPONENT_VALUE,{value:t,dataType:n})},e.onMessageEvent=function(t){var n=t.data.type;switch(n){case e.RENDER_EVENT:e.onRenderMessage(t.data);break}},e.onRenderMessage=function(t){var n=t.args;n==null&&(console.error("Got null args in onRenderMessage. This should never happen"),n={});var r=t.dfs&&t.dfs.length>0?e.argsDataframeToObject(t.dfs):{};n=Si(Si({},n),r);var i=Boolean(t.disabled),o=t.theme;o&&W0(o);var s={disabled:i,args:n,theme:o},a=new CustomEvent(e.RENDER_EVENT,{detail:s});e.events.dispatchEvent(a)},e.argsDataframeToObject=function(t){var n=t.map(function(r){var i=r.key,o=r.value;return[i,e.toArrowTable(o)]});return Object.fromEntries(n)},e.toArrowTable=function(t){var n=t.data,r=n.data,i=n.index,o=n.columns,s=n.styler;return new Eu(r,i,o,s)},e.sendBackMsg=function(t,n){window.parent.postMessage(Si({isStreamlitMessage:!0,type:t},n),"*")},e}(),W0=function(e){var t=document.createElement("style");document.head.appendChild(t),t.innerHTML=`
    :root {
      --primary-color: `+e.primaryColor+`;
      --background-color: `+e.backgroundColor+`;
      --secondary-background-color: `+e.secondaryBackgroundColor+`;
      --text-color: `+e.textColor+`;
      --font: `+e.font+`;
    }

    body {
      background-color: var(--background-color);
      color: var(--text-color);
    }
  `};function Y0(e){var t=!1;try{t=e instanceof BigInt64Array||e instanceof BigUint64Array}catch{}return e instanceof Int8Array||e instanceof Uint8Array||e instanceof Uint8ClampedArray||e instanceof Int16Array||e instanceof Uint16Array||e instanceof Int32Array||e instanceof Uint32Array||e instanceof Float32Array||e instanceof Float64Array||t}/**
 * @license
 * Copyright 2018-2021 Streamlit Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */var H0=globalThis&&globalThis.__extends||function(){var e=function(t,n){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(r,i){r.__proto__=i}||function(r,i){for(var o in i)i.hasOwnProperty(o)&&(r[o]=i[o])},e(t,n)};return function(t,n){e(t,n);function r(){this.constructor=t}t.prototype=n===null?Object.create(n):(r.prototype=n.prototype,new r)}}();(function(e){H0(t,e);function t(){return e!==null&&e.apply(this,arguments)||this}return t.prototype.componentDidMount=function(){un.setFrameHeight()},t.prototype.componentDidUpdate=function(){un.setFrameHeight()},t})(Vb.PureComponent);function K0(){Xa(()=>{un.setFrameHeight()}),Qa(()=>{un.setFrameHeight()})}var rl=(e,t)=>{const n=e.__vccOpts||e;for(const[r,i]of t)n[r]=i;return n};const q0={class:"bootstrap-wrapper pt-4"},J0={class:"concept-gallery row"},G0={class:"concept-card p-4 container-fluid"},Z0={class:"concept-card-content-wrapper"},X0={class:"card-header row no-gutters"},Q0={class:"col"},tI={class:"concept-title pl-1"},eI=Pt("span",{class:"token-char pr-0"},"<",-1),nI=Pt("span",{class:"pl-0 token-char"},">",-1),rI={class:"concept-img-wrapper p-0 row no-gutters"},iI={key:0,class:"col-12 p-4 no-preview"},sI=Pt("svg",{class:"no-preview-icon",xmlns:"http://www.w3.org/2000/svg",viewBox:"0 0 32 32"},[Pt("path",{d:"M 2 5 L 2 27 L 30 27 L 30 5 Z M 4 7 L 28 7 L 28 20.90625 L 22.71875 15.59375 L 22 14.875 L 17.46875 19.40625 L 11.71875 13.59375 L 11 12.875 L 4 19.875 Z M 24 9 C 22.894531 9 22 9.894531 22 11 C 22 12.105469 22.894531 13 24 13 C 25.105469 13 26 12.105469 26 11 C 26 9.894531 25.105469 9 24 9 Z M 11 15.71875 L 20.1875 25 L 4 25 L 4 22.71875 Z M 22 17.71875 L 28 23.71875 L 28 25 L 23.03125 25 L 18.875 20.8125 Z"})],-1),oI=Pt("p",{style:{opacity:"0.8"}},"No preview available",-1),aI=[sI,oI],cI={class:"concept-card-footer row no-gutters pt-4"},lI={class:"col pl-1"},uI={class:"col-auto"},fI=["onClick"],dI=ic(" Copy to clipboard "),hI=Ga({__name:"Component",props:{args:{type:null,required:!0},disabled:{type:Boolean,required:!0},theme:{type:null,required:!0}},setup(e){K0();const t=n=>{console.log("sending copy to clipboard event",n),navigator.clipboard.writeText(n)};return(n,r)=>(_e(),ln("div",q0,[Pt("div",J0,[(_e(!0),ln(ie,null,Ml(e.args.concepts,i=>(_e(),ln("div",{key:i.name,class:"col-12 col-sm-6 col-md-4 col-lg-4 col-xl-3"},[Pt("div",G0,[Pt("div",Z0,[Pt("div",X0,[Pt("div",Q0,[Pt("h1",tI,[eI,ic(ia(i.name),1),nI])]),Sn(" Favorite feature, not implemented yet "),Sn(` <div class="col-auto card-favorite" >\r
              <img width="24"\r
                   height="24"\r
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================================================
FILE: frontend/dists/concept-browser/dist/index.html
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<!DOCTYPE html>
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================================================
FILE: frontend/dists/sd-gallery/dist/assets/index.4194368f.css
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n?D.Int32:D.Uint32;case 64:return n?D.Int64:D.Uint64}return D.Int;case D.Float:switch(e.precision){case Tn.HALF:return D.Float16;case Tn.SINGLE:return D.Float32;case Tn.DOUBLE:return D.Float64}return D.Float;case D.Binary:return D.Binary;case D.Utf8:return D.Utf8;case D.Bool:return D.Bool;case D.Decimal:return D.Decimal;case D.Time:switch(e.unit){case he.SECOND:return D.TimeSecond;case he.MILLISECOND:return D.TimeMillisecond;case he.MICROSECOND:return D.TimeMicrosecond;case he.NANOSECOND:return D.TimeNanosecond}return D.Time;case D.Timestamp:switch(e.unit){case he.SECOND:return D.TimestampSecond;case he.MILLISECOND:return D.TimestampMillisecond;case he.MICROSECOND:return D.TimestampMicrosecond;case he.NANOSECOND:return D.TimestampNanosecond}return D.Timestamp;case D.Date:switch(e.unit){case Gn.DAY:return D.DateDay;case Gn.MILLISECOND:return D.DateMillisecond}return D.Date;case D.Interval:switch(e.unit){case Ji.DAY_TIME:return D.IntervalDayTime;case Ji.YEAR_MONTH:return D.IntervalYearMonth}return D.Interval;case D.Map:return D.Map;case D.List:return D.List;case D.Struct:return D.Struct;case D.Union:switch(e.mode){case ur.Dense:return D.DenseUnion;case ur.Sparse:return D.SparseUnion}return D.Union;case D.FixedSizeBinary:return D.FixedSizeBinary;case D.FixedSizeList:return D.FixedSizeList;case D.Dictionary:return D.Dictionary}throw new Error(`Unrecognized type 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t.constructor&&pn.compareFields(t.fields,n.fields)}compareFields(t,n){return t===n||Array.isArray(t)&&Array.isArray(n)&&t.length===n.length&&t.every((r,i)=>pn.compareField(r,n[i]))}compareField(t,n){return t===n||n instanceof t.constructor&&t.name===n.name&&t.nullable===n.nullable&&pn.visit(t.type,n.type)}}function rn(e,t){return t instanceof e.constructor}function po(e,t){return e===t||rn(e,t)}function pr(e,t){return e===t||rn(e,t)&&e.bitWidth===t.bitWidth&&e.isSigned===t.isSigned}function qa(e,t){return e===t||rn(e,t)&&e.precision===t.precision}function Ww(e,t){return e===t||rn(e,t)&&e.byteWidth===t.byteWidth}function ou(e,t){return e===t||rn(e,t)&&e.unit===t.unit}function go(e,t){return e===t||rn(e,t)&&e.unit===t.unit&&e.timezone===t.timezone}function mo(e,t){return e===t||rn(e,t)&&e.unit===t.unit&&e.bitWidth===t.bitWidth}function Yw(e,t){return e===t||rn(e,t)&&e.children.length===t.children.length&&pn.compareFields(e.children,t.children)}function Gw(e,t){return e===t||rn(e,t)&&e.children.length===t.children.length&&pn.compareFields(e.children,t.children)}function au(e,t){return e===t||rn(e,t)&&e.mode===t.mode&&e.typeIds.every((n,r)=>n===t.typeIds[r])&&pn.compareFields(e.children,t.children)}function Kw(e,t){return e===t||rn(e,t)&&e.id===t.id&&e.isOrdered===t.isOrdered&&pn.visit(e.indices,t.indices)&&pn.visit(e.dictionary,t.dictionary)}function lu(e,t){return e===t||rn(e,t)&&e.unit===t.unit}function Xw(e,t){return e===t||rn(e,t)&&e.listSize===t.listSize&&e.children.length===t.children.length&&pn.compareFields(e.children,t.children)}function Jw(e,t){return e===t||rn(e,t)&&e.keysSorted===t.keysSorted&&e.children.length===t.children.length&&pn.compareFields(e.children,t.children)}Pt.prototype.visitNull=po;Pt.prototype.visitBool=po;Pt.prototype.visitInt=pr;Pt.prototype.visitInt8=pr;Pt.prototype.visitInt16=pr;Pt.prototype.visitInt32=pr;Pt.prototype.visitInt64=pr;Pt.prototype.visitUint8=pr;Pt.prototype.visitUint16=pr;Pt.prototype.visitUint32=pr;Pt.prototype.visitUint64=pr;Pt.prototype.visitFloat=qa;Pt.prototype.visitFloat16=qa;Pt.prototype.visitFloat32=qa;Pt.prototype.visitFloat64=qa;Pt.prototype.visitUtf8=po;Pt.prototype.visitBinary=po;Pt.prototype.visitFixedSizeBinary=Ww;Pt.prototype.visitDate=ou;Pt.prototype.visitDateDay=ou;Pt.prototype.visitDateMillisecond=ou;Pt.prototype.visitTimestamp=go;Pt.prototype.visitTimestampSecond=go;Pt.prototype.visitTimestampMillisecond=go;Pt.prototype.visitTimestampMicrosecond=go;Pt.prototype.visitTimestampNanosecond=go;Pt.prototype.visitTime=mo;Pt.prototype.visitTimeSecond=mo;Pt.prototype.visitTimeMillisecond=mo;Pt.prototype.visitTimeMicrosecond=mo;Pt.prototype.visitTimeNanosecond=mo;Pt.prototype.visitDecimal=po;Pt.prototype.visitList=Yw;Pt.prototype.visitStruct=Gw;Pt.prototype.visitUnion=au;Pt.prototype.visitDenseUnion=au;Pt.prototype.visitSparseUnion=au;Pt.prototype.visitDictionary=Kw;Pt.prototype.visitInterval=lu;Pt.prototype.visitIntervalDayTime=lu;Pt.prototype.visitIntervalYearMonth=lu;Pt.prototype.visitFixedSizeList=Xw;Pt.prototype.visitMap=Jw;const 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Float64Array}throw new Error(`Unrecognized ${this[Symbol.toStringTag]} type`)}toString(){return`Float${this.precision<<5||16}`}}hi[Symbol.toStringTag]=(e=>(e.precision=null,e[Symbol.toStringTag]="Float"))(hi.prototype);class tl extends hi{constructor(){super(Tn.HALF)}}class pu extends hi{constructor(){super(Tn.SINGLE)}}class gu extends hi{constructor(){super(Tn.DOUBLE)}}Object.defineProperty(tl.prototype,"ArrayType",{value:Uint16Array});Object.defineProperty(pu.prototype,"ArrayType",{value:Float32Array});Object.defineProperty(gu.prototype,"ArrayType",{value:Float64Array});class Gs extends Wt{constructor(){super()}get typeId(){return D.Binary}toString(){return"Binary"}}Gs[Symbol.toStringTag]=(e=>(e.ArrayType=Uint8Array,e[Symbol.toStringTag]="Binary"))(Gs.prototype);class ts extends Wt{constructor(){super()}get typeId(){return D.Utf8}toString(){return"Utf8"}}ts[Symbol.toStringTag]=(e=>(e.ArrayType=Uint8Array,e[Symbol.toStringTag]="Utf8"))(ts.prototype);class Ks extends 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D.Time}toString(){return`Time${this.bitWidth}<${he[this.unit]}>`}}pa[Symbol.toStringTag]=(e=>(e.unit=null,e.bitWidth=null,e.ArrayType=Int32Array,e[Symbol.toStringTag]="Time"))(pa.prototype);class ga extends Wt{constructor(t,n){super(),this.unit=t,this.timezone=n}get typeId(){return D.Timestamp}toString(){return`Timestamp<${he[this.unit]}${this.timezone?`, ${this.timezone}`:""}>`}}ga[Symbol.toStringTag]=(e=>(e.unit=null,e.timezone=null,e.ArrayType=Int32Array,e[Symbol.toStringTag]="Timestamp"))(ga.prototype);class ma extends Wt{constructor(t){super(),this.unit=t}get typeId(){return D.Interval}toString(){return`Interval<${Ji[this.unit]}>`}}ma[Symbol.toStringTag]=(e=>(e.unit=null,e.ArrayType=Int32Array,e[Symbol.toStringTag]="Interval"))(ma.prototype);class ns extends Wt{constructor(t){super(),this.children=[t]}get typeId(){return D.List}toString(){return`List<${this.valueType}>`}get valueType(){return this.children[0].type}get valueField(){return this.children[0]}get ArrayType(){return this.valueType.ArrayType}}ns[Symbol.toStringTag]=(e=>(e.children=null,e[Symbol.toStringTag]="List"))(ns.prototype);class Ln extends Wt{constructor(t){super(),this.children=t}get typeId(){return D.Struct}toString(){return`Struct<{${this.children.map(t=>`${t.name}:${t.type}`).join(", ")}}>`}}Ln[Symbol.toStringTag]=(e=>(e.children=null,e[Symbol.toStringTag]="Struct"))(Ln.prototype);class Xs extends Wt{constructor(t,n,r){super(),this.mode=t,this.children=r,this.typeIds=n=Int32Array.from(n),this.typeIdToChildIndex=n.reduce((i,o,s)=>(i[o]=s)&&i||i,Object.create(null))}get typeId(){return D.Union}toString(){return`${this[Symbol.toStringTag]}<${this.children.map(t=>`${t.type}`).join(" | ")}>`}}Xs[Symbol.toStringTag]=(e=>(e.mode=null,e.typeIds=null,e.children=null,e.typeIdToChildIndex=null,e.ArrayType=Int8Array,e[Symbol.toStringTag]="Union"))(Xs.prototype);class ya extends Wt{constructor(t){super(),this.byteWidth=t}get typeId(){return D.FixedSizeBinary}toString(){return`FixedSizeBinary[${this.byteWidth}]`}}ya[Symbol.toStringTag]=(e=>(e.byteWidth=null,e.ArrayType=Uint8Array,e[Symbol.toStringTag]="FixedSizeBinary"))(ya.prototype);class Js extends Wt{constructor(t,n){super(),this.listSize=t,this.children=[n]}get typeId(){return D.FixedSizeList}get valueType(){return this.children[0].type}get valueField(){return this.children[0]}get ArrayType(){return this.valueType.ArrayType}toString(){return`FixedSizeList[${this.listSize}]<${this.valueType}>`}}Js[Symbol.toStringTag]=(e=>(e.children=null,e.listSize=null,e[Symbol.toStringTag]="FixedSizeList"))(Js.prototype);class Zs extends Wt{constructor(t,n=!1){super(),this.children=[t],this.keysSorted=n}get typeId(){return D.Map}get keyType(){return this.children[0].type.children[0].type}get valueType(){return this.children[0].type.children[1].type}toString(){return`Map<{${this.children[0].type.children.map(t=>`${t.name}:${t.type}`).join(", 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qw=-1;class ft{constructor(t,n,r,i,o,s,a){this.type=t,this.dictionary=a,this.offset=Math.floor(Math.max(n||0,0)),this.length=Math.floor(Math.max(r||0,0)),this._nullCount=Math.floor(Math.max(i||0,-1)),this.childData=(s||[]).map(c=>c instanceof ft?c:c.data);let l;o instanceof ft?(this.stride=o.stride,this.values=o.values,this.typeIds=o.typeIds,this.nullBitmap=o.nullBitmap,this.valueOffsets=o.valueOffsets):(this.stride=Qp(t),o&&((l=o[0])&&(this.valueOffsets=l),(l=o[1])&&(this.values=l),(l=o[2])&&(this.nullBitmap=l),(l=o[3])&&(this.typeIds=l)))}get typeId(){return this.type.typeId}get ArrayType(){return this.type.ArrayType}get buffers(){return[this.valueOffsets,this.values,this.nullBitmap,this.typeIds]}get byteLength(){let t=0,{valueOffsets:n,values:r,nullBitmap:i,typeIds:o}=this;return n&&(t+=n.byteLength),r&&(t+=r.byteLength),i&&(t+=i.byteLength),o&&(t+=o.byteLength),this.childData.reduce((s,a)=>s+a.byteLength,t)}get nullCount(){let t=this._nullCount,n;return t<=qw&&(n=this.nullBitmap)&&(this._nullCount=t=this.length-da(n,this.offset,this.offset+this.length)),t}clone(t,n=this.offset,r=this.length,i=this._nullCount,o=this,s=this.childData){return new ft(t,n,r,i,o,s,this.dictionary)}slice(t,n){const{stride:r,typeId:i,childData:o}=this,s=+(this._nullCount===0)-1,a=i===16?r:1,l=this._sliceBuffers(t,n,r,i);return this.clone(this.type,this.offset+t,n,s,l,!o.length||this.valueOffsets?o:this._sliceChildren(o,a*t,a*n))}_changeLengthAndBackfillNullBitmap(t){if(this.typeId===D.Null)return this.clone(this.type,0,t,0);const{length:n,nullCount:r}=this,i=new Uint8Array((t+63&-64)>>3).fill(255,0,n>>3);i[n>>3]=(1<<n-(n&-8))-1,r>0&&i.set(Qa(this.offset,n,this.nullBitmap),0);const o=this.buffers;return o[Ot.VALIDITY]=i,this.clone(this.type,0,t,r+(t-n),o)}_sliceBuffers(t,n,r,i){let o,{buffers:s}=this;return(o=s[Ot.TYPE])&&(s[Ot.TYPE]=o.subarray(t,t+n)),(o=s[Ot.OFFSET])&&(s[Ot.OFFSET]=o.subarray(t,t+n+1))||(o=s[Ot.DATA])&&(s[Ot.DATA]=i===6?o:o.subarray(r*t,r*(t+n))),s}_sliceChildren(t,n,r){return t.map(i=>i.slice(n,r))}static new(t,n,r,i,o,s,a){switch(o instanceof ft?o=o.buffers:o||(o=[]),t.typeId){case D.Null:return ft.Null(t,n,r);case D.Int:return ft.Int(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.DATA]||[]);case D.Dictionary:return ft.Dictionary(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.DATA]||[],a);case D.Float:return ft.Float(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.DATA]||[]);case D.Bool:return ft.Bool(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.DATA]||[]);case D.Decimal:return ft.Decimal(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.DATA]||[]);case D.Date:return ft.Date(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.DATA]||[]);case D.Time:return ft.Time(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.DATA]||[]);case D.Timestamp:return ft.Timestamp(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.DATA]||[]);case D.Interval:return ft.Interval(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.DATA]||[]);case D.FixedSizeBinary:return ft.FixedSizeBinary(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.DATA]||[]);case D.Binary:return ft.Binary(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.OFFSET]||[],o[Ot.DATA]||[]);case D.Utf8:return ft.Utf8(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.OFFSET]||[],o[Ot.DATA]||[]);case D.List:return ft.List(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.OFFSET]||[],(s||[])[0]);case D.FixedSizeList:return ft.FixedSizeList(t,n,r,i||0,o[Ot.VALIDITY],(s||[])[0]);case D.Struct:return ft.Struct(t,n,r,i||0,o[Ot.VALIDITY],s||[]);case D.Map:return ft.Map(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.OFFSET]||[],(s||[])[0]);case D.Union:return ft.Union(t,n,r,i||0,o[Ot.VALIDITY],o[Ot.TYPE]||[],o[Ot.OFFSET]||s,s)}throw new Error(`Unrecognized typeId ${t.typeId}`)}static Null(t,n,r){return new ft(t,n,r,0)}static Int(t,n,r,i,o,s){return new ft(t,n,r,i,[void 0,Jt(t.ArrayType,s),Qt(o)])}static Dictionary(t,n,r,i,o,s,a){return new ft(t,n,r,i,[void 0,Jt(t.indices.ArrayType,s),Qt(o)],[],a)}static 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new ft(t,n,r,i,[void 0,void 0,Qt(o)],s)}static Map(t,n,r,i,o,s,a){return new ft(t,n,r,i,[Fi(s),void 0,Qt(o)],[a])}static Union(t,n,r,i,o,s,a,l){const c=[void 0,void 0,Qt(o),Jt(t.ArrayType,s)];return t.mode===ur.Sparse?new ft(t,n,r,i,c,a):(c[Ot.OFFSET]=Fi(a),new ft(t,n,r,i,c,l))}}ft.prototype.childData=Object.freeze([]);const tS=void 0;function Ms(e){if(e===null)return"null";if(e===tS)return"undefined";switch(typeof e){case"number":return`${e}`;case"bigint":return`${e}`;case"string":return`"${e}"`}return typeof e[Symbol.toPrimitive]=="function"?e[Symbol.toPrimitive]("string"):ArrayBuffer.isView(e)?`[${e}]`:JSON.stringify(e)}function eS(e){if(!e||e.length<=0)return function(i){return!0};let t="",n=e.filter(r=>r===r);return n.length>0&&(t=`
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this.ArrayType(t),this.buffer)}}yo.prototype.offset=0;class vo extends yo{last(){return this.get(this.length-1)}get(t){return this.buffer[t]}set(t,n){return this.reserve(t-this.length+1),this.buffer[t*this.stride]=n,this}}class qp extends vo{constructor(t=new Uint8Array(0)){super(t,1/8),this.numValid=0}get numInvalid(){return this.length-this.numValid}get(t){return this.buffer[t>>3]>>t%8&1}set(t,n){const{buffer:r}=this.reserve(t-this.length+1),i=t>>3,o=t%8,s=r[i]>>o&1;return n?s===0&&(r[i]|=1<<o,++this.numValid):s===1&&(r[i]&=~(1<<o),--this.numValid),this}clear(){return this.numValid=0,super.clear()}}class tg extends vo{constructor(t=new Int32Array(1)){super(t,1)}append(t){return this.set(this.length-1,t)}set(t,n){const r=this.length-1,i=this.reserve(t-r+1).buffer;return r<t++&&i.fill(i[r],r,t),i[t]=i[t-1]+n,this}flush(t=this.length-1){return t>this.length&&this.set(t-1,0),super.flush(t+1)}}class eg extends yo{get ArrayType64(){return this._ArrayType64||(this._ArrayType64=this.buffer instanceof Int32Array?us:fo)}set(t,n){switch(this.reserve(t-this.length+1),typeof n){case"bigint":this.buffer64[t]=n;break;case"number":this.buffer[t*this.stride]=n;break;default:this.buffer.set(n,t*this.stride)}return this}_resize(t){const n=super._resize(t),r=n.byteLength/(this.BYTES_PER_ELEMENT*this.stride);return Ja&&(this.buffer64=new this.ArrayType64(n.buffer,n.byteOffset,r)),n}}class Ce{constructor({type:t,nullValues:n}){this.length=0,this.finished=!1,this.type=t,this.children=[],this.nullValues=n,this.stride=Qp(t),this._nulls=new qp,n&&n.length>0&&(this._isValid=eS(n))}static new(t){}static throughNode(t){throw new Error('"throughNode" not available in this environment')}static throughDOM(t){throw new Error('"throughDOM" not available in this environment')}static throughIterable(t){return iS(t)}static throughAsyncIterable(t){return sS(t)}toVector(){return ne.new(this.flush())}get ArrayType(){return this.type.ArrayType}get nullCount(){return this._nulls.numInvalid}get numChildren(){return this.children.length}get byteLength(){let t=0;return this._offsets&&(t+=this._offsets.byteLength),this._values&&(t+=this._values.byteLength),this._nulls&&(t+=this._nulls.byteLength),this._typeIds&&(t+=this._typeIds.byteLength),this.children.reduce((n,r)=>n+r.byteLength,t)}get reservedLength(){return this._nulls.reservedLength}get reservedByteLength(){let t=0;return this._offsets&&(t+=this._offsets.reservedByteLength),this._values&&(t+=this._values.reservedByteLength),this._nulls&&(t+=this._nulls.reservedByteLength),this._typeIds&&(t+=this._typeIds.reservedByteLength),this.children.reduce((n,r)=>n+r.reservedByteLength,t)}get valueOffsets(){return this._offsets?this._offsets.buffer:null}get values(){return this._values?this._values.buffer:null}get nullBitmap(){return this._nulls?this._nulls.buffer:null}get typeIds(){return this._typeIds?this._typeIds.buffer:null}append(t){return this.set(this.length,t)}isValid(t){return this._isValid(t)}set(t,n){return this.setValid(t,this.isValid(n))&&this.setValue(t,n),this}setValue(t,n){this._setValue(this,t,n)}setValid(t,n){return this.length=this._nulls.set(t,+n).length,n}addChild(t,n=`${this.numChildren}`){throw new Error(`Cannot append children to non-nested type "${this.type}"`)}getChildAt(t){return this.children[t]||null}flush(){const t=[],n=this._values,r=this._offsets,i=this._typeIds,{length:o,nullCount:s}=this;i?(t[Ot.TYPE]=i.flush(o),r&&(t[Ot.OFFSET]=r.flush(o))):r?(n&&(t[Ot.DATA]=n.flush(r.last())),t[Ot.OFFSET]=r.flush(o)):n&&(t[Ot.DATA]=n.flush(o)),s>0&&(t[Ot.VALIDITY]=this._nulls.flush(o));const a=ft.new(this.type,0,o,s,t,this.children.map(l=>l.flush()));return this.clear(),a}finish(){return this.finished=!0,this.children.forEach(t=>t.finish()),this}clear(){return this.length=0,this._offsets&&this._offsets.clear(),this._values&&this._values.clear(),this._nulls&&this._nulls.clear(),this._typeIds&&this._typeIds.clear(),this.children.forEach(t=>t.clear()),this}}Ce.prototype.length=1;Ce.prototype.stride=1;Ce.prototype.children=null;Ce.prototype.finished=!1;Ce.prototype.nullValues=null;Ce.prototype._isValid=()=>!0;class Wr extends Ce{constructor(t){super(t),this._values=new vo(new this.ArrayType(0),this.stride)}setValue(t,n){const r=this._values;return r.reserve(t-r.length+1),super.setValue(t,n)}}class el extends Ce{constructor(t){super(t),this._pendingLength=0,this._offsets=new tg}setValue(t,n){const r=this._pending||(this._pending=new Map),i=r.get(t);i&&(this._pendingLength-=i.length),this._pendingLength+=n.length,r.set(t,n)}setValid(t,n){return super.setValid(t,n)?!0:((this._pending||(this._pending=new Map)).set(t,void 0),!1)}clear(){return this._pendingLength=0,this._pending=void 0,super.clear()}flush(){return this._flush(),super.flush()}finish(){return this._flush(),super.finish()}_flush(){const t=this._pending,n=this._pendingLength;return this._pendingLength=0,this._pending=void 0,t&&t.size>0&&this._flushPending(t,n),this}}function iS(e){const{["queueingStrategy"]:t="count"}=e,{["highWaterMark"]:n=t!=="bytes"?1e3:2**14}=e,r=t!=="bytes"?"length":"byteLength";return function*(i){let o=0,s=Ce.new(e);for(const a of i)s.append(a)[r]>=n&&++o&&(yield s.toVector());(s.finish().length>0||o===0)&&(yield s.toVector())}}function sS(e){const{["queueingStrategy"]:t="count"}=e,{["highWaterMark"]:n=t!=="bytes"?1e3:2**14}=e,r=t!=="bytes"?"length":"byteLength";return async function*(i){let o=0,s=Ce.new(e);for await(const a of i)s.append(a)[r]>=n&&++o&&(yield s.toVector());(s.finish().length>0||o===0)&&(yield s.toVector())}}class oS extends Ce{constructor(t){super(t),this._values=new qp}setValue(t,n){this._values.set(t,+n)}}class aS extends Ce{setValue(t,n){}setValid(t,n){return this.length=Math.max(t+1,this.length),n}}class mu extends Wr{}class lS extends mu{}class cS extends mu{}class uS extends Wr{}class fS extends Ce{constructor({type:t,nullValues:n,dictionaryHashFunction:r}){super({type:new zr(t.dictionary,t.indices,t.id,t.isOrdered)}),this._nulls=null,this._dictionaryOffset=0,this._keysToIndices=Object.create(null),this.indices=Ce.new({type:this.type.indices,nullValues:n}),this.dictionary=Ce.new({type:this.type.dictionary,nullValues:null}),typeof r=="function"&&(this.valueToKey=r)}get values(){return this.indices.values}get nullCount(){return this.indices.nullCount}get nullBitmap(){return this.indices.nullBitmap}get byteLength(){return this.indices.byteLength+this.dictionary.byteLength}get reservedLength(){return this.indices.reservedLength+this.dictionary.reservedLength}get reservedByteLength(){return this.indices.reservedByteLength+this.dictionary.reservedByteLength}isValid(t){return this.indices.isValid(t)}setValid(t,n){const r=this.indices;return n=r.setValid(t,n),this.length=r.length,n}setValue(t,n){let r=this._keysToIndices,i=this.valueToKey(n),o=r[i];return o===void 0&&(r[i]=o=this._dictionaryOffset+this.dictionary.append(n).length-1),this.indices.setValue(t,o)}flush(){const t=this.type,n=this._dictionary,r=this.dictionary.toVector(),i=this.indices.flush().clone(t);return i.dictionary=n?n.concat(r):r,this.finished||(this._dictionaryOffset+=r.length),this._dictionary=i.dictionary,this.clear(),i}finish(){return this.indices.finish(),this.dictionary.finish(),this._dictionaryOffset=0,this._keysToIndices=Object.create(null),super.finish()}clear(){return this.indices.clear(),this.dictionary.clear(),super.clear()}valueToKey(t){return typeof t=="string"?t:`${t}`}}class dS extends Wr{}const ng=new Float64Array(1),Ei=new Uint32Array(ng.buffer);function rg(e){let t=(e&31744)>>10,n=(e&1023)/1024,r=(-1)**((e&32768)>>15);switch(t){case 31:return r*(n?NaN:1/0);case 0:return r*(n?6103515625e-14*n:0)}return r*2**(t-15)*(1+n)}function yu(e){if(e!==e)return 32256;ng[0]=e;let t=(Ei[1]&2147483648)>>16&65535,n=Ei[1]&2146435072,r=0;return n>=1089470464?Ei[0]>0?n=31744:(n=(n&2080374784)>>16,r=(Ei[1]&1048575)>>10):n<=1056964608?(r=1048576+(Ei[1]&1048575),r=1048576+(r<<(n>>20)-998)>>21,n=0):(n=n-1056964608>>10,r=(Ei[1]&1048575)+512>>10),t|n|r&65535}var hS=Object.freeze(Object.defineProperty({__proto__:null,uint16ToFloat64:rg,float64ToUint16:yu},Symbol.toStringTag,{value:"Module"}));class nl extends Wr{}class pS extends nl{setValue(t,n){this._values.set(t,yu(n))}}class gS extends nl{setValue(t,n){this._values.set(t,n)}}class mS extends nl{setValue(t,n){this._values.set(t,n)}}const ig=Symbol.for("isArrowBigNum");function Nn(e,...t){return t.length===0?Object.setPrototypeOf(Jt(this.TypedArray,e),this.constructor.prototype):Object.setPrototypeOf(new this.TypedArray(e,...t),this.constructor.prototype)}Nn.prototype[ig]=!0;Nn.prototype.toJSON=function(){return`"${Mr(this)}"`};Nn.prototype.valueOf=function(){return sg(this)};Nn.prototype.toString=function(){return Mr(this)};Nn.prototype[Symbol.toPrimitive]=function(e="default"){switch(e){case"number":return sg(this);case"string":return Mr(this);case"default":return qs(this)}return Mr(this)};function Ui(...e){return Nn.apply(this,e)}function ji(...e){return Nn.apply(this,e)}function Qs(...e){return Nn.apply(this,e)}Object.setPrototypeOf(Ui.prototype,Object.create(Int32Array.prototype));Object.setPrototypeOf(ji.prototype,Object.create(Uint32Array.prototype));Object.setPrototypeOf(Qs.prototype,Object.create(Uint32Array.prototype));Object.assign(Ui.prototype,Nn.prototype,{constructor:Ui,signed:!0,TypedArray:Int32Array,BigIntArray:us});Object.assign(ji.prototype,Nn.prototype,{constructor:ji,signed:!1,TypedArray:Uint32Array,BigIntArray:fo});Object.assign(Qs.prototype,Nn.prototype,{constructor:Qs,signed:!0,TypedArray:Uint32Array,BigIntArray:fo});function sg(e){let{buffer:t,byteOffset:n,length:r,signed:i}=e,o=new 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Qs(t)}}var yS=Object.freeze(Object.defineProperty({__proto__:null,isArrowBigNumSymbol:ig,get bignumToString(){return Mr},get bignumToBigInt(){return qs},BN:Ti},Symbol.toStringTag,{value:"Module"}));class gr extends Wr{setValue(t,n){this._values.set(t,n)}}class vS extends gr{}class bS extends gr{}class wS extends gr{}class SS extends gr{constructor(t){t.nullValues&&(t.nullValues=t.nullValues.map(va)),super(t),this._values=new eg(new Int32Array(0),2)}get values64(){return this._values.buffer64}isValid(t){return super.isValid(va(t))}}class IS extends gr{}class TS extends gr{}class xS extends gr{}class OS extends gr{constructor(t){t.nullValues&&(t.nullValues=t.nullValues.map(va)),super(t),this._values=new eg(new Uint32Array(0),2)}get values64(){return this._values.buffer64}isValid(t){return super.isValid(va(t))}}const va=(e=>t=>(ArrayBuffer.isView(t)&&(e.buffer=t.buffer,e.byteOffset=t.byteOffset,e.byteLength=t.byteLength,t=qs(e),e.buffer=null),t))({BigIntArray:us});class bo extends 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Uint32Array(this.buffer.buffer,this.buffer.byteOffset+8,2)).plus(le.multiply(r,c)).plus(le.multiply(i,l)).plus(le.multiply(o,a)),this.buffer[3]+=le.multiply(n,c).plus(le.multiply(r,l)).plus(le.multiply(i,a)).plus(le.multiply(o,s)).low(),this}plus(t){let n=new Uint32Array(4);return n[3]=this.buffer[3]+t.buffer[3]>>>0,n[2]=this.buffer[2]+t.buffer[2]>>>0,n[1]=this.buffer[1]+t.buffer[1]>>>0,n[0]=this.buffer[0]+t.buffer[0]>>>0,n[0]<this.buffer[0]>>>0&&++n[1],n[1]<this.buffer[1]>>>0&&++n[2],n[2]<this.buffer[2]>>>0&&++n[3],this.buffer[3]=n[3],this.buffer[2]=n[2],this.buffer[1]=n[1],this.buffer[0]=n[0],this}hex(){return`${Ri(this.buffer[3])} ${Ri(this.buffer[2])} ${Ri(this.buffer[1])} ${Ri(this.buffer[0])}`}static multiply(t,n){return new En(new Uint32Array(t.buffer)).times(n)}static add(t,n){return new En(new Uint32Array(t.buffer)).plus(n)}static from(t,n=new Uint32Array(4)){return En.fromString(typeof t=="string"?t:t.toString(),n)}static fromNumber(t,n=new Uint32Array(4)){return 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ft.Timestamp(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitTime(t,{length:n,nullCount:r}=this.nextFieldNode()){return ft.Time(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitDecimal(t,{length:n,nullCount:r}=this.nextFieldNode()){return ft.Decimal(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitList(t,{length:n,nullCount:r}=this.nextFieldNode()){return ft.List(t,0,n,r,this.readNullBitmap(t,r),this.readOffsets(t),this.visit(t.children[0]))}visitStruct(t,{length:n,nullCount:r}=this.nextFieldNode()){return ft.Struct(t,0,n,r,this.readNullBitmap(t,r),this.visitMany(t.children))}visitUnion(t){return t.mode===ur.Sparse?this.visitSparseUnion(t):this.visitDenseUnion(t)}visitDenseUnion(t,{length:n,nullCount:r}=this.nextFieldNode()){return ft.Union(t,0,n,r,this.readNullBitmap(t,r),this.readTypeIds(t),this.readOffsets(t),this.visitMany(t.children))}visitSparseUnion(t,{length:n,nullCount:r}=this.nextFieldNode()){return ft.Union(t,0,n,r,this.readNullBitmap(t,r),this.readTypeIds(t),this.visitMany(t.children))}visitDictionary(t,{length:n,nullCount:r}=this.nextFieldNode()){return ft.Dictionary(t,0,n,r,this.readNullBitmap(t,r),this.readData(t.indices),this.readDictionary(t))}visitInterval(t,{length:n,nullCount:r}=this.nextFieldNode()){return ft.Interval(t,0,n,r,this.readNullBitmap(t,r),this.readData(t))}visitFixedSizeList(t,{length:n,nullCount:r}=this.nextFieldNode()){return ft.FixedSizeList(t,0,n,r,this.readNullBitmap(t,r),this.visit(t.children[0]))}visitMap(t,{length:n,nullCount:r}=this.nextFieldNode()){return ft.Map(t,0,n,r,this.readNullBitmap(t,r),this.readOffsets(t),this.visit(t.children[0]))}nextFieldNode(){return this.nodes[++this.nodesIndex]}nextBufferRange(){return this.buffers[++this.buffersIndex]}readNullBitmap(t,n,r=this.nextBufferRange()){return n>0&&this.readData(t,r)||new Uint8Array(0)}readOffsets(t,n){return this.readData(t,n)}readTypeIds(t,n){return 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md.startStruct_(n),md.endStruct_(n)}visitUnion(t,n){Ci.startTypeIdsVector(n,t.typeIds.length);const r=Ci.createTypeIdsVector(n,t.typeIds);return Ci.startUnion(n),Ci.addMode(n,t.mode),Ci.addTypeIds(n,r),Ci.endUnion(n)}visitDictionary(t,n){const r=this.visit(t.indices,n);return gs.startDictionaryEncoding(n),gs.addId(n,new jI(t.id,0)),gs.addIsOrdered(n,t.isOrdered),r!==void 0&&gs.addIndexType(n,r),gs.endDictionaryEncoding(n)}visitFixedSizeBinary(t,n){return Ll.startFixedSizeBinary(n),Ll.addByteWidth(n,t.byteWidth),Ll.endFixedSizeBinary(n)}visitFixedSizeList(t,n){return _l.startFixedSizeList(n),_l.addListSize(n,t.listSize),_l.endFixedSizeList(n)}visitMap(t,n){return Nl.startMap(n),Nl.addKeysSorted(n,t.keysSorted),Nl.endMap(n)}}const Ul=new $I;function VI(e,t=new Map){return new pe(kI(e,t),Go(e.customMetadata),t)}function Mg(e){return new vn(e.count,Pg(e.columns),Lg(e.columns))}function zI(e){return new Kn(Mg(e.data),e.id,e.isDelta)}function 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n=e.typeType();switch(n){case _e.NONE:return new Zi;case _e.Null:return new Zi;case _e.Binary:return new Gs;case _e.Utf8:return new ts;case _e.Bool:return new Ks;case _e.List:return new ns((t||[])[0]);case _e.Struct_:return new Ln(t||[])}switch(n){case _e.Int:{const r=e.type(new K.apache.arrow.flatbuf.Int);return new nn(r.isSigned(),r.bitWidth())}case _e.FloatingPoint:{const r=e.type(new K.apache.arrow.flatbuf.FloatingPoint);return new hi(r.precision())}case _e.Decimal:{const r=e.type(new K.apache.arrow.flatbuf.Decimal);return new ha(r.scale(),r.precision())}case _e.Date:{const r=e.type(new K.apache.arrow.flatbuf.Date);return new es(r.unit())}case _e.Time:{const r=e.type(new K.apache.arrow.flatbuf.Time);return new pa(r.unit(),r.bitWidth())}case _e.Timestamp:{const r=e.type(new K.apache.arrow.flatbuf.Timestamp);return new ga(r.unit(),r.timezone())}case _e.Interval:{const r=e.type(new K.apache.arrow.flatbuf.Interval);return new ma(r.unit())}case _e.Union:{const r=e.type(new K.apache.arrow.flatbuf.Union);return new Xs(r.mode(),r.typeIdsArray()||[],t||[])}case _e.FixedSizeBinary:{const r=e.type(new K.apache.arrow.flatbuf.FixedSizeBinary);return new ya(r.byteWidth())}case _e.FixedSizeList:{const r=e.type(new K.apache.arrow.flatbuf.FixedSizeList);return new Js(r.listSize(),(t||[])[0])}case _e.Map:{const r=e.type(new K.apache.arrow.flatbuf.Map);return new Zs((t||[])[0],r.keysSorted())}}throw new Error(`Unrecognized type: "${_e[n]}" (${n})`)}function aT(e,t){const n=t.fields.map(o=>ie.encode(e,o));nr.startFieldsVector(e,n.length);const r=nr.createFieldsVector(e,n),i=t.metadata&&t.metadata.size>0?nr.createCustomMetadataVector(e,[...t.metadata].map(([o,s])=>{const a=e.createString(`${o}`),l=e.createString(`${s}`);return Er.startKeyValue(e),Er.addKey(e,a),Er.addValue(e,l),Er.endKeyValue(e)})):-1;return nr.startSchema(e),nr.addFields(e,r),nr.addEndianness(e,hT?wd.Little:wd.Big),i!==-1&&nr.addCustomMetadata(e,i),nr.endSchema(e)}function lT(e,t){let n=-1,r=-1,i=-1,o=t.type,s=t.typeId;Wt.isDictionary(o)?(s=o.dictionary.typeId,i=Ul.visit(o,e),r=Ul.visit(o.dictionary,e)):r=Ul.visit(o,e);const a=(o.children||[]).map(f=>ie.encode(e,f)),l=An.createChildrenVector(e,a),c=t.metadata&&t.metadata.size>0?An.createCustomMetadataVector(e,[...t.metadata].map(([f,d])=>{const h=e.createString(`${f}`),p=e.createString(`${d}`);return Er.startKeyValue(e),Er.addKey(e,h),Er.addValue(e,p),Er.endKeyValue(e)})):-1;return t.name&&(n=e.createString(t.name)),An.startField(e),An.addType(e,r),An.addTypeType(e,s),An.addChildren(e,l),An.addNullable(e,!!t.nullable),n!==-1&&An.addName(e,n),i!==-1&&An.addDictionary(e,i),c!==-1&&An.addCustomMetadata(e,c),An.endField(e)}function cT(e,t){const n=t.nodes||[],r=t.buffers||[];Sr.startNodesVector(e,n.length),n.slice().reverse().forEach(s=>Oi.encode(e,s));const i=e.endVector();Sr.startBuffersVector(e,r.length),r.slice().reverse().forEach(s=>Wn.encode(e,s));const o=e.endVector();return Sr.startRecordBatch(e),Sr.addLength(e,new gi(t.length,0)),Sr.addNodes(e,i),Sr.addBuffers(e,o),Sr.endRecordBatch(e)}function uT(e,t){const n=vn.encode(e,t.data);return Di.startDictionaryBatch(e),Di.addId(e,new gi(t.id,0)),Di.addIsDelta(e,t.isDelta),Di.addData(e,n),Di.endDictionaryBatch(e)}function fT(e,t){return XI.createFieldNode(e,new gi(t.length,0),new gi(t.nullCount,0))}function dT(e,t){return KI.createBuffer(e,new gi(t.offset,0),new gi(t.length,0))}const hT=function(){const e=new ArrayBuffer(2);return new DataView(e).setInt16(0,256,!0),new Int16Array(e)[0]===256}();var _g=$.ByteBuffer;const Cu=e=>`Expected ${ge[e]} Message in stream, but was null or length 0.`,Bu=e=>`Header pointer of flatbuffer-encoded ${ge[e]} Message is null or length 0.`,Ng=(e,t)=>`Expected to read ${e} metadata bytes, but only read ${t}.`,Ug=(e,t)=>`Expected to read ${e} bytes for message body, but only read ${t}.`;class jg{constructor(t){this.source=t instanceof ba?t:new ba(t)}[Symbol.iterator](){return this}next(){let t;return(t=this.readMetadataLength()).done||t.value===-1&&(t=this.readMetadataLength()).done||(t=this.readMetadata(t.value)).done?Ee:t}throw(t){return this.source.throw(t)}return(t){return this.source.return(t)}readMessage(t){let n;if((n=this.next()).done)return null;if(t!=null&&n.value.headerType!==t)throw new Error(Cu(t));return n.value}readMessageBody(t){if(t<=0)return new Uint8Array(0);const n=Qt(this.source.read(t));if(n.byteLength<t)throw new Error(Ug(t,n.byteLength));return n.byteOffset%8===0&&n.byteOffset+n.byteLength<=n.buffer.byteLength?n:n.slice()}readSchema(t=!1){const n=ge.Schema,r=this.readMessage(n),i=r&&r.header();if(t&&!i)throw new Error(Bu(n));return i}readMetadataLength(){const t=this.source.read(sl),n=t&&new _g(t),r=n&&n.readInt32(0)||0;return{done:r===0,value:r}}readMetadata(t){const n=this.source.read(t);if(!n)return Ee;if(n.byteLength<t)throw new Error(Ng(t,n.byteLength));return{done:!1,value:Je.decode(n)}}}class pT{constructor(t,n){this.source=t instanceof pi?t:$p(t)?new wa(t,n):new pi(t)}[Symbol.asyncIterator](){return this}async next(){let t;return(t=await this.readMetadataLength()).done||t.value===-1&&(t=await this.readMetadataLength()).done||(t=await this.readMetadata(t.value)).done?Ee:t}async throw(t){return await this.source.throw(t)}async return(t){return await this.source.return(t)}async readMessage(t){let n;if((n=await this.next()).done)return null;if(t!=null&&n.value.headerType!==t)throw new Error(Cu(t));return n.value}async readMessageBody(t){if(t<=0)return new Uint8Array(0);const n=Qt(await this.source.read(t));if(n.byteLength<t)throw new Error(Ug(t,n.byteLength));return n.byteOffset%8===0&&n.byteOffset+n.byteLength<=n.buffer.byteLength?n:n.slice()}async readSchema(t=!1){const n=ge.Schema,r=await this.readMessage(n),i=r&&r.header();if(t&&!i)throw new Error(Bu(n));return i}async readMetadataLength(){const t=await this.source.read(sl),n=t&&new _g(t),r=n&&n.readInt32(0)||0;return{done:r===0,value:r}}async readMetadata(t){const n=await this.source.read(t);if(!n)return Ee;if(n.byteLength<t)throw new Error(Ng(t,n.byteLength));return{done:!1,value:Je.decode(n)}}}class gT extends jg{constructor(t){super(new Uint8Array(0)),this._schema=!1,this._body=[],this._batchIndex=0,this._dictionaryIndex=0,this._json=t instanceof rd?t:new rd(t)}next(){const{_json:t}=this;if(!this._schema){this._schema=!0;const n=Je.fromJSON(t.schema,ge.Schema);return{done:!1,value:n}}if(this._dictionaryIndex<t.dictionaries.length){const n=t.dictionaries[this._dictionaryIndex++];this._body=n.data.columns;const r=Je.fromJSON(n,ge.DictionaryBatch);return{done:!1,value:r}}if(this._batchIndex<t.batches.length){const n=t.batches[this._batchIndex++];this._body=n.columns;const r=Je.fromJSON(n,ge.RecordBatch);return{done:!1,value:r}}return this._body=[],Ee}readMessageBody(t){return n(this._body);function n(r){return(r||[]).reduce((i,o)=>[...i,...o.VALIDITY&&[o.VALIDITY]||[],...o.TYPE&&[o.TYPE]||[],...o.OFFSET&&[o.OFFSET]||[],...o.DATA&&[o.DATA]||[],...n(o.children)],[])}}readMessage(t){let n;if((n=this.next()).done)return null;if(t!=null&&n.value.headerType!==t)throw new Error(Cu(t));return n.value}readSchema(){const t=ge.Schema,n=this.readMessage(t),r=n&&n.header();if(!n||!r)throw new Error(Bu(t));return r}}const sl=4,Sc="ARROW1",eo=new Uint8Array(Sc.length);for(let e=0;e<Sc.length;e+=1)eo[e]=Sc.charCodeAt(e);function Fu(e,t=0){for(let n=-1,r=eo.length;++n<r;)if(eo[n]!==e[t+n])return!1;return!0}const So=eo.length,$g=So+sl,mT=So*2+sl;class Le extends Zt{constructor(){super(),this._byteLength=0,this._nodes=[],this._buffers=[],this._bufferRegions=[]}static assemble(...t){const n=new Le,r=ZS(en,t),[i=n]=n.visitMany(r);return i}visit(t){if(!Wt.isDictionary(t.type)){const{data:n,length:r,nullCount:i}=t;if(r>2147483647)throw new RangeError("Cannot write arrays larger than 2^31 - 1 in length");Wt.isNull(t.type)||Dn.call(this,i<=0?new Uint8Array(0):Qa(n.offset,r,n.nullBitmap)),this.nodes.push(new Oi(r,i))}return super.visit(t)}visitNull(t){return this}visitDictionary(t){return this.visit(t.indices)}get nodes(){return this._nodes}get buffers(){return this._buffers}get byteLength(){return this._byteLength}get bufferRegions(){return this._bufferRegions}}function Dn(e){const t=e.byteLength+7&-8;return this.buffers.push(e),this.bufferRegions.push(new Wn(this._byteLength,t)),this._byteLength+=t,this}function yT(e){const{type:t,length:n,typeIds:r,valueOffsets:i}=e;if(Dn.call(this,r),t.mode===ur.Sparse)return Ic.call(this,e);if(t.mode===ur.Dense){if(e.offset<=0)return Dn.call(this,i),Ic.call(this,e);{const o=r.reduce((f,d)=>Math.max(f,d),r[0]),s=new Int32Array(o+1),a=new Int32Array(o+1).fill(-1),l=new Int32Array(n),c=Za(-i[0],n,i);for(let f,d,h=-1;++h<n;)(d=a[f=r[h]])===-1&&(d=a[f]=c[f]),l[h]=c[h]-d,++s[f];Dn.call(this,l);for(let f,d=-1,h=t.children.length;++d<h;)if(f=e.getChildAt(d)){const p=t.typeIds[d],b=Math.min(n,s[p]);this.visit(f.slice(a[p],b))}}}return this}function vT(e){let t;return e.nullCount>=e.length?Dn.call(this,new Uint8Array(0)):(t=e.values)instanceof Uint8Array?Dn.call(this,Qa(e.offset,e.length,t)):Dn.call(this,Ys(e))}function Yr(e){return Dn.call(this,e.values.subarray(0,e.length*e.stride))}function Vg(e){const{length:t,values:n,valueOffsets:r}=e,i=r[0],o=r[t],s=Math.min(o-i,n.byteLength-i);return Dn.call(this,Za(-r[0],t,r)),Dn.call(this,n.subarray(i,i+s)),this}function Du(e){const{length:t,valueOffsets:n}=e;return n&&Dn.call(this,Za(n[0],t,n)),this.visit(e.getChildAt(0))}function Ic(e){return this.visitMany(e.type.children.map((t,n)=>e.getChildAt(n)).filter(Boolean))[0]}Le.prototype.visitBool=vT;Le.prototype.visitInt=Yr;Le.prototype.visitFloat=Yr;Le.prototype.visitUtf8=Vg;Le.prototype.visitBinary=Vg;Le.prototype.visitFixedSizeBinary=Yr;Le.prototype.visitDate=Yr;Le.prototype.visitTimestamp=Yr;Le.prototype.visitTime=Yr;Le.prototype.visitDecimal=Yr;Le.prototype.visitList=Du;Le.prototype.visitStruct=Ic;Le.prototype.visitUnion=yT;Le.prototype.visitInterval=Yr;Le.prototype.visitFixedSizeList=Du;Le.prototype.visitMap=Du;class Ru extends Ii{constructor(t){super(),this._position=0,this._started=!1,this._sink=new Ps,this._schema=null,this._dictionaryBlocks=[],this._recordBatchBlocks=[],this._dictionaryDeltaOffsets=new Map,yn(t)||(t={autoDestroy:!0,writeLegacyIpcFormat:!1}),this._autoDestroy=typeof t.autoDestroy=="boolean"?t.autoDestroy:!0,this._writeLegacyIpcFormat=typeof t.writeLegacyIpcFormat=="boolean"?t.writeLegacyIpcFormat:!1}static throughNode(t){throw new Error('"throughNode" not available in this environment')}static throughDOM(t,n){throw new Error('"throughDOM" not available in this environment')}toString(t=!1){return this._sink.toString(t)}toUint8Array(t=!1){return this._sink.toUint8Array(t)}writeAll(t){return Vr(t)?t.then(n=>this.writeAll(n)):hr(t)?_u(this,t):Lu(this,t)}get closed(){return this._sink.closed}[Symbol.asyncIterator](){return this._sink[Symbol.asyncIterator]()}toDOMStream(t){return this._sink.toDOMStream(t)}toNodeStream(t){return this._sink.toNodeStream(t)}close(){return this.reset()._sink.close()}abort(t){return this.reset()._sink.abort(t)}finish(){return this._autoDestroy?this.close():this.reset(this._sink,this._schema),this}reset(t=this._sink,n=null){return t===this._sink||t instanceof Ps?this._sink=t:(this._sink=new Ps,t&&uw(t)?this.toDOMStream({type:"bytes"}).pipeTo(t):t&&fw(t)&&this.toNodeStream({objectMode:!1}).pipe(t)),this._started&&this._schema&&this._writeFooter(this._schema),this._started=!1,this._dictionaryBlocks=[],this._recordBatchBlocks=[],this._dictionaryDeltaOffsets=new Map,(!n||!n.compareTo(this._schema))&&(n===null?(this._position=0,this._schema=null):(this._started=!0,this._schema=n,this._writeSchema(n))),this}write(t){let n=null;if(this._sink){if(t==null)return this.finish()&&void 0;if(t instanceof oe&&!(n=t.schema))return this.finish()&&void 0;if(t instanceof en&&!(n=t.schema))return this.finish()&&void 0}else throw new Error("RecordBatchWriter is closed");if(n&&!n.compareTo(this._schema)){if(this._started&&this._autoDestroy)return this.close();this.reset(this._sink,n)}t instanceof en?t instanceof cl||this._writeRecordBatch(t):t instanceof oe?this.writeAll(t.chunks):Mn(t)&&this.writeAll(t)}_writeMessage(t,n=8){const r=n-1,i=Je.encode(t),o=i.byteLength,s=this._writeLegacyIpcFormat?4:8,a=o+s+r&~r,l=a-o-s;return t.headerType===ge.RecordBatch?this._recordBatchBlocks.push(new kr(a,t.bodyLength,this._position)):t.headerType===ge.DictionaryBatch&&this._dictionaryBlocks.push(new kr(a,t.bodyLength,this._position)),this._writeLegacyIpcFormat||this._write(Int32Array.of(-1)),this._write(Int32Array.of(a-s)),o>0&&this._write(i),this._writePadding(l)}_write(t){if(this._started){const n=Qt(t);n&&n.byteLength>0&&(this._sink.write(n),this._position+=n.byteLength)}return this}_writeSchema(t){return this._writeMessage(Je.from(t))}_writeFooter(t){return this._writeLegacyIpcFormat?this._write(Int32Array.of(0)):this._write(Int32Array.of(-1,0))}_writeMagic(){return this._write(eo)}_writePadding(t){return t>0?this._write(new Uint8Array(t)):this}_writeRecordBatch(t){const{byteLength:n,nodes:r,bufferRegions:i,buffers:o}=Le.assemble(t),s=new vn(t.length,r,i),a=Je.from(s,n);return this._writeDictionaries(t)._writeMessage(a)._writeBodyBuffers(o)}_writeDictionaryBatch(t,n,r=!1){this._dictionaryDeltaOffsets.set(n,t.length+(this._dictionaryDeltaOffsets.get(n)||0));const{byteLength:i,nodes:o,bufferRegions:s,buffers:a}=Le.assemble(t),l=new vn(t.length,o,s),c=new Kn(l,n,r),f=Je.from(c,i);return this._writeMessage(f)._writeBodyBuffers(a)}_writeBodyBuffers(t){let n,r,i;for(let o=-1,s=t.length;++o<s;)(n=t[o])&&(r=n.byteLength)>0&&(this._write(n),(i=(r+7&-8)-r)>0&&this._writePadding(i));return this}_writeDictionaries(t){for(let[n,r]of t.dictionaries){let i=this._dictionaryDeltaOffsets.get(n)||0;if(i===0||(r=r.slice(i)).length>0){const o="chunks"in r?r.chunks:[r];for(const s of o)this._writeDictionaryBatch(s,n,i>0),i+=s.length}}return this}}class Mu extends Ru{static writeAll(t,n){const r=new Mu(n);return Vr(t)?t.then(i=>r.writeAll(i)):hr(t)?_u(r,t):Lu(r,t)}}class Pu extends Ru{constructor(){super(),this._autoDestroy=!0}static writeAll(t){const n=new Pu;return Vr(t)?t.then(r=>n.writeAll(r)):hr(t)?_u(n,t):Lu(n,t)}_writeSchema(t){return this._writeMagic()._writePadding(2)}_writeFooter(t){const n=to.encode(new to(t,Cn.V4,this._recordBatchBlocks,this._dictionaryBlocks));return super._writeFooter(t)._write(n)._write(Int32Array.of(n.byteLength))._writeMagic()}}function Lu(e,t){let n=t;t instanceof oe&&(n=t.chunks,e.reset(void 0,t.schema));for(const r of n)e.write(r);return e.finish()}async function _u(e,t){for await(const n of t)e.write(n);return e.finish()}const jl=new Uint8Array(0),zg=e=>[jl,jl,new Uint8Array(e),jl];function bT(e,t,n=t.reduce((r,i)=>Math.max(r,i.length),0)){let r,i,o=-1,s=t.length;const a=[...e.fields],l=[],c=(n+63&-64)>>3;for(;++o<s;)(r=t[o])&&r.length===n?l[o]=r:((i=a[o]).nullable||(a[o]=a[o].clone({nullable:!0})),l[o]=r?r._changeLengthAndBackfillNullBitmap(n):ft.new(i.type,0,n,n,zg(c)));return[new pe(a),n,l]}function wT(e){return kg(new pe(e.map(({field:t})=>t)),e)}function kg(e,t){return ST(e,t.map(n=>n instanceof 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schema(){return this._schema}get length(){return this._length}get chunks(){return this._chunks}get numCols(){return this._numChildren}clone(t=this._chunks){return new oe(this._schema,t)}getColumn(t){return this.getColumnAt(this.getColumnIndex(t))}getColumnAt(t){return this.getChildAt(t)}getColumnIndex(t){return this._schema.fields.findIndex(n=>n.name===t)}getChildAt(t){if(t<0||t>=this.numChildren)return null;let n,r;const i=this._schema.fields,o=this._children||(this._children=[]);if(r=o[t])return r;if(n=i[t]){const s=this._chunks.map(a=>a.getChildAt(t)).filter(a=>a!=null);if(s.length>0)return o[t]=new Bn(n,s)}return null}serialize(t="binary",n=!0){return(n?Mu:Pu).writeAll(this).toUint8Array(!0)}count(){return this._length}select(...t){const n=this._schema.fields.reduce((r,i,o)=>r.set(i.name,o),new Map);return this.selectAt(...t.map(r=>n.get(r)).filter(r=>r>-1))}selectAt(...t){const n=this._schema.selectAt(...t);return new oe(n,this._chunks.map(({length:r,data:{childData:i}})=>new en(n,r,t.map(o=>i[o]).filter(Boolean))))}assign(t){const n=this._schema.fields,[r,i]=t.schema.fields.reduce((a,l,c)=>{const[f,d]=a,h=n.findIndex(p=>p.name===l.name);return~h?d[h]=c:f.push(c),a},[[],[]]),o=this._schema.assign(t.schema),s=[...n.map((a,l,c,f=i[l])=>f===void 0?this.getColumnAt(l):t.getColumnAt(f)),...r.map(a=>t.getColumnAt(a))].filter(Boolean);return new oe(...kg(o,s))}}function Px(e){const{type:t}=e;return t instanceof Ln?oe.fromStruct(al.from(e)):null}function Lx(e){const{type:t}=e;return t instanceof Ln?al.from(e).then(n=>oe.fromStruct(n)):null}class en extends al{constructor(...t){let n,r=t[0],i;if(t[1]instanceof ft)[,n,i]=t;else{const o=r.fields,[,s,a]=t;n=ft.Struct(new Ln(o),0,s,0,null,a)}super(n,i),this._schema=r}static from(t){return Mn(t.values),oe.from(t)}static new(...t){const[n,r]=dg(t),i=r.filter(o=>o instanceof ne);return new en(...bT(new pe(n),i.map(o=>o.data)))}clone(t,n=this._children){return new en(this._schema,t,n)}concat(...t){const n=this._schema,r=ke.flatten(this,...t);return new oe(n,r.map(({data:i})=>new en(n,i)))}get schema(){return this._schema}get numCols(){return this._schema.fields.length}get dictionaries(){return this._dictionaries||(this._dictionaries=Hu.collect(this))}select(...t){const n=this._schema.fields.reduce((r,i,o)=>r.set(i.name,o),new Map);return this.selectAt(...t.map(r=>n.get(r)).filter(r=>r>-1))}selectAt(...t){const n=this._schema.selectAt(...t),r=t.map(i=>this.data.childData[i]).filter(Boolean);return new en(n,this.length,r)}}class cl extends en{constructor(t){super(t,0,t.fields.map(n=>ft.new(n.type,0,0,0)))}}class Hu extends Zt{constructor(){super(...arguments),this.dictionaries=new Map}static collect(t){return new Hu().visit(t.data,new Ln(t.schema.fields)).dictionaries}visit(t,n){return Wt.isDictionary(n)?this.visitDictionary(t,n):(t.childData.forEach((r,i)=>this.visit(r,n.children[i].type)),this)}visitDictionary(t,n){const r=t.dictionary;return r&&r.length>0&&this.dictionaries.set(n.id,r),this}}class Rn extends Ii{constructor(t){super(),this._impl=t}get closed(){return this._impl.closed}get schema(){return this._impl.schema}get autoDestroy(){return this._impl.autoDestroy}get dictionaries(){return this._impl.dictionaries}get numDictionaries(){return this._impl.numDictionaries}get numRecordBatches(){return this._impl.numRecordBatches}get footer(){return this._impl.isFile()?this._impl.footer:null}isSync(){return this._impl.isSync()}isAsync(){return this._impl.isAsync()}isFile(){return this._impl.isFile()}isStream(){return this._impl.isStream()}next(){return this._impl.next()}throw(t){return this._impl.throw(t)}return(t){return this._impl.return(t)}cancel(){return this._impl.cancel()}reset(t){return this._impl.reset(t),this._DOMStream=void 0,this._nodeStream=void 0,this}open(t){const n=this._impl.open(t);return Vr(n)?n.then(()=>this):this}readRecordBatch(t){return this._impl.isFile()?this._impl.readRecordBatch(t):null}[Symbol.iterator](){return this._impl[Symbol.iterator]()}[Symbol.asyncIterator](){return this._impl[Symbol.asyncIterator]()}toDOMStream(){return dn.toDOMStream(this.isSync()?{[Symbol.iterator]:()=>this}:{[Symbol.asyncIterator]:()=>this})}toNodeStream(){return dn.toNodeStream(this.isSync()?{[Symbol.iterator]:()=>this}:{[Symbol.asyncIterator]:()=>this},{objectMode:!0})}static throughNode(t){throw new Error('"throughNode" not available in this environment')}static throughDOM(t,n){throw new Error('"throughDOM" not available in this environment')}static from(t){return t instanceof Rn?t:mc(t)?jx(t):$p(t)?zx(t):Vr(t)?(async()=>await Rn.from(await t))():Vp(t)||ru(t)||zp(t)||hr(t)?Vx(new pi(t)):$x(new ba(t))}static readAll(t){return t instanceof Rn?t.isSync()?Ad(t):Ed(t):mc(t)||ArrayBuffer.isView(t)||Mn(t)||jp(t)?Ad(t):Ed(t)}}class Sa extends Rn{constructor(t){super(t),this._impl=t}[Symbol.iterator](){return this._impl[Symbol.iterator]()}async*[Symbol.asyncIterator](){yield*this[Symbol.iterator]()}}class Ia extends Rn{constructor(t){super(t),this._impl=t}[Symbol.iterator](){throw new Error("AsyncRecordBatchStreamReader is not Iterable")}[Symbol.asyncIterator](){return this._impl[Symbol.asyncIterator]()}}class Tm extends Sa{constructor(t){super(t),this._impl=t}}class _x extends Ia{constructor(t){super(t),this._impl=t}}class xm{constructor(t=new Map){this.closed=!1,this.autoDestroy=!0,this._dictionaryIndex=0,this._recordBatchIndex=0,this.dictionaries=t}get numDictionaries(){return this._dictionaryIndex}get numRecordBatches(){return this._recordBatchIndex}isSync(){return!1}isAsync(){return!1}isFile(){return!1}isStream(){return!1}reset(t){return this._dictionaryIndex=0,this._recordBatchIndex=0,this.schema=t,this.dictionaries=new Map,this}_loadRecordBatch(t,n){return new en(this.schema,t.length,this._loadVectors(t,n,this.schema.fields))}_loadDictionaryBatch(t,n){const{id:r,isDelta:i,data:o}=t,{dictionaries:s,schema:a}=this,l=s.get(r);if(i||!l){const c=a.dictionaries.get(r);return l&&i?l.concat(ne.new(this._loadVectors(o,n,[c])[0])):ne.new(this._loadVectors(o,n,[c])[0])}return l}_loadVectors(t,n,r){return new Rg(n,t.nodes,t.buffers,this.dictionaries).visitMany(r)}}class Ta extends xm{constructor(t,n){super(n),this._reader=mc(t)?new gT(this._handle=t):new jg(this._handle=t)}isSync(){return!0}isStream(){return!0}[Symbol.iterator](){return this}cancel(){!this.closed&&(this.closed=!0)&&(this.reset()._reader.return(),this._reader=null,this.dictionaries=null)}open(t){return this.closed||(this.autoDestroy=Am(this,t),this.schema||(this.schema=this._reader.readSchema())||this.cancel()),this}throw(t){return!this.closed&&this.autoDestroy&&(this.closed=!0)?this.reset()._reader.throw(t):Ee}return(t){return!this.closed&&this.autoDestroy&&(this.closed=!0)?this.reset()._reader.return(t):Ee}next(){if(this.closed)return Ee;let t,{_reader:n}=this;for(;t=this._readNextMessageAndValidate();)if(t.isSchema())this.reset(t.header());else if(t.isRecordBatch()){this._recordBatchIndex++;const r=t.header(),i=n.readMessageBody(t.bodyLength),o=this._loadRecordBatch(r,i);return{done:!1,value:o}}else if(t.isDictionaryBatch()){this._dictionaryIndex++;const r=t.header(),i=n.readMessageBody(t.bodyLength),o=this._loadDictionaryBatch(r,i);this.dictionaries.set(r.id,o)}return this.schema&&this._recordBatchIndex===0?(this._recordBatchIndex++,{done:!1,value:new cl(this.schema)}):this.return()}_readNextMessageAndValidate(t){return this._reader.readMessage(t)}}class xa extends xm{constructor(t,n){super(n),this._reader=new pT(this._handle=t)}isAsync(){return!0}isStream(){return!0}[Symbol.asyncIterator](){return this}async cancel(){!this.closed&&(this.closed=!0)&&(await this.reset()._reader.return(),this._reader=null,this.dictionaries=null)}async open(t){return this.closed||(this.autoDestroy=Am(this,t),this.schema||(this.schema=await this._reader.readSchema())||await this.cancel()),this}async throw(t){return!this.closed&&this.autoDestroy&&(this.closed=!0)?await this.reset()._reader.throw(t):Ee}async return(t){return!this.closed&&this.autoDestroy&&(this.closed=!0)?await this.reset()._reader.return(t):Ee}async next(){if(this.closed)return Ee;let t,{_reader:n}=this;for(;t=await this._readNextMessageAndValidate();)if(t.isSchema())await this.reset(t.header());else if(t.isRecordBatch()){this._recordBatchIndex++;const r=t.header(),i=await n.readMessageBody(t.bodyLength),o=this._loadRecordBatch(r,i);return{done:!1,value:o}}else if(t.isDictionaryBatch()){this._dictionaryIndex++;const r=t.header(),i=await n.readMessageBody(t.bodyLength),o=this._loadDictionaryBatch(r,i);this.dictionaries.set(r.id,o)}return this.schema&&this._recordBatchIndex===0?(this._recordBatchIndex++,{done:!1,value:new cl(this.schema)}):await this.return()}async _readNextMessageAndValidate(t){return await this._reader.readMessage(t)}}class Om extends Ta{constructor(t,n){super(t instanceof ud?t:new ud(t),n)}get footer(){return this._footer}get numDictionaries(){return this._footer?this._footer.numDictionaries:0}get numRecordBatches(){return this._footer?this._footer.numRecordBatches:0}isSync(){return!0}isFile(){return!0}open(t){if(!this.closed&&!this._footer){this.schema=(this._footer=this._readFooter()).schema;for(const n of this._footer.dictionaryBatches())n&&this._readDictionaryBatch(this._dictionaryIndex++)}return super.open(t)}readRecordBatch(t){if(this.closed)return null;this._footer||this.open();const n=this._footer&&this._footer.getRecordBatch(t);if(n&&this._handle.seek(n.offset)){const r=this._reader.readMessage(ge.RecordBatch);if(r&&r.isRecordBatch()){const i=r.header(),o=this._reader.readMessageBody(r.bodyLength);return this._loadRecordBatch(i,o)}}return null}_readDictionaryBatch(t){const n=this._footer&&this._footer.getDictionaryBatch(t);if(n&&this._handle.seek(n.offset)){const r=this._reader.readMessage(ge.DictionaryBatch);if(r&&r.isDictionaryBatch()){const i=r.header(),o=this._reader.readMessageBody(r.bodyLength),s=this._loadDictionaryBatch(i,o);this.dictionaries.set(i.id,s)}}}_readFooter(){const{_handle:t}=this,n=t.size-$g,r=t.readInt32(n),i=t.readAt(n-r,r);return to.decode(i)}_readNextMessageAndValidate(t){if(this._footer||this.open(),this._footer&&this._recordBatchIndex<this.numRecordBatches){const n=this._footer&&this._footer.getRecordBatch(this._recordBatchIndex);if(n&&this._handle.seek(n.offset))return this._reader.readMessage(t)}return null}}class Nx extends xa{constructor(t,...n){const r=typeof n[0]!="number"?n.shift():void 0,i=n[0]instanceof Map?n.shift():void 0;super(t instanceof wa?t:new wa(t,r),i)}get footer(){return this._footer}get numDictionaries(){return this._footer?this._footer.numDictionaries:0}get numRecordBatches(){return this._footer?this._footer.numRecordBatches:0}isFile(){return!0}isAsync(){return!0}async open(t){if(!this.closed&&!this._footer){this.schema=(this._footer=await this._readFooter()).schema;for(const n of this._footer.dictionaryBatches())n&&await this._readDictionaryBatch(this._dictionaryIndex++)}return await super.open(t)}async readRecordBatch(t){if(this.closed)return null;this._footer||await this.open();const n=this._footer&&this._footer.getRecordBatch(t);if(n&&await this._handle.seek(n.offset)){const r=await this._reader.readMessage(ge.RecordBatch);if(r&&r.isRecordBatch()){const i=r.header(),o=await this._reader.readMessageBody(r.bodyLength);return this._loadRecordBatch(i,o)}}return null}async _readDictionaryBatch(t){const n=this._footer&&this._footer.getDictionaryBatch(t);if(n&&await this._handle.seek(n.offset)){const r=await this._reader.readMessage(ge.DictionaryBatch);if(r&&r.isDictionaryBatch()){const i=r.header(),o=await this._reader.readMessageBody(r.bodyLength),s=this._loadDictionaryBatch(i,o);this.dictionaries.set(i.id,s)}}}async _readFooter(){const{_handle:t}=this;t._pending&&await t._pending;const n=t.size-$g,r=await t.readInt32(n),i=await t.readAt(n-r,r);return to.decode(i)}async _readNextMessageAndValidate(t){if(this._footer||await this.open(),this._footer&&this._recordBatchIndex<this.numRecordBatches){const n=this._footer.getRecordBatch(this._recordBatchIndex);if(n&&await this._handle.seek(n.offset))return await this._reader.readMessage(t)}return null}}class Ux extends Ta{constructor(t,n){super(t,n)}_loadVectors(t,n,r){return new NI(n,t.nodes,t.buffers,this.dictionaries).visitMany(r)}}function Am(e,t){return t&&typeof t.autoDestroy=="boolean"?t.autoDestroy:e.autoDestroy}function*Ad(e){const t=Rn.from(e);try{if(!t.open({autoDestroy:!1}).closed)do yield t;while(!t.reset().open().closed)}finally{t.cancel()}}async function*Ed(e){const t=await Rn.from(e);try{if(!(await t.open({autoDestroy:!1})).closed)do yield t;while(!(await t.reset().open()).closed)}finally{await t.cancel()}}function jx(e){return new Sa(new Ux(e))}function $x(e){const t=e.peek(So+7&-8);return t&&t.byteLength>=4?Fu(t)?new Tm(new Om(e.read())):new Sa(new Ta(e)):new Sa(new Ta(function*(){}()))}async function Vx(e){const t=await e.peek(So+7&-8);return t&&t.byteLength>=4?Fu(t)?new Tm(new Om(await e.read())):new Ia(new xa(e)):new Ia(new xa(async function*(){}()))}async function zx(e){const{size:t}=await e.stat(),n=new wa(e,t);return t>=mT&&Fu(await n.readAt(0,So+7&-8))?new _x(new Nx(n)):new Ia(new xa(n))}function kx(e,t){if(hr(e))return Wx(e,t);if(Mn(e))return Hx(e,t);throw new Error("toDOMStream() must be called with an Iterable or AsyncIterable")}function Hx(e,t){let n=null;const r=t&&t.type==="bytes"||!1,i=t&&t.highWaterMark||2**24;return new ReadableStream({...t,start(s){o(s,n||(n=e[Symbol.iterator]()))},pull(s){n?o(s,n):s.close()},cancel(){(n&&n.return&&n.return()||!0)&&(n=null)}},{highWaterMark:r?i:void 0,...t});function o(s,a){let l,c=null,f=s.desiredSize||null;for(;!(c=a.next(r?f:null)).done;)if(ArrayBuffer.isView(c.value)&&(l=Qt(c.value))&&(f!=null&&r&&(f=f-l.byteLength+1),c.value=l),s.enqueue(c.value),f!=null&&--f<=0)return;s.close()}}function Wx(e,t){let n=null;const r=t&&t.type==="bytes"||!1,i=t&&t.highWaterMark||2**24;return new ReadableStream({...t,async start(s){await o(s,n||(n=e[Symbol.asyncIterator]()))},async pull(s){n?await o(s,n):s.close()},async cancel(){(n&&n.return&&await n.return()||!0)&&(n=null)}},{highWaterMark:r?i:void 0,...t});async function o(s,a){let l,c=null,f=s.desiredSize||null;for(;!(c=await a.next(r?f:null)).done;)if(ArrayBuffer.isView(c.value)&&(l=Qt(c.value))&&(f!=null&&r&&(f=f-l.byteLength+1),c.value=l),s.enqueue(c.value),f!=null&&--f<=0)return;s.close()}}function Yx(e){return new Gx(e)}class Gx{constructor(t){this._numChunks=0,this._finished=!1,this._bufferedSize=0;const{["readableStrategy"]:n,["writableStrategy"]:r,["queueingStrategy"]:i="count",...o}=t;this._controller=null,this._builder=Ce.new(o),this._getSize=i!=="bytes"?Cd:Bd;const{["highWaterMark"]:s=i==="bytes"?2**14:1e3}={...n},{["highWaterMark"]:a=i==="bytes"?2**14:1e3}={...r};this.readable=new ReadableStream({cancel:()=>{this._builder.clear()},pull:l=>{this._maybeFlush(this._builder,this._controller=l)},start:l=>{this._maybeFlush(this._builder,this._controller=l)}},{highWaterMark:s,size:i!=="bytes"?Cd:Bd}),this.writable=new WritableStream({abort:()=>{this._builder.clear()},write:()=>{this._maybeFlush(this._builder,this._controller)},close:()=>{this._maybeFlush(this._builder.finish(),this._controller)}},{highWaterMark:a,size:l=>this._writeValueAndReturnChunkSize(l)})}_writeValueAndReturnChunkSize(t){const n=this._bufferedSize;return this._bufferedSize=this._getSize(this._builder.append(t)),this._bufferedSize-n}_maybeFlush(t,n){n!==null&&(this._bufferedSize>=n.desiredSize&&++this._numChunks&&this._enqueue(n,t.toVector()),t.finished&&((t.length>0||this._numChunks===0)&&++this._numChunks&&this._enqueue(n,t.toVector()),!this._finished&&(this._finished=!0)&&this._enqueue(n,null)))}_enqueue(t,n){this._bufferedSize=0,this._controller=null,n===null?t.close():t.enqueue(n)}}const Cd=e=>e.length,Bd=e=>e.byteLength;function Kx(e,t){const n=new Ps;let r=null;const i=new ReadableStream({async cancel(){await n.close()},async start(a){await s(a,r||(r=await o()))},async pull(a){r?await s(a,r):a.close()}});return{writable:new WritableStream(n,{highWaterMark:2**14,...e}),readable:i};async function o(){return await(await Rn.from(n)).open(t)}async function s(a,l){let c=a.desiredSize,f=null;for(;!(f=await l.next()).done;)if(a.enqueue(f.value),c!=null&&--c<=0)return;a.close()}}function Xx(e,t){const n=new this(e),r=new pi(n),i=new ReadableStream({type:"bytes",async cancel(){await r.cancel()},async pull(s){await o(s)},async start(s){await o(s)}},{highWaterMark:2**14,...t});return{writable:new WritableStream(n,e),readable:i};async function o(s){let a=null,l=s.desiredSize;for(;a=await r.read(l||null);)if(s.enqueue(a),l!=null&&(l-=a.byteLength)<=0)return;s.close()}}class Vi{eq(t){return t instanceof Vi||(t=new zi(t)),new Jx(this,t)}le(t){return t instanceof Vi||(t=new zi(t)),new Zx(this,t)}ge(t){return t instanceof Vi||(t=new zi(t)),new Qx(this,t)}lt(t){return new Xo(this.ge(t))}gt(t){return new Xo(this.le(t))}ne(t){return new Xo(this.eq(t))}}class zi extends Vi{constructor(t){super(),this.v=t}}class Em extends Vi{constructor(t){super(),this.name=t}bind(t){if(!this.colidx){this.colidx=-1;const r=t.schema.fields;for(let i=-1;++i<r.length;)if(r[i].name===this.name){this.colidx=i;break}if(this.colidx<0)throw new Error(`Failed to bind Col "${this.name}"`)}const n=this.vector=t.getChildAt(this.colidx);return r=>n.get(r)}}class Wu{and(...t){return new Ku(this,...t)}or(...t){return new Xu(this,...t)}not(){return new Xo(this)}}class Yu extends Wu{constructor(t,n){super(),this.left=t,this.right=n}bind(t){return this.left instanceof zi?this.right instanceof zi?this._bindLitLit(t,this.left,this.right):this._bindLitCol(t,this.left,this.right):this.right instanceof zi?this._bindColLit(t,this.left,this.right):this._bindColCol(t,this.left,this.right)}}class Gu extends Wu{constructor(...t){super(),this.children=t}}Gu.prototype.children=Object.freeze([]);class Ku extends Gu{constructor(...t){t=t.reduce((n,r)=>n.concat(r instanceof Ku?r.children:r),[]),super(...t)}bind(t){const n=this.children.map(r=>r.bind(t));return(r,i)=>n.every(o=>o(r,i))}}class Xu extends Gu{constructor(...t){t=t.reduce((n,r)=>n.concat(r instanceof Xu?r.children:r),[]),super(...t)}bind(t){const n=this.children.map(r=>r.bind(t));return(r,i)=>n.some(o=>o(r,i))}}class Jx extends Yu{_bindLitLit(t,n,r){const i=n.v==r.v;return()=>i}_bindColCol(t,n,r){const i=n.bind(t),o=r.bind(t);return(s,a)=>i(s,a)==o(s,a)}_bindColLit(t,n,r){const i=n.bind(t);if(n.vector instanceof Uu){let o;const s=n.vector;return s.dictionary!==this.lastDictionary?(o=s.reverseLookup(r.v),this.lastDictionary=s.dictionary,this.lastKey=o):o=this.lastKey,o===-1?()=>!1:a=>s.getKey(a)===o}else return(o,s)=>i(o,s)==r.v}_bindLitCol(t,n,r){return this._bindColLit(t,r,n)}}class Zx extends Yu{_bindLitLit(t,n,r){const i=n.v<=r.v;return()=>i}_bindColCol(t,n,r){const i=n.bind(t),o=r.bind(t);return(s,a)=>i(s,a)<=o(s,a)}_bindColLit(t,n,r){const i=n.bind(t);return(o,s)=>i(o,s)<=r.v}_bindLitCol(t,n,r){const i=r.bind(t);return(o,s)=>n.v<=i(o,s)}}class Qx extends Yu{_bindLitLit(t,n,r){const i=n.v>=r.v;return()=>i}_bindColCol(t,n,r){const i=n.bind(t),o=r.bind(t);return(s,a)=>i(s,a)>=o(s,a)}_bindColLit(t,n,r){const i=n.bind(t);return(o,s)=>i(o,s)>=r.v}_bindLitCol(t,n,r){const i=r.bind(t);return(o,s)=>n.v>=i(o,s)}}class Xo extends Wu{constructor(t){super(),this.child=t}bind(t){const n=this.child.bind(t);return(r,i)=>!n(r,i)}}oe.prototype.countBy=function(e){return new xo(this.chunks).countBy(e)};oe.prototype.scan=function(e,t){return new xo(this.chunks).scan(e,t)};oe.prototype.scanReverse=function(e,t){return new xo(this.chunks).scanReverse(e,t)};oe.prototype.filter=function(e){return new xo(this.chunks).filter(e)};class xo extends oe{filter(t){return new Ju(this.chunks,t)}scan(t,n){const r=this.chunks,i=r.length;for(let o=-1;++o<i;){const s=r[o];n&&n(s);for(let a=-1,l=s.length;++a<l;)t(a,s)}}scanReverse(t,n){const r=this.chunks,i=r.length;for(let o=i;--o>=0;){const s=r[o];n&&n(s);for(let a=s.length;--a>=0;)t(a,s)}}countBy(t){const n=this.chunks,r=n.length,i=typeof t=="string"?new Em(t):t;i.bind(n[r-1]);const o=i.vector;if(!Wt.isDictionary(o.type))throw new Error("countBy currently only supports dictionary-encoded columns");const s=Math.ceil(Math.log(o.length)/Math.log(256)),a=s==4?Uint32Array:s>=2?Uint16Array:Uint8Array,l=new a(o.dictionary.length);for(let c=-1;++c<r;){const f=n[c];i.bind(f);const d=i.vector.indices;for(let h=-1,p=f.length;++h<p;){let b=d.get(h);b!==null&&l[b]++}}return new Cm(o.dictionary,Un.from(l))}}class Cm extends oe{constructor(t,n){const r=new pe([new ie("values",t.type),new ie("counts",n.type)]);super(new en(r,n.length,[t,n]))}toJSON(){const t=this.getColumnAt(0),n=this.getColumnAt(1),r={};for(let i=-1;++i<this.length;)r[t.get(i)]=n.get(i);return r}}class Ju extends xo{constructor(t,n){super(t),this._predicate=n}scan(t,n){const r=this._chunks,i=r.length;for(let o=-1;++o<i;){const s=r[o],a=this._predicate.bind(s);let l=!1;for(let c=-1,f=s.length;++c<f;)a(c,s)&&(n&&!l&&(n(s),l=!0),t(c,s))}}scanReverse(t,n){const r=this._chunks,i=r.length;for(let o=i;--o>=0;){const s=r[o],a=this._predicate.bind(s);let l=!1;for(let c=s.length;--c>=0;)a(c,s)&&(n&&!l&&(n(s),l=!0),t(c,s))}}count(){let t=0;const n=this._chunks,r=n.length;for(let i=-1;++i<r;){const o=n[i],s=this._predicate.bind(o);for(let a=-1,l=o.length;++a<l;)s(a,o)&&++t}return t}*[Symbol.iterator](){const t=this._chunks,n=t.length;for(let r=-1;++r<n;){const i=t[r],o=this._predicate.bind(i);for(let s=-1,a=i.length;++s<a;)o(s,i)&&(yield i.get(s))}}filter(t){return new Ju(this._chunks,this._predicate.and(t))}countBy(t){const n=this._chunks,r=n.length,i=typeof t=="string"?new Em(t):t;i.bind(n[r-1]);const o=i.vector;if(!Wt.isDictionary(o.type))throw new Error("countBy currently only supports dictionary-encoded columns");const s=Math.ceil(Math.log(o.length)/Math.log(256)),a=s==4?Uint32Array:s>=2?Uint16Array:Uint8Array,l=new a(o.dictionary.length);for(let c=-1;++c<r;){const f=n[c],d=this._predicate.bind(f);i.bind(f);const h=i.vector.indices;for(let p=-1,b=f.length;++p<b;){let A=h.get(p);A!==null&&d(p,f)&&l[A]++}}return new Cm(o.dictionary,Un.from(l))}}({...yS,..._I,...kw,...hS,...Nw,...WS});dn.toDOMStream=kx;Ce.throughDOM=Yx;Rn.throughDOM=Kx;Ru.throughDOM=Xx;/**
 * @license
 * Copyright 2018-2021 Streamlit Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */var Fd=function(){function e(t,n,r,i){var o=this;this.getCell=function(s,a){var l=s<o.headerRows&&a<o.headerColumns,c=s>=o.headerRows&&a<o.headerColumns,f=s<o.headerRows&&a>=o.headerColumns;if(l){var d=["blank"];return a>0&&d.push("level"+s),{type:"blank",classNames:d.join(" "),content:""}}else if(f){var h=a-o.headerColumns,d=["col_heading","level"+s,"col"+h];return{type:"columns",classNames:d.join(" "),content:o.getContent(o.columnsTable,h,s)}}else if(c){var p=s-o.headerRows,d=["row_heading","level"+a,"row"+p];return{type:"index",id:"T_"+o.uuid+"level"+a+"_row"+p,classNames:d.join(" "),content:o.getContent(o.indexTable,p,a)}}else{var p=s-o.headerRows,h=a-o.headerColumns,d=["data","row"+p,"col"+h],b=o.styler?o.getContent(o.styler.displayValuesTable,p,h):o.getContent(o.dataTable,p,h);return{type:"data",id:"T_"+o.uuid+"row"+p+"_col"+h,classNames:d.join(" "),content:b}}},this.getContent=function(s,a,l){var c=s.getColumnAt(l);if(c===null)return"";var f=o.getColumnTypeId(s,l);switch(f){case D.Timestamp:return o.nanosToDate(c.get(a));default:return c.get(a)}},this.dataTable=oe.from(t),this.indexTable=oe.from(n),this.columnsTable=oe.from(r),this.styler=i?{caption:i.caption,displayValuesTable:oe.from(i.displayValues),styles:i.styles,uuid:i.uuid}:void 0}return Object.defineProperty(e.prototype,"rows",{get:function(){return this.indexTable.length+this.columnsTable.numCols},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"columns",{get:function(){return this.indexTable.numCols+this.columnsTable.length},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"headerRows",{get:function(){return this.rows-this.dataRows},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"headerColumns",{get:function(){return this.columns-this.dataColumns},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"dataRows",{get:function(){return this.dataTable.length},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"dataColumns",{get:function(){return this.dataTable.numCols},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"uuid",{get:function(){return this.styler&&this.styler.uuid},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"caption",{get:function(){return this.styler&&this.styler.caption},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"styles",{get:function(){return this.styler&&this.styler.styles},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"table",{get:function(){return this.dataTable},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"index",{get:function(){return this.indexTable},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"columnTable",{get:function(){return this.columnsTable},enumerable:!0,configurable:!0}),e.prototype.serialize=function(){return{data:this.dataTable.serialize(),index:this.indexTable.serialize(),columns:this.columnsTable.serialize()}},e.prototype.getColumnTypeId=function(t,n){return t.schema.fields[n].type.typeId},e.prototype.nanosToDate=function(t){return new Date(t/1e6)},e}();/**
 * @license
 * Copyright 2018-2021 Streamlit Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */var Ls=globalThis&&globalThis.__assign||function(){return Ls=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++){t=arguments[n];for(var i in t)Object.prototype.hasOwnProperty.call(t,i)&&(e[i]=t[i])}return e},Ls.apply(this,arguments)},_s;(function(e){e.COMPONENT_READY="streamlit:componentReady",e.SET_COMPONENT_VALUE="streamlit:setComponentValue",e.SET_FRAME_HEIGHT="streamlit:setFrameHeight"})(_s||(_s={}));var Sn=function(){function e(){}return e.API_VERSION=1,e.RENDER_EVENT="streamlit:render",e.events=new $r,e.registeredMessageListener=!1,e.setComponentReady=function(){e.registeredMessageListener||(window.addEventListener("message",e.onMessageEvent),e.registeredMessageListener=!0),e.sendBackMsg(_s.COMPONENT_READY,{apiVersion:e.API_VERSION})},e.setFrameHeight=function(t){t===void 0&&(t=document.body.scrollHeight),t!==e.lastFrameHeight&&(e.lastFrameHeight=t,e.sendBackMsg(_s.SET_FRAME_HEIGHT,{height:t}))},e.setComponentValue=function(t){var n;t instanceof Fd?(n="dataframe",t=t.serialize()):tO(t)?(n="bytes",t=new Uint8Array(t.buffer)):t instanceof ArrayBuffer?(n="bytes",t=new Uint8Array(t)):n="json",e.sendBackMsg(_s.SET_COMPONENT_VALUE,{value:t,dataType:n})},e.onMessageEvent=function(t){var n=t.data.type;switch(n){case e.RENDER_EVENT:e.onRenderMessage(t.data);break}},e.onRenderMessage=function(t){var n=t.args;n==null&&(console.error("Got null args in onRenderMessage. This should never happen"),n={});var r=t.dfs&&t.dfs.length>0?e.argsDataframeToObject(t.dfs):{};n=Ls(Ls({},n),r);var i=Boolean(t.disabled),o=t.theme;o&&qx(o);var s={disabled:i,args:n,theme:o},a=new CustomEvent(e.RENDER_EVENT,{detail:s});e.events.dispatchEvent(a)},e.argsDataframeToObject=function(t){var n=t.map(function(r){var i=r.key,o=r.value;return[i,e.toArrowTable(o)]});return Object.fromEntries(n)},e.toArrowTable=function(t){var n=t.data,r=n.data,i=n.index,o=n.columns,s=n.styler;return new Fd(r,i,o,s)},e.sendBackMsg=function(t,n){window.parent.postMessage(Ls({isStreamlitMessage:!0,type:t},n),"*")},e}(),qx=function(e){var t=document.createElement("style");document.head.appendChild(t),t.innerHTML=`
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            `).concat(u||"",`
            <img class="lg-object lg-video-poster" src="`).concat(v,`" />
        </div>`)},getFocusableElements:function(v){var u=v.querySelectorAll('a[href]:not([disabled]), button:not([disabled]), textarea:not([disabled]), input[type="text"]:not([disabled]), input[type="radio"]:not([disabled]), input[type="checkbox"]:not([disabled]), select:not([disabled])'),g=[].filter.call(u,function(S){var x=window.getComputedStyle(S);return x.display!=="none"&&x.visibility!=="hidden"});return g},getDynamicOptions:function(v,u,g,S){var x=[],_=[].concat(wt,Z(u));return[].forEach.call(v,function(H){for(var ht={},Et=0;Et<H.attributes.length;Et++){var St=H.attributes[Et];if(St.specified){var Ct=Dt(St.name),jt="";_.indexOf(Ct)>-1&&(jt=Ct),jt&&(ht[jt]=St.value)}}var Bt=X(H),Ae=Bt.find("img").first().attr("alt"),ue=Bt.attr("title"),Fe=S?Bt.attr(S):Bt.find("img").first().attr("src");ht.thumb=Fe,g&&!ht.subHtml&&(ht.subHtml=ue||Ae||""),ht.alt=Ae||ue||"",x.push(ht)}),x},isMobile:function(){return/iPhone|iPad|iPod|Android/i.test(navigator.userAgent)},isVideo:function(v,u,g){if(!v){if(u)return{html5:!0};console.error("lightGallery :- data-src is not provided on slide item "+(g+1)+". Please make sure the selector property is properly configured. More info - https://www.lightgalleryjs.com/demos/html-markup/");return}var S=v.match(/\/\/(?:www\.)?youtu(?:\.be|be\.com|be-nocookie\.com)\/(?:watch\?v=|embed\/)?([a-z0-9\-\_\%]+)([\&|?][\S]*)*/i),x=v.match(/\/\/(?:www\.)?(?:player\.)?vimeo.com\/(?:video\/)?([0-9a-z\-_]+)(.*)?/i),_=v.match(/https?:\/\/(.+)?(wistia\.com|wi\.st)\/(medias|embed)\/([0-9a-z\-_]+)(.*)/);if(S)return{youtube:S};if(x)return{vimeo:x};if(_)return{wistia:_}}},Tt=Ht,J=0,mt=function(){function T(v,u){if(p(this,T),this.lgOpened=!1,this.index=0,this.plugins=[],this.lGalleryOn=!1,this.lgBusy=!1,this.currentItemsInDom=[],this.prevScrollTop=0,this.bodyPaddingRight=0,this.isDummyImageRemoved=!1,this.dragOrSwipeEnabled=!1,this.mediaContainerPosition={top:0,bottom:0},!v)return this;if(J++,this.lgId=J,this.el=v,this.LGel=X(v),this.generateSettings(u),this.buildModules(),this.settings.dynamic&&this.settings.dynamicEl!==void 0&&!Array.isArray(this.settings.dynamicEl))throw"When using dynamic mode, you must also define dynamicEl as an Array.";return this.galleryItems=this.getItems(),this.normalizeSettings(),this.init(),this.validateLicense(),this}return A(T,[{key:"generateSettings",value:function(u){if(this.settings=h(h({},Y),u),this.settings.isMobile&&typeof this.settings.isMobile=="function"?this.settings.isMobile():Tt.isMobile()){var g=h(h({},this.settings.mobileSettings),this.settings.mobileSettings);this.settings=h(h({},this.settings),g)}}},{key:"normalizeSettings",value:function(){this.settings.slideEndAnimation&&(this.settings.hideControlOnEnd=!1),this.settings.closable||(this.settings.swipeToClose=!1),this.zoomFromOrigin=this.settings.zoomFromOrigin,this.settings.dynamic&&(this.zoomFromOrigin=!1),this.settings.container||(this.settings.container=document.body),this.settings.preload=Math.min(this.settings.preload,this.galleryItems.length)}},{key:"init",value:function(){var u=this;this.addSlideVideoInfo(this.galleryItems),this.buildStructure(),this.LGel.trigger(j.init,{instance:this}),this.settings.keyPress&&this.keyPress(),setTimeout(function(){u.enableDrag(),u.enableSwipe(),u.triggerPosterClick()},50),this.arrow(),this.settings.mousewheel&&this.mousewheel(),this.settings.dynamic||this.openGalleryOnItemClick()}},{key:"openGalleryOnItemClick",value:function(){for(var u=this,g=function(_){var H=u.items[_],ht=X(H),Et=ut.generateUUID();ht.attr("data-lg-id",Et).on("click.lgcustom-item-".concat(Et),function(St){St.preventDefault();var Ct=u.settings.index||_;u.openGallery(Ct,H)})},S=0;S<this.items.length;S++)g(S)}},{key:"buildModules",value:function(){var u=this;this.settings.plugins.forEach(function(g){u.plugins.push(new g(u,X))})}},{key:"validateLicense",value:function(){this.settings.licenseKey?this.settings.licenseKey==="0000-0000-000-0000"&&console.warn("lightGallery: ".concat(this.settings.licenseKey," license key is not valid for production use")):console.error("Please provide a valid license key")}},{key:"getSlideItem",value:function(u){return X(this.getSlideItemId(u))}},{key:"getSlideItemId",value:function(u){return"#lg-item-".concat(this.lgId,"-").concat(u)}},{key:"getIdName",value:function(u){return"".concat(u,"-").concat(this.lgId)}},{key:"getElementById",value:function(u){return X("#".concat(this.getIdName(u)))}},{key:"manageSingleSlideClassName",value:function(){this.galleryItems.length<2?this.outer.addClass("lg-single-item"):this.outer.removeClass("lg-single-item")}},{key:"buildStructure",value:function(){var u=this,g=this.$container&&this.$container.get();if(!g){var S="",x="";this.settings.controls&&(S='<button type="button" id="'.concat(this.getIdName("lg-prev"),'" aria-label="').concat(this.settings.strings.previousSlide,'" class="lg-prev lg-icon"> ').concat(this.settings.prevHtml,` </button>
                <button type="button" id="`).concat(this.getIdName("lg-next"),'" aria-label="').concat(this.settings.strings.nextSlide,'" class="lg-next lg-icon"> ').concat(this.settings.nextHtml," </button>")),this.settings.appendSubHtmlTo!==".lg-item"&&(x='<div class="lg-sub-html" role="status" aria-live="polite"></div>');var _="";this.settings.allowMediaOverlap&&(_+="lg-media-overlap ");var H=this.settings.ariaLabelledby?'aria-labelledby="'+this.settings.ariaLabelledby+'"':"",ht=this.settings.ariaDescribedby?'aria-describedby="'+this.settings.ariaDescribedby+'"':"",Et="lg-container ".concat(this.settings.addClass," ").concat(document.body!==this.settings.container?"lg-inline":""),St=this.settings.closable&&this.settings.showCloseIcon?'<button type="button" aria-label="'.concat(this.settings.strings.closeGallery,'" id="').concat(this.getIdName("lg-close"),'" class="lg-close lg-icon"></button>'):"",Ct=this.settings.showMaximizeIcon?'<button type="button" aria-label="'.concat(this.settings.strings.toggleMaximize,'" id="').concat(this.getIdName("lg-maximize"),'" class="lg-maximize lg-icon"></button>'):"",jt=`
        <div class="`.concat(Et,'" id="').concat(this.getIdName("lg-container"),'" tabindex="-1" aria-modal="true" ').concat(H," ").concat(ht,` role="dialog"
        >
            <div id="`).concat(this.getIdName("lg-backdrop"),`" class="lg-backdrop"></div>

            <div id="`).concat(this.getIdName("lg-outer"),'" class="lg-outer lg-use-css3 lg-css3 lg-hide-items ').concat(_,` ">

              <div id="`).concat(this.getIdName("lg-content"),`" class="lg-content">
                <div id="`).concat(this.getIdName("lg-inner"),`" class="lg-inner">
                </div>
                `).concat(S,`
              </div>
                <div id="`).concat(this.getIdName("lg-toolbar"),`" class="lg-toolbar lg-group">
                    `).concat(Ct,`
                    `).concat(St,`
                    </div>
                    `).concat(this.settings.appendSubHtmlTo===".lg-outer"?x:"",`
                <div id="`).concat(this.getIdName("lg-components"),`" class="lg-components">
                    `).concat(this.settings.appendSubHtmlTo===".lg-sub-html"?x:"",`
                </div>
            </div>
        </div>
        `);X(this.settings.container).append(jt),document.body!==this.settings.container&&X(this.settings.container).css("position","relative"),this.outer=this.getElementById("lg-outer"),this.$lgComponents=this.getElementById("lg-components"),this.$backdrop=this.getElementById("lg-backdrop"),this.$container=this.getElementById("lg-container"),this.$inner=this.getElementById("lg-inner"),this.$content=this.getElementById("lg-content"),this.$toolbar=this.getElementById("lg-toolbar"),this.$backdrop.css("transition-duration",this.settings.backdropDuration+"ms");var Bt="".concat(this.settings.mode," ");this.manageSingleSlideClassName(),this.settings.enableDrag&&(Bt+="lg-grab "),this.outer.addClass(Bt),this.$inner.css("transition-timing-function",this.settings.easing),this.$inner.css("transition-duration",this.settings.speed+"ms"),this.settings.download&&this.$toolbar.append('<a id="'.concat(this.getIdName("lg-download"),'" target="_blank" rel="noopener" aria-label="').concat(this.settings.strings.download,'" download class="lg-download lg-icon"></a>')),this.counter(),X(window).on("resize.lg.global".concat(this.lgId," orientationchange.lg.global").concat(this.lgId),function(){u.refreshOnResize()}),this.hideBars(),this.manageCloseGallery(),this.toggleMaximize(),this.initModules()}}},{key:"refreshOnResize",value:function(){if(this.lgOpened){var u=this.galleryItems[this.index],g=u.__slideVideoInfo;this.mediaContainerPosition=this.getMediaContainerPosition();var S=this.mediaContainerPosition,x=S.top,_=S.bottom;if(this.currentImageSize=Tt.getSize(this.items[this.index],this.outer,x+_,g&&this.settings.videoMaxSize),g&&this.resizeVideoSlide(this.index,this.currentImageSize),this.zoomFromOrigin&&!this.isDummyImageRemoved){var H=this.getDummyImgStyles(this.currentImageSize);this.outer.find(".lg-current .lg-dummy-img").first().attr("style",H)}this.LGel.trigger(j.containerResize)}}},{key:"resizeVideoSlide",value:function(u,g){var S=this.getVideoContStyle(g),x=this.getSlideItem(u);x.find(".lg-video-cont").attr("style",S)}},{key:"updateSlides",value:function(u,g){if(this.index>u.length-1&&(this.index=u.length-1),u.length===1&&(this.index=0),!u.length){this.closeGallery();return}var S=this.galleryItems[g].src;this.galleryItems=u,this.updateControls(),this.$inner.empty(),this.currentItemsInDom=[];var x=0;this.galleryItems.some(function(_,H){return _.src===S?(x=H,!0):!1}),this.currentItemsInDom=this.organizeSlideItems(x,-1),this.loadContent(x,!0),this.getSlideItem(x).addClass("lg-current"),this.index=x,this.updateCurrentCounter(x),this.LGel.trigger(j.updateSlides)}},{key:"getItems",value:function(){if(this.items=[],this.settings.dynamic)return this.settings.dynamicEl||[];if(this.settings.selector==="this")this.items.push(this.el);else if(this.settings.selector)if(typeof this.settings.selector=="string")if(this.settings.selectWithin){var u=X(this.settings.selectWithin);this.items=u.find(this.settings.selector).get()}else this.items=this.el.querySelectorAll(this.settings.selector);else this.items=this.settings.selector;else this.items=this.el.children;return Tt.getDynamicOptions(this.items,this.settings.extraProps,this.settings.getCaptionFromTitleOrAlt,this.settings.exThumbImage)}},{key:"shouldHideScrollbar",value:function(){return this.settings.hideScrollbar&&document.body===this.settings.container}},{key:"hideScrollbar",value:function(){if(!!this.shouldHideScrollbar()){this.bodyPaddingRight=parseFloat(X("body").style().paddingRight);var u=document.documentElement.getBoundingClientRect(),g=window.innerWidth-u.width;X(document.body).css("padding-right",g+this.bodyPaddingRight+"px"),X(document.body).addClass("lg-overlay-open")}}},{key:"resetScrollBar",value:function(){!this.shouldHideScrollbar()||(X(document.body).css("padding-right",this.bodyPaddingRight+"px"),X(document.body).removeClass("lg-overlay-open"))}},{key:"openGallery",value:function(){var u=this,g=arguments.length>0&&arguments[0]!==void 0?arguments[0]:this.settings.index,S=arguments.length>1?arguments[1]:void 0;if(!this.lgOpened){this.lgOpened=!0,this.outer.removeClass("lg-hide-items"),this.hideScrollbar(),this.$container.addClass("lg-show");var x=this.getItemsToBeInsertedToDom(g,g);this.currentItemsInDom=x;var _="";x.forEach(function(Bt){_=_+'<div id="'.concat(Bt,'" class="lg-item"></div>')}),this.$inner.append(_),this.addHtml(g);var H="";this.mediaContainerPosition=this.getMediaContainerPosition();var ht=this.mediaContainerPosition,Et=ht.top,St=ht.bottom;this.settings.allowMediaOverlap||this.setMediaContainerPosition(Et,St);var Ct=this.galleryItems[g].__slideVideoInfo;this.zoomFromOrigin&&S&&(this.currentImageSize=Tt.getSize(S,this.outer,Et+St,Ct&&this.settings.videoMaxSize),H=Tt.getTransform(S,this.outer,Et,St,this.currentImageSize)),(!this.zoomFromOrigin||!H)&&(this.outer.addClass(this.settings.startClass),this.getSlideItem(g).removeClass("lg-complete"));var jt=this.settings.zoomFromOrigin?100:this.settings.backdropDuration;setTimeout(function(){u.outer.addClass("lg-components-open")},jt),this.index=g,this.LGel.trigger(j.beforeOpen),this.getSlideItem(g).addClass("lg-current"),this.lGalleryOn=!1,this.prevScrollTop=X(window).scrollTop(),setTimeout(function(){if(u.zoomFromOrigin&&H){var Bt=u.getSlideItem(g);Bt.css("transform",H),setTimeout(function(){Bt.addClass("lg-start-progress lg-start-end-progress").css("transition-duration",u.settings.startAnimationDuration+"ms"),u.outer.addClass("lg-zoom-from-image")}),setTimeout(function(){Bt.css("transform","translate3d(0, 0, 0)")},100)}setTimeout(function(){u.$backdrop.addClass("in"),u.$container.addClass("lg-show-in")},10),setTimeout(function(){u.settings.trapFocus&&document.body===u.settings.container&&u.trapFocus()},u.settings.backdropDuration+50),(!u.zoomFromOrigin||!H)&&setTimeout(function(){u.outer.addClass("lg-visible")},u.settings.backdropDuration),u.slide(g,!1,!1,!1),u.LGel.trigger(j.afterOpen)}),document.body===this.settings.container&&X("html").addClass("lg-on")}}},{key:"getMediaContainerPosition",value:function(){if(this.settings.allowMediaOverlap)return{top:0,bottom:0};var u=this.$toolbar.get().clientHeight||0,g=this.outer.find(".lg-components .lg-sub-html").get(),S=this.settings.defaultCaptionHeight||g&&g.clientHeight||0,x=this.outer.find(".lg-thumb-outer").get(),_=x?x.clientHeight:0,H=_+S;return{top:u,bottom:H}}},{key:"setMediaContainerPosition",value:function(){var u=arguments.length>0&&arguments[0]!==void 0?arguments[0]:0,g=arguments.length>1&&arguments[1]!==void 0?arguments[1]:0;this.$content.css("top",u+"px").css("bottom",g+"px")}},{key:"hideBars",value:function(){var u=this;setTimeout(function(){u.outer.removeClass("lg-hide-items"),u.settings.hideBarsDelay>0&&(u.outer.on("mousemove.lg click.lg touchstart.lg",function(){u.outer.removeClass("lg-hide-items"),clearTimeout(u.hideBarTimeout),u.hideBarTimeout=setTimeout(function(){u.outer.addClass("lg-hide-items")},u.settings.hideBarsDelay)}),u.outer.trigger("mousemove.lg"))},this.settings.showBarsAfter)}},{key:"initPictureFill",value:function(u){if(this.settings.supportLegacyBrowser)try{picturefill({elements:[u.get()]})}catch{console.warn("lightGallery :- If you want srcset or picture tag to be supported for older browser please include picturefil javascript library in your document.")}}},{key:"counter",value:function(){if(this.settings.counter){var u=`<div class="lg-counter" role="status" aria-live="polite">
                <span id="`.concat(this.getIdName("lg-counter-current"),'" class="lg-counter-current">').concat(this.index+1,` </span> /
                <span id="`).concat(this.getIdName("lg-counter-all"),'" class="lg-counter-all">').concat(this.galleryItems.length," </span></div>");this.outer.find(this.settings.appendCounterTo).append(u)}}},{key:"addHtml",value:function(u){var g,S;if(this.galleryItems[u].subHtmlUrl?S=this.galleryItems[u].subHtmlUrl:g=this.galleryItems[u].subHtml,!S)if(g){var x=g.substring(0,1);(x==="."||x==="#")&&(this.settings.subHtmlSelectorRelative&&!this.settings.dynamic?g=X(this.items).eq(u).find(g).first().html():g=X(g).first().html())}else g="";if(this.settings.appendSubHtmlTo!==".lg-item")S?this.outer.find(".lg-sub-html").load(S):this.outer.find(".lg-sub-html").html(g);else{var _=X(this.getSlideItemId(u));S?_.load(S):_.append('<div class="lg-sub-html">'.concat(g,"</div>"))}typeof g!="undefined"&&g!==null&&(g===""?this.outer.find(this.settings.appendSubHtmlTo).addClass("lg-empty-html"):this.outer.find(this.settings.appendSubHtmlTo).removeClass("lg-empty-html")),this.LGel.trigger(j.afterAppendSubHtml,{index:u})}},{key:"preload",value:function(u){for(var g=1;g<=this.settings.preload&&!(g>=this.galleryItems.length-u);g++)this.loadContent(u+g,!1);for(var S=1;S<=this.settings.preload&&!(u-S<0);S++)this.loadContent(u-S,!1)}},{key:"getDummyImgStyles",value:function(u){return u?"width:".concat(u.width,`px;
                margin-left: -`).concat(u.width/2,`px;
                margin-top: -`).concat(u.height/2,`px;
                height:`).concat(u.height,"px"):""}},{key:"getVideoContStyle",value:function(u){return u?"width:".concat(u.width,`px;
                height:`).concat(u.height,"px"):""}},{key:"getDummyImageContent",value:function(u,g,S){var x;if(this.settings.dynamic||(x=X(this.items).eq(g)),x){var _;if(this.settings.exThumbImage?_=x.attr(this.settings.exThumbImage):_=x.find("img").first().attr("src"),!_)return"";var H=this.getDummyImgStyles(this.currentImageSize),ht="<img ".concat(S,' style="').concat(H,'" class="lg-dummy-img" src="').concat(_,'" />');return u.addClass("lg-first-slide"),this.outer.addClass("lg-first-slide-loading"),ht}return""}},{key:"setImgMarkup",value:function(u,g,S){var x=this.galleryItems[S],_=x.alt,H=x.srcset,ht=x.sizes,Et=x.sources,St="",Ct=_?'alt="'+_+'"':"";this.isFirstSlideWithZoomAnimation()?St=this.getDummyImageContent(g,S,Ct):St=Tt.getImgMarkup(S,u,Ct,H,ht,Et);var jt='<picture class="lg-img-wrap"> '.concat(St,"</picture>");g.prepend(jt)}},{key:"onSlideObjectLoad",value:function(u,g,S,x){var _=u.find(".lg-object").first();Tt.isImageLoaded(_.get())||g?S():(_.on("load.lg error.lg",function(){S&&S()}),_.on("error.lg",function(){x&&x()}))}},{key:"onLgObjectLoad",value:function(u,g,S,x,_,H){var ht=this;this.onSlideObjectLoad(u,H,function(){ht.triggerSlideItemLoad(u,g,S,x,_)},function(){u.addClass("lg-complete lg-complete_"),u.html('<span class="lg-error-msg">Oops... Failed to load content...</span>')})}},{key:"triggerSlideItemLoad",value:function(u,g,S,x,_){var H=this,ht=this.galleryItems[g],Et=_&&this.getSlideType(ht)==="video"&&!ht.poster?x:0;setTimeout(function(){u.addClass("lg-complete lg-complete_"),H.LGel.trigger(j.slideItemLoad,{index:g,delay:S||0,isFirstSlide:_})},Et)}},{key:"isFirstSlideWithZoomAnimation",value:function(){return!!(!this.lGalleryOn&&this.zoomFromOrigin&&this.currentImageSize)}},{key:"addSlideVideoInfo",value:function(u){var g=this;u.forEach(function(S,x){S.__slideVideoInfo=Tt.isVideo(S.src,!!S.video,x),S.__slideVideoInfo&&g.settings.loadYouTubePoster&&!S.poster&&S.__slideVideoInfo.youtube&&(S.poster="//img.youtube.com/vi/".concat(S.__slideVideoInfo.youtube[1],"/maxresdefault.jpg"))})}},{key:"loadContent",value:function(u,g){var S=this,x=this.galleryItems[u],_=X(this.getSlideItemId(u)),H=x.poster,ht=x.srcset,Et=x.sizes,St=x.sources,Ct=x.src,jt=x.video,Bt=jt&&typeof jt=="string"?JSON.parse(jt):jt;if(x.responsive){var Ae=x.responsive.split(",");Ct=Tt.getResponsiveSrc(Ae)||Ct}var ue=x.__slideVideoInfo,Fe="",ye=!!x.iframe,je=!this.lGalleryOn,sn=0;if(je&&(this.zoomFromOrigin&&this.currentImageSize?sn=this.settings.startAnimationDuration+10:sn=this.settings.backdropDuration+10),!_.hasClass("lg-loaded")){if(ue){var on=this.mediaContainerPosition,ul=on.top,fl=on.bottom,dl=Tt.getSize(this.items[u],this.outer,ul+fl,ue&&this.settings.videoMaxSize);Fe=this.getVideoContStyle(dl)}if(ye){var hl=Tt.getIframeMarkup(this.settings.iframeWidth,this.settings.iframeHeight,this.settings.iframeMaxWidth,this.settings.iframeMaxHeight,Ct,x.iframeTitle);_.prepend(hl)}else if(H){var Oo="",pl=je&&this.zoomFromOrigin&&this.currentImageSize;pl&&(Oo=this.getDummyImageContent(_,u,""));var gl=Tt.getVideoPosterMarkup(H,Oo||"",Fe,this.settings.strings.playVideo,ue);_.prepend(gl)}else if(ue){var ml='<div class="lg-video-cont " style="'.concat(Fe,'"></div>');_.prepend(ml)}else if(this.setImgMarkup(Ct,_,u),ht||St){var yl=_.find(".lg-object");this.initPictureFill(yl)}(H||ue)&&this.LGel.trigger(j.hasVideo,{index:u,src:Ct,html5Video:Bt,hasPoster:!!H}),this.LGel.trigger(j.afterAppendSlide,{index:u}),this.lGalleryOn&&this.settings.appendSubHtmlTo===".lg-item"&&this.addHtml(u)}var Gr=0;sn&&!X(document.body).hasClass("lg-from-hash")&&(Gr=sn),this.isFirstSlideWithZoomAnimation()&&(setTimeout(function(){_.removeClass("lg-start-end-progress lg-start-progress").removeAttr("style")},this.settings.startAnimationDuration+100),_.hasClass("lg-loaded")||setTimeout(function(){if(S.getSlideType(x)==="image"){var Ai=x.alt,Ao=Ai?'alt="'+Ai+'"':"";if(_.find(".lg-img-wrap").append(Tt.getImgMarkup(u,Ct,Ao,ht,Et,x.sources)),ht||St){var vl=_.find(".lg-object");S.initPictureFill(vl)}}(S.getSlideType(x)==="image"||S.getSlideType(x)==="video"&&H)&&(S.onLgObjectLoad(_,u,sn,Gr,!0,!1),S.onSlideObjectLoad(_,!!(ue&&ue.html5&&!H),function(){S.loadContentOnFirstSlideLoad(u,_,Gr)},function(){S.loadContentOnFirstSlideLoad(u,_,Gr)}))},this.settings.startAnimationDuration+100)),_.addClass("lg-loaded"),(!this.isFirstSlideWithZoomAnimation()||this.getSlideType(x)==="video"&&!H)&&this.onLgObjectLoad(_,u,sn,Gr,je,!!(ue&&ue.html5&&!H)),(!this.zoomFromOrigin||!this.currentImageSize)&&_.hasClass("lg-complete_")&&!this.lGalleryOn&&setTimeout(function(){_.addClass("lg-complete")},this.settings.backdropDuration),this.lGalleryOn=!0,g===!0&&(_.hasClass("lg-complete_")?this.preload(u):_.find(".lg-object").first().on("load.lg error.lg",function(){S.preload(u)}))}},{key:"loadContentOnFirstSlideLoad",value:function(u,g,S){var x=this;setTimeout(function(){g.find(".lg-dummy-img").remove(),g.removeClass("lg-first-slide"),x.outer.removeClass("lg-first-slide-loading"),x.isDummyImageRemoved=!0,x.preload(u)},S+300)}},{key:"getItemsToBeInsertedToDom",value:function(u,g){var S=this,x=arguments.length>2&&arguments[2]!==void 0?arguments[2]:0,_=[],H=Math.max(x,3);H=Math.min(H,this.galleryItems.length);var ht="lg-item-".concat(this.lgId,"-").concat(g);if(this.galleryItems.length<=3)return this.galleryItems.forEach(function(ue,Fe){_.push("lg-item-".concat(S.lgId,"-").concat(Fe))}),_;if(u<(this.galleryItems.length-1)/2){for(var Et=u;Et>u-H/2&&Et>=0;Et--)_.push("lg-item-".concat(this.lgId,"-").concat(Et));for(var St=_.length,Ct=0;Ct<H-St;Ct++)_.push("lg-item-".concat(this.lgId,"-").concat(u+Ct+1))}else{for(var jt=u;jt<=this.galleryItems.length-1&&jt<u+H/2;jt++)_.push("lg-item-".concat(this.lgId,"-").concat(jt));for(var Bt=_.length,Ae=0;Ae<H-Bt;Ae++)_.push("lg-item-".concat(this.lgId,"-").concat(u-Ae-1))}return this.settings.loop&&(u===this.galleryItems.length-1?_.push("lg-item-".concat(this.lgId,"-",0)):u===0&&_.push("lg-item-".concat(this.lgId,"-").concat(this.galleryItems.length-1))),_.indexOf(ht)===-1&&_.push("lg-item-".concat(this.lgId,"-").concat(g)),_}},{key:"organizeSlideItems",value:function(u,g){var S=this,x=this.getItemsToBeInsertedToDom(u,g,this.settings.numberOfSlideItemsInDom);return x.forEach(function(_){S.currentItemsInDom.indexOf(_)===-1&&S.$inner.append('<div id="'.concat(_,'" class="lg-item"></div>'))}),this.currentItemsInDom.forEach(function(_){x.indexOf(_)===-1&&X("#".concat(_)).remove()}),x}},{key:"getPreviousSlideIndex",value:function(){var u=0;try{var g=this.outer.find(".lg-current").first().attr("id");u=parseInt(g.split("-")[3])||0}catch{u=0}return u}},{key:"setDownloadValue",value:function(u){if(this.settings.download){var g=this.galleryItems[u],S=g.downloadUrl===!1||g.downloadUrl==="false";if(S)this.outer.addClass("lg-hide-download");else{var x=this.getElementById("lg-download");this.outer.removeClass("lg-hide-download"),x.attr("href",g.downloadUrl||g.src),g.download&&x.attr("download",g.download)}}}},{key:"makeSlideAnimation",value:function(u,g,S){var x=this;this.lGalleryOn&&S.addClass("lg-slide-progress"),setTimeout(function(){x.outer.addClass("lg-no-trans"),x.outer.find(".lg-item").removeClass("lg-prev-slide lg-next-slide"),u==="prev"?(g.addClass("lg-prev-slide"),S.addClass("lg-next-slide")):(g.addClass("lg-next-slide"),S.addClass("lg-prev-slide")),setTimeout(function(){x.outer.find(".lg-item").removeClass("lg-current"),g.addClass("lg-current"),x.outer.removeClass("lg-no-trans")},50)},this.lGalleryOn?this.settings.slideDelay:0)}},{key:"slide",value:function(u,g,S,x){var _=this,H=this.getPreviousSlideIndex();if(this.currentItemsInDom=this.organizeSlideItems(u,H),!(this.lGalleryOn&&H===u)){var ht=this.galleryItems.length;if(!this.lgBusy){this.settings.counter&&this.updateCurrentCounter(u);var Et=this.getSlideItem(u),St=this.getSlideItem(H),Ct=this.galleryItems[u],jt=Ct.__slideVideoInfo;if(this.outer.attr("data-lg-slide-type",this.getSlideType(Ct)),this.setDownloadValue(u),jt){var Bt=this.mediaContainerPosition,Ae=Bt.top,ue=Bt.bottom,Fe=Tt.getSize(this.items[u],this.outer,Ae+ue,jt&&this.settings.videoMaxSize);this.resizeVideoSlide(u,Fe)}if(this.LGel.trigger(j.beforeSlide,{prevIndex:H,index:u,fromTouch:!!g,fromThumb:!!S}),this.lgBusy=!0,clearTimeout(this.hideBarTimeout),this.arrowDisable(u),x||(u<H?x="prev":u>H&&(x="next")),!g)this.makeSlideAnimation(x,Et,St);else{this.outer.find(".lg-item").removeClass("lg-prev-slide lg-current lg-next-slide");var ye,je;ht>2?(ye=u-1,je=u+1,(u===0&&H===ht-1||u===ht-1&&H===0)&&(je=0,ye=ht-1)):(ye=0,je=1),x==="prev"?this.getSlideItem(je).addClass("lg-next-slide"):this.getSlideItem(ye).addClass("lg-prev-slide"),Et.addClass("lg-current")}this.lGalleryOn?setTimeout(function(){_.loadContent(u,!0),_.settings.appendSubHtmlTo!==".lg-item"&&_.addHtml(u)},this.settings.speed+50+(g?0:this.settings.slideDelay)):this.loadContent(u,!0),setTimeout(function(){_.lgBusy=!1,St.removeClass("lg-slide-progress"),_.LGel.trigger(j.afterSlide,{prevIndex:H,index:u,fromTouch:g,fromThumb:S})},(this.lGalleryOn?this.settings.speed+100:100)+(g?0:this.settings.slideDelay))}this.index=u}}},{key:"updateCurrentCounter",value:function(u){this.getElementById("lg-counter-current").html(u+1+"")}},{key:"updateCounterTotal",value:function(){this.getElementById("lg-counter-all").html(this.galleryItems.length+"")}},{key:"getSlideType",value:function(u){return u.__slideVideoInfo?"video":u.iframe?"iframe":"image"}},{key:"touchMove",value:function(u,g,S){var x=g.pageX-u.pageX,_=g.pageY-u.pageY,H=!1;if(this.swipeDirection?H=!0:Math.abs(x)>15?(this.swipeDirection="horizontal",H=!0):Math.abs(_)>15&&(this.swipeDirection="vertical",H=!0),!!H){var ht=this.getSlideItem(this.index);if(this.swipeDirection==="horizontal"){S==null||S.preventDefault(),this.outer.addClass("lg-dragging"),this.setTranslate(ht,x,0);var Et=ht.get().offsetWidth,St=Et*15/100,Ct=St-Math.abs(x*10/100);this.setTranslate(this.outer.find(".lg-prev-slide").first(),-Et+x-Ct,0),this.setTranslate(this.outer.find(".lg-next-slide").first(),Et+x+Ct,0)}else if(this.swipeDirection==="vertical"&&this.settings.swipeToClose){S==null||S.preventDefault(),this.$container.addClass("lg-dragging-vertical");var jt=1-Math.abs(_)/window.innerHeight;this.$backdrop.css("opacity",jt);var Bt=1-Math.abs(_)/(window.innerWidth*2);this.setTranslate(ht,0,_,Bt,Bt),Math.abs(_)>100&&this.outer.addClass("lg-hide-items").removeClass("lg-components-open")}}}},{key:"touchEnd",value:function(u,g,S){var x=this,_;this.settings.mode!=="lg-slide"&&this.outer.addClass("lg-slide"),setTimeout(function(){x.$container.removeClass("lg-dragging-vertical"),x.outer.removeClass("lg-dragging lg-hide-items").addClass("lg-components-open");var H=!0;if(x.swipeDirection==="horizontal"){_=u.pageX-g.pageX;var ht=Math.abs(u.pageX-g.pageX);_<0&&ht>x.settings.swipeThreshold?(x.goToNextSlide(!0),H=!1):_>0&&ht>x.settings.swipeThreshold&&(x.goToPrevSlide(!0),H=!1)}else if(x.swipeDirection==="vertical")if(_=Math.abs(u.pageY-g.pageY),x.settings.closable&&x.settings.swipeToClose&&_>100){x.closeGallery();return}else x.$backdrop.css("opacity",1);if(x.outer.find(".lg-item").removeAttr("style"),H&&Math.abs(u.pageX-g.pageX)<5){var Et=X(S.target);x.isPosterElement(Et)&&x.LGel.trigger(j.posterClick)}x.swipeDirection=void 0}),setTimeout(function(){!x.outer.hasClass("lg-dragging")&&x.settings.mode!=="lg-slide"&&x.outer.removeClass("lg-slide")},this.settings.speed+100)}},{key:"enableSwipe",value:function(){var u=this,g={},S={},x=!1,_=!1;this.settings.enableSwipe&&(this.$inner.on("touchstart.lg",function(H){u.dragOrSwipeEnabled=!0;var ht=u.getSlideItem(u.index);(X(H.target).hasClass("lg-item")||ht.get().contains(H.target))&&!u.outer.hasClass("lg-zoomed")&&!u.lgBusy&&H.touches.length===1&&(_=!0,u.touchAction="swipe",u.manageSwipeClass(),g={pageX:H.touches[0].pageX,pageY:H.touches[0].pageY})}),this.$inner.on("touchmove.lg",function(H){_&&u.touchAction==="swipe"&&H.touches.length===1&&(S={pageX:H.touches[0].pageX,pageY:H.touches[0].pageY},u.touchMove(g,S,H),x=!0)}),this.$inner.on("touchend.lg",function(H){if(u.touchAction==="swipe"){if(x)x=!1,u.touchEnd(S,g,H);else if(_){var ht=X(H.target);u.isPosterElement(ht)&&u.LGel.trigger(j.posterClick)}u.touchAction=void 0,_=!1}}))}},{key:"enableDrag",value:function(){var u=this,g={},S={},x=!1,_=!1;this.settings.enableDrag&&(this.outer.on("mousedown.lg",function(H){u.dragOrSwipeEnabled=!0;var ht=u.getSlideItem(u.index);(X(H.target).hasClass("lg-item")||ht.get().contains(H.target))&&!u.outer.hasClass("lg-zoomed")&&!u.lgBusy&&(H.preventDefault(),u.lgBusy||(u.manageSwipeClass(),g={pageX:H.pageX,pageY:H.pageY},x=!0,u.outer.get().scrollLeft+=1,u.outer.get().scrollLeft-=1,u.outer.removeClass("lg-grab").addClass("lg-grabbing"),u.LGel.trigger(j.dragStart)))}),X(window).on("mousemove.lg.global".concat(this.lgId),function(H){x&&u.lgOpened&&(_=!0,S={pageX:H.pageX,pageY:H.pageY},u.touchMove(g,S),u.LGel.trigger(j.dragMove))}),X(window).on("mouseup.lg.global".concat(this.lgId),function(H){if(!!u.lgOpened){var ht=X(H.target);_?(_=!1,u.touchEnd(S,g,H),u.LGel.trigger(j.dragEnd)):u.isPosterElement(ht)&&u.LGel.trigger(j.posterClick),x&&(x=!1,u.outer.removeClass("lg-grabbing").addClass("lg-grab"))}}))}},{key:"triggerPosterClick",value:function(){var u=this;this.$inner.on("click.lg",function(g){!u.dragOrSwipeEnabled&&u.isPosterElement(X(g.target))&&u.LGel.trigger(j.posterClick)})}},{key:"manageSwipeClass",value:function(){var u=this.index+1,g=this.index-1;this.settings.loop&&this.galleryItems.length>2&&(this.index===0?g=this.galleryItems.length-1:this.index===this.galleryItems.length-1&&(u=0)),this.outer.find(".lg-item").removeClass("lg-next-slide lg-prev-slide"),g>-1&&this.getSlideItem(g).addClass("lg-prev-slide"),this.getSlideItem(u).addClass("lg-next-slide")}},{key:"goToNextSlide",value:function(u){var g=this,S=this.settings.loop;u&&this.galleryItems.length<3&&(S=!1),this.lgBusy||(this.index+1<this.galleryItems.length?(this.index++,this.LGel.trigger(j.beforeNextSlide,{index:this.index}),this.slide(this.index,!!u,!1,"next")):S?(this.index=0,this.LGel.trigger(j.beforeNextSlide,{index:this.index}),this.slide(this.index,!!u,!1,"next")):this.settings.slideEndAnimation&&!u&&(this.outer.addClass("lg-right-end"),setTimeout(function(){g.outer.removeClass("lg-right-end")},400)))}},{key:"goToPrevSlide",value:function(u){var g=this,S=this.settings.loop;u&&this.galleryItems.length<3&&(S=!1),this.lgBusy||(this.index>0?(this.index--,this.LGel.trigger(j.beforePrevSlide,{index:this.index,fromTouch:u}),this.slide(this.index,!!u,!1,"prev")):S?(this.index=this.galleryItems.length-1,this.LGel.trigger(j.beforePrevSlide,{index:this.index,fromTouch:u}),this.slide(this.index,!!u,!1,"prev")):this.settings.slideEndAnimation&&!u&&(this.outer.addClass("lg-left-end"),setTimeout(function(){g.outer.removeClass("lg-left-end")},400)))}},{key:"keyPress",value:function(){var u=this;X(window).on("keydown.lg.global".concat(this.lgId),function(g){u.lgOpened&&u.settings.escKey===!0&&g.keyCode===27&&(g.preventDefault(),u.settings.allowMediaOverlap&&u.outer.hasClass("lg-can-toggle")&&u.outer.hasClass("lg-components-open")?u.outer.removeClass("lg-components-open"):u.closeGallery()),u.lgOpened&&u.galleryItems.length>1&&(g.keyCode===37&&(g.preventDefault(),u.goToPrevSlide()),g.keyCode===39&&(g.preventDefault(),u.goToNextSlide()))})}},{key:"arrow",value:function(){var u=this;this.getElementById("lg-prev").on("click.lg",function(){u.goToPrevSlide()}),this.getElementById("lg-next").on("click.lg",function(){u.goToNextSlide()})}},{key:"arrowDisable",value:function(u){if(!this.settings.loop&&this.settings.hideControlOnEnd){var g=this.getElementById("lg-prev"),S=this.getElementById("lg-next");u+1===this.galleryItems.length?S.attr("disabled","disabled").addClass("disabled"):S.removeAttr("disabled").removeClass("disabled"),u===0?g.attr("disabled","disabled").addClass("disabled"):g.removeAttr("disabled").removeClass("disabled")}}},{key:"setTranslate",value:function(u,g,S){var x=arguments.length>3&&arguments[3]!==void 0?arguments[3]:1,_=arguments.length>4&&arguments[4]!==void 0?arguments[4]:1;u.css("transform","translate3d("+g+"px, "+S+"px, 0px) scale3d("+x+", "+_+", 1)")}},{key:"mousewheel",value:function(){var u=this,g=0;this.outer.on("wheel.lg",function(S){if(!(!S.deltaY||u.galleryItems.length<2)){S.preventDefault();var x=new Date().getTime();x-g<1e3||(g=x,S.deltaY>0?u.goToNextSlide():S.deltaY<0&&u.goToPrevSlide())}})}},{key:"isSlideElement",value:function(u){return u.hasClass("lg-outer")||u.hasClass("lg-item")||u.hasClass("lg-img-wrap")}},{key:"isPosterElement",value:function(u){var g=this.getSlideItem(this.index).find(".lg-video-play-button").get();return u.hasClass("lg-video-poster")||u.hasClass("lg-video-play-button")||g&&g.contains(u.get())}},{key:"toggleMaximize",value:function(){var u=this;this.getElementById("lg-maximize").on("click.lg",function(){u.$container.toggleClass("lg-inline"),u.refreshOnResize()})}},{key:"invalidateItems",value:function(){for(var u=0;u<this.items.length;u++){var g=this.items[u],S=X(g);S.off("click.lgcustom-item-".concat(S.attr("data-lg-id")))}}},{key:"trapFocus",value:function(){var u=this;this.$container.get().focus({preventScroll:!0}),X(window).on("keydown.lg.global".concat(this.lgId),function(g){if(!!u.lgOpened){var S=g.key==="Tab"||g.keyCode===9;if(!!S){var x=Tt.getFocusableElements(u.$container.get()),_=x[0],H=x[x.length-1];g.shiftKey?document.activeElement===_&&(H.focus(),g.preventDefault()):document.activeElement===H&&(_.focus(),g.preventDefault())}}})}},{key:"manageCloseGallery",value:function(){var u=this;if(!!this.settings.closable){var g=!1;this.getElementById("lg-close").on("click.lg",function(){u.closeGallery()}),this.settings.closeOnTap&&(this.outer.on("mousedown.lg",function(S){var x=X(S.target);u.isSlideElement(x)?g=!0:g=!1}),this.outer.on("mousemove.lg",function(){g=!1}),this.outer.on("mouseup.lg",function(S){var x=X(S.target);u.isSlideElement(x)&&g&&(u.outer.hasClass("lg-dragging")||u.closeGallery())}))}}},{key:"closeGallery",value:function(u){var g=this;if(!this.lgOpened||!this.settings.closable&&!u)return 0;this.LGel.trigger(j.beforeClose),this.settings.resetScrollPosition&&!this.settings.hideScrollbar&&X(window).scrollTop(this.prevScrollTop);var S=this.items[this.index],x;if(this.zoomFromOrigin&&S){var _=this.mediaContainerPosition,H=_.top,ht=_.bottom,Et=this.galleryItems[this.index],St=Et.__slideVideoInfo,Ct=Et.poster,jt=Tt.getSize(S,this.outer,H+ht,St&&Ct&&this.settings.videoMaxSize);x=Tt.getTransform(S,this.outer,H,ht,jt)}this.zoomFromOrigin&&x?(this.outer.addClass("lg-closing lg-zoom-from-image"),this.getSlideItem(this.index).addClass("lg-start-end-progress").css("transition-duration",this.settings.startAnimationDuration+"ms").css("transform",x)):(this.outer.addClass("lg-hide-items"),this.outer.removeClass("lg-zoom-from-image")),this.destroyModules(),this.lGalleryOn=!1,this.isDummyImageRemoved=!1,this.zoomFromOrigin=this.settings.zoomFromOrigin,clearTimeout(this.hideBarTimeout),this.hideBarTimeout=!1,X("html").removeClass("lg-on"),this.outer.removeClass("lg-visible lg-components-open"),this.$backdrop.removeClass("in").css("opacity",0);var Bt=this.zoomFromOrigin&&x?Math.max(this.settings.startAnimationDuration,this.settings.backdropDuration):this.settings.backdropDuration;return this.$container.removeClass("lg-show-in"),setTimeout(function(){g.zoomFromOrigin&&x&&g.outer.removeClass("lg-zoom-from-image"),g.$container.removeClass("lg-show"),g.resetScrollBar(),g.$backdrop.removeAttr("style").css("transition-duration",g.settings.backdropDuration+"ms"),g.outer.removeClass("lg-closing ".concat(g.settings.startClass)),g.getSlideItem(g.index).removeClass("lg-start-end-progress"),g.$inner.empty(),g.lgOpened&&g.LGel.trigger(j.afterClose,{instance:g}),g.$container.get()&&g.$container.get().blur(),g.lgOpened=!1},Bt+100),Bt+100}},{key:"initModules",value:function(){this.plugins.forEach(function(u){try{u.init()}catch{console.warn("lightGallery:- make sure lightGallery module is properly initiated")}})}},{key:"destroyModules",value:function(u){this.plugins.forEach(function(g){try{u?g.destroy():g.closeGallery&&g.closeGallery()}catch{console.warn("lightGallery:- make sure lightGallery module is properly destroyed")}})}},{key:"refresh",value:function(u){this.settings.dynamic||this.invalidateItems(),u?this.galleryItems=u:this.galleryItems=this.getItems(),this.updateControls(),this.openGalleryOnItemClick(),this.LGel.trigger(j.updateSlides)}},{key:"updateControls",value:function(){this.addSlideVideoInfo(this.galleryItems),this.updateCounterTotal(),this.manageSingleSlideClassName()}},{key:"destroyGallery",value:function(){this.destroyModules(!0),this.settings.dynamic||this.invalidateItems(),X(window).off(".lg.global".concat(this.lgId)),this.LGel.off(".lg"),this.$container.remove()}},{key:"destroy",value:function(){var u=this.closeGallery(!0);return u?setTimeout(this.destroyGallery.bind(this),u):this.destroyGallery(),u}}]),T}();function xt(T,v){return new mt(T,v)}var Rt=xt,ot=function(T){O(u,T);var v=C(u);function u(){return p(this,u),v.apply(this,arguments)}return A(u,[{key:"mounted",value:function(){this.registerEvents.call(this),this.LG=Rt(this.$refs.container,h({},this.settings))}},{key:"unmounted",value:function(){this.LG.destroy()}},{key:"getMethodName",value:function(S){return"on".concat(S.charAt(0).toUpperCase()+S.slice(1))}},{key:"registerEvents",value:function(){var S=this;Object.keys(j).forEach(function(x){S.$refs.container.addEventListener(j[x].split(".")[0],function(_){S[S.getMethodName(x)]&&S[S.getMethodName.call(S,x)](_.detail)})})}}]),u}(w);ot=L([B({props:{settings:{type:Object},onAfterAppendSlide:{type:Function},onInit:{type:Function},onHasVideo:{type:Function},onContainerResize:{type:Function},onAfterAppendSubHtml:{type:Function},onBeforeOpen:{type:Function},onAfterOpen:{type:Function},onSlideItemLoad:{type:Function},onBeforeSlide:{type:Function},onAfterSlide:{type:Function},onPosterClick:{type:Function},onDragStart:{type:Function},onDragMove:{type:Function},onDragEnd:{type:Function},onBeforeNextSlide:{type:Function},onBeforePrevSlide:{type:Function},onBeforeClose:{type:Function},onAfterClose:{type:Function},onRotateLeft:{type:Function},onRotateRight:{type:Function},onFlipHorizontal:{type:Function},onFlipVertical:{type:Function}}})],ot);var ct=ot;ct.render=c;var at=ct;n.default=at},fb6a:function(t,n,r){var i=r("23e7"),o=r("861d"),s=r("e8b5"),a=r("23cb"),l=r("50c4"),c=r("fc6a"),f=r("8418"),d=r("b622"),h=r("1dde"),p=h("slice"),b=d("species"),A=[].slice,F=Math.max;i({target:"Array",proto:!0,forced:!p},{slice:function(m,I){var E=c(this),N=l(E.length),P=a(m,N),R=a(I===void 0?N:I,N),C,L,U;if(s(E)&&(C=E.constructor,typeof C=="function"&&(C===Array||s(C.prototype))?C=void 0:o(C)&&(C=C[b],C===null&&(C=void 0)),C===Array||C===void 0))return A.call(E,P,R);for(L=new(C===void 0?Array:C)(F(R-P,0)),U=0;P<R;P++,U++)P in E&&f(L,U,E[P]);return L.length=U,L}})},fc6a:function(t,n,r){var i=r("44ad"),o=r("1d80");t.exports=function(s){return i(o(s))}},fdbc:function(t,n){t.exports={CSSRuleList:0,CSSStyleDeclaration:0,CSSValueList:0,ClientRectList:0,DOMRectList:0,DOMStringList:0,DOMTokenList:1,DataTransferItemList:0,FileList:0,HTMLAllCollection:0,HTMLCollection:0,HTMLFormElement:0,HTMLSelectElement:0,MediaList:0,MimeTypeArray:0,NamedNodeMap:0,NodeList:1,PaintRequestList:0,Plugin:0,PluginArray:0,SVGLengthList:0,SVGNumberList:0,SVGPathSegList:0,SVGPointList:0,SVGStringList:0,SVGTransformList:0,SourceBufferList:0,StyleSheetList:0,TextTrackCueList:0,TextTrackList:0,TouchList:0}},fdbf:function(t,n,r){var i=r("4930");t.exports=i&&!Symbol.sham&&typeof Symbol.iterator=="symbol"},fea9:function(t,n,r){var i=r("da84");t.exports=i.Promise}}).default})(Bm);var iO=M0(Bm.exports);/*!
 * lightgallery | 2.6.1 | September 14th 2022
 * http://www.lightgalleryjs.com/
 * Copyright (c) 2020 Sachin Neravath;
 * @license GPLv3
 *//*! *****************************************************************************
Copyright (c) Microsoft Corporation.

Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby granted.

THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
PERFORMANCE OF THIS SOFTWARE.
***************************************************************************** */var Oa=function(){return Oa=Object.assign||function(t){for(var n,r=1,i=arguments.length;r<i;r++){n=arguments[r];for(var o in n)Object.prototype.hasOwnProperty.call(n,o)&&(t[o]=n[o])}return t},Oa.apply(this,arguments)},sO={thumbnail:!0,animateThumb:!0,currentPagerPosition:"middle",alignThumbnails:"middle",thumbWidth:100,thumbHeight:"80px",thumbMargin:5,appendThumbnailsTo:".lg-components",toggleThumb:!1,enableThumbDrag:!0,enableThumbSwipe:!0,thumbnailSwipeThreshold:10,loadYouTubeThumbnail:!0,youTubeThumbSize:1,thumbnailPluginStrings:{toggleThumbnails:"Toggle thumbnails"}},ys={afterAppendSlide:"lgAfterAppendSlide",init:"lgInit",hasVideo:"lgHasVideo",containerResize:"lgContainerResize",updateSlides:"lgUpdateSlides",afterAppendSubHtml:"lgAfterAppendSubHtml",beforeOpen:"lgBeforeOpen",afterOpen:"lgAfterOpen",slideItemLoad:"lgSlideItemLoad",beforeSlide:"lgBeforeSlide",afterSlide:"lgAfterSlide",posterClick:"lgPosterClick",dragStart:"lgDragStart",dragMove:"lgDragMove",dragEnd:"lgDragEnd",beforeNextSlide:"lgBeforeNextSlide",beforePrevSlide:"lgBeforePrevSlide",beforeClose:"lgBeforeClose",afterClose:"lgAfterClose",rotateLeft:"lgRotateLeft",rotateRight:"lgRotateRight",flipHorizontal:"lgFlipHorizontal",flipVertical:"lgFlipVertical",autoplay:"lgAutoplay",autoplayStart:"lgAutoplayStart",autoplayStop:"lgAutoplayStop"},oO=function(){function e(t,n){return this.thumbOuterWidth=0,this.thumbTotalWidth=0,this.translateX=0,this.thumbClickable=!1,this.core=t,this.$LG=n,this}return e.prototype.init=function(){this.settings=Oa(Oa({},sO),this.core.settings),this.thumbOuterWidth=0,this.thumbTotalWidth=this.core.galleryItems.length*(this.settings.thumbWidth+this.settings.thumbMargin),this.translateX=0,this.setAnimateThumbStyles(),this.core.settings.allowMediaOverlap||(this.settings.toggleThumb=!1),this.settings.thumbnail&&(this.build(),this.settings.animateThumb?(this.settings.enableThumbDrag&&this.enableThumbDrag(),this.settings.enableThumbSwipe&&this.enableThumbSwipe(),this.thumbClickable=!1):this.thumbClickable=!0,this.toggleThumbBar(),this.thumbKeyPress())},e.prototype.build=function(){var t=this;this.setThumbMarkup(),this.manageActiveClassOnSlideChange(),this.$lgThumb.first().on("click.lg touchend.lg",function(n){var r=t.$LG(n.target);!r.hasAttribute("data-lg-item-id")||setTimeout(function(){if(t.thumbClickable&&!t.core.lgBusy){var i=parseInt(r.attr("data-lg-item-id"));t.core.slide(i,!1,!0,!1)}},50)}),this.core.LGel.on(ys.beforeSlide+".thumb",function(n){var r=n.detail.index;t.animateThumb(r)}),this.core.LGel.on(ys.beforeOpen+".thumb",function(){t.thumbOuterWidth=t.core.outer.get().offsetWidth}),this.core.LGel.on(ys.updateSlides+".thumb",function(){t.rebuildThumbnails()}),this.core.LGel.on(ys.containerResize+".thumb",function(){!t.core.lgOpened||setTimeout(function(){t.thumbOuterWidth=t.core.outer.get().offsetWidth,t.animateThumb(t.core.index),t.thumbOuterWidth=t.core.outer.get().offsetWidth},50)})},e.prototype.setThumbMarkup=function(){var t="lg-thumb-outer ";this.settings.alignThumbnails&&(t+="lg-thumb-align-"+this.settings.alignThumbnails);var n='<div class="'+t+`">
        <div class="lg-thumb lg-group">
        </div>
        </div>`;this.core.outer.addClass("lg-has-thumb"),this.settings.appendThumbnailsTo===".lg-components"?this.core.$lgComponents.append(n):this.core.outer.append(n),this.$thumbOuter=this.core.outer.find(".lg-thumb-outer").first(),this.$lgThumb=this.core.outer.find(".lg-thumb").first(),this.settings.animateThumb&&this.core.outer.find(".lg-thumb").css("transition-duration",this.core.settings.speed+"ms").css("width",this.thumbTotalWidth+"px").css("position","relative"),this.setThumbItemHtml(this.core.galleryItems)},e.prototype.enableThumbDrag=function(){var t=this,n={cords:{startX:0,endX:0},isMoved:!1,newTranslateX:0,startTime:new Date,endTime:new Date,touchMoveTime:0},r=!1;this.$thumbOuter.addClass("lg-grab"),this.core.outer.find(".lg-thumb").first().on("mousedown.lg.thumb",function(i){t.thumbTotalWidth>t.thumbOuterWidth&&(i.preventDefault(),n.cords.startX=i.pageX,n.startTime=new Date,t.thumbClickable=!1,r=!0,t.core.outer.get().scrollLeft+=1,t.core.outer.get().scrollLeft-=1,t.$thumbOuter.removeClass("lg-grab").addClass("lg-grabbing"))}),this.$LG(window).on("mousemove.lg.thumb.global"+this.core.lgId,function(i){!t.core.lgOpened||r&&(n.cords.endX=i.pageX,n=t.onThumbTouchMove(n))}),this.$LG(window).on("mouseup.lg.thumb.global"+this.core.lgId,function(){!t.core.lgOpened||(n.isMoved?n=t.onThumbTouchEnd(n):t.thumbClickable=!0,r&&(r=!1,t.$thumbOuter.removeClass("lg-grabbing").addClass("lg-grab")))})},e.prototype.enableThumbSwipe=function(){var t=this,n={cords:{startX:0,endX:0},isMoved:!1,newTranslateX:0,startTime:new Date,endTime:new Date,touchMoveTime:0};this.$lgThumb.on("touchstart.lg",function(r){t.thumbTotalWidth>t.thumbOuterWidth&&(r.preventDefault(),n.cords.startX=r.targetTouches[0].pageX,t.thumbClickable=!1,n.startTime=new Date)}),this.$lgThumb.on("touchmove.lg",function(r){t.thumbTotalWidth>t.thumbOuterWidth&&(r.preventDefault(),n.cords.endX=r.targetTouches[0].pageX,n=t.onThumbTouchMove(n))}),this.$lgThumb.on("touchend.lg",function(){n.isMoved?n=t.onThumbTouchEnd(n):t.thumbClickable=!0})},e.prototype.rebuildThumbnails=function(){var t=this;this.$thumbOuter.addClass("lg-rebuilding-thumbnails"),setTimeout(function(){t.thumbTotalWidth=t.core.galleryItems.length*(t.settings.thumbWidth+t.settings.thumbMargin),t.$lgThumb.css("width",t.thumbTotalWidth+"px"),t.$lgThumb.empty(),t.setThumbItemHtml(t.core.galleryItems),t.animateThumb(t.core.index)},50),setTimeout(function(){t.$thumbOuter.removeClass("lg-rebuilding-thumbnails")},200)},e.prototype.setTranslate=function(t){this.$lgThumb.css("transform","translate3d(-"+t+"px, 0px, 0px)")},e.prototype.getPossibleTransformX=function(t){return t>this.thumbTotalWidth-this.thumbOuterWidth&&(t=this.thumbTotalWidth-this.thumbOuterWidth),t<0&&(t=0),t},e.prototype.animateThumb=function(t){if(this.$lgThumb.css("transition-duration",this.core.settings.speed+"ms"),this.settings.animateThumb){var n=0;switch(this.settings.currentPagerPosition){case"left":n=0;break;case"middle":n=this.thumbOuterWidth/2-this.settings.thumbWidth/2;break;case"right":n=this.thumbOuterWidth-this.settings.thumbWidth}this.translateX=(this.settings.thumbWidth+this.settings.thumbMargin)*t-1-n,this.translateX>this.thumbTotalWidth-this.thumbOuterWidth&&(this.translateX=this.thumbTotalWidth-this.thumbOuterWidth),this.translateX<0&&(this.translateX=0),this.setTranslate(this.translateX)}},e.prototype.onThumbTouchMove=function(t){return t.newTranslateX=this.translateX,t.isMoved=!0,t.touchMoveTime=new Date().valueOf(),t.newTranslateX-=t.cords.endX-t.cords.startX,t.newTranslateX=this.getPossibleTransformX(t.newTranslateX),this.setTranslate(t.newTranslateX),this.$thumbOuter.addClass("lg-dragging"),t},e.prototype.onThumbTouchEnd=function(t){t.isMoved=!1,t.endTime=new Date,this.$thumbOuter.removeClass("lg-dragging");var n=t.endTime.valueOf()-t.startTime.valueOf(),r=t.cords.endX-t.cords.startX,i=Math.abs(r)/n;return i>.15&&t.endTime.valueOf()-t.touchMoveTime<30?(i+=1,i>2&&(i+=1),i=i+i*(Math.abs(r)/this.thumbOuterWidth),this.$lgThumb.css("transition-duration",Math.min(i-1,2)+"settings"),r=r*i,this.translateX=this.getPossibleTransformX(this.translateX-r),this.setTranslate(this.translateX)):this.translateX=t.newTranslateX,Math.abs(t.cords.endX-t.cords.startX)<this.settings.thumbnailSwipeThreshold&&(this.thumbClickable=!0),t},e.prototype.getThumbHtml=function(t,n){var r=this.core.galleryItems[n].__slideVideoInfo||{},i;return r.youtube&&this.settings.loadYouTubeThumbnail?i="//img.youtube.com/vi/"+r.youtube[1]+"/"+this.settings.youTubeThumbSize+".jpg":i=t,'<div data-lg-item-id="'+n+'" class="lg-thumb-item '+(n===this.core.index?" active":"")+`"
        style="width:`+this.settings.thumbWidth+"px; height: "+this.settings.thumbHeight+`;
            margin-right: `+this.settings.thumbMargin+`px;">
            <img data-lg-item-id="`+n+'" src="'+i+`" />
        </div>`},e.prototype.getThumbItemHtml=function(t){for(var n="",r=0;r<t.length;r++)n+=this.getThumbHtml(t[r].thumb,r);return n},e.prototype.setThumbItemHtml=function(t){var n=this.getThumbItemHtml(t);this.$lgThumb.html(n)},e.prototype.setAnimateThumbStyles=function(){this.settings.animateThumb&&this.core.outer.addClass("lg-animate-thumb")},e.prototype.manageActiveClassOnSlideChange=function(){var t=this;this.core.LGel.on(ys.beforeSlide+".thumb",function(n){var r=t.core.outer.find(".lg-thumb-item"),i=n.detail.index;r.removeClass("active"),r.eq(i).addClass("active")})},e.prototype.toggleThumbBar=function(){var t=this;this.settings.toggleThumb&&(this.core.outer.addClass("lg-can-toggle"),this.core.$toolbar.append('<button type="button" aria-label="'+this.settings.thumbnailPluginStrings.toggleThumbnails+'" class="lg-toggle-thumb lg-icon"></button>'),this.core.outer.find(".lg-toggle-thumb").first().on("click.lg",function(){t.core.outer.toggleClass("lg-components-open")}))},e.prototype.thumbKeyPress=function(){var t=this;this.$LG(window).on("keydown.lg.thumb.global"+this.core.lgId,function(n){!t.core.lgOpened||!t.settings.toggleThumb||(n.keyCode===38?(n.preventDefault(),t.core.outer.addClass("lg-components-open")):n.keyCode===40&&(n.preventDefault(),t.core.outer.removeClass("lg-components-open")))})},e.prototype.destroy=function(){this.settings.thumbnail&&(this.$LG(window).off(".lg.thumb.global"+this.core.lgId),this.core.LGel.off(".lg.thumb"),this.core.LGel.off(".thumb"),this.$thumbOuter.remove(),this.core.outer.removeClass("lg-has-thumb"))},e}();/*!
 * lightgallery | 2.6.1 | September 14th 2022
 * http://www.lightgalleryjs.com/
 * Copyright (c) 2020 Sachin Neravath;
 * @license GPLv3
 *//*! *****************************************************************************
Copyright (c) Microsoft Corporation.

Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby granted.

THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
PERFORMANCE OF THIS SOFTWARE.
***************************************************************************** */var Aa=function(){return Aa=Object.assign||function(t){for(var n,r=1,i=arguments.length;r<i;r++){n=arguments[r];for(var o in n)Object.prototype.hasOwnProperty.call(n,o)&&(t[o]=n[o])}return t},Aa.apply(this,arguments)},aO={scale:1,zoom:!0,actualSize:!0,showZoomInOutIcons:!1,actualSizeIcons:{zoomIn:"lg-zoom-in",zoomOut:"lg-zoom-out"},enableZoomAfter:300,zoomPluginStrings:{zoomIn:"Zoom in",zoomOut:"Zoom out",viewActualSize:"View actual size"}},Qn={afterAppendSlide:"lgAfterAppendSlide",init:"lgInit",hasVideo:"lgHasVideo",containerResize:"lgContainerResize",updateSlides:"lgUpdateSlides",afterAppendSubHtml:"lgAfterAppendSubHtml",beforeOpen:"lgBeforeOpen",afterOpen:"lgAfterOpen",slideItemLoad:"lgSlideItemLoad",beforeSlide:"lgBeforeSlide",afterSlide:"lgAfterSlide",posterClick:"lgPosterClick",dragStart:"lgDragStart",dragMove:"lgDragMove",dragEnd:"lgDragEnd",beforeNextSlide:"lgBeforeNextSlide",beforePrevSlide:"lgBeforePrevSlide",beforeClose:"lgBeforeClose",afterClose:"lgAfterClose",rotateLeft:"lgRotateLeft",rotateRight:"lgRotateRight",flipHorizontal:"lgFlipHorizontal",flipVertical:"lgFlipVertical",autoplay:"lgAutoplay",autoplayStart:"lgAutoplayStart",autoplayStop:"lgAutoplayStop"},lO=function(){function e(t,n){return this.core=t,this.$LG=n,this.settings=Aa(Aa({},aO),this.core.settings),this}return e.prototype.buildTemplates=function(){var t=this.settings.showZoomInOutIcons?'<button id="'+this.core.getIdName("lg-zoom-in")+'" type="button" aria-label="'+this.settings.zoomPluginStrings.zoomIn+'" class="lg-zoom-in lg-icon"></button><button id="'+this.core.getIdName("lg-zoom-out")+'" type="button" aria-label="'+this.settings.zoomPluginStrings.zoomIn+'" class="lg-zoom-out lg-icon"></button>':"";this.settings.actualSize&&(t+='<button id="'+this.core.getIdName("lg-actual-size")+'" type="button" aria-label="'+this.settings.zoomPluginStrings.viewActualSize+'" class="'+this.settings.actualSizeIcons.zoomIn+' lg-icon"></button>'),this.core.outer.addClass("lg-use-transition-for-zoom"),this.core.$toolbar.first().append(t)},e.prototype.enableZoom=function(t){var n=this,r=this.settings.enableZoomAfter+t.detail.delay;this.$LG("body").first().hasClass("lg-from-hash")&&t.detail.delay?r=0:this.$LG("body").first().removeClass("lg-from-hash"),this.zoomableTimeout=setTimeout(function(){!n.isImageSlide(n.core.index)||(n.core.getSlideItem(t.detail.index).addClass("lg-zoomable"),t.detail.index===n.core.index&&n.setZoomEssentials())},r+30)},e.prototype.enableZoomOnSlideItemLoad=function(){this.core.LGel.on(Qn.slideItemLoad+".zoom",this.enableZoom.bind(this))},e.prototype.getDragCords=function(t){return{x:t.pageX,y:t.pageY}},e.prototype.getSwipeCords=function(t){var n=t.touches[0].pageX,r=t.touches[0].pageY;return{x:n,y:r}},e.prototype.getDragAllowedAxises=function(t,n){var r=this.core.getSlideItem(this.core.index).find(".lg-image").first().get(),i=0,o=0,s=r.getBoundingClientRect();t?(i=r.offsetHeight*t,o=r.offsetWidth*t):n?(i=s.height+n*s.height,o=s.width+n*s.width):(i=s.height,o=s.width);var a=i>this.containerRect.height,l=o>this.containerRect.width;return{allowX:l,allowY:a}},e.prototype.setZoomEssentials=function(){this.containerRect=this.core.$content.get().getBoundingClientRect()},e.prototype.zoomImage=function(t,n,r,i){if(!(Math.abs(n)<=0)){var o=this.containerRect.width/2+this.containerRect.left,s=this.containerRect.height/2+this.containerRect.top+this.scrollTop,a,l;t===1&&(this.positionChanged=!1);var c=this.getDragAllowedAxises(0,n),f=c.allowY,d=c.allowX;this.positionChanged&&(a=this.left/(this.scale-n),l=this.top/(this.scale-n),this.pageX=o-a,this.pageY=s-l,this.positionChanged=!1);var h=this.getPossibleSwipeDragCords(n),p,b,A=o-this.pageX,F=s-this.pageY;if(t-n>1){var O=(t-n)/Math.abs(n);A=(n<0?-A:A)+this.left*(O+(n<0?-1:1)),F=(n<0?-F:F)+this.top*(O+(n<0?-1:1)),p=A/O,b=F/O}else{var O=(t-n)*n;p=A*O,b=F*O}if(r&&(d?this.isBeyondPossibleLeft(p,h.minX)?p=h.minX:this.isBeyondPossibleRight(p,h.maxX)&&(p=h.maxX):t>1&&(p<h.minX?p=h.minX:p>h.maxX&&(p=h.maxX)),f?this.isBeyondPossibleTop(b,h.minY)?b=h.minY:this.isBeyondPossibleBottom(b,h.maxY)&&(b=h.maxY):t>1&&(b<h.minY?b=h.minY:b>h.maxY&&(b=h.maxY))),this.setZoomStyles({x:p,y:b,scale:t}),this.left=p,this.top=b,i){var m=this.getCurrentImageActualSizeScale();t>=m&&this.setZoomImageSize()}}},e.prototype.resetImageTranslate=function(t){if(!!this.isImageSlide(t)){var n=this.core.getSlideItem(t).find(".lg-image").first();this.imageReset=!1,n.removeClass("reset-transition reset-transition-y reset-transition-x"),this.core.outer.removeClass("lg-actual-size"),n.css("width","auto").css("height","auto"),setTimeout(function(){n.removeClass("no-transition")},10)}},e.prototype.setZoomImageSize=function(){var t=this,n=this.core.getSlideItem(this.core.index).find(".lg-image").first();setTimeout(function(){n.addClass("no-transition"),t.imageReset=!0},500),setTimeout(function(){var r=t.getDragAllowedAxises(t.scale);n.css("width",n.get().naturalWidth+"px").css("height",n.get().naturalHeight+"px"),t.core.outer.addClass("lg-actual-size"),r.allowX&&r.allowY?n.addClass("reset-transition"):r.allowX&&!r.allowY?n.addClass("reset-transition-x"):!r.allowX&&r.allowY&&n.addClass("reset-transition-y")},550)},e.prototype.setZoomStyles=function(t){var n=this.core.getSlideItem(this.core.index).find(".lg-img-wrap").first(),r=this.core.getSlideItem(this.core.index).find(".lg-image").first(),i=this.core.outer.find(".lg-current .lg-dummy-img").first();this.scale=t.scale,r.css("transform","scale3d("+t.scale+", "+t.scale+", 1)"),i.css("transform","scale3d("+t.scale+", "+t.scale+", 1)");var o="translate3d("+t.x+"px, "+t.y+"px, 0)";n.css("transform",o)},e.prototype.setActualSize=function(t,n){var r=this,i=this.core.galleryItems[this.core.index];this.resetImageTranslate(t),setTimeout(function(){if(!(!i.src||r.core.outer.hasClass("lg-first-slide-loading"))){var o=r.getCurrentImageActualSizeScale(),s=r.scale;r.core.outer.hasClass("lg-zoomed")?r.scale=1:r.scale=r.getScale(o),r.setPageCords(n),r.beginZoom(r.scale),r.zoomImage(r.scale,r.scale-s,!0,!0),setTimeout(function(){r.core.outer.removeClass("lg-grabbing").addClass("lg-grab")},10)}},50)},e.prototype.getNaturalWidth=function(t){var n=this.core.getSlideItem(t).find(".lg-image").first(),r=this.core.galleryItems[t].width;return r?parseFloat(r):n.get().naturalWidth},e.prototype.getActualSizeScale=function(t,n){var r,i;return t>=n?(r=t/n,i=r||2):i=1,i},e.prototype.getCurrentImageActualSizeScale=function(){var t=this.core.getSlideItem(this.core.index).find(".lg-image").first(),n=t.get().offsetWidth,r=this.getNaturalWidth(this.core.index)||n;return this.getActualSizeScale(r,n)},e.prototype.getPageCords=function(t){var n={};if(t)n.x=t.pageX||t.touches[0].pageX,n.y=t.pageY||t.touches[0].pageY;else{var r=this.core.$content.get().getBoundingClientRect();n.x=r.width/2+r.left,n.y=r.height/2+this.scrollTop+r.top}return n},e.prototype.setPageCords=function(t){var n=this.getPageCords(t);this.pageX=n.x,this.pageY=n.y},e.prototype.manageActualPixelClassNames=function(){var t=this.core.getElementById("lg-actual-size");t.removeClass(this.settings.actualSizeIcons.zoomIn).addClass(this.settings.actualSizeIcons.zoomOut)},e.prototype.beginZoom=function(t){return this.core.outer.removeClass("lg-zoom-drag-transition lg-zoom-dragging"),t>1?(this.core.outer.addClass("lg-zoomed"),this.manageActualPixelClassNames()):this.resetZoom(),t>1},e.prototype.getScale=function(t){var n=this.getCurrentImageActualSizeScale();return t<1?t=1:t>n&&(t=n),t},e.prototype.init=function(){var t=this;if(!!this.settings.zoom){this.buildTemplates(),this.enableZoomOnSlideItemLoad();var n=null;this.core.outer.on("dblclick.lg",function(r){!t.$LG(r.target).hasClass("lg-image")||t.setActualSize(t.core.index,r)}),this.core.outer.on("touchstart.lg",function(r){var i=t.$LG(r.target);r.touches.length===1&&i.hasClass("lg-image")&&(n?(clearTimeout(n),n=null,r.preventDefault(),t.setActualSize(t.core.index,r)):n=setTimeout(function(){n=null},300))}),this.core.LGel.on(Qn.containerResize+".zoom "+Qn.rotateRight+".zoom "+Qn.rotateLeft+".zoom "+Qn.flipHorizontal+".zoom "+Qn.flipVertical+".zoom",function(){if(!(!t.core.lgOpened||!t.isImageSlide(t.core.index)||t.core.touchAction)){var r=t.core.getSlideItem(t.core.index).find(".lg-img-wrap").first();t.top=0,t.left=0,t.setZoomEssentials(),t.setZoomSwipeStyles(r,{x:0,y:0}),t.positionChanged=!0}}),this.$LG(window).on("scroll.lg.zoom.global"+this.core.lgId,function(){!t.core.lgOpened||(t.scrollTop=t.$LG(window).scrollTop())}),this.core.getElementById("lg-zoom-out").on("click.lg",function(){if(!!t.isImageSlide(t.core.index)){var r=0;t.imageReset&&(t.resetImageTranslate(t.core.index),r=50),setTimeout(function(){var i=t.scale-t.settings.scale;i<1&&(i=1),t.beginZoom(i),t.zoomImage(i,-t.settings.scale,!0,!0)},r)}}),this.core.getElementById("lg-zoom-in").on("click.lg",function(){t.zoomIn()}),this.core.getElementById("lg-actual-size").on("click.lg",function(){t.setActualSize(t.core.index)}),this.core.LGel.on(Qn.beforeOpen+".zoom",function(){t.core.outer.find(".lg-item").removeClass("lg-zoomable")}),this.core.LGel.on(Qn.afterOpen+".zoom",function(){t.scrollTop=t.$LG(window).scrollTop(),t.pageX=t.core.outer.width()/2,t.pageY=t.core.outer.height()/2+t.scrollTop,t.scale=1}),this.core.LGel.on(Qn.afterSlide+".zoom",function(r){var i=r.detail.prevIndex;t.scale=1,t.positionChanged=!1,t.resetZoom(i),t.resetImageTranslate(i),t.isImageSlide(t.core.index)&&t.setZoomEssentials()}),this.zoomDrag(),this.pinchZoom(),this.zoomSwipe(),this.zoomableTimeout=!1,this.positionChanged=!1}},e.prototype.zoomIn=function(){if(!!this.isImageSlide(this.core.index)){var t=this.scale+this.settings.scale;t=this.getScale(t),this.beginZoom(t),this.zoomImage(t,this.settings.scale,!0,!0)}},e.prototype.resetZoom=function(t){this.core.outer.removeClass("lg-zoomed lg-zoom-drag-transition");var n=this.core.getElementById("lg-actual-size"),r=this.core.getSlideItem(t!==void 0?t:this.core.index);n.removeClass(this.settings.actualSizeIcons.zoomOut).addClass(this.settings.actualSizeIcons.zoomIn),r.find(".lg-img-wrap").first().removeAttr("style"),r.find(".lg-image").first().removeAttr("style"),this.scale=1,this.left=0,this.top=0,this.setPageCords()},e.prototype.getTouchDistance=function(t){return Math.sqrt((t.touches[0].pageX-t.touches[1].pageX)*(t.touches[0].pageX-t.touches[1].pageX)+(t.touches[0].pageY-t.touches[1].pageY)*(t.touches[0].pageY-t.touches[1].pageY))},e.prototype.pinchZoom=function(){var t=this,n=0,r=!1,i=1,o=0,s=this.core.getSlideItem(this.core.index);this.core.outer.on("touchstart.lg",function(a){if(s=t.core.getSlideItem(t.core.index),!!t.isImageSlide(t.core.index)&&a.touches.length===2){if(a.preventDefault(),t.core.outer.hasClass("lg-first-slide-loading"))return;i=t.scale||1,t.core.outer.removeClass("lg-zoom-drag-transition lg-zoom-dragging"),t.setPageCords(a),t.resetImageTranslate(t.core.index),t.core.touchAction="pinch",n=t.getTouchDistance(a)}}),this.core.$inner.on("touchmove.lg",function(a){if(a.touches.length===2&&t.core.touchAction==="pinch"&&(t.$LG(a.target).hasClass("lg-item")||s.get().contains(a.target))){a.preventDefault();var l=t.getTouchDistance(a),c=n-l;if(!r&&Math.abs(c)>5&&(r=!0),r){o=t.scale;var f=Math.max(1,i+-c*.02);t.scale=Math.round((f+Number.EPSILON)*100)/100;var d=t.scale-o;t.zoomImage(t.scale,Math.round((d+Number.EPSILON)*100)/100,!1,!1)}}}),this.core.$inner.on("touchend.lg",function(a){if(t.core.touchAction==="pinch"&&(t.$LG(a.target).hasClass("lg-item")||s.get().contains(a.target))){if(r=!1,n=0,t.scale<=1)t.resetZoom();else{var l=t.getCurrentImageActualSizeScale();if(t.scale>=l){var c=l-t.scale;c===0&&(c=.01),t.zoomImage(l,c,!1,!0)}t.manageActualPixelClassNames(),t.core.outer.addClass("lg-zoomed")}t.core.touchAction=void 0}})},e.prototype.touchendZoom=function(t,n,r,i,o){var s=n.x-t.x,a=n.y-t.y,l=Math.abs(s)/o+1,c=Math.abs(a)/o+1;l>2&&(l+=1),c>2&&(c+=1),s=s*l,a=a*c;var f=this.core.getSlideItem(this.core.index).find(".lg-img-wrap").first(),d={};d.x=this.left+s,d.y=this.top+a;var h=this.getPossibleSwipeDragCords();(Math.abs(s)>15||Math.abs(a)>15)&&(i&&(this.isBeyondPossibleTop(d.y,h.minY)?d.y=h.minY:this.isBeyondPossibleBottom(d.y,h.maxY)&&(d.y=h.maxY)),r&&(this.isBeyondPossibleLeft(d.x,h.minX)?d.x=h.minX:this.isBeyondPossibleRight(d.x,h.maxX)&&(d.x=h.maxX)),i?this.top=d.y:d.y=this.top,r?this.left=d.x:d.x=this.left,this.setZoomSwipeStyles(f,d),this.positionChanged=!0)},e.prototype.getZoomSwipeCords=function(t,n,r,i,o){var s={};if(i){if(s.y=this.top+(n.y-t.y),this.isBeyondPossibleTop(s.y,o.minY)){var a=o.minY-s.y;s.y=o.minY-a/6}else if(this.isBeyondPossibleBottom(s.y,o.maxY)){var l=s.y-o.maxY;s.y=o.maxY+l/6}}else s.y=this.top;if(r){if(s.x=this.left+(n.x-t.x),this.isBeyondPossibleLeft(s.x,o.minX)){var c=o.minX-s.x;s.x=o.minX-c/6}else if(this.isBeyondPossibleRight(s.x,o.maxX)){var f=s.x-o.maxX;s.x=o.maxX+f/6}}else s.x=this.left;return s},e.prototype.isBeyondPossibleLeft=function(t,n){return t>=n},e.prototype.isBeyondPossibleRight=function(t,n){return t<=n},e.prototype.isBeyondPossibleTop=function(t,n){return t>=n},e.prototype.isBeyondPossibleBottom=function(t,n){return t<=n},e.prototype.isImageSlide=function(t){var n=this.core.galleryItems[t];return this.core.getSlideType(n)==="image"},e.prototype.getPossibleSwipeDragCords=function(t){var n=this.core.getSlideItem(this.core.index).find(".lg-image").first(),r=this.core.mediaContainerPosition.bottom,i=n.get().getBoundingClientRect(),o=i.height,s=i.width;t&&(o=o+t*o,s=s+t*s);var a=(o-this.containerRect.height)/2,l=(this.containerRect.height-o)/2+r,c=(s-this.containerRect.width)/2,f=(this.containerRect.width-s)/2,d={minY:a,maxY:l,minX:c,maxX:f};return d},e.prototype.setZoomSwipeStyles=function(t,n){t.css("transform","translate3d("+n.x+"px, "+n.y+"px, 0)")},e.prototype.zoomSwipe=function(){var t=this,n={},r={},i=!1,o=!1,s=!1,a=new Date,l=new Date,c,f,d=this.core.getSlideItem(this.core.index);this.core.$inner.on("touchstart.lg",function(h){if(!!t.isImageSlide(t.core.index)&&(d=t.core.getSlideItem(t.core.index),(t.$LG(h.target).hasClass("lg-item")||d.get().contains(h.target))&&h.touches.length===1&&t.core.outer.hasClass("lg-zoomed"))){h.preventDefault(),a=new Date,t.core.touchAction="zoomSwipe",f=t.core.getSlideItem(t.core.index).find(".lg-img-wrap").first();var p=t.getDragAllowedAxises(0);s=p.allowY,o=p.allowX,(o||s)&&(n=t.getSwipeCords(h)),c=t.getPossibleSwipeDragCords(),t.core.outer.addClass("lg-zoom-dragging lg-zoom-drag-transition")}}),this.core.$inner.on("touchmove.lg",function(h){if(h.touches.length===1&&t.core.touchAction==="zoomSwipe"&&(t.$LG(h.target).hasClass("lg-item")||d.get().contains(h.target))){h.preventDefault(),t.core.touchAction="zoomSwipe",r=t.getSwipeCords(h);var p=t.getZoomSwipeCords(n,r,o,s,c);(Math.abs(r.x-n.x)>15||Math.abs(r.y-n.y)>15)&&(i=!0,t.setZoomSwipeStyles(f,p))}}),this.core.$inner.on("touchend.lg",function(h){if(t.core.touchAction==="zoomSwipe"&&(t.$LG(h.target).hasClass("lg-item")||d.get().contains(h.target))){if(h.preventDefault(),t.core.touchAction=void 0,t.core.outer.removeClass("lg-zoom-dragging"),!i)return;i=!1,l=new Date;var p=l.valueOf()-a.valueOf();t.touchendZoom(n,r,o,s,p)}})},e.prototype.zoomDrag=function(){var t=this,n={},r={},i=!1,o=!1,s=!1,a=!1,l,c,f,d;this.core.outer.on("mousedown.lg.zoom",function(h){if(!!t.isImageSlide(t.core.index)){var p=t.core.getSlideItem(t.core.index);if(t.$LG(h.target).hasClass("lg-item")||p.get().contains(h.target)){l=new Date,d=t.core.getSlideItem(t.core.index).find(".lg-img-wrap").first();var b=t.getDragAllowedAxises(0);a=b.allowY,s=b.allowX,t.core.outer.hasClass("lg-zoomed")&&t.$LG(h.target).hasClass("lg-object")&&(s||a)&&(h.preventDefault(),n=t.getDragCords(h),f=t.getPossibleSwipeDragCords(),i=!0,t.core.outer.removeClass("lg-grab").addClass("lg-grabbing lg-zoom-drag-transition lg-zoom-dragging"))}}}),this.$LG(window).on("mousemove.lg.zoom.global"+this.core.lgId,function(h){if(i){o=!0,r=t.getDragCords(h);var p=t.getZoomSwipeCords(n,r,s,a,f);t.setZoomSwipeStyles(d,p)}}),this.$LG(window).on("mouseup.lg.zoom.global"+this.core.lgId,function(h){if(i){if(c=new Date,i=!1,t.core.outer.removeClass("lg-zoom-dragging"),o&&(n.x!==r.x||n.y!==r.y)){r=t.getDragCords(h);var p=c.valueOf()-l.valueOf();t.touchendZoom(n,r,s,a,p)}o=!1}t.core.outer.removeClass("lg-grabbing").addClass("lg-grab")})},e.prototype.closeGallery=function(){this.resetZoom()},e.prototype.destroy=function(){this.$LG(window).off(".lg.zoom.global"+this.core.lgId),this.core.LGel.off(".lg.zoom"),this.core.LGel.off(".zoom"),clearTimeout(this.zoomableTimeout),this.zoomableTimeout=!1},e}();var Zu=(e,t)=>{const n=e.__vccOpts||e;for(const[r,i]of t)n[r]=i;return n};const cO={id:"lightgallery"},uO=["href"],fO=["src"],dO=os({__name:"Component",props:{args:{type:null,required:!0},disabled:{type:Boolean,required:!0},theme:{type:null,required:!0}},setup(e){nO();const t=[oO,lO];let n=null;const r=function(o){n=o.instance,n.openGallery(0),Sn.setFrameHeight(640)},i=function(){Sn.setFrameHeight(640)};return(o,s)=>(Fn(),ai("div",cO,[me(Mc(iO),{ref:"gallery",settings:{speed:250,showMaximizeIcon:!1,closable:!1,plugins:t},onInit:r,onBeforeSlide:i},{default:lo(()=>[(Fn(!0),ai(Re,null,Ch(e.args.images,a=>(Fn(),ai("a",{class:"gallery-item",href:a,"data-sub-html":""},[cs("img",{class:"hide-img",src:a},null,8,fO)],8,uO))),256))]),_:1},8,["settings"])]))}});var hO=Zu(dO,[["__file","E:/Projets/Perso/AI Generated Art/Stable Diffusion/gallery-component/streamlit-gallery-component/sd_gallery/frontend/src/Component.vue"]]);const pO=os({name:"WithStreamlitConnection",setup(){const e=Pi(void 0),t=Pi(""),n=r=>{const i=r;e.value=i.detail,t.value=""};return Si(()=>{Sn.events.addEventListener(Sn.RENDER_EVENT,n),Sn.setComponentReady()}),ls(()=>{t.value!=""&&Sn.setFrameHeight()}),uo(()=>{Sn.events.removeEventListener(Sn.RENDER_EVENT,n)}),Yc(r=>{t.value=String(r)}),{renderData:e,componentError:t}}}),gO=e=>(uh("data-v-4d5bb78e"),e=e(),fh(),e),mO={key:0},yO=gO(()=>cs("h1",{class:"err__title"},"Component Error",-1)),vO={class:"err__msg"};function bO(e,t,n,r,i,o){return Fn(),ai("div",null,[ko("  Error boundary. If our wrapped component threw an error, display it. "),e.componentError!==""?(Fn(),ai("div",mO,[yO,cs("div",vO,"Message: "+Rd(e.componentError),1)])):e.renderData!=null?(Fn(),ai(Re,{key:1},[ko(` \r
      Else render the component slot and pass Streamlit event data in \`args\` props to it.\r
      Don't render until we've gotten our first RENDER_EVENT from Streamlit.\r
      All components get disabled while the app is being re-run, and become re-enabled when the re-run has finished.\r
    `),Bh(e.$slots,"default",{args:e.renderData.args,theme:e.renderData.theme,disabled:e.renderData.disabled},void 0,!0)],2112)):ko("v-if",!0)])}var wO=Zu(pO,[["render",bO],["__scopeId","data-v-4d5bb78e"],["__file","E:/Projets/Perso/AI Generated Art/Stable Diffusion/gallery-component/streamlit-gallery-component/sd_gallery/frontend/src/streamlit/WithStreamlitConnection.vue"]]);const SO=os({name:"App",components:{Component:hO,WithStreamlitConnection:wO}});function IO(e,t,n,r,i,o){const s=Xl("Component"),a=Xl("WithStreamlitConnection");return Fn(),za(a,null,{default:lo(({args:l,theme:c,disabled:f})=>[me(s,{args:l,theme:c,disabled:f},null,8,["args","theme","disabled"])]),_:1})}var TO=Zu(SO,[["render",IO],["__file","E:/Projets/Perso/AI Generated Art/Stable Diffusion/gallery-component/streamlit-gallery-component/sd_gallery/frontend/src/app.vue"]]);xp(TO).mount("#app");


================================================
FILE: frontend/dists/sd-gallery/dist/index.html
================================================
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Component Template</title>
  <script type="module" crossorigin src="assets/index.aeaed602.js"></script>
  <link rel="stylesheet" href="assets/index.4194368f.css">
</head>
<body>
<div id="app"></div>

</body>
</html>


================================================
FILE: frontend/frontend.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
import gradio as gr
from frontend.css_and_js import css, js, call_JS
from frontend.job_manager import JobManager
import frontend.ui_functions as uifn
import torch
import os


def draw_gradio_ui(
    opt,
    img2img=lambda x: x,
    txt2img=lambda x: x,
    imgproc=lambda x: x,
    scn2img=lambda x: x,
    txt2img_defaults={},
    RealESRGAN=True,
    GFPGAN=True,
    LDSR=True,
    txt2img_toggles={},
    txt2img_toggle_defaults="k_euler",
    show_embeddings=False,
    img2img_defaults={},
    img2img_toggles={},
    img2img_toggle_defaults={},
    sample_img2img=None,
    img2img_mask_modes=None,
    img2img_resize_modes=None,
    imgproc_defaults={},
    imgproc_mode_toggles={},
    scn2img_defaults={},
    scn2img_toggles={},
    scn2img_toggle_defaults={},
    scn2img_define_args=lambda: ({}, {}, {}),
    user_defaults={},
    run_GFPGAN=lambda x: x,
    run_RealESRGAN=lambda x: x,
    job_manager: JobManager = None,
) -> gr.Blocks:
    with gr.Blocks(
        css=css(opt), analytics_enabled=False, title="Stable Diffusion WebUI"
    ) as demo:
        with gr.Tabs(elem_id="tabss") as tabs:
            with gr.TabItem("Text-to-Image", id="txt2img_tab"):
                with gr.Row(elem_id="prompt_row"):
                    txt2img_prompt = gr.Textbox(
                        label="Prompt",
                        elem_id="prompt_input",
                        placeholder="A corgi wearing a top hat as an oil painting.",
                        lines=1,
                        max_lines=1
                        if txt2img_defaults["submit_on_enter"] == "Yes"
                        else 25,
                        value=txt2img_defaults["prompt"],
                        show_label=False,
                    )
                    txt2img_btn = gr.Button(
                        "Generate", elem_id="generate", variant="primary"
                    )

                with gr.Row(elem_id="body").style(equal_height=False):
                    with gr.Column():
                        txt2img_width = gr.Slider(
                            minimum=64,
                            maximum=1024,
                            step=64,
                            label="Width",
                            value=txt2img_defaults["width"],
                        )
                        txt2img_height = gr.Slider(
                            minimum=64,
                            maximum=1024,
                            step=64,
                            label="Height",
                            value=txt2img_defaults["height"],
                        )
                        txt2img_cfg = gr.Slider(
                            minimum=-40.0,
                            maximum=30.0,
                            step=0.5,
                            label="Classifier Free Guidance Scale (how strongly the image should follow the prompt)",
                            value=txt2img_defaults["cfg_scale"],
                            elem_id="cfg_slider",
                        )
                        txt2img_seed = gr.Textbox(
                            label="Seed (blank to randomize)",
                            lines=1,
                            max_lines=1,
                            value=txt2img_defaults["seed"],
                        )
                        txt2img_batch_size = gr.Slider(
                            minimum=1,
                            maximum=50,
                            step=1,
                            label="Images per batch",
                            value=txt2img_defaults["batch_size"],
                        )
                        txt2img_batch_count = gr.Slider(
                            minimum=1,
                            maximum=50,
                            step=1,
                            label="Number of batches to generate",
                            value=txt2img_defaults["n_iter"],
                        )

                        txt2img_job_ui = (
                            job_manager.draw_gradio_ui() if job_manager else None
                        )

                        txt2img_dimensions_info_text_box = gr.Textbox(
                            label="Aspect ratio (4:3 = 1.333 | 16:9 = 1.777 | 21:9 = 2.333)"
                        )
                    with gr.Column(elem_id="text2img_col2"):
                        with gr.Box():
                            output_txt2img_gallery = gr.Gallery(
                                label="Images", elem_id="txt2img_gallery_output"
                            ).style(grid=[4, 4])
                            gr.Markdown(
                                "Select an image from the gallery, then click one of the buttons below to perform an action."
                            )
                            with gr.Row(elem_id="txt2img_actions_row"):
                                gr.Button("Copy to clipboard").click(
                                    fn=None,
                                    inputs=output_txt2img_gallery,
                                    outputs=[],
                                    _js=call_JS(
                                        "copyImageFromGalleryToClipboard",
                                        fromId="txt2img_gallery_output",
                                    ),
                                )
                                output_txt2img_copy_to_input_btn = gr.Button(
                                    "Push to img2img"
                                )
                                output_txt2img_to_imglab = gr.Button(
                                    "Send to Lab", visible=True
                                )

                        output_txt2img_params = gr.Highlightedtext(
                            label="Generation parameters",
                            interactive=False,
                            elem_id="txt2img_highlight",
                        )
                        with gr.Group():
                            with gr.Row(elem_id="txt2img_output_row"):
                                gr.Button("Copy full parameters").click(
                                    inputs=[output_txt2img_params],
                                    outputs=[],
                                    _js=call_JS(
                                        "copyFullOutput", fromId="txt2img_highlight"
                                    ),
                                    fn=None,
                                    show_progress=False,
                                )
                                output_txt2img_seed = gr.Number(
                                    label="Seed", interactive=False, visible=False
                                )
                                gr.Button("Copy only seed").click(
                                    inputs=[output_txt2img_seed],
                                    outputs=[],
                                    _js=call_JS("gradioInputToClipboard"),
                                    fn=None,
                                    show_progress=False,
                                )
                            output_txt2img_stats = gr.HTML(label="Stats")
                    with gr.Column():
                        txt2img_steps = gr.Slider(
                            minimum=1,
                            maximum=250,
                            step=1,
                            label="Sampling Steps",
                            value=txt2img_defaults["ddim_steps"],
                        )
                        txt2img_sampling = gr.Dropdown(
                            label="Sampling method (k_lms is default k-diffusion sampler)",
                            choices=[
                                "DDIM",
                                "PLMS",
                                "k_dpm_2_a",
                                "k_dpm_2",
                                "k_euler_a",
                                "k_euler",
                                "k_heun",
                                "k_lms",
                            ],
                            value=txt2img_defaults["sampler_name"],
                        )
                        with gr.Tabs():
                            with gr.TabItem("Simple"):
                                txt2img_submit_on_enter = gr.Radio(
                                    ["Yes", "No"],
                                    label="Submit on enter? (no means multiline)",
                                    value=txt2img_defaults["submit_on_enter"],
                                    interactive=True,
                                    elem_id="submit_on_enter",
                                )
                                txt2img_submit_on_enter.change(
                                    lambda x: gr.update(
                                        max_lines=1 if x == "Yes" else 25
                                    ),
                                    txt2img_submit_on_enter,
                                    txt2img_prompt,
                                )
                            with gr.TabItem("Advanced"):
                                txt2img_toggles = gr.CheckboxGroup(
                                    label="",
                                    choices=txt2img_toggles,
                                    value=txt2img_toggle_defaults,
                                    type="index",
                                )
                                txt2img_realesrgan_model_name = gr.Dropdown(
                                    label="RealESRGAN model",
                                    choices=[
                                        "RealESRGAN_x4plus",
                                        "RealESRGAN_x4plus_anime_6B",
                                    ],
                                    value=txt2img_defaults["realesrgan_model_name"],
                                    visible=False,
                                )  # RealESRGAN is not None # invisible until removed)  # TODO: Feels like I shouldnt slot it in here.

                                txt2img_ddim_eta = gr.Slider(
                                    minimum=0.0,
                                    maximum=1.0,
                                    step=0.01,
                                    label="DDIM ETA",
                                    value=txt2img_defaults["ddim_eta"],
                                    visible=False,
                                )
                                txt2img_variant_amount = gr.Slider(
                                    minimum=0.0,
                                    maximum=1.0,
                                    label="Variation Amount",
                                    value=txt2img_defaults["variant_amount"],
                                )
                                txt2img_variant_seed = gr.Textbox(
                                    label="Variant Seed (blank to randomize)",
                                    lines=1,
                                    max_lines=1,
                                    value=txt2img_defaults["variant_seed"],
                                )
                        txt2img_embeddings = gr.File(
                            label="Embeddings file for textual inversion",
                            visible=show_embeddings,
                        )

                txt2img_func = txt2img
                txt2img_inputs = [
                    txt2img_prompt,
                    txt2img_steps,
                    txt2img_sampling,
                    txt2img_toggles,
                    txt2img_realesrgan_model_name,
                    txt2img_ddim_eta,
                    txt2img_batch_count,
                    txt2img_batch_size,
                    txt2img_cfg,
                    txt2img_seed,
                    txt2img_height,
                    txt2img_width,
                    txt2img_embeddings,
                    txt2img_variant_amount,
                    txt2img_variant_seed,
                ]
                txt2img_outputs = [
                    output_txt2img_gallery,
                    output_txt2img_seed,
                    output_txt2img_params,
                    output_txt2img_stats,
                ]

                # If a JobManager was passed in then wrap the Generate functions
                if txt2img_job_ui:
                    (
                        txt2img_func,
                        txt2img_inputs,
                        txt2img_outputs,
                    ) = txt2img_job_ui.wrap_func(
                        func=txt2img_func,
                        inputs=txt2img_inputs,
                        outputs=txt2img_outputs,
                    )
                    use_queue = False
                else:
                    use_queue = True

                txt2img_btn.click(
                    txt2img_func,
                    txt2img_inputs,
                    txt2img_outputs,
                    api_name="txt2img",
                    queue=use_queue,
                )
                txt2img_prompt.submit(
                    txt2img_func, txt2img_inputs, txt2img_outputs, queue=use_queue
                )

                txt2img_width.change(
                    fn=uifn.update_dimensions_info,
                    inputs=[txt2img_width, txt2img_height],
                    outputs=txt2img_dimensions_info_text_box,
                )
                txt2img_height.change(
                    fn=uifn.update_dimensions_info,
                    inputs=[txt2img_width, txt2img_height],
                    outputs=txt2img_dimensions_info_text_box,
                )
                txt2img_dimensions_info_text_box.value = uifn.update_dimensions_info(
                    txt2img_width.value, txt2img_height.value
                )

                # Temporarily disable prompt parsing until memory issues could be solved
                # See #676
                # live_prompt_params = [txt2img_prompt, txt2img_width, txt2img_height, txt2img_steps, txt2img_seed,
                #                       txt2img_batch_count, txt2img_cfg]
                # txt2img_prompt.change(
                #     fn=None,
                #     inputs=live_prompt_params,
                #     outputs=live_prompt_params,
                #     _js=js_parse_prompt
                # )

            with gr.TabItem("Image-to-Image Unified", id="img2img_tab"):
                with gr.Row(elem_id="prompt_row"):
                    img2img_prompt = gr.Textbox(
                        label="Prompt",
                        elem_id="img2img_prompt_input",
                        placeholder="A fantasy landscape, trending on artstation.",
                        lines=1,
                        max_lines=1
                        if txt2img_defaults["submit_on_enter"] == "Yes"
                        else 25,
                        value=img2img_defaults["prompt"],
                        show_label=False,
                    ).style()

                    img2img_btn_mask = gr.Button(
                        "Generate",
                        variant="primary",
                        visible=False,
                        elem_id="img2img_mask_btn",
                    )
                    img2img_btn_editor = gr.Button(
                        "Generate", variant="primary", elem_id="img2img_edit_btn"
                    )
                with gr.Row().style(equal_height=False):
                    with gr.Column():
                        gr.Markdown("#### Img2Img Input")
                        img2img_image_mask = gr.Image(
                            value=sample_img2img,
                            source="upload",
                            interactive=True,
                            type="pil",
                            tool="sketch",
                            elem_id="img2img_mask",
                            image_mode="RGBA",
                        )
                        img2img_image_editor = gr.Image(
                            value=sample_img2img,
                            source="upload",
                            interactive=True,
                            type="pil",
                            tool="select",
                            visible=False,
                            image_mode="RGBA",
                            elem_id="img2img_editor",
                        )

                        with gr.Tabs():
                            with gr.TabItem("Editor Options"):
                                with gr.Row():
                                    # disable Uncrop for now
                                    choices = ["Mask", "Crop", "Uncrop"]
                                    # choices=["Mask", "Crop"]
                                    img2img_image_editor_mode = gr.Radio(
                                        choices=choices,
                                        label="Image Editor Mode",
                                        value="Mask",
                                        elem_id="edit_mode_select",
                                        visible=True,
                                    )
                                    img2img_mask = gr.Radio(
                                        choices=[
                                            "Keep masked area",
                                            "Regenerate only masked area",
                                        ],
                                        label="Mask Mode",
                                        type="index",
                                        value=img2img_mask_modes[
                                            img2img_defaults["mask_mode"]
                                        ],
                                        visible=True,
                                    )

                                    img2img_mask_restore = gr.Checkbox(
                                        label="Only modify regenerated parts of image",
                                        value=img2img_defaults["mask_restore"],
                                        visible=True,
                                    )

                                    img2img_mask_blur_strength = gr.Slider(
                                        minimum=1,
                                        maximum=100,
                                        step=1,
                                        label="How much blurry should the mask be? (to avoid hard edges)",
                                        value=img2img_defaults["mask_blur_strength"],
                                        visible=True,
                                    )

                                    img2img_resize = gr.Radio(
                                        label="Resize mode",
                                        choices=[
                                            "Just resize",
                                            "Crop and resize",
                                            "Resize and fill",
                                        ],
                                        type="index",
                                        value=img2img_resize_modes[
                                            img2img_defaults["resize_mode"]
                                        ],
                                        visible=False,
                                    )

                                img2img_painterro_btn = gr.Button("Advanced Editor")
                            with gr.TabItem("Hints"):
                                gr.Markdown(visible=False, value=uifn.help_text)

                    with gr.Column():
                        gr.Markdown("#### Img2Img Results")
                        output_img2img_gallery = gr.Gallery(
                            label="Images", elem_id="img2img_gallery_output"
                        ).style(grid=[4, 4, 4])
                        img2img_job_ui = (
                            job_manager.draw_gradio_ui() if job_manager else None
                        )
                        with gr.Tabs():
                            with gr.TabItem(
                                "Generated image actions", id="img2img_actions_tab"
                            ):
                                gr.Markdown(
                                    "Select an image, then press one of the buttons below"
                                )
                                with gr.Row():
                                    output_img2img_copy_to_clipboard_btn = gr.Button(
                                        "Copy to clipboard"
                                    )
                                    output_img2img_copy_to_input_btn = gr.Button(
                                        "Push to img2img input"
                                    )
                                    output_img2img_copy_to_mask_btn = gr.Button(
                                        "Push to img2img input mask"
                                    )

                                gr.Markdown(
                                    "Warning: This will clear your current image and mask settings!"
                                )
                            with gr.TabItem(
                                "Output info", id="img2img_output_info_tab"
                            ):
                                output_img2img_params = gr.Highlightedtext(
                                    label="Generation parameters",
                                    interactive=False,
                                    elem_id="img2img_highlight",
                                )
                                with gr.Row():
                                    gr.Button("Copy full parameters").click(
                                        inputs=output_img2img_params,
                                        outputs=[],
                                        _js=call_JS(
                                            "copyFullOutput", fromId="img2img_highlight"
                                        ),
                                        fn=None,
                                        show_progress=False,
                                    )
                                    output_img2img_seed = gr.Number(
                                        label="Seed", interactive=False, visible=False
                                    )
                                    gr.Button("Copy only seed").click(
                                        inputs=output_img2img_seed,
                                        outputs=[],
                                        _js=call_JS("gradioInputToClipboard"),
                                        fn=None,
                                        show_progress=False,
                                    )
                                output_img2img_stats = gr.HTML(label="Stats")

                gr.Markdown("# img2img settings")

                with gr.Row():
                    with gr.Column():
                        img2img_width = gr.Slider(
                            minimum=64,
                            maximum=2048,
                            step=64,
                            label="Width",
                            value=img2img_defaults["width"],
                        )
                        img2img_height = gr.Slider(
                            minimum=64,
                            maximum=2048,
                            step=64,
                            label="Height",
                            value=img2img_defaults["height"],
                        )
                        img2img_cfg = gr.Slider(
                            minimum=-40.0,
                            maximum=30.0,
                            step=0.5,
                            label="Classifier Free Guidance Scale (how strongly the image should follow the prompt)",
                            value=img2img_defaults["cfg_scale"],
                            elem_id="cfg_slider",
                        )
                        img2img_seed = gr.Textbox(
                            label="Seed (blank to randomize)",
                            lines=1,
                            max_lines=1,
                            value=img2img_defaults["seed"],
                        )
                        img2img_batch_count = gr.Slider(
                            minimum=1,
                            maximum=50,
                            step=1,
                            label="Batch count (how many batches of images to generate)",
                            value=img2img_defaults["n_iter"],
                        )
                        img2img_dimensions_info_text_box = gr.Textbox(
                            label="Aspect ratio (4:3 = 1.333 | 16:9 = 1.777 | 21:9 = 2.333)",
                            lines="2",
                        )
                    with gr.Column():
                        img2img_steps = gr.Slider(
                            minimum=1,
                            maximum=250,
                            step=1,
                            label="Sampling Steps",
                            value=img2img_defaults["ddim_steps"],
                        )

                        img2img_sampling = gr.Dropdown(
                            label="Sampling method (k_lms is default k-diffusion sampler)",
                            choices=[
                                "DDIM",
                                "k_dpm_2_a",
                                "k_dpm_2",
                                "k_euler_a",
                                "k_euler",
                                "k_heun",
                                "k_lms",
                            ],
                            value=img2img_defaults["sampler_name"],
                        )

                        img2img_denoising = gr.Slider(
                            minimum=0.0,
                            maximum=1.0,
                            step=0.01,
                            label="Denoising Strength",
                            value=img2img_defaults["denoising_strength"],
                        )

                        img2img_toggles = gr.CheckboxGroup(
                            label="",
                            choices=img2img_toggles,
                            value=img2img_toggle_defaults,
                            type="index",
                        )

                        img2img_realesrgan_model_name = gr.Dropdown(
                            label="RealESRGAN model",
                            choices=["RealESRGAN_x4plus", "RealESRGAN_x4plus_anime_6B"],
                            value=img2img_defaults["realesrgan_model_name"],
                            visible=RealESRGAN is not None,
                        )  # TODO: Feels like I shouldnt slot it in here.

                        img2img_embeddings = gr.File(
                            label="Embeddings file for textual inversion",
                            visible=show_embeddings,
                        )

                img2img_image_editor_mode.change(
                    uifn.change_image_editor_mode,
                    [
                        img2img_image_editor_mode,
                        img2img_image_editor,
                        img2img_image_mask,
                        img2img_resize,
                        img2img_width,
                        img2img_height,
                    ],
                    [
                        img2img_image_editor,
                        img2img_image_mask,
                        img2img_btn_editor,
                        img2img_btn_mask,
                        img2img_painterro_btn,
                        img2img_mask,
                        img2img_mask_blur_strength,
                        img2img_mask_restore,
                    ],
                )

                # img2img_image_editor_mode.change(
                #     uifn.update_image_mask,
                #     [img2img_image_editor, img2img_resize, img2img_width, img2img_height],
                #     img2img_image_mask
                # )

                output_txt2img_copy_to_input_btn.click(
                    uifn.copy_img_to_input,
                    [output_txt2img_gallery],
                    [img2img_image_editor, img2img_image_mask, tabs],
                    _js=call_JS(
                        "moveImageFromGallery",
                        fromId="txt2img_gallery_output",
                        toId="img2img_editor",
                    ),
                )

                output_img2img_copy_to_input_btn.click(
                    uifn.copy_img_to_edit,
                    [output_img2img_gallery],
                    [img2img_image_editor, tabs, img2img_image_editor_mode],
                    _js=call_JS(
                        "moveImageFromGallery",
                        fromId="img2img_gallery_output",
                        toId="img2img_editor",
                    ),
                )
                output_img2img_copy_to_mask_btn.click(
                    uifn.copy_img_to_mask,
                    [output_img2img_gallery],
                    [img2img_image_mask, tabs, img2img_image_editor_mode],
                    _js=call_JS(
                        "moveImageFromGallery",
                        fromId="img2img_gallery_output",
                        toId="img2img_editor",
                    ),
                )

                output_img2img_copy_to_clipboard_btn.click(
                    fn=None,
                    inputs=output_img2img_gallery,
                    outputs=[],
                    _js=call_JS(
                        "copyImageFromGalleryToClipboard",
                        fromId="img2img_gallery_output",
                    ),
                )

                img2img_func = img2img
                img2img_inputs = [
                    img2img_prompt,
                    img2img_image_editor_mode,
                    img2img_mask,
                    img2img_mask_blur_strength,
                    img2img_mask_restore,
                    img2img_steps,
                    img2img_sampling,
                    img2img_toggles,
                    img2img_realesrgan_model_name,
                    img2img_batch_count,
                    img2img_cfg,
                    img2img_denoising,
                    img2img_seed,
                    img2img_height,
                    img2img_width,
                    img2img_resize,
                    img2img_image_editor,
                    img2img_image_mask,
                    img2img_embeddings,
                ]
                img2img_outputs = [
                    output_img2img_gallery,
                    output_img2img_seed,
                    output_img2img_params,
                    output_img2img_stats,
                ]

                # If a JobManager was passed in then wrap the Generate functions
                if img2img_job_ui:
                    (
                        img2img_func,
                        img2img_inputs,
                        img2img_outputs,
                    ) = img2img_job_ui.wrap_func(
                        func=img2img_func,
                        inputs=img2img_inputs,
                        outputs=img2img_outputs,
                    )
                    use_queue = False
                else:
                    use_queue = True

                img2img_btn_mask.click(
                    img2img_func,
                    img2img_inputs,
                    img2img_outputs,
                    api_name="img2img",
                    queue=use_queue,
                )

                def img2img_submit_params():
                    # print([img2img_prompt, img2img_image_editor_mode, img2img_mask,
                    #              img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles,
                    #              img2img_realesrgan_model_name, img2img_batch_count, img2img_cfg,
                    #              img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize,
                    #              img2img_image_editor, img2img_image_mask, img2img_embeddings])
                    return (img2img_func, img2img_inputs, img2img_outputs)

                img2img_btn_editor.click(*img2img_submit_params())

                # GENERATE ON ENTER
                img2img_prompt.submit(
                    None,
                    None,
                    None,
                    _js=call_JS("clickFirstVisibleButton", rowId="prompt_row"),
                )

                img2img_painterro_btn.click(
                    None,
                    [
                        img2img_image_editor,
                        img2img_image_mask,
                        img2img_image_editor_mode,
                    ],
                    [img2img_image_editor, img2img_image_mask],
                    _js=call_JS("Painterro.init", toId="img2img_editor"),
                )

                img2img_width.change(
                    fn=uifn.update_dimensions_info,
                    inputs=[img2img_width, img2img_height],
                    outputs=img2img_dimensions_info_text_box,
                )
                img2img_height.change(
                    fn=uifn.update_dimensions_info,
                    inputs=[img2img_width, img2img_height],
                    outputs=img2img_dimensions_info_text_box,
                )
                img2img_dimensions_info_text_box.value = uifn.update_dimensions_info(
                    img2img_width.value, img2img_height.value
                )

            with gr.TabItem("Image Lab", id="imgproc_tab"):
                gr.Markdown("Post-process results")
                with gr.Row():
                    with gr.Column():
                        with gr.Tabs():
                            with gr.TabItem("Single Image"):
                                imgproc_source = gr.Image(
                                    label="Source",
                                    source="upload",
                                    interactive=True,
                                    type="pil",
                                    elem_id="imglab_input",
                                )

                            # gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength",
                            #                            value=gfpgan_defaults['strength'])
                            # select folder with images to process
                            with gr.TabItem("Batch Process"):
                                imgproc_folder = gr.File(
                                    label="Batch Process",
                                    file_count="multiple",
                                    interactive=True,
                                    type="file",
                                )
                        imgproc_pngnfo = gr.Textbox(
                            label="PNG Metadata",
                            placeholder="PngNfo",
                            visible=False,
                            max_lines=5,
                        )
                        with gr.Row():
                            imgproc_btn = gr.Button("Process", variant="primary")
                        gr.HTML(
                            """
        <div id="90" style="max-width: 100%; font-size: 14px; text-align: center;" class="output-markdown gr-prose border-solid border border-gray-200 rounded gr-panel">
            <p><b>Upscale Modes Guide</b></p>
            <p></p>
            <p><b>RealESRGAN</b></p>
            <p>A 4X/2X fast upscaler that works well for stylized content, will smooth more detailed compositions.</p>
            <p><b>GoBIG</b></p>
            <p>A 2X upscaler that uses RealESRGAN to upscale the image and then slice it into small parts, each part gets diffused further by SD to create more details, great for adding and increasing details but will change the composition, might also fix issues like eyes etc, use the settings like img2img etc</p>
            <p><b>Latent Diffusion Super Resolution</b></p>
            <p>A 4X upscaler with high VRAM usage that uses a Latent Diffusion model to upscale the image, this will accentuate the details but won't change the composition, might introduce sharpening, great for textures or compositions with plenty of details, is slower.</p>
            <p><b>GoLatent</b></p>
            <p>A 8X upscaler with high VRAM usage, uses GoBig to add details and then uses a Latent Diffusion model to upscale the image, this will result in less artifacting/sharpeninng, use the settings to feed GoBig settings that will contribute to the result, this mode is considerbly slower</p>
        </div>
        """
                        )
                    with gr.Column():
                        with gr.Tabs():
                            with gr.TabItem("Output"):
                                imgproc_output = gr.Gallery(
                                    label="Output", elem_id="imgproc_gallery_output"
                                )
                        with gr.Row(elem_id="proc_options_row"):
                            with gr.Box():
                                with gr.Column():
                                    gr.Markdown("<b>Processor Selection</b>")
                                    imgproc_toggles = gr.CheckboxGroup(
                                        label="",
                                        choices=imgproc_mode_toggles,
                                        type="index",
                                    )
                                    # .change toggles to show options
                                    # imgproc_toggles.change()
                        with gr.Box(visible=False) as gfpgan_group:
                            gfpgan_defaults = {
                                "strength": 100,
                            }

                            if "gfpgan" in user_defaults:
                                gfpgan_defaults.update(user_defaults["gfpgan"])
                            if GFPGAN is None:
                                gr.HTML(
                                    """
    <div id="90" style="max-width: 100%; font-size: 14px; text-align: center;" class="output-markdown gr-prose border-solid border border-gray-200 rounded gr-panel">
        <p><b> Please download GFPGAN to activate face fixing features</b>, instructions are available at the <a href='https://github.com/Sygil-Dev/sygil-webui'>Github</a></p>
    </div>
    """
                                )
                                # gr.Markdown("")
                                # gr.Markdown("<b> Please download GFPGAN to activate face fixing features</b>, instructions are available at the <a href='https://github.com/Sygil-Dev/sygil-webui'>Github</a>")
                            with gr.Column():
                                gr.Markdown("<b>GFPGAN Settings</b>")
                                imgproc_gfpgan_strength = gr.Slider(
                                    minimum=0.0,
                                    maximum=1.0,
                                    step=0.001,
                                    label="Effect strength",
                                    value=gfpgan_defaults["strength"],
                                    visible=GFPGAN is not None,
                                )
                        with gr.Box(visible=False) as upscale_group:
                            if LDSR:
                                upscaleModes = [
                                    "RealESRGAN",
                                    "GoBig",
                                    "Latent Diffusion SR",
                                    "GoLatent ",
                                ]
                            else:
                                gr.HTML(
                                    """
    <div id="90" style="max-width: 100%; font-size: 14px; text-align: center;" class="output-markdown gr-prose border-solid border border-gray-200 rounded gr-panel">
        <p><b> Please download LDSR to activate more upscale features</b>, instructions are available at the <a href='https://github.com/Sygil-Dev/sygil-webui'>Github</a></p>
    </div>
    """
                                )
                                upscaleModes = ["RealESRGAN", "GoBig"]
                            with gr.Column():
                                gr.Markdown("<b>Upscaler Selection</b>")
                                imgproc_upscale_toggles = gr.Radio(
                                    label="",
                                    choices=upscaleModes,
                                    type="index",
                                    visible=RealESRGAN is not None,
                                    value="RealESRGAN",
                                )
                        with gr.Box(visible=False) as upscalerSettings_group:
                            with gr.Box(visible=True) as realesrgan_group:
                                with gr.Column():
                                    gr.Markdown("<b>RealESRGAN Settings</b>")
                                    imgproc_realesrgan_model_name = gr.Dropdown(
                                        label="RealESRGAN model",
                                        interactive=RealESRGAN is not None,
                                        choices=[
                                            "RealESRGAN_x4plus",
                                            "RealESRGAN_x4plus_anime_6B",
                                            "RealESRGAN_x2plus",
                                            "RealESRGAN_x2plus_anime_6B",
                                        ],
                                        value="RealESRGAN_x4plus",
                                        visible=RealESRGAN is not None,
                                    )  # TODO: Feels like I shouldnt slot it in here.
                            with gr.Box(visible=False) as ldsr_group:
                                with gr.Row(elem_id="ldsr_settings_row"):
                                    with gr.Column():
                                        gr.Markdown(
                                            "<b>Latent Diffusion Super Sampling Settings</b>"
                                        )
                                        imgproc_ldsr_steps = gr.Slider(
                                            minimum=0,
                                            maximum=500,
                                            step=10,
                                            label="LDSR Sampling Steps",
                                            value=100,
                                            visible=LDSR is not None,
                                        )
                                        imgproc_ldsr_pre_downSample = gr.Dropdown(
                                            label="LDSR Pre Downsample mode (Lower resolution before processing for speed)",
                                            choices=["None", "1/2", "1/4"],
                                            value="None",
                                            visible=LDSR is not None,
                                        )
                                        imgproc_ldsr_post_downSample = gr.Dropdown(
                                            label="LDSR Post Downsample mode (aka SuperSampling)",
                                            choices=[
                                                "None",
                                                "Original Size",
                                                "1/2",
                                                "1/4",
                                            ],
                                            value="None",
                                            visible=LDSR is not None,
                                        )
                            with gr.Box(visible=False) as gobig_group:
                                with gr.Row(elem_id="proc_prompt_row"):
                                    with gr.Column():
                                        gr.Markdown("<b>GoBig Settings</b>")
                                        imgproc_prompt = gr.Textbox(
                                            label="",
                                            elem_id="prompt_input",
                                            placeholder="A corgi wearing a top hat as an oil painting.",
                                            lines=1,
                                            max_lines=1,
                                            value=imgproc_defaults["prompt"],
                                            show_label=True,
                                            visible=RealESRGAN is not None,
                                        )
                                        imgproc_sampling = gr.Dropdown(
                                            label="Sampling method (k_lms is default k-diffusion sampler)",
                                            choices=[
                                                "DDIM",
                                                "k_dpm_2_a",
                                                "k_dpm_2",
                                                "k_euler_a",
                                                "k_euler",
                                                "k_heun",
                                                "k_lms",
                                            ],
                                            value=imgproc_defaults["sampler_name"],
                                            visible=RealESRGAN is not None,
                                        )
                                        imgproc_steps = gr.Slider(
                                            minimum=1,
                                            maximum=250,
                                            step=1,
                                            label="Sampling Steps",
                                            value=imgproc_defaults["ddim_steps"],
                                            visible=RealESRGAN is not None,
                                        )
                                        imgproc_cfg = gr.Slider(
                                            minimum=1.0,
                                            maximum=30.0,
                                            step=0.5,
                                            label="Classifier Free Guidance Scale (how strongly the image should follow the prompt)",
                                            value=imgproc_defaults["cfg_scale"],
                                            visible=RealESRGAN is not None,
                                        )
                                        imgproc_denoising = gr.Slider(
                                            minimum=0.0,
                                            maximum=1.0,
                                            step=0.01,
                                            label="Denoising Strength",
                                            value=imgproc_defaults[
                                                "denoising_strength"
                                            ],
                                            visible=RealESRGAN is not None,
                                        )
                                        imgproc_height = gr.Slider(
                                            minimum=64,
                                            maximum=2048,
                                            step=64,
                                            label="Height",
                                            value=imgproc_defaults["height"],
                                            visible=False,
                                        )  # not currently implemented
                                        imgproc_width = gr.Slider(
                                            minimum=64,
                                            maximum=2048,
                                            step=64,
                                            label="Width",
                                            value=imgproc_defaults["width"],
                                            visible=False,
                                        )  # not currently implemented
                                        imgproc_seed = gr.Textbox(
                                            label="Seed (blank to randomize)",
                                            lines=1,
                                            max_lines=1,
                                            value=imgproc_defaults["seed"],
                                            visible=RealESRGAN is not None,
                                        )
                                        imgproc_btn.click(
                                            imgproc,
                                            [
                                                imgproc_source,
                                                imgproc_folder,
                                                imgproc_prompt,
                                                imgproc_toggles,
                                                imgproc_upscale_toggles,
                                                imgproc_realesrgan_model_name,
                                                imgproc_sampling,
                                                imgproc_steps,
                                                imgproc_height,
                                                imgproc_width,
                                                imgproc_cfg,
                                                imgproc_denoising,
                                                imgproc_seed,
                                                imgproc_gfpgan_strength,
                                                imgproc_ldsr_steps,
                                                imgproc_ldsr_pre_downSample,
                                                imgproc_ldsr_post_downSample,
                                            ],
                                            [imgproc_output],
                                            api_name="imgproc",
                                        )

                                        imgproc_source.change(
                                            uifn.get_png_nfo,
                                            [imgproc_source],
                                            [imgproc_pngnfo],
                                        )

                                output_txt2img_to_imglab.click(
                                    fn=uifn.copy_img_params_to_lab,
                                    inputs=[output_txt2img_params],
                                    outputs=[
                                        imgproc_prompt,
                                        imgproc_seed,
                                        imgproc_steps,
                                        imgproc_cfg,
                                        imgproc_sampling,
                                    ],
                                )

                                output_txt2img_to_imglab.click(
                                    fn=uifn.copy_img_to_lab,
                                    inputs=[output_txt2img_gallery],
                                    outputs=[imgproc_source, tabs],
                                    _js=call_JS(
                                        "moveImageFromGallery",
                                        fromId="txt2img_gallery_output",
                                        toId="imglab_input",
                                    ),
                                )
                                if RealESRGAN is None:
                                    with gr.Row():
                                        with gr.Column():
                                            # seperator
                                            gr.HTML(
                                                """
        <div id="90" style="max-width: 100%; font-size: 14px; text-align: center;" class="output-markdown gr-prose border-solid border border-gray-200 rounded gr-panel">
            <p><b> Please download RealESRGAN to activate upscale features</b>, instructions are available at the <a href='https://github.com/Sygil-Dev/sygil-webui'>Github</a></p>
        </div>
        """
                                            )
            imgproc_toggles.change(
                fn=uifn.toggle_options_gfpgan,
                inputs=[imgproc_toggles],
                outputs=[gfpgan_group],
            )
            imgproc_toggles.change(
                fn=uifn.toggle_options_upscalers,
                inputs=[imgproc_toggles],
                outputs=[upscale_group],
            )
            imgproc_toggles.change(
                fn=uifn.toggle_options_upscalers,
                inputs=[imgproc_toggles],
                outputs=[upscalerSettings_group],
            )
            imgproc_upscale_toggles.change(
                fn=uifn.toggle_options_realesrgan,
                inputs=[imgproc_upscale_toggles],
                outputs=[realesrgan_group],
            )
            imgproc_upscale_toggles.change(
                fn=uifn.toggle_options_ldsr,
                inputs=[imgproc_upscale_toggles],
                outputs=[ldsr_group],
            )
            imgproc_upscale_toggles.change(
                fn=uifn.toggle_options_gobig,
                inputs=[imgproc_upscale_toggles],
                outputs=[gobig_group],
            )

            with gr.TabItem("Scene-to-Image", id="scn2img_tab"):
                example_path = os.path.join("data", "scn2img_examples")
                files = os.listdir(example_path)
                examples = {}
                for fn in files:
                    filepath = os.path.join(example_path, str(fn))
                    with open(filepath, "r") as file:
                        examples[fn] = file.read()
                with gr.Row(elem_id="tools_row"):
                    scn2img_btn = gr.Button(
                        "Generate", elem_id="generate", variant="primary"
                    )

                with gr.Row().style(equal_height=False):
                    with gr.Column():
                        scn2img_seed = gr.Textbox(
                            label="Seed (blank to randomize, specify to use cache)",
                            lines=1,
                            max_lines=1,
                            value=scn2img_defaults["seed"],
                        )
                        scn2img_prompt = gr.Textbox(
                            label="Prompt Scene",
                            elem_id="scn2_img_input",
                            placeholder=examples[list(examples.keys())[0]],
                            lines=50,
                            max_lines=50,
                            value=scn2img_defaults["prompt"],
                            show_label=False,
                        )

                    with gr.Column():
                        with gr.Tabs():
                            with gr.TabItem("Results", id="scn2img_results_tab"):
                                # gr.Markdown('#### Scn2Img Results')
                                output_scn2img_gallery = gr.Gallery(
                                    label="Images", elem_id="scn2img_gallery_output"
                                ).style(grid=[3, 3, 3], height=80)
                                scn2img_job_ui = (
                                    job_manager.draw_gradio_ui()
                                    if job_manager
                                    else None
                                )

                                with gr.Tabs():
                                    with gr.TabItem(
                                        "Generated image actions",
                                        id="scn2img_actions_tab",
                                    ):
                                        gr.Markdown(
                                            "Select an image, then press one of the buttons below"
                                        )
                                        with gr.Row():
                                            output_scn2img_copy_to_clipboard_btn = (
                                                gr.Button("Copy to clipboard")
                                            )
                                            output_scn2img_copy_to_img2img_input_btn = (
                                                gr.Button("Push to img2img input")
                                            )
                                            output_scn2img_copy_to_img2img_mask_btn = (
                                                gr.Button("Push to img2img input mask")
                                            )

                                        gr.Markdown(
                                            "Warning: This will clear your current img2img image and mask settings!"
                                        )

                                    with gr.TabItem(
                                        "Output info", id="scn2img_output_info_tab"
                                    ):
                                        output_scn2img_params = gr.Highlightedtext(
                                            label="Generation parameters",
                                            interactive=False,
                                            elem_id="scn2img_highlight",
                                        )
                                        with gr.Row():
                                            gr.Button("Copy full parameters").click(
                                                inputs=[output_scn2img_params],
                                                outputs=[],
                                                _js=call_JS(
                                                    "copyFullOutput",
                                                    fromId="scn2img_highlight",
                                                ),
                                                fn=None,
                                                show_progress=False,
                                            )
                                            output_scn2img_seed = gr.Number(
                                                label="Seed",
                                                interactive=False,
                                                visible=False,
                                            )
                                            gr.Button("Copy only initial seed").click(
                                                inputs=output_scn2img_seed,
                                                outputs=[],
                                                _js=call_JS("gradioInputToClipboard"),
                                                fn=None,
                                                show_progress=False,
                                            )
                                        output_scn2img_stats = gr.HTML(label="Stats")
                                    with gr.TabItem(
                                        "SceneCode", id="scn2img_scncode_tab"
                                    ):
                                        output_scn2img_scncode = gr.HTML(
                                            label="SceneCode"
                                        )
                                scn2img_toggles = gr.CheckboxGroup(
                                    label="",
                                    choices=scn2img_toggles,
                                    value=scn2img_toggle_defaults,
                                    type="index",
                                )

                                scn2img_embeddings = gr.File(
                                    label="Embeddings file for textual inversion",
                                    visible=show_embeddings,
                                )
                            with gr.TabItem("Docs", id="scn2img_docs_tab"):
                                (
                                    parse_arg,
                                    function_args,
                                    function_args_ext,
                                ) = scn2img_define_args()
                                with gr.Tabs():
                                    with gr.TabItem(
                                        "syntax", id="scn2img_docs_syntax_tab"
                                    ):
                                        lines = [
                                            "Scene-to-Image defines layers of images in markdown-like syntax.",
                                            "",
                                            "Markdown headings, e.g. '# layer0', define layers.",
                                            "Layers are hierarchical, i.e. each layer can contain more layers.",
                                            "Child layers are blended together by their image masks, like layers in image editors.",
                                            "",
                                            "The content of sections define the arguments for image generation.",
                                            "Arguments are defined by lines of the form 'arg:value' or 'arg=value'.",
                                            "",
                                            "To invoke txt2img or img2img, they layer must contain the 'prompt' argument.",
                                            "For img2img the layer must have child layers, the result of blending them will be the input image for img2img.",
                                            "When no prompt is specified the layer can still be used for image composition and mask selection.",
                                        ]
                                        gr.Markdown("\n".join(lines))

                                    for func, ext in function_args_ext.items():
                                        with gr.TabItem(
                                            func, id=f"scn2img_docs_{func}_tab"
                                        ):
                                            lines = []
                                            for e in ext:
                                                lines.append(f"#### Arguments for {e}")
                                                if e not in function_args:
                                                    continue
                                                for argname, argtype in function_args[
                                                    e
                                                ].items():
                                                    lines.append(
                                                        f" - {argname}: {argtype}"
                                                    )
                                            gr.Markdown("\n".join(lines))

                            with gr.TabItem("Examples", id="scn2img_examples_tab"):
                                scn2img_examples = {}
                                with gr.Tabs():
                                    for k, (example, content) in enumerate(
                                        examples.items()
                                    ):
                                        with gr.TabItem(
                                            example, id=f"scn2img_example_{k}_tab"
                                        ):
                                            scn2img_examples[example] = gr.Textbox(
                                                label="Prompt Scene",
                                                elem_id=f"scn2img_example_{k}",
                                                value=content,
                                                lines=50,
                                                max_lines=50,
                                                show_label=False,
                                                interactive=True,
                                            )
                output_scn2img_copy_to_img2img_input_btn.click(
                    uifn.copy_img_to_edit,
                    [output_scn2img_gallery],
                    [img2img_image_editor, tabs, img2img_image_editor_mode],
                    _js=call_JS(
                        "moveImageFromGallery",
                        fromId="scn2img_gallery_output",
                        toId="img2img_editor",
                    ),
                )
                output_scn2img_copy_to_img2img_mask_btn.click(
                    uifn.copy_img_to_mask,
                    [output_scn2img_gallery],
                    [img2img_image_mask, tabs, img2img_image_editor_mode],
                    _js=call_JS(
                        "moveImageFromGallery",
                        fromId="scn2img_gallery_output",
                        toId="img2img_editor",
                    ),
                )

                output_scn2img_copy_to_clipboard_btn.click(
                    fn=None,
                    inputs=output_scn2img_gallery,
                    outputs=[],
                    _js=call_JS(
                        "copyImageFromGalleryToClipboard",
                        fromId="scn2img_gallery_output",
                    ),
                )

                scn2img_func = scn2img
                scn2img_inputs = [
                    scn2img_prompt,
                    scn2img_toggles,
                    scn2img_seed,
                    scn2img_embeddings,
                ]
                scn2img_outputs = [
                    output_scn2img_gallery,
                    output_scn2img_seed,
                    output_scn2img_params,
                    output_scn2img_stats,
                    output_scn2img_scncode,
                ]
                # If a JobManager was passed in then wrap the Generate functions
                if scn2img_job_ui:
                    (
                        scn2img_func,
                        scn2img_inputs,
                        scn2img_outputs,
                    ) = scn2img_job_ui.wrap_func(
                        func=scn2img_func,
                        inputs=scn2img_inputs,
                        outputs=scn2img_outputs,
                    )

                scn2img_btn.click(scn2img_func, scn2img_inputs, scn2img_outputs)

            """
            if GFPGAN is not None:
                gfpgan_defaults = {
                    'strength': 100,
                }

                if 'gfpgan' in user_defaults:
                    gfpgan_defaults.update(user_defaults['gfpgan'])

                with gr.TabItem("GFPGAN", id='cfpgan_tab'):
                    gr.Markdown("Fix faces on images")
                    with gr.Row():
                        with gr.Column():
                            gfpgan_source = gr.Image(label="Source", source="upload", interactive=True, type="pil")
                            gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Effect strength",
                                                        value=gfpgan_defaults['strength'])
                            gfpgan_btn = gr.Button("Generate", variant="primary")
                        with gr.Column():
                            gfpgan_output = gr.Image(label="Output", elem_id='gan_image')
                    gfpgan_btn.click(
                        run_GFPGAN,
                        [gfpgan_source, gfpgan_strength],
                        [gfpgan_output]
                    )
            if RealESRGAN is not None:
                with gr.TabItem("RealESRGAN", id='realesrgan_tab'):
                    gr.Markdown("Upscale images")
                    with gr.Row():
                        with gr.Column():
                            realesrgan_source = gr.Image(label="Source", source="upload", interactive=True, type="pil")
                            realesrgan_model_name = gr.Dropdown(label='RealESRGAN model', choices=['RealESRGAN_x4plus',
                                                                                                   'RealESRGAN_x4plus_anime_6B'],
                                                                value='RealESRGAN_x4plus')
                            realesrgan_btn = gr.Button("Generate")
                        with gr.Column():
                            realesrgan_output = gr.Image(label="Output", elem_id='gan_image')
                    realesrgan_btn.click(
                        run_RealESRGAN,
                        [realesrgan_source, realesrgan_model_name],
                        [realesrgan_output]
                    )
                output_txt2img_to_upscale_esrgan.click(
                    uifn.copy_img_to_upscale_esrgan,
                    output_txt2img_gallery,
                    [realesrgan_source, tabs],
                    _js=js_move_image('txt2img_gallery_output', 'img2img_editor'))
        """
        gr.HTML(
            """
    <div id="90" style="max-width: 100%; font-size: 14px; text-align: center;" class="output-markdown gr-prose border-solid border border-gray-200 rounded gr-panel">
        <p>For help and advanced usage guides, visit the <a href="https://github.com/Sygil-Dev/sygil-webui/wiki" target="_blank">Project Wiki</a></p>
        <p>Stable Diffusion WebUI is an open-source project. You can find the latest stable builds on the <a href="https://github.com/Sygil-Dev/stable-diffusion" target="_blank">main repository</a>.
        If you would like to contribute to development or test bleeding edge builds, you can visit the <a href="https://github.com/Sygil-Dev/sygil-webui" target="_blank">developement repository</a>.</p>
        <p>Device ID {current_device_index}: {current_device_name}<br/>{total_device_count} total devices</p>
    </div>
    """.format(
                current_device_name=torch.cuda.get_device_name(),
                current_device_index=torch.cuda.current_device(),
                total_device_count=torch.cuda.device_count(),
            )
        )
        # Hack: Detect the load event on the frontend
        # Won't be needed in the next version of gradio
        # See the relevant PR: https://github.com/gradio-app/gradio/pull/2108
        load_detector = gr.Number(value=0, label="Load Detector", visible=False)
        load_detector.change(None, None, None, _js=js(opt))
        demo.load(lambda x: 42, inputs=load_detector, outputs=load_detector)
    return demo


================================================
FILE: frontend/image_metadata.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
""" Class to store image generation parameters to be stored as metadata in the image"""
from __future__ import annotations
from dataclasses import dataclass, asdict
from typing import Dict, Optional
from PIL import Image
from PIL.PngImagePlugin import PngInfo
import copy


@dataclass
class ImageMetadata:
    prompt: str = None
    seed: str = None
    width: str = None
    height: str = None
    steps: str = None
    cfg_scale: str = None
    normalize_prompt_weights: str = None
    denoising_strength: str = None
    GFPGAN: str = None

    def as_png_info(self) -> PngInfo:
        info = PngInfo()
        for key, value in self.as_dict().items():
            info.add_text(key, value)
        return info

    def as_dict(self) -> Dict[str, str]:
        return {
            f"SD:{key}": str(value)
            for key, value in asdict(self).items()
            if value is not None
        }

    @classmethod
    def set_on_image(cls, image: Image, metadata: ImageMetadata) -> None:
        """Sets metadata on image, in both text form and as an ImageMetadata object"""
        if metadata:
            image.info = metadata.as_dict()
        else:
            metadata = ImageMetadata()
        image.info["ImageMetadata"] = copy.copy(metadata)

    @classmethod
    def get_from_image(cls, image: Image) -> Optional[ImageMetadata]:
        """Gets metadata from an image, first looking for an ImageMetadata,
        then if not found tries to construct one from the info"""
        metadata = image.info.get("ImageMetadata", None)
        if not metadata:
            found_metadata = False
            metadata = ImageMetadata()
            for key, value in image.info.items():
                if key.lower().startswith("sd:"):
                    key = key[3:]
                    if f"{key}" in metadata.__dict__:
                        metadata.__dict__[key] = value
                        found_metadata = True
            if not found_metadata:
                metadata = None
        if not metadata:
            print("Couldn't find metadata on image")
        return metadata


================================================
FILE: frontend/index.html
================================================
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Component Template</title>
</head>
<body>
<div id="app"></div>
<script type="module" src="/src/main.ts"></script>
</body>
</html>


================================================
FILE: frontend/job_manager.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
""" Provides simple job management for gradio, allowing viewing and stopping in-progress multi-batch generations """
from __future__ import annotations
import gradio as gr
from gradio.components import Component, Gallery
from threading import Event, Timer
from typing import Callable, List, Dict, Tuple, Optional, Any
from dataclasses import dataclass, field
from functools import partial
from PIL.Image import Image
import uuid
import traceback
import time


@dataclass(eq=True, frozen=True)
class FuncKey:
    job_id: str
    func: Callable


@dataclass(eq=True, frozen=True)
class JobKey:
    func_key: FuncKey
    session_key: str


@dataclass
class JobInfo:
    inputs: List[Component]
    func: Callable
    session_key: str
    job_token: Optional[int] = None
    images: List[Image] = field(default_factory=list)
    active_image: Image = None
    rec_steps_enabled: bool = False
    rec_steps_imgs: List[Image] = field(default_factory=list)
    rec_steps_intrvl: int = None
    rec_steps_to_gallery: bool = False
    rec_steps_to_file: bool = False
    should_stop: Event = field(default_factory=Event)
    refresh_active_image_requested: Event = field(default_factory=Event)
    refresh_active_image_done: Event = field(default_factory=Event)
    stop_cur_iter: Event = field(default_factory=Event)
    active_iteration_cnt: int = field(default_factory=int)
    job_status: str = field(default_factory=str)
    finished: bool = False
    started: bool = False
    timestamp: float = None
    removed_output_idxs: List[int] = field(default_factory=list)


@dataclass
class SessionInfo:
    jobs: Dict[FuncKey, JobInfo] = field(default_factory=dict)
    finished_jobs: Dict[FuncKey, JobInfo] = field(default_factory=dict)


@dataclass
class QueueItem:
    wait_event: Event


def triggerChangeEvent():
    return uuid.uuid4().hex


@dataclass
class JobManagerUi:
    def wrap_func(
        self, func: Callable, inputs: List[Component], outputs: List[Component]
    ) -> Tuple[Callable, List[Component], List[Component]]:
        """Takes a gradio event listener function and its input/outputs and returns wrapped replacements which will
            be managed by JobManager
        Parameters:
        func (Callable) the original event listener to be wrapped.
                        This listener should be modified to take a 'job_info' parameter which, if not None, should can
                        be used by the function to check for stop events and to store intermediate image results
        inputs (List[Component]) the original inputs
        outputs (List[Component]) the original outputs. The first gallery, if any, will be used for refreshing images
        refresh_btn: (gr.Button, optional) a button to use for updating the gallery with intermediate results
        stop_btn: (gr.Button, optional) a button to use for stopping the function
        status_text: (gr.Textbox) a textbox to display job status updates

        Returns:
        Tuple(newFunc (Callable), newInputs (List[Component]), newOutputs (List[Component]), which should be used as
        replacements for the passed in function, inputs and outputs
        """
        return self._job_manager._wrap_func(
            func=func, inputs=inputs, outputs=outputs, job_ui=self
        )

    _refresh_btn: gr.Button
    _stop_btn: gr.Button
    _status_text: gr.Textbox
    _stop_all_session_btn: gr.Button
    _free_done_sessions_btn: gr.Button
    _active_image: gr.Image
    _active_image_stop_btn: gr.Button
    _active_image_refresh_btn: gr.Button
    _rec_steps_intrvl_sldr: gr.Slider
    _rec_steps_checkbox: gr.Checkbox
    _save_rec_steps_to_gallery_chkbx: gr.Checkbox
    _save_rec_steps_to_file_chkbx: gr.Checkbox
    _job_manager: JobManager


class JobManager:
    JOB_MAX_START_TIME = (
        5.0  # How long can a job be stuck 'starting' before assuming it isn't running
    )

    def __init__(self, max_jobs: int):
        self._max_jobs: int = max_jobs
        self._avail_job_tokens: List[Any] = list(range(max_jobs))
        self._job_queue: List[QueueItem] = []
        self._sessions: Dict[str, SessionInfo] = {}
        self._session_key: gr.JSON = None

    def draw_gradio_ui(self) -> JobManagerUi:
        """draws the job manager ui in gradio
        Returns:
        ui (JobManagerUi): object which can connect functions to the ui
        """
        assert (
            gr.context.Context.block is not None
        ), "draw_gradio_ui must be called within a 'gr.Blocks' 'with' context"
        with gr.Tabs():
            with gr.TabItem("Job Controls"):
                with gr.Row():
                    stop_btn = gr.Button(
                        "Stop All Batches", elem_id="stop", variant="secondary"
                    )
                    refresh_btn = gr.Button(
                        "Refresh Finished Batches",
                        elem_id="refresh",
                        variant="secondary",
                    )
                status_text = gr.Textbox(
                    placeholder="Job Status", interactive=False, show_label=False
                )
                with gr.Row():
                    active_image_stop_btn = gr.Button(
                        "Skip Active Batch", variant="secondary"
                    )
                    active_image_refresh_btn = gr.Button(
                        "View Batch Progress", variant="secondary"
                    )
                active_image = gr.Image(
                    type="pil",
                    interactive=False,
                    visible=False,
                    elem_id="active_iteration_image",
                )
            with gr.TabItem("Batch Progress Settings"):
                with gr.Row():
                    record_steps_checkbox = gr.Checkbox(
                        value=False, label="Enable Batch Progress Grid"
                    )
                    record_steps_interval_slider = gr.Slider(
                        value=3,
                        label="Record Interval (steps)",
                        minimum=1,
                        maximum=25,
                        step=1,
                    )
                with gr.Row():
                    steps_to_gallery_checkbox = gr.Checkbox(
                        value=False, label="Save Progress Grid to Gallery"
                    )
                    steps_to_file_checkbox = gr.Checkbox(
                        value=False, label="Save Progress Grid to File"
                    )
            with gr.TabItem("Maintenance"):
                with gr.Row():
                    gr.Markdown(
                        "Stop all concurrent sessions, or free memory associated with jobs which were finished after the browser was closed"
                    )
                with gr.Row():
                    stop_all_sessions_btn = gr.Button(
                        "Stop All Sessions", elem_id="stop_all", variant="secondary"
                    )
                    free_done_sessions_btn = gr.Button(
                        "Clear Finished Jobs",
                        elem_id="clear_finished",
                        variant="secondary",
                    )

        return JobManagerUi(
            _refresh_btn=refresh_btn,
            _stop_btn=stop_btn,
            _status_text=status_text,
            _stop_all_session_btn=stop_all_sessions_btn,
            _free_done_sessions_btn=free_done_sessions_btn,
            _active_image=active_image,
            _active_image_stop_btn=active_image_stop_btn,
            _active_image_refresh_btn=active_image_refresh_btn,
            _rec_steps_checkbox=record_steps_checkbox,
            _save_rec_steps_to_gallery_chkbx=steps_to_gallery_checkbox,
            _save_rec_steps_to_file_chkbx=steps_to_file_checkbox,
            _rec_steps_intrvl_sldr=record_steps_interval_slider,
            _job_manager=self,
        )

    def clear_all_finished_jobs(self):
        """Removes all currently finished jobs, across all sessions.
        Useful to free memory if a job is started and the browser is closed
        before it finishes"""
        for session in self._sessions.values():
            session.finished_jobs.clear()

    def stop_all_jobs(self):
        """Stops all active jobs, across all sessions"""
        for session in self._sessions.values():
            for job in session.jobs.values():
                job.should_stop.set()
                job.stop_cur_iter.set()

    def _get_job_token(self, block: bool = False) -> Optional[int]:
        """Attempts to acquire a job token, optionally blocking until available"""
        token = None
        while token is None:
            try:
                token = self._avail_job_tokens.pop()
                break
            except IndexError:
                pass

            if not block:
                break

            # No token and requested to block, so queue up
            wait_event = Event()
            self._job_queue.append(QueueItem(wait_event))
            wait_event.wait()

        return token

    def _release_job_token(self, token: int) -> None:
        """Returns a job token to allow another job to start"""
        self._avail_job_tokens.append(token)
        self._run_queued_jobs()

    def _refresh_func(self, func_key: FuncKey, session_key: str) -> List[Component]:
        """Updates information from the active job"""
        session_info, job_info = self._get_call_info(func_key, session_key)
        if job_info is None:
            return [None, f"Session {session_key} was not running function {func_key}"]
        return [triggerChangeEvent(), job_info.job_status]

    def _stop_wrapped_func(
        self, func_key: FuncKey, session_key: str
    ) -> List[Component]:
        """Marks that the job should be stopped"""
        session_info, job_info = self._get_call_info(func_key, session_key)
        if job_info is None:
            return f"Session {session_key} was not running function {func_key}"
        job_info.should_stop.set()
        return "Stopping after current batch finishes"

    def _refresh_cur_iter_func(
        self, func_key: FuncKey, session_key: str
    ) -> List[Component]:
        """Updates information from the active iteration"""
        session_info, job_info = self._get_call_info(func_key, session_key)
        if job_info is None:
            return [None, f"Session {session_key} was not running function {func_key}"]

        job_info.refresh_active_image_requested.set()
        if job_info.refresh_active_image_done.wait(timeout=20.0):
            job_info.refresh_active_image_done.clear()
            return [
                gr.Image.update(value=job_info.active_image, visible=True),
                f"Sample iteration {job_info.active_iteration_cnt}",
            ]
        return [gr.Image.update(visible=False), "Timed out getting image"]

    def _stop_cur_iter_func(
        self, func_key: FuncKey, session_key: str
    ) -> List[Component]:
        """Marks that the active iteration should be stopped"""
        session_info, job_info = self._get_call_info(func_key, session_key)
        if job_info is None:
            return [None, f"Session {session_key} was not running function {func_key}"]
        job_info.stop_cur_iter.set()
        return [gr.Image.update(visible=False), "Stopping current iteration"]

    def _get_call_info(
        self, func_key: FuncKey, session_key: str
    ) -> Tuple[SessionInfo, JobInfo]:
        """Helper to get the SessionInfo and JobInfo."""
        session_info = self._sessions.get(session_key, None)
        if not session_info:
            print(f"Couldn't find session {session_key} for call to {func_key}")
            return None, None

        job_info = session_info.jobs.get(func_key, None)
        if not job_info:
            job_info = session_info.finished_jobs.get(func_key, None)
        if not job_info:
            print(f"Couldn't find job {func_key} in session {session_key}")
            return session_info, None

        return session_info, job_info

    def _run_queued_jobs(self) -> None:
        """Runs queued jobs for any available slots"""
        if self._avail_job_tokens:
            try:
                # Notify next queued job it may begin
                queue_item = self._job_queue.pop(0)
                queue_item.wait_event.set()

                # Check again in a few seconds, just in case the queued
                # waiter closed the browser while still queued
                Timer(3.0, self._run_queued_jobs).start()
            except IndexError:
                pass  # No queued jobs

    def _pre_call_func(
        self,
        func_key: FuncKey,
        output_dummy_obj: Component,
        refresh_btn: gr.Button,
        stop_btn: gr.Button,
        status_text: gr.Textbox,
        active_image: gr.Image,
        active_refresh_btn: gr.Button,
        active_stop_btn: gr.Button,
        session_key: str,
    ) -> List[Component]:
        """Called when a job is about to start"""
        session_info, job_info = self._get_call_info(func_key, session_key)

        # If we didn't already get a token then queue up for one
        if job_info.job_token is None:
            job_info.job_token = self._get_job_token(block=True)

        # Buttons don't seem to update unless value is set on them as well...
        return {
            output_dummy_obj: triggerChangeEvent(),
            refresh_btn: gr.Button.update(variant="primary", value=refresh_btn.value),
            stop_btn: gr.Button.update(variant="primary", value=stop_btn.value),
            status_text: gr.Textbox.update(
                value="Generation has started. Click 'Refresh' to see finished images, 'View Batch Progress' for active images"
            ),
            active_refresh_btn: gr.Button.update(
                variant="primary", value=active_refresh_btn.value
            ),
            active_stop_btn: gr.Button.update(
                variant="primary", value=active_stop_btn.value
            ),
        }

    def _call_func(self, func_key: FuncKey, session_key: str) -> List[Component]:
        """Runs the real function with job management."""
        session_info, job_info = self._get_call_info(func_key, session_key)
        if session_info is None or job_info is None:
            return []

        job_info.started = True
        try:
            if job_info.should_stop.is_set():
                raise Exception(
                    f"Job {job_info} requested a stop before execution began"
                )
            outputs = job_info.func(*job_info.inputs, job_info=job_info)
        except Exception as e:
            job_info.job_status = f"Error: {e}"
            print(f"Exception processing job {job_info}: {e}\n{traceback.format_exc()}")
            raise
        finally:
            job_info.finished = True
            session_info.finished_jobs[func_key] = session_info.jobs.pop(func_key)
            self._release_job_token(job_info.job_token)

        # Filter the function output for any removed outputs
        filtered_output = []
        for idx, output in enumerate(outputs):
            if idx not in job_info.removed_output_idxs:
                filtered_output.append(output)

        # The wrapper added a dummy JSON output. Append a random text string
        # to fire the dummy objects 'change' event to notify that the job is done
        filtered_output.append(triggerChangeEvent())

        return tuple(filtered_output)

    def _post_call_func(
        self,
        func_key: FuncKey,
        output_dummy_obj: Component,
        refresh_btn: gr.Button,
        stop_btn: gr.Button,
        status_text: gr.Textbox,
        active_image: gr.Image,
        active_refresh_btn: gr.Button,
        active_stop_btn: gr.Button,
        session_key: str,
    ) -> List[Component]:
        """Called when a job completes"""
        return {
            output_dummy_obj: triggerChangeEvent(),
            refresh_btn: gr.Button.update(variant="secondary", value=refresh_btn.value),
            stop_btn: gr.Button.update(variant="secondary", value=stop_btn.value),
            status_text: gr.Textbox.update(value="Generation has finished!"),
            active_refresh_btn: gr.Button.update(
                variant="secondary", value=active_refresh_btn.value
            ),
            active_stop_btn: gr.Button.update(
                variant="secondary", value=active_stop_btn.value
            ),
            active_image: gr.Image.update(visible=False),
        }

    def _update_gallery_event(
        self, func_key: FuncKey, session_key: str
    ) -> List[Component]:
        """Updates the gallery with results from the given job.
        Frees the images after return if the job is finished.
        Triggered by changing the update_gallery_obj dummy object"""
        session_info, job_info = self._get_call_info(func_key, session_key)
        if session_info is None or job_info is None:
            return []

        return job_info.images

    def _wrap_func(
        self,
        func: Callable,
        inputs: List[Component],
        outputs: List[Component],
        job_ui: JobManagerUi,
    ) -> Tuple[Callable, List[Component]]:
        """handles JobManageUI's wrap_func"""

        assert (
            gr.context.Context.block is not None
        ), "wrap_func must be called within a 'gr.Blocks' 'with' context"

        # Create a unique key for this job
        func_key = FuncKey(job_id=uuid.uuid4().hex, func=func)

        # Create a unique session key (next gradio release can use gr.State, see https://gradio.app/state_in_blocks/)
        if self._session_key is None:
            # When this gradio object is received as an event handler input it will resolve to a unique per-session id
            self._session_key = gr.JSON(
                value=lambda: uuid.uuid4().hex,
                visible=False,
                elem_id="JobManagerDummyObject_sessionKey",
            )

        # Pull the gallery out of the original outputs and assign it to the gallery update dummy object
        gallery_comp = None
        removed_idxs = []
        for idx, comp in enumerate(outputs):
            if isinstance(comp, Gallery):
                removed_idxs.append(idx)
                gallery_comp = comp
                del outputs[idx]
                break

        # Create dummy objects
        update_gallery_obj = gr.JSON(visible=False, elem_id="JobManagerDummyObject")
        update_gallery_obj.change(
            partial(self._update_gallery_event, func_key),
            [self._session_key],
            [gallery_comp],
            queue=False,
        )

        if job_ui._refresh_btn:
            job_ui._refresh_btn.variant = "secondary"
            job_ui._refresh_btn.click(
                partial(self._refresh_func, func_key),
                [self._session_key],
                [update_gallery_obj, job_ui._status_text],
                queue=False,
            )

        if job_ui._stop_btn:
            job_ui._stop_btn.variant = "secondary"
            job_ui._stop_btn.click(
                partial(self._stop_wrapped_func, func_key),
                [self._session_key],
                [job_ui._status_text],
                queue=False,
            )

        if job_ui._active_image and job_ui._active_image_refresh_btn:
            job_ui._active_image_refresh_btn.click(
                partial(self._refresh_cur_iter_func, func_key),
                [self._session_key],
                [job_ui._active_image, job_ui._status_text],
                queue=False,
            )

        if job_ui._active_image_stop_btn:
            job_ui._active_image_stop_btn.click(
                partial(self._stop_cur_iter_func, func_key),
                [self._session_key],
                [job_ui._active_image, job_ui._status_text],
                queue=False,
            )

        if job_ui._stop_all_session_btn:
            job_ui._stop_all_session_btn.click(self.stop_all_jobs, [], [], queue=False)

        if job_ui._free_done_sessions_btn:
            job_ui._free_done_sessions_btn.click(
                self.clear_all_finished_jobs, [], [], queue=False
            )

        # (ab)use gr.JSON to forward events.
        # The gr.JSON object will fire its 'change' event when it is modified by being the output
        # of another component. This allows a method to forward events and allow multiple components
        # to update the gallery (without locking it).

        # For example, the update_gallery_obj will update the gallery as in output of its 'change' event.
        # When its content changes it will update the gallery with the most recent images available from
        # the JobInfo. Now, eg, testComponent can have update_gallery_obj as an output and write random text
        # to it. This will trigger an update to the gallery, but testComponent didn't need to have
        # update_gallery_obj listed as an output, which would have locked it.

        # Since some parameters are optional it makes sense to use the 'dict' return value type, which requires
        # the Component as a key... so group together the UI components that the event listeners are going to update
        # to make it easy to append to function calls and outputs
        job_ui_params = [
            job_ui._refresh_btn,
            job_ui._stop_btn,
            job_ui._status_text,
            job_ui._active_image,
            job_ui._active_image_refresh_btn,
            job_ui._active_image_stop_btn,
        ]
        job_ui_outputs = [comp for comp in job_ui_params if comp is not None]

        # Here a chain is constructed that will make a 'pre' call, a 'run' call, and a 'post' call,
        # to be able to update the UI before and after, as well as run the actual call
        post_call_dummyobj = gr.JSON(
            visible=False, elem_id="JobManagerDummyObject_postCall"
        )
        post_call_dummyobj.change(
            partial(self._post_call_func, func_key, update_gallery_obj, *job_ui_params),
            [self._session_key],
            [update_gallery_obj] + job_ui_outputs,
            queue=False,
        )

        call_dummyobj = gr.JSON(visible=False, elem_id="JobManagerDummyObject_runCall")
        call_dummyobj.change(
            partial(self._call_func, func_key),
            [self._session_key],
            outputs + [post_call_dummyobj],
            queue=False,
        )

        pre_call_dummyobj = gr.JSON(
            visible=False, elem_id="JobManagerDummyObject_preCall"
        )
        pre_call_dummyobj.change(
            partial(self._pre_call_func, func_key, call_dummyobj, *job_ui_params),
            [self._session_key],
            [call_dummyobj] + job_ui_outputs,
            queue=False,
        )

        # Add any components that we want the runtime values for
        added_inputs = [
            self._session_key,
            job_ui._rec_steps_checkbox,
            job_ui._save_rec_steps_to_gallery_chkbx,
            job_ui._save_rec_steps_to_file_chkbx,
            job_ui._rec_steps_intrvl_sldr,
        ]

        # Now replace the original function with one that creates a JobInfo and triggers the dummy obj
        def wrapped_func(*wrapped_inputs):
            # Remove the added_inputs (pop opposite order of list)

            wrapped_inputs = list(wrapped_inputs)
            rec_steps_interval: int = wrapped_inputs.pop()
            save_rec_steps_file: bool = wrapped_inputs.pop()
            save_rec_steps_grid: bool = wrapped_inputs.pop()
            record_steps_enabled: bool = wrapped_inputs.pop()
            session_key: str = wrapped_inputs.pop()
            job_inputs = tuple(wrapped_inputs)

            # Get or create a session for this key
            session_info = self._sessions.setdefault(session_key, SessionInfo())

            # Is this session already running this job?
            if func_key in session_info.jobs:
                job_info = session_info.jobs[func_key]
                # If the job seems stuck in 'starting' then go ahead and toss it
                if (
                    not job_info.started
                    and time.time() > job_info.timestamp + JobManager.JOB_MAX_START_TIME
                ):
                    job_info.should_stop.set()
                    job_info.stop_cur_iter.set()
                    session_info.jobs.pop(func_key)
                    return {
                        job_ui._status_text: "Canceled possibly hung job. Try again"
                    }
                return {
                    job_ui._status_text: "This session is already running that function!"
                }

            # Is this a new run of a previously finished job? Clear old info
            if func_key in session_info.finished_jobs:
                session_info.finished_jobs.pop(func_key)

            job_token = self._get_job_token(block=False)
            job = JobInfo(
                inputs=job_inputs,
                func=func,
                removed_output_idxs=removed_idxs,
                session_key=session_key,
                job_token=job_token,
                rec_steps_enabled=record_steps_enabled,
                rec_steps_intrvl=rec_steps_interval,
                rec_steps_to_gallery=save_rec_steps_grid,
                rec_steps_to_file=save_rec_steps_file,
                timestamp=time.time(),
            )
            session_info.jobs[func_key] = job

            ret = {pre_call_dummyobj: triggerChangeEvent()}
            if job_token is None:
                ret[job_ui._status_text] = "Job is queued"
            return ret

        return (
            wrapped_func,
            inputs + added_inputs,
            [pre_call_dummyobj, job_ui._status_text],
        )


================================================
FILE: frontend/js/index.js
================================================
window.SD = (() => {
  /*
   * Painterro is made a field of the SD global object
   * To provide convinience when using w() method in css_and_js.py
   */
  class PainterroClass {
    static isOpen = false;
    static async init ({ x, toId }) {
      console.log(x)

      const originalImage = x[2] === 'Mask' ? x[1]?.image : x[0];

      if (window.Painterro === undefined) {
        try {
          await this.load();
        } catch (e) {
          SDClass.error(e);

          return this.fallback(originalImage);
        }
      }

      if (this.isOpen) {
        return this.fallback(originalImage);
      }
      this.isOpen = true;

      let resolveResult;
      const paintClient = Painterro({
        hiddenTools: ['arrow'],
        onHide: () => {
          resolveResult?.(null);
        },
        saveHandler: (image, done) => {
          const data = image.asDataURL();

          // ensures stable performance even
          // when the editor is in interactive mode
          SD.clearImageInput(SD.el.get(`#${toId}`));

          resolveResult(data);

          done(true);
          paintClient.hide();
        },
      });

      const result = await new Promise((resolve) => {
        resolveResult = resolve;
        paintClient.show(originalImage);
      });
      this.isOpen = false;

      return result ? this.success(result) : this.fallback(originalImage);
    }
    static success (result) { return [result, { image: result, mask: result }] };
    static fallback (image) { return [image, { image: image, mask: image }] };
    static load () {
      return new Promise((resolve, reject) => {
        const scriptId = '__painterro-script';
        if (document.getElementById(scriptId)) {
          reject(new Error('Tried to load painterro script, but script tag already exists.'));
          return;
        }

        const styleId = '__painterro-css-override';
        if (!document.getElementById(styleId)) {
          /* Ensure Painterro window is always on top */
          const style = document.createElement('style');
          style.id = styleId;
          style.setAttribute('type', 'text/css');
          style.appendChild(document.createTextNode(`
            .ptro-holder-wrapper {
                z-index: 100;
            }
          `));
          document.head.appendChild(style);
        }

        const script = document.createElement('script');
        script.id = scriptId;
        script.src = 'https://unpkg.com/painterro@1.2.78/build/painterro.min.js';
        script.onload = () => resolve(true);
        script.onerror = (e) => {
          // remove self on error to enable reattempting load
          document.head.removeChild(script);
          reject(e);
        };
        document.head.appendChild(script);
      });
    }
  }

  /*
   * Turns out caching elements doesn't actually work in gradio
   * As elements in tabs might get recreated
   */
  class ElementCache {
    #el;
    constructor () {
      this.root = document.querySelector('gradio-app').shadowRoot;
    }
    get (selector) {
      return this.root.querySelector(selector);
    }
  }

  /*
   * The main helper class to incapsulate functions
   * that change gradio ui functionality
   */
  class SDClass {
    el = new ElementCache();
    Painterro = PainterroClass;
    moveImageFromGallery ({ x, fromId, toId }) {
      x = x[0];
      if (!Array.isArray(x) || x.length === 0) return;

      this.clearImageInput(this.el.get(`#${toId}`));

      const i = this.#getGallerySelectedIndex(this.el.get(`#${fromId}`));

      return [x[i].replace('data:;','data:image/png;')];
    }
    async copyImageFromGalleryToClipboard ({ x, fromId }) {
      x = x[0];
      if (!Array.isArray(x) || x.length === 0) return;

      const i = this.#getGallerySelectedIndex(this.el.get(`#${fromId}`));

      const data = x[i];
      const blob = await (await fetch(data.replace('data:;','data:image/png;'))).blob();
      const item = new ClipboardItem({'image/png': blob});

      await this.copyToClipboard([item]);
    }
    async copyFullOutput ({ fromId }) {
      const textField = this.el.get(`#${fromId} .textfield`);
      if (!textField) {
        SDclass.error(new Error(`Can't find textfield with the output!`));
      }

      const value = textField.textContent.replace(/\s+/g,' ').replace(/: /g,':');

      await this.copyToClipboard(value)
    }
    clickFirstVisibleButton({ rowId }) {
      const generateButtons = this.el.get(`#${rowId}`).querySelectorAll('.gr-button-primary');

      if (!generateButtons) return;

      for (let i = 0, arr = [...generateButtons]; i < arr.length; i++) {
        const cs = window.getComputedStyle(arr[i]);

        if (cs.display !== 'none' && cs.visibility !== 'hidden') {
          console.log(arr[i]);

          arr[i].click();
          break;
        }
      }
    }
    async gradioInputToClipboard ({ x }) { return this.copyToClipboard(x[0]); }
    async copyToClipboard (value) {
      if (!value || typeof value === 'boolean') return;
      try {
        if (Array.isArray(value) &&
            value.length &&
            value[0] instanceof ClipboardItem) {
          await navigator.clipboard.write(value);
        } else {
          await navigator.clipboard.writeText(value);
        }
      } catch (e) {
        SDClass.error(e);
      }
    }
    static error (e) {
      console.error(e);
      if (typeof e === 'string') {
        alert(e);
      } else if(typeof e === 'object' && Object.hasOwn(e, 'message')) {
        alert(e.message);
      }
    }
    clearImageInput (imageEditor) {
      imageEditor?.querySelector('.modify-upload button:last-child')?.click();
    }
    #getGallerySelectedIndex (gallery) {
      const selected = gallery.querySelector(`.\\!ring-2`);
      return selected ? [...selected.parentNode.children].indexOf(selected) : 0;
    }
  }

  return new SDClass();
})();


================================================
FILE: frontend/package.json
================================================
{
  "name": "@codedealer/streamlit_template_vue",
  "version": "0.1.0",
  "description": "Starter template to build Streamlit component with vue 3",
  "license": "MIT",
  "author": "Thomas Mello",
  "repository": {
    "type": "git",
    "url": "https://github.com/codedealer/streamlit-component-template-vue"
  },
  "scripts": {
    "clean:dist": "rimraf dist",
    "dev": "vite --mode dev",
    "build": "yarn clean:dist && vite build --mode prod",
    "preview": "vite preview",
    "lint": "eslint .",
    "lint:fix": "eslint . --fix"
  },
  "dependencies": {
    "vue": "^3.2.38",
    "streamlit-component-lib": "^1.4.0"
  },
  "devDependencies": {
    "@antfu/eslint-config": "^0.18.9",
    "@types/node": "^17.0.21",
    "@vitejs/plugin-vue": "^2.2.0",
    "eslint": "^8.11.0",
    "rimraf": "^3.0.2",
    "typescript": "^4.5.4",
    "vite": "^2.8.0",
    "vue": "^3.2.38",
    "vue-tsc": "^0.29.8"
  }
}


================================================
FILE: frontend/src/Component.vue
================================================
<template>
  <div class="bootstrap-wrapper pt-4">
    <div class="concept-gallery row">
      <div v-for="concept in args.concepts"
           :key="concept.name"
           class="col-12 col-sm-6 col-md-4 col-lg-4 col-xl-3">

        <div class="concept-card p-4 container-fluid">
          <div class="concept-card-content-wrapper">
            <div class="card-header row no-gutters">
              <div class="col">
                <h1 class="concept-title pl-1"><span class="token-char pr-0">&lt;</span>{{ concept.name }}<span class="pl-0 token-char">&gt;</span></h1>
              </div>

              <!-- Favorite feature, not implemented yet -->
              <!-- <div class="col-auto card-favorite" >
              <img width="24"
                   height="24"
                   class="icon-star"
                   src="./icons/star.svg" />
              </div>  -->

            </div>

            <div class="concept-img-wrapper p-0 row no-gutters">

              <div v-for="(img, img_index) in concept.images"
                   :key="'concept_img'+img_index"
                   :class="{
                     'p-1': true,
                      'col-6': concept.images.length % 2 == 0 || img_index < concept.images.length - 1,
                      'col-12': concept.images.length % 2 == 1 && img_index == concept.images.length - 1
                   }">
                   <div class="img-bg" :style="{'background-image': 'url(data:image/png;base64,' + img + ')'}"></div>

              </div>

              <div v-if="concept.images.length == 0"
                   class="col-12 p-4 no-preview">
                   <svg class="no-preview-icon" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32"><path d="M 2 5 L 2 27 L 30 27 L 30 5 Z M 4 7 L 28 7 L 28 20.90625 L 22.71875 15.59375 L 22 14.875 L 17.46875 19.40625 L 11.71875 13.59375 L 11 12.875 L 4 19.875 Z M 24 9 C 22.894531 9 22 9.894531 22 11 C 22 12.105469 22.894531 13 24 13 C 25.105469 13 26 12.105469 26 11 C 26 9.894531 25.105469 9 24 9 Z M 11 15.71875 L 20.1875 25 L 4 25 L 4 22.71875 Z M 22 17.71875 L 28 23.71875 L 28 25 L 23.03125 25 L 18.875 20.8125 Z"/></svg>
                 <p style="opacity: 0.8">No preview available</p>
              </div>

            </div>

            <div class="concept-card-footer row no-gutters pt-4">
              <div class="col pl-1">
                <div v-if="concept.type"
                     :class="{

                      'concept-type-tag': true,
                      'concept-type-style': concept.type.toLowerCase() === 'style',
                      'concept-type-object': concept.type.toLowerCase() === 'object'
                    }
                     ">
                  {{ concept.type.toUpperCase() }}
                </div>
              </div>
              <div class="col-auto">
                <!-- Copy to clipboard button -->
                <button class="button"
                        @click="copyToClipboard(concept.token)">
                        <!-- <svg class="icon-clipboard" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 32 32" ><path d="M 15 3 C 13.742188 3 12.847656 3.890625 12.40625 5 L 5 5 L 5 28 L 13 28 L 13 30 L 27 30 L 27 14 L 25 14 L 25 5 L 17.59375 5 C 17.152344 3.890625 16.257813 3 15 3 Z M 15 5 C 15.554688 5 16 5.445313 16 6 L 16 7 L 19 7 L 19 9 L 11 9 L 11 7 L 14 7 L 14 6 C 14 5.445313 14.445313 5 15 5 Z M 7 7 L 9 7 L 9 11 L 21 11 L 21 7 L 23 7 L 23 14 L 13 14 L 13 26 L 7 26 Z M 15 16 L 25 16 L 25 28 L 15 28 Z"/></svg> -->
                  Copy to clipboard
                </button>
              </div>
            </div>
          </div>

        </div>
      </div>
    </div>
  </div>
</template>

<script setup lang="ts">
import { ref } from "vue"
import { Streamlit, Theme } from "streamlit-component-lib"
import { useStreamlit } from "./streamlit"
interface IProps {
  args: any;
  disabled: boolean;
  theme: Theme;
}

useStreamlit(); // lifecycle hooks for automatic Streamlit resize
const props = defineProps<IProps>();

const copyToClipboard = (text: string) => {
  console.log("sending copy to clipboard event", text)
  // Streamlit.setComponentValue({
  //   action: "copy_to_clipboard",
  //   value: text
  // })
  // copy to clipboard
  navigator.clipboard.writeText(text)
}

// const enable_favorites = ref(false)
// const enable_copy_to_clipboard = ref(false)


</script>
<style>

/* svg.icon-clipboard  {
    fill: var(--text-color);
    width: 18px;
    height: 18px;
} */

svg.no-preview-icon {
    fill: var(--text-color);
    width: 72px;
    height: 72px;
    opacity: 0.2;

}

.no-preview {
  align-self: center;
    text-align: center;
    color: var(--text-color);
}
.concept-card {
  background-color: var(--secondary-background-color);
  border-radius: 5px;
  margin-bottom: 20px;

}

.concept-card-content-wrapper {
  flex-direction: column !important;
  display: flex !important;
  height: 360px;
}

.concept-title {
  margin-top: 0px;
  margin-bottom: 24px;
  font-size: 1em;
  color: var(--text-color);
}

.concept-img-wrapper {
  flex-grow: 1 !important;

}

.card-favorite {
  text-align: end;
}

.concept-img {
  max-height: 100%;
  height: 100%;
}

.concept-img img {
  border-radius: 8px;
  object-fit: cover;
}

.img-bg {
  background-size: cover;
  background-position: center;
  background-origin: content-box;
  background-repeat: no-repeat;
  height: 100%;
  width: 100%;
  border-radius: 8px;
}

.icon-star {
  cursor: pointer;
  position: relative;
  top: -3px;
}

.token-char {
  color: #939393;
  font-weight: 700;
  position: relative;
  top: 1px;
}

.concept-card-footer {
  align-items: center;
}

.concept-type-tag {
  background-color: #898989;
  border-radius: 16px;
  padding: 5px 16px;
  font-size: 0.7em;
  color: #fff;
  display: inline-block;
  font-weight: bold;
}

.concept-type-style {
  background-color: #0095ff;
}

.concept-type-object {
  background-color: #ff9031;
}

.button {
  height: 35px;
  cursor: pointer;
  display: inline-flex;
  -webkit-box-align: center;
  align-items: center;
  -webkit-box-pack: center;
  justify-content: center;
  font-weight: 400;
  padding: 0.25rem 0.75rem;
  border-radius: 0.25rem;
  margin: 0px;
  line-height: 1.6;
  color: inherit;
  width: auto;
  user-select: none;
  background-color: var(--background-color);
  border: 1px solid rgba(128, 128, 128, 0.8);
}

.button:hover {
  color: var(--primary-color);
  border-color: var(--primary-color);
}

/* .button:focus {
  box-shadow: rgb(var(--primary-color) / 50%) 0px 0px 0px 0.2rem;
  outline: none;
}

.button:focus:not(:active) {
    border-color: var(--primary-color);
    color: var(--primary-color);
} */
</style>


================================================
FILE: frontend/src/app.vue
================================================
<!--
  We bootstrap our Component to Streamlit with our scoped slot in the top-level App.
  This is where scoped slot passes Streamlit `args` data from itself to children MyComponent.
  You should not have to edit this, but are free to do so :)
-->
<template>
  <WithStreamlitConnection v-slot="{ args, theme, disabled }">
    <Component :args="args" :theme="theme" :disabled="disabled" />
  </WithStreamlitConnection>
</template>

<script lang="ts">
import { defineComponent } from 'vue'
import Component from './Component.vue'

// "withStreamlitConnection" is a scoped slot. It bootstraps the
// connection between your component and the Streamlit app, and handles
// passing arguments from Python -> Component.
//
// You don't need to edit withStreamlitConnection (but you're welcome to!).
import WithStreamlitConnection from "./streamlit/WithStreamlitConnection.vue"

export default defineComponent({
  name: 'App',
  components: {
    Component,
    WithStreamlitConnection,
  },
})
</script>

<style>
body, html {
  margin: 0;
  font-family: "Source Sans Pro", sans-serif;
}


/*!
 * Bootstrap Grid Only (grid and responsive utilities extracted from Bootstrap 4.1.3)
 * GitHub: https://github.com/dmhendricks/bootstrap-grid-css
 * Bootstrap License: MIT (https://github.com/twbs/bootstrap/blob/v4-dev/LICENSE)
 * Credits: Twitter, Inc. & The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
 */
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 /* Padding Margin helpers */
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(min-width:576px){.m-sm-0{margin:0!important}.mt-sm-0,.my-sm-0{margin-top:0!important}.mr-sm-0,.mx-sm-0{margin-right:0!important}.mb-sm-0,.my-sm-0{margin-bottom:0!important}.ml-sm-0,.mx-sm-0{margin-left:0!important}.m-sm-1{margin:.25rem!important}.mt-sm-1,.my-sm-1{margin-top:.25rem!important}.mr-sm-1,.mx-sm-1{margin-right:.25rem!important}.mb-sm-1,.my-sm-1{margin-bottom:.25rem!important}.ml-sm-1,.mx-sm-1{margin-left:.25rem!important}.m-sm-2{margin:.5rem!important}.mt-sm-2,.my-sm-2{margin-top:.5rem!important}.mr-sm-2,.mx-sm-2{margin-right:.5rem!important}.mb-sm-2,.my-sm-2{margin-bottom:.5rem!important}.ml-sm-2,.mx-sm-2{margin-left:.5rem!important}.m-sm-3{margin:1rem!important}.mt-sm-3,.my-sm-3{margin-top:1rem!important}.mr-sm-3,.mx-sm-3{margin-right:1rem!important}.mb-sm-3,.my-sm-3{margin-bottom:1rem!important}.ml-sm-3,.mx-sm-3{margin-left:1rem!important}.m-sm-4{margin:1.5rem!important}.mt-sm-4,.my-sm-4{margin-top:1.5rem!important}.mr-sm-4,.mx-sm-4{margin-right:1.5rem!important}.mb-sm-4,.my-sm-4{margin-bottom:1.5rem!important}.ml-sm-4,.mx-sm-4{margin-left:1.5rem!important}.m-sm-5{margin:3rem!important}.mt-sm-5,.my-sm-5{margin-top:3rem!important}.mr-sm-5,.mx-sm-5{margin-right:3rem!important}.mb-sm-5,.my-sm-5{margin-bottom:3rem!important}.ml-sm-5,.mx-sm-5{margin-left:3rem!important}.p-sm-0{padding:0!important}.pt-sm-0,.py-sm-0{padding-top:0!important}.pr-sm-0,.px-sm-0{padding-right:0!important}.pb-sm-0,.py-sm-0{padding-bottom:0!important}.pl-sm-0,.px-sm-0{padding-left:0!important}.p-sm-1{padding:.25rem!important}.pt-sm-1,.py-sm-1{padding-top:.25rem!important}.pr-sm-1,.px-sm-1{padding-right:.25rem!important}.pb-sm-1,.py-sm-1{padding-bottom:.25rem!important}.pl-sm-1,.px-sm-1{padding-left:.25rem!important}.p-sm-2{padding:.5rem!important}.pt-sm-2,.py-sm-2{padding-top:.5rem!important}.pr-sm-2,.px-sm-2{padding-right:.5rem!important}.pb-sm-2,.py-sm-2{padding-bottom:.5rem!important}.pl-sm-2,.px-sm-2{padding-left:.5rem!important}.p-sm-3{padding:1rem!important}.pt-sm-3,.py-sm-3{padding-top:1rem!important}.pr-sm-3,.px-sm-3{padding-right:1rem!important}.pb-sm-3,.py-sm-3{padding-bottom:1rem!important}.pl-sm-3,.px-sm-3{padding-left:1rem!important}.p-sm-4{padding:1.5rem!important}.pt-sm-4,.py-sm-4{padding-top:1.5rem!important}.pr-sm-4,.px-sm-4{padding-right:1.5rem!important}.pb-sm-4,.py-sm-4{padding-bottom:1.5rem!important}.pl-sm-4,.px-sm-4{padding-left:1.5rem!important}.p-sm-5{padding:3rem!important}.pt-sm-5,.py-sm-5{padding-top:3rem!important}.pr-sm-5,.px-sm-5{padding-right:3rem!important}.pb-sm-5,.py-sm-5{padding-bottom:3rem!important}.pl-sm-5,.px-sm-5{padding-left:3rem!important}.m-sm-n1{margin:-.25rem!important}.mt-sm-n1,.my-sm-n1{margin-top:-.25rem!important}.mr-sm-n1,.mx-sm-n1{margin-right:-.25rem!important}.mb-sm-n1,.my-sm-n1{margin-bottom:-.25rem!important}.ml-sm-n1,.mx-sm-n1{margin-left:-.25rem!important}.m-sm-n2{margin:-.5rem!important}.mt-sm-n2,.my-sm-n2{margin-top:-.5rem!important}.mr-sm-n2,.mx-sm-n2{margin-right:-.5rem!important}.mb-sm-n2,.my-sm-n2{margin-bottom:-.5rem!important}.ml-sm-n2,.mx-sm-n2{margin-left:-.5rem!important}.m-sm-n3{margin:-1rem!important}.mt-sm-n3,.my-sm-n3{margin-top:-1rem!important}.mr-sm-n3,.mx-sm-n3{margin-right:-1rem!important}.mb-sm-n3,.my-sm-n3{margin-bottom:-1rem!important}.ml-sm-n3,.mx-sm-n3{margin-left:-1rem!important}.m-sm-n4{margin:-1.5rem!important}.mt-sm-n4,.my-sm-n4{margin-top:-1.5rem!important}.mr-sm-n4,.mx-sm-n4{margin-right:-1.5rem!important}.mb-sm-n4,.my-sm-n4{margin-bottom:-1.5rem!important}.ml-sm-n4,.mx-sm-n4{margin-left:-1.5rem!important}.m-sm-n5{margin:-3rem!important}.mt-sm-n5,.my-sm-n5{margin-top:-3rem!important}.mr-sm-n5,.mx-sm-n5{margin-right:-3rem!important}.mb-sm-n5,.my-sm-n5{margin-bottom:-3rem!important}.ml-sm-n5,.mx-sm-n5{margin-left:-3rem!important}.m-sm-auto{margin:auto!important}.mt-sm-auto,.my-sm-auto{margin-top:auto!important}.mr-sm-auto,.mx-sm-auto{margin-right:auto!important}.mb-sm-auto,.my-sm-auto{margin-bottom:auto!important}.ml-sm-auto,.mx-sm-auto{margin-left:auto!important}}@media (min-width:768px){.m-md-0{margin:0!important}.mt-md-0,.my-md-0{margin-top:0!important}.mr-md-0,.mx-md-0{margin-right:0!important}.mb-md-0,.my-md-0{margin-bottom:0!important}.ml-md-0,.mx-md-0{margin-left:0!important}.m-md-1{margin:.25rem!important}.mt-md-1,.my-md-1{margin-top:.25rem!important}.mr-md-1,.mx-md-1{margin-right:.25rem!important}.mb-md-1,.my-md-1{margin-bottom:.25rem!important}.ml-md-1,.mx-md-1{margin-left:.25rem!important}.m-md-2{margin:.5rem!important}.mt-md-2,.my-md-2{margin-top:.5rem!important}.mr-md-2,.mx-md-2{margin-right:.5rem!important}.mb-md-2,.my-md-2{margin-bottom:.5rem!important}.ml-md-2,.mx-md-2{margin-left:.5rem!important}.m-md-3{margin:1rem!important}.mt-md-3,.my-md-3{margin-top:1rem!important}.mr-md-3,.mx-md-3{margin-right:1rem!important}.mb-md-3,.my-md-3{margin-bottom:1rem!important}.ml-md-3,.mx-md-3{margin-left:1rem!important}.m-md-4{margin:1.5rem!important}.mt-md-4,.my-md-4{margin-top:1.5rem!important}.mr-md-4,.mx-md-4{margin-right:1.5rem!important}.mb-md-4,.my-md-4{margin-bottom:1.5rem!important}.ml-md-4,.mx-md-4{margin-left:1.5rem!important}.m-md-5{margin:3rem!important}.mt-md-5,.my-md-5{margin-top:3rem!important}.mr-md-5,.mx-md-5{margin-right:3rem!important}.mb-md-5,.my-md-5{margin-bottom:3rem!important}.ml-md-5,.mx-md-5{margin-left:3rem!important}.p-md-0{padding:0!important}.pt-md-0,.py-md-0{padding-top:0!important}.pr-md-0,.px-md-0{padding-right:0!important}.pb-md-0,.py-md-0{padding-bottom:0!important}.pl-md-0,.px-md-0{padding-left:0!important}.p-md-1{padding:.25rem!important}.pt-md-1,.py-md-1{padding-top:.25rem!important}.pr-md-1,.px-md-1{padding-right:.25rem!important}.pb-md-1,.py-md-1{padding-bottom:.25rem!important}.pl-md-1,.px-md-1{padding-left:.25rem!important}.p-md-2{padding:.5rem!important}.pt-md-2,.py-md-2{padding-top:.5rem!important}.pr-md-2,.px-md-2{padding-right:.5rem!important}.pb-md-2,.py-md-2{padding-bottom:.5rem!important}.pl-md-2,.px-md-2{padding-left:.5rem!important}.p-md-3{padding:1rem!important}.pt-md-3,.py-md-3{padding-top:1rem!important}.pr-md-3,.px-md-3{padding-right:1rem!important}.pb-md-3,.py-md-3{padding-bottom:1rem!important}.pl-md-3,.px-md-3{padding-left:1rem!important}.p-md-4{padding:1.5rem!important}.pt-md-4,.py-md-4{padding-top:1.5rem!important}.pr-md-4,.px-md-4{padding-right:1.5rem!important}.pb-md-4,.py-md-4{padding-bottom:1.5rem!important}.pl-md-4,.px-md-4{padding-left:1.5rem!important}.p-md-5{padding:3rem!important}.pt-md-5,.py-md-5{padding-top:3rem!important}.pr-md-5,.px-md-5{padding-right:3rem!important}.pb-md-5,.py-md-5{padding-bottom:3rem!important}.pl-md-5,.px-md-5{padding-left:3rem!important}.m-md-n1{margin:-.25rem!important}.mt-md-n1,.my-md-n1{margin-top:-.25rem!important}.mr-md-n1,.mx-md-n1{margin-right:-.25rem!important}.mb-md-n1,.my-md-n1{margin-bottom:-.25rem!important}.ml-md-n1,.mx-md-n1{margin-left:-.25rem!important}.m-md-n2{margin:-.5rem!important}.mt-md-n2,.my-md-n2{margin-top:-.5rem!important}.mr-md-n2,.mx-md-n2{margin-right:-.5rem!important}.mb-md-n2,.my-md-n2{margin-bottom:-.5rem!important}.ml-md-n2,.mx-md-n2{margin-left:-.5rem!important}.m-md-n3{margin:-1rem!important}.mt-md-n3,.my-md-n3{margin-top:-1rem!important}.mr-md-n3,.mx-md-n3{margin-right:-1rem!important}.mb-md-n3,.my-md-n3{margin-bottom:-1rem!important}.ml-md-n3,.mx-md-n3{margin-left:-1rem!important}.m-md-n4{margin:-1.5rem!important}.mt-md-n4,.my-md-n4{margin-top:-1.5rem!important}.mr-md-n4,.mx-md-n4{margin-right:-1.5rem!important}.mb-md-n4,.my-md-n4{margin-bottom:-1.5rem!important}.ml-md-n4,.mx-md-n4{margin-left:-1.5rem!important}.m-md-n5{margin:-3rem!important}.mt-md-n5,.my-md-n5{margin-top:-3rem!important}.mr-md-n5,.mx-md-n5{margin-right:-3rem!important}.mb-md-n5,.my-md-n5{margin-bottom:-3rem!important}.ml-md-n5,.mx-md-n5{margin-left:-3rem!important}.m-md-auto{margin:auto!important}.mt-md-auto,.my-md-auto{margin-top:auto!important}.mr-md-auto,.mx-md-auto{margin-right:auto!important}.mb-md-auto,.my-md-auto{margin-bottom:auto!important}.ml-md-auto,.mx-md-auto{margin-left:auto!important}}@media (min-width:992px){.m-lg-0{margin:0!important}.mt-lg-0,.my-lg-0{margin-top:0!important}.mr-lg-0,.mx-lg-0{margin-right:0!important}.mb-lg-0,.my-lg-0{margin-bottom:0!important}.ml-lg-0,.mx-lg-0{margin-left:0!important}.m-lg-1{margin:.25rem!important}.mt-lg-1,.my-lg-1{margin-top:.25rem!important}.mr-lg-1,.mx-lg-1{margin-right:.25rem!important}.mb-lg-1,.my-lg-1{margin-bottom:.25rem!important}.ml-lg-1,.mx-lg-1{margin-left:.25rem!important}.m-lg-2{margin:.5rem!important}.mt-lg-2,.my-lg-2{margin-top:.5rem!important}.mr-lg-2,.mx-lg-2{margin-right:.5rem!important}.mb-lg-2,.my-lg-2{margin-bottom:.5rem!important}.ml-lg-2,.mx-lg-2{margin-left:.5rem!important}.m-lg-3{margin:1rem!important}.mt-lg-3,.my-lg-3{margin-top:1rem!important}.mr-lg-3,.mx-lg-3{margin-right:1rem!important}.mb-lg-3,.my-lg-3{margin-bottom:1rem!important}.ml-lg-3,.mx-lg-3{margin-left:1rem!important}.m-lg-4{margin:1.5rem!important}.mt-lg-4,.my-lg-4{margin-top:1.5rem!important}.mr-lg-4,.mx-lg-4{margin-right:1.5rem!important}.mb-lg-4,.my-lg-4{margin-bottom:1.5rem!important}.ml-lg-4,.mx-lg-4{margin-left:1.5rem!important}.m-lg-5{margin:3rem!important}.mt-lg-5,.my-lg-5{margin-top:3rem!important}.mr-lg-5,.mx-lg-5{margin-right:3rem!important}.mb-lg-5,.my-lg-5{margin-bottom:3rem!important}.ml-lg-5,.mx-lg-5{margin-left:3rem!important}.p-lg-0{padding:0!important}.pt-lg-0,.py-lg-0{padding-top:0!important}.pr-lg-0,.px-lg-0{padding-right:0!important}.pb-lg-0,.py-lg-0{padding-bottom:0!important}.pl-lg-0,.px-lg-0{padding-left:0!important}.p-lg-1{padding:.25rem!important}.pt-lg-1,.py-lg-1{padding-top:.25rem!important}.pr-lg-1,.px-lg-1{padding-right:.25rem!important}.pb-lg-1,.py-lg-1{padding-bottom:.25rem!important}.pl-lg-1,.px-lg-1{padding-left:.25rem!important}.p-lg-2{padding:.5rem!important}.pt-lg-2,.py-lg-2{padding-top:.5rem!important}.pr-lg-2,.px-lg-2{padding-right:.5rem!important}.pb-lg-2,.py-lg-2{padding-bottom:.5rem!important}.pl-lg-2,.px-lg-2{padding-left:.5rem!important}.p-lg-3{padding:1rem!important}.pt-lg-3,.py-lg-3{padding-top:1rem!important}.pr-lg-3,.px-lg-3{padding-right:1rem!important}.pb-lg-3,.py-lg-3{padding-bottom:1rem!important}.pl-lg-3,.px-lg-3{padding-left:1rem!important}.p-lg-4{padding:1.5rem!important}.pt-lg-4,.py-lg-4{padding-top:1.5rem!important}.pr-lg-4,.px-lg-4{padding-right:1.5rem!important}.pb-lg-4,.py-lg-4{padding-bottom:1.5rem!important}.pl-lg-4,.px-lg-4{padding-left:1.5rem!important}.p-lg-5{padding:3rem!important}.pt-lg-5,.py-lg-5{padding-top:3rem!important}.pr-lg-5,.px-lg-5{padding-right:3rem!important}.pb-lg-5,.py-lg-5{padding-bottom:3rem!important}.pl-lg-5,.px-lg-5{padding-left:3rem!important}.m-lg-n1{margin:-.25rem!important}.mt-lg-n1,.my-lg-n1{margin-top:-.25rem!important}.mr-lg-n1,.mx-lg-n1{margin-right:-.25rem!important}.mb-lg-n1,.my-lg-n1{margin-bottom:-.25rem!important}.ml-lg-n1,.mx-lg-n1{margin-left:-.25rem!important}.m-lg-n2{margin:-.5rem!important}.mt-lg-n2,.my-lg-n2{margin-top:-.5rem!important}.mr-lg-n2,.mx-lg-n2{margin-right:-.5rem!important}.mb-lg-n2,.my-lg-n2{margin-bottom:-.5rem!important}.ml-lg-n2,.mx-lg-n2{margin-left:-.5rem!important}.m-lg-n3{margin:-1rem!important}.mt-lg-n3,.my-lg-n3{margin-top:-1rem!important}.mr-lg-n3,.mx-lg-n3{margin-right:-1rem!important}.mb-lg-n3,.my-lg-n3{margin-bottom:-1rem!important}.ml-lg-n3,.mx-lg-n3{margin-left:-1rem!important}.m-lg-n4{margin:-1.5rem!important}.mt-lg-n4,.my-lg-n4{margin-top:-1.5rem!important}.mr-lg-n4,.mx-lg-n4{margin-right:-1.5rem!important}.mb-lg-n4,.my-lg-n4{margin-bottom:-1.5rem!important}.ml-lg-n4,.mx-lg-n4{margin-left:-1.5rem!important}.m-lg-n5{margin:-3rem!important}.mt-lg-n5,.my-lg-n5{margin-top:-3rem!important}.mr-lg-n5,.mx-lg-n5{margin-right:-3rem!important}.mb-lg-n5,.my-lg-n5{margin-bottom:-3rem!important}.ml-lg-n5,.mx-lg-n5{margin-left:-3rem!important}.m-lg-auto{margin:auto!important}.mt-lg-auto,.my-lg-auto{margin-top:auto!important}.mr-lg-auto,.mx-lg-auto{margin-right:auto!important}.mb-lg-auto,.my-lg-auto{margin-bottom:auto!important}.ml-lg-auto,.mx-lg-auto{margin-left:auto!important}}


 </style>


================================================
FILE: frontend/src/env.d.ts
================================================
/// <reference types="vite/client" />

declare module '*.vue' {
  import type { DefineComponent } from 'vue'
  // eslint-disable-next-line @typescript-eslint/no-explicit-any, @typescript-eslint/ban-types
  const component: DefineComponent<{}, {}, any>
  export default component
}


================================================
FILE: frontend/src/main.ts
================================================
import { createApp } from 'vue'
import App from './app.vue'

createApp(App).mount('#app')


================================================
FILE: frontend/src/streamlit/StreamlitVue.ts
================================================
/**
 * Vue.js specific composables
 */
import { onMounted, onUpdated } from "vue"
import { Streamlit } from "streamlit-component-lib"

export function useStreamlit() {
  /**
   * Optional Streamlit Vue-based setup.
   *
   * You are not required call this function on your Streamlit
   * component. If you decide not to call it, you should implement the
   * `onMounted` and `onUpdated` functions in your own component,
   * so that your plugin properly resizes.
   */

  onMounted((): void => {
    // After we're rendered for the first time, tell Streamlit that our height
    // has changed.
    Streamlit.setFrameHeight()
  })

  onUpdated((): void => {
    // After we're updated, tell Streamlit that our height may have changed.
    Streamlit.setFrameHeight()
  })
}


================================================
FILE: frontend/src/streamlit/WithStreamlitConnection.vue
================================================
<template>
  <div>
    <!--  Error boundary. If our wrapped component threw an error, display it. -->
    <div v-if="componentError !== ''">
      <h1 class="err__title">Component Error</h1>
      <div class="err__msg">Message: {{ componentError }}</div>
    </div>
    <!--
      Else render the component slot and pass Streamlit event data in `args` props to it.
      Don't render until we've gotten our first RENDER_EVENT from Streamlit.
      All components get disabled while the app is being re-run, and become re-enabled when the re-run has finished.
    -->
    <slot
      v-else-if="renderData != null"
      :args="renderData.args"
      :theme="renderData.theme"
      :disabled="renderData.disabled"
    ></slot>
  </div>
</template>

<script lang="ts">
import {
  ref,
  defineComponent,
  onMounted,
  onUpdated,
  onUnmounted,
  onErrorCaptured,
} from "vue"
import { Streamlit, RenderData } from "streamlit-component-lib"

export default defineComponent({
  name: "WithStreamlitConnection",
  setup() {
    const renderData = ref<RenderData>((undefined as unknown) as RenderData)
    const componentError = ref("")

    const onRenderEvent = (event: Event): void => {
      const renderEvent = event as CustomEvent<RenderData>
      renderData.value = renderEvent.detail
      componentError.value = ""
    }

    // Set up event listeners, and signal to Streamlit that we're ready.
    // We won't render the component until we receive the first RENDER_EVENT.
    onMounted(() => {
      Streamlit.events.addEventListener(Streamlit.RENDER_EVENT, onRenderEvent)
      Streamlit.setComponentReady()
    })
    onUpdated(() => {
      // If our slot threw an error, we display it in render(). In this
      // case, the slot won't be mounted and therefore won't call
      // `setFrameHeight` on its own. We do it here so that the rendered
      // error will be visible.
      if (componentError.value != "") {
        Streamlit.setFrameHeight()
      }
    })
    onUnmounted(() => {
      Streamlit.events.removeEventListener(
        Streamlit.RENDER_EVENT,
        onRenderEvent
      )
    })
    onErrorCaptured(err => {
      componentError.value = String(err)
    })

    return {
      renderData,
      componentError,
    }
  },
})
</script>

<style scoped>
.err__title,
.err__msg {
  margin: 0;
}
</style>


================================================
FILE: frontend/src/streamlit/index.ts
================================================
/**
 * @license
 * Copyright 2018-2020 Streamlit Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
export { useStreamlit } from "./StreamlitVue"


================================================
FILE: frontend/tsconfig.json
================================================
{
  "compilerOptions": {
    "baseUrl": ".",
    "outDir": "dist",
    "declaration": false,
    "target": "esnext",
    "useDefineForClassFields": true,
    "module": "esnext",
    "moduleResolution": "node",
    "isolatedModules": true,
    "strict": true,
    "jsx": "preserve",
    "sourceMap": true,
    "resolveJsonModule": true,
    "esModuleInterop": true,
    "skipLibCheck": true,
    "lib": [
      "esnext",
      "dom"
    ],
    "paths": {
      "@/*": ["src/*"]
    },
  },
  "include": [
    "src/**/*.ts",
    "src/**/*.d.ts",
    "src/**/*.tsx",
    "src/**/*.vue"
  ],
  "exclude": [
    "node_modules",
    "dist",
    "public"
  ],
  "references": [
    { "path": "./tsconfig.node.json" }
  ]
}


================================================
FILE: frontend/tsconfig.node.json
================================================
{
  "compilerOptions": {
    "composite": false,
    "module": "esnext",
    "moduleResolution": "node"
  },
  "include": ["vite.config.ts"]
}


================================================
FILE: frontend/ui_functions.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
import re
import gradio as gr
from PIL import Image
from io import BytesIO
import base64
import re


def change_image_editor_mode(
    choice, cropped_image, masked_image, resize_mode, width, height
):
    if choice == "Mask":
        update_image_result = update_image_mask(
            cropped_image, resize_mode, width, height
        )
        return [
            gr.update(visible=False),
            update_image_result,
            gr.update(visible=False),
            gr.update(visible=True),
            gr.update(visible=False),
            gr.update(visible=True),
            gr.update(visible=True),
            gr.update(visible=True),
        ]

    update_image_result = update_image_mask(
        masked_image["image"] if masked_image is not None else None,
        resize_mode,
        width,
        height,
    )
    return [
        update_image_result,
        gr.update(visible=False),
        gr.update(visible=True),
        gr.update(visible=False),
        gr.update(visible=True),
        gr.update(visible=False),
        gr.update(visible=False),
        gr.update(visible=False),
    ]


def update_image_mask(cropped_image, resize_mode, width, height):
    resized_cropped_image = (
        resize_image(resize_mode, cropped_image, width, height)
        if cropped_image
        else None
    )
    return gr.update(value=resized_cropped_image, visible=True)


def toggle_options_gfpgan(selection):
    if 0 in selection:
        return gr.update(visible=True)
    else:
        return gr.update(visible=False)


def toggle_options_upscalers(selection):
    if 1 in selection:
        return gr.update(visible=True)
    else:
        return gr.update(visible=False)


def toggle_options_realesrgan(selection):
    if selection == 0 or selection == 1 or selection == 3:
        return gr.update(visible=True)
    else:
        return gr.update(visible=False)


def toggle_options_gobig(selection):
    if selection == 1:
        # print(selection)
        return gr.update(visible=True)
    if selection == 3:
        return gr.update(visible=True)
    else:
        return gr.update(visible=False)


def toggle_options_ldsr(selection):
    if selection == 2 or selection == 3:
        return gr.update(visible=True)
    else:
        return gr.update(visible=False)


def increment_down(value):
    return value - 1


def increment_up(value):
    return value + 1


def copy_img_to_lab(img):
    try:
        image_data = re.sub("^data:image/.+;base64,", "", img)
        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
        tab_update = gr.update(selected="imgproc_tab")
        gr.update(value=processed_image)
        return (
            processed_image,
            tab_update,
        )
    except IndexError:
        return [None, None]


def copy_img_params_to_lab(params):
    try:
        prompt = params[0][0].replace("\n", " ").replace("\r", "")
        seed = int(params[1][1])
        steps = int(params[7][1])
        cfg_scale = float(params[9][1])
        sampler = params[11][1]
        return prompt, seed, steps, cfg_scale, sampler
    except IndexError:
        return [None, None]


def copy_img_to_input(img):
    try:
        image_data = re.sub("^data:image/.+;base64,", "", img)
        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
        tab_update = gr.update(selected="img2img_tab")
        gr.update(value=processed_image)
        return processed_image, processed_image, tab_update
    except IndexError:
        return [None, None]


def copy_img_to_edit(img):
    try:
        image_data = re.sub("^data:image/.+;base64,", "", img)
        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
        tab_update = gr.update(selected="img2img_tab")
        gr.update(value=processed_image)
        mode_update = gr.update(value="Crop")
        return processed_image, tab_update, mode_update
    except IndexError:
        return [None, None]


def copy_img_to_mask(img):
    try:
        image_data = re.sub("^data:image/.+;base64,", "", img)
        processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
        tab_update = gr.update(selected="img2img_tab")
        gr.update(value=processed_image)
        mode_update = gr.update(value="Mask")
        return processed_image, tab_update, mode_update
    except IndexError:
        return [None, None]


def copy_img_to_upscale_esrgan(img):
    tabs_update = gr.update(selected="realesrgan_tab")
    image_data = re.sub("^data:image/.+;base64,", "", img)
    processed_image = Image.open(BytesIO(base64.b64decode(image_data)))
    return processed_image, tabs_update


help_text = """
    ## Mask/Crop
    * Masking is not inpainting. You will probably get better results manually masking your images in photoshop instead.
    * Built-in masking/cropping is very temperamental.
    * It may take some time for the image to show when switching from Crop to Mask.
    * If the image doesn't appear after switching to Mask, switch back to Crop and then back again to Mask
    * If the mask appears distorted (the brush is weirdly shaped instead of round), switch back to Crop and then back again to Mask.

    ## Advanced Editor
    * Click 💾 Save to send your editor changes to the img2img workflow
    * Click ❌ Clear to discard your editor changes

    If anything breaks, try switching modes again, switch tabs, clear the image, or reload.
"""


def resize_image(resize_mode, im, width, height):
    LANCZOS = (
        Image.Resampling.LANCZOS if hasattr(Image, "Resampling") else Image.LANCZOS
    )
    if resize_mode == 0:
        res = im.resize((width, height), resample=LANCZOS)
    elif resize_mode == 1:
        ratio = width / height
        src_ratio = im.width / im.height

        src_w = width if ratio > src_ratio else im.width * height // im.height
        src_h = height if ratio <= src_ratio else im.height * width // im.width

        resized = im.resize((src_w, src_h), resample=LANCZOS)
        res = Image.new("RGBA", (width, height))
        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
    else:
        ratio = width / height
        src_ratio = im.width / im.height

        src_w = width if ratio < src_ratio else im.width * height // im.height
        src_h = height if ratio >= src_ratio else im.height * width // im.width

        resized = im.resize((src_w, src_h), resample=LANCZOS)
        res = Image.new("RGBA", (width, height))
        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))

        if ratio < src_ratio:
            fill_height = height // 2 - src_h // 2
            res.paste(
                resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)
            )
            res.paste(
                resized.resize(
                    (width, fill_height), box=(0, resized.height, width, resized.height)
                ),
                box=(0, fill_height + src_h),
            )
        elif ratio > src_ratio:
            fill_width = width // 2 - src_w // 2
            res.paste(
                resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)
            )
            res.paste(
                resized.resize(
                    (fill_width, height), box=(resized.width, 0, resized.width, height)
                ),
                box=(fill_width + src_w, 0),
            )

    return res


def update_dimensions_info(width, height):
    pixel_count_formated = "{:,.0f}".format(width * height)
    return f"Aspect ratio: {round(width / height, 5)}\nTotal pixel count: {pixel_count_formated}"


def get_png_nfo(image: Image):
    info_text = ""
    visible = bool(image and any(image.info))
    if visible:
        for key, value in image.info.items():
            info_text += f"{key}: {value}\n"
        info_text = info_text.rstrip("\n")
    return gr.Textbox.update(value=info_text, visible=visible)


def load_settings(*values):
    new_settings, key_names, checkboxgroup_info = values[-3:]
    values = list(values[:-3])

    if new_settings:
        if type(new_settings) is str:
            if os.path.exists(new_settings):
                with open(new_settings, "r", encoding="utf8") as f:
                    new_settings = yaml.safe_load(f)
            elif new_settings.startswith("file://") and os.path.exists(
                new_settings[7:]
            ):
                with open(new_settings[7:], "r", encoding="utf8") as f:
                    new_settings = yaml.safe_load(f)
            else:
                new_settings = yaml.safe_load(new_settings)
        if type(new_settings) is not dict:
            new_settings = {"prompt": new_settings}
        if "txt2img" in new_settings:
            new_settings = new_settings["txt2img"]
        target = new_settings.pop("target", "txt2img")
        if target != "txt2img":
            print(
                f"Warning: applying settings to txt2img even though {target} is specified as target.",
                file=sys.stderr,
            )

        skipped_settings = {}
        for key in new_settings.keys():
            if key in key_names:
                values[key_names.index(key)] = new_settings[key]
            else:
                skipped_settings[key] = new_settings[key]
        if skipped_settings:
            print(f"Settings could not be applied: {skipped_settings}", file=sys.stderr)

    # Convert lists of checkbox indices to lists of checkbox labels:
    for cbg_index, cbg_choices in checkboxgroup_info:
        values[cbg_index] = [cbg_choices[i] for i in values[cbg_index]]

    return values


================================================
FILE: frontend/vite.config.ts
================================================
import { resolve } from 'path'
import { defineConfig } from 'vite'
import vue from '@vitejs/plugin-vue'

export default defineConfig(({ mode }) => {
  return {
    plugins: [vue()],
    base: "",
    server: {
      port: 3001,
    },
    /**
     * DESC:
     * defining aliases
     */
    resolve: {
      alias: [
        {
          find: '@',
          replacement: resolve(__dirname, './src'),
        },
      ],
    },
  }
})


================================================
FILE: horde_bridge.cmd
================================================
@echo off
:: This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

:: Copyright 2022 Sygil-Dev team.
:: This program is free software: you can redistribute it and/or modify
:: it under the terms of the GNU Affero General Public License as published by
:: the Free Software Foundation, either version 3 of the License, or
:: (at your option) any later version.

:: This program is distributed in the hope that it will be useful,
:: but WITHOUT ANY WARRANTY; without even the implied warranty of
:: MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
:: GNU Affero General Public License for more details.

:: You should have received a copy of the GNU Affero General Public License
:: along with this program.  If not, see <http://www.gnu.org/licenses/>.
:: Run all commands using this script's directory as the working directory
cd %~dp0

:: copy over the first line from environment.yaml, e.g. name: ldm, and take the second word after splitting by ":" delimiter
for /F "tokens=2 delims=: " %%i in (environment.yaml) DO (
  set v_conda_env_name=%%i
  goto EOL
)
:EOL

echo Environment name is set as %v_conda_env_name% as per environment.yaml

:: Put the path to conda directory in a file called "custom-conda-path.txt" if it's installed at non-standard path
IF EXIST custom-conda-path.txt (
  FOR /F %%i IN (custom-conda-path.txt) DO set v_custom_path=%%i
)

set v_paths=%ProgramData%\miniconda3
set v_paths=%v_paths%;%USERPROFILE%\miniconda3
set v_paths=%v_paths%;%ProgramData%\anaconda3
set v_paths=%v_paths%;%USERPROFILE%\anaconda3

for %%a in (%v_paths%) do (
  IF NOT "%v_custom_path%"=="" (
    set v_paths=%v_custom_path%;%v_paths%
  )
)

for %%a in (%v_paths%) do (
  if EXIST "%%a\Scripts\activate.bat" (
    SET v_conda_path=%%a
    echo anaconda3/miniconda3 detected in %%a
    goto :CONDA_FOUND
  )
)

IF "%v_conda_path%"=="" (
  echo anaconda3/miniconda3 not found. Install from here https://docs.conda.io/en/latest/miniconda.html
  pause
  exit /b 1
)

:CONDA_FOUND
echo Stashing local changes and pulling latest update...
git status --porcelain=1 -uno | findstr . && set "HasChanges=1" || set "HasChanges=0"
call git stash
call git pull
IF "%HasChanges%" == "0" GOTO SKIP_RESTORE

set /P restore="Do you want to restore changes you made before updating? (Y/N): "
IF /I "%restore%" == "N" (
  echo Removing changes...
  call git stash drop
  echo "Changes removed"
) ELSE IF /I "%restore%" == "Y" (
  echo Restoring changes...
  call git stash pop --quiet
  echo "Changes restored"
)

:SKIP_RESTORE
call "%v_conda_path%\Scripts\activate.bat"

for /f "delims=" %%a in ('git log -1 --format^="%%H" -- environment.yaml')  DO set v_cur_hash=%%a
set /p "v_last_hash="<"z_version_env.tmp"
echo %v_cur_hash%>z_version_env.tmp

echo Current  environment.yaml hash: %v_cur_hash%
echo Previous environment.yaml hash: %v_last_hash%

if "%v_last_hash%" == "%v_cur_hash%" (
  echo environment.yaml unchanged. dependencies should be up to date.
  echo if you still have unresolved dependencies, delete "z_version_env.tmp"
) else (
  echo environment.yaml changed. updating dependencies
  call conda env create --name "%v_conda_env_name%" -f environment.yaml
  call conda env update --name "%v_conda_env_name%" -f environment.yaml
)


call "%v_conda_path%\Scripts\activate.bat" "%v_conda_env_name%"

:PROMPT
set SETUPTOOLS_USE_DISTUTILS=stdlib
IF EXIST "models\ldm\stable-diffusion-v1\model.ckpt" (
  set "PYTHONPATH=%~dp0"
  python scripts\relauncher.py --bridge -v %*
) ELSE (
  echo Your model file does not exist! Place it in 'models\ldm\stable-diffusion-v1' with the name 'model.ckpt'.
  pause
)

::cmd /k


================================================
FILE: horde_bridge.sh
================================================
#!/bin/bash -i
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# Start the Stable Diffusion WebUI for Linux Users

DIRECTORY="."
ENV_FILE="environment.yaml"
ENV_NAME="ldm"
ENV_MODIFIED=$(date -r $ENV_FILE "+%s")
ENV_MODIFED_FILE=".env_updated"
ENV_UPDATED=0

# Models used for upscaling
GFPGAN_MODEL="https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth"
LATENT_DIFFUSION_REPO="https://github.com/devilismyfriend/latent-diffusion.git"
LSDR_CONFIG="https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
LSDR_MODEL="https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
REALESRGAN_MODEL="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"
REALESRGAN_ANIME_MODEL="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth"
SD_CONCEPT_REPO="https://github.com/Sygil-Dev/sd-concepts-library/archive/refs/heads/main.zip"


if [[ -f $ENV_MODIFED_FILE ]]; then
    ENV_MODIFIED_CACHED=$(<${ENV_MODIFED_FILE})
else
    ENV_MODIFIED_CACHED=0
fi

# Setup the Conda env for the project. This will also handle updating the env as needed too.
conda_env_setup () {
    # Set conda path if it is not already in default environment
    CUSTOM_CONDA_PATH=

    # Allow setting custom path via file to allow updates of this script without undoing custom path
    if [ -f custom-conda-path.txt ]; then
        CUSTOM_CONDA_PATH=$(cat custom-conda-path.txt)
    fi

    # If custom path is set above, try to setup conda environment
    if [ -f "${CUSTOM_CONDA_PATH}/etc/profile.d/conda.sh" ]; then
        . "${CUSTOM_CONDA_PATH}/etc/profile.d/conda.sh"
    elif [ -n "${CUSTOM_CONDA_PATH}" ] && [ -f "${CUSTOM_CONDA_PATH}/bin" ]; then
        export PATH="${CUSTOM_CONDA_PATH}/bin:$PATH"
    fi

    if ! command -v conda >/dev/null; then
        printf "Anaconda3 not found. Install from here https://www.anaconda.com/products/distribution\n"
        exit 1
    fi

    # Create/update conda env if needed
    if ! conda env list | grep ".*${ENV_NAME}.*" >/dev/null 2>&1; then
        printf "Could not find conda env: ${ENV_NAME} ... creating ... \n\n"
        conda env create -f $ENV_FILE
        ENV_UPDATED=1
    elif [[ ! -z $CONDA_FORCE_UPDATE && $CONDA_FORCE_UPDATE == "true" ]] || (( $ENV_MODIFIED > $ENV_MODIFIED_CACHED )); then
        printf "Updating conda env: ${ENV_NAME} ...\n\n"
        PIP_EXISTS_ACTION=w conda env update --file $ENV_FILE --prune
        ENV_UPDATED=1
    fi

    # Clear artifacts from conda after create/update
    if (( $ENV_UPDATED > 0 )); then
        conda clean --all
        echo -n $ENV_MODIFIED > $ENV_MODIFED_FILE
    fi
}

# Activate conda environment
conda_env_activation () {
    conda activate $ENV_NAME
    conda info | grep active
}

# Check to see if the SD model already exists, if not then it creates it and prompts the user to add the SD AI models to the repo directory
sd_model_loading () {
    if [ -f "$DIRECTORY/models/ldm/stable-diffusion-v1/model.ckpt" ]; then
        printf "AI Model already in place. Continuing...\n\n"
    else
        printf "\n\n########## MOVE MODEL FILE ##########\n\n"
        printf "Please download the 1.4 AI Model from Huggingface (or another source) and place it inside of the sygil-webui folder\n\n"
        read -p "Once you have sd-v1-4.ckpt in the project root, Press Enter...\n\n"

        # Check to make sure checksum of models is the original one from HuggingFace and not a fake model set
        printf "fe4efff1e174c627256e44ec2991ba279b3816e364b49f9be2abc0b3ff3f8556 sd-v1-4.ckpt" | sha256sum --check || exit 1
        mv sd-v1-4.ckpt $DIRECTORY/models/ldm/stable-diffusion-v1/model.ckpt
        rm -r ./Models
    fi
}

# Checks to see if the upscaling models exist in their correct locations. If they do not they will be downloaded as required
post_processor_model_loading () {
    # Check to see if GFPGAN has been added yet, if not it will download it and place it in the proper directory
    if [ -f "$DIRECTORY/models/gfpgan/GFPGANv1.3.pth" ]; then
        printf "GFPGAN already exists. Continuing...\n\n"
    else
        printf "Downloading GFPGAN model. Please wait...\n"
        wget $GFPGAN_MODEL -P $DIRECTORY/models/gfpgan
    fi

    # Check to see if realESRGAN has been added yet, if not it will download it and place it in the proper directory
    if [ -f "$DIRECTORY/models/realesrgan/RealESRGAN_x4plus.pth" ]; then
        printf "realESRGAN already exists. Continuing...\n\n"
    else
        printf "Downloading realESRGAN model. Please wait...\n"
        wget $REALESRGAN_MODEL -P $DIRECTORY/models/realesrgan
        wget $REALESRGAN_ANIME_MODEL -P $DIRECTORY/models/realesrgan
    fi

    # Check to see if LDSR has been added yet, if not it will be cloned and its models downloaded to the correct directory
    if [ -f "$DIRECTORY/models/ldsr/model.ckpt" ]; then
        printf "LDSR already exists. Continuing...\n\n"
    else
        printf "Cloning LDSR and downloading model. Please wait...\n"
        git clone $LATENT_DIFFUSION_REPO
        mv latent-diffusion $DIRECTORY/models/ldsr
        mkdir $DIRECTORY/models/ldsr/experiments
        mkdir $DIRECTORY/models/ldsr
        wget $LSDR_CONFIG -P $DIRECTORY/models/ldsr
        mv $DIRECTORY/models/ldsr/index.html?dl=1 $DIRECTORY/models/ldsr/project.yaml
        wget $LSDR_MODEL -P $DIRECTORY/models/ldsr
        mv $DIRECTORY/models/ldsr/index.html?dl=1 $DIRECTORY/models/ldsr/model.ckpt
    fi

    # Check to see if SD Concepts has been added yet, if not it will download it and place it in the proper directory
    if [ -d "$DIRECTORY/models/custom/sd-concepts-library" ]; then
        printf "SD Concepts Library already exists. Continuing...\n\n"
    else
        printf "Downloading and Extracting SD Concepts Library model. Please wait...\n"
        mkdir $DIRECTORY/models/custom
        wget $SD_CONCEPT_REPO
        if ! command -v unzip &> /dev/null
        then
            printf "Warning: unzip could not be found. \nPlease install 'unzip' from your package manager and rerun this program.\n"
            exit 1
        fi
        unzip main.zip
        mv sd-concepts-library-main/sd-concepts-library $DIRECTORY/models/custom
    fi
}

# Function to initialize the other functions
start_initialization () {
    conda_env_setup
    sd_model_loading
    post_processor_model_loading
    conda_env_activation
    if [ ! -e "models/ldm/stable-diffusion-v1/model.ckpt" ]; then
        echo "Your model file does not exist! Place it in 'models/ldm/stable-diffusion-v1' with the name 'model.ckpt'."
        exit 1
    fi
    printf "\nStarting Stable Horde Bridge: Please Wait...\n"; python scripts/relauncher.py --bridge -v "$@"; break;

}

start_initialization "$@"


================================================
FILE: installer/create_installers.sh
================================================
#!/bin/bash

# For developers only! Not for users!
# This creates the installer zip files that will be distributed to users
# It packs install.{sh,bat} along with a readme, and ensures that the user
# has the install script inside a new empty folder (after unzipping),
# otherwise the git repo will extract into whatever folder the script is in.

cd "$(dirname "${BASH_SOURCE[0]}")"

# make the installer zip for linux and mac
rm -rf sygil
mkdir -p sygil
cp install.sh sygil
cp readme.txt sygil

zip -r sygil-linux.zip sygil
zip -r sygil-mac.zip sygil

# make the installer zip for windows
rm -rf sygil
mkdir -p sygil
cp install.bat sygil
cp readme.txt sygil

zip -r sygil-windows.zip sygil

echo "The installer zips are ready to be distributed.."


================================================
FILE: installer/install.bat
================================================
@echo off

@rem This script will install git and conda (if not found on the PATH variable)
@rem  using micromamba (an 8mb static-linked single-file binary, conda replacement).
@rem For users who already have git and conda, this step will be skipped.

@rem Then, it'll run the webui.cmd file to continue with the installation as usual.

@rem This enables a user to install this project without manually installing conda and git.

echo "Installing Sygil WebUI.."
echo.

@rem config
set MAMBA_ROOT_PREFIX=%cd%\installer_files\mamba
set INSTALL_ENV_DIR=%cd%\installer_files\env
set MICROMAMBA_DOWNLOAD_URL=https://github.com/cmdr2/stable-diffusion-ui/releases/download/v1.1/micromamba.exe
set REPO_URL=https://github.com/Sygil-Dev/sygil-webui.git
@rem Change the download URL to Sygil repo's release URL
@rem We need to mirror micromamba.exe, because the official download URL uses tar.bz2 compression
@rem  which Windows can't unzip natively.
@rem https://mamba.readthedocs.io/en/latest/installation.html#windows
set umamba_exists=F

@rem figure out whether git and conda needs to be installed
if exist "%INSTALL_ENV_DIR%" set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%

set PACKAGES_TO_INSTALL=

call conda --version >.tmp1 2>.tmp2
if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% conda

call git --version >.tmp1 2>.tmp2
if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% git

call "%MAMBA_ROOT_PREFIX%\micromamba.exe" --version >.tmp1 2>.tmp2
if "%ERRORLEVEL%" EQU "0" set umamba_exists=T

@rem (if necessary) install git and conda into a contained environment
if "%PACKAGES_TO_INSTALL%" NEQ "" (
    @rem download micromamba
    if "%umamba_exists%" == "F" (
        echo "Downloading micromamba from %MICROMAMBA_DOWNLOAD_URL% to %MAMBA_ROOT_PREFIX%\micromamba.exe"

        mkdir "%MAMBA_ROOT_PREFIX%"
        call curl -L "%MICROMAMBA_DOWNLOAD_URL%" > "%MAMBA_ROOT_PREFIX%\micromamba.exe"

        @rem test the mamba binary
        echo Micromamba version:
        call "%MAMBA_ROOT_PREFIX%\micromamba.exe" --version
    )

    @rem create the installer env
    if not exist "%INSTALL_ENV_DIR%" (
        call "%MAMBA_ROOT_PREFIX%\micromamba.exe" create -y --prefix "%INSTALL_ENV_DIR%"
    )

    echo "Packages to install:%PACKAGES_TO_INSTALL%"

    call "%MAMBA_ROOT_PREFIX%\micromamba.exe" install -y --prefix "%INSTALL_ENV_DIR%" -c conda-forge %PACKAGES_TO_INSTALL%

    if not exist "%INSTALL_ENV_DIR%" (
        echo "There was a problem while installing%PACKAGES_TO_INSTALL% using micromamba. Cannot continue."
        pause
        exit /b
    )
)

set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%

@rem get the repo (and load into the current directory)
if not exist ".git" (
    call git config --global init.defaultBranch master
    call git init
    call git remote add origin %REPO_URL%
    call git fetch
    call git checkout origin/master -ft
)

@rem activate the base env
call conda activate

@rem make the models dir
mkdir models\ldm\stable-diffusion-v1

@rem install the project
call webui.cmd

@rem finally, tell the user that they need to download the ckpt
echo.
echo "Now you need to install the weights for the stable diffusion model."
echo "Please follow the steps related to models weights at https://sd-webui.github.io/stable-diffusion-webui/docs/1.windows-installation.html#cloning-the-repo to complete the installation"

@rem it would be nice if the weights downloaded automatically, and didn't need the user to do this manually.

pause


================================================
FILE: installer/install.sh
================================================
#!/bin/bash

# This script will install git and conda (if not found on the PATH variable)
#  using micromamba (an 8mb static-linked single-file binary, conda replacement).
# For users who already have git and conda, this step will be skipped.

# Then, it'll run the webui.cmd file to continue with the installation as usual.

# This enables a user to install this project without manually installing conda and git.

cd "$(dirname "${BASH_SOURCE[0]}")"

echo "Installing Sygil WebUI.."
echo ""

OS_ARCH=$(uname -m)
case "${OS_ARCH}" in
    x86_64*)    OS_ARCH="64";;
    arm64*)     OS_ARCH="aarch64";;
    *)          echo "Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64" && exit
esac

# config
export MAMBA_ROOT_PREFIX="$(pwd)/installer_files/mamba"
INSTALL_ENV_DIR="$(pwd)/installer_files/env"
MICROMAMBA_DOWNLOAD_URL="https://micro.mamba.pm/api/micromamba/linux-${OS_ARCH}/latest"
umamba_exists="F"

# figure out whether git and conda needs to be installed
if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi

PACKAGES_TO_INSTALL=""

if ! hash "conda" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL conda"; fi
if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi

if "$MAMBA_ROOT_PREFIX/micromamba" --version &>/dev/null; then umamba_exists="T"; fi

# (if necessary) install git and conda into a contained environment
if [ "$PACKAGES_TO_INSTALL" != "" ]; then
    # download micromamba
    if [ "$umamba_exists" == "F" ]; then
        echo "Downloading micromamba from $MICROMAMBA_DOWNLOAD_URL to $MAMBA_ROOT_PREFIX/micromamba"

        mkdir -p "$MAMBA_ROOT_PREFIX"
        curl -L "$MICROMAMBA_DOWNLOAD_URL" | tar -xvj bin/micromamba -O > "$MAMBA_ROOT_PREFIX/micromamba"

        chmod u+x "$MAMBA_ROOT_PREFIX/micromamba"

        # test the mamba binary
        echo "Micromamba version:"
        "$MAMBA_ROOT_PREFIX/micromamba" --version
    fi

    # create the installer env
    if [ ! -e "$INSTALL_ENV_DIR" ]; then
        "$MAMBA_ROOT_PREFIX/micromamba" create -y --prefix "$INSTALL_ENV_DIR"
    fi

    echo "Packages to install:$PACKAGES_TO_INSTALL"

    "$MAMBA_ROOT_PREFIX/micromamba" install -y --prefix "$INSTALL_ENV_DIR" -c conda-forge $PACKAGES_TO_INSTALL

    if [ ! -e "$INSTALL_ENV_DIR" ]; then
        echo "There was a problem while initializing micromamba. Cannot continue."
        exit
    fi
fi

if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi

CONDA_BASEPATH=$(conda info --base)
source "$CONDA_BASEPATH/etc/profile.d/conda.sh" # otherwise conda complains about 'shell not initialized' (needed when running in a script)

conda activate

# run the installer script for linux
curl "https://raw.githubusercontent.com/JoshuaKimsey/Linux-StableDiffusion-Script/main/linux-sd.sh" > linux-sd.sh
chmod u+x linux-sd.sh

./linux-sd.sh

# tell the user that they need to download the ckpt
WEIGHTS_DOC_URL="https://sd-webui.github.io/stable-diffusion-webui/docs/2.linux-installation.html#initial-start-guide"

echo ""
echo "Now you need to install the weights for the stable diffusion model."
echo "Please follow the steps at $WEIGHTS_DOC_URL to complete the installation"

# it would be nice if the weights downloaded automatically, and didn't need the user to do this manually.


================================================
FILE: ldm/__init__.py
================================================


================================================
FILE: ldm/data/__init__.py
================================================
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from data.coco_karpathy_dataset import (
    coco_karpathy_train,
    coco_karpathy_caption_eval,
    coco_karpathy_retrieval_eval,
)
from data.nocaps_dataset import nocaps_eval
from data.flickr30k_dataset import flickr30k_train, flickr30k_retrieval_eval
from data.vqa_dataset import vqa_dataset
from data.nlvr_dataset import nlvr_dataset
from data.pretrain_dataset import pretrain_dataset
from transform.randaugment import RandomAugment


def create_dataset(dataset, config, min_scale=0.5):
    normalize = transforms.Normalize(
        (0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)
    )

    transform_train = transforms.Compose(
        [
            transforms.RandomResizedCrop(
                config["image_size"],
                scale=(min_scale, 1.0),
                interpolation=InterpolationMode.BICUBIC,
            ),
            transforms.RandomHorizontalFlip(),
            RandomAugment(
                2,
                5,
                isPIL=True,
                augs=[
                    "Identity",
                    "AutoContrast",
                    "Brightness",
                    "Sharpness",
                    "Equalize",
                    "ShearX",
                    "ShearY",
                    "TranslateX",
                    "TranslateY",
                    "Rotate",
                ],
            ),
            transforms.ToTensor(),
            normalize,
        ]
    )
    transform_test = transforms.Compose(
        [
            transforms.Resize(
                (config["image_size"], config["image_size"]),
                interpolation=InterpolationMode.BICUBIC,
            ),
            transforms.ToTensor(),
            normalize,
        ]
    )

    if dataset == "pretrain":
        dataset = pretrain_dataset(
            config["train_file"], config["laion_path"], transform_train
        )
        return dataset

    elif dataset == "caption_coco":
        train_dataset = coco_karpathy_train(
            transform_train,
            config["image_root"],
            config["ann_root"],
            prompt=config["prompt"],
        )
        val_dataset = coco_karpathy_caption_eval(
            transform_test, config["image_root"], config["ann_root"], "val"
        )
        test_dataset = coco_karpathy_caption_eval(
            transform_test, config["image_root"], config["ann_root"], "test"
        )
        return train_dataset, val_dataset, test_dataset

    elif dataset == "nocaps":
        val_dataset = nocaps_eval(
            transform_test, config["image_root"], config["ann_root"], "val"
        )
        test_dataset = nocaps_eval(
            transform_test, config["image_root"], config["ann_root"], "test"
        )
        return val_dataset, test_dataset

    elif dataset == "retrieval_coco":
        train_dataset = coco_karpathy_train(
            transform_train, config["image_root"], config["ann_root"]
        )
        val_dataset = coco_karpathy_retrieval_eval(
            transform_test, config["image_root"], config["ann_root"], "val"
        )
        test_dataset = coco_karpathy_retrieval_eval(
            transform_test, config["image_root"], config["ann_root"], "test"
        )
        return train_dataset, val_dataset, test_dataset

    elif dataset == "retrieval_flickr":
        train_dataset = flickr30k_train(
            transform_train, config["image_root"], config["ann_root"]
        )
        val_dataset = flickr30k_retrieval_eval(
            transform_test, config["image_root"], config["ann_root"], "val"
        )
        test_dataset = flickr30k_retrieval_eval(
            transform_test, config["image_root"], config["ann_root"], "test"
        )
        return train_dataset, val_dataset, test_dataset

    elif dataset == "vqa":
        train_dataset = vqa_dataset(
            transform_train,
            config["ann_root"],
            config["vqa_root"],
            config["vg_root"],
            train_files=config["train_files"],
            split="train",
        )
        test_dataset = vqa_dataset(
            transform_test,
            config["ann_root"],
            config["vqa_root"],
            config["vg_root"],
            split="test",
        )
        return train_dataset, test_dataset

    elif dataset == "nlvr":
        train_dataset = nlvr_dataset(
            transform_train, config["image_root"], config["ann_root"], "train"
        )
        val_dataset = nlvr_dataset(
            transform_test, config["image_root"], config["ann_root"], "val"
        )
        test_dataset = nlvr_dataset(
            transform_test, config["image_root"], config["ann_root"], "test"
        )
        return train_dataset, val_dataset, test_dataset


def create_sampler(datasets, shuffles, num_tasks, global_rank):
    samplers = []
    for dataset, shuffle in zip(datasets, shuffles):
        sampler = torch.utils.data.DistributedSampler(
            dataset, num_replicas=num_tasks, rank=global_rank, shuffle=shuffle
        )
        samplers.append(sampler)
    return samplers


def create_loader(datasets, samplers, batch_size, num_workers, is_trains, collate_fns):
    loaders = []
    for dataset, sampler, bs, n_worker, is_train, collate_fn in zip(
        datasets, samplers, batch_size, num_workers, is_trains, collate_fns
    ):
        if is_train:
            shuffle = sampler is None
            drop_last = True
        else:
            shuffle = False
            drop_last = False
        loader = DataLoader(
            dataset,
            batch_size=bs,
            num_workers=n_worker,
            pin_memory=True,
            sampler=sampler,
            shuffle=shuffle,
            collate_fn=collate_fn,
            drop_last=drop_last,
        )
        loaders.append(loader)
    return loaders


================================================
FILE: ldm/data/base.py
================================================
from abc import abstractmethod
from torch.utils.data import IterableDataset


class Txt2ImgIterableBaseDataset(IterableDataset):
    """
    Define an interface to make the IterableDatasets for text2img data chainable
    """

    def __init__(self, num_records=0, valid_ids=None, size=256):
        super().__init__()
        self.num_records = num_records
        self.valid_ids = valid_ids
        self.sample_ids = valid_ids
        self.size = size

        print(f"{self.__class__.__name__} dataset contains {self.__len__()} examples.")

    def __len__(self):
        return self.num_records

    @abstractmethod
    def __iter__(self):
        pass


================================================
FILE: ldm/data/coco_karpathy_dataset.py
================================================
import os
import json

from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url

from PIL import Image

from data.utils import pre_caption


class coco_karpathy_train(Dataset):
    def __init__(self, transform, image_root, ann_root, max_words=30, prompt=""):
        """
        image_root (string): Root directory of images (e.g. coco/images/)
        ann_root (string): directory to store the annotation file
        """
        url = "https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json"
        filename = "coco_karpathy_train.json"

        download_url(url, ann_root)

        self.annotation = json.load(open(os.path.join(ann_root, filename), "r"))
        self.transform = transform
        self.image_root = image_root
        self.max_words = max_words
        self.prompt = prompt

        self.img_ids = {}
        n = 0
        for ann in self.annotation:
            img_id = ann["image_id"]
            if img_id not in self.img_ids.keys():
                self.img_ids[img_id] = n
                n += 1

    def __len__(self):
        return len(self.annotation)

    def __getitem__(self, index):
        ann = self.annotation[index]

        image_path = os.path.join(self.image_root, ann["image"])
        image = Image.open(image_path).convert("RGB")
        image = self.transform(image)

        caption = self.prompt + pre_caption(ann["caption"], self.max_words)

        return image, caption, self.img_ids[ann["image_id"]]


class coco_karpathy_caption_eval(Dataset):
    def __init__(self, transform, image_root, ann_root, split):
        """
        image_root (string): Root directory of images (e.g. coco/images/)
        ann_root (string): directory to store the annotation file
        split (string): val or test
        """
        urls = {
            "val": "https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json",
            "test": "https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json",
        }
        filenames = {"val": "coco_karpathy_val.json", "test": "coco_karpathy_test.json"}

        download_url(urls[split], ann_root)

        self.annotation = json.load(open(os.path.join(ann_root, filenames[split]), "r"))
        self.transform = transform
        self.image_root = image_root

    def __len__(self):
        return len(self.annotation)

    def __getitem__(self, index):
        ann = self.annotation[index]

        image_path = os.path.join(self.image_root, ann["image"])
        image = Image.open(image_path).convert("RGB")
        image = self.transform(image)

        img_id = ann["image"].split("/")[-1].strip(".jpg").split("_")[-1]

        return image, int(img_id)


class coco_karpathy_retrieval_eval(Dataset):
    def __init__(self, transform, image_root, ann_root, split, max_words=30):
        """
        image_root (string): Root directory of images (e.g. coco/images/)
        ann_root (string): directory to store the annotation file
        split (string): val or test
        """
        urls = {
            "val": "https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json",
            "test": "https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json",
        }
        filenames = {"val": "coco_karpathy_val.json", "test": "coco_karpathy_test.json"}

        download_url(urls[split], ann_root)

        self.annotation = json.load(open(os.path.join(ann_root, filenames[split]), "r"))
        self.transform = transform
        self.image_root = image_root

        self.text = []
        self.image = []
        self.txt2img = {}
        self.img2txt = {}

        txt_id = 0
        for img_id, ann in enumerate(self.annotation):
            self.image.append(ann["image"])
            self.img2txt[img_id] = []
            for i, caption in enumerate(ann["caption"]):
                self.text.append(pre_caption(caption, max_words))
                self.img2txt[img_id].append(txt_id)
                self.txt2img[txt_id] = img_id
                txt_id += 1

    def __len__(self):
        return len(self.annotation)

    def __getitem__(self, index):
        image_path = os.path.join(self.image_root, self.annotation[index]["image"])
        image = Image.open(image_path).convert("RGB")
        image = self.transform(image)

        return image, index


================================================
FILE: ldm/data/flickr30k_dataset.py
================================================
import os
import json

from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url

from PIL import Image

from data.utils import pre_caption


class flickr30k_train(Dataset):
    def __init__(self, transform, image_root, ann_root, max_words=30, prompt=""):
        """
        image_root (string): Root directory of images (e.g. flickr30k/)
        ann_root (string): directory to store the annotation file
        """
        url = "https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_train.json"
        filename = "flickr30k_train.json"

        download_url(url, ann_root)

        self.annotation = json.load(open(os.path.join(ann_root, filename), "r"))
        self.transform = transform
        self.image_root = image_root
        self.max_words = max_words
        self.prompt = prompt

        self.img_ids = {}
        n = 0
        for ann in self.annotation:
            img_id = ann["image_id"]
            if img_id not in self.img_ids.keys():
                self.img_ids[img_id] = n
                n += 1

    def __len__(self):
        return len(self.annotation)

    def __getitem__(self, index):
        ann = self.annotation[index]

        image_path = os.path.join(self.image_root, ann["image"])
        image = Image.open(image_path).convert("RGB")
        image = self.transform(image)

        caption = self.prompt + pre_caption(ann["caption"], self.max_words)

        return image, caption, self.img_ids[ann["image_id"]]


class flickr30k_retrieval_eval(Dataset):
    def __init__(self, transform, image_root, ann_root, split, max_words=30):
        """
        image_root (string): Root directory of images (e.g. flickr30k/)
        ann_root (string): directory to store the annotation file
        split (string): val or test
        """
        urls = {
            "val": "https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_val.json",
            "test": "https://storage.googleapis.com/sfr-vision-language-research/datasets/flickr30k_test.json",
        }
        filenames = {"val": "flickr30k_val.json", "test": "flickr30k_test.json"}

        download_url(urls[split], ann_root)

        self.annotation = json.load(open(os.path.join(ann_root, filenames[split]), "r"))
        self.transform = transform
        self.image_root = image_root

        self.text = []
        self.image = []
        self.txt2img = {}
        self.img2txt = {}

        txt_id = 0
        for img_id, ann in enumerate(self.annotation):
            self.image.append(ann["image"])
            self.img2txt[img_id] = []
            for i, caption in enumerate(ann["caption"]):
                self.text.append(pre_caption(caption, max_words))
                self.img2txt[img_id].append(txt_id)
                self.txt2img[txt_id] = img_id
                txt_id += 1

    def __len__(self):
        return len(self.annotation)

    def __getitem__(self, index):
        image_path = os.path.join(self.image_root, self.annotation[index]["image"])
        image = Image.open(image_path).convert("RGB")
        image = self.transform(image)

        return image, index


================================================
FILE: ldm/data/imagenet.py
================================================
import os, yaml, pickle, shutil, tarfile, glob
import cv2
import albumentations
import PIL
import numpy as np
import torchvision.transforms.functional as TF
from omegaconf import OmegaConf
from functools import partial
from PIL import Image
from tqdm import tqdm
from torch.utils.data import Dataset, Subset

import taming.data.utils as tdu
from taming.data.imagenet import (
    str_to_indices,
    give_synsets_from_indices,
    download,
    retrieve,
)
from taming.data.imagenet import ImagePaths

from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light


def synset2idx(path_to_yaml="data/index_synset.yaml"):
    with open(path_to_yaml) as f:
        di2s = yaml.load(f)
    return dict((v, k) for k, v in di2s.items())


class ImageNetBase(Dataset):
    def __init__(self, config=None):
        self.config = config or OmegaConf.create()
        if not type(self.config) == dict:
            self.config = OmegaConf.to_container(self.config)
        self.keep_orig_class_label = self.config.get("keep_orig_class_label", False)
        self.process_images = True  # if False we skip loading & processing images and self.data contains filepaths
        self._prepare()
        self._prepare_synset_to_human()
        self._prepare_idx_to_synset()
        self._prepare_human_to_integer_label()
        self._load()

    def __len__(self):
        return len(self.data)

    def __getitem__(self, i):
        return self.data[i]

    def _prepare(self):
        raise NotImplementedError()

    def _filter_relpaths(self, relpaths):
        ignore = set(
            [
                "n06596364_9591.JPEG",
            ]
        )
        relpaths = [rpath for rpath in relpaths if rpath.split("/")[-1] not in ignore]
        if "sub_indices" in self.config:
            indices = str_to_indices(self.config["sub_indices"])
            synsets = give_synsets_from_indices(
                indices, path_to_yaml=self.idx2syn
            )  # returns a list of strings
            self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)
            files = []
            for rpath in relpaths:
                syn = rpath.split("/")[0]
                if syn in synsets:
                    files.append(rpath)
            return files
        else:
            return relpaths

    def _prepare_synset_to_human(self):
        SIZE = 2655750
        URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
        self.human_dict = os.path.join(self.root, "synset_human.txt")
        if (
            not os.path.exists(self.human_dict)
            or not os.path.getsize(self.human_dict) == SIZE
        ):
            download(URL, self.human_dict)

    def _prepare_idx_to_synset(self):
        URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
        self.idx2syn = os.path.join(self.root, "index_synset.yaml")
        if not os.path.exists(self.idx2syn):
            download(URL, self.idx2syn)

    def _prepare_human_to_integer_label(self):
        URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
        self.human2integer = os.path.join(
            self.root, "imagenet1000_clsidx_to_labels.txt"
        )
        if not os.path.exists(self.human2integer):
            download(URL, self.human2integer)
        with open(self.human2integer, "r") as f:
            lines = f.read().splitlines()
            assert len(lines) == 1000
            self.human2integer_dict = dict()
            for line in lines:
                value, key = line.split(":")
                self.human2integer_dict[key] = int(value)

    def _load(self):
        with open(self.txt_filelist, "r") as f:
            self.relpaths = f.read().splitlines()
            l1 = len(self.relpaths)
            self.relpaths = self._filter_relpaths(self.relpaths)
            print(
                "Removed {} files from filelist during filtering.".format(
                    l1 - len(self.relpaths)
                )
            )

        self.synsets = [p.split("/")[0] for p in self.relpaths]
        self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]

        unique_synsets = np.unique(self.synsets)
        class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
        if not self.keep_orig_class_label:
            self.class_labels = [class_dict[s] for s in self.synsets]
        else:
            self.class_labels = [self.synset2idx[s] for s in self.synsets]

        with open(self.human_dict, "r") as f:
            human_dict = f.read().splitlines()
            human_dict = dict(line.split(maxsplit=1) for line in human_dict)

        self.human_labels = [human_dict[s] for s in self.synsets]

        labels = {
            "relpath": np.array(self.relpaths),
            "synsets": np.array(self.synsets),
            "class_label": np.array(self.class_labels),
            "human_label": np.array(self.human_labels),
        }

        if self.process_images:
            self.size = retrieve(self.config, "size", default=256)
            self.data = ImagePaths(
                self.abspaths,
                labels=labels,
                size=self.size,
                random_crop=self.random_crop,
            )
        else:
            self.data = self.abspaths


class ImageNetTrain(ImageNetBase):
    NAME = "ILSVRC2012_train"
    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
    AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
    FILES = [
        "ILSVRC2012_img_train.tar",
    ]
    SIZES = [
        147897477120,
    ]

    def __init__(self, process_images=True, data_root=None, **kwargs):
        self.process_images = process_images
        self.data_root = data_root
        super().__init__(**kwargs)

    def _prepare(self):
        if self.data_root:
            self.root = os.path.join(self.data_root, self.NAME)
        else:
            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)

        self.datadir = os.path.join(self.root, "data")
        self.txt_filelist = os.path.join(self.root, "filelist.txt")
        self.expected_length = 1281167
        self.random_crop = retrieve(
            self.config, "ImageNetTrain/random_crop", default=True
        )
        if not tdu.is_prepared(self.root):
            # prep
            print("Preparing dataset {} in {}".format(self.NAME, self.root))

            datadir = self.datadir
            if not os.path.exists(datadir):
                path = os.path.join(self.root, self.FILES[0])
                if (
                    not os.path.exists(path)
                    or not os.path.getsize(path) == self.SIZES[0]
                ):
                    import academictorrents as at

                    atpath = at.get(self.AT_HASH, datastore=self.root)
                    assert atpath == path

                print("Extracting {} to {}".format(path, datadir))
                os.makedirs(datadir, exist_ok=True)
                with tarfile.open(path, "r:") as tar:
                    tar.extractall(path=datadir)

                print("Extracting sub-tars.")
                subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
                for subpath in tqdm(subpaths):
                    subdir = subpath[: -len(".tar")]
                    os.makedirs(subdir, exist_ok=True)
                    with tarfile.open(subpath, "r:") as tar:
                        tar.extractall(path=subdir)

            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
            filelist = sorted(filelist)
            filelist = "\n".join(filelist) + "\n"
            with open(self.txt_filelist, "w") as f:
                f.write(filelist)

            tdu.mark_prepared(self.root)


class ImageNetValidation(ImageNetBase):
    NAME = "ILSVRC2012_validation"
    URL = "http://www.image-net.org/challenges/LSVRC/2012/"
    AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
    VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
    FILES = [
        "ILSVRC2012_img_val.tar",
        "validation_synset.txt",
    ]
    SIZES = [
        6744924160,
        1950000,
    ]

    def __init__(self, process_images=True, data_root=None, **kwargs):
        self.data_root = data_root
        self.process_images = process_images
        super().__init__(**kwargs)

    def _prepare(self):
        if self.data_root:
            self.root = os.path.join(self.data_root, self.NAME)
        else:
            cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
            self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
        self.datadir = os.path.join(self.root, "data")
        self.txt_filelist = os.path.join(self.root, "filelist.txt")
        self.expected_length = 50000
        self.random_crop = retrieve(
            self.config, "ImageNetValidation/random_crop", default=False
        )
        if not tdu.is_prepared(self.root):
            # prep
            print("Preparing dataset {} in {}".format(self.NAME, self.root))

            datadir = self.datadir
            if not os.path.exists(datadir):
                path = os.path.join(self.root, self.FILES[0])
                if (
                    not os.path.exists(path)
                    or not os.path.getsize(path) == self.SIZES[0]
                ):
                    import academictorrents as at

                    atpath = at.get(self.AT_HASH, datastore=self.root)
                    assert atpath == path

                print("Extracting {} to {}".format(path, datadir))
                os.makedirs(datadir, exist_ok=True)
                with tarfile.open(path, "r:") as tar:
                    tar.extractall(path=datadir)

                vspath = os.path.join(self.root, self.FILES[1])
                if (
                    not os.path.exists(vspath)
                    or not os.path.getsize(vspath) == self.SIZES[1]
                ):
                    download(self.VS_URL, vspath)

                with open(vspath, "r") as f:
                    synset_dict = f.read().splitlines()
                    synset_dict = dict(line.split() for line in synset_dict)

                print("Reorganizing into synset folders")
                synsets = np.unique(list(synset_dict.values()))
                for s in synsets:
                    os.makedirs(os.path.join(datadir, s), exist_ok=True)
                for k, v in synset_dict.items():
                    src = os.path.join(datadir, k)
                    dst = os.path.join(datadir, v)
                    shutil.move(src, dst)

            filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
            filelist = [os.path.relpath(p, start=datadir) for p in filelist]
            filelist = sorted(filelist)
            filelist = "\n".join(filelist) + "\n"
            with open(self.txt_filelist, "w") as f:
                f.write(filelist)

            tdu.mark_prepared(self.root)


class ImageNetSR(Dataset):
    def __init__(
        self,
        size=None,
        degradation=None,
        downscale_f=4,
        min_crop_f=0.5,
        max_crop_f=1.0,
        random_crop=True,
    ):
        """
        Imagenet Superresolution Dataloader
        Performs following ops in order:
        1.  crops a crop of size s from image either as random or center crop
        2.  resizes crop to size with cv2.area_interpolation
        3.  degrades resized crop with degradation_fn

        :param size: resizing to size after cropping
        :param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light
        :param downscale_f: Low Resolution Downsample factor
        :param min_crop_f: determines crop size s,
          where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f)
        :param max_crop_f: ""
        :param data_root:
        :param random_crop:
        """
        self.base = self.get_base()
        assert size
        assert (size / downscale_f).is_integer()
        self.size = size
        self.LR_size = int(size / downscale_f)
        self.min_crop_f = min_crop_f
        self.max_crop_f = max_crop_f
        assert max_crop_f <= 1.0
        self.center_crop = not random_crop

        self.image_rescaler = albumentations.SmallestMaxSize(
            max_size=size, interpolation=cv2.INTER_AREA
        )

        self.pil_interpolation = (
            False  # gets reset later if incase interp_op is from pillow
        )

        if degradation == "bsrgan":
            self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)

        elif degradation == "bsrgan_light":
            self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)

        else:
            interpolation_fn = {
                "cv_nearest": cv2.INTER_NEAREST,
                "cv_bilinear": cv2.INTER_LINEAR,
                "cv_bicubic": cv2.INTER_CUBIC,
                "cv_area": cv2.INTER_AREA,
                "cv_lanczos": cv2.INTER_LANCZOS4,
                "pil_nearest": PIL.Image.NEAREST,
                "pil_bilinear": PIL.Image.BILINEAR,
                "pil_bicubic": PIL.Image.BICUBIC,
                "pil_box": PIL.Image.BOX,
                "pil_hamming": PIL.Image.HAMMING,
                "pil_lanczos": PIL.Image.LANCZOS,
            }[degradation]

            self.pil_interpolation = degradation.startswith("pil_")

            if self.pil_interpolation:
                self.degradation_process = partial(
                    TF.resize, size=self.LR_size, interpolation=interpolation_fn
                )

            else:
                self.degradation_process = albumentations.SmallestMaxSize(
                    max_size=self.LR_size, interpolation=interpolation_fn
                )

    def __len__(self):
        return len(self.base)

    def __getitem__(self, i):
        example = self.base[i]
        image = Image.open(example["file_path_"])

        if not image.mode == "RGB":
            image = image.convert("RGB")

        image = np.array(image).astype(np.uint8)

        min_side_len = min(image.shape[:2])
        crop_side_len = min_side_len * np.random.uniform(
            self.min_crop_f, self.max_crop_f, size=None
        )
        crop_side_len = int(crop_side_len)

        if self.center_crop:
            self.cropper = albumentations.CenterCrop(
                height=crop_side_len, width=crop_side_len
            )

        else:
            self.cropper = albumentations.RandomCrop(
                height=crop_side_len, width=crop_side_len
            )

        image = self.cropper(image=image)["image"]
        image = self.image_rescaler(image=image)["image"]

        if self.pil_interpolation:
            image_pil = PIL.Image.fromarray(image)
            LR_image = self.degradation_process(image_pil)
            LR_image = np.array(LR_image).astype(np.uint8)

        else:
            LR_image = self.degradation_process(image=image)["image"]

        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
        example["LR_image"] = (LR_image / 127.5 - 1.0).astype(np.float32)

        return example


class ImageNetSRTrain(ImageNetSR):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def get_base(self):
        with open("data/imagenet_train_hr_indices.p", "rb") as f:
            indices = pickle.load(f)
        dset = ImageNetTrain(
            process_images=False,
        )
        return Subset(dset, indices)


class ImageNetSRValidation(ImageNetSR):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def get_base(self):
        with open("data/imagenet_val_hr_indices.p", "rb") as f:
            indices = pickle.load(f)
        dset = ImageNetValidation(
            process_images=False,
        )
        return Subset(dset, indices)


================================================
FILE: ldm/data/lsun.py
================================================
import os
import numpy as np
import PIL
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms


class LSUNBase(Dataset):
    def __init__(
        self, txt_file, data_root, size=None, interpolation="bicubic", flip_p=0.5
    ):
        self.data_paths = txt_file
        self.data_root = data_root
        with open(self.data_paths, "r") as f:
            self.image_paths = f.read().splitlines()
        self._length = len(self.image_paths)
        self.labels = {
            "relative_file_path_": [l for l in self.image_paths],
            "file_path_": [os.path.join(self.data_root, l) for l in self.image_paths],
        }

        self.size = size
        self.interpolation = {
            "linear": PIL.Image.LINEAR,
            "bilinear": PIL.Image.BILINEAR,
            "bicubic": PIL.Image.BICUBIC,
            "lanczos": PIL.Image.LANCZOS,
        }[interpolation]
        self.flip = transforms.RandomHorizontalFlip(p=flip_p)

    def __len__(self):
        return self._length

    def __getitem__(self, i):
        example = dict((k, self.labels[k][i]) for k in self.labels)
        image = Image.open(example["file_path_"])
        if not image.mode == "RGB":
            image = image.convert("RGB")

        # default to score-sde preprocessing
        img = np.array(image).astype(np.uint8)
        crop = min(img.shape[0], img.shape[1])
        (
            h,
            w,
        ) = (
            img.shape[0],
            img.shape[1],
        )
        img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]

        image = Image.fromarray(img)
        if self.size is not None:
            image = image.resize((self.size, self.size), resample=self.interpolation)

        image = self.flip(image)
        image = np.array(image).astype(np.uint8)
        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
        return example


class LSUNChurchesTrain(LSUNBase):
    def __init__(self, **kwargs):
        super().__init__(
            txt_file="data/lsun/church_outdoor_train.txt",
            data_root="data/lsun/churches",
            **kwargs
        )


class LSUNChurchesValidation(LSUNBase):
    def __init__(self, flip_p=0.0, **kwargs):
        super().__init__(
            txt_file="data/lsun/church_outdoor_val.txt",
            data_root="data/lsun/churches",
            flip_p=flip_p,
            **kwargs
        )


class LSUNBedroomsTrain(LSUNBase):
    def __init__(self, **kwargs):
        super().__init__(
            txt_file="data/lsun/bedrooms_train.txt",
            data_root="data/lsun/bedrooms",
            **kwargs
        )


class LSUNBedroomsValidation(LSUNBase):
    def __init__(self, flip_p=0.0, **kwargs):
        super().__init__(
            txt_file="data/lsun/bedrooms_val.txt",
            data_root="data/lsun/bedrooms",
            flip_p=flip_p,
            **kwargs
        )


class LSUNCatsTrain(LSUNBase):
    def __init__(self, **kwargs):
        super().__init__(
            txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs
        )


class LSUNCatsValidation(LSUNBase):
    def __init__(self, flip_p=0.0, **kwargs):
        super().__init__(
            txt_file="data/lsun/cat_val.txt",
            data_root="data/lsun/cats",
            flip_p=flip_p,
            **kwargs
        )


================================================
FILE: ldm/data/nlvr_dataset.py
================================================
import os
import json
import random

from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url

from PIL import Image

from data.utils import pre_caption


class nlvr_dataset(Dataset):
    def __init__(self, transform, image_root, ann_root, split):
        """
        image_root (string): Root directory of images
        ann_root (string): directory to store the annotation file
        split (string): train, val or test
        """
        urls = {
            "train": "https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_train.json",
            "val": "https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_dev.json",
            "test": "https://storage.googleapis.com/sfr-vision-language-research/datasets/nlvr_test.json",
        }
        filenames = {
            "train": "nlvr_train.json",
            "val": "nlvr_dev.json",
            "test": "nlvr_test.json",
        }

        download_url(urls[split], ann_root)
        self.annotation = json.load(open(os.path.join(ann_root, filenames[split]), "r"))

        self.transform = transform
        self.image_root = image_root

    def __len__(self):
        return len(self.annotation)

    def __getitem__(self, index):
        ann = self.annotation[index]

        image0_path = os.path.join(self.image_root, ann["images"][0])
        image0 = Image.open(image0_path).convert("RGB")
        image0 = self.transform(image0)

        image1_path = os.path.join(self.image_root, ann["images"][1])
        image1 = Image.open(image1_path).convert("RGB")
        image1 = self.transform(image1)

        sentence = pre_caption(ann["sentence"], 40)

        if ann["label"] == "True":
            label = 1
        else:
            label = 0

        words = sentence.split(" ")

        if "left" not in words and "right" not in words:
            if random.random() < 0.5:
                return image0, image1, sentence, label
            else:
                return image1, image0, sentence, label
        else:
            if random.random() < 0.5:
                return image0, image1, sentence, label
            else:
                new_words = []
                for word in words:
                    if word == "left":
                        new_words.append("right")
                    elif word == "right":
                        new_words.append("left")
                    else:
                        new_words.append(word)

                sentence = " ".join(new_words)
                return image1, image0, sentence, label


================================================
FILE: ldm/data/nocaps_dataset.py
================================================
import os
import json

from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url

from PIL import Image


class nocaps_eval(Dataset):
    def __init__(self, transform, image_root, ann_root, split):
        urls = {
            "val": "https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_val.json",
            "test": "https://storage.googleapis.com/sfr-vision-language-research/datasets/nocaps_test.json",
        }
        filenames = {"val": "nocaps_val.json", "test": "nocaps_test.json"}

        download_url(urls[split], ann_root)

        self.annotation = json.load(open(os.path.join(ann_root, filenames[split]), "r"))
        self.transform = transform
        self.image_root = image_root

    def __len__(self):
        return len(self.annotation)

    def __getitem__(self, index):
        ann = self.annotation[index]

        image_path = os.path.join(self.image_root, ann["image"])
        image = Image.open(image_path).convert("RGB")
        image = self.transform(image)

        return image, int(ann["img_id"])


================================================
FILE: ldm/data/personalized.py
================================================
import os
import numpy as np
import PIL
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms

import random

imagenet_templates_smallest = [
    "a photo of a {}",
]

imagenet_templates_small = [
    "a photo of a {}",
    "a rendering of a {}",
    "a cropped photo of the {}",
    "the photo of a {}",
    "a photo of a clean {}",
    "a photo of a dirty {}",
    "a dark photo of the {}",
    "a photo of my {}",
    "a photo of the cool {}",
    "a close-up photo of a {}",
    "a bright photo of the {}",
    "a cropped photo of a {}",
    "a photo of the {}",
    "a good photo of the {}",
    "a photo of one {}",
    "a close-up photo of the {}",
    "a rendition of the {}",
    "a photo of the clean {}",
    "a rendition of a {}",
    "a photo of a nice {}",
    "a good photo of a {}",
    "a photo of the nice {}",
    "a photo of the small {}",
    "a photo of the weird {}",
    "a photo of the large {}",
    "a photo of a cool {}",
    "a photo of a small {}",
]

imagenet_dual_templates_small = [
    "a photo of a {} with {}",
    "a rendering of a {} with {}",
    "a cropped photo of the {} with {}",
    "the photo of a {} with {}",
    "a photo of a clean {} with {}",
    "a photo of a dirty {} with {}",
    "a dark photo of the {} with {}",
    "a photo of my {} with {}",
    "a photo of the cool {} with {}",
    "a close-up photo of a {} with {}",
    "a bright photo of the {} with {}",
    "a cropped photo of a {} with {}",
    "a photo of the {} with {}",
    "a good photo of the {} with {}",
    "a photo of one {} with {}",
    "a close-up photo of the {} with {}",
    "a rendition of the {} with {}",
    "a photo of the clean {} with {}",
    "a rendition of a {} with {}",
    "a photo of a nice {} with {}",
    "a good photo of a {} with {}",
    "a photo of the nice {} with {}",
    "a photo of the small {} with {}",
    "a photo of the weird {} with {}",
    "a photo of the large {} with {}",
    "a photo of a cool {} with {}",
    "a photo of a small {} with {}",
]

per_img_token_list = [
    "א",
    "ב",
    "ג",
    "ד",
    "ה",
    "ו",
    "ז",
    "ח",
    "ט",
    "י",
    "כ",
    "ל",
    "מ",
    "נ",
    "ס",
    "ע",
    "פ",
    "צ",
    "ק",
    "ר",
    "ש",
    "ת",
]


class PersonalizedBase(Dataset):
    def __init__(
        self,
        data_root,
        size=None,
        repeats=100,
        interpolation="bicubic",
        flip_p=0.5,
        set="train",
        placeholder_token="*",
        per_image_tokens=False,
        center_crop=False,
        mixing_prob=0.25,
        coarse_class_text=None,
    ):
        self.data_root = data_root

        self.image_paths = [
            os.path.join(self.data_root, file_path)
            for file_path in os.listdir(self.data_root)
        ]

        # self._length = len(self.image_paths)
        self.num_images = len(self.image_paths)
        self._length = self.num_images

        self.placeholder_token = placeholder_token

        self.per_image_tokens = per_image_tokens
        self.center_crop = center_crop
        self.mixing_prob = mixing_prob

        self.coarse_class_text = coarse_class_text

        if per_image_tokens:
            assert self.num_images < len(
                per_img_token_list
            ), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."

        if set == "train":
            self._length = self.num_images * repeats

        self.size = size
        self.interpolation = {
            "linear": PIL.Image.LINEAR,
            "bilinear": PIL.Image.BILINEAR,
            "bicubic": PIL.Image.BICUBIC,
            "lanczos": PIL.Image.LANCZOS,
        }[interpolation]
        self.flip = transforms.RandomHorizontalFlip(p=flip_p)

    def __len__(self):
        return self._length

    def __getitem__(self, i):
        example = {}
        image = Image.open(self.image_paths[i % self.num_images])

        if not image.mode == "RGB":
            image = image.convert("RGB")

        placeholder_string = self.placeholder_token
        if self.coarse_class_text:
            placeholder_string = f"{self.coarse_class_text} {placeholder_string}"

        if self.per_image_tokens and np.random.uniform() < self.mixing_prob:
            text = random.choice(imagenet_dual_templates_small).format(
                placeholder_string, per_img_token_list[i % self.num_images]
            )
        else:
            text = random.choice(imagenet_templates_small).format(placeholder_string)

        example["caption"] = text

        # default to score-sde preprocessing
        img = np.array(image).astype(np.uint8)

        if self.center_crop:
            crop = min(img.shape[0], img.shape[1])
            (
                h,
                w,
            ) = (
                img.shape[0],
                img.shape[1],
            )
            img = img[
                (h - crop) // 2 : (h + crop) // 2,
                (w - crop) // 2 : (w + crop) // 2,
            ]

        image = Image.fromarray(img)
        if self.size is not None:
            image = image.resize((self.size, self.size), resample=self.interpolation)

        image = self.flip(image)
        image = np.array(image).astype(np.uint8)
        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
        return example


================================================
FILE: ldm/data/personalized_file.py
================================================
import os
import numpy as np
import PIL
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms

import random

imagenet_templates_small = [
    "a painting in the style of {}",
    "a rendering in the style of {}",
    "a cropped painting in the style of {}",
    "the painting in the style of {}",
    "a clean painting in the style of {}",
    "a dirty painting in the style of {}",
    "a dark painting in the style of {}",
    "a picture in the style of {}",
    "a cool painting in the style of {}",
    "a close-up painting in the style of {}",
    "a bright painting in the style of {}",
    "a cropped painting in the style of {}",
    "a good painting in the style of {}",
    "a close-up painting in the style of {}",
    "a rendition in the style of {}",
    "a nice painting in the style of {}",
    "a small painting in the style of {}",
    "a weird painting in the style of {}",
    "a large painting in the style of {}",
]

imagenet_dual_templates_small = [
    "a painting in the style of {} with {}",
    "a rendering in the style of {} with {}",
    "a cropped painting in the style of {} with {}",
    "the painting in the style of {} with {}",
    "a clean painting in the style of {} with {}",
    "a dirty painting in the style of {} with {}",
    "a dark painting in the style of {} with {}",
    "a cool painting in the style of {} with {}",
    "a close-up painting in the style of {} with {}",
    "a bright painting in the style of {} with {}",
    "a cropped painting in the style of {} with {}",
    "a good painting in the style of {} with {}",
    "a painting of one {} in the style of {}",
    "a nice painting in the style of {} with {}",
    "a small painting in the style of {} with {}",
    "a weird painting in the style of {} with {}",
    "a large painting in the style of {} with {}",
]

per_img_token_list = [
    "א",
    "ב",
    "ג",
    "ד",
    "ה",
    "ו",
    "ז",
    "ח",
    "ט",
    "י",
    "כ",
    "ל",
    "מ",
    "נ",
    "ס",
    "ע",
    "פ",
    "צ",
    "ק",
    "ר",
    "ש",
    "ת",
]


class PersonalizedBase(Dataset):
    def __init__(
        self,
        data_root,
        size=None,
        repeats=100,
        interpolation="bicubic",
        flip_p=0.5,
        set="train",
        placeholder_token="*",
        per_image_tokens=False,
        center_crop=False,
    ):
        self.data_root = data_root

        self.image_paths = [
            os.path.join(self.data_root, file_path)
            for file_path in os.listdir(self.data_root)
        ]

        # self._length = len(self.image_paths)
        self.num_images = len(self.image_paths)
        self._length = self.num_images

        self.placeholder_token = placeholder_token

        self.per_image_tokens = per_image_tokens
        self.center_crop = center_crop

        if per_image_tokens:
            assert self.num_images < len(
                per_img_token_list
            ), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."

        if set == "train":
            self._length = self.num_images * repeats

        self.size = size
        self.interpolation = {
            "linear": PIL.Image.LINEAR,
            "bilinear": PIL.Image.BILINEAR,
            "bicubic": PIL.Image.BICUBIC,
            "lanczos": PIL.Image.LANCZOS,
        }[interpolation]
        self.flip = transforms.RandomHorizontalFlip(p=flip_p)

    def __len__(self):
        return self._length

    def __getitem__(self, i):
        example = {}
        image = Image.open(self.image_paths[i % self.num_images])

        if not image.mode == "RGB":
            image = image.convert("RGB")

        if self.per_image_tokens and np.random.uniform() < 0.25:
            text = random.choice(imagenet_dual_templates_small).format(
                self.placeholder_token, per_img_token_list[i % self.num_images]
            )
        else:
            text = random.choice(imagenet_templates_small).format(
                self.placeholder_token
            )

        example["caption"] = text

        # default to score-sde preprocessing
        img = np.array(image).astype(np.uint8)

        if self.center_crop:
            crop = min(img.shape[0], img.shape[1])
            (
                h,
                w,
            ) = (
                img.shape[0],
                img.shape[1],
            )
            img = img[
                (h - crop) // 2 : (h + crop) // 2,
                (w - crop) // 2 : (w + crop) // 2,
            ]

        image = Image.fromarray(img)
        if self.size is not None:
            image = image.resize((self.size, self.size), resample=self.interpolation)

        image = self.flip(image)
        image = np.array(image).astype(np.uint8)
        example["image"] = (image / 127.5 - 1.0).astype(np.float32)
        return example


================================================
FILE: ldm/data/pretrain_dataset.py
================================================
import json
import os

from torch.utils.data import Dataset

from PIL import Image
from PIL import ImageFile

ImageFile.LOAD_TRUNCATED_IMAGES = True
Image.MAX_IMAGE_PIXELS = None

from data.utils import pre_caption
import os, glob


class pretrain_dataset(Dataset):
    def __init__(self, ann_file, laion_path, transform):
        self.ann_pretrain = []
        for f in ann_file:
            print("loading " + f)
            ann = json.load(open(f, "r"))
            self.ann_pretrain += ann

        self.laion_path = laion_path
        if self.laion_path:
            self.laion_files = glob.glob(os.path.join(laion_path, "*.json"))

            print("loading " + self.laion_files[0])
            with open(self.laion_files[0], "r") as f:
                self.ann_laion = json.load(f)

            self.annotation = self.ann_pretrain + self.ann_laion
        else:
            self.annotation = self.ann_pretrain

        self.transform = transform

    def reload_laion(self, epoch):
        n = epoch % len(self.laion_files)
        print("loading " + self.laion_files[n])
        with open(self.laion_files[n], "r") as f:
            self.ann_laion = json.load(f)

        self.annotation = self.ann_pretrain + self.ann_laion

    def __len__(self):
        return len(self.annotation)

    def __getitem__(self, index):
        ann = self.annotation[index]

        image = Image.open(ann["image"]).convert("RGB")
        image = self.transform(image)
        caption = pre_caption(ann["caption"], 30)

        return image, caption


================================================
FILE: ldm/data/util.py
================================================
import torch

from ldm.modules.midas.api import load_midas_transform


class AddMiDaS(object):
    def __init__(self, model_type):
        super().__init__()
        self.transform = load_midas_transform(model_type)

    def pt2np(self, x):
        x = ((x + 1.0) * 0.5).detach().cpu().numpy()
        return x

    def np2pt(self, x):
        x = torch.from_numpy(x) * 2 - 1.0
        return x

    def __call__(self, sample):
        # sample['jpg'] is tensor hwc in [-1, 1] at this point
        x = self.pt2np(sample["jpg"])
        x = self.transform({"image": x})["image"]
        sample["midas_in"] = x
        return sample


================================================
FILE: ldm/data/video_dataset.py
================================================
from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url

import torch
import numpy as np
import random
import decord
from decord import VideoReader
import json
import os
from data.utils import pre_caption

decord.bridge.set_bridge("torch")


class ImageNorm(object):
    """Apply Normalization to Image Pixels on GPU"""

    def __init__(self, mean, std):
        self.mean = torch.tensor(mean).view(1, 3, 1, 1)
        self.std = torch.tensor(std).view(1, 3, 1, 1)

    def __call__(self, img):
        if torch.max(img) > 1 and self.mean.max() <= 1:
            img.div_(255.0)
        return img.sub_(self.mean).div_(self.std)


def load_jsonl(filename):
    with open(filename, "r") as f:
        return [json.loads(l.strip("\n")) for l in f.readlines()]


class VideoDataset(Dataset):
    def __init__(
        self,
        video_root,
        ann_root,
        num_frm=4,
        frm_sampling_strategy="rand",
        max_img_size=384,
        video_fmt=".mp4",
    ):
        """
        image_root (string): Root directory of video
        ann_root (string): directory to store the annotation file
        """
        url = "https://storage.googleapis.com/sfr-vision-language-research/datasets/msrvtt_test.jsonl"
        filename = "msrvtt_test.jsonl"

        download_url(url, ann_root)
        self.annotation = load_jsonl(os.path.join(ann_root, filename))

        self.num_frm = num_frm
        self.frm_sampling_strategy = frm_sampling_strategy
        self.max_img_size = max_img_size
        self.video_root = video_root
        self.video_fmt = video_fmt
        self.img_norm = ImageNorm(
            mean=(0.48145466, 0.4578275, 0.40821073),
            std=(0.26862954, 0.26130258, 0.27577711),
        )

        self.text = [pre_caption(ann["caption"], 40) for ann in self.annotation]
        self.txt2video = [i for i in range(len(self.annotation))]
        self.video2txt = self.txt2video

    def __len__(self):
        return len(self.annotation)

    def __getitem__(self, index):
        ann = self.annotation[index]

        video_path = os.path.join(self.video_root, ann["clip_name"] + self.video_fmt)

        vid_frm_array = self._load_video_from_path_decord(
            video_path, height=self.max_img_size, width=self.max_img_size
        )

        video = self.img_norm(vid_frm_array.float())

        return video, ann["clip_name"]

    def _load_video_from_path_decord(
        self,
        video_path,
        height=None,
        width=None,
        start_time=None,
        end_time=None,
        fps=-1,
    ):
        try:
            if not height or not width:
                vr = VideoReader(video_path)
            else:
                vr = VideoReader(video_path, width=width, height=height)

            vlen = len(vr)

            if start_time or end_time:
                assert (
                    fps > 0
                ), "must provide video fps if specifying start and end time."

                start_idx = min(int(start_time * fps), vlen)
                end_idx = min(int(end_time * fps), vlen)
            else:
                start_idx, end_idx = 0, vlen

            if self.frm_sampling_strategy == "uniform":
                frame_indices = np.arange(
                    start_idx, end_idx, vlen / self.num_frm, dtype=int
                )
            elif self.frm_sampling_strategy == "rand":
                frame_indices = sorted(random.sample(range(vlen), self.num_frm))
            elif self.frm_sampling_strategy == "headtail":
                frame_indices_head = sorted(
                    random.sample(range(vlen // 2), self.num_frm // 2)
                )
                frame_indices_tail = sorted(
                    random.sample(range(vlen // 2, vlen), self.num_frm // 2)
                )
                frame_indices = frame_indices_head + frame_indices_tail
            else:
                raise NotImplementedError(
                    "Invalid sampling strategy {} ".format(self.frm_sampling_strategy)
                )

            raw_sample_frms = vr.get_batch(frame_indices)
        except Exception:
            return None

        raw_sample_frms = raw_sample_frms.permute(0, 3, 1, 2)

        return raw_sample_frms


================================================
FILE: ldm/data/vqa_dataset.py
================================================
import os
import json
from PIL import Image

import torch
from torch.utils.data import Dataset
from data.utils import pre_question

from torchvision.datasets.utils import download_url


class vqa_dataset(Dataset):
    def __init__(
        self, transform, ann_root, vqa_root, vg_root, train_files=[], split="train"
    ):
        self.split = split

        self.transform = transform
        self.vqa_root = vqa_root
        self.vg_root = vg_root

        if split == "train":
            urls = {
                "vqa_train": "https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_train.json",
                "vqa_val": "https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_val.json",
                "vg_qa": "https://storage.googleapis.com/sfr-vision-language-research/datasets/vg_qa.json",
            }

            self.annotation = []
            for f in train_files:
                download_url(urls[f], ann_root)
                self.annotation += json.load(
                    open(os.path.join(ann_root, "%s.json" % f), "r")
                )
        else:
            download_url(
                "https://storage.googleapis.com/sfr-vision-language-research/datasets/vqa_test.json",
                ann_root,
            )
            self.annotation = json.load(
                open(os.path.join(ann_root, "vqa_test.json"), "r")
            )

            download_url(
                "https://storage.googleapis.com/sfr-vision-language-research/datasets/answer_list.json",
                ann_root,
            )
            self.answer_list = json.load(
                open(os.path.join(ann_root, "answer_list.json"), "r")
            )

    def __len__(self):
        return len(self.annotation)

    def __getitem__(self, index):
        ann = self.annotation[index]

        if ann["dataset"] == "vqa":
            image_path = os.path.join(self.vqa_root, ann["image"])
        elif ann["dataset"] == "vg":
            image_path = os.path.join(self.vg_root, ann["image"])

        image = Image.open(image_path).convert("RGB")
        image = self.transform(image)

        if self.split == "test":
            question = pre_question(ann["question"])
            question_id = ann["question_id"]
            return image, question, question_id

        elif self.split == "train":
            question = pre_question(ann["question"])

            if ann["dataset"] == "vqa":
                answer_weight = {}
                for answer in ann["answer"]:
                    if answer in answer_weight.keys():
                        answer_weight[answer] += 1 / len(ann["answer"])
                    else:
                        answer_weight[answer] = 1 / len(ann["answer"])

                answers = list(answer_weight.keys())
                weights = list(answer_weight.values())

            elif ann["dataset"] == "vg":
                answers = [ann["answer"]]
                weights = [0.2]

            return image, question, answers, weights


def vqa_collate_fn(batch):
    image_list, question_list, answer_list, weight_list, n = [], [], [], [], []
    for image, question, answer, weights in batch:
        image_list.append(image)
        question_list.append(question)
        weight_list += weights
        answer_list += answer
        n.append(len(answer))
    return (
        torch.stack(image_list, dim=0),
        question_list,
        answer_list,
        torch.Tensor(weight_list),
        n,
    )


================================================
FILE: ldm/devices/__init__.py
================================================


================================================
FILE: ldm/devices/devices.py
================================================
import torch
from torch import autocast
from contextlib import nullcontext


def choose_torch_device() -> str:
    """Convenience routine for guessing which GPU device to run model on"""
    if torch.cuda.is_available():
        return "cuda"
    if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
        return "mps"
    return "cpu"


def choose_autocast_device(device):
    """Returns an autocast compatible device from a torch device"""
    device_type = device.type  # this returns 'mps' on M1
    # autocast only for cuda, but GTX 16xx have issues with it
    if device_type == "cuda":
        device_name = torch.cuda.get_device_name()
        if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name:
            return device_type, nullcontext
        else:
            return device_type, autocast
    else:
        return "cpu", nullcontext


================================================
FILE: ldm/lr_scheduler.py
================================================
import numpy as np


class LambdaWarmUpCosineScheduler:
    """
    note: use with a base_lr of 1.0
    """

    def __init__(
        self,
        warm_up_steps,
        lr_min,
        lr_max,
        lr_start,
        max_decay_steps,
        verbosity_interval=0,
    ):
        self.lr_warm_up_steps = warm_up_steps
        self.lr_start = lr_start
        self.lr_min = lr_min
        self.lr_max = lr_max
        self.lr_max_decay_steps = max_decay_steps
        self.last_lr = 0.0
        self.verbosity_interval = verbosity_interval

    def schedule(self, n, **kwargs):
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0:
                print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
        if n < self.lr_warm_up_steps:
            lr = (
                self.lr_max - self.lr_start
            ) / self.lr_warm_up_steps * n + self.lr_start
            self.last_lr = lr
            return lr
        else:
            t = (n - self.lr_warm_up_steps) / (
                self.lr_max_decay_steps - self.lr_warm_up_steps
            )
            t = min(t, 1.0)
            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
                1 + np.cos(t * np.pi)
            )
            self.last_lr = lr
            return lr

    def __call__(self, n, **kwargs):
        return self.schedule(n, **kwargs)


class LambdaWarmUpCosineScheduler2:
    """
    supports repeated iterations, configurable via lists
    note: use with a base_lr of 1.0.
    """

    def __init__(
        self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0
    ):
        assert (
            len(warm_up_steps)
            == len(f_min)
            == len(f_max)
            == len(f_start)
            == len(cycle_lengths)
        )
        self.lr_warm_up_steps = warm_up_steps
        self.f_start = f_start
        self.f_min = f_min
        self.f_max = f_max
        self.cycle_lengths = cycle_lengths
        self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
        self.last_f = 0.0
        self.verbosity_interval = verbosity_interval

    def find_in_interval(self, n):
        interval = 0
        for cl in self.cum_cycles[1:]:
            if n <= cl:
                return interval
            interval += 1

    def schedule(self, n, **kwargs):
        cycle = self.find_in_interval(n)
        n = n - self.cum_cycles[cycle]
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0:
                print(
                    f"current step: {n}, recent lr-multiplier: {self.last_f}, "
                    f"current cycle {cycle}"
                )
        if n < self.lr_warm_up_steps[cycle]:
            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[
                cycle
            ] * n + self.f_start[cycle]
            self.last_f = f
            return f
        else:
            t = (n - self.lr_warm_up_steps[cycle]) / (
                self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]
            )
            t = min(t, 1.0)
            f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
                1 + np.cos(t * np.pi)
            )
            self.last_f = f
            return f

    def __call__(self, n, **kwargs):
        return self.schedule(n, **kwargs)


class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
    def schedule(self, n, **kwargs):
        cycle = self.find_in_interval(n)
        n = n - self.cum_cycles[cycle]
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0:
                print(
                    f"current step: {n}, recent lr-multiplier: {self.last_f}, "
                    f"current cycle {cycle}"
                )

        if n < self.lr_warm_up_steps[cycle]:
            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[
                cycle
            ] * n + self.f_start[cycle]
            self.last_f = f
            return f
        else:
            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (
                self.cycle_lengths[cycle] - n
            ) / (self.cycle_lengths[cycle])
            self.last_f = f
            return f


================================================
FILE: ldm/models/__init__.py
================================================


================================================
FILE: ldm/models/autoencoder.py
================================================
import torch
import pytorch_lightning as pl
import torch.nn.functional as F
from contextlib import contextmanager

from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer

from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution

from ldm.util import instantiate_from_config


class VQModel(pl.LightningModule):
    def __init__(
        self,
        ddconfig,
        lossconfig,
        n_embed,
        embed_dim,
        ckpt_path=None,
        ignore_keys=[],
        image_key="image",
        colorize_nlabels=None,
        monitor=None,
        batch_resize_range=None,
        scheduler_config=None,
        lr_g_factor=1.0,
        remap=None,
        sane_index_shape=False,  # tell vector quantizer to return indices as bhw
        use_ema=False,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.n_embed = n_embed
        self.image_key = image_key
        self.encoder = Encoder(**ddconfig)
        self.decoder = Decoder(**ddconfig)
        self.loss = instantiate_from_config(lossconfig)
        self.quantize = VectorQuantizer(
            n_embed,
            embed_dim,
            beta=0.25,
            remap=remap,
            sane_index_shape=sane_index_shape,
        )
        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
        if colorize_nlabels is not None:
            assert type(colorize_nlabels) == int
            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
        if monitor is not None:
            self.monitor = monitor
        self.batch_resize_range = batch_resize_range
        if self.batch_resize_range is not None:
            print(
                f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}."
            )

        self.use_ema = use_ema
        if self.use_ema:
            self.model_ema = LitEma(self)
            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")

        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
        self.scheduler_config = scheduler_config
        self.lr_g_factor = lr_g_factor

    @contextmanager
    def ema_scope(self, context=None):
        if self.use_ema:
            self.model_ema.store(self.parameters())
            self.model_ema.copy_to(self)
            if context is not None:
                print(f"{context}: Switched to EMA weights")
        try:
            yield None
        finally:
            if self.use_ema:
                self.model_ema.restore(self.parameters())
                if context is not None:
                    print(f"{context}: Restored training weights")

    def init_from_ckpt(self, path, ignore_keys=list()):
        sd = torch.load(path, map_location="cpu")["state_dict"]
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        missing, unexpected = self.load_state_dict(sd, strict=False)
        print(
            f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
        )
        if len(missing) > 0:
            print(f"Missing Keys: {missing}")
            print(f"Unexpected Keys: {unexpected}")

    def on_train_batch_end(self, *args, **kwargs):
        if self.use_ema:
            self.model_ema(self)

    def encode(self, x):
        h = self.encoder(x)
        h = self.quant_conv(h)
        quant, emb_loss, info = self.quantize(h)
        return quant, emb_loss, info

    def encode_to_prequant(self, x):
        h = self.encoder(x)
        h = self.quant_conv(h)
        return h

    def decode(self, quant):
        quant = self.post_quant_conv(quant)
        dec = self.decoder(quant)
        return dec

    def decode_code(self, code_b):
        quant_b = self.quantize.embed_code(code_b)
        dec = self.decode(quant_b)
        return dec

    def forward(self, input, return_pred_indices=False):
        quant, diff, (_, _, ind) = self.encode(input)
        dec = self.decode(quant)
        if return_pred_indices:
            return dec, diff, ind
        return dec, diff

    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
        if self.batch_resize_range is not None:
            lower_size = self.batch_resize_range[0]
            upper_size = self.batch_resize_range[1]
            if self.global_step <= 4:
                # do the first few batches with max size to avoid later oom
                new_resize = upper_size
            else:
                new_resize = np.random.choice(
                    np.arange(lower_size, upper_size + 16, 16)
                )
            if new_resize != x.shape[2]:
                x = F.interpolate(x, size=new_resize, mode="bicubic")
            x = x.detach()
        return x

    def training_step(self, batch, batch_idx, optimizer_idx):
        # https://github.com/pytorch/pytorch/issues/37142
        # try not to fool the heuristics
        x = self.get_input(batch, self.image_key)
        xrec, qloss, ind = self(x, return_pred_indices=True)

        if optimizer_idx == 0:
            # autoencode
            aeloss, log_dict_ae = self.loss(
                qloss,
                x,
                xrec,
                optimizer_idx,
                self.global_step,
                last_layer=self.get_last_layer(),
                split="train",
                predicted_indices=ind,
            )

            self.log_dict(
                log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True
            )
            return aeloss

        if optimizer_idx == 1:
            # discriminator
            discloss, log_dict_disc = self.loss(
                qloss,
                x,
                xrec,
                optimizer_idx,
                self.global_step,
                last_layer=self.get_last_layer(),
                split="train",
            )
            self.log_dict(
                log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True
            )
            return discloss

    def validation_step(self, batch, batch_idx):
        log_dict = self._validation_step(batch, batch_idx)
        with self.ema_scope():
            self._validation_step(batch, batch_idx, suffix="_ema")
        return log_dict

    def _validation_step(self, batch, batch_idx, suffix=""):
        x = self.get_input(batch, self.image_key)
        xrec, qloss, ind = self(x, return_pred_indices=True)
        aeloss, log_dict_ae = self.loss(
            qloss,
            x,
            xrec,
            0,
            self.global_step,
            last_layer=self.get_last_layer(),
            split="val" + suffix,
            predicted_indices=ind,
        )

        discloss, log_dict_disc = self.loss(
            qloss,
            x,
            xrec,
            1,
            self.global_step,
            last_layer=self.get_last_layer(),
            split="val" + suffix,
            predicted_indices=ind,
        )
        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
        self.log(
            f"val{suffix}/rec_loss",
            rec_loss,
            prog_bar=True,
            logger=True,
            on_step=False,
            on_epoch=True,
            sync_dist=True,
        )
        self.log(
            f"val{suffix}/aeloss",
            aeloss,
            prog_bar=True,
            logger=True,
            on_step=False,
            on_epoch=True,
            sync_dist=True,
        )
        if version.parse(pl.__version__) >= version.parse("1.4.0"):
            del log_dict_ae[f"val{suffix}/rec_loss"]
        self.log_dict(log_dict_ae)
        self.log_dict(log_dict_disc)
        return self.log_dict

    def configure_optimizers(self):
        lr_d = self.learning_rate
        lr_g = self.lr_g_factor * self.learning_rate
        print("lr_d", lr_d)
        print("lr_g", lr_g)
        opt_ae = torch.optim.Adam(
            list(self.encoder.parameters())
            + list(self.decoder.parameters())
            + list(self.quantize.parameters())
            + list(self.quant_conv.parameters())
            + list(self.post_quant_conv.parameters()),
            lr=lr_g,
            betas=(0.5, 0.9),
        )
        opt_disc = torch.optim.Adam(
            self.loss.discriminator.parameters(), lr=lr_d, betas=(0.5, 0.9)
        )

        if self.scheduler_config is not None:
            scheduler = instantiate_from_config(self.scheduler_config)

            print("Setting up LambdaLR scheduler...")
            scheduler = [
                {
                    "scheduler": LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
                    "interval": "step",
                    "frequency": 1,
                },
                {
                    "scheduler": LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
                    "interval": "step",
                    "frequency": 1,
                },
            ]
            return [opt_ae, opt_disc], scheduler
        return [opt_ae, opt_disc], []

    def get_last_layer(self):
        return self.decoder.conv_out.weight

    def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
        log = dict()
        x = self.get_input(batch, self.image_key)
        x = x.to(self.device)
        if only_inputs:
            log["inputs"] = x
            return log
        xrec, _ = self(x)
        if x.shape[1] > 3:
            # colorize with random projection
            assert xrec.shape[1] > 3
            x = self.to_rgb(x)
            xrec = self.to_rgb(xrec)
        log["inputs"] = x
        log["reconstructions"] = xrec
        if plot_ema:
            with self.ema_scope():
                xrec_ema, _ = self(x)
                if x.shape[1] > 3:
                    xrec_ema = self.to_rgb(xrec_ema)
                log["reconstructions_ema"] = xrec_ema
        return log

    def to_rgb(self, x):
        assert self.image_key == "segmentation"
        if not hasattr(self, "colorize"):
            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
        x = F.conv2d(x, weight=self.colorize)
        x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
        return x


class VQModelInterface(VQModel):
    def __init__(self, embed_dim, *args, **kwargs):
        super().__init__(embed_dim=embed_dim, *args, **kwargs)
        self.embed_dim = embed_dim

    def encode(self, x):
        h = self.encoder(x)
        h = self.quant_conv(h)
        return h

    def decode(self, h, force_not_quantize=False):
        # also go through quantization layer
        if not force_not_quantize:
            quant, emb_loss, info = self.quantize(h)
        else:
            quant = h
        quant = self.post_quant_conv(quant)
        dec = self.decoder(quant)
        return dec


class AutoencoderKL(pl.LightningModule):
    def __init__(
        self,
        ddconfig,
        lossconfig,
        embed_dim,
        ckpt_path=None,
        ignore_keys=[],
        image_key="image",
        colorize_nlabels=None,
        monitor=None,
    ):
        super().__init__()
        self.image_key = image_key
        self.encoder = Encoder(**ddconfig)
        self.decoder = Decoder(**ddconfig)
        self.loss = instantiate_from_config(lossconfig)
        assert ddconfig["double_z"]
        self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1)
        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
        self.embed_dim = embed_dim
        if colorize_nlabels is not None:
            assert type(colorize_nlabels) == int
            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
        if monitor is not None:
            self.monitor = monitor
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)

    def init_from_ckpt(self, path, ignore_keys=list()):
        sd = torch.load(path, map_location="cpu")["state_dict"]
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        self.load_state_dict(sd, strict=False)
        print(f"Restored from {path}")

    def encode(self, x):
        h = self.encoder(x)
        moments = self.quant_conv(h)
        posterior = DiagonalGaussianDistribution(moments)
        return posterior

    def decode(self, z):
        z = self.post_quant_conv(z)
        dec = self.decoder(z)
        return dec

    def forward(self, input, sample_posterior=True):
        posterior = self.encode(input)
        if sample_posterior:
            z = posterior.sample()
        else:
            z = posterior.mode()
        dec = self.decode(z)
        return dec, posterior

    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
        return x

    def training_step(self, batch, batch_idx, optimizer_idx):
        inputs = self.get_input(batch, self.image_key)
        reconstructions, posterior = self(inputs)

        if optimizer_idx == 0:
            # train encoder+decoder+logvar
            aeloss, log_dict_ae = self.loss(
                inputs,
                reconstructions,
                posterior,
                optimizer_idx,
                self.global_step,
                last_layer=self.get_last_layer(),
                split="train",
            )
            self.log(
                "aeloss",
                aeloss,
                prog_bar=True,
                logger=True,
                on_step=True,
                on_epoch=True,
            )
            self.log_dict(
                log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False
            )
            return aeloss

        if optimizer_idx == 1:
            # train the discriminator
            discloss, log_dict_disc = self.loss(
                inputs,
                reconstructions,
                posterior,
                optimizer_idx,
                self.global_step,
                last_layer=self.get_last_layer(),
                split="train",
            )

            self.log(
                "discloss",
                discloss,
                prog_bar=True,
                logger=True,
                on_step=True,
                on_epoch=True,
            )
            self.log_dict(
                log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False
            )
            return discloss

    def validation_step(self, batch, batch_idx):
        inputs = self.get_input(batch, self.image_key)
        reconstructions, posterior = self(inputs)
        aeloss, log_dict_ae = self.loss(
            inputs,
            reconstructions,
            posterior,
            0,
            self.global_step,
            last_layer=self.get_last_layer(),
            split="val",
        )

        discloss, log_dict_disc = self.loss(
            inputs,
            reconstructions,
            posterior,
            1,
            self.global_step,
            last_layer=self.get_last_layer(),
            split="val",
        )

        self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
        self.log_dict(log_dict_ae)
        self.log_dict(log_dict_disc)
        return self.log_dict

    def configure_optimizers(self):
        lr = self.learning_rate
        opt_ae = torch.optim.Adam(
            list(self.encoder.parameters())
            + list(self.decoder.parameters())
            + list(self.quant_conv.parameters())
            + list(self.post_quant_conv.parameters()),
            lr=lr,
            betas=(0.5, 0.9),
        )
        opt_disc = torch.optim.Adam(
            self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)
        )
        return [opt_ae, opt_disc], []

    def get_last_layer(self):
        return self.decoder.conv_out.weight

    @torch.no_grad()
    def log_images(self, batch, only_inputs=False, **kwargs):
        log = dict()
        x = self.get_input(batch, self.image_key)
        x = x.to(self.device)
        if not only_inputs:
            xrec, posterior = self(x)
            if x.shape[1] > 3:
                # colorize with random projection
                assert xrec.shape[1] > 3
                x = self.to_rgb(x)
                xrec = self.to_rgb(xrec)
            log["samples"] = self.decode(torch.randn_like(posterior.sample()))
            log["reconstructions"] = xrec
        log["inputs"] = x
        return log

    def to_rgb(self, x):
        assert self.image_key == "segmentation"
        if not hasattr(self, "colorize"):
            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
        x = F.conv2d(x, weight=self.colorize)
        x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
        return x


class IdentityFirstStage(torch.nn.Module):
    def __init__(self, *args, vq_interface=False, **kwargs):
        self.vq_interface = vq_interface  # TODO: Should be true by default but check to not break older stuff
        super().__init__()

    def encode(self, x, *args, **kwargs):
        return x

    def decode(self, x, *args, **kwargs):
        return x

    def quantize(self, x, *args, **kwargs):
        if self.vq_interface:
            return x, None, [None, None, None]
        return x

    def forward(self, x, *args, **kwargs):
        return x


================================================
FILE: ldm/models/blip.py
================================================
"""
 * Copyright (c) 2022, salesforce.com, inc.
 * All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
 * By Junnan Li
"""
import warnings

warnings.filterwarnings("ignore")

from .vit import VisionTransformer, interpolate_pos_embed
from .med import BertConfig, BertModel, BertLMHeadModel
from transformers import BertTokenizer

import torch
from torch import nn

# import torch.nn.functional as F

import os
from urllib.parse import urlparse
from timm.models.hub import download_cached_file


class BLIP_Base(nn.Module):
    def __init__(
        self,
        med_config="configs/blip/med_config.json",
        image_size=224,
        vit="base",
        vit_grad_ckpt=False,
        vit_ckpt_layer=0,
    ):
        """
        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
        """
        super().__init__()

        self.visual_encoder, vision_width = create_vit(
            vit, image_size, vit_grad_ckpt, vit_ckpt_layer
        )
        self.tokenizer = init_tokenizer()
        med_config = BertConfig.from_json_file(med_config)
        med_config.encoder_width = vision_width
        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)

    def forward(self, image, caption, mode):
        assert mode in [
            "image",
            "text",
            "multimodal",
        ], "mode parameter must be image, text, or multimodal"
        text = self.tokenizer(caption, return_tensors="pt").to(image.device)

        if mode == "image":
            # return image features
            image_embeds = self.visual_encoder(image)
            return image_embeds

        elif mode == "text":
            # return text features
            text_output = self.text_encoder(
                text.input_ids,
                attention_mask=text.attention_mask,
                return_dict=True,
                mode="text",
            )
            return text_output.last_hidden_state

        elif mode == "multimodal":
            # return multimodel features
            image_embeds = self.visual_encoder(image)
            image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
                image.device
            )

            text.input_ids[:, 0] = self.tokenizer.enc_token_id
            output = self.text_encoder(
                text.input_ids,
                attention_mask=text.attention_mask,
                encoder_hidden_states=image_embeds,
                encoder_attention_mask=image_atts,
                return_dict=True,
            )
            return output.last_hidden_state


class BLIP_Decoder(nn.Module):
    def __init__(
        self,
        med_config="configs/blip/med_config.json",
        image_size=384,
        vit="base",
        vit_grad_ckpt=False,
        vit_ckpt_layer=0,
        prompt="a picture of ",
    ):
        """
        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
        """
        super().__init__()

        self.visual_encoder, vision_width = create_vit(
            vit, image_size, vit_grad_ckpt, vit_ckpt_layer
        )
        self.tokenizer = init_tokenizer()
        med_config = BertConfig.from_json_file(med_config)
        med_config.encoder_width = vision_width
        self.text_decoder = BertLMHeadModel(config=med_config)

        self.prompt = prompt
        self.prompt_length = len(self.tokenizer(self.prompt).input_ids) - 1

    def forward(self, image, caption):
        image_embeds = self.visual_encoder(image)
        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
            image.device
        )

        text = self.tokenizer(
            caption,
            padding="longest",
            truncation=True,
            max_length=40,
            return_tensors="pt",
        ).to(image.device)

        text.input_ids[:, 0] = self.tokenizer.bos_token_id

        decoder_targets = text.input_ids.masked_fill(
            text.input_ids == self.tokenizer.pad_token_id, -100
        )
        decoder_targets[:, : self.prompt_length] = -100

        decoder_output = self.text_decoder(
            text.input_ids,
            attention_mask=text.attention_mask,
            encoder_hidden_states=image_embeds,
            encoder_attention_mask=image_atts,
            labels=decoder_targets,
            return_dict=True,
        )
        loss_lm = decoder_output.loss

        return loss_lm

    def generate(
        self,
        image,
        sample=False,
        num_beams=3,
        max_length=30,
        min_length=10,
        top_p=0.9,
        repetition_penalty=1.0,
    ):
        image_embeds = self.visual_encoder(image)

        if not sample:
            image_embeds = image_embeds.repeat_interleave(num_beams, dim=0)

        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
            image.device
        )
        model_kwargs = {
            "encoder_hidden_states": image_embeds,
            "encoder_attention_mask": image_atts,
        }

        prompt = [self.prompt] * image.size(0)
        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(
            image.device
        )
        input_ids[:, 0] = self.tokenizer.bos_token_id
        input_ids = input_ids[:, :-1]

        if sample:
            # nucleus sampling
            outputs = self.text_decoder.generate(
                input_ids=input_ids,
                max_length=max_length,
                min_length=min_length,
                do_sample=True,
                top_p=top_p,
                num_return_sequences=1,
                eos_token_id=self.tokenizer.sep_token_id,
                pad_token_id=self.tokenizer.pad_token_id,
                repetition_penalty=1.1,
                **model_kwargs
            )
        else:
            # beam search
            outputs = self.text_decoder.generate(
                input_ids=input_ids,
                max_length=max_length,
                min_length=min_length,
                num_beams=num_beams,
                eos_token_id=self.tokenizer.sep_token_id,
                pad_token_id=self.tokenizer.pad_token_id,
                repetition_penalty=repetition_penalty,
                **model_kwargs
            )

        captions = []
        for output in outputs:
            caption = self.tokenizer.decode(output, skip_special_tokens=True)
            captions.append(caption[len(self.prompt) :])
        return captions


def blip_decoder(pretrained="", **kwargs):
    model = BLIP_Decoder(**kwargs)
    if pretrained:
        model, msg = load_checkpoint(model, pretrained)
        assert len(msg.missing_keys) == 0
    return model


def blip_feature_extractor(pretrained="", **kwargs):
    model = BLIP_Base(**kwargs)
    if pretrained:
        model, msg = load_checkpoint(model, pretrained)
        assert len(msg.missing_keys) == 0
    return model


def init_tokenizer():
    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
    tokenizer.add_special_tokens({"bos_token": "[DEC]"})
    tokenizer.add_special_tokens({"additional_special_tokens": ["[ENC]"]})
    tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0]
    return tokenizer


def create_vit(
    vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0
):
    assert vit in ["base", "large"], "vit parameter must be base or large"
    if vit == "base":
        vision_width = 768
        visual_encoder = VisionTransformer(
            img_size=image_size,
            patch_size=16,
            embed_dim=vision_width,
            depth=12,
            num_heads=12,
            use_grad_checkpointing=use_grad_checkpointing,
            ckpt_layer=ckpt_layer,
            drop_path_rate=0 or drop_path_rate,
        )
    elif vit == "large":
        vision_width = 1024
        visual_encoder = VisionTransformer(
            img_size=image_size,
            patch_size=16,
            embed_dim=vision_width,
            depth=24,
            num_heads=16,
            use_grad_checkpointing=use_grad_checkpointing,
            ckpt_layer=ckpt_layer,
            drop_path_rate=0.1 or drop_path_rate,
        )
    return visual_encoder, vision_width


def is_url(url_or_filename):
    parsed = urlparse(url_or_filename)
    return parsed.scheme in ("http", "https")


def load_checkpoint(model, url_or_filename):
    if is_url(url_or_filename):
        cached_file = download_cached_file(
            url_or_filename, check_hash=False, progress=True
        )
        checkpoint = torch.load(cached_file, map_location="cpu")
    elif os.path.isfile(url_or_filename):
        checkpoint = torch.load(url_or_filename, map_location="cpu")
    else:
        raise RuntimeError("checkpoint url or path is invalid")

    state_dict = checkpoint["model"]

    state_dict["visual_encoder.pos_embed"] = interpolate_pos_embed(
        state_dict["visual_encoder.pos_embed"], model.visual_encoder
    )
    if "visual_encoder_m.pos_embed" in model.state_dict().keys():
        state_dict["visual_encoder_m.pos_embed"] = interpolate_pos_embed(
            state_dict["visual_encoder_m.pos_embed"], model.visual_encoder_m
        )
    for key in model.state_dict().keys():
        if key in state_dict.keys():
            if state_dict[key].shape != model.state_dict()[key].shape:
                del state_dict[key]

    msg = model.load_state_dict(state_dict, strict=False)
    print("load checkpoint from %s" % url_or_filename)
    return model, msg


================================================
FILE: ldm/models/blip_itm.py
================================================
from models.med import BertConfig, BertModel

import torch
from torch import nn
import torch.nn.functional as F

from models.blip import create_vit, init_tokenizer, load_checkpoint


class BLIP_ITM(nn.Module):
    def __init__(
        self,
        med_config="configs/med_config.json",
        image_size=384,
        vit="base",
        vit_grad_ckpt=False,
        vit_ckpt_layer=0,
        embed_dim=256,
    ):
        """
        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
        """
        super().__init__()

        self.visual_encoder, vision_width = create_vit(
            vit, image_size, vit_grad_ckpt, vit_ckpt_layer
        )
        self.tokenizer = init_tokenizer()
        med_config = BertConfig.from_json_file(med_config)
        med_config.encoder_width = vision_width
        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)

        text_width = self.text_encoder.config.hidden_size

        self.vision_proj = nn.Linear(vision_width, embed_dim)
        self.text_proj = nn.Linear(text_width, embed_dim)

        self.itm_head = nn.Linear(text_width, 2)

    def forward(self, image, caption, match_head="itm"):
        image_embeds = self.visual_encoder(image)
        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
            image.device
        )

        text = self.tokenizer(
            caption,
            padding="max_length",
            truncation=True,
            max_length=35,
            return_tensors="pt",
        ).to(image.device)

        if match_head == "itm":
            output = self.text_encoder(
                text.input_ids,
                attention_mask=text.attention_mask,
                encoder_hidden_states=image_embeds,
                encoder_attention_mask=image_atts,
                return_dict=True,
            )
            itm_output = self.itm_head(output.last_hidden_state[:, 0, :])
            return itm_output

        elif match_head == "itc":
            text_output = self.text_encoder(
                text.input_ids,
                attention_mask=text.attention_mask,
                return_dict=True,
                mode="text",
            )
            image_feat = F.normalize(self.vision_proj(image_embeds[:, 0, :]), dim=-1)
            text_feat = F.normalize(
                self.text_proj(text_output.last_hidden_state[:, 0, :]), dim=-1
            )

            sim = image_feat @ text_feat.t()
            return sim


def blip_itm(pretrained="", **kwargs):
    model = BLIP_ITM(**kwargs)
    if pretrained:
        model, msg = load_checkpoint(model, pretrained)
        assert len(msg.missing_keys) == 0
    return model


================================================
FILE: ldm/models/blip_nlvr.py
================================================
from models.med import BertConfig
from models.nlvr_encoder import BertModel
from models.vit import interpolate_pos_embed
from models.blip import create_vit, init_tokenizer, is_url

from timm.models.hub import download_cached_file

import torch
from torch import nn
import torch.nn.functional as F


class BLIP_NLVR(nn.Module):
    def __init__(
        self,
        med_config="configs/med_config.json",
        image_size=480,
        vit="base",
        vit_grad_ckpt=False,
        vit_ckpt_layer=0,
    ):
        """
        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
        """
        super().__init__()

        self.visual_encoder, vision_width = create_vit(
            vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1
        )
        self.tokenizer = init_tokenizer()
        med_config = BertConfig.from_json_file(med_config)
        med_config.encoder_width = vision_width
        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)

        self.cls_head = nn.Sequential(
            nn.Linear(
                self.text_encoder.config.hidden_size,
                self.text_encoder.config.hidden_size,
            ),
            nn.ReLU(),
            nn.Linear(self.text_encoder.config.hidden_size, 2),
        )

    def forward(self, image, text, targets, train=True):
        image_embeds = self.visual_encoder(image)
        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
            image.device
        )
        image0_embeds, image1_embeds = torch.split(image_embeds, targets.size(0))

        text = self.tokenizer(text, padding="longest", return_tensors="pt").to(
            image.device
        )
        text.input_ids[:, 0] = self.tokenizer.enc_token_id

        output = self.text_encoder(
            text.input_ids,
            attention_mask=text.attention_mask,
            encoder_hidden_states=[image0_embeds, image1_embeds],
            encoder_attention_mask=[
                image_atts[: image0_embeds.size(0)],
                image_atts[image0_embeds.size(0) :],
            ],
            return_dict=True,
        )
        hidden_state = output.last_hidden_state[:, 0, :]
        prediction = self.cls_head(hidden_state)

        if train:
            loss = F.cross_entropy(prediction, targets)
            return loss
        else:
            return prediction


def blip_nlvr(pretrained="", **kwargs):
    model = BLIP_NLVR(**kwargs)
    if pretrained:
        model, msg = load_checkpoint(model, pretrained)
        print("missing keys:")
        print(msg.missing_keys)
    return model


def load_checkpoint(model, url_or_filename):
    if is_url(url_or_filename):
        cached_file = download_cached_file(
            url_or_filename, check_hash=False, progress=True
        )
        checkpoint = torch.load(cached_file, map_location="cpu")
    elif os.path.isfile(url_or_filename):
        checkpoint = torch.load(url_or_filename, map_location="cpu")
    else:
        raise RuntimeError("checkpoint url or path is invalid")
    state_dict = checkpoint["model"]

    state_dict["visual_encoder.pos_embed"] = interpolate_pos_embed(
        state_dict["visual_encoder.pos_embed"], model.visual_encoder
    )

    for key in list(state_dict.keys()):
        if "crossattention.self." in key:
            new_key0 = key.replace("self", "self0")
            new_key1 = key.replace("self", "self1")
            state_dict[new_key0] = state_dict[key]
            state_dict[new_key1] = state_dict[key]
        elif "crossattention.output.dense." in key:
            new_key0 = key.replace("dense", "dense0")
            new_key1 = key.replace("dense", "dense1")
            state_dict[new_key0] = state_dict[key]
            state_dict[new_key1] = state_dict[key]

    msg = model.load_state_dict(state_dict, strict=False)
    print("load checkpoint from %s" % url_or_filename)
    return model, msg


================================================
FILE: ldm/models/blip_pretrain.py
================================================
"""
 * Copyright (c) 2022, salesforce.com, inc.
 * All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
 * By Junnan Li
"""
from models.med import BertConfig, BertModel, BertLMHeadModel
import transformers

transformers.logging.set_verbosity_error()

import torch
from torch import nn
import torch.nn.functional as F

from models.blip import create_vit, init_tokenizer


class BLIP_Pretrain(nn.Module):
    def __init__(
        self,
        med_config="configs/bert_config.json",
        image_size=224,
        vit="base",
        vit_grad_ckpt=False,
        vit_ckpt_layer=0,
        embed_dim=256,
        queue_size=57600,
        momentum=0.995,
    ):
        """
        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
        """
        super().__init__()

        self.visual_encoder, vision_width = create_vit(
            vit, image_size, vit_grad_ckpt, vit_ckpt_layer, 0
        )

        if vit == "base":
            checkpoint = torch.hub.load_state_dict_from_url(
                url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
                map_location="cpu",
                check_hash=True,
            )
            state_dict = checkpoint["model"]
            self.visual_encoder.load_state_dict(state_dict, strict=False)
        elif vit == "large":
            from timm.models.helpers import load_custom_pretrained
            from timm.models.vision_transformer import default_cfgs

            load_custom_pretrained(
                self.visual_encoder, default_cfgs["vit_large_patch16_224_in21k"]
            )

        self.tokenizer = init_tokenizer()
        encoder_config = BertConfig.from_json_file(med_config)
        encoder_config.encoder_width = vision_width
        self.text_encoder = BertModel.from_pretrained(
            "bert-base-uncased", config=encoder_config, add_pooling_layer=False
        )
        self.text_encoder.resize_token_embeddings(len(self.tokenizer))

        text_width = self.text_encoder.config.hidden_size

        self.vision_proj = nn.Linear(vision_width, embed_dim)
        self.text_proj = nn.Linear(text_width, embed_dim)

        self.itm_head = nn.Linear(text_width, 2)

        # create momentum encoders
        self.visual_encoder_m, vision_width = create_vit(vit, image_size)
        self.vision_proj_m = nn.Linear(vision_width, embed_dim)
        self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False)
        self.text_proj_m = nn.Linear(text_width, embed_dim)

        self.model_pairs = [
            [self.visual_encoder, self.visual_encoder_m],
            [self.vision_proj, self.vision_proj_m],
            [self.text_encoder, self.text_encoder_m],
            [self.text_proj, self.text_proj_m],
        ]
        self.copy_params()

        # create the queue
        self.register_buffer("image_queue", torch.randn(embed_dim, queue_size))
        self.register_buffer("text_queue", torch.randn(embed_dim, queue_size))
        self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long))

        self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
        self.text_queue = nn.functional.normalize(self.text_queue, dim=0)

        self.queue_size = queue_size
        self.momentum = momentum
        self.temp = nn.Parameter(0.07 * torch.ones([]))

        # create the decoder
        decoder_config = BertConfig.from_json_file(med_config)
        decoder_config.encoder_width = vision_width
        self.text_decoder = BertLMHeadModel.from_pretrained(
            "bert-base-uncased", config=decoder_config
        )
        self.text_decoder.resize_token_embeddings(len(self.tokenizer))
        tie_encoder_decoder_weights(
            self.text_encoder, self.text_decoder.bert, "", "/attention"
        )

    def forward(self, image, caption, alpha):
        with torch.no_grad():
            self.temp.clamp_(0.001, 0.5)

        image_embeds = self.visual_encoder(image)
        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
            image.device
        )
        image_feat = F.normalize(self.vision_proj(image_embeds[:, 0, :]), dim=-1)

        text = self.tokenizer(
            caption,
            padding="max_length",
            truncation=True,
            max_length=30,
            return_tensors="pt",
        ).to(image.device)
        text_output = self.text_encoder(
            text.input_ids,
            attention_mask=text.attention_mask,
            return_dict=True,
            mode="text",
        )
        text_feat = F.normalize(
            self.text_proj(text_output.last_hidden_state[:, 0, :]), dim=-1
        )

        # get momentum features
        with torch.no_grad():
            self._momentum_update()
            image_embeds_m = self.visual_encoder_m(image)
            image_feat_m = F.normalize(
                self.vision_proj_m(image_embeds_m[:, 0, :]), dim=-1
            )
            image_feat_all = torch.cat(
                [image_feat_m.t(), self.image_queue.clone().detach()], dim=1
            )

            text_output_m = self.text_encoder_m(
                text.input_ids,
                attention_mask=text.attention_mask,
                return_dict=True,
                mode="text",
            )
            text_feat_m = F.normalize(
                self.text_proj_m(text_output_m.last_hidden_state[:, 0, :]), dim=-1
            )
            text_feat_all = torch.cat(
                [text_feat_m.t(), self.text_queue.clone().detach()], dim=1
            )

            sim_i2t_m = image_feat_m @ text_feat_all / self.temp
            sim_t2i_m = text_feat_m @ image_feat_all / self.temp

            sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device)
            sim_targets.fill_diagonal_(1)

            sim_i2t_targets = (
                alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets
            )
            sim_t2i_targets = (
                alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets
            )

        sim_i2t = image_feat @ text_feat_all / self.temp
        sim_t2i = text_feat @ image_feat_all / self.temp

        loss_i2t = -torch.sum(
            F.log_softmax(sim_i2t, dim=1) * sim_i2t_targets, dim=1
        ).mean()
        loss_t2i = -torch.sum(
            F.log_softmax(sim_t2i, dim=1) * sim_t2i_targets, dim=1
        ).mean()

        loss_ita = (loss_i2t + loss_t2i) / 2

        self._dequeue_and_enqueue(image_feat_m, text_feat_m)

        ###============== Image-text Matching ===================###
        encoder_input_ids = text.input_ids.clone()
        encoder_input_ids[:, 0] = self.tokenizer.enc_token_id

        # forward the positve image-text pair
        bs = image.size(0)
        output_pos = self.text_encoder(
            encoder_input_ids,
            attention_mask=text.attention_mask,
            encoder_hidden_states=image_embeds,
            encoder_attention_mask=image_atts,
            return_dict=True,
        )
        with torch.no_grad():
            weights_t2i = F.softmax(sim_t2i[:, :bs], dim=1) + 1e-4
            weights_t2i.fill_diagonal_(0)
            weights_i2t = F.softmax(sim_i2t[:, :bs], dim=1) + 1e-4
            weights_i2t.fill_diagonal_(0)

        # select a negative image for each text
        image_embeds_neg = []
        for b in range(bs):
            neg_idx = torch.multinomial(weights_t2i[b], 1).item()
            image_embeds_neg.append(image_embeds[neg_idx])
        image_embeds_neg = torch.stack(image_embeds_neg, dim=0)

        # select a negative text for each image
        text_ids_neg = []
        text_atts_neg = []
        for b in range(bs):
            neg_idx = torch.multinomial(weights_i2t[b], 1).item()
            text_ids_neg.append(encoder_input_ids[neg_idx])
            text_atts_neg.append(text.attention_mask[neg_idx])

        text_ids_neg = torch.stack(text_ids_neg, dim=0)
        text_atts_neg = torch.stack(text_atts_neg, dim=0)

        text_ids_all = torch.cat([encoder_input_ids, text_ids_neg], dim=0)
        text_atts_all = torch.cat([text.attention_mask, text_atts_neg], dim=0)

        image_embeds_all = torch.cat([image_embeds_neg, image_embeds], dim=0)
        image_atts_all = torch.cat([image_atts, image_atts], dim=0)

        output_neg = self.text_encoder(
            text_ids_all,
            attention_mask=text_atts_all,
            encoder_hidden_states=image_embeds_all,
            encoder_attention_mask=image_atts_all,
            return_dict=True,
        )

        vl_embeddings = torch.cat(
            [
                output_pos.last_hidden_state[:, 0, :],
                output_neg.last_hidden_state[:, 0, :],
            ],
            dim=0,
        )
        vl_output = self.itm_head(vl_embeddings)

        itm_labels = torch.cat(
            [torch.ones(bs, dtype=torch.long), torch.zeros(2 * bs, dtype=torch.long)],
            dim=0,
        ).to(image.device)
        loss_itm = F.cross_entropy(vl_output, itm_labels)

        ##================= LM ========================##
        decoder_input_ids = text.input_ids.clone()
        decoder_input_ids[:, 0] = self.tokenizer.bos_token_id
        decoder_targets = decoder_input_ids.masked_fill(
            decoder_input_ids == self.tokenizer.pad_token_id, -100
        )

        decoder_output = self.text_decoder(
            decoder_input_ids,
            attention_mask=text.attention_mask,
            encoder_hidden_states=image_embeds,
            encoder_attention_mask=image_atts,
            labels=decoder_targets,
            return_dict=True,
        )

        loss_lm = decoder_output.loss
        return loss_ita, loss_itm, loss_lm

    @torch.no_grad()
    def copy_params(self):
        for model_pair in self.model_pairs:
            for param, param_m in zip(
                model_pair[0].parameters(), model_pair[1].parameters()
            ):
                param_m.data.copy_(param.data)  # initialize
                param_m.requires_grad = False  # not update by gradient

    @torch.no_grad()
    def _momentum_update(self):
        for model_pair in self.model_pairs:
            for param, param_m in zip(
                model_pair[0].parameters(), model_pair[1].parameters()
            ):
                param_m.data = param_m.data * self.momentum + param.data * (
                    1.0 - self.momentum
                )

    @torch.no_grad()
    def _dequeue_and_enqueue(self, image_feat, text_feat):
        # gather keys before updating queue
        image_feats = concat_all_gather(image_feat)
        text_feats = concat_all_gather(text_feat)

        batch_size = image_feats.shape[0]

        ptr = int(self.queue_ptr)
        assert self.queue_size % batch_size == 0  # for simplicity

        # replace the keys at ptr (dequeue and enqueue)
        self.image_queue[:, ptr : ptr + batch_size] = image_feats.T
        self.text_queue[:, ptr : ptr + batch_size] = text_feats.T
        ptr = (ptr + batch_size) % self.queue_size  # move pointer

        self.queue_ptr[0] = ptr


def blip_pretrain(**kwargs):
    model = BLIP_Pretrain(**kwargs)
    return model


@torch.no_grad()
def concat_all_gather(tensor):
    """
    Performs all_gather operation on the provided tensors.
    *** Warning ***: torch.distributed.all_gather has no gradient.
    """
    tensors_gather = [
        torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size())
    ]
    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)

    output = torch.cat(tensors_gather, dim=0)
    return output


from typing import List


def tie_encoder_decoder_weights(
    encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key: str
):
    uninitialized_encoder_weights: List[str] = []
    if decoder.__class__ != encoder.__class__:
        logger.info(
            f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
        )

    def tie_encoder_to_decoder_recursively(
        decoder_pointer: nn.Module,
        encoder_pointer: nn.Module,
        module_name: str,
        uninitialized_encoder_weights: List[str],
        skip_key: str,
        depth=0,
    ):
        assert isinstance(decoder_pointer, nn.Module) and isinstance(
            encoder_pointer, nn.Module
        ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module"
        if hasattr(decoder_pointer, "weight") and skip_key not in module_name:
            assert hasattr(encoder_pointer, "weight")
            encoder_pointer.weight = decoder_pointer.weight
            if hasattr(decoder_pointer, "bias"):
                assert hasattr(encoder_pointer, "bias")
                encoder_pointer.bias = decoder_pointer.bias
            print(module_name + " is tied")
            return

        encoder_modules = encoder_pointer._modules
        decoder_modules = decoder_pointer._modules
        if len(decoder_modules) > 0:
            assert (
                len(encoder_modules) > 0
            ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"

            all_encoder_weights = set(
                [module_name + "/" + sub_name for sub_name in encoder_modules.keys()]
            )
            encoder_layer_pos = 0
            for name, module in decoder_modules.items():
                if name.isdigit():
                    encoder_name = str(int(name) + encoder_layer_pos)
                    decoder_name = name
                    if not isinstance(
                        decoder_modules[decoder_name],
                        type(encoder_modules[encoder_name]),
                    ) and len(encoder_modules) != len(decoder_modules):
                        # this can happen if the name corresponds to the position in a list module list of layers
                        # in this case the decoder has added a cross-attention that the encoder does not have
                        # thus skip this step and subtract one layer pos from encoder
                        encoder_layer_pos -= 1
                        continue
                elif name not in encoder_modules:
                    continue
                elif depth > 500:
                    raise ValueError(
                        "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model."
                    )
                else:
                    decoder_name = encoder_name = name
                tie_encoder_to_decoder_recursively(
                    decoder_modules[decoder_name],
                    encoder_modules[encoder_name],
                    module_name + "/" + name,
                    uninitialized_encoder_weights,
                    skip_key,
                    depth=depth + 1,
                )
                all_encoder_weights.remove(module_name + "/" + encoder_name)

            uninitialized_encoder_weights += list(all_encoder_weights)

    # tie weights recursively
    tie_encoder_to_decoder_recursively(
        decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key
    )


================================================
FILE: ldm/models/blip_retrieval.py
================================================
from models.med import BertConfig, BertModel

import torch
from torch import nn
import torch.nn.functional as F

from models.blip import create_vit, init_tokenizer, load_checkpoint


class BLIP_Retrieval(nn.Module):
    def __init__(
        self,
        med_config="configs/med_config.json",
        image_size=384,
        vit="base",
        vit_grad_ckpt=False,
        vit_ckpt_layer=0,
        embed_dim=256,
        queue_size=57600,
        momentum=0.995,
        negative_all_rank=False,
    ):
        """
        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
        """
        super().__init__()

        self.visual_encoder, vision_width = create_vit(
            vit, image_size, vit_grad_ckpt, vit_ckpt_layer
        )
        self.tokenizer = init_tokenizer()
        med_config = BertConfig.from_json_file(med_config)
        med_config.encoder_width = vision_width
        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)

        text_width = self.text_encoder.config.hidden_size

        self.vision_proj = nn.Linear(vision_width, embed_dim)
        self.text_proj = nn.Linear(text_width, embed_dim)

        self.itm_head = nn.Linear(text_width, 2)

        # create momentum encoders
        self.visual_encoder_m, vision_width = create_vit(vit, image_size)
        self.vision_proj_m = nn.Linear(vision_width, embed_dim)
        self.text_encoder_m = BertModel(config=med_config, add_pooling_layer=False)
        self.text_proj_m = nn.Linear(text_width, embed_dim)

        self.model_pairs = [
            [self.visual_encoder, self.visual_encoder_m],
            [self.vision_proj, self.vision_proj_m],
            [self.text_encoder, self.text_encoder_m],
            [self.text_proj, self.text_proj_m],
        ]
        self.copy_params()

        # create the queue
        self.register_buffer("image_queue", torch.randn(embed_dim, queue_size))
        self.register_buffer("text_queue", torch.randn(embed_dim, queue_size))
        self.register_buffer("idx_queue", torch.full((1, queue_size), -100))
        self.register_buffer("ptr_queue", torch.zeros(1, dtype=torch.long))

        self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
        self.text_queue = nn.functional.normalize(self.text_queue, dim=0)

        self.queue_size = queue_size
        self.momentum = momentum
        self.temp = nn.Parameter(0.07 * torch.ones([]))

        self.negative_all_rank = negative_all_rank

    def forward(self, image, caption, alpha, idx):
        with torch.no_grad():
            self.temp.clamp_(0.001, 0.5)

        image_embeds = self.visual_encoder(image)
        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
            image.device
        )
        image_feat = F.normalize(self.vision_proj(image_embeds[:, 0, :]), dim=-1)

        text = self.tokenizer(
            caption,
            padding="max_length",
            truncation=True,
            max_length=35,
            return_tensors="pt",
        ).to(image.device)

        text_output = self.text_encoder(
            text.input_ids,
            attention_mask=text.attention_mask,
            return_dict=True,
            mode="text",
        )
        text_feat = F.normalize(
            self.text_proj(text_output.last_hidden_state[:, 0, :]), dim=-1
        )

        ###============== Image-text Contrastive Learning ===================###
        idx = idx.view(-1, 1)
        idx_all = torch.cat([idx.t(), self.idx_queue.clone().detach()], dim=1)
        pos_idx = torch.eq(idx, idx_all).float()
        sim_targets = pos_idx / pos_idx.sum(1, keepdim=True)

        # get momentum features
        with torch.no_grad():
            self._momentum_update()
            image_embeds_m = self.visual_encoder_m(image)
            image_feat_m = F.normalize(
                self.vision_proj_m(image_embeds_m[:, 0, :]), dim=-1
            )
            image_feat_m_all = torch.cat(
                [image_feat_m.t(), self.image_queue.clone().detach()], dim=1
            )

            text_output_m = self.text_encoder_m(
                text.input_ids,
                attention_mask=text.attention_mask,
                return_dict=True,
                mode="text",
            )
            text_feat_m = F.normalize(
                self.text_proj_m(text_output_m.last_hidden_state[:, 0, :]), dim=-1
            )
            text_feat_m_all = torch.cat(
                [text_feat_m.t(), self.text_queue.clone().detach()], dim=1
            )

            sim_i2t_m = image_feat_m @ text_feat_m_all / self.temp
            sim_t2i_m = text_feat_m @ image_feat_m_all / self.temp

            sim_i2t_targets = (
                alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets
            )
            sim_t2i_targets = (
                alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets
            )

        sim_i2t = image_feat @ text_feat_m_all / self.temp
        sim_t2i = text_feat @ image_feat_m_all / self.temp

        loss_i2t = -torch.sum(
            F.log_softmax(sim_i2t, dim=1) * sim_i2t_targets, dim=1
        ).mean()
        loss_t2i = -torch.sum(
            F.log_softmax(sim_t2i, dim=1) * sim_t2i_targets, dim=1
        ).mean()

        loss_ita = (loss_i2t + loss_t2i) / 2

        idxs = concat_all_gather(idx)
        self._dequeue_and_enqueue(image_feat_m, text_feat_m, idxs)

        ###============== Image-text Matching ===================###
        encoder_input_ids = text.input_ids.clone()
        encoder_input_ids[:, 0] = self.tokenizer.enc_token_id

        # forward the positve image-text pair
        bs = image.size(0)
        output_pos = self.text_encoder(
            encoder_input_ids,
            attention_mask=text.attention_mask,
            encoder_hidden_states=image_embeds,
            encoder_attention_mask=image_atts,
            return_dict=True,
        )

        if self.negative_all_rank:
            # compute sample similarity
            with torch.no_grad():
                mask = torch.eq(idx, idxs.t())

                image_feat_world = concat_all_gather(image_feat)
                text_feat_world = concat_all_gather(text_feat)

                sim_i2t = image_feat @ text_feat_world.t() / self.temp
                sim_t2i = text_feat @ image_feat_world.t() / self.temp

                weights_i2t = F.softmax(sim_i2t, dim=1)
                weights_i2t.masked_fill_(mask, 0)

                weights_t2i = F.softmax(sim_t2i, dim=1)
                weights_t2i.masked_fill_(mask, 0)

            image_embeds_world = all_gather_with_grad(image_embeds)

            # select a negative image (from all ranks) for each text
            image_embeds_neg = []
            for b in range(bs):
                neg_idx = torch.multinomial(weights_t2i[b], 1).item()
                image_embeds_neg.append(image_embeds_world[neg_idx])
            image_embeds_neg = torch.stack(image_embeds_neg, dim=0)

            # select a negative text (from all ranks) for each image
            input_ids_world = concat_all_gather(encoder_input_ids)
            att_mask_world = concat_all_gather(text.attention_mask)

            text_ids_neg = []
            text_atts_neg = []
            for b in range(bs):
                neg_idx = torch.multinomial(weights_i2t[b], 1).item()
                text_ids_neg.append(input_ids_world[neg_idx])
                text_atts_neg.append(att_mask_world[neg_idx])

        else:
            with torch.no_grad():
                mask = torch.eq(idx, idx.t())

                sim_i2t = image_feat @ text_feat.t() / self.temp
                sim_t2i = text_feat @ image_feat.t() / self.temp

                weights_i2t = F.softmax(sim_i2t, dim=1)
                weights_i2t.masked_fill_(mask, 0)

                weights_t2i = F.softmax(sim_t2i, dim=1)
                weights_t2i.masked_fill_(mask, 0)

            # select a negative image (from same rank) for each text
            image_embeds_neg = []
            for b in range(bs):
                neg_idx = torch.multinomial(weights_t2i[b], 1).item()
                image_embeds_neg.append(image_embeds[neg_idx])
            image_embeds_neg = torch.stack(image_embeds_neg, dim=0)

            # select a negative text (from same rank) for each image
            text_ids_neg = []
            text_atts_neg = []
            for b in range(bs):
                neg_idx = torch.multinomial(weights_i2t[b], 1).item()
                text_ids_neg.append(encoder_input_ids[neg_idx])
                text_atts_neg.append(text.attention_mask[neg_idx])

        text_ids_neg = torch.stack(text_ids_neg, dim=0)
        text_atts_neg = torch.stack(text_atts_neg, dim=0)

        text_ids_all = torch.cat([encoder_input_ids, text_ids_neg], dim=0)
        text_atts_all = torch.cat([text.attention_mask, text_atts_neg], dim=0)

        image_embeds_all = torch.cat([image_embeds_neg, image_embeds], dim=0)
        image_atts_all = torch.cat([image_atts, image_atts], dim=0)

        output_neg = self.text_encoder(
            text_ids_all,
            attention_mask=text_atts_all,
            encoder_hidden_states=image_embeds_all,
            encoder_attention_mask=image_atts_all,
            return_dict=True,
        )

        vl_embeddings = torch.cat(
            [
                output_pos.last_hidden_state[:, 0, :],
                output_neg.last_hidden_state[:, 0, :],
            ],
            dim=0,
        )
        vl_output = self.itm_head(vl_embeddings)

        itm_labels = torch.cat(
            [torch.ones(bs, dtype=torch.long), torch.zeros(2 * bs, dtype=torch.long)],
            dim=0,
        ).to(image.device)
        loss_itm = F.cross_entropy(vl_output, itm_labels)

        return loss_ita, loss_itm

    @torch.no_grad()
    def copy_params(self):
        for model_pair in self.model_pairs:
            for param, param_m in zip(
                model_pair[0].parameters(), model_pair[1].parameters()
            ):
                param_m.data.copy_(param.data)  # initialize
                param_m.requires_grad = False  # not update by gradient

    @torch.no_grad()
    def _momentum_update(self):
        for model_pair in self.model_pairs:
            for param, param_m in zip(
                model_pair[0].parameters(), model_pair[1].parameters()
            ):
                param_m.data = param_m.data * self.momentum + param.data * (
                    1.0 - self.momentum
                )

    @torch.no_grad()
    def _dequeue_and_enqueue(self, image_feat, text_feat, idxs):
        # gather keys before updating queue
        image_feats = concat_all_gather(image_feat)
        text_feats = concat_all_gather(text_feat)

        batch_size = image_feats.shape[0]

        ptr = int(self.ptr_queue)
        assert self.queue_size % batch_size == 0  # for simplicity

        # replace the keys at ptr (dequeue and enqueue)
        self.image_queue[:, ptr : ptr + batch_size] = image_feats.T
        self.text_queue[:, ptr : ptr + batch_size] = text_feats.T
        self.idx_queue[:, ptr : ptr + batch_size] = idxs.T
        ptr = (ptr + batch_size) % self.queue_size  # move pointer

        self.ptr_queue[0] = ptr


def blip_retrieval(pretrained="", **kwargs):
    model = BLIP_Retrieval(**kwargs)
    if pretrained:
        model, msg = load_checkpoint(model, pretrained)
        print("missing keys:")
        print(msg.missing_keys)
    return model


@torch.no_grad()
def concat_all_gather(tensor):
    """
    Performs all_gather operation on the provided tensors.
    *** Warning ***: torch.distributed.all_gather has no gradient.
    """
    tensors_gather = [
        torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size())
    ]
    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)

    output = torch.cat(tensors_gather, dim=0)
    return output


class GatherLayer(torch.autograd.Function):
    """
    Gather tensors from all workers with support for backward propagation:
    This implementation does not cut the gradients as torch.distributed.all_gather does.
    """

    @staticmethod
    def forward(ctx, x):
        output = [
            torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())
        ]
        torch.distributed.all_gather(output, x)
        return tuple(output)

    @staticmethod
    def backward(ctx, *grads):
        all_gradients = torch.stack(grads)
        torch.distributed.all_reduce(all_gradients)
        return all_gradients[torch.distributed.get_rank()]


def all_gather_with_grad(tensors):
    """
    Performs all_gather operation on the provided tensors.
    Graph remains connected for backward grad computation.
    """
    # Queue the gathered tensors
    world_size = torch.distributed.get_world_size()
    # There is no need for reduction in the single-proc case
    if world_size == 1:
        return tensors

    tensor_all = GatherLayer.apply(tensors)

    return torch.cat(tensor_all, dim=0)


================================================
FILE: ldm/models/blip_vqa.py
================================================
from models.med import BertConfig, BertModel, BertLMHeadModel
from models.blip import create_vit, init_tokenizer, load_checkpoint

import torch
from torch import nn
import torch.nn.functional as F
import numpy as np


class BLIP_VQA(nn.Module):
    def __init__(
        self,
        med_config="configs/med_config.json",
        image_size=480,
        vit="base",
        vit_grad_ckpt=False,
        vit_ckpt_layer=0,
    ):
        """
        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
        """
        super().__init__()

        self.visual_encoder, vision_width = create_vit(
            vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1
        )
        self.tokenizer = init_tokenizer()

        encoder_config = BertConfig.from_json_file(med_config)
        encoder_config.encoder_width = vision_width
        self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False)

        decoder_config = BertConfig.from_json_file(med_config)
        self.text_decoder = BertLMHeadModel(config=decoder_config)

    def forward(
        self,
        image,
        question,
        answer=None,
        n=None,
        weights=None,
        train=True,
        inference="rank",
        k_test=128,
    ):
        image_embeds = self.visual_encoder(image)
        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
            image.device
        )

        question = self.tokenizer(
            question,
            padding="longest",
            truncation=True,
            max_length=35,
            return_tensors="pt",
        ).to(image.device)
        question.input_ids[:, 0] = self.tokenizer.enc_token_id

        if train:
            """
            n: number of answers for each question
            weights: weight for each answer
            """
            answer = self.tokenizer(answer, padding="longest", return_tensors="pt").to(
                image.device
            )
            answer.input_ids[:, 0] = self.tokenizer.bos_token_id
            answer_targets = answer.input_ids.masked_fill(
                answer.input_ids == self.tokenizer.pad_token_id, -100
            )

            question_output = self.text_encoder(
                question.input_ids,
                attention_mask=question.attention_mask,
                encoder_hidden_states=image_embeds,
                encoder_attention_mask=image_atts,
                return_dict=True,
            )

            question_states = []
            question_atts = []
            for b, n in enumerate(n):
                question_states += [question_output.last_hidden_state[b]] * n
                question_atts += [question.attention_mask[b]] * n
            question_states = torch.stack(question_states, 0)
            question_atts = torch.stack(question_atts, 0)

            answer_output = self.text_decoder(
                answer.input_ids,
                attention_mask=answer.attention_mask,
                encoder_hidden_states=question_states,
                encoder_attention_mask=question_atts,
                labels=answer_targets,
                return_dict=True,
                reduction="none",
            )

            loss = weights * answer_output.loss
            loss = loss.sum() / image.size(0)

            return loss

        else:
            question_output = self.text_encoder(
                question.input_ids,
                attention_mask=question.attention_mask,
                encoder_hidden_states=image_embeds,
                encoder_attention_mask=image_atts,
                return_dict=True,
            )

            if inference == "generate":
                num_beams = 3
                question_states = question_output.last_hidden_state.repeat_interleave(
                    num_beams, dim=0
                )
                question_atts = torch.ones(
                    question_states.size()[:-1], dtype=torch.long
                ).to(question_states.device)
                model_kwargs = {
                    "encoder_hidden_states": question_states,
                    "encoder_attention_mask": question_atts,
                }

                bos_ids = torch.full(
                    (image.size(0), 1),
                    fill_value=self.tokenizer.bos_token_id,
                    device=image.device,
                )

                outputs = self.text_decoder.generate(
                    input_ids=bos_ids,
                    max_length=10,
                    min_length=1,
                    num_beams=num_beams,
                    eos_token_id=self.tokenizer.sep_token_id,
                    pad_token_id=self.tokenizer.pad_token_id,
                    **model_kwargs
                )

                answers = []
                for output in outputs:
                    answer = self.tokenizer.decode(output, skip_special_tokens=True)
                    answers.append(answer)
                return answers

            elif inference == "rank":
                max_ids = self.rank_answer(
                    question_output.last_hidden_state,
                    question.attention_mask,
                    answer.input_ids,
                    answer.attention_mask,
                    k_test,
                )
                return max_ids

    def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k):
        num_ques = question_states.size(0)
        start_ids = answer_ids[0, 0].repeat(num_ques, 1)  # bos token

        start_output = self.text_decoder(
            start_ids,
            encoder_hidden_states=question_states,
            encoder_attention_mask=question_atts,
            return_dict=True,
            reduction="none",
        )
        logits = start_output.logits[:, 0, :]  # first token's logit

        # topk_probs: top-k probability
        # topk_ids: [num_question, k]
        answer_first_token = answer_ids[:, 1]
        prob_first_token = F.softmax(logits, dim=1).index_select(
            dim=1, index=answer_first_token
        )
        topk_probs, topk_ids = prob_first_token.topk(k, dim=1)

        # answer input: [num_question*k, answer_len]
        input_ids = []
        input_atts = []
        for b, topk_id in enumerate(topk_ids):
            input_ids.append(answer_ids.index_select(dim=0, index=topk_id))
            input_atts.append(answer_atts.index_select(dim=0, index=topk_id))
        input_ids = torch.cat(input_ids, dim=0)
        input_atts = torch.cat(input_atts, dim=0)

        targets_ids = input_ids.masked_fill(
            input_ids == self.tokenizer.pad_token_id, -100
        )

        # repeat encoder's output for top-k answers
        question_states = tile(question_states, 0, k)
        question_atts = tile(question_atts, 0, k)

        output = self.text_decoder(
            input_ids,
            attention_mask=input_atts,
            encoder_hidden_states=question_states,
            encoder_attention_mask=question_atts,
            labels=targets_ids,
            return_dict=True,
            reduction="none",
        )

        log_probs_sum = -output.loss
        log_probs_sum = log_probs_sum.view(num_ques, k)

        max_topk_ids = log_probs_sum.argmax(dim=1)
        max_ids = topk_ids[max_topk_ids >= 0, max_topk_ids]

        return max_ids


def blip_vqa(pretrained="", **kwargs):
    model = BLIP_VQA(**kwargs)
    if pretrained:
        model, msg = load_checkpoint(model, pretrained)
    #         assert(len(msg.missing_keys)==0)
    return model


def tile(x, dim, n_tile):
    init_dim = x.size(dim)
    repeat_idx = [1] * x.dim()
    repeat_idx[dim] = n_tile
    x = x.repeat(*(repeat_idx))
    order_index = torch.LongTensor(
        np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])
    )
    return torch.index_select(x, dim, order_index.to(x.device))


================================================
FILE: ldm/models/diffusion/__init__.py
================================================


================================================
FILE: ldm/models/diffusion/classifier.py
================================================
import os
import torch
import pytorch_lightning as pl
from omegaconf import OmegaConf
from torch.nn import functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from copy import deepcopy
from einops import rearrange
from glob import glob
from natsort import natsorted

from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config

__models__ = {"class_label": EncoderUNetModel, "segmentation": UNetModel}


def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


class NoisyLatentImageClassifier(pl.LightningModule):
    def __init__(
        self,
        diffusion_path,
        num_classes,
        ckpt_path=None,
        pool="attention",
        label_key=None,
        diffusion_ckpt_path=None,
        scheduler_config=None,
        weight_decay=1.0e-2,
        log_steps=10,
        monitor="val/loss",
        *args,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        self.num_classes = num_classes
        # get latest config of diffusion model
        diffusion_config = natsorted(
            glob(os.path.join(diffusion_path, "configs", "*-project.yaml"))
        )[-1]
        self.diffusion_config = OmegaConf.load(diffusion_config).model
        self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
        self.load_diffusion()

        self.monitor = monitor
        self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
        self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
        self.log_steps = log_steps

        self.label_key = (
            label_key
            if not hasattr(self.diffusion_model, "cond_stage_key")
            else self.diffusion_model.cond_stage_key
        )

        assert (
            self.label_key is not None
        ), "label_key neither in diffusion model nor in model.params"

        if self.label_key not in __models__:
            raise NotImplementedError()

        self.load_classifier(ckpt_path, pool)

        self.scheduler_config = scheduler_config
        self.use_scheduler = self.scheduler_config is not None
        self.weight_decay = weight_decay

    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
        sd = torch.load(path, map_location="cpu")
        if "state_dict" in list(sd.keys()):
            sd = sd["state_dict"]
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        missing, unexpected = (
            self.load_state_dict(sd, strict=False)
            if not only_model
            else self.model.load_state_dict(sd, strict=False)
        )
        print(
            f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
        )
        if len(missing) > 0:
            print(f"Missing Keys: {missing}")
        if len(unexpected) > 0:
            print(f"Unexpected Keys: {unexpected}")

    def load_diffusion(self):
        model = instantiate_from_config(self.diffusion_config)
        self.diffusion_model = model.eval()
        self.diffusion_model.train = disabled_train
        for param in self.diffusion_model.parameters():
            param.requires_grad = False

    def load_classifier(self, ckpt_path, pool):
        model_config = deepcopy(self.diffusion_config.params.unet_config.params)
        model_config.in_channels = (
            self.diffusion_config.params.unet_config.params.out_channels
        )
        model_config.out_channels = self.num_classes
        if self.label_key == "class_label":
            model_config.pool = pool

        self.model = __models__[self.label_key](**model_config)
        if ckpt_path is not None:
            print(
                "#####################################################################"
            )
            print(f'load from ckpt "{ckpt_path}"')
            print(
                "#####################################################################"
            )
            self.init_from_ckpt(ckpt_path)

    @torch.no_grad()
    def get_x_noisy(self, x, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x))
        continuous_sqrt_alpha_cumprod = None
        if self.diffusion_model.use_continuous_noise:
            continuous_sqrt_alpha_cumprod = (
                self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
            )
            # todo: make sure t+1 is correct here

        return self.diffusion_model.q_sample(
            x_start=x,
            t=t,
            noise=noise,
            continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod,
        )

    def forward(self, x_noisy, t, *args, **kwargs):
        return self.model(x_noisy, t)

    @torch.no_grad()
    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = rearrange(x, "b h w c -> b c h w")
        x = x.to(memory_format=torch.contiguous_format).float()
        return x

    @torch.no_grad()
    def get_conditioning(self, batch, k=None):
        if k is None:
            k = self.label_key
        assert k is not None, "Needs to provide label key"

        targets = batch[k].to(self.device)

        if self.label_key == "segmentation":
            targets = rearrange(targets, "b h w c -> b c h w")
            for down in range(self.numd):
                h, w = targets.shape[-2:]
                targets = F.interpolate(targets, size=(h // 2, w // 2), mode="nearest")

            # targets = rearrange(targets,'b c h w -> b h w c')

        return targets

    def compute_top_k(self, logits, labels, k, reduction="mean"):
        _, top_ks = torch.topk(logits, k, dim=1)
        if reduction == "mean":
            return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
        elif reduction == "none":
            return (top_ks == labels[:, None]).float().sum(dim=-1)

    def on_train_epoch_start(self):
        # save some memory
        self.diffusion_model.model.to("cpu")

    @torch.no_grad()
    def write_logs(self, loss, logits, targets):
        log_prefix = "train" if self.training else "val"
        log = {}
        log[f"{log_prefix}/loss"] = loss.mean()
        log[f"{log_prefix}/acc@1"] = self.compute_top_k(
            logits, targets, k=1, reduction="mean"
        )
        log[f"{log_prefix}/acc@5"] = self.compute_top_k(
            logits, targets, k=5, reduction="mean"
        )

        self.log_dict(
            log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True
        )
        self.log("loss", log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
        self.log(
            "global_step", self.global_step, logger=False, on_epoch=False, prog_bar=True
        )
        lr = self.optimizers().param_groups[0]["lr"]
        self.log("lr_abs", lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)

    def shared_step(self, batch, t=None):
        x, *_ = self.diffusion_model.get_input(
            batch, k=self.diffusion_model.first_stage_key
        )
        targets = self.get_conditioning(batch)
        if targets.dim() == 4:
            targets = targets.argmax(dim=1)
        if t is None:
            t = torch.randint(
                0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device
            ).long()
        else:
            t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
        x_noisy = self.get_x_noisy(x, t)
        logits = self(x_noisy, t)

        loss = F.cross_entropy(logits, targets, reduction="none")

        self.write_logs(loss.detach(), logits.detach(), targets.detach())

        loss = loss.mean()
        return loss, logits, x_noisy, targets

    def training_step(self, batch, batch_idx):
        loss, *_ = self.shared_step(batch)
        return loss

    def reset_noise_accs(self):
        self.noisy_acc = {
            t: {"acc@1": [], "acc@5": []}
            for t in range(
                0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t
            )
        }

    def on_validation_start(self):
        self.reset_noise_accs()

    @torch.no_grad()
    def validation_step(self, batch, batch_idx):
        loss, *_ = self.shared_step(batch)

        for t in self.noisy_acc:
            _, logits, _, targets = self.shared_step(batch, t)
            self.noisy_acc[t]["acc@1"].append(
                self.compute_top_k(logits, targets, k=1, reduction="mean")
            )
            self.noisy_acc[t]["acc@5"].append(
                self.compute_top_k(logits, targets, k=5, reduction="mean")
            )

        return loss

    def configure_optimizers(self):
        optimizer = AdamW(
            self.model.parameters(),
            lr=self.learning_rate,
            weight_decay=self.weight_decay,
        )

        if self.use_scheduler:
            scheduler = instantiate_from_config(self.scheduler_config)

            print("Setting up LambdaLR scheduler...")
            scheduler = [
                {
                    "scheduler": LambdaLR(optimizer, lr_lambda=scheduler.schedule),
                    "interval": "step",
                    "frequency": 1,
                }
            ]
            return [optimizer], scheduler

        return optimizer

    @torch.no_grad()
    def log_images(self, batch, N=8, *args, **kwargs):
        log = dict()
        x = self.get_input(batch, self.diffusion_model.first_stage_key)
        log["inputs"] = x

        y = self.get_conditioning(batch)

        if self.label_key == "class_label":
            y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
            log["labels"] = y

        if ismap(y):
            log["labels"] = self.diffusion_model.to_rgb(y)

            for step in range(self.log_steps):
                current_time = step * self.log_time_interval

                _, logits, x_noisy, _ = self.shared_step(batch, t=current_time)

                log[f"inputs@t{current_time}"] = x_noisy

                pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
                pred = rearrange(pred, "b h w c -> b c h w")

                log[f"pred@t{current_time}"] = self.diffusion_model.to_rgb(pred)

        for key in log:
            log[key] = log[key][:N]

        return log


================================================
FILE: ldm/models/diffusion/ddim.py
================================================
"""SAMPLING ONLY."""

import torch
import numpy as np
from tqdm import tqdm

from ldm.modules.diffusionmodules.util import (
    make_ddim_sampling_parameters,
    make_ddim_timesteps,
    noise_like,
    extract_into_tensor,
)


class DDIMSampler(object):
    def __init__(self, model, schedule="linear", **kwargs):
        super().__init__()
        self.model = model
        self.ddpm_num_timesteps = model.num_timesteps
        self.schedule = schedule

    def register_buffer(self, name, attr):
        if type(attr) == torch.Tensor:
            if attr.device != torch.device("cuda"):
                attr = attr.to(torch.device("cuda"))
        setattr(self, name, attr)

    def make_schedule(
        self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True
    ):
        self.ddim_timesteps = make_ddim_timesteps(
            ddim_discr_method=ddim_discretize,
            num_ddim_timesteps=ddim_num_steps,
            num_ddpm_timesteps=self.ddpm_num_timesteps,
            verbose=verbose,
        )
        alphas_cumprod = self.model.alphas_cumprod
        assert (
            alphas_cumprod.shape[0] == self.ddpm_num_timesteps
        ), "alphas have to be defined for each timestep"

        def to_torch(x):
            return x.clone().detach().to(torch.float32).to(self.model.device)

        self.register_buffer("betas", to_torch(self.model.betas))
        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
        self.register_buffer(
            "alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)
        )

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer(
            "sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_one_minus_alphas_cumprod",
            to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
        )
        self.register_buffer(
            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_recipm1_alphas_cumprod",
            to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
        )

        # ddim sampling parameters
        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(
            alphacums=alphas_cumprod.cpu(),
            ddim_timesteps=self.ddim_timesteps,
            eta=ddim_eta,
            verbose=verbose,
        )
        self.register_buffer("ddim_sigmas", ddim_sigmas)
        self.register_buffer("ddim_alphas", ddim_alphas)
        self.register_buffer("ddim_alphas_prev", ddim_alphas_prev)
        self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas))
        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
            (1 - self.alphas_cumprod_prev)
            / (1 - self.alphas_cumprod)
            * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
        )
        self.register_buffer(
            "ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps
        )

    @torch.no_grad()
    def sample(
        self,
        S,
        batch_size,
        shape,
        conditioning=None,
        callback=None,
        normals_sequence=None,
        img_callback=None,
        quantize_x0=False,
        eta=0.0,
        mask=None,
        x0=None,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        verbose=True,
        x_T=None,
        log_every_t=100,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
        **kwargs,
    ):
        if conditioning is not None:
            if isinstance(conditioning, dict):
                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
                if cbs != batch_size:
                    print(
                        f"Warning: Got {cbs} conditionings but batch-size is {batch_size}"
                    )
            else:
                if conditioning.shape[0] != batch_size:
                    print(
                        f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}"
                    )

        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
        # sampling
        C, H, W = shape
        size = (batch_size, C, H, W)
        print(f"Data shape for DDIM sampling is {size}, eta {eta}")

        samples, intermediates = self.ddim_sampling(
            conditioning,
            size,
            callback=callback,
            img_callback=img_callback,
            quantize_denoised=quantize_x0,
            mask=mask,
            x0=x0,
            ddim_use_original_steps=False,
            noise_dropout=noise_dropout,
            temperature=temperature,
            score_corrector=score_corrector,
            corrector_kwargs=corrector_kwargs,
            x_T=x_T,
            log_every_t=log_every_t,
            unconditional_guidance_scale=unconditional_guidance_scale,
            unconditional_conditioning=unconditional_conditioning,
        )
        return samples, intermediates

    @torch.no_grad()
    def ddim_sampling(
        self,
        cond,
        shape,
        x_T=None,
        ddim_use_original_steps=False,
        callback=None,
        timesteps=None,
        quantize_denoised=False,
        mask=None,
        x0=None,
        img_callback=None,
        log_every_t=100,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
    ):
        device = self.model.betas.device
        b = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T

        if timesteps is None:
            timesteps = (
                self.ddpm_num_timesteps
                if ddim_use_original_steps
                else self.ddim_timesteps
            )
        elif timesteps is not None and not ddim_use_original_steps:
            subset_end = (
                int(
                    min(timesteps / self.ddim_timesteps.shape[0], 1)
                    * self.ddim_timesteps.shape[0]
                )
                - 1
            )
            timesteps = self.ddim_timesteps[:subset_end]

        intermediates = {"x_inter": [img], "pred_x0": [img]}
        time_range = (
            reversed(range(0, timesteps))
            if ddim_use_original_steps
            else np.flip(timesteps)
        )
        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
        print(f"Running DDIM Sampling with {total_steps} timesteps")

        iterator = tqdm(time_range, desc="DDIM Sampler", total=total_steps)

        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full((b,), step, device=device, dtype=torch.long)

            if mask is not None:
                assert x0 is not None
                img_orig = self.model.q_sample(
                    x0, ts
                )  # TODO: deterministic forward pass?
                img = img_orig * mask + (1.0 - mask) * img

            outs = self.p_sample_ddim(
                img,
                cond,
                ts,
                index=index,
                use_original_steps=ddim_use_original_steps,
                quantize_denoised=quantize_denoised,
                temperature=temperature,
                noise_dropout=noise_dropout,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
            )
            img, pred_x0 = outs
            if callback:
                callback(i)
            if img_callback:
                img_callback(pred_x0, i)

            if index % log_every_t == 0 or index == total_steps - 1:
                intermediates["x_inter"].append(img)
                intermediates["pred_x0"].append(pred_x0)

        return img, intermediates

    @torch.no_grad()
    def p_sample_ddim(
        self,
        x,
        c,
        t,
        index,
        repeat_noise=False,
        use_original_steps=False,
        quantize_denoised=False,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
    ):
        b, *_, device = *x.shape, x.device

        if unconditional_conditioning is None or unconditional_guidance_scale == 1.0:
            e_t = self.model.apply_model(x, t, c)
        else:
            x_in = torch.cat([x] * 2)
            t_in = torch.cat([t] * 2)
            c_in = torch.cat([unconditional_conditioning, c])
            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

        if score_corrector is not None:
            assert self.model.parameterization == "eps"
            e_t = score_corrector.modify_score(
                self.model, e_t, x, t, c, **corrector_kwargs
            )

        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
        alphas_prev = (
            self.model.alphas_cumprod_prev
            if use_original_steps
            else self.ddim_alphas_prev
        )
        sqrt_one_minus_alphas = (
            self.model.sqrt_one_minus_alphas_cumprod
            if use_original_steps
            else self.ddim_sqrt_one_minus_alphas
        )
        sigmas = (
            self.model.ddim_sigmas_for_original_num_steps
            if use_original_steps
            else self.ddim_sigmas
        )
        # select parameters corresponding to the currently considered timestep
        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
        sqrt_one_minus_at = torch.full(
            (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
        )

        # current prediction for x_0
        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
        if quantize_denoised:
            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
        # direction pointing to x_t
        dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.0:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
        return x_prev, pred_x0

    @torch.no_grad()
    def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
        # fast, but does not allow for exact reconstruction
        # t serves as an index to gather the correct alphas
        if use_original_steps:
            sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
            sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
        else:
            sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
            sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas

        if noise is None:
            noise = torch.randn_like(x0)
        return (
            extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
            + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise
        )

    @torch.no_grad()
    def decode(
        self,
        x_latent,
        cond,
        t_start,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        use_original_steps=False,
        z_mask=None,
        x0=None,
    ):
        timesteps = (
            np.arange(self.ddpm_num_timesteps)
            if use_original_steps
            else self.ddim_timesteps
        )
        timesteps = timesteps[:t_start]

        time_range = np.flip(timesteps)
        total_steps = timesteps.shape[0]
        print(f"Running DDIM Sampling with {total_steps} timesteps")

        iterator = tqdm(time_range, desc="Decoding image", total=total_steps)
        x_dec = x_latent
        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full(
                (x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long
            )

            if z_mask is not None and i < total_steps - 2:
                assert x0 is not None
                img_orig = self.model.q_sample(
                    x0, ts
                )  # TODO: deterministic forward pass?
                mask_inv = 1.0 - z_mask
                x_dec = (img_orig * mask_inv) + (z_mask * x_dec)

            x_dec, _ = self.p_sample_ddim(
                x_dec,
                cond,
                ts,
                index=index,
                use_original_steps=use_original_steps,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
            )
        return x_dec


================================================
FILE: ldm/models/diffusion/ddpm.py
================================================
"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""

import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only

from ldm.util import (
    log_txt_as_img,
    exists,
    default,
    ismap,
    isimage,
    mean_flat,
    count_params,
    instantiate_from_config,
)
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import (
    normal_kl,
    DiagonalGaussianDistribution,
)
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import (
    make_beta_schedule,
    extract_into_tensor,
    noise_like,
)
from ldm.models.diffusion.ddim import DDIMSampler


__conditioning_keys__ = {"concat": "c_concat", "crossattn": "c_crossattn", "adm": "y"}


def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


def uniform_on_device(r1, r2, shape, device):
    return (r1 - r2) * torch.rand(*shape, device=device) + r2


class DDPM(pl.LightningModule):
    # classic DDPM with Gaussian diffusion, in image space
    def __init__(
        self,
        unet_config,
        timesteps=1000,
        beta_schedule="linear",
        loss_type="l2",
        ckpt_path=None,
        ignore_keys=[],
        load_only_unet=False,
        monitor="val/loss",
        use_ema=True,
        first_stage_key="image",
        image_size=256,
        channels=3,
        log_every_t=100,
        clip_denoised=True,
        linear_start=1e-4,
        linear_end=2e-2,
        cosine_s=8e-3,
        given_betas=None,
        original_elbo_weight=0.0,
        v_posterior=0.0,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
        l_simple_weight=1.0,
        conditioning_key=None,
        parameterization="eps",  # all assuming fixed variance schedules
        scheduler_config=None,
        use_positional_encodings=False,
        learn_logvar=False,
        logvar_init=0.0,
    ):
        super().__init__()
        assert parameterization in [
            "eps",
            "x0",
        ], 'currently only supporting "eps" and "x0"'
        self.parameterization = parameterization
        print(
            f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode"
        )
        self.cond_stage_model = None
        self.clip_denoised = clip_denoised
        self.log_every_t = log_every_t
        self.first_stage_key = first_stage_key
        self.image_size = image_size  # try conv?
        self.channels = channels
        self.use_positional_encodings = use_positional_encodings
        self.model = DiffusionWrapper(unet_config, conditioning_key)
        count_params(self.model, verbose=True)
        self.use_ema = use_ema
        if self.use_ema:
            self.model_ema = LitEma(self.model)
            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")

        self.use_scheduler = scheduler_config is not None
        if self.use_scheduler:
            self.scheduler_config = scheduler_config

        self.v_posterior = v_posterior
        self.original_elbo_weight = original_elbo_weight
        self.l_simple_weight = l_simple_weight

        if monitor is not None:
            self.monitor = monitor
        if ckpt_path is not None:
            self.init_from_ckpt(
                ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet
            )

        self.register_schedule(
            given_betas=given_betas,
            beta_schedule=beta_schedule,
            timesteps=timesteps,
            linear_start=linear_start,
            linear_end=linear_end,
            cosine_s=cosine_s,
        )

        self.loss_type = loss_type

        self.learn_logvar = learn_logvar
        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
        if self.learn_logvar:
            self.logvar = nn.Parameter(self.logvar, requires_grad=True)

    def register_schedule(
        self,
        given_betas=None,
        beta_schedule="linear",
        timesteps=1000,
        linear_start=1e-4,
        linear_end=2e-2,
        cosine_s=8e-3,
    ):
        if exists(given_betas):
            betas = given_betas
        else:
            betas = make_beta_schedule(
                beta_schedule,
                timesteps,
                linear_start=linear_start,
                linear_end=linear_end,
                cosine_s=cosine_s,
            )
        alphas = 1.0 - betas
        alphas_cumprod = np.cumprod(alphas, axis=0)
        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])

        (timesteps,) = betas.shape
        self.num_timesteps = int(timesteps)
        self.linear_start = linear_start
        self.linear_end = linear_end
        assert (
            alphas_cumprod.shape[0] == self.num_timesteps
        ), "alphas have to be defined for each timestep"

        to_torch = partial(torch.tensor, dtype=torch.float32)

        self.register_buffer("betas", to_torch(betas))
        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
        self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
        self.register_buffer(
            "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
        )
        self.register_buffer(
            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
        )
        self.register_buffer(
            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
        )
        self.register_buffer(
            "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
        )

        # calculations for posterior q(x_{t-1} | x_t, x_0)
        posterior_variance = (1 - self.v_posterior) * betas * (
            1.0 - alphas_cumprod_prev
        ) / (1.0 - alphas_cumprod) + self.v_posterior * betas
        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
        self.register_buffer("posterior_variance", to_torch(posterior_variance))
        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
        self.register_buffer(
            "posterior_log_variance_clipped",
            to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
        )
        self.register_buffer(
            "posterior_mean_coef1",
            to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
        )
        self.register_buffer(
            "posterior_mean_coef2",
            to_torch(
                (1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
            ),
        )

        if self.parameterization == "eps":
            lvlb_weights = self.betas**2 / (
                2
                * self.posterior_variance
                * to_torch(alphas)
                * (1 - self.alphas_cumprod)
            )
        elif self.parameterization == "x0":
            lvlb_weights = (
                0.5
                * np.sqrt(torch.Tensor(alphas_cumprod))
                / (2.0 * 1 - torch.Tensor(alphas_cumprod))
            )
        else:
            raise NotImplementedError("mu not supported")
        # TODO how to choose this term
        lvlb_weights[0] = lvlb_weights[1]
        self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
        assert not torch.isnan(self.lvlb_weights).all()

    @contextmanager
    def ema_scope(self, context=None):
        if self.use_ema:
            self.model_ema.store(self.model.parameters())
            self.model_ema.copy_to(self.model)
            if context is not None:
                print(f"{context}: Switched to EMA weights")
        try:
            yield None
        finally:
            if self.use_ema:
                self.model_ema.restore(self.model.parameters())
                if context is not None:
                    print(f"{context}: Restored training weights")

    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
        sd = torch.load(path, map_location="cpu")
        if "state_dict" in list(sd.keys()):
            sd = sd["state_dict"]
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        missing, unexpected = (
            self.load_state_dict(sd, strict=False)
            if not only_model
            else self.model.load_state_dict(sd, strict=False)
        )
        print(
            f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
        )
        if len(missing) > 0:
            print(f"Missing Keys: {missing}")
        if len(unexpected) > 0:
            print(f"Unexpected Keys: {unexpected}")

    def q_mean_variance(self, x_start, t):
        """
        Get the distribution q(x_t | x_0).
        :param x_start: the [N x C x ...] tensor of noiseless inputs.
        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
        """
        mean = extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
        log_variance = extract_into_tensor(
            self.log_one_minus_alphas_cumprod, t, x_start.shape
        )
        return mean, variance, log_variance

    def predict_start_from_noise(self, x_t, t, noise):
        return (
            extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
            - extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
            * noise
        )

    def q_posterior(self, x_start, x_t, t):
        posterior_mean = (
            extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
            + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
        )
        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
        posterior_log_variance_clipped = extract_into_tensor(
            self.posterior_log_variance_clipped, t, x_t.shape
        )
        return posterior_mean, posterior_variance, posterior_log_variance_clipped

    def p_mean_variance(self, x, t, clip_denoised: bool):
        model_out = self.model(x, t)
        if self.parameterization == "eps":
            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
        elif self.parameterization == "x0":
            x_recon = model_out
        if clip_denoised:
            x_recon.clamp_(-1.0, 1.0)

        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
            x_start=x_recon, x_t=x, t=t
        )
        return model_mean, posterior_variance, posterior_log_variance

    @torch.no_grad()
    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
        b, *_, device = *x.shape, x.device
        model_mean, _, model_log_variance = self.p_mean_variance(
            x=x, t=t, clip_denoised=clip_denoised
        )
        noise = noise_like(x.shape, device, repeat_noise)
        # no noise when t == 0
        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise

    @torch.no_grad()
    def p_sample_loop(self, shape, return_intermediates=False):
        device = self.betas.device
        b = shape[0]
        img = torch.randn(shape, device=device)
        intermediates = [img]
        for i in tqdm(
            reversed(range(0, self.num_timesteps)),
            desc="Sampling t",
            total=self.num_timesteps,
        ):
            img = self.p_sample(
                img,
                torch.full((b,), i, device=device, dtype=torch.long),
                clip_denoised=self.clip_denoised,
            )
            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
                intermediates.append(img)
        if return_intermediates:
            return img, intermediates
        return img

    @torch.no_grad()
    def sample(self, batch_size=16, return_intermediates=False):
        image_size = self.image_size
        channels = self.channels
        return self.p_sample_loop(
            (batch_size, channels, image_size, image_size),
            return_intermediates=return_intermediates,
        )

    def q_sample(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        return (
            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
            + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
            * noise
        )

    def get_loss(self, pred, target, mean=True):
        if self.loss_type == "l1":
            loss = (target - pred).abs()
            if mean:
                loss = loss.mean()
        elif self.loss_type == "l2":
            if mean:
                loss = torch.nn.functional.mse_loss(target, pred)
            else:
                loss = torch.nn.functional.mse_loss(target, pred, reduction="none")
        else:
            raise NotImplementedError("unknown loss type '{loss_type}'")

        return loss

    def p_losses(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
        model_out = self.model(x_noisy, t)

        loss_dict = {}
        if self.parameterization == "eps":
            target = noise
        elif self.parameterization == "x0":
            target = x_start
        else:
            raise NotImplementedError(
                f"Paramterization {self.parameterization} not yet supported"
            )

        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])

        log_prefix = "train" if self.training else "val"

        loss_dict.update({f"{log_prefix}/loss_simple": loss.mean()})
        loss_simple = loss.mean() * self.l_simple_weight

        loss_vlb = (self.lvlb_weights[t] * loss).mean()
        loss_dict.update({f"{log_prefix}/loss_vlb": loss_vlb})

        loss = loss_simple + self.original_elbo_weight * loss_vlb

        loss_dict.update({f"{log_prefix}/loss": loss})

        return loss, loss_dict

    def forward(self, x, *args, **kwargs):
        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
        t = torch.randint(
            0, self.num_timesteps, (x.shape[0],), device=self.device
        ).long()
        return self.p_losses(x, t, *args, **kwargs)

    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = rearrange(x, "b h w c -> b c h w")
        x = x.to(memory_format=torch.contiguous_format).float()
        return x

    def shared_step(self, batch):
        x = self.get_input(batch, self.first_stage_key)
        loss, loss_dict = self(x)
        return loss, loss_dict

    def training_step(self, batch, batch_idx):
        loss, loss_dict = self.shared_step(batch)

        self.log_dict(
            loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True
        )

        self.log(
            "global_step",
            self.global_step,
            prog_bar=True,
            logger=True,
            on_step=True,
            on_epoch=False,
        )

        if self.use_scheduler:
            lr = self.optimizers().param_groups[0]["lr"]
            self.log(
                "lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False
            )

        return loss

    @torch.no_grad()
    def validation_step(self, batch, batch_idx):
        _, loss_dict_no_ema = self.shared_step(batch)
        with self.ema_scope():
            _, loss_dict_ema = self.shared_step(batch)
            loss_dict_ema = {key + "_ema": loss_dict_ema[key] for key in loss_dict_ema}
        self.log_dict(
            loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
        )
        self.log_dict(
            loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True
        )

    def on_train_batch_end(self, *args, **kwargs):
        if self.use_ema:
            self.model_ema(self.model)

    def _get_rows_from_list(self, samples):
        n_imgs_per_row = len(samples)
        denoise_grid = rearrange(samples, "n b c h w -> b n c h w")
        denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w")
        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
        return denoise_grid

    @torch.no_grad()
    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
        log = dict()
        x = self.get_input(batch, self.first_stage_key)
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        x = x.to(self.device)[:N]
        log["inputs"] = x

        # get diffusion row
        diffusion_row = list()
        x_start = x[:n_row]

        for t in range(self.num_timesteps):
            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
                t = repeat(torch.tensor([t]), "1 -> b", b=n_row)
                t = t.to(self.device).long()
                noise = torch.randn_like(x_start)
                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
                diffusion_row.append(x_noisy)

        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)

        if sample:
            # get denoise row
            with self.ema_scope("Plotting"):
                samples, denoise_row = self.sample(
                    batch_size=N, return_intermediates=True
                )

            log["samples"] = samples
            log["denoise_row"] = self._get_rows_from_list(denoise_row)

        if return_keys:
            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
                return log
            else:
                return {key: log[key] for key in return_keys}
        return log

    def configure_optimizers(self):
        lr = self.learning_rate
        params = list(self.model.parameters())
        if self.learn_logvar:
            params = params + [self.logvar]
        opt = torch.optim.AdamW(params, lr=lr)
        return opt


class LatentDiffusion(DDPM):
    """main class"""

    def __init__(
        self,
        first_stage_config,
        cond_stage_config,
        num_timesteps_cond=None,
        cond_stage_key="image",
        cond_stage_trainable=False,
        concat_mode=True,
        cond_stage_forward=None,
        conditioning_key=None,
        scale_factor=1.0,
        scale_by_std=False,
        *args,
        **kwargs,
    ):
        self.num_timesteps_cond = default(num_timesteps_cond, 1)
        self.scale_by_std = scale_by_std
        assert self.num_timesteps_cond <= kwargs["timesteps"]
        # for backwards compatibility after implementation of DiffusionWrapper
        if conditioning_key is None:
            conditioning_key = "concat" if concat_mode else "crossattn"
        if cond_stage_config == "__is_unconditional__":
            conditioning_key = None
        ckpt_path = kwargs.pop("ckpt_path", None)
        ignore_keys = kwargs.pop("ignore_keys", [])
        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
        self.concat_mode = concat_mode
        self.cond_stage_trainable = cond_stage_trainable
        self.cond_stage_key = cond_stage_key
        try:
            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
        except:
            self.num_downs = 0
        if not scale_by_std:
            self.scale_factor = scale_factor
        else:
            self.register_buffer("scale_factor", torch.tensor(scale_factor))
        self.instantiate_first_stage(first_stage_config)
        self.instantiate_cond_stage(cond_stage_config)
        self.cond_stage_forward = cond_stage_forward
        self.clip_denoised = False
        self.bbox_tokenizer = None

        self.restarted_from_ckpt = False
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys)
            self.restarted_from_ckpt = True

    def make_cond_schedule(
        self,
    ):
        self.cond_ids = torch.full(
            size=(self.num_timesteps,),
            fill_value=self.num_timesteps - 1,
            dtype=torch.long,
        )
        ids = torch.round(
            torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
        ).long()
        self.cond_ids[: self.num_timesteps_cond] = ids

    @rank_zero_only
    @torch.no_grad()
    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
        # only for very first batch
        if (
            self.scale_by_std
            and self.current_epoch == 0
            and self.global_step == 0
            and batch_idx == 0
            and not self.restarted_from_ckpt
        ):
            assert (
                self.scale_factor == 1.0
            ), "rather not use custom rescaling and std-rescaling simultaneously"
            # set rescale weight to 1./std of encodings
            print("### USING STD-RESCALING ###")
            x = super().get_input(batch, self.first_stage_key)
            x = x.to(self.device)
            encoder_posterior = self.encode_first_stage(x)
            z = self.get_first_stage_encoding(encoder_posterior).detach()
            del self.scale_factor
            self.register_buffer("scale_factor", 1.0 / z.flatten().std())
            print(f"setting self.scale_factor to {self.scale_factor}")
            print("### USING STD-RESCALING ###")

    def register_schedule(
        self,
        given_betas=None,
        beta_schedule="linear",
        timesteps=1000,
        linear_start=1e-4,
        linear_end=2e-2,
        cosine_s=8e-3,
    ):
        super().register_schedule(
            given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
        )

        self.shorten_cond_schedule = self.num_timesteps_cond > 1
        if self.shorten_cond_schedule:
            self.make_cond_schedule()

    def instantiate_first_stage(self, config):
        model = instantiate_from_config(config)
        self.first_stage_model = model.eval()
        self.first_stage_model.train = disabled_train
        for param in self.first_stage_model.parameters():
            param.requires_grad = False

    def instantiate_cond_stage(self, config):
        if not self.cond_stage_trainable:
            if config == "__is_first_stage__":
                print("Using first stage also as cond stage.")
                self.cond_stage_model = self.first_stage_model
            elif config == "__is_unconditional__":
                print(f"Training {self.__class__.__name__} as an unconditional model.")
                self.cond_stage_model = None
                # self.be_unconditional = True
            else:
                model = instantiate_from_config(config)
                self.cond_stage_model = model.eval()
                self.cond_stage_model.train = disabled_train
                for param in self.cond_stage_model.parameters():
                    param.requires_grad = False
        else:
            assert config != "__is_first_stage__"
            assert config != "__is_unconditional__"
            model = instantiate_from_config(config)
            self.cond_stage_model = model

    def _get_denoise_row_from_list(
        self, samples, desc="", force_no_decoder_quantization=False
    ):
        denoise_row = []
        for zd in tqdm(samples, desc=desc):
            denoise_row.append(
                self.decode_first_stage(
                    zd.to(self.device), force_not_quantize=force_no_decoder_quantization
                )
            )
        n_imgs_per_row = len(denoise_row)
        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
        denoise_grid = rearrange(denoise_row, "n b c h w -> b n c h w")
        denoise_grid = rearrange(denoise_grid, "b n c h w -> (b n) c h w")
        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
        return denoise_grid

    def get_first_stage_encoding(self, encoder_posterior):
        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
            z = encoder_posterior.sample()
        elif isinstance(encoder_posterior, torch.Tensor):
            z = encoder_posterior
        else:
            raise NotImplementedError(
                f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented"
            )
        return self.scale_factor * z

    def get_learned_conditioning(self, c):
        if self.cond_stage_forward is None:
            if hasattr(self.cond_stage_model, "encode") and callable(
                self.cond_stage_model.encode
            ):
                c = self.cond_stage_model.encode(c)
                if isinstance(c, DiagonalGaussianDistribution):
                    c = c.mode()
            else:
                c = self.cond_stage_model(c)
        else:
            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
        return c

    def meshgrid(self, h, w):
        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)

        arr = torch.cat([y, x], dim=-1)
        return arr

    def delta_border(self, h, w):
        """
        :param h: height
        :param w: width
        :return: normalized distance to image border,
         wtith min distance = 0 at border and max dist = 0.5 at image center
        """
        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
        arr = self.meshgrid(h, w) / lower_right_corner
        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
        edge_dist = torch.min(
            torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1
        )[0]
        return edge_dist

    def get_weighting(self, h, w, Ly, Lx, device):
        weighting = self.delta_border(h, w)
        weighting = torch.clip(
            weighting,
            self.split_input_params["clip_min_weight"],
            self.split_input_params["clip_max_weight"],
        )
        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)

        if self.split_input_params["tie_braker"]:
            L_weighting = self.delta_border(Ly, Lx)
            L_weighting = torch.clip(
                L_weighting,
                self.split_input_params["clip_min_tie_weight"],
                self.split_input_params["clip_max_tie_weight"],
            )

            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
            weighting = weighting * L_weighting
        return weighting

    def get_fold_unfold(
        self, x, kernel_size, stride, uf=1, df=1
    ):  # todo load once not every time, shorten code
        """
        :param x: img of size (bs, c, h, w)
        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
        """
        bs, nc, h, w = x.shape

        # number of crops in image
        Ly = (h - kernel_size[0]) // stride[0] + 1
        Lx = (w - kernel_size[1]) // stride[1] + 1

        if uf == 1 and df == 1:
            fold_params = dict(
                kernel_size=kernel_size, dilation=1, padding=0, stride=stride
            )
            unfold = torch.nn.Unfold(**fold_params)

            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)

            weighting = self.get_weighting(
                kernel_size[0], kernel_size[1], Ly, Lx, x.device
            ).to(x.dtype)
            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))

        elif uf > 1 and df == 1:
            fold_params = dict(
                kernel_size=kernel_size, dilation=1, padding=0, stride=stride
            )
            unfold = torch.nn.Unfold(**fold_params)

            fold_params2 = dict(
                kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
                dilation=1,
                padding=0,
                stride=(stride[0] * uf, stride[1] * uf),
            )
            fold = torch.nn.Fold(
                output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2
            )

            weighting = self.get_weighting(
                kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device
            ).to(x.dtype)
            normalization = fold(weighting).view(
                1, 1, h * uf, w * uf
            )  # normalizes the overlap
            weighting = weighting.view(
                (1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx)
            )

        elif df > 1 and uf == 1:
            fold_params = dict(
                kernel_size=kernel_size, dilation=1, padding=0, stride=stride
            )
            unfold = torch.nn.Unfold(**fold_params)

            fold_params2 = dict(
                kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
                dilation=1,
                padding=0,
                stride=(stride[0] // df, stride[1] // df),
            )
            fold = torch.nn.Fold(
                output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2
            )

            weighting = self.get_weighting(
                kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device
            ).to(x.dtype)
            normalization = fold(weighting).view(
                1, 1, h // df, w // df
            )  # normalizes the overlap
            weighting = weighting.view(
                (1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx)
            )

        else:
            raise NotImplementedError

        return fold, unfold, normalization, weighting

    @torch.no_grad()
    def get_input(
        self,
        batch,
        k,
        return_first_stage_outputs=False,
        force_c_encode=False,
        cond_key=None,
        return_original_cond=False,
        bs=None,
    ):
        x = super().get_input(batch, k)
        if bs is not None:
            x = x[:bs]
        x = x.to(self.device)
        encoder_posterior = self.encode_first_stage(x)
        z = self.get_first_stage_encoding(encoder_posterior).detach()

        if self.model.conditioning_key is not None:
            if cond_key is None:
                cond_key = self.cond_stage_key
            if cond_key != self.first_stage_key:
                if cond_key in ["caption", "coordinates_bbox"]:
                    xc = batch[cond_key]
                elif cond_key == "class_label":
                    xc = batch
                else:
                    xc = super().get_input(batch, cond_key).to(self.device)
            else:
                xc = x
            if not self.cond_stage_trainable or force_c_encode:
                if isinstance(xc, dict) or isinstance(xc, list):
                    # import pudb; pudb.set_trace()
                    c = self.get_learned_conditioning(xc)
                else:
                    c = self.get_learned_conditioning(xc.to(self.device))
            else:
                c = xc
            if bs is not None:
                c = c[:bs]

            if self.use_positional_encodings:
                pos_x, pos_y = self.compute_latent_shifts(batch)
                ckey = __conditioning_keys__[self.model.conditioning_key]
                c = {ckey: c, "pos_x": pos_x, "pos_y": pos_y}

        else:
            c = None
            xc = None
            if self.use_positional_encodings:
                pos_x, pos_y = self.compute_latent_shifts(batch)
                c = {"pos_x": pos_x, "pos_y": pos_y}
        out = [z, c]
        if return_first_stage_outputs:
            xrec = self.decode_first_stage(z)
            out.extend([x, xrec])
        if return_original_cond:
            out.append(xc)
        return out

    @torch.no_grad()
    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
        if predict_cids:
            if z.dim() == 4:
                z = torch.argmax(z.exp(), dim=1).long()
            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
            z = rearrange(z, "b h w c -> b c h w").contiguous()

        z = 1.0 / self.scale_factor * z

        if hasattr(self, "split_input_params"):
            if self.split_input_params["patch_distributed_vq"]:
                ks = self.split_input_params["ks"]  # eg. (128, 128)
                stride = self.split_input_params["stride"]  # eg. (64, 64)
                uf = self.split_input_params["vqf"]
                bs, nc, h, w = z.shape
                if ks[0] > h or ks[1] > w:
                    ks = (min(ks[0], h), min(ks[1], w))
                    print("reducing Kernel")

                if stride[0] > h or stride[1] > w:
                    stride = (min(stride[0], h), min(stride[1], w))
                    print("reducing stride")

                fold, unfold, normalization, weighting = self.get_fold_unfold(
                    z, ks, stride, uf=uf
                )

                z = unfold(z)  # (bn, nc * prod(**ks), L)
                # 1. Reshape to img shape
                z = z.view(
                    (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
                )  # (bn, nc, ks[0], ks[1], L )

                # 2. apply model loop over last dim
                if isinstance(self.first_stage_model, VQModelInterface):
                    output_list = [
                        self.first_stage_model.decode(
                            z[:, :, :, :, i],
                            force_not_quantize=predict_cids or force_not_quantize,
                        )
                        for i in range(z.shape[-1])
                    ]
                else:
                    output_list = [
                        self.first_stage_model.decode(z[:, :, :, :, i])
                        for i in range(z.shape[-1])
                    ]

                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
                o = o * weighting
                # Reverse 1. reshape to img shape
                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
                # stitch crops together
                decoded = fold(o)
                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
                return decoded
            else:
                if isinstance(self.first_stage_model, VQModelInterface):
                    return self.first_stage_model.decode(
                        z, force_not_quantize=predict_cids or force_not_quantize
                    )
                else:
                    return self.first_stage_model.decode(z)

        else:
            if isinstance(self.first_stage_model, VQModelInterface):
                return self.first_stage_model.decode(
                    z, force_not_quantize=predict_cids or force_not_quantize
                )
            else:
                return self.first_stage_model.decode(z)

    # same as above but without decorator
    def differentiable_decode_first_stage(
        self, z, predict_cids=False, force_not_quantize=False
    ):
        if predict_cids:
            if z.dim() == 4:
                z = torch.argmax(z.exp(), dim=1).long()
            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
            z = rearrange(z, "b h w c -> b c h w").contiguous()

        z = 1.0 / self.scale_factor * z

        if hasattr(self, "split_input_params"):
            if self.split_input_params["patch_distributed_vq"]:
                ks = self.split_input_params["ks"]  # eg. (128, 128)
                stride = self.split_input_params["stride"]  # eg. (64, 64)
                uf = self.split_input_params["vqf"]
                bs, nc, h, w = z.shape
                if ks[0] > h or ks[1] > w:
                    ks = (min(ks[0], h), min(ks[1], w))
                    print("reducing Kernel")

                if stride[0] > h or stride[1] > w:
                    stride = (min(stride[0], h), min(stride[1], w))
                    print("reducing stride")

                fold, unfold, normalization, weighting = self.get_fold_unfold(
                    z, ks, stride, uf=uf
                )

                z = unfold(z)  # (bn, nc * prod(**ks), L)
                # 1. Reshape to img shape
                z = z.view(
                    (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
                )  # (bn, nc, ks[0], ks[1], L )

                # 2. apply model loop over last dim
                if isinstance(self.first_stage_model, VQModelInterface):
                    output_list = [
                        self.first_stage_model.decode(
                            z[:, :, :, :, i],
                            force_not_quantize=predict_cids or force_not_quantize,
                        )
                        for i in range(z.shape[-1])
                    ]
                else:
                    output_list = [
                        self.first_stage_model.decode(z[:, :, :, :, i])
                        for i in range(z.shape[-1])
                    ]

                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
                o = o * weighting
                # Reverse 1. reshape to img shape
                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
                # stitch crops together
                decoded = fold(o)
                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
                return decoded
            else:
                if isinstance(self.first_stage_model, VQModelInterface):
                    return self.first_stage_model.decode(
                        z, force_not_quantize=predict_cids or force_not_quantize
                    )
                else:
                    return self.first_stage_model.decode(z)

        else:
            if isinstance(self.first_stage_model, VQModelInterface):
                return self.first_stage_model.decode(
                    z, force_not_quantize=predict_cids or force_not_quantize
                )
            else:
                return self.first_stage_model.decode(z)

    @torch.no_grad()
    def encode_first_stage(self, x):
        if hasattr(self, "split_input_params"):
            if self.split_input_params["patch_distributed_vq"]:
                ks = self.split_input_params["ks"]  # eg. (128, 128)
                stride = self.split_input_params["stride"]  # eg. (64, 64)
                df = self.split_input_params["vqf"]
                self.split_input_params["original_image_size"] = x.shape[-2:]
                bs, nc, h, w = x.shape
                if ks[0] > h or ks[1] > w:
                    ks = (min(ks[0], h), min(ks[1], w))
                    print("reducing Kernel")

                if stride[0] > h or stride[1] > w:
                    stride = (min(stride[0], h), min(stride[1], w))
                    print("reducing stride")

                fold, unfold, normalization, weighting = self.get_fold_unfold(
                    x, ks, stride, df=df
                )
                z = unfold(x)  # (bn, nc * prod(**ks), L)
                # Reshape to img shape
                z = z.view(
                    (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
                )  # (bn, nc, ks[0], ks[1], L )

                output_list = [
                    self.first_stage_model.encode(z[:, :, :, :, i])
                    for i in range(z.shape[-1])
                ]

                o = torch.stack(output_list, axis=-1)
                o = o * weighting

                # Reverse reshape to img shape
                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
                # stitch crops together
                decoded = fold(o)
                decoded = decoded / normalization
                return decoded

            else:
                return self.first_stage_model.encode(x)
        else:
            return self.first_stage_model.encode(x)

    def shared_step(self, batch, **kwargs):
        x, c = self.get_input(batch, self.first_stage_key)
        loss = self(x, c)
        return loss

    def forward(self, x, c, *args, **kwargs):
        t = torch.randint(
            0, self.num_timesteps, (x.shape[0],), device=self.device
        ).long()
        if self.model.conditioning_key is not None:
            assert c is not None
            if self.cond_stage_trainable:
                c = self.get_learned_conditioning(c)
            if self.shorten_cond_schedule:  # TODO: drop this option
                tc = self.cond_ids[t].to(self.device)
                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
        return self.p_losses(x, c, t, *args, **kwargs)

    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
        def rescale_bbox(bbox):
            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
            return x0, y0, w, h

        return [rescale_bbox(b) for b in bboxes]

    def apply_model(self, x_noisy, t, cond, return_ids=False):
        if isinstance(cond, dict):
            # hybrid case, cond is exptected to be a dict
            pass
        else:
            if not isinstance(cond, list):
                cond = [cond]
            key = (
                "c_concat" if self.model.conditioning_key == "concat" else "c_crossattn"
            )
            cond = {key: cond}

        if hasattr(self, "split_input_params"):
            assert len(cond) == 1  # todo can only deal with one conditioning atm
            assert not return_ids
            ks = self.split_input_params["ks"]  # eg. (128, 128)
            stride = self.split_input_params["stride"]  # eg. (64, 64)

            h, w = x_noisy.shape[-2:]

            fold, unfold, normalization, weighting = self.get_fold_unfold(
                x_noisy, ks, stride
            )

            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
            # Reshape to img shape
            z = z.view(
                (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
            )  # (bn, nc, ks[0], ks[1], L )
            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]

            if (
                self.cond_stage_key in ["image", "LR_image", "segmentation", "bbox_img"]
                and self.model.conditioning_key
            ):  # todo check for completeness
                c_key = next(iter(cond.keys()))  # get key
                c = next(iter(cond.values()))  # get value
                assert len(c) == 1  # todo extend to list with more than one elem
                c = c[0]  # get element

                c = unfold(c)
                c = c.view(
                    (c.shape[0], -1, ks[0], ks[1], c.shape[-1])
                )  # (bn, nc, ks[0], ks[1], L )

                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]

            elif self.cond_stage_key == "coordinates_bbox":
                assert (
                    "original_image_size" in self.split_input_params
                ), "BoudingBoxRescaling is missing original_image_size"

                # assuming padding of unfold is always 0 and its dilation is always 1
                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
                full_img_h, full_img_w = self.split_input_params["original_image_size"]
                # as we are operating on latents, we need the factor from the original image size to the
                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
                num_downs = self.first_stage_model.encoder.num_resolutions - 1
                rescale_latent = 2 ** (num_downs)

                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
                # need to rescale the tl patch coordinates to be in between (0,1)
                tl_patch_coordinates = [
                    (
                        rescale_latent
                        * stride[0]
                        * (patch_nr % n_patches_per_row)
                        / full_img_w,
                        rescale_latent
                        * stride[1]
                        * (patch_nr // n_patches_per_row)
                        / full_img_h,
                    )
                    for patch_nr in range(z.shape[-1])
                ]

                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
                patch_limits = [
                    (
                        x_tl,
                        y_tl,
                        rescale_latent * ks[0] / full_img_w,
                        rescale_latent * ks[1] / full_img_h,
                    )
                    for x_tl, y_tl in tl_patch_coordinates
                ]
                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]

                # tokenize crop coordinates for the bounding boxes of the respective patches
                patch_limits_tknzd = [
                    torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(
                        self.device
                    )
                    for bbox in patch_limits
                ]  # list of length l with tensors of shape (1, 2)
                print(patch_limits_tknzd[0].shape)
                # cut tknzd crop position from conditioning
                assert isinstance(cond, dict), "cond must be dict to be fed into model"
                cut_cond = cond["c_crossattn"][0][..., :-2].to(self.device)
                print(cut_cond.shape)

                adapted_cond = torch.stack(
                    [torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd]
                )
                adapted_cond = rearrange(adapted_cond, "l b n -> (l b) n")
                print(adapted_cond.shape)
                adapted_cond = self.get_learned_conditioning(adapted_cond)
                print(adapted_cond.shape)
                adapted_cond = rearrange(
                    adapted_cond, "(l b) n d -> l b n d", l=z.shape[-1]
                )
                print(adapted_cond.shape)

                cond_list = [{"c_crossattn": [e]} for e in adapted_cond]

            else:
                cond_list = [
                    cond for i in range(z.shape[-1])
                ]  # Todo make this more efficient

            # apply model by loop over crops
            output_list = [
                self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])
            ]
            assert not isinstance(
                output_list[0], tuple
            )  # todo cant deal with multiple model outputs check this never happens

            o = torch.stack(output_list, axis=-1)
            o = o * weighting
            # Reverse reshape to img shape
            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
            # stitch crops together
            x_recon = fold(o) / normalization

        else:
            x_recon = self.model(x_noisy, t, **cond)

        if isinstance(x_recon, tuple) and not return_ids:
            return x_recon[0]
        else:
            return x_recon

    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
        return (
            extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
            - pred_xstart
        ) / extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)

    def _prior_bpd(self, x_start):
        """
        Get the prior KL term for the variational lower-bound, measured in
        bits-per-dim.
        This term can't be optimized, as it only depends on the encoder.
        :param x_start: the [N x C x ...] tensor of inputs.
        :return: a batch of [N] KL values (in bits), one per batch element.
        """
        batch_size = x_start.shape[0]
        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
        kl_prior = normal_kl(
            mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
        )
        return mean_flat(kl_prior) / np.log(2.0)

    def p_losses(self, x_start, cond, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
        model_output = self.apply_model(x_noisy, t, cond)

        loss_dict = {}
        prefix = "train" if self.training else "val"

        if self.parameterization == "x0":
            target = x_start
        elif self.parameterization == "eps":
            target = noise
        else:
            raise NotImplementedError()

        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
        loss_dict.update({f"{prefix}/loss_simple": loss_simple.mean()})

        logvar_t = self.logvar[t].to(self.device)
        loss = loss_simple / torch.exp(logvar_t) + logvar_t
        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
        if self.learn_logvar:
            loss_dict.update({f"{prefix}/loss_gamma": loss.mean()})
            loss_dict.update({"logvar": self.logvar.data.mean()})

        loss = self.l_simple_weight * loss.mean()

        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
        loss_dict.update({f"{prefix}/loss_vlb": loss_vlb})
        loss += self.original_elbo_weight * loss_vlb
        loss_dict.update({f"{prefix}/loss": loss})

        return loss, loss_dict

    def p_mean_variance(
        self,
        x,
        c,
        t,
        clip_denoised: bool,
        return_codebook_ids=False,
        quantize_denoised=False,
        return_x0=False,
        score_corrector=None,
        corrector_kwargs=None,
    ):
        t_in = t
        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)

        if score_corrector is not None:
            assert self.parameterization == "eps"
            model_out = score_corrector.modify_score(
                self, model_out, x, t, c, **corrector_kwargs
            )

        if return_codebook_ids:
            model_out, logits = model_out

        if self.parameterization == "eps":
            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
        elif self.parameterization == "x0":
            x_recon = model_out
        else:
            raise NotImplementedError()

        if clip_denoised:
            x_recon.clamp_(-1.0, 1.0)
        if quantize_denoised:
            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(
            x_start=x_recon, x_t=x, t=t
        )
        if return_codebook_ids:
            return model_mean, posterior_variance, posterior_log_variance, logits
        elif return_x0:
            return model_mean, posterior_variance, posterior_log_variance, x_recon
        else:
            return model_mean, posterior_variance, posterior_log_variance

    @torch.no_grad()
    def p_sample(
        self,
        x,
        c,
        t,
        clip_denoised=False,
        repeat_noise=False,
        return_codebook_ids=False,
        quantize_denoised=False,
        return_x0=False,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
    ):
        b, *_, device = *x.shape, x.device
        outputs = self.p_mean_variance(
            x=x,
            c=c,
            t=t,
            clip_denoised=clip_denoised,
            return_codebook_ids=return_codebook_ids,
            quantize_denoised=quantize_denoised,
            return_x0=return_x0,
            score_corrector=score_corrector,
            corrector_kwargs=corrector_kwargs,
        )
        if return_codebook_ids:
            raise DeprecationWarning("Support dropped.")
            model_mean, _, model_log_variance, logits = outputs
        elif return_x0:
            model_mean, _, model_log_variance, x0 = outputs
        else:
            model_mean, _, model_log_variance = outputs

        noise = noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.0:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
        # no noise when t == 0
        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))

        if return_codebook_ids:
            return model_mean + nonzero_mask * (
                0.5 * model_log_variance
            ).exp() * noise, logits.argmax(dim=1)
        if return_x0:
            return (
                model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise,
                x0,
            )
        else:
            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise

    @torch.no_grad()
    def progressive_denoising(
        self,
        cond,
        shape,
        verbose=True,
        callback=None,
        quantize_denoised=False,
        img_callback=None,
        mask=None,
        x0=None,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        batch_size=None,
        x_T=None,
        start_T=None,
        log_every_t=None,
    ):
        if not log_every_t:
            log_every_t = self.log_every_t
        timesteps = self.num_timesteps
        if batch_size is not None:
            b = batch_size if batch_size is not None else shape[0]
            shape = [batch_size] + list(shape)
        else:
            b = batch_size = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=self.device)
        else:
            img = x_T
        intermediates = []
        if cond is not None:
            if isinstance(cond, dict):
                cond = {
                    key: cond[key][:batch_size]
                    if not isinstance(cond[key], list)
                    else list(map(lambda x: x[:batch_size], cond[key]))
                    for key in cond
                }
            else:
                cond = (
                    [c[:batch_size] for c in cond]
                    if isinstance(cond, list)
                    else cond[:batch_size]
                )

        if start_T is not None:
            timesteps = min(timesteps, start_T)
        iterator = (
            tqdm(
                reversed(range(0, timesteps)),
                desc="Progressive Generation",
                total=timesteps,
            )
            if verbose
            else reversed(range(0, timesteps))
        )
        if type(temperature) == float:
            temperature = [temperature] * timesteps

        for i in iterator:
            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
            if self.shorten_cond_schedule:
                assert self.model.conditioning_key != "hybrid"
                tc = self.cond_ids[ts].to(cond.device)
                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))

            img, x0_partial = self.p_sample(
                img,
                cond,
                ts,
                clip_denoised=self.clip_denoised,
                quantize_denoised=quantize_denoised,
                return_x0=True,
                temperature=temperature[i],
                noise_dropout=noise_dropout,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs,
            )
            if mask is not None:
                assert x0 is not None
                img_orig = self.q_sample(x0, ts)
                img = img_orig * mask + (1.0 - mask) * img

            if i % log_every_t == 0 or i == timesteps - 1:
                intermediates.append(x0_partial)
            if callback:
                callback(i)
            if img_callback:
                img_callback(img, i)
        return img, intermediates

    @torch.no_grad()
    def p_sample_loop(
        self,
        cond,
        shape,
        return_intermediates=False,
        x_T=None,
        verbose=True,
        callback=None,
        timesteps=None,
        quantize_denoised=False,
        mask=None,
        x0=None,
        img_callback=None,
        start_T=None,
        log_every_t=None,
    ):
        if not log_every_t:
            log_every_t = self.log_every_t
        device = self.betas.device
        b = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T

        intermediates = [img]
        if timesteps is None:
            timesteps = self.num_timesteps

        if start_T is not None:
            timesteps = min(timesteps, start_T)
        iterator = (
            tqdm(reversed(range(0, timesteps)), desc="Sampling t", total=timesteps)
            if verbose
            else reversed(range(0, timesteps))
        )

        if mask is not None:
            assert x0 is not None
            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match

        for i in iterator:
            ts = torch.full((b,), i, device=device, dtype=torch.long)
            if self.shorten_cond_schedule:
                assert self.model.conditioning_key != "hybrid"
                tc = self.cond_ids[ts].to(cond.device)
                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))

            img = self.p_sample(
                img,
                cond,
                ts,
                clip_denoised=self.clip_denoised,
                quantize_denoised=quantize_denoised,
            )
            if mask is not None:
                img_orig = self.q_sample(x0, ts)
                img = img_orig * mask + (1.0 - mask) * img

            if i % log_every_t == 0 or i == timesteps - 1:
                intermediates.append(img)
            if callback:
                callback(i)
            if img_callback:
                img_callback(img, i)

        if return_intermediates:
            return img, intermediates
        return img

    @torch.no_grad()
    def sample(
        self,
        cond,
        batch_size=16,
        return_intermediates=False,
        x_T=None,
        verbose=True,
        timesteps=None,
        quantize_denoised=False,
        mask=None,
        x0=None,
        shape=None,
        **kwargs,
    ):
        if shape is None:
            shape = (batch_size, self.channels, self.image_size, self.image_size)
        if cond is not None:
            if isinstance(cond, dict):
                cond = {
                    key: cond[key][:batch_size]
                    if not isinstance(cond[key], list)
                    else list(map(lambda x: x[:batch_size], cond[key]))
                    for key in cond
                }
            else:
                cond = (
                    [c[:batch_size] for c in cond]
                    if isinstance(cond, list)
                    else cond[:batch_size]
                )
        return self.p_sample_loop(
            cond,
            shape,
            return_intermediates=return_intermediates,
            x_T=x_T,
            verbose=verbose,
            timesteps=timesteps,
            quantize_denoised=quantize_denoised,
            mask=mask,
            x0=x0,
        )

    @torch.no_grad()
    def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
        if ddim:
            ddim_sampler = DDIMSampler(self)
            shape = (self.channels, self.image_size, self.image_size)
            samples, intermediates = ddim_sampler.sample(
                ddim_steps, batch_size, shape, cond, verbose=False, **kwargs
            )

        else:
            samples, intermediates = self.sample(
                cond=cond, batch_size=batch_size, return_intermediates=True, **kwargs
            )

        return samples, intermediates

    @torch.no_grad()
    def log_images(
        self,
        batch,
        N=8,
        n_row=4,
        sample=True,
        ddim_steps=200,
        ddim_eta=1.0,
        return_keys=None,
        quantize_denoised=True,
        inpaint=True,
        plot_denoise_rows=False,
        plot_progressive_rows=True,
        plot_diffusion_rows=True,
        **kwargs,
    ):
        use_ddim = ddim_steps is not None

        log = dict()
        z, c, x, xrec, xc = self.get_input(
            batch,
            self.first_stage_key,
            return_first_stage_outputs=True,
            force_c_encode=True,
            return_original_cond=True,
            bs=N,
        )
        N = min(x.shape[0], N)
        n_row = min(x.shape[0], n_row)
        log["inputs"] = x
        log["reconstruction"] = xrec
        if self.model.conditioning_key is not None:
            if hasattr(self.cond_stage_model, "decode"):
                xc = self.cond_stage_model.decode(c)
                log["conditioning"] = xc
            elif self.cond_stage_key in ["caption"]:
                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
                log["conditioning"] = xc
            elif self.cond_stage_key == "class_label":
                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
                log["conditioning"] = xc
            elif isimage(xc):
                log["conditioning"] = xc
            if ismap(xc):
                log["original_conditioning"] = self.to_rgb(xc)

        if plot_diffusion_rows:
            # get diffusion row
            diffusion_row = list()
            z_start = z[:n_row]
            for t in range(self.num_timesteps):
                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
                    t = repeat(torch.tensor([t]), "1 -> b", b=n_row)
                    t = t.to(self.device).long()
                    noise = torch.randn_like(z_start)
                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
                    diffusion_row.append(self.decode_first_stage(z_noisy))

            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
            diffusion_grid = rearrange(diffusion_row, "n b c h w -> b n c h w")
            diffusion_grid = rearrange(diffusion_grid, "b n c h w -> (b n) c h w")
            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
            log["diffusion_row"] = diffusion_grid

        if sample:
            # get denoise row
            with self.ema_scope("Plotting"):
                samples, z_denoise_row = self.sample_log(
                    cond=c,
                    batch_size=N,
                    ddim=use_ddim,
                    ddim_steps=ddim_steps,
                    eta=ddim_eta,
                )
                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
            x_samples = self.decode_first_stage(samples)
            log["samples"] = x_samples
            if plot_denoise_rows:
                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
                log["denoise_row"] = denoise_grid

            if (
                quantize_denoised
                and not isinstance(self.first_stage_model, AutoencoderKL)
                and not isinstance(self.first_stage_model, IdentityFirstStage)
            ):
                # also display when quantizing x0 while sampling
                with self.ema_scope("Plotting Quantized Denoised"):
                    samples, z_denoise_row = self.sample_log(
                        cond=c,
                        batch_size=N,
                        ddim=use_ddim,
                        ddim_steps=ddim_steps,
                        eta=ddim_eta,
                        quantize_denoised=True,
                    )
                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
                    #                                      quantize_denoised=True)
                x_samples = self.decode_first_stage(samples.to(self.device))
                log["samples_x0_quantized"] = x_samples

            if inpaint:
                # make a simple center square
                b, h, w = z.shape[0], z.shape[2], z.shape[3]
                mask = torch.ones(N, h, w).to(self.device)
                # zeros will be filled in
                mask[:, h // 4 : 3 * h // 4, w // 4 : 3 * w // 4] = 0.0
                mask = mask[:, None, ...]
                with self.ema_scope("Plotting Inpaint"):
                    samples, _ = self.sample_log(
                        cond=c,
                        batch_size=N,
                        ddim=use_ddim,
                        eta=ddim_eta,
                        ddim_steps=ddim_steps,
                        x0=z[:N],
                        mask=mask,
                    )
                x_samples = self.decode_first_stage(samples.to(self.device))
                log["samples_inpainting"] = x_samples
                log["mask"] = mask

                # outpaint
                with self.ema_scope("Plotting Outpaint"):
                    samples, _ = self.sample_log(
                        cond=c,
                        batch_size=N,
                        ddim=use_ddim,
                        eta=ddim_eta,
                        ddim_steps=ddim_steps,
                        x0=z[:N],
                        mask=mask,
                    )
                x_samples = self.decode_first_stage(samples.to(self.device))
                log["samples_outpainting"] = x_samples

        if plot_progressive_rows:
            with self.ema_scope("Plotting Progressives"):
                img, progressives = self.progressive_denoising(
                    c,
                    shape=(self.channels, self.image_size, self.image_size),
                    batch_size=N,
                )
            prog_row = self._get_denoise_row_from_list(
                progressives, desc="Progressive Generation"
            )
            log["progressive_row"] = prog_row

        if return_keys:
            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
                return log
            else:
                return {key: log[key] for key in return_keys}
        return log

    def configure_optimizers(self):
        lr = self.learning_rate
        params = list(self.model.parameters())
        if self.cond_stage_trainable:
            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
            params = params + list(self.cond_stage_model.parameters())
        if self.learn_logvar:
            print("Diffusion model optimizing logvar")
            params.append(self.logvar)
        opt = torch.optim.AdamW(params, lr=lr)
        if self.use_scheduler:
            assert "target" in self.scheduler_config
            scheduler = instantiate_from_config(self.scheduler_config)

            print("Setting up LambdaLR scheduler...")
            scheduler = [
                {
                    "scheduler": LambdaLR(opt, lr_lambda=scheduler.schedule),
                    "interval": "step",
                    "frequency": 1,
                }
            ]
            return [opt], scheduler
        return opt

    @torch.no_grad()
    def to_rgb(self, x):
        x = x.float()
        if not hasattr(self, "colorize"):
            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
        x = nn.functional.conv2d(x, weight=self.colorize)
        x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
        return x


class DiffusionWrapper(pl.LightningModule):
    def __init__(self, diff_model_config, conditioning_key):
        super().__init__()
        self.diffusion_model = instantiate_from_config(diff_model_config)
        self.conditioning_key = conditioning_key
        assert self.conditioning_key in [None, "concat", "crossattn", "hybrid", "adm"]

    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
        if self.conditioning_key is None:
            out = self.diffusion_model(x, t)
        elif self.conditioning_key == "concat":
            xc = torch.cat([x] + c_concat, dim=1)
            out = self.diffusion_model(xc, t)
        elif self.conditioning_key == "crossattn":
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(x, t, context=cc)
        elif self.conditioning_key == "hybrid":
            xc = torch.cat([x] + c_concat, dim=1)
            cc = torch.cat(c_crossattn, 1)
            out = self.diffusion_model(xc, t, context=cc)
        elif self.conditioning_key == "adm":
            cc = c_crossattn[0]
            out = self.diffusion_model(x, t, y=cc)
        else:
            raise NotImplementedError()

        return out


class Layout2ImgDiffusion(LatentDiffusion):
    # TODO: move all layout-specific hacks to this class
    def __init__(self, cond_stage_key, *args, **kwargs):
        assert (
            cond_stage_key == "coordinates_bbox"
        ), 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)

    def log_images(self, batch, N=8, *args, **kwargs):
        logs = super().log_images(batch=batch, N=N, *args, **kwargs)

        key = "train" if self.training else "validation"
        dset = self.trainer.datamodule.datasets[key]
        mapper = dset.conditional_builders[self.cond_stage_key]

        bbox_imgs = []

        def map_fn(catno):
            return dset.get_textual_label(dset.get_category_id(catno))

        for tknzd_bbox in batch[self.cond_stage_key][:N]:
            bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
            bbox_imgs.append(bboximg)

        cond_img = torch.stack(bbox_imgs, dim=0)
        logs["bbox_image"] = cond_img
        return logs


================================================
FILE: ldm/models/diffusion/dpm_solver/__init__.py
================================================


================================================
FILE: ldm/models/diffusion/dpm_solver/dpm_solver.py
================================================
import torch
import math
from tqdm import tqdm


class NoiseScheduleVP:
    def __init__(
        self,
        schedule="discrete",
        betas=None,
        alphas_cumprod=None,
        continuous_beta_0=0.1,
        continuous_beta_1=20.0,
    ):
        """Create a wrapper class for the forward SDE (VP type).
        ***
        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
        ***
        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
            log_alpha_t = self.marginal_log_mean_coeff(t)
            sigma_t = self.marginal_std(t)
            lambda_t = self.marginal_lambda(t)
        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
            t = self.inverse_lambda(lambda_t)
        ===============================================================
        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
        1. For discrete-time DPMs:
            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
                t_i = (i + 1) / N
            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
            Args:
                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
                and
                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
        2. For continuous-time DPMs:
            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
            schedule are the default settings in DDPM and improved-DDPM:
            Args:
                beta_min: A `float` number. The smallest beta for the linear schedule.
                beta_max: A `float` number. The largest beta for the linear schedule.
                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
                T: A `float` number. The ending time of the forward process.
        ===============================================================
        Args:
            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
                    'linear' or 'cosine' for continuous-time DPMs.
        Returns:
            A wrapper object of the forward SDE (VP type).

        ===============================================================
        Example:
        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
        >>> ns = NoiseScheduleVP('discrete', betas=betas)
        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
        # For continuous-time DPMs (VPSDE), linear schedule:
        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
        """

        if schedule not in ["discrete", "linear", "cosine"]:
            raise ValueError(
                "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(
                    schedule
                )
            )

        self.schedule = schedule
        if schedule == "discrete":
            if betas is not None:
                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
            else:
                assert alphas_cumprod is not None
                log_alphas = 0.5 * torch.log(alphas_cumprod)
            self.total_N = len(log_alphas)
            self.T = 1.0
            self.t_array = torch.linspace(0.0, 1.0, self.total_N + 1)[1:].reshape(
                (1, -1)
            )
            self.log_alpha_array = log_alphas.reshape(
                (
                    1,
                    -1,
                )
            )
        else:
            self.total_N = 1000
            self.beta_0 = continuous_beta_0
            self.beta_1 = continuous_beta_1
            self.cosine_s = 0.008
            self.cosine_beta_max = 999.0
            self.cosine_t_max = (
                math.atan(self.cosine_beta_max * (1.0 + self.cosine_s) / math.pi)
                * 2.0
                * (1.0 + self.cosine_s)
                / math.pi
                - self.cosine_s
            )
            self.cosine_log_alpha_0 = math.log(
                math.cos(self.cosine_s / (1.0 + self.cosine_s) * math.pi / 2.0)
            )
            self.schedule = schedule
            if schedule == "cosine":
                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
                self.T = 0.9946
            else:
                self.T = 1.0

    def marginal_log_mean_coeff(self, t):
        """
        Compute log(alpha_t) of a given continuous-time label t in [0, T].
        """
        if self.schedule == "discrete":
            return interpolate_fn(
                t.reshape((-1, 1)),
                self.t_array.to(t.device),
                self.log_alpha_array.to(t.device),
            ).reshape((-1))
        elif self.schedule == "linear":
            return -0.25 * t**2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
        elif self.schedule == "cosine":

            def log_alpha_fn(s):
                return torch.log(
                    torch.cos(
                        (s + self.cosine_s) / (1.0 + self.cosine_s) * math.pi / 2.0
                    )
                )

            log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
            return log_alpha_t

    def marginal_alpha(self, t):
        """
        Compute alpha_t of a given continuous-time label t in [0, T].
        """
        return torch.exp(self.marginal_log_mean_coeff(t))

    def marginal_std(self, t):
        """
        Compute sigma_t of a given continuous-time label t in [0, T].
        """
        return torch.sqrt(1.0 - torch.exp(2.0 * self.marginal_log_mean_coeff(t)))

    def marginal_lambda(self, t):
        """
        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
        """
        log_mean_coeff = self.marginal_log_mean_coeff(t)
        log_std = 0.5 * torch.log(1.0 - torch.exp(2.0 * log_mean_coeff))
        return log_mean_coeff - log_std

    def inverse_lambda(self, lamb):
        """
        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
        """
        if self.schedule == "linear":
            tmp = (
                2.0
                * (self.beta_1 - self.beta_0)
                * torch.logaddexp(-2.0 * lamb, torch.zeros((1,)).to(lamb))
            )
            Delta = self.beta_0**2 + tmp
            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
        elif self.schedule == "discrete":
            log_alpha = -0.5 * torch.logaddexp(
                torch.zeros((1,)).to(lamb.device), -2.0 * lamb
            )
            t = interpolate_fn(
                log_alpha.reshape((-1, 1)),
                torch.flip(self.log_alpha_array.to(lamb.device), [1]),
                torch.flip(self.t_array.to(lamb.device), [1]),
            )
            return t.reshape((-1,))
        else:
            log_alpha = -0.5 * torch.logaddexp(-2.0 * lamb, torch.zeros((1,)).to(lamb))

            def t_fn(log_alpha_t):
                return (
                    torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0))
                    * 2.0
                    * (1.0 + self.cosine_s)
                    / math.pi
                    - self.cosine_s
                )

            t = t_fn(log_alpha)
            return t


def model_wrapper(
    model,
    noise_schedule,
    model_type="noise",
    model_kwargs={},
    guidance_type="uncond",
    condition=None,
    unconditional_condition=None,
    guidance_scale=1.0,
    classifier_fn=None,
    classifier_kwargs={},
):
    """Create a wrapper function for the noise prediction model.
    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
    We support four types of the diffusion model by setting `model_type`:
        1. "noise": noise prediction model. (Trained by predicting noise).
        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
        3. "v": velocity prediction model. (Trained by predicting the velocity).
            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
                arXiv preprint arXiv:2202.00512 (2022).
            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
                arXiv preprint arXiv:2210.02303 (2022).

        4. "score": marginal score function. (Trained by denoising score matching).
            Note that the score function and the noise prediction model follows a simple relationship:
            ```
                noise(x_t, t) = -sigma_t * score(x_t, t)
            ```
    We support three types of guided sampling by DPMs by setting `guidance_type`:
        1. "uncond": unconditional sampling by DPMs.
            The input `model` has the following format:
            ``
                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
            ``
        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
            The input `model` has the following format:
            ``
                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
            ``
            The input `classifier_fn` has the following format:
            ``
                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
            ``
            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
            The input `model` has the following format:
            ``
                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
            ``
            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
                arXiv preprint arXiv:2207.12598 (2022).

    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
    or continuous-time labels (i.e. epsilon to T).
    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
    ``
        def model_fn(x, t_continuous) -> noise:
            t_input = get_model_input_time(t_continuous)
            return noise_pred(model, x, t_input, **model_kwargs)
    ``
    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
    ===============================================================
    Args:
        model: A diffusion model with the corresponding format described above.
        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
        model_type: A `str`. The parameterization type of the diffusion model.
                    "noise" or "x_start" or "v" or "score".
        model_kwargs: A `dict`. A dict for the other inputs of the model function.
        guidance_type: A `str`. The type of the guidance for sampling.
                    "uncond" or "classifier" or "classifier-free".
        condition: A pytorch tensor. The condition for the guided sampling.
                    Only used for "classifier" or "classifier-free" guidance type.
        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
                    Only used for "classifier-free" guidance type.
        guidance_scale: A `float`. The scale for the guided sampling.
        classifier_fn: A classifier function. Only used for the classifier guidance.
        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
    Returns:
        A noise prediction model that accepts the noised data and the continuous time as the inputs.
    """

    def get_model_input_time(t_continuous):
        """
        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
        For continuous-time DPMs, we just use `t_continuous`.
        """
        if noise_schedule.schedule == "discrete":
            return (t_continuous - 1.0 / noise_schedule.total_N) * 1000.0
        else:
            return t_continuous

    def noise_pred_fn(x, t_continuous, cond=None):
        if t_continuous.reshape((-1,)).shape[0] == 1:
            t_continuous = t_continuous.expand((x.shape[0]))
        t_input = get_model_input_time(t_continuous)
        if cond is None:
            output = model(x, t_input, **model_kwargs)
        else:
            output = model(x, t_input, cond, **model_kwargs)
        if model_type == "noise":
            return output
        elif model_type == "x_start":
            alpha_t, sigma_t = noise_schedule.marginal_alpha(
                t_continuous
            ), noise_schedule.marginal_std(t_continuous)
            dims = x.dim()
            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(
                sigma_t, dims
            )
        elif model_type == "v":
            alpha_t, sigma_t = noise_schedule.marginal_alpha(
                t_continuous
            ), noise_schedule.marginal_std(t_continuous)
            dims = x.dim()
            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
        elif model_type == "score":
            sigma_t = noise_schedule.marginal_std(t_continuous)
            dims = x.dim()
            return -expand_dims(sigma_t, dims) * output

    def cond_grad_fn(x, t_input):
        """
        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
        """
        with torch.enable_grad():
            x_in = x.detach().requires_grad_(True)
            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
            return torch.autograd.grad(log_prob.sum(), x_in)[0]

    def model_fn(x, t_continuous):
        """
        The noise predicition model function that is used for DPM-Solver.
        """
        if t_continuous.reshape((-1,)).shape[0] == 1:
            t_continuous = t_continuous.expand((x.shape[0]))
        if guidance_type == "uncond":
            return noise_pred_fn(x, t_continuous)
        elif guidance_type == "classifier":
            assert classifier_fn is not None
            t_input = get_model_input_time(t_continuous)
            cond_grad = cond_grad_fn(x, t_input)
            sigma_t = noise_schedule.marginal_std(t_continuous)
            noise = noise_pred_fn(x, t_continuous)
            return (
                noise
                - guidance_scale
                * expand_dims(sigma_t, dims=cond_grad.dim())
                * cond_grad
            )
        elif guidance_type == "classifier-free":
            if guidance_scale == 1.0 or unconditional_condition is None:
                return noise_pred_fn(x, t_continuous, cond=condition)
            else:
                x_in = torch.cat([x] * 2)
                t_in = torch.cat([t_continuous] * 2)
                c_in = torch.cat([unconditional_condition, condition])
                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
                return noise_uncond + guidance_scale * (noise - noise_uncond)

    assert model_type in ["noise", "x_start", "v"]
    assert guidance_type in ["uncond", "classifier", "classifier-free"]
    return model_fn


class DPM_Solver:
    def __init__(
        self,
        model_fn,
        noise_schedule,
        predict_x0=False,
        thresholding=False,
        max_val=1.0,
    ):
        """Construct a DPM-Solver.
        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
        Args:
            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
                ``
                def model_fn(x, t_continuous):
                    return noise
                ``
            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.

        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
        """
        self.model = model_fn
        self.noise_schedule = noise_schedule
        self.predict_x0 = predict_x0
        self.thresholding = thresholding
        self.max_val = max_val

    def noise_prediction_fn(self, x, t):
        """
        Return the noise prediction model.
        """
        return self.model(x, t)

    def data_prediction_fn(self, x, t):
        """
        Return the data prediction model (with thresholding).
        """
        noise = self.noise_prediction_fn(x, t)
        dims = x.dim()
        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(
            t
        ), self.noise_schedule.marginal_std(t)
        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
        if self.thresholding:
            p = 0.995  # A hyperparameter in the paper of "Imagen" [1].
            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
            s = expand_dims(
                torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims
            )
            x0 = torch.clamp(x0, -s, s) / s
        return x0

    def model_fn(self, x, t):
        """
        Convert the model to the noise prediction model or the data prediction model.
        """
        if self.predict_x0:
            return self.data_prediction_fn(x, t)
        else:
            return self.noise_prediction_fn(x, t)

    def get_time_steps(self, skip_type, t_T, t_0, N, device):
        """Compute the intermediate time steps for sampling.
        Args:
            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
                - 'logSNR': uniform logSNR for the time steps.
                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
            t_T: A `float`. The starting time of the sampling (default is T).
            t_0: A `float`. The ending time of the sampling (default is epsilon).
            N: A `int`. The total number of the spacing of the time steps.
            device: A torch device.
        Returns:
            A pytorch tensor of the time steps, with the shape (N + 1,).
        """
        if skip_type == "logSNR":
            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
            logSNR_steps = torch.linspace(
                lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1
            ).to(device)
            return self.noise_schedule.inverse_lambda(logSNR_steps)
        elif skip_type == "time_uniform":
            return torch.linspace(t_T, t_0, N + 1).to(device)
        elif skip_type == "time_quadratic":
            t_order = 2
            t = (
                torch.linspace(t_T ** (1.0 / t_order), t_0 ** (1.0 / t_order), N + 1)
                .pow(t_order)
                .to(device)
            )
            return t
        else:
            raise ValueError(
                "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(
                    skip_type
                )
            )

    def get_orders_and_timesteps_for_singlestep_solver(
        self, steps, order, skip_type, t_T, t_0, device
    ):
        """
        Get the order of each step for sampling by the singlestep DPM-Solver.
        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
            - If order == 1:
                We take `steps` of DPM-Solver-1 (i.e. DDIM).
            - If order == 2:
                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
            - If order == 3:
                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
        ============================================
        Args:
            order: A `int`. The max order for the solver (2 or 3).
            steps: A `int`. The total number of function evaluations (NFE).
            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
                - 'logSNR': uniform logSNR for the time steps.
                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
            t_T: A `float`. The starting time of the sampling (default is T).
            t_0: A `float`. The ending time of the sampling (default is epsilon).
            device: A torch device.
        Returns:
            orders: A list of the solver order of each step.
        """
        if order == 3:
            K = steps // 3 + 1
            if steps % 3 == 0:
                orders = [
                    3,
                ] * (
                    K - 2
                ) + [2, 1]
            elif steps % 3 == 1:
                orders = [
                    3,
                ] * (
                    K - 1
                ) + [1]
            else:
                orders = [
                    3,
                ] * (
                    K - 1
                ) + [2]
        elif order == 2:
            if steps % 2 == 0:
                K = steps // 2
                orders = [
                    2,
                ] * K
            else:
                K = steps // 2 + 1
                orders = [
                    2,
                ] * (
                    K - 1
                ) + [1]
        elif order == 1:
            K = 1
            orders = [
                1,
            ] * steps
        else:
            raise ValueError("'order' must be '1' or '2' or '3'.")
        if skip_type == "logSNR":
            # To reproduce the results in DPM-Solver paper
            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
        else:
            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
                torch.cumsum(
                    torch.tensor(
                        [
                            0,
                        ]
                        + orders
                    )
                ).to(device)
            ]
        return timesteps_outer, orders

    def denoise_to_zero_fn(self, x, s):
        """
        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
        """
        return self.data_prediction_fn(x, s)

    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
        """
        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
        Args:
            x: A pytorch tensor. The initial value at time `s`.
            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
            model_s: A pytorch tensor. The model function evaluated at time `s`.
                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
            return_intermediate: A `bool`. If true, also return the model value at time `s`.
        Returns:
            x_t: A pytorch tensor. The approximated solution at time `t`.
        """
        ns = self.noise_schedule
        dims = x.dim()
        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
        h = lambda_t - lambda_s
        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(
            s
        ), ns.marginal_log_mean_coeff(t)
        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
        alpha_t = torch.exp(log_alpha_t)

        if self.predict_x0:
            phi_1 = torch.expm1(-h)
            if model_s is None:
                model_s = self.model_fn(x, s)
            x_t = (
                expand_dims(sigma_t / sigma_s, dims) * x
                - expand_dims(alpha_t * phi_1, dims) * model_s
            )
            if return_intermediate:
                return x_t, {"model_s": model_s}
            else:
                return x_t
        else:
            phi_1 = torch.expm1(h)
            if model_s is None:
                model_s = self.model_fn(x, s)
            x_t = (
                expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
                - expand_dims(sigma_t * phi_1, dims) * model_s
            )
            if return_intermediate:
                return x_t, {"model_s": model_s}
            else:
                return x_t

    def singlestep_dpm_solver_second_update(
        self,
        x,
        s,
        t,
        r1=0.5,
        model_s=None,
        return_intermediate=False,
        solver_type="dpm_solver",
    ):
        """
        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
        Args:
            x: A pytorch tensor. The initial value at time `s`.
            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
            r1: A `float`. The hyperparameter of the second-order solver.
            model_s: A pytorch tensor. The model function evaluated at time `s`.
                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
        Returns:
            x_t: A pytorch tensor. The approximated solution at time `t`.
        """
        if solver_type not in ["dpm_solver", "taylor"]:
            raise ValueError(
                "'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(
                    solver_type
                )
            )
        if r1 is None:
            r1 = 0.5
        ns = self.noise_schedule
        dims = x.dim()
        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
        h = lambda_t - lambda_s
        lambda_s1 = lambda_s + r1 * h
        s1 = ns.inverse_lambda(lambda_s1)
        log_alpha_s, log_alpha_s1, log_alpha_t = (
            ns.marginal_log_mean_coeff(s),
            ns.marginal_log_mean_coeff(s1),
            ns.marginal_log_mean_coeff(t),
        )
        sigma_s, sigma_s1, sigma_t = (
            ns.marginal_std(s),
            ns.marginal_std(s1),
            ns.marginal_std(t),
        )
        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)

        if self.predict_x0:
            phi_11 = torch.expm1(-r1 * h)
            phi_1 = torch.expm1(-h)

            if model_s is None:
                model_s = self.model_fn(x, s)
            x_s1 = (
                expand_dims(sigma_s1 / sigma_s, dims) * x
                - expand_dims(alpha_s1 * phi_11, dims) * model_s
            )
            model_s1 = self.model_fn(x_s1, s1)
            if solver_type == "dpm_solver":
                x_t = (
                    expand_dims(sigma_t / sigma_s, dims) * x
                    - expand_dims(alpha_t * phi_1, dims) * model_s
                    - (0.5 / r1)
                    * expand_dims(alpha_t * phi_1, dims)
                    * (model_s1 - model_s)
                )
            elif solver_type == "taylor":
                x_t = (
                    expand_dims(sigma_t / sigma_s, dims) * x
                    - expand_dims(alpha_t * phi_1, dims) * model_s
                    + (1.0 / r1)
                    * expand_dims(alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0), dims)
                    * (model_s1 - model_s)
                )
        else:
            phi_11 = torch.expm1(r1 * h)
            phi_1 = torch.expm1(h)

            if model_s is None:
                model_s = self.model_fn(x, s)
            x_s1 = (
                expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
                - expand_dims(sigma_s1 * phi_11, dims) * model_s
            )
            model_s1 = self.model_fn(x_s1, s1)
            if solver_type == "dpm_solver":
                x_t = (
                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
                    - expand_dims(sigma_t * phi_1, dims) * model_s
                    - (0.5 / r1)
                    * expand_dims(sigma_t * phi_1, dims)
                    * (model_s1 - model_s)
                )
            elif solver_type == "taylor":
                x_t = (
                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
                    - expand_dims(sigma_t * phi_1, dims) * model_s
                    - (1.0 / r1)
                    * expand_dims(sigma_t * ((torch.exp(h) - 1.0) / h - 1.0), dims)
                    * (model_s1 - model_s)
                )
        if return_intermediate:
            return x_t, {"model_s": model_s, "model_s1": model_s1}
        else:
            return x_t

    def singlestep_dpm_solver_third_update(
        self,
        x,
        s,
        t,
        r1=1.0 / 3.0,
        r2=2.0 / 3.0,
        model_s=None,
        model_s1=None,
        return_intermediate=False,
        solver_type="dpm_solver",
    ):
        """
        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
        Args:
            x: A pytorch tensor. The initial value at time `s`.
            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
            r1: A `float`. The hyperparameter of the third-order solver.
            r2: A `float`. The hyperparameter of the third-order solver.
            model_s: A pytorch tensor. The model function evaluated at time `s`.
                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
        Returns:
            x_t: A pytorch tensor. The approximated solution at time `t`.
        """
        if solver_type not in ["dpm_solver", "taylor"]:
            raise ValueError(
                "'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(
                    solver_type
                )
            )
        if r1 is None:
            r1 = 1.0 / 3.0
        if r2 is None:
            r2 = 2.0 / 3.0
        ns = self.noise_schedule
        dims = x.dim()
        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
        h = lambda_t - lambda_s
        lambda_s1 = lambda_s + r1 * h
        lambda_s2 = lambda_s + r2 * h
        s1 = ns.inverse_lambda(lambda_s1)
        s2 = ns.inverse_lambda(lambda_s2)
        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = (
            ns.marginal_log_mean_coeff(s),
            ns.marginal_log_mean_coeff(s1),
            ns.marginal_log_mean_coeff(s2),
            ns.marginal_log_mean_coeff(t),
        )
        sigma_s, sigma_s1, sigma_s2, sigma_t = (
            ns.marginal_std(s),
            ns.marginal_std(s1),
            ns.marginal_std(s2),
            ns.marginal_std(t),
        )
        alpha_s1, alpha_s2, alpha_t = (
            torch.exp(log_alpha_s1),
            torch.exp(log_alpha_s2),
            torch.exp(log_alpha_t),
        )

        if self.predict_x0:
            phi_11 = torch.expm1(-r1 * h)
            phi_12 = torch.expm1(-r2 * h)
            phi_1 = torch.expm1(-h)
            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.0
            phi_2 = phi_1 / h + 1.0
            phi_3 = phi_2 / h - 0.5

            if model_s is None:
                model_s = self.model_fn(x, s)
            if model_s1 is None:
                x_s1 = (
                    expand_dims(sigma_s1 / sigma_s, dims) * x
                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
                )
                model_s1 = self.model_fn(x_s1, s1)
            x_s2 = (
                expand_dims(sigma_s2 / sigma_s, dims) * x
                - expand_dims(alpha_s2 * phi_12, dims) * model_s
                + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
            )
            model_s2 = self.model_fn(x_s2, s2)
            if solver_type == "dpm_solver":
                x_t = (
                    expand_dims(sigma_t / sigma_s, dims) * x
                    - expand_dims(alpha_t * phi_1, dims) * model_s
                    + (1.0 / r2)
                    * expand_dims(alpha_t * phi_2, dims)
                    * (model_s2 - model_s)
                )
            elif solver_type == "taylor":
                D1_0 = (1.0 / r1) * (model_s1 - model_s)
                D1_1 = (1.0 / r2) * (model_s2 - model_s)
                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
                D2 = 2.0 * (D1_1 - D1_0) / (r2 - r1)
                x_t = (
                    expand_dims(sigma_t / sigma_s, dims) * x
                    - expand_dims(alpha_t * phi_1, dims) * model_s
                    + expand_dims(alpha_t * phi_2, dims) * D1
                    - expand_dims(alpha_t * phi_3, dims) * D2
                )
        else:
            phi_11 = torch.expm1(r1 * h)
            phi_12 = torch.expm1(r2 * h)
            phi_1 = torch.expm1(h)
            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.0
            phi_2 = phi_1 / h - 1.0
            phi_3 = phi_2 / h - 0.5

            if model_s is None:
                model_s = self.model_fn(x, s)
            if model_s1 is None:
                x_s1 = (
                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
                )
                model_s1 = self.model_fn(x_s1, s1)
            x_s2 = (
                expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
                - expand_dims(sigma_s2 * phi_12, dims) * model_s
                - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
            )
            model_s2 = self.model_fn(x_s2, s2)
            if solver_type == "dpm_solver":
                x_t = (
                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
                    - expand_dims(sigma_t * phi_1, dims) * model_s
                    - (1.0 / r2)
                    * expand_dims(sigma_t * phi_2, dims)
                    * (model_s2 - model_s)
                )
            elif solver_type == "taylor":
                D1_0 = (1.0 / r1) * (model_s1 - model_s)
                D1_1 = (1.0 / r2) * (model_s2 - model_s)
                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
                D2 = 2.0 * (D1_1 - D1_0) / (r2 - r1)
                x_t = (
                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
                    - expand_dims(sigma_t * phi_1, dims) * model_s
                    - expand_dims(sigma_t * phi_2, dims) * D1
                    - expand_dims(sigma_t * phi_3, dims) * D2
                )

        if return_intermediate:
            return x_t, {"model_s": model_s, "model_s1": model_s1, "model_s2": model_s2}
        else:
            return x_t

    def multistep_dpm_solver_second_update(
        self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"
    ):
        """
        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
        Args:
            x: A pytorch tensor. The initial value at time `s`.
            model_prev_list: A list of pytorch tensor. The previous computed model values.
            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
        Returns:
            x_t: A pytorch tensor. The approximated solution at time `t`.
        """
        if solver_type not in ["dpm_solver", "taylor"]:
            raise ValueError(
                "'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(
                    solver_type
                )
            )
        ns = self.noise_schedule
        dims = x.dim()
        model_prev_1, model_prev_0 = model_prev_list
        t_prev_1, t_prev_0 = t_prev_list
        lambda_prev_1, lambda_prev_0, lambda_t = (
            ns.marginal_lambda(t_prev_1),
            ns.marginal_lambda(t_prev_0),
            ns.marginal_lambda(t),
        )
        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(
            t_prev_0
        ), ns.marginal_log_mean_coeff(t)
        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
        alpha_t = torch.exp(log_alpha_t)

        h_0 = lambda_prev_0 - lambda_prev_1
        h = lambda_t - lambda_prev_0
        r0 = h_0 / h
        D1_0 = expand_dims(1.0 / r0, dims) * (model_prev_0 - model_prev_1)
        if self.predict_x0:
            if solver_type == "dpm_solver":
                x_t = (
                    expand_dims(sigma_t / sigma_prev_0, dims) * x
                    - expand_dims(alpha_t * (torch.exp(-h) - 1.0), dims) * model_prev_0
                    - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.0), dims) * D1_0
                )
            elif solver_type == "taylor":
                x_t = (
                    expand_dims(sigma_t / sigma_prev_0, dims) * x
                    - expand_dims(alpha_t * (torch.exp(-h) - 1.0), dims) * model_prev_0
                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0), dims)
                    * D1_0
                )
        else:
            if solver_type == "dpm_solver":
                x_t = (
                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
                    - expand_dims(sigma_t * (torch.exp(h) - 1.0), dims) * model_prev_0
                    - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.0), dims) * D1_0
                )
            elif solver_type == "taylor":
                x_t = (
                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
                    - expand_dims(sigma_t * (torch.exp(h) - 1.0), dims) * model_prev_0
                    - expand_dims(sigma_t * ((torch.exp(h) - 1.0) / h - 1.0), dims)
                    * D1_0
                )
        return x_t

    def multistep_dpm_solver_third_update(
        self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"
    ):
        """
        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
        Args:
            x: A pytorch tensor. The initial value at time `s`.
            model_prev_list: A list of pytorch tensor. The previous computed model values.
            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
        Returns:
            x_t: A pytorch tensor. The approximated solution at time `t`.
        """
        ns = self.noise_schedule
        dims = x.dim()
        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = (
            ns.marginal_lambda(t_prev_2),
            ns.marginal_lambda(t_prev_1),
            ns.marginal_lambda(t_prev_0),
            ns.marginal_lambda(t),
        )
        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(
            t_prev_0
        ), ns.marginal_log_mean_coeff(t)
        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
        alpha_t = torch.exp(log_alpha_t)

        h_1 = lambda_prev_1 - lambda_prev_2
        h_0 = lambda_prev_0 - lambda_prev_1
        h = lambda_t - lambda_prev_0
        r0, r1 = h_0 / h, h_1 / h
        D1_0 = expand_dims(1.0 / r0, dims) * (model_prev_0 - model_prev_1)
        D1_1 = expand_dims(1.0 / r1, dims) * (model_prev_1 - model_prev_2)
        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
        D2 = expand_dims(1.0 / (r0 + r1), dims) * (D1_0 - D1_1)
        if self.predict_x0:
            x_t = (
                expand_dims(sigma_t / sigma_prev_0, dims) * x
                - expand_dims(alpha_t * (torch.exp(-h) - 1.0), dims) * model_prev_0
                + expand_dims(alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0), dims) * D1
                - expand_dims(
                    alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5), dims
                )
                * D2
            )
        else:
            x_t = (
                expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
                - expand_dims(sigma_t * (torch.exp(h) - 1.0), dims) * model_prev_0
                - expand_dims(sigma_t * ((torch.exp(h) - 1.0) / h - 1.0), dims) * D1
                - expand_dims(sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5), dims)
                * D2
            )
        return x_t

    def singlestep_dpm_solver_update(
        self,
        x,
        s,
        t,
        order,
        return_intermediate=False,
        solver_type="dpm_solver",
        r1=None,
        r2=None,
    ):
        """
        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
        Args:
            x: A pytorch tensor. The initial value at time `s`.
            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
            r1: A `float`. The hyperparameter of the second-order or third-order solver.
            r2: A `float`. The hyperparameter of the third-order solver.
        Returns:
            x_t: A pytorch tensor. The approximated solution at time `t`.
        """
        if order == 1:
            return self.dpm_solver_first_update(
                x, s, t, return_intermediate=return_intermediate
            )
        elif order == 2:
            return self.singlestep_dpm_solver_second_update(
                x,
                s,
                t,
                return_intermediate=return_intermediate,
                solver_type=solver_type,
                r1=r1,
            )
        elif order == 3:
            return self.singlestep_dpm_solver_third_update(
                x,
                s,
                t,
                return_intermediate=return_intermediate,
                solver_type=solver_type,
                r1=r1,
                r2=r2,
            )
        else:
            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))

    def multistep_dpm_solver_update(
        self, x, model_prev_list, t_prev_list, t, order, solver_type="dpm_solver"
    ):
        """
        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
        Args:
            x: A pytorch tensor. The initial value at time `s`.
            model_prev_list: A list of pytorch tensor. The previous computed model values.
            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
        Returns:
            x_t: A pytorch tensor. The approximated solution at time `t`.
        """
        if order == 1:
            return self.dpm_solver_first_update(
                x, t_prev_list[-1], t, model_s=model_prev_list[-1]
            )
        elif order == 2:
            return self.multistep_dpm_solver_second_update(
                x, model_prev_list, t_prev_list, t, solver_type=solver_type
            )
        elif order == 3:
            return self.multistep_dpm_solver_third_update(
                x, model_prev_list, t_prev_list, t, solver_type=solver_type
            )
        else:
            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))

    def dpm_solver_adaptive(
        self,
        x,
        order,
        t_T,
        t_0,
        h_init=0.05,
        atol=0.0078,
        rtol=0.05,
        theta=0.9,
        t_err=1e-5,
        solver_type="dpm_solver",
        callback=None,
        img_callback=None,
    ):
        """
        The adaptive step size solver based on singlestep DPM-Solver.
        Args:
            x: A pytorch tensor. The initial value at time `t_T`.
            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
            t_T: A `float`. The starting time of the sampling (default is T).
            t_0: A `float`. The ending time of the sampling (default is epsilon).
            h_init: A `float`. The initial step size (for logSNR).
            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
        Returns:
            x_0: A pytorch tensor. The approximated solution at time `t_0`.
        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
        """
        ns = self.noise_schedule
        s = t_T * torch.ones((x.shape[0],)).to(x)
        lambda_s = ns.marginal_lambda(s)
        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
        h = h_init * torch.ones_like(s).to(x)
        x_prev = x
        nfe = 0
        if order == 2:
            r1 = 0.5

            def lower_update(x, s, t):
                return self.dpm_solver_first_update(x, s, t, return_intermediate=True)

            def higher_update(x, s, t, **kwargs):
                return self.singlestep_dpm_solver_second_update(
                    x, s, t, r1=r1, solver_type=solver_type, **kwargs
                )

        elif order == 3:
            r1, r2 = 1.0 / 3.0, 2.0 / 3.0

            def lower_update(x, s, t):
                return self.singlestep_dpm_solver_second_update(
                    x, s, t, r1=r1, return_intermediate=True, solver_type=solver_type
                )

            def higher_update(x, s, t, **kwargs):
                return self.singlestep_dpm_solver_third_update(
                    x, s, t, r1=r1, r2=r2, solver_type=solver_type, **kwargs
                )

        else:
            raise ValueError(
                "For adaptive step size solver, order must be 2 or 3, got {}".format(
                    order
                )
            )
        while torch.abs((s - t_0)).mean() > t_err:
            t = ns.inverse_lambda(lambda_s + h)
            x_lower, lower_noise_kwargs = lower_update(x, s, t)
            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
            delta = torch.max(
                torch.ones_like(x).to(x) * atol,
                rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)),
            )

            def norm_fn(v):
                return torch.sqrt(
                    torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True)
                )

            E = norm_fn((x_higher - x_lower) / delta).max()
            if torch.all(E <= 1.0):
                x = x_higher
                s = t
                x_prev = x_lower
                lambda_s = ns.marginal_lambda(s)
                if callback:
                    callback(nfe)
                if img_callback:
                    img_callback(x, nfe)
            h = torch.min(
                theta * h * torch.float_power(E, -1.0 / order).float(),
                lambda_0 - lambda_s,
            )
            nfe += order
        print("adaptive solver nfe", nfe)
        return x

    def sample(
        self,
        x,
        steps=20,
        t_start=None,
        t_end=None,
        order=3,
        skip_type="time_uniform",
        method="singlestep",
        lower_order_final=True,
        denoise_to_zero=False,
        solver_type="dpm_solver",
        atol=0.0078,
        rtol=0.05,
        callback=None,
        img_callback=None,
    ):
        """
        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
        =====================================================
        We support the following algorithms for both noise prediction model and data prediction model:
            - 'singlestep':
                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
                The total number of function evaluations (NFE) == `steps`.
                Given a fixed NFE == `steps`, the sampling procedure is:
                    - If `order` == 1:
                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
                    - If `order` == 2:
                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
                    - If `order` == 3:
                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
            - 'multistep':
                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
                We initialize the first `order` values by lower order multistep solvers.
                Given a fixed NFE == `steps`, the sampling procedure is:
                    Denote K = steps.
                    - If `order` == 1:
                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
                    - If `order` == 2:
                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
                    - If `order` == 3:
                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
            - 'singlestep_fixed':
                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
            - 'adaptive':
                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
                (NFE) and the sample quality.
                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
        =====================================================
        Some advices for choosing the algorithm:
            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
                e.g.
                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
                            skip_type='time_uniform', method='singlestep')
            - For **guided sampling with large guidance scale** by DPMs:
                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
                e.g.
                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
                            skip_type='time_uniform', method='multistep')
        We support three types of `skip_type`:
            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
            - 'time_quadratic': quadratic time for the time steps.
        =====================================================
        Args:
            x: A pytorch tensor. The initial value at time `t_start`
                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
            steps: A `int`. The total number of function evaluations (NFE).
            t_start: A `float`. The starting time of the sampling.
                If `T` is None, we use self.noise_schedule.T (default is 1.0).
            t_end: A `float`. The ending time of the sampling.
                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
                e.g. if total_N == 1000, we have `t_end` == 1e-3.
                For discrete-time DPMs:
                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
                For continuous-time DPMs:
                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
            order: A `int`. The order of DPM-Solver.
            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
            denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
                Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
                This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
                score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
                for diffusion models sampling by diffusion SDEs for low-resolutional images
                (such as CIFAR-10). However, we observed that such trick does not matter for
                high-resolutional images. As it needs an additional NFE, we do not recommend
                it for high-resolutional images.
            lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
                Only valid for `method=multistep` and `steps < 15`. We empirically find that
                this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
                (especially for steps <= 10). So we recommend to set it to be `True`.
            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
        Returns:
            x_end: A pytorch tensor. The approximated solution at time `t_end`.
        """
        t_0 = 1.0 / self.noise_schedule.total_N if t_end is None else t_end
        t_T = self.noise_schedule.T if t_start is None else t_start
        device = x.device
        if method == "adaptive":
            with torch.no_grad():
                x = self.dpm_solver_adaptive(
                    x,
                    order=order,
                    t_T=t_T,
                    t_0=t_0,
                    atol=atol,
                    rtol=rtol,
                    solver_type=solver_type,
                    callback=callback,
                    img_callback=img_callback,
                )
        elif method == "multistep":
            assert steps >= order
            timesteps = self.get_time_steps(
                skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device
            )
            assert timesteps.shape[0] - 1 == steps
            with torch.no_grad():
                vec_t = timesteps[0].expand((x.shape[0]))
                model_prev_list = [self.model_fn(x, vec_t)]
                t_prev_list = [vec_t]
                # Init the first `order` values by lower order multistep DPM-Solver.
                for init_order in tqdm(range(1, order), desc="DPM init order"):
                    vec_t = timesteps[init_order].expand(x.shape[0])
                    x = self.multistep_dpm_solver_update(
                        x,
                        model_prev_list,
                        t_prev_list,
                        vec_t,
                        init_order,
                        solver_type=solver_type,
                    )
                    model_prev_list.append(self.model_fn(x, vec_t))
                    t_prev_list.append(vec_t)
                # Compute the remaining values by `order`-th order multistep DPM-Solver.
                for step in tqdm(range(order, steps + 1), desc="DPM multistep"):
                    vec_t = timesteps[step].expand(x.shape[0])
                    if lower_order_final and steps < 15:
                        step_order = min(order, steps + 1 - step)
                    else:
                        step_order = order
                    x = self.multistep_dpm_solver_update(
                        x,
                        model_prev_list,
                        t_prev_list,
                        vec_t,
                        step_order,
                        solver_type=solver_type,
                    )
                    if callback:
                        callback(step)
                    if img_callback:
                        img_callback(x, step)
                    for i in range(order - 1):
                        t_prev_list[i] = t_prev_list[i + 1]
                        model_prev_list[i] = model_prev_list[i + 1]
                    t_prev_list[-1] = vec_t
                    # We do not need to evaluate the final model value.
                    if step < steps:
                        model_prev_list[-1] = self.model_fn(x, vec_t)
        elif method in ["singlestep", "singlestep_fixed"]:
            if method == "singlestep":
                (
                    timesteps_outer,
                    orders,
                ) = self.get_orders_and_timesteps_for_singlestep_solver(
                    steps=steps,
                    order=order,
                    skip_type=skip_type,
                    t_T=t_T,
                    t_0=t_0,
                    device=device,
                )
            elif method == "singlestep_fixed":
                K = steps // order
                orders = [
                    order,
                ] * K
                timesteps_outer = self.get_time_steps(
                    skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device
                )
            for i, order in enumerate(orders):
                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
                timesteps_inner = self.get_time_steps(
                    skip_type=skip_type,
                    t_T=t_T_inner.item(),
                    t_0=t_0_inner.item(),
                    N=order,
                    device=device,
                )
                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
                vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
                h = lambda_inner[-1] - lambda_inner[0]
                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
                x = self.singlestep_dpm_solver_update(
                    x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2
                )
                if callback:
                    callback(i)
                if img_callback:
                    img_callback(x, i)
        if denoise_to_zero:
            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
            if callback:
                callback(len(orders) - 1)
            if img_callback:
                img_callback(x, len(orders) - 1)
        return x


#############################################################
# other utility functions
#############################################################


def interpolate_fn(x, xp, yp):
    """
    A piecewise linear function y = f(x), using xp and yp as keypoints.
    We implement f(x) in a differentiable way (i.e. applicable for autograd).
    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
    Args:
        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
        yp: PyTorch tensor with shape [C, K].
    Returns:
        The function values f(x), with shape [N, C].
    """
    N, K = x.shape[0], xp.shape[1]
    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
    x_idx = torch.argmin(x_indices, dim=2)
    cand_start_idx = x_idx - 1
    start_idx = torch.where(
        torch.eq(x_idx, 0),
        torch.tensor(1, device=x.device),
        torch.where(
            torch.eq(x_idx, K),
            torch.tensor(K - 2, device=x.device),
            cand_start_idx,
        ),
    )
    end_idx = torch.where(
        torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1
    )
    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
    start_idx2 = torch.where(
        torch.eq(x_idx, 0),
        torch.tensor(0, device=x.device),
        torch.where(
            torch.eq(x_idx, K),
            torch.tensor(K - 2, device=x.device),
            cand_start_idx,
        ),
    )
    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
    start_y = torch.gather(
        y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)
    ).squeeze(2)
    end_y = torch.gather(
        y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)
    ).squeeze(2)
    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
    return cand


def expand_dims(v, dims):
    """
    Expand the tensor `v` to the dim `dims`.
    Args:
        `v`: a PyTorch tensor with shape [N].
        `dim`: a `int`.
    Returns:
        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
    """
    return v[(...,) + (None,) * (dims - 1)]


================================================
FILE: ldm/models/diffusion/dpm_solver/sampler.py
================================================
"""SAMPLING ONLY."""
import torch

from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver


MODEL_TYPES = {"eps": "noise", "v": "v"}


class DPMSolverSampler(object):
    def __init__(self, model, **kwargs):
        super().__init__()
        self.model = model

        def to_torch(x):
            return x.clone().detach().to(torch.float32).to(model.device)

        self.register_buffer("alphas_cumprod", to_torch(model.alphas_cumprod))

    def register_buffer(self, name, attr):
        if type(attr) == torch.Tensor:
            if attr.device != torch.device(self.model.device):
                attr = attr.to(torch.device(self.model.device))
        setattr(self, name, attr)

    @torch.no_grad()
    def sample(
        self,
        S,
        batch_size,
        shape,
        conditioning=None,
        callback=None,
        normals_sequence=None,
        img_callback=None,
        quantize_x0=False,
        eta=0.0,
        mask=None,
        x0=None,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        verbose=True,
        x_T=None,
        log_every_t=100,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
        **kwargs,
    ):
        if conditioning is not None:
            if isinstance(conditioning, dict):
                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
                if cbs != batch_size:
                    print(
                        f"Warning: Got {cbs} conditionings but batch-size is {batch_size}"
                    )
            else:
                if conditioning.shape[0] != batch_size:
                    print(
                        f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}"
                    )

        # sampling
        C, H, W = shape
        size = (batch_size, C, H, W)

        print(f"Data shape for DPM-Solver sampling is {size}, sampling steps {S}")

        device = self.model.betas.device
        if x_T is None:
            img = torch.randn(size, device=device)
        else:
            img = x_T

        ns = NoiseScheduleVP("discrete", alphas_cumprod=self.alphas_cumprod)

        model_fn = model_wrapper(
            lambda x, t, c: self.model.apply_model(x, t, c),
            ns,
            model_type=MODEL_TYPES[self.model.parameterization],
            guidance_type="classifier-free",
            condition=conditioning,
            unconditional_condition=unconditional_conditioning,
            guidance_scale=unconditional_guidance_scale,
        )

        dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
        method = "multistep" if S > 1 else "adaptive"
        x = dpm_solver.sample(
            img,
            steps=S,
            skip_type="time_uniform",
            method=method,
            order=2,
            lower_order_final=True,
            callback=callback,
            img_callback=img_callback,
        )

        return x.to(device), None


================================================
FILE: ldm/models/diffusion/kdiffusion.py
================================================
import k_diffusion as K
import torch
import torch.nn as nn


class KDiffusionSampler:
    def __init__(self, m, sampler, callback=None):
        self.model = m
        self.model_wrap = K.external.CompVisDenoiser(m)
        self.schedule = sampler
        self.generation_callback = callback

    def get_sampler_name(self):
        return self.schedule

    def sample(
        self,
        S,
        conditioning,
        unconditional_guidance_scale,
        unconditional_conditioning,
        x_T,
    ):
        sigmas = self.model_wrap.get_sigmas(S)
        x = x_T * sigmas[0]
        model_wrap_cfg = CFGDenoiser(self.model_wrap)
        samples_ddim = None
        samples_ddim = K.sampling.__dict__[f"sample_{self.schedule}"](
            model_wrap_cfg,
            x,
            sigmas,
            extra_args={
                "cond": conditioning,
                "uncond": unconditional_conditioning,
                "cond_scale": unconditional_guidance_scale,
            },
            disable=False,
            callback=self.generation_callback,
        )
        #
        return samples_ddim, None


class CFGMaskedDenoiser(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.inner_model = model

    def forward(self, x, sigma, uncond, cond, cond_scale, mask, x0, xi):
        x_in = x
        x_in = torch.cat([x_in] * 2)
        sigma_in = torch.cat([sigma] * 2)
        cond_in = torch.cat([uncond, cond])
        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
        denoised = uncond + (cond - uncond) * cond_scale

        if mask is not None:
            assert x0 is not None
            img_orig = x0
            mask_inv = 1.0 - mask
            denoised = (img_orig * mask_inv) + (mask * denoised)

        return denoised


class CFGDenoiser(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.inner_model = model

    def forward(self, x, sigma, uncond, cond, cond_scale):
        x_in = torch.cat([x] * 2)
        sigma_in = torch.cat([sigma] * 2)
        cond_in = torch.cat([uncond, cond])
        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
        return uncond + (cond - uncond) * cond_scale


================================================
FILE: ldm/models/diffusion/plms.py
================================================
"""SAMPLING ONLY."""

import torch
import numpy as np
from tqdm import tqdm

from ldm.modules.diffusionmodules.util import (
    make_ddim_sampling_parameters,
    make_ddim_timesteps,
    noise_like,
)


class PLMSSampler(object):
    def __init__(self, model, schedule="linear", **kwargs):
        super().__init__()
        self.model = model
        self.ddpm_num_timesteps = model.num_timesteps
        self.schedule = schedule

    def register_buffer(self, name, attr):
        if type(attr) == torch.Tensor:
            if attr.device != torch.device("cuda"):
                attr = attr.to(torch.device("cuda"))
        setattr(self, name, attr)

    def make_schedule(
        self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True
    ):
        if ddim_eta != 0:
            raise ValueError("ddim_eta must be 0 for PLMS")
        self.ddim_timesteps = make_ddim_timesteps(
            ddim_discr_method=ddim_discretize,
            num_ddim_timesteps=ddim_num_steps,
            num_ddpm_timesteps=self.ddpm_num_timesteps,
            verbose=verbose,
        )
        alphas_cumprod = self.model.alphas_cumprod
        assert (
            alphas_cumprod.shape[0] == self.ddpm_num_timesteps
        ), "alphas have to be defined for each timestep"

        def to_torch(x):
            return x.clone().detach().to(torch.float32).to(self.model.device)

        self.register_buffer("betas", to_torch(self.model.betas))
        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
        self.register_buffer(
            "alphas_cumprod_prev", to_torch(self.model.alphas_cumprod_prev)
        )

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer(
            "sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_one_minus_alphas_cumprod",
            to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
        )
        self.register_buffer(
            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod.cpu()))
        )
        self.register_buffer(
            "sqrt_recipm1_alphas_cumprod",
            to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
        )

        # ddim sampling parameters
        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(
            alphacums=alphas_cumprod.cpu(),
            ddim_timesteps=self.ddim_timesteps,
            eta=ddim_eta,
            verbose=verbose,
        )
        self.register_buffer("ddim_sigmas", ddim_sigmas)
        self.register_buffer("ddim_alphas", ddim_alphas)
        self.register_buffer("ddim_alphas_prev", ddim_alphas_prev)
        self.register_buffer("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas))
        sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
            (1 - self.alphas_cumprod_prev)
            / (1 - self.alphas_cumprod)
            * (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
        )
        self.register_buffer(
            "ddim_sigmas_for_original_num_steps", sigmas_for_original_sampling_steps
        )

    @torch.no_grad()
    def sample(
        self,
        S,
        batch_size,
        shape,
        conditioning=None,
        callback=None,
        normals_sequence=None,
        img_callback=None,
        quantize_x0=False,
        eta=0.0,
        mask=None,
        x0=None,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        verbose=True,
        x_T=None,
        log_every_t=100,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
        **kwargs,
    ):
        if conditioning is not None:
            if isinstance(conditioning, dict):
                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
                if cbs != batch_size:
                    print(
                        f"Warning: Got {cbs} conditionings but batch-size is {batch_size}"
                    )
            else:
                if conditioning.shape[0] != batch_size:
                    print(
                        f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}"
                    )

        self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
        # sampling
        C, H, W = shape
        size = (batch_size, C, H, W)
        print(f"Data shape for PLMS sampling is {size}")

        samples, intermediates = self.plms_sampling(
            conditioning,
            size,
            callback=callback,
            img_callback=img_callback,
            quantize_denoised=quantize_x0,
            mask=mask,
            x0=x0,
            ddim_use_original_steps=False,
            noise_dropout=noise_dropout,
            temperature=temperature,
            score_corrector=score_corrector,
            corrector_kwargs=corrector_kwargs,
            x_T=x_T,
            log_every_t=log_every_t,
            unconditional_guidance_scale=unconditional_guidance_scale,
            unconditional_conditioning=unconditional_conditioning,
        )
        return samples, intermediates

    @torch.no_grad()
    def plms_sampling(
        self,
        cond,
        shape,
        x_T=None,
        ddim_use_original_steps=False,
        callback=None,
        timesteps=None,
        quantize_denoised=False,
        mask=None,
        x0=None,
        img_callback=None,
        log_every_t=100,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
    ):
        device = self.model.betas.device
        b = shape[0]
        if x_T is None:
            img = torch.randn(shape, device=device)
        else:
            img = x_T

        if timesteps is None:
            timesteps = (
                self.ddpm_num_timesteps
                if ddim_use_original_steps
                else self.ddim_timesteps
            )
        elif timesteps is not None and not ddim_use_original_steps:
            subset_end = (
                int(
                    min(timesteps / self.ddim_timesteps.shape[0], 1)
                    * self.ddim_timesteps.shape[0]
                )
                - 1
            )
            timesteps = self.ddim_timesteps[:subset_end]

        intermediates = {"x_inter": [img], "pred_x0": [img]}
        time_range = (
            list(reversed(range(0, timesteps)))
            if ddim_use_original_steps
            else np.flip(timesteps)
        )
        total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
        print(f"Running PLMS Sampling with {total_steps} timesteps")

        iterator = tqdm(time_range, desc="PLMS Sampler", total=total_steps)
        old_eps = []

        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full((b,), step, device=device, dtype=torch.long)
            ts_next = torch.full(
                (b,),
                time_range[min(i + 1, len(time_range) - 1)],
                device=device,
                dtype=torch.long,
            )

            if mask is not None:
                assert x0 is not None
                img_orig = self.model.q_sample(
                    x0, ts
                )  # TODO: deterministic forward pass?
                img = img_orig * mask + (1.0 - mask) * img

            outs = self.p_sample_plms(
                img,
                cond,
                ts,
                index=index,
                use_original_steps=ddim_use_original_steps,
                quantize_denoised=quantize_denoised,
                temperature=temperature,
                noise_dropout=noise_dropout,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
                old_eps=old_eps,
                t_next=ts_next,
            )
            img, pred_x0, e_t = outs
            old_eps.append(e_t)
            if len(old_eps) >= 4:
                old_eps.pop(0)
            if callback:
                callback(i)
            if img_callback:
                img_callback(pred_x0, i)

            if index % log_every_t == 0 or index == total_steps - 1:
                intermediates["x_inter"].append(img)
                intermediates["pred_x0"].append(pred_x0)

        return img, intermediates

    @torch.no_grad()
    def p_sample_plms(
        self,
        x,
        c,
        t,
        index,
        repeat_noise=False,
        use_original_steps=False,
        quantize_denoised=False,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        old_eps=None,
        t_next=None,
    ):
        b, *_, device = *x.shape, x.device

        def get_model_output(x, t):
            if (
                unconditional_conditioning is None
                or unconditional_guidance_scale == 1.0
            ):
                e_t = self.model.apply_model(x, t, c)
            else:
                x_in = torch.cat([x] * 2)
                t_in = torch.cat([t] * 2)
                c_in = torch.cat([unconditional_conditioning, c])
                e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

            if score_corrector is not None:
                assert self.model.parameterization == "eps"
                e_t = score_corrector.modify_score(
                    self.model, e_t, x, t, c, **corrector_kwargs
                )

            return e_t

        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
        alphas_prev = (
            self.model.alphas_cumprod_prev
            if use_original_steps
            else self.ddim_alphas_prev
        )
        sqrt_one_minus_alphas = (
            self.model.sqrt_one_minus_alphas_cumprod
            if use_original_steps
            else self.ddim_sqrt_one_minus_alphas
        )
        sigmas = (
            self.model.ddim_sigmas_for_original_num_steps
            if use_original_steps
            else self.ddim_sigmas
        )

        def get_x_prev_and_pred_x0(e_t, index):
            # select parameters corresponding to the currently considered timestep
            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
            sqrt_one_minus_at = torch.full(
                (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
            )

            # current prediction for x_0
            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
            if quantize_denoised:
                pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
            # direction pointing to x_t
            dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
            if noise_dropout > 0.0:
                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
            return x_prev, pred_x0

        e_t = get_model_output(x, t)
        if len(old_eps) == 0:
            # Pseudo Improved Euler (2nd order)
            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
            e_t_next = get_model_output(x_prev, t_next)
            e_t_prime = (e_t + e_t_next) / 2
        elif len(old_eps) == 1:
            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
            e_t_prime = (3 * e_t - old_eps[-1]) / 2
        elif len(old_eps) == 2:
            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
        elif len(old_eps) >= 3:
            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
            e_t_prime = (
                55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]
            ) / 24

        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)

        return x_prev, pred_x0, e_t


================================================
FILE: ldm/models/diffusion/sampling_util.py
================================================
def append_dims(x, target_dims):
    """Appends dimensions to the end of a tensor until it has target_dims dimensions.
    From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
    dims_to_append = target_dims - x.ndim
    if dims_to_append < 0:
        raise ValueError(
            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
        )
    return x[(...,) + (None,) * dims_to_append]


def norm_thresholding(x0, value):
    s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
    return x0 * (value / s)


def spatial_norm_thresholding(x0, value):
    # b c h w
    s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
    return x0 * (value / s)


================================================
FILE: ldm/models/med.py
================================================
"""
 * Copyright (c) 2022, salesforce.com, inc.
 * All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
 * By Junnan Li
 * Based on huggingface code base
 * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
"""

import math
from typing import Tuple

import torch
from torch import Tensor, device, nn
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss

from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    BaseModelOutputWithPoolingAndCrossAttentions,
    CausalLMOutputWithCrossAttentions,
)
from transformers.modeling_utils import (
    PreTrainedModel,
    apply_chunking_to_forward,
    find_pruneable_heads_and_indices,
    prune_linear_layer,
)
from transformers.utils import logging
from transformers.models.bert.configuration_bert import BertConfig


logger = logging.get_logger(__name__)


class BertEmbeddings(nn.Module):
    """Construct the embeddings from word and position embeddings."""

    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(
            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
        )
        self.position_embeddings = nn.Embedding(
            config.max_position_embeddings, config.hidden_size
        )

        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer(
            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))
        )
        self.position_embedding_type = getattr(
            config, "position_embedding_type", "absolute"
        )

        self.config = config

    def forward(
        self,
        input_ids=None,
        position_ids=None,
        inputs_embeds=None,
        past_key_values_length=0,
    ):
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = self.position_ids[
                :, past_key_values_length : seq_length + past_key_values_length
            ]

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        embeddings = inputs_embeds

        if self.position_embedding_type == "absolute":
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class BertSelfAttention(nn.Module):
    def __init__(self, config, is_cross_attention):
        super().__init__()
        self.config = config
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
            config, "embedding_size"
        ):
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        if is_cross_attention:
            self.key = nn.Linear(config.encoder_width, self.all_head_size)
            self.value = nn.Linear(config.encoder_width, self.all_head_size)
        else:
            self.key = nn.Linear(config.hidden_size, self.all_head_size)
            self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = getattr(
            config, "position_embedding_type", "absolute"
        )
        if (
            self.position_embedding_type == "relative_key"
            or self.position_embedding_type == "relative_key_query"
        ):
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(
                2 * config.max_position_embeddings - 1, self.attention_head_size
            )
        self.save_attention = False

    def save_attn_gradients(self, attn_gradients):
        self.attn_gradients = attn_gradients

    def get_attn_gradients(self):
        return self.attn_gradients

    def save_attention_map(self, attention_map):
        self.attention_map = attention_map

    def get_attention_map(self):
        return self.attention_map

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        mixed_query_layer = self.query(hidden_states)

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))

        query_layer = self.transpose_for_scores(mixed_query_layer)

        past_key_value = (key_layer, value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        if (
            self.position_embedding_type == "relative_key"
            or self.position_embedding_type == "relative_key_query"
        ):
            seq_length = hidden_states.size()[1]
            position_ids_l = torch.arange(
                seq_length, dtype=torch.long, device=hidden_states.device
            ).view(-1, 1)
            position_ids_r = torch.arange(
                seq_length, dtype=torch.long, device=hidden_states.device
            ).view(1, -1)
            distance = position_ids_l - position_ids_r
            positional_embedding = self.distance_embedding(
                distance + self.max_position_embeddings - 1
            )
            positional_embedding = positional_embedding.to(
                dtype=query_layer.dtype
            )  # fp16 compatibility

            if self.position_embedding_type == "relative_key":
                relative_position_scores = torch.einsum(
                    "bhld,lrd->bhlr", query_layer, positional_embedding
                )
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = torch.einsum(
                    "bhld,lrd->bhlr", query_layer, positional_embedding
                )
                relative_position_scores_key = torch.einsum(
                    "bhrd,lrd->bhlr", key_layer, positional_embedding
                )
                attention_scores = (
                    attention_scores
                    + relative_position_scores_query
                    + relative_position_scores_key
                )

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.Softmax(dim=-1)(attention_scores)

        if is_cross_attention and self.save_attention:
            self.save_attention_map(attention_probs)
            attention_probs.register_hook(self.save_attn_gradients)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs_dropped = self.dropout(attention_probs)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs_dropped = attention_probs_dropped * head_mask

        context_layer = torch.matmul(attention_probs_dropped, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)

        outputs = (
            (context_layer, attention_probs) if output_attentions else (context_layer,)
        )

        outputs = outputs + (past_key_value,)
        return outputs


class BertSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertAttention(nn.Module):
    def __init__(self, config, is_cross_attention=False):
        super().__init__()
        self.self = BertSelfAttention(config, is_cross_attention)
        self.output = BertSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads,
            self.self.num_attention_heads,
            self.self.attention_head_size,
            self.pruned_heads,
        )

        # Prune linear layers
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = (
            self.self.attention_head_size * self.self.num_attention_heads
        )
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        self_outputs = self.self(
            hidden_states,
            attention_mask,
            head_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            past_key_value,
            output_attentions,
        )
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[
            1:
        ]  # add attentions if we output them
        return outputs


class BertIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


class BertOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertLayer(nn.Module):
    def __init__(self, config, layer_num):
        super().__init__()
        self.config = config
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = BertAttention(config)
        self.layer_num = layer_num
        if self.config.add_cross_attention:
            self.crossattention = BertAttention(
                config, is_cross_attention=self.config.add_cross_attention
            )
        self.intermediate = BertIntermediate(config)
        self.output = BertOutput(config)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
        mode=None,
    ):
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = (
            past_key_value[:2] if past_key_value is not None else None
        )
        self_attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            head_mask,
            output_attentions=output_attentions,
            past_key_value=self_attn_past_key_value,
        )
        attention_output = self_attention_outputs[0]

        outputs = self_attention_outputs[1:-1]
        present_key_value = self_attention_outputs[-1]

        if mode == "multimodal":
            assert (
                encoder_hidden_states is not None
            ), "encoder_hidden_states must be given for cross-attention layers"

            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                output_attentions=output_attentions,
            )
            attention_output = cross_attention_outputs[0]
            outputs = (
                outputs + cross_attention_outputs[1:-1]
            )  # add cross attentions if we output attention weights
        layer_output = apply_chunking_to_forward(
            self.feed_forward_chunk,
            self.chunk_size_feed_forward,
            self.seq_len_dim,
            attention_output,
        )
        outputs = (layer_output,) + outputs

        outputs = outputs + (present_key_value,)

        return outputs

    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output


class BertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList(
            [BertLayer(config, i) for i in range(config.num_hidden_layers)]
        )
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
        mode="multimodal",
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = (
            () if output_attentions and self.config.add_cross_attention else None
        )

        next_decoder_cache = () if use_cache else None

        for i in range(self.config.num_hidden_layers):
            layer_module = self.layer[i]
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_head_mask = head_mask[i] if head_mask is not None else None
            past_key_value = past_key_values[i] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:
                if use_cache:
                    logger.warn(
                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                    )
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, past_key_value, output_attentions)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer_module),
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    mode=mode,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    past_key_value,
                    output_attentions,
                    mode=mode,
                )

            hidden_states = layer_outputs[0]
            if use_cache:
                next_decoder_cache += (layer_outputs[-1],)
            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    next_decoder_cache,
                    all_hidden_states,
                    all_self_attentions,
                    all_cross_attentions,
                ]
                if v is not None
            )
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_decoder_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            cross_attentions=all_cross_attentions,
        )


class BertPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class BertPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


class BertLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states


class BertOnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = BertLMPredictionHead(config)

    def forward(self, sequence_output):
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores


class BertPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = BertConfig
    base_model_prefix = "bert"
    _keys_to_ignore_on_load_missing = [r"position_ids"]

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None:
            module.bias.data.zero_()


class BertModel(BertPreTrainedModel):
    """
    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
    input to the forward pass.
    """

    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config

        self.embeddings = BertEmbeddings(config)

        self.encoder = BertEncoder(config)

        self.pooler = BertPooler(config) if add_pooling_layer else None

        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def get_extended_attention_mask(
        self,
        attention_mask: Tensor,
        input_shape: Tuple[int],
        device: device,
        is_decoder: bool,
    ) -> Tensor:
        """
        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

        Arguments:
            attention_mask (:obj:`torch.Tensor`):
                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
            input_shape (:obj:`Tuple[int]`):
                The shape of the input to the model.
            device: (:obj:`torch.device`):
                The device of the input to the model.

        Returns:
            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
        """
        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        if attention_mask.dim() == 3:
            extended_attention_mask = attention_mask[:, None, :, :]
        elif attention_mask.dim() == 2:
            # Provided a padding mask of dimensions [batch_size, seq_length]
            # - if the model is a decoder, apply a causal mask in addition to the padding mask
            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
            if is_decoder:
                batch_size, seq_length = input_shape

                seq_ids = torch.arange(seq_length, device=device)
                causal_mask = (
                    seq_ids[None, None, :].repeat(batch_size, seq_length, 1)
                    <= seq_ids[None, :, None]
                )
                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
                # causal and attention masks must have same type with pytorch version < 1.3
                causal_mask = causal_mask.to(attention_mask.dtype)

                if causal_mask.shape[1] < attention_mask.shape[1]:
                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
                    causal_mask = torch.cat(
                        [
                            torch.ones(
                                (batch_size, seq_length, prefix_seq_len),
                                device=device,
                                dtype=causal_mask.dtype,
                            ),
                            causal_mask,
                        ],
                        axis=-1,
                    )

                extended_attention_mask = (
                    causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
                )
            else:
                extended_attention_mask = attention_mask[:, None, None, :]
        else:
            raise ValueError(
                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
                    input_shape, attention_mask.shape
                )
            )

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
        # positions we want to attend and -10000.0 for masked positions.
        # Since we are adding it to the raw scores before the softmax, this is
        # effectively the same as removing these entirely.
        extended_attention_mask = extended_attention_mask.to(
            dtype=self.dtype
        )  # fp16 compatibility
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
        return extended_attention_mask

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        is_decoder=False,
        mode="multimodal",
    ):
        r"""
        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
        use_cache (:obj:`bool`, `optional`):
            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
            decoding (see :obj:`past_key_values`).
        """
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        if is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache
        else:
            use_cache = False

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time"
            )
        elif input_ids is not None:
            input_shape = input_ids.size()
            batch_size, seq_length = input_shape
            device = input_ids.device
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            batch_size, seq_length = input_shape
            device = inputs_embeds.device
        elif encoder_embeds is not None:
            input_shape = encoder_embeds.size()[:-1]
            batch_size, seq_length = input_shape
            device = encoder_embeds.device
        else:
            raise ValueError(
                "You have to specify either input_ids or inputs_embeds or encoder_embeds"
            )

        # past_key_values_length
        past_key_values_length = (
            past_key_values[0][0].shape[2] if past_key_values is not None else 0
        )

        if attention_mask is None:
            attention_mask = torch.ones(
                ((batch_size, seq_length + past_key_values_length)), device=device
            )

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
            attention_mask, input_shape, device, is_decoder
        )

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if encoder_hidden_states is not None:
            if type(encoder_hidden_states) == list:
                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[
                    0
                ].size()
            else:
                (
                    encoder_batch_size,
                    encoder_sequence_length,
                    _,
                ) = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)

            if type(encoder_attention_mask) == list:
                encoder_extended_attention_mask = [
                    self.invert_attention_mask(mask) for mask in encoder_attention_mask
                ]
            elif encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
                encoder_extended_attention_mask = self.invert_attention_mask(
                    encoder_attention_mask
                )
            else:
                encoder_extended_attention_mask = self.invert_attention_mask(
                    encoder_attention_mask
                )
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        if encoder_embeds is None:
            embedding_output = self.embeddings(
                input_ids=input_ids,
                position_ids=position_ids,
                inputs_embeds=inputs_embeds,
                past_key_values_length=past_key_values_length,
            )
        else:
            embedding_output = encoder_embeds

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            mode=mode,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = (
            self.pooler(sequence_output) if self.pooler is not None else None
        )

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_attentions,
        )


class BertLMHeadModel(BertPreTrainedModel):
    _keys_to_ignore_on_load_unexpected = [r"pooler"]
    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]

    def __init__(self, config):
        super().__init__(config)

        self.bert = BertModel(config, add_pooling_layer=False)
        self.cls = BertOnlyMLMHead(config)

        self.init_weights()

    def get_output_embeddings(self):
        return self.cls.predictions.decoder

    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        labels=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        return_logits=False,
        is_decoder=True,
        reduction="mean",
        mode="multimodal",
    ):
        r"""
        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
        use_cache (:obj:`bool`, `optional`):
            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
            decoding (see :obj:`past_key_values`).
        Returns:
        Example::
            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
            >>> import torch
            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
            >>> config = BertConfig.from_pretrained("bert-base-cased")
            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
            >>> outputs = model(**inputs)
            >>> prediction_logits = outputs.logits
        """
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )
        if labels is not None:
            use_cache = False

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            is_decoder=is_decoder,
            mode=mode,
        )

        sequence_output = outputs[0]
        prediction_scores = self.cls(sequence_output)

        if return_logits:
            return prediction_scores[:, :-1, :].contiguous()

        lm_loss = None
        if labels is not None:
            # we are doing next-token prediction; shift prediction scores and input ids by one
            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
            labels = labels[:, 1:].contiguous()
            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1)
            lm_loss = loss_fct(
                shifted_prediction_scores.view(-1, self.config.vocab_size),
                labels.view(-1),
            )
            if reduction == "none":
                lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1)

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((lm_loss,) + output) if lm_loss is not None else output

        return CausalLMOutputWithCrossAttentions(
            loss=lm_loss,
            logits=prediction_scores,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past=None, attention_mask=None, **model_kwargs
    ):
        input_shape = input_ids.shape
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = input_ids.new_ones(input_shape)

        # cut decoder_input_ids if past is used
        if past is not None:
            input_ids = input_ids[:, -1:]

        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "past_key_values": past,
            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
            "is_decoder": True,
        }

    def _reorder_cache(self, past, beam_idx):
        reordered_past = ()
        for layer_past in past:
            reordered_past += (
                tuple(
                    past_state.index_select(0, beam_idx) for past_state in layer_past
                ),
            )
        return reordered_past


================================================
FILE: ldm/models/nlvr_encoder.py
================================================
import math
from typing import Tuple

import torch
from torch import Tensor, device, nn
import torch.utils.checkpoint
from torch import nn

from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
    BaseModelOutputWithPastAndCrossAttentions,
    BaseModelOutputWithPoolingAndCrossAttentions,
)
from transformers.modeling_utils import (
    PreTrainedModel,
    apply_chunking_to_forward,
    find_pruneable_heads_and_indices,
    prune_linear_layer,
)
from transformers.utils import logging
from transformers.models.bert.configuration_bert import BertConfig


logger = logging.get_logger(__name__)


class BertEmbeddings(nn.Module):
    """Construct the embeddings from word and position embeddings."""

    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(
            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
        )
        self.position_embeddings = nn.Embedding(
            config.max_position_embeddings, config.hidden_size
        )

        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
        # any TensorFlow checkpoint file
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer(
            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))
        )
        self.position_embedding_type = getattr(
            config, "position_embedding_type", "absolute"
        )

        self.config = config

    def forward(
        self,
        input_ids=None,
        position_ids=None,
        inputs_embeds=None,
        past_key_values_length=0,
    ):
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = self.position_ids[
                :, past_key_values_length : seq_length + past_key_values_length
            ]

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        embeddings = inputs_embeds

        if self.position_embedding_type == "absolute":
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class BertSelfAttention(nn.Module):
    def __init__(self, config, is_cross_attention):
        super().__init__()
        self.config = config
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
            config, "embedding_size"
        ):
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        if is_cross_attention:
            self.key = nn.Linear(config.encoder_width, self.all_head_size)
            self.value = nn.Linear(config.encoder_width, self.all_head_size)
        else:
            self.key = nn.Linear(config.hidden_size, self.all_head_size)
            self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = getattr(
            config, "position_embedding_type", "absolute"
        )
        if (
            self.position_embedding_type == "relative_key"
            or self.position_embedding_type == "relative_key_query"
        ):
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(
                2 * config.max_position_embeddings - 1, self.attention_head_size
            )
        self.save_attention = False

    def save_attn_gradients(self, attn_gradients):
        self.attn_gradients = attn_gradients

    def get_attn_gradients(self):
        return self.attn_gradients

    def save_attention_map(self, attention_map):
        self.attention_map = attention_map

    def get_attention_map(self):
        return self.attention_map

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (
            self.num_attention_heads,
            self.attention_head_size,
        )
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        mixed_query_layer = self.query(hidden_states)

        # If this is instantiated as a cross-attention module, the keys
        # and values come from an encoder; the attention mask needs to be
        # such that the encoder's padding tokens are not attended to.
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
            attention_mask = encoder_attention_mask
        elif past_key_value is not None:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))
            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))

        query_layer = self.transpose_for_scores(mixed_query_layer)

        past_key_value = (key_layer, value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

        if (
            self.position_embedding_type == "relative_key"
            or self.position_embedding_type == "relative_key_query"
        ):
            seq_length = hidden_states.size()[1]
            position_ids_l = torch.arange(
                seq_length, dtype=torch.long, device=hidden_states.device
            ).view(-1, 1)
            position_ids_r = torch.arange(
                seq_length, dtype=torch.long, device=hidden_states.device
            ).view(1, -1)
            distance = position_ids_l - position_ids_r
            positional_embedding = self.distance_embedding(
                distance + self.max_position_embeddings - 1
            )
            positional_embedding = positional_embedding.to(
                dtype=query_layer.dtype
            )  # fp16 compatibility

            if self.position_embedding_type == "relative_key":
                relative_position_scores = torch.einsum(
                    "bhld,lrd->bhlr", query_layer, positional_embedding
                )
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = torch.einsum(
                    "bhld,lrd->bhlr", query_layer, positional_embedding
                )
                relative_position_scores_key = torch.einsum(
                    "bhrd,lrd->bhlr", key_layer, positional_embedding
                )
                attention_scores = (
                    attention_scores
                    + relative_position_scores_query
                    + relative_position_scores_key
                )

        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.Softmax(dim=-1)(attention_scores)

        if is_cross_attention and self.save_attention:
            self.save_attention_map(attention_probs)
            attention_probs.register_hook(self.save_attn_gradients)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs_dropped = self.dropout(attention_probs)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs_dropped = attention_probs_dropped * head_mask

        context_layer = torch.matmul(attention_probs_dropped, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)

        outputs = (
            (context_layer, attention_probs) if output_attentions else (context_layer,)
        )

        outputs = outputs + (past_key_value,)
        return outputs


class BertSelfOutput(nn.Module):
    def __init__(self, config, twin=False, merge=False):
        super().__init__()
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        if twin:
            self.dense0 = nn.Linear(config.hidden_size, config.hidden_size)
            self.dense1 = nn.Linear(config.hidden_size, config.hidden_size)
        else:
            self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if merge:
            self.act = ACT2FN[config.hidden_act]
            self.merge_layer = nn.Linear(config.hidden_size * 2, config.hidden_size)
            self.merge = True
        else:
            self.merge = False

    def forward(self, hidden_states, input_tensor):
        if type(hidden_states) == list:
            hidden_states0 = self.dense0(hidden_states[0])
            hidden_states1 = self.dense1(hidden_states[1])
            if self.merge:
                # hidden_states = self.merge_layer(self.act(torch.cat([hidden_states0,hidden_states1],dim=-1)))
                hidden_states = self.merge_layer(
                    torch.cat([hidden_states0, hidden_states1], dim=-1)
                )
            else:
                hidden_states = (hidden_states0 + hidden_states1) / 2
        else:
            hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertAttention(nn.Module):
    def __init__(self, config, is_cross_attention=False, layer_num=-1):
        super().__init__()
        if is_cross_attention:
            self.self0 = BertSelfAttention(config, is_cross_attention)
            self.self1 = BertSelfAttention(config, is_cross_attention)
        else:
            self.self = BertSelfAttention(config, is_cross_attention)
        self.output = BertSelfOutput(
            config,
            twin=is_cross_attention,
            merge=(is_cross_attention and layer_num >= 6),
        )
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads,
            self.self.num_attention_heads,
            self.self.attention_head_size,
            self.pruned_heads,
        )

        # Prune linear layers
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = (
            self.self.attention_head_size * self.self.num_attention_heads
        )
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
    ):
        if type(encoder_hidden_states) == list:
            self_outputs0 = self.self0(
                hidden_states,
                attention_mask,
                head_mask,
                encoder_hidden_states[0],
                encoder_attention_mask[0],
                past_key_value,
                output_attentions,
            )
            self_outputs1 = self.self1(
                hidden_states,
                attention_mask,
                head_mask,
                encoder_hidden_states[1],
                encoder_attention_mask[1],
                past_key_value,
                output_attentions,
            )
            attention_output = self.output(
                [self_outputs0[0], self_outputs1[0]], hidden_states
            )

            outputs = (attention_output,) + self_outputs0[
                1:
            ]  # add attentions if we output them
        else:
            self_outputs = self.self(
                hidden_states,
                attention_mask,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                past_key_value,
                output_attentions,
            )
            attention_output = self.output(self_outputs[0], hidden_states)
            outputs = (attention_output,) + self_outputs[
                1:
            ]  # add attentions if we output them
        return outputs


class BertIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else:
            self.intermediate_act_fn = config.hidden_act

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


class BertOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class BertLayer(nn.Module):
    def __init__(self, config, layer_num):
        super().__init__()
        self.config = config
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = BertAttention(config)
        self.layer_num = layer_num
        if self.config.add_cross_attention:
            self.crossattention = BertAttention(
                config,
                is_cross_attention=self.config.add_cross_attention,
                layer_num=layer_num,
            )
        self.intermediate = BertIntermediate(config)
        self.output = BertOutput(config)

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_value=None,
        output_attentions=False,
        mode=None,
    ):
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = (
            past_key_value[:2] if past_key_value is not None else None
        )
        self_attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            head_mask,
            output_attentions=output_attentions,
            past_key_value=self_attn_past_key_value,
        )
        attention_output = self_attention_outputs[0]

        outputs = self_attention_outputs[1:-1]
        present_key_value = self_attention_outputs[-1]

        if mode == "multimodal":
            assert (
                encoder_hidden_states is not None
            ), "encoder_hidden_states must be given for cross-attention layers"
            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                output_attentions=output_attentions,
            )
            attention_output = cross_attention_outputs[0]
            outputs = (
                outputs + cross_attention_outputs[1:-1]
            )  # add cross attentions if we output attention weights
        layer_output = apply_chunking_to_forward(
            self.feed_forward_chunk,
            self.chunk_size_feed_forward,
            self.seq_len_dim,
            attention_output,
        )
        outputs = (layer_output,) + outputs

        outputs = outputs + (present_key_value,)

        return outputs

    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output


class BertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList(
            [BertLayer(config, i) for i in range(config.num_hidden_layers)]
        )
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
        mode="multimodal",
    ):
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        all_cross_attentions = (
            () if output_attentions and self.config.add_cross_attention else None
        )

        next_decoder_cache = () if use_cache else None

        for i in range(self.config.num_hidden_layers):
            layer_module = self.layer[i]
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_head_mask = head_mask[i] if head_mask is not None else None
            past_key_value = past_key_values[i] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:
                if use_cache:
                    logger.warn(
                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                    )
                    use_cache = False

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs, past_key_value, output_attentions)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(layer_module),
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    mode=mode,
                )
            else:
                layer_outputs = layer_module(
                    hidden_states,
                    attention_mask,
                    layer_head_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    past_key_value,
                    output_attentions,
                    mode=mode,
                )

            hidden_states = layer_outputs[0]
            if use_cache:
                next_decoder_cache += (layer_outputs[-1],)
            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_outputs[1],)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    next_decoder_cache,
                    all_hidden_states,
                    all_self_attentions,
                    all_cross_attentions,
                ]
                if v is not None
            )
        return BaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=next_decoder_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
            cross_attentions=all_cross_attentions,
        )


class BertPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class BertPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


class BertLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states


class BertOnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = BertLMPredictionHead(config)

    def forward(self, sequence_output):
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores


class BertPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = BertConfig
    base_model_prefix = "bert"
    _keys_to_ignore_on_load_missing = [r"position_ids"]

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None:
            module.bias.data.zero_()


class BertModel(BertPreTrainedModel):
    """
    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
    input to the forward pass.
    """

    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config

        self.embeddings = BertEmbeddings(config)

        self.encoder = BertEncoder(config)

        self.pooler = BertPooler(config) if add_pooling_layer else None

        self.init_weights()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def get_extended_attention_mask(
        self,
        attention_mask: Tensor,
        input_shape: Tuple[int],
        device: device,
        is_decoder: bool,
    ) -> Tensor:
        """
        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

        Arguments:
            attention_mask (:obj:`torch.Tensor`):
                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
            input_shape (:obj:`Tuple[int]`):
                The shape of the input to the model.
            device: (:obj:`torch.device`):
                The device of the input to the model.

        Returns:
            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
        """
        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        if attention_mask.dim() == 3:
            extended_attention_mask = attention_mask[:, None, :, :]
        elif attention_mask.dim() == 2:
            # Provided a padding mask of dimensions [batch_size, seq_length]
            # - if the model is a decoder, apply a causal mask in addition to the padding mask
            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
            if is_decoder:
                batch_size, seq_length = input_shape

                seq_ids = torch.arange(seq_length, device=device)
                causal_mask = (
                    seq_ids[None, None, :].repeat(batch_size, seq_length, 1)
                    <= seq_ids[None, :, None]
                )
                # in case past_key_values are used we need to add a prefix ones mask to the causal mask
                # causal and attention masks must have same type with pytorch version < 1.3
                causal_mask = causal_mask.to(attention_mask.dtype)

                if causal_mask.shape[1] < attention_mask.shape[1]:
                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
                    causal_mask = torch.cat(
                        [
                            torch.ones(
                                (batch_size, seq_length, prefix_seq_len),
                                device=device,
                                dtype=causal_mask.dtype,
                            ),
                            causal_mask,
                        ],
                        axis=-1,
                    )

                extended_attention_mask = (
                    causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
                )
            else:
                extended_attention_mask = attention_mask[:, None, None, :]
        else:
            raise ValueError(
                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
                    input_shape, attention_mask.shape
                )
            )

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
        # positions we want to attend and -10000.0 for masked positions.
        # Since we are adding it to the raw scores before the softmax, this is
        # effectively the same as removing these entirely.
        extended_attention_mask = extended_attention_mask.to(
            dtype=self.dtype
        )  # fp16 compatibility
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
        return extended_attention_mask

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        head_mask=None,
        inputs_embeds=None,
        encoder_embeds=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
        is_decoder=False,
        mode="multimodal",
    ):
        r"""
        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
        use_cache (:obj:`bool`, `optional`):
            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
            decoding (see :obj:`past_key_values`).
        """
        output_attentions = (
            output_attentions
            if output_attentions is not None
            else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        return_dict = (
            return_dict if return_dict is not None else self.config.use_return_dict
        )

        if is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache
        else:
            use_cache = False

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time"
            )
        elif input_ids is not None:
            input_shape = input_ids.size()
            batch_size, seq_length = input_shape
            device = input_ids.device
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
            batch_size, seq_length = input_shape
            device = inputs_embeds.device
        elif encoder_embeds is not None:
            input_shape = encoder_embeds.size()[:-1]
            batch_size, seq_length = input_shape
            device = encoder_embeds.device
        else:
            raise ValueError(
                "You have to specify either input_ids or inputs_embeds or encoder_embeds"
            )

        # past_key_values_length
        past_key_values_length = (
            past_key_values[0][0].shape[2] if past_key_values is not None else 0
        )

        if attention_mask is None:
            attention_mask = torch.ones(
                ((batch_size, seq_length + past_key_values_length)), device=device
            )

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
            attention_mask, input_shape, device, is_decoder
        )

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if encoder_hidden_states is not None:
            if type(encoder_hidden_states) == list:
                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[
                    0
                ].size()
            else:
                (
                    encoder_batch_size,
                    encoder_sequence_length,
                    _,
                ) = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)

            if type(encoder_attention_mask) == list:
                encoder_extended_attention_mask = [
                    self.invert_attention_mask(mask) for mask in encoder_attention_mask
                ]
            elif encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
                encoder_extended_attention_mask = self.invert_attention_mask(
                    encoder_attention_mask
                )
            else:
                encoder_extended_attention_mask = self.invert_attention_mask(
                    encoder_attention_mask
                )
        else:
            encoder_extended_attention_mask = None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        if encoder_embeds is None:
            embedding_output = self.embeddings(
                input_ids=input_ids,
                position_ids=position_ids,
                inputs_embeds=inputs_embeds,
                past_key_values_length=past_key_values_length,
            )
        else:
            embedding_output = encoder_embeds

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            mode=mode,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = (
            self.pooler(sequence_output) if self.pooler is not None else None
        )

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_attentions,
        )


================================================
FILE: ldm/models/vit.py
================================================
"""
 * Copyright (c) 2022, salesforce.com, inc.
 * All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
 * By Junnan Li
 * Based on timm code base
 * https://github.com/rwightman/pytorch-image-models/tree/master/timm
"""

import torch
import torch.nn as nn
from functools import partial

from timm.models.vision_transformer import PatchEmbed
from timm.models.layers import trunc_normal_, DropPath
from timm.models.helpers import adapt_input_conv

from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper


class Mlp(nn.Module):
    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""

    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        drop=0.0,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
    ):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
        self.scale = qk_scale or head_dim**-0.5
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)
        self.attn_gradients = None
        self.attention_map = None

    def save_attn_gradients(self, attn_gradients):
        self.attn_gradients = attn_gradients

    def get_attn_gradients(self):
        return self.attn_gradients

    def save_attention_map(self, attention_map):
        self.attention_map = attention_map

    def get_attention_map(self):
        return self.attention_map

    def forward(self, x, register_hook=False):
        B, N, C = x.shape
        qkv = (
            self.qkv(x)
            .reshape(B, N, 3, self.num_heads, C // self.num_heads)
            .permute(2, 0, 3, 1, 4)
        )
        q, k, v = (
            qkv[0],
            qkv[1],
            qkv[2],
        )  # make torchscript happy (cannot use tensor as tuple)

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        if register_hook:
            self.save_attention_map(attn)
            attn.register_hook(self.save_attn_gradients)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        use_grad_checkpointing=False,
    ):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
        )
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )

        if use_grad_checkpointing:
            self.attn = checkpoint_wrapper(self.attn)
            self.mlp = checkpoint_wrapper(self.mlp)

    def forward(self, x, register_hook=False):
        x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class VisionTransformer(nn.Module):
    """Vision Transformer
    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
        https://arxiv.org/abs/2010.11929
    """

    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_chans=3,
        num_classes=1000,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        representation_size=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.0,
        norm_layer=None,
        use_grad_checkpointing=False,
        ckpt_layer=0,
    ):
        """
        Args:
            img_size (int, tuple): input image size
            patch_size (int, tuple): patch size
            in_chans (int): number of input channels
            num_classes (int): number of classes for classification head
            embed_dim (int): embedding dimension
            depth (int): depth of transformer
            num_heads (int): number of attention heads
            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
            qkv_bias (bool): enable bias for qkv if True
            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
            drop_rate (float): dropout rate
            attn_drop_rate (float): attention dropout rate
            drop_path_rate (float): stochastic depth rate
            norm_layer: (nn.Module): normalization layer
        """
        super().__init__()
        self.num_features = (
            self.embed_dim
        ) = embed_dim  # num_features for consistency with other models
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)

        self.patch_embed = PatchEmbed(
            img_size=img_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
        )

        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [
            x.item() for x in torch.linspace(0, drop_path_rate, depth)
        ]  # stochastic depth decay rule
        self.blocks = nn.ModuleList(
            [
                Block(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop_rate,
                    attn_drop=attn_drop_rate,
                    drop_path=dpr[i],
                    norm_layer=norm_layer,
                    use_grad_checkpointing=(
                        use_grad_checkpointing and i >= depth - ckpt_layer
                    ),
                )
                for i in range(depth)
            ]
        )
        self.norm = norm_layer(embed_dim)

        trunc_normal_(self.pos_embed, std=0.02)
        trunc_normal_(self.cls_token, std=0.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {"pos_embed", "cls_token"}

    def forward(self, x, register_blk=-1):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(
            B, -1, -1
        )  # stole cls_tokens impl from Phil Wang, thanks
        x = torch.cat((cls_tokens, x), dim=1)

        x = x + self.pos_embed[:, : x.size(1), :]
        x = self.pos_drop(x)

        for i, blk in enumerate(self.blocks):
            x = blk(x, register_blk == i)
        x = self.norm(x)

        return x

    @torch.jit.ignore()
    def load_pretrained(self, checkpoint_path, prefix=""):
        _load_weights(self, checkpoint_path, prefix)


@torch.no_grad()
def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ""):
    """Load weights from .npz checkpoints for official Google Brain Flax implementation"""
    import numpy as np

    def _n2p(w, t=True):
        if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
            w = w.flatten()
        if t:
            if w.ndim == 4:
                w = w.transpose([3, 2, 0, 1])
            elif w.ndim == 3:
                w = w.transpose([2, 0, 1])
            elif w.ndim == 2:
                w = w.transpose([1, 0])
        return torch.from_numpy(w)

    w = np.load(checkpoint_path)
    if not prefix and "opt/target/embedding/kernel" in w:
        prefix = "opt/target/"

    if hasattr(model.patch_embed, "backbone"):
        # hybrid
        backbone = model.patch_embed.backbone
        stem_only = not hasattr(backbone, "stem")
        stem = backbone if stem_only else backbone.stem
        stem.conv.weight.copy_(
            adapt_input_conv(
                stem.conv.weight.shape[1], _n2p(w[f"{prefix}conv_root/kernel"])
            )
        )
        stem.norm.weight.copy_(_n2p(w[f"{prefix}gn_root/scale"]))
        stem.norm.bias.copy_(_n2p(w[f"{prefix}gn_root/bias"]))
        if not stem_only:
            for i, stage in enumerate(backbone.stages):
                for j, block in enumerate(stage.blocks):
                    bp = f"{prefix}block{i + 1}/unit{j + 1}/"
                    for r in range(3):
                        getattr(block, f"conv{r + 1}").weight.copy_(
                            _n2p(w[f"{bp}conv{r + 1}/kernel"])
                        )
                        getattr(block, f"norm{r + 1}").weight.copy_(
                            _n2p(w[f"{bp}gn{r + 1}/scale"])
                        )
                        getattr(block, f"norm{r + 1}").bias.copy_(
                            _n2p(w[f"{bp}gn{r + 1}/bias"])
                        )
                    if block.downsample is not None:
                        block.downsample.conv.weight.copy_(
                            _n2p(w[f"{bp}conv_proj/kernel"])
                        )
                        block.downsample.norm.weight.copy_(
                            _n2p(w[f"{bp}gn_proj/scale"])
                        )
                        block.downsample.norm.bias.copy_(_n2p(w[f"{bp}gn_proj/bias"]))
        embed_conv_w = _n2p(w[f"{prefix}embedding/kernel"])
    else:
        embed_conv_w = adapt_input_conv(
            model.patch_embed.proj.weight.shape[1], _n2p(w[f"{prefix}embedding/kernel"])
        )
    model.patch_embed.proj.weight.copy_(embed_conv_w)
    model.patch_embed.proj.bias.copy_(_n2p(w[f"{prefix}embedding/bias"]))
    model.cls_token.copy_(_n2p(w[f"{prefix}cls"], t=False))
    pos_embed_w = _n2p(w[f"{prefix}Transformer/posembed_input/pos_embedding"], t=False)
    if pos_embed_w.shape != model.pos_embed.shape:
        pos_embed_w = resize_pos_embed(  # resize pos embedding when different size from pretrained weights
            pos_embed_w,
            model.pos_embed,
            getattr(model, "num_tokens", 1),
            model.patch_embed.grid_size,
        )
    model.pos_embed.copy_(pos_embed_w)
    model.norm.weight.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/scale"]))
    model.norm.bias.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/bias"]))
    #     if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
    #         model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
    #         model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
    #     if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
    #         model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
    #         model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
    for i, block in enumerate(model.blocks.children()):
        block_prefix = f"{prefix}Transformer/encoderblock_{i}/"
        mha_prefix = block_prefix + "MultiHeadDotProductAttention_1/"
        block.norm1.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/scale"]))
        block.norm1.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/bias"]))
        block.attn.qkv.weight.copy_(
            torch.cat(
                [
                    _n2p(w[f"{mha_prefix}{n}/kernel"], t=False).flatten(1).T
                    for n in ("query", "key", "value")
                ]
            )
        )
        block.attn.qkv.bias.copy_(
            torch.cat(
                [
                    _n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1)
                    for n in ("query", "key", "value")
                ]
            )
        )
        block.attn.proj.weight.copy_(_n2p(w[f"{mha_prefix}out/kernel"]).flatten(1))
        block.attn.proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"]))
        for r in range(2):
            getattr(block.mlp, f"fc{r + 1}").weight.copy_(
                _n2p(w[f"{block_prefix}MlpBlock_3/Dense_{r}/kernel"])
            )
            getattr(block.mlp, f"fc{r + 1}").bias.copy_(
                _n2p(w[f"{block_prefix}MlpBlock_3/Dense_{r}/bias"])
            )
        block.norm2.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_2/scale"]))
        block.norm2.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_2/bias"]))


def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):
    # interpolate position embedding
    embedding_size = pos_embed_checkpoint.shape[-1]
    num_patches = visual_encoder.patch_embed.num_patches
    num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
    # height (== width) for the checkpoint position embedding
    orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
    # height (== width) for the new position embedding
    new_size = int(num_patches**0.5)

    if orig_size != new_size:
        # class_token and dist_token are kept unchanged
        extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
        # only the position tokens are interpolated
        pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
        pos_tokens = pos_tokens.reshape(
            -1, orig_size, orig_size, embedding_size
        ).permute(0, 3, 1, 2)
        pos_tokens = torch.nn.functional.interpolate(
            pos_tokens, size=(new_size, new_size), mode="bicubic", align_corners=False
        )
        pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
        new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
        print(
            "reshape position embedding from %d to %d" % (orig_size**2, new_size**2)
        )

        return new_pos_embed
    else:
        return pos_embed_checkpoint


================================================
FILE: ldm/modules/__init__.py
================================================


================================================
FILE: ldm/modules/attention.py
================================================
from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange

from ldm.modules.diffusionmodules.util import checkpoint

import psutil


def exists(val):
    return val is not None


def uniq(arr):
    return {el: True for el in arr}.keys()


def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d


def max_neg_value(t):
    return -torch.finfo(t.dtype).max


def init_(tensor):
    dim = tensor.shape[-1]
    std = 1 / math.sqrt(dim)
    tensor.uniform_(-std, std)
    return tensor


# feedforward
class GEGLU(nn.Module):
    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

    def forward(self, x):
        x, gate = self.proj(x).chunk(2, dim=-1)
        return x * F.gelu(gate)


class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = default(dim_out, dim)
        project_in = (
            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
            if not glu
            else GEGLU(dim, inner_dim)
        )

        self.net = nn.Sequential(
            project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
        )

    def forward(self, x):
        return self.net(x)


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


def Normalize(in_channels):
    return torch.nn.GroupNorm(
        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
    )


class LinearAttention(nn.Module):
    def __init__(self, dim, heads=4, dim_head=32):
        super().__init__()
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
        self.to_out = nn.Conv2d(hidden_dim, dim, 1)

    def forward(self, x):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x)
        q, k, v = rearrange(
            qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
        )
        k = k.softmax(dim=-1)
        context = torch.einsum("bhdn,bhen->bhde", k, v)
        out = torch.einsum("bhde,bhdn->bhen", context, q)
        out = rearrange(
            out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
        )
        return self.to_out(out)


class SpatialSelfAttention(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.k = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.v = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.proj_out = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q.shape
        q = rearrange(q, "b c h w -> b (h w) c")
        k = rearrange(k, "b c h w -> b c (h w)")
        w_ = torch.einsum("bij,bjk->bik", q, k)

        w_ = w_ * (int(c) ** (-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = rearrange(v, "b c h w -> b c (h w)")
        w_ = rearrange(w_, "b i j -> b j i")
        h_ = torch.einsum("bij,bjk->bik", v, w_)
        h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
        h_ = self.proj_out(h_)

        return x + h_


class CrossAttention(nn.Module):
    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.scale = dim_head**-0.5
        self.heads = heads

        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
        )

        if torch.cuda.is_available():
            self.einsum_op = self.einsum_op_cuda
        else:
            self.mem_total = psutil.virtual_memory().total / (1024**3)
            self.einsum_op = (
                self.einsum_op_mps_v1 if self.mem_total >= 32 else self.einsum_op_mps_v2
            )

    def einsum_op_compvis(self, q, k, v, r1):
        s1 = einsum("b i d, b j d -> b i j", q, k) * self.scale  # faster
        s2 = s1.softmax(dim=-1, dtype=q.dtype)
        del s1
        r1 = einsum("b i j, b j d -> b i d", s2, v)
        del s2
        return r1

    def einsum_op_mps_v1(self, q, k, v, r1):
        if q.shape[1] <= 4096:  # (512x512) max q.shape[1]: 4096
            r1 = self.einsum_op_compvis(q, k, v, r1)
        else:
            slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
            for i in range(0, q.shape[1], slice_size):
                end = i + slice_size
                s1 = einsum("b i d, b j d -> b i j", q[:, i:end], k) * self.scale
                s2 = s1.softmax(dim=-1, dtype=r1.dtype)
                del s1
                r1[:, i:end] = einsum("b i j, b j d -> b i d", s2, v)
                del s2
        return r1

    def einsum_op_mps_v2(self, q, k, v, r1):
        if self.mem_total >= 8 and q.shape[1] <= 4096:
            r1 = self.einsum_op_compvis(q, k, v, r1)
        else:
            slice_size = 1
            for i in range(0, q.shape[0], slice_size):
                end = min(q.shape[0], i + slice_size)
                s1 = einsum("b i d, b j d -> b i j", q[i:end], k[i:end])
                s1 *= self.scale
                s2 = s1.softmax(dim=-1, dtype=r1.dtype)
                del s1
                r1[i:end] = einsum("b i j, b j d -> b i d", s2, v[i:end])
                del s2
        return r1

    def einsum_op_cuda(self, q, k, v, r1):
        stats = torch.cuda.memory_stats(q.device)
        mem_active = stats["active_bytes.all.current"]
        mem_reserved = stats["reserved_bytes.all.current"]
        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
        mem_free_torch = mem_reserved - mem_active
        mem_free_total = mem_free_cuda + mem_free_torch

        gb = 1024**3
        tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * 4
        mem_required = tensor_size * 2.5
        steps = 1

        if mem_required > mem_free_total:
            steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))

        if steps > 64:
            max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
            raise RuntimeError(
                f"Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). "
                f"Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free"
            )

        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
        for i in range(0, q.shape[1], slice_size):
            end = min(q.shape[1], i + slice_size)
            s1 = einsum("b i d, b j d -> b i j", q[:, i:end], k)  # * self.scale
            s2 = s1.softmax(dim=-1, dtype=r1.dtype)
            del s1
            r1[:, i:end] = einsum("b i j, b j d -> b i d", s2, v)
            del s2
        return r1

    def forward(self, x, context=None, mask=None):
        h = self.heads

        q = self.to_q(x)
        context = default(context, x)
        del x
        k = self.to_k(context) * self.scale
        v = self.to_v(context)
        del context

        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))

        r1 = torch.zeros(
            q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype
        )
        r1 = self.einsum_op(q, k, v, r1)
        del q, k, v
        r2 = rearrange(r1, "(b h) n d -> b n (h d)", h=h)
        del r1
        return self.to_out(r2)


class BasicTransformerBlock(nn.Module):
    def __init__(
        self,
        dim,
        n_heads,
        d_head,
        dropout=0.0,
        context_dim=None,
        gated_ff=True,
        checkpoint=True,
    ):
        super().__init__()
        self.attn1 = CrossAttention(
            query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
        )  # is a self-attention
        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.attn2 = CrossAttention(
            query_dim=dim,
            context_dim=context_dim,
            heads=n_heads,
            dim_head=d_head,
            dropout=dropout,
        )  # is self-attn if context is none
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
        self.norm3 = nn.LayerNorm(dim)
        self.checkpoint = checkpoint

    def forward(self, x, context=None):
        return checkpoint(
            self._forward, (x, context), self.parameters(), self.checkpoint
        )

    def _forward(self, x, context=None):
        x = x.contiguous() if x.device.type == "mps" else x
        x += self.attn1(self.norm1(x))
        x += self.attn2(self.norm2(x), context=context)
        x += self.ff(self.norm3(x))
        return x


class SpatialTransformer(nn.Module):
    """
    Transformer block for image-like data.
    First, project the input (aka embedding)
    and reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    """

    def __init__(
        self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None
    ):
        super().__init__()
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = Normalize(in_channels)

        self.proj_in = nn.Conv2d(
            in_channels, inner_dim, kernel_size=1, stride=1, padding=0
        )

        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim
                )
                for d in range(depth)
            ]
        )

        self.proj_out = zero_module(
            nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
        )

    def forward(self, x, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        b, c, h, w = x.shape
        x_in = x
        x = self.norm(x)
        x = self.proj_in(x)
        x = rearrange(x, "b c h w -> b (h w) c")
        for block in self.transformer_blocks:
            x = block(x, context=context)
        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
        x = self.proj_out(x)
        return x + x_in


================================================
FILE: ldm/modules/diffusionmodules/__init__.py
================================================


================================================
FILE: ldm/modules/diffusionmodules/model.py
================================================
# pytorch_diffusion + derived encoder decoder
import gc
import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange

from ldm.util import instantiate_from_config
from ldm.modules.attention import LinearAttention


def get_timestep_embedding(timesteps, embedding_dim):
    """
    This matches the implementation in Denoising Diffusion Probabilistic Models:
    From Fairseq.
    Build sinusoidal embeddings.
    This matches the implementation in tensor2tensor, but differs slightly
    from the description in Section 3.5 of "Attention Is All You Need".
    """
    assert len(timesteps.shape) == 1

    half_dim = embedding_dim // 2
    emb = math.log(10000) / (half_dim - 1)
    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
    emb = emb.to(device=timesteps.device)
    emb = timesteps.float()[:, None] * emb[None, :]
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
    if embedding_dim % 2 == 1:  # zero pad
        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
    return emb


def nonlinearity(x):
    # swish
    return x * torch.sigmoid(x)


def Normalize(in_channels, num_groups=32):
    return torch.nn.GroupNorm(
        num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
    )


class Upsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            self.conv = torch.nn.Conv2d(
                in_channels, in_channels, kernel_size=3, stride=1, padding=1
            )

    def forward(self, x):
        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
        if self.with_conv:
            x = self.conv(x)
        return x


class Downsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            # no asymmetric padding in torch conv, must do it ourselves
            self.conv = torch.nn.Conv2d(
                in_channels, in_channels, kernel_size=3, stride=2, padding=0
            )

    def forward(self, x):
        if self.with_conv:
            pad = (0, 1, 0, 1)
            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
            x = self.conv(x)
        else:
            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
        return x


class ResnetBlock(nn.Module):
    def __init__(
        self,
        *,
        in_channels,
        out_channels=None,
        conv_shortcut=False,
        dropout,
        temb_channels=512,
    ):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels
        self.use_conv_shortcut = conv_shortcut

        self.norm1 = Normalize(in_channels)
        self.conv1 = torch.nn.Conv2d(
            in_channels, out_channels, kernel_size=3, stride=1, padding=1
        )
        if temb_channels > 0:
            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
        self.norm2 = Normalize(out_channels)
        self.dropout = torch.nn.Dropout(dropout)
        self.conv2 = torch.nn.Conv2d(
            out_channels, out_channels, kernel_size=3, stride=1, padding=1
        )
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                self.conv_shortcut = torch.nn.Conv2d(
                    in_channels, out_channels, kernel_size=3, stride=1, padding=1
                )
            else:
                self.nin_shortcut = torch.nn.Conv2d(
                    in_channels, out_channels, kernel_size=1, stride=1, padding=0
                )

    def forward(self, x, temb):
        h1 = x
        h2 = self.norm1(h1)
        del h1

        h3 = nonlinearity(h2)
        del h2

        h4 = self.conv1(h3)
        del h3

        if temb is not None:
            h4 = h4 + self.temb_proj(nonlinearity(temb))[:, :, None, None]

        h5 = self.norm2(h4)
        del h4

        h6 = nonlinearity(h5)
        del h5

        h7 = self.dropout(h6)
        del h6

        h8 = self.conv2(h7)
        del h7

        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                x = self.conv_shortcut(x)
            else:
                x = self.nin_shortcut(x)

        return x + h8


class LinAttnBlock(LinearAttention):
    """to match AttnBlock usage"""

    def __init__(self, in_channels):
        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)


class AttnBlock(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.k = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.v = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.proj_out = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q1 = self.q(h_)
        k1 = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q1.shape

        q2 = q1.reshape(b, c, h * w)
        del q1

        q = q2.permute(0, 2, 1)  # b,hw,c
        del q2

        k = k1.reshape(b, c, h * w)  # b,c,hw
        del k1

        h_ = torch.zeros_like(k, device=q.device)

        stats = torch.cuda.memory_stats(q.device)
        mem_active = stats["active_bytes.all.current"]
        mem_reserved = stats["reserved_bytes.all.current"]
        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
        mem_free_torch = mem_reserved - mem_active
        mem_free_total = mem_free_cuda + mem_free_torch

        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * 4
        mem_required = tensor_size * 2.5
        steps = 1

        if mem_required > mem_free_total:
            steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))

        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
        for i in range(0, q.shape[1], slice_size):
            end = i + slice_size

            w1 = torch.bmm(q[:, i:end], k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
            w2 = w1 * (int(c) ** (-0.5))
            del w1
            w3 = torch.nn.functional.softmax(w2, dim=2)
            del w2

            # attend to values
            v1 = v.reshape(b, c, h * w)
            w4 = w3.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
            del w3

            h_[:, :, i:end] = torch.bmm(
                v1, w4
            )  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
            del v1, w4

        h2 = h_.reshape(b, c, h, w)
        del h_

        h3 = self.proj_out(h2)
        del h2

        h3 += x

        return h3


def make_attn(in_channels, attn_type="vanilla"):
    assert attn_type in ["vanilla", "linear", "none"], f"attn_type {attn_type} unknown"
    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
    if attn_type == "vanilla":
        return AttnBlock(in_channels)
    elif attn_type == "none":
        return nn.Identity(in_channels)
    else:
        return LinAttnBlock(in_channels)


class Model(nn.Module):
    def __init__(
        self,
        *,
        ch,
        out_ch,
        ch_mult=(1, 2, 4, 8),
        num_res_blocks,
        attn_resolutions,
        dropout=0.0,
        resamp_with_conv=True,
        in_channels,
        resolution,
        use_timestep=True,
        use_linear_attn=False,
        attn_type="vanilla",
    ):
        super().__init__()
        if use_linear_attn:
            attn_type = "linear"
        self.ch = ch
        self.temb_ch = self.ch * 4
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels

        self.use_timestep = use_timestep
        if self.use_timestep:
            # timestep embedding
            self.temb = nn.Module()
            self.temb.dense = nn.ModuleList(
                [
                    torch.nn.Linear(self.ch, self.temb_ch),
                    torch.nn.Linear(self.temb_ch, self.temb_ch),
                ]
            )

        # downsampling
        self.conv_in = torch.nn.Conv2d(
            in_channels, self.ch, kernel_size=3, stride=1, padding=1
        )

        curr_res = resolution
        in_ch_mult = (1,) + tuple(ch_mult)
        self.down = nn.ModuleList()
        for i_level in range(self.num_resolutions):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_in = ch * in_ch_mult[i_level]
            block_out = ch * ch_mult[i_level]
            for i_block in range(self.num_res_blocks):
                block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(make_attn(block_in, attn_type=attn_type))
            down = nn.Module()
            down.block = block
            down.attn = attn
            if i_level != self.num_resolutions - 1:
                down.downsample = Downsample(block_in, resamp_with_conv)
                curr_res = curr_res // 2
            self.down.append(down)

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )
        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
        self.mid.block_2 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )

        # upsampling
        self.up = nn.ModuleList()
        for i_level in reversed(range(self.num_resolutions)):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_out = ch * ch_mult[i_level]
            skip_in = ch * ch_mult[i_level]
            for i_block in range(self.num_res_blocks + 1):
                if i_block == self.num_res_blocks:
                    skip_in = ch * in_ch_mult[i_level]
                block.append(
                    ResnetBlock(
                        in_channels=block_in + skip_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(make_attn(block_in, attn_type=attn_type))
            up = nn.Module()
            up.block = block
            up.attn = attn
            if i_level != 0:
                up.upsample = Upsample(block_in, resamp_with_conv)
                curr_res = curr_res * 2
            self.up.insert(0, up)  # prepend to get consistent order

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv2d(
            block_in, out_ch, kernel_size=3, stride=1, padding=1
        )

    def forward(self, x, t=None, context=None):
        # assert x.shape[2] == x.shape[3] == self.resolution
        if context is not None:
            # assume aligned context, cat along channel axis
            x = torch.cat((x, context), dim=1)
        if self.use_timestep:
            # timestep embedding
            assert t is not None
            temb = get_timestep_embedding(t, self.ch)
            temb = self.temb.dense[0](temb)
            temb = nonlinearity(temb)
            temb = self.temb.dense[1](temb)
        else:
            temb = None

        # downsampling
        hs = [self.conv_in(x)]
        for i_level in range(self.num_resolutions):
            for i_block in range(self.num_res_blocks):
                h = self.down[i_level].block[i_block](hs[-1], temb)
                if len(self.down[i_level].attn) > 0:
                    h = self.down[i_level].attn[i_block](h)
                hs.append(h)
            if i_level != self.num_resolutions - 1:
                hs.append(self.down[i_level].downsample(hs[-1]))

        # middle
        h = hs[-1]
        h = self.mid.block_1(h, temb)
        h = self.mid.attn_1(h)
        h = self.mid.block_2(h, temb)

        # upsampling
        for i_level in reversed(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks + 1):
                h = self.up[i_level].block[i_block](
                    torch.cat([h, hs.pop()], dim=1), temb
                )
                if len(self.up[i_level].attn) > 0:
                    h = self.up[i_level].attn[i_block](h)
            if i_level != 0:
                h = self.up[i_level].upsample(h)

        # end
        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h

    def get_last_layer(self):
        return self.conv_out.weight


class Encoder(nn.Module):
    def __init__(
        self,
        *,
        ch,
        out_ch,
        ch_mult=(1, 2, 4, 8),
        num_res_blocks,
        attn_resolutions,
        dropout=0.0,
        resamp_with_conv=True,
        in_channels,
        resolution,
        z_channels,
        double_z=True,
        use_linear_attn=False,
        attn_type="vanilla",
        **ignore_kwargs,
    ):
        super().__init__()
        if use_linear_attn:
            attn_type = "linear"
        self.ch = ch
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels

        # downsampling
        self.conv_in = torch.nn.Conv2d(
            in_channels, self.ch, kernel_size=3, stride=1, padding=1
        )

        curr_res = resolution
        in_ch_mult = (1,) + tuple(ch_mult)
        self.in_ch_mult = in_ch_mult
        self.down = nn.ModuleList()
        for i_level in range(self.num_resolutions):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_in = ch * in_ch_mult[i_level]
            block_out = ch * ch_mult[i_level]
            for i_block in range(self.num_res_blocks):
                block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(make_attn(block_in, attn_type=attn_type))
            down = nn.Module()
            down.block = block
            down.attn = attn
            if i_level != self.num_resolutions - 1:
                down.downsample = Downsample(block_in, resamp_with_conv)
                curr_res = curr_res // 2
            self.down.append(down)

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )
        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
        self.mid.block_2 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv2d(
            block_in,
            2 * z_channels if double_z else z_channels,
            kernel_size=3,
            stride=1,
            padding=1,
        )

    def forward(self, x):
        # timestep embedding
        temb = None

        # downsampling
        hs = [self.conv_in(x)]
        for i_level in range(self.num_resolutions):
            for i_block in range(self.num_res_blocks):
                h = self.down[i_level].block[i_block](hs[-1], temb)
                if len(self.down[i_level].attn) > 0:
                    h = self.down[i_level].attn[i_block](h)
                hs.append(h)
            if i_level != self.num_resolutions - 1:
                hs.append(self.down[i_level].downsample(hs[-1]))

        # middle
        h = hs[-1]
        h = self.mid.block_1(h, temb)
        h = self.mid.attn_1(h)
        h = self.mid.block_2(h, temb)

        # end
        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h


class Decoder(nn.Module):
    def __init__(
        self,
        *,
        ch,
        out_ch,
        ch_mult=(1, 2, 4, 8),
        num_res_blocks,
        attn_resolutions,
        dropout=0.0,
        resamp_with_conv=True,
        in_channels,
        resolution,
        z_channels,
        give_pre_end=False,
        tanh_out=False,
        use_linear_attn=False,
        attn_type="vanilla",
        **ignorekwargs,
    ):
        super().__init__()
        if use_linear_attn:
            attn_type = "linear"
        self.ch = ch
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels
        self.give_pre_end = give_pre_end
        self.tanh_out = tanh_out

        # compute in_ch_mult, block_in and curr_res at lowest res
        (1,) + tuple(ch_mult)
        block_in = ch * ch_mult[self.num_resolutions - 1]
        curr_res = resolution // 2 ** (self.num_resolutions - 1)
        self.z_shape = (1, z_channels, curr_res, curr_res)
        print(
            "Working with z of shape {} = {} dimensions.".format(
                self.z_shape, np.prod(self.z_shape)
            )
        )

        # z to block_in
        self.conv_in = torch.nn.Conv2d(
            z_channels, block_in, kernel_size=3, stride=1, padding=1
        )

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )
        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
        self.mid.block_2 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )

        # upsampling
        self.up = nn.ModuleList()
        for i_level in reversed(range(self.num_resolutions)):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_out = ch * ch_mult[i_level]
            for i_block in range(self.num_res_blocks + 1):
                block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(make_attn(block_in, attn_type=attn_type))
            up = nn.Module()
            up.block = block
            up.attn = attn
            if i_level != 0:
                up.upsample = Upsample(block_in, resamp_with_conv)
                curr_res = curr_res * 2
            self.up.insert(0, up)  # prepend to get consistent order

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv2d(
            block_in, out_ch, kernel_size=3, stride=1, padding=1
        )

    def forward(self, z):
        # assert z.shape[1:] == self.z_shape[1:]
        self.last_z_shape = z.shape

        # timestep embedding
        temb = None

        # z to block_in
        h1 = self.conv_in(z)

        # middle
        h2 = self.mid.block_1(h1, temb)
        del h1

        h3 = self.mid.attn_1(h2)
        del h2

        h = self.mid.block_2(h3, temb)
        del h3

        # prepare for up sampling
        gc.collect()
        torch.cuda.empty_cache()

        # upsampling
        for i_level in reversed(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks + 1):
                h = self.up[i_level].block[i_block](h, temb)
                if len(self.up[i_level].attn) > 0:
                    t = h
                    h = self.up[i_level].attn[i_block](t)
                    del t

            if i_level != 0:
                t = h
                h = self.up[i_level].upsample(t)
                del t

        # end
        if self.give_pre_end:
            return h

        h1 = self.norm_out(h)
        del h

        h2 = nonlinearity(h1)
        del h1

        h = self.conv_out(h2)
        del h2

        if self.tanh_out:
            t = h
            h = torch.tanh(t)
            del t

        return h


class SimpleDecoder(nn.Module):
    def __init__(self, in_channels, out_channels, *args, **kwargs):
        super().__init__()
        self.model = nn.ModuleList(
            [
                nn.Conv2d(in_channels, in_channels, 1),
                ResnetBlock(
                    in_channels=in_channels,
                    out_channels=2 * in_channels,
                    temb_channels=0,
                    dropout=0.0,
                ),
                ResnetBlock(
                    in_channels=2 * in_channels,
                    out_channels=4 * in_channels,
                    temb_channels=0,
                    dropout=0.0,
                ),
                ResnetBlock(
                    in_channels=4 * in_channels,
                    out_channels=2 * in_channels,
                    temb_channels=0,
                    dropout=0.0,
                ),
                nn.Conv2d(2 * in_channels, in_channels, 1),
                Upsample(in_channels, with_conv=True),
            ]
        )
        # end
        self.norm_out = Normalize(in_channels)
        self.conv_out = torch.nn.Conv2d(
            in_channels, out_channels, kernel_size=3, stride=1, padding=1
        )

    def forward(self, x):
        for i, layer in enumerate(self.model):
            if i in [1, 2, 3]:
                x = layer(x, None)
            else:
                x = layer(x)

        h = self.norm_out(x)
        h = nonlinearity(h)
        x = self.conv_out(h)
        return x


class UpsampleDecoder(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        ch,
        num_res_blocks,
        resolution,
        ch_mult=(2, 2),
        dropout=0.0,
    ):
        super().__init__()
        # upsampling
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        block_in = in_channels
        curr_res = resolution // 2 ** (self.num_resolutions - 1)
        self.res_blocks = nn.ModuleList()
        self.upsample_blocks = nn.ModuleList()
        for i_level in range(self.num_resolutions):
            res_block = []
            block_out = ch * ch_mult[i_level]
            for i_block in range(self.num_res_blocks + 1):
                res_block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
            self.res_blocks.append(nn.ModuleList(res_block))
            if i_level != self.num_resolutions - 1:
                self.upsample_blocks.append(Upsample(block_in, True))
                curr_res = curr_res * 2

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv2d(
            block_in, out_channels, kernel_size=3, stride=1, padding=1
        )

    def forward(self, x):
        # upsampling
        h = x
        for k, i_level in enumerate(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks + 1):
                h = self.res_blocks[i_level][i_block](h, None)
            if i_level != self.num_resolutions - 1:
                h = self.upsample_blocks[k](h)
        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h


class LatentRescaler(nn.Module):
    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
        super().__init__()
        # residual block, interpolate, residual block
        self.factor = factor
        self.conv_in = nn.Conv2d(
            in_channels, mid_channels, kernel_size=3, stride=1, padding=1
        )
        self.res_block1 = nn.ModuleList(
            [
                ResnetBlock(
                    in_channels=mid_channels,
                    out_channels=mid_channels,
                    temb_channels=0,
                    dropout=0.0,
                )
                for _ in range(depth)
            ]
        )
        self.attn = AttnBlock(mid_channels)
        self.res_block2 = nn.ModuleList(
            [
                ResnetBlock(
                    in_channels=mid_channels,
                    out_channels=mid_channels,
                    temb_channels=0,
                    dropout=0.0,
                )
                for _ in range(depth)
            ]
        )

        self.conv_out = nn.Conv2d(
            mid_channels,
            out_channels,
            kernel_size=1,
        )

    def forward(self, x):
        x = self.conv_in(x)
        for block in self.res_block1:
            x = block(x, None)
        x = torch.nn.functional.interpolate(
            x,
            size=(
                int(round(x.shape[2] * self.factor)),
                int(round(x.shape[3] * self.factor)),
            ),
        )
        x = self.attn(x)
        for block in self.res_block2:
            x = block(x, None)
        x = self.conv_out(x)
        return x


class MergedRescaleEncoder(nn.Module):
    def __init__(
        self,
        in_channels,
        ch,
        resolution,
        out_ch,
        num_res_blocks,
        attn_resolutions,
        dropout=0.0,
        resamp_with_conv=True,
        ch_mult=(1, 2, 4, 8),
        rescale_factor=1.0,
        rescale_module_depth=1,
    ):
        super().__init__()
        intermediate_chn = ch * ch_mult[-1]
        self.encoder = Encoder(
            in_channels=in_channels,
            num_res_blocks=num_res_blocks,
            ch=ch,
            ch_mult=ch_mult,
            z_channels=intermediate_chn,
            double_z=False,
            resolution=resolution,
            attn_resolutions=attn_resolutions,
            dropout=dropout,
            resamp_with_conv=resamp_with_conv,
            out_ch=None,
        )
        self.rescaler = LatentRescaler(
            factor=rescale_factor,
            in_channels=intermediate_chn,
            mid_channels=intermediate_chn,
            out_channels=out_ch,
            depth=rescale_module_depth,
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.rescaler(x)
        return x


class MergedRescaleDecoder(nn.Module):
    def __init__(
        self,
        z_channels,
        out_ch,
        resolution,
        num_res_blocks,
        attn_resolutions,
        ch,
        ch_mult=(1, 2, 4, 8),
        dropout=0.0,
        resamp_with_conv=True,
        rescale_factor=1.0,
        rescale_module_depth=1,
    ):
        super().__init__()
        tmp_chn = z_channels * ch_mult[-1]
        self.decoder = Decoder(
            out_ch=out_ch,
            z_channels=tmp_chn,
            attn_resolutions=attn_resolutions,
            dropout=dropout,
            resamp_with_conv=resamp_with_conv,
            in_channels=None,
            num_res_blocks=num_res_blocks,
            ch_mult=ch_mult,
            resolution=resolution,
            ch=ch,
        )
        self.rescaler = LatentRescaler(
            factor=rescale_factor,
            in_channels=z_channels,
            mid_channels=tmp_chn,
            out_channels=tmp_chn,
            depth=rescale_module_depth,
        )

    def forward(self, x):
        x = self.rescaler(x)
        x = self.decoder(x)
        return x


class Upsampler(nn.Module):
    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
        super().__init__()
        assert out_size >= in_size
        num_blocks = int(np.log2(out_size // in_size)) + 1
        factor_up = 1.0 + (out_size % in_size)
        print(
            f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}"
        )
        self.rescaler = LatentRescaler(
            factor=factor_up,
            in_channels=in_channels,
            mid_channels=2 * in_channels,
            out_channels=in_channels,
        )
        self.decoder = Decoder(
            out_ch=out_channels,
            resolution=out_size,
            z_channels=in_channels,
            num_res_blocks=2,
            attn_resolutions=[],
            in_channels=None,
            ch=in_channels,
            ch_mult=[ch_mult for _ in range(num_blocks)],
        )

    def forward(self, x):
        x = self.rescaler(x)
        x = self.decoder(x)
        return x


class Resize(nn.Module):
    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
        super().__init__()
        self.with_conv = learned
        self.mode = mode
        if self.with_conv:
            print(
                f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode"
            )
            raise NotImplementedError()
            assert in_channels is not None
            # no asymmetric padding in torch conv, must do it ourselves
            self.conv = torch.nn.Conv2d(
                in_channels, in_channels, kernel_size=4, stride=2, padding=1
            )

    def forward(self, x, scale_factor=1.0):
        if scale_factor == 1.0:
            return x
        else:
            x = torch.nn.functional.interpolate(
                x, mode=self.mode, align_corners=False, scale_factor=scale_factor
            )
        return x


class FirstStagePostProcessor(nn.Module):
    def __init__(
        self,
        ch_mult: list,
        in_channels,
        pretrained_model: nn.Module = None,
        reshape=False,
        n_channels=None,
        dropout=0.0,
        pretrained_config=None,
    ):
        super().__init__()
        if pretrained_config is None:
            assert (
                pretrained_model is not None
            ), 'Either "pretrained_model" or "pretrained_config" must not be None'
            self.pretrained_model = pretrained_model
        else:
            assert (
                pretrained_config is not None
            ), 'Either "pretrained_model" or "pretrained_config" must not be None'
            self.instantiate_pretrained(pretrained_config)

        self.do_reshape = reshape

        if n_channels is None:
            n_channels = self.pretrained_model.encoder.ch

        self.proj_norm = Normalize(in_channels, num_groups=in_channels // 2)
        self.proj = nn.Conv2d(
            in_channels, n_channels, kernel_size=3, stride=1, padding=1
        )

        blocks = []
        downs = []
        ch_in = n_channels
        for m in ch_mult:
            blocks.append(
                ResnetBlock(
                    in_channels=ch_in, out_channels=m * n_channels, dropout=dropout
                )
            )
            ch_in = m * n_channels
            downs.append(Downsample(ch_in, with_conv=False))

        self.model = nn.ModuleList(blocks)
        self.downsampler = nn.ModuleList(downs)

    def instantiate_pretrained(self, config):
        model = instantiate_from_config(config)
        self.pretrained_model = model.eval()
        # self.pretrained_model.train = False
        for param in self.pretrained_model.parameters():
            param.requires_grad = False

    @torch.no_grad()
    def encode_with_pretrained(self, x):
        c = self.pretrained_model.encode(x)
        if isinstance(c, DiagonalGaussianDistribution):
            c = c.mode()
        return c

    def forward(self, x):
        z_fs = self.encode_with_pretrained(x)
        z = self.proj_norm(z_fs)
        z = self.proj(z)
        z = nonlinearity(z)

        for submodel, downmodel in zip(self.model, self.downsampler):
            z = submodel(z, temb=None)
            z = downmodel(z)

        if self.do_reshape:
            z = rearrange(z, "b c h w -> b (h w) c")
        return z


================================================
FILE: ldm/modules/diffusionmodules/openaimodel.py
================================================
from abc import abstractmethod
import math

import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F

from ldm.modules.diffusionmodules.util import (
    checkpoint,
    conv_nd,
    linear,
    avg_pool_nd,
    zero_module,
    normalization,
    timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer


# dummy replace
def convert_module_to_f16(x):
    pass


def convert_module_to_f32(x):
    pass


## go
class AttentionPool2d(nn.Module):
    """
    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
    """

    def __init__(
        self,
        spacial_dim: int,
        embed_dim: int,
        num_heads_channels: int,
        output_dim: int = None,
    ):
        super().__init__()
        self.positional_embedding = nn.Parameter(
            th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5
        )
        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
        self.num_heads = embed_dim // num_heads_channels
        self.attention = QKVAttention(self.num_heads)

    def forward(self, x):
        b, c, *_spatial = x.shape
        x = x.reshape(b, c, -1)  # NC(HW)
        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
        x = self.qkv_proj(x)
        x = self.attention(x)
        x = self.c_proj(x)
        return x[:, :, 0]


class TimestepBlock(nn.Module):
    """
    Any module where forward() takes timestep embeddings as a second argument.
    """

    @abstractmethod
    def forward(self, x, emb):
        """
        Apply the module to `x` given `emb` timestep embeddings.
        """


class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    """
    A sequential module that passes timestep embeddings to the children that
    support it as an extra input.
    """

    def forward(self, x, emb, context=None):
        for layer in self:
            if isinstance(layer, TimestepBlock):
                x = layer(x, emb)
            elif isinstance(layer, SpatialTransformer):
                x = layer(x, context)
            else:
                x = layer(x)
        return x


class Upsample(nn.Module):
    """
    An upsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 upsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        if use_conv:
            self.conv = conv_nd(
                dims, self.channels, self.out_channels, 3, padding=padding
            )

    def forward(self, x):
        assert x.shape[1] == self.channels
        if self.dims == 3:
            x = F.interpolate(
                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
            )
        else:
            x = F.interpolate(x, scale_factor=2, mode="nearest")
        if self.use_conv:
            x = self.conv(x)
        return x


class TransposedUpsample(nn.Module):
    "Learned 2x upsampling without padding"

    def __init__(self, channels, out_channels=None, ks=5):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels

        self.up = nn.ConvTranspose2d(
            self.channels, self.out_channels, kernel_size=ks, stride=2
        )

    def forward(self, x):
        return self.up(x)


class Downsample(nn.Module):
    """
    A downsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 downsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        stride = 2 if dims != 3 else (1, 2, 2)
        if use_conv:
            self.op = conv_nd(
                dims,
                self.channels,
                self.out_channels,
                3,
                stride=stride,
                padding=padding,
            )
        else:
            assert self.channels == self.out_channels
            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)

    def forward(self, x):
        assert x.shape[1] == self.channels
        return self.op(x)


class ResBlock(TimestepBlock):
    """
    A residual block that can optionally change the number of channels.
    :param channels: the number of input channels.
    :param emb_channels: the number of timestep embedding channels.
    :param dropout: the rate of dropout.
    :param out_channels: if specified, the number of out channels.
    :param use_conv: if True and out_channels is specified, use a spatial
        convolution instead of a smaller 1x1 convolution to change the
        channels in the skip connection.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param use_checkpoint: if True, use gradient checkpointing on this module.
    :param up: if True, use this block for upsampling.
    :param down: if True, use this block for downsampling.
    """

    def __init__(
        self,
        channels,
        emb_channels,
        dropout,
        out_channels=None,
        use_conv=False,
        use_scale_shift_norm=False,
        dims=2,
        use_checkpoint=False,
        up=False,
        down=False,
    ):
        super().__init__()
        self.channels = channels
        self.emb_channels = emb_channels
        self.dropout = dropout
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.use_checkpoint = use_checkpoint
        self.use_scale_shift_norm = use_scale_shift_norm

        self.in_layers = nn.Sequential(
            normalization(channels),
            nn.SiLU(),
            conv_nd(dims, channels, self.out_channels, 3, padding=1),
        )

        self.updown = up or down

        if up:
            self.h_upd = Upsample(channels, False, dims)
            self.x_upd = Upsample(channels, False, dims)
        elif down:
            self.h_upd = Downsample(channels, False, dims)
            self.x_upd = Downsample(channels, False, dims)
        else:
            self.h_upd = self.x_upd = nn.Identity()

        self.emb_layers = nn.Sequential(
            nn.SiLU(),
            linear(
                emb_channels,
                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
            ),
        )
        self.out_layers = nn.Sequential(
            normalization(self.out_channels),
            nn.SiLU(),
            nn.Dropout(p=dropout),
            zero_module(
                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
            ),
        )

        if self.out_channels == channels:
            self.skip_connection = nn.Identity()
        elif use_conv:
            self.skip_connection = conv_nd(
                dims, channels, self.out_channels, 3, padding=1
            )
        else:
            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)

    def forward(self, x, emb):
        """
        Apply the block to a Tensor, conditioned on a timestep embedding.
        :param x: an [N x C x ...] Tensor of features.
        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
        :return: an [N x C x ...] Tensor of outputs.
        """
        return checkpoint(
            self._forward, (x, emb), self.parameters(), self.use_checkpoint
        )

    def _forward(self, x, emb):
        if self.updown:
            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
            h = in_rest(x)
            h = self.h_upd(h)
            x = self.x_upd(x)
            h = in_conv(h)
        else:
            h = self.in_layers(x)
        emb_out = self.emb_layers(emb).type(h.dtype)
        while len(emb_out.shape) < len(h.shape):
            emb_out = emb_out[..., None]
        if self.use_scale_shift_norm:
            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
            scale, shift = th.chunk(emb_out, 2, dim=1)
            h = out_norm(h) * (1 + scale) + shift
            h = out_rest(h)
        else:
            h = h + emb_out
            h = self.out_layers(h)
        return self.skip_connection(x) + h


class AttentionBlock(nn.Module):
    """
    An attention block that allows spatial positions to attend to each other.
    Originally ported from here, but adapted to the N-d case.
    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
    """

    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        use_checkpoint=False,
        use_new_attention_order=False,
    ):
        super().__init__()
        self.channels = channels
        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        self.use_checkpoint = use_checkpoint
        self.norm = normalization(channels)
        self.qkv = conv_nd(1, channels, channels * 3, 1)
        if use_new_attention_order:
            # split qkv before split heads
            self.attention = QKVAttention(self.num_heads)
        else:
            # split heads before split qkv
            self.attention = QKVAttentionLegacy(self.num_heads)

        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))

    def forward(self, x):
        return checkpoint(
            self._forward, (x,), self.parameters(), True
        )  # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
        # return pt_checkpoint(self._forward, x)  # pytorch

    def _forward(self, x):
        b, c, *spatial = x.shape
        x = x.reshape(b, c, -1)
        qkv = self.qkv(self.norm(x))
        h = self.attention(qkv)
        h = self.proj_out(h)
        return (x + h).reshape(b, c, *spatial)


def count_flops_attn(model, _x, y):
    """
    A counter for the `thop` package to count the operations in an
    attention operation.
    Meant to be used like:
        macs, params = thop.profile(
            model,
            inputs=(inputs, timestamps),
            custom_ops={QKVAttention: QKVAttention.count_flops},
        )
    """
    b, c, *spatial = y[0].shape
    num_spatial = int(np.prod(spatial))
    # We perform two matmuls with the same number of ops.
    # The first computes the weight matrix, the second computes
    # the combination of the value vectors.
    matmul_ops = 2 * b * (num_spatial**2) * c
    model.total_ops += th.DoubleTensor([matmul_ops])


class QKVAttentionLegacy(nn.Module):
    """
    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv):
        """
        Apply QKV attention.
        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts", q * scale, k * scale
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v)
        return a.reshape(bs, -1, length)

    @staticmethod
    def count_flops(model, _x, y):
        return count_flops_attn(model, _x, y)


class QKVAttention(nn.Module):
    """
    A module which performs QKV attention and splits in a different order.
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv):
        """
        Apply QKV attention.
        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.chunk(3, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts",
            (q * scale).view(bs * self.n_heads, ch, length),
            (k * scale).view(bs * self.n_heads, ch, length),
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
        return a.reshape(bs, -1, length)

    @staticmethod
    def count_flops(model, _x, y):
        return count_flops_attn(model, _x, y)


class UNetModel(nn.Module):
    """
    The full UNet model with attention and timestep embedding.
    :param in_channels: channels in the input Tensor.
    :param model_channels: base channel count for the model.
    :param out_channels: channels in the output Tensor.
    :param num_res_blocks: number of residual blocks per downsample.
    :param attention_resolutions: a collection of downsample rates at which
        attention will take place. May be a set, list, or tuple.
        For example, if this contains 4, then at 4x downsampling, attention
        will be used.
    :param dropout: the dropout probability.
    :param channel_mult: channel multiplier for each level of the UNet.
    :param conv_resample: if True, use learned convolutions for upsampling and
        downsampling.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param num_classes: if specified (as an int), then this model will be
        class-conditional with `num_classes` classes.
    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
    :param num_heads: the number of attention heads in each attention layer.
    :param num_heads_channels: if specified, ignore num_heads and instead use
                               a fixed channel width per attention head.
    :param num_heads_upsample: works with num_heads to set a different number
                               of heads for upsampling. Deprecated.
    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
    :param resblock_updown: use residual blocks for up/downsampling.
    :param use_new_attention_order: use a different attention pattern for potentially
                                    increased efficiency.
    """

    def __init__(
        self,
        image_size,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        attention_resolutions,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=2,
        num_classes=None,
        use_checkpoint=False,
        use_fp16=False,
        num_heads=-1,
        num_head_channels=-1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        resblock_updown=False,
        use_new_attention_order=False,
        use_spatial_transformer=False,  # custom transformer support
        transformer_depth=1,  # custom transformer support
        context_dim=None,  # custom transformer support
        n_embed=None,  # custom support for prediction of discrete ids into codebook of first stage vq model
        legacy=True,
    ):
        super().__init__()
        if use_spatial_transformer:
            assert (
                context_dim is not None
            ), "Fool!! You forgot to include the dimension of your cross-attention conditioning..."

        if context_dim is not None:
            assert (
                use_spatial_transformer
            ), "Fool!! You forgot to use the spatial transformer for your cross-attention conditioning..."
            from omegaconf.listconfig import ListConfig

            if type(context_dim) == ListConfig:
                context_dim = list(context_dim)

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        if num_heads == -1:
            assert (
                num_head_channels != -1
            ), "Either num_heads or num_head_channels has to be set"

        if num_head_channels == -1:
            assert (
                num_heads != -1
            ), "Either num_heads or num_head_channels has to be set"

        self.image_size = image_size
        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        self.num_res_blocks = num_res_blocks
        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.num_classes = num_classes
        self.use_checkpoint = use_checkpoint
        self.dtype = th.float16 if use_fp16 else th.float32
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample
        self.predict_codebook_ids = n_embed is not None

        time_embed_dim = model_channels * 4
        self.time_embed = nn.Sequential(
            linear(model_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )

        if self.num_classes is not None:
            self.label_emb = nn.Embedding(num_classes, time_embed_dim)

        self.input_blocks = nn.ModuleList(
            [
                TimestepEmbedSequential(
                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
                )
            ]
        )
        self._feature_size = model_channels
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1
        for level, mult in enumerate(channel_mult):
            for _ in range(num_res_blocks):
                layers = [
                    ResBlock(
                        ch,
                        time_embed_dim,
                        dropout,
                        out_channels=mult * model_channels,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = mult * model_channels
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        # num_heads = 1
                        dim_head = (
                            ch // num_heads
                            if use_spatial_transformer
                            else num_head_channels
                        )
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads,
                            num_head_channels=dim_head,
                            use_new_attention_order=use_new_attention_order,
                        )
                        if not use_spatial_transformer
                        else SpatialTransformer(
                            ch,
                            num_heads,
                            dim_head,
                            depth=transformer_depth,
                            context_dim=context_dim,
                        )
                    )
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            down=True,
                        )
                        if resblock_updown
                        else Downsample(
                            ch, conv_resample, dims=dims, out_channels=out_ch
                        )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2
                self._feature_size += ch

        if num_head_channels == -1:
            dim_head = ch // num_heads
        else:
            num_heads = ch // num_head_channels
            dim_head = num_head_channels
        if legacy:
            # num_heads = 1
            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
        self.middle_block = TimestepEmbedSequential(
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
            AttentionBlock(
                ch,
                use_checkpoint=use_checkpoint,
                num_heads=num_heads,
                num_head_channels=dim_head,
                use_new_attention_order=use_new_attention_order,
            )
            if not use_spatial_transformer
            else SpatialTransformer(
                ch,
                num_heads,
                dim_head,
                depth=transformer_depth,
                context_dim=context_dim,
            ),
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
        )
        self._feature_size += ch

        self.output_blocks = nn.ModuleList([])
        for level, mult in list(enumerate(channel_mult))[::-1]:
            for i in range(num_res_blocks + 1):
                ich = input_block_chans.pop()
                layers = [
                    ResBlock(
                        ch + ich,
                        time_embed_dim,
                        dropout,
                        out_channels=model_channels * mult,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = model_channels * mult
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        # num_heads = 1
                        dim_head = (
                            ch // num_heads
                            if use_spatial_transformer
                            else num_head_channels
                        )
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads_upsample,
                            num_head_channels=dim_head,
                            use_new_attention_order=use_new_attention_order,
                        )
                        if not use_spatial_transformer
                        else SpatialTransformer(
                            ch,
                            num_heads,
                            dim_head,
                            depth=transformer_depth,
                            context_dim=context_dim,
                        )
                    )
                if level and i == num_res_blocks:
                    out_ch = ch
                    layers.append(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            up=True,
                        )
                        if resblock_updown
                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
                    )
                    ds //= 2
                self.output_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch

        self.out = nn.Sequential(
            normalization(ch),
            nn.SiLU(),
            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
        )
        if self.predict_codebook_ids:
            self.id_predictor = nn.Sequential(
                normalization(ch),
                conv_nd(dims, model_channels, n_embed, 1),
                # nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
            )

    def convert_to_fp16(self):
        """
        Convert the torso of the model to float16.
        """
        self.input_blocks.apply(convert_module_to_f16)
        self.middle_block.apply(convert_module_to_f16)
        self.output_blocks.apply(convert_module_to_f16)

    def convert_to_fp32(self):
        """
        Convert the torso of the model to float32.
        """
        self.input_blocks.apply(convert_module_to_f32)
        self.middle_block.apply(convert_module_to_f32)
        self.output_blocks.apply(convert_module_to_f32)

    def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
        """
        Apply the model to an input batch.
        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :param context: conditioning plugged in via crossattn
        :param y: an [N] Tensor of labels, if class-conditional.
        :return: an [N x C x ...] Tensor of outputs.
        """
        assert (y is not None) == (
            self.num_classes is not None
        ), "must specify y if and only if the model is class-conditional"
        hs = []
        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
        emb = self.time_embed(t_emb)

        if self.num_classes is not None:
            assert y.shape == (x.shape[0],)
            emb = emb + self.label_emb(y)

        h = x.type(self.dtype)
        for module in self.input_blocks:
            h = module(h, emb, context)
            hs.append(h)
        h = self.middle_block(h, emb, context)
        for module in self.output_blocks:
            h = th.cat([h, hs.pop()], dim=1)
            h = module(h, emb, context)
        h = h.type(x.dtype)
        if self.predict_codebook_ids:
            return self.id_predictor(h)
        else:
            return self.out(h)


class EncoderUNetModel(nn.Module):
    """
    The half UNet model with attention and timestep embedding.
    For usage, see UNet.
    """

    def __init__(
        self,
        image_size,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        attention_resolutions,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=2,
        use_checkpoint=False,
        use_fp16=False,
        num_heads=1,
        num_head_channels=-1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        resblock_updown=False,
        use_new_attention_order=False,
        pool="adaptive",
        *args,
        **kwargs,
    ):
        super().__init__()

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        self.num_res_blocks = num_res_blocks
        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.use_checkpoint = use_checkpoint
        self.dtype = th.float16 if use_fp16 else th.float32
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample

        time_embed_dim = model_channels * 4
        self.time_embed = nn.Sequential(
            linear(model_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )

        self.input_blocks = nn.ModuleList(
            [
                TimestepEmbedSequential(
                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
                )
            ]
        )
        self._feature_size = model_channels
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1
        for level, mult in enumerate(channel_mult):
            for _ in range(num_res_blocks):
                layers = [
                    ResBlock(
                        ch,
                        time_embed_dim,
                        dropout,
                        out_channels=mult * model_channels,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = mult * model_channels
                if ds in attention_resolutions:
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads,
                            num_head_channels=num_head_channels,
                            use_new_attention_order=use_new_attention_order,
                        )
                    )
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            down=True,
                        )
                        if resblock_updown
                        else Downsample(
                            ch, conv_resample, dims=dims, out_channels=out_ch
                        )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2
                self._feature_size += ch

        self.middle_block = TimestepEmbedSequential(
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
            AttentionBlock(
                ch,
                use_checkpoint=use_checkpoint,
                num_heads=num_heads,
                num_head_channels=num_head_channels,
                use_new_attention_order=use_new_attention_order,
            ),
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
        )
        self._feature_size += ch
        self.pool = pool
        if pool == "adaptive":
            self.out = nn.Sequential(
                normalization(ch),
                nn.SiLU(),
                nn.AdaptiveAvgPool2d((1, 1)),
                zero_module(conv_nd(dims, ch, out_channels, 1)),
                nn.Flatten(),
            )
        elif pool == "attention":
            assert num_head_channels != -1
            self.out = nn.Sequential(
                normalization(ch),
                nn.SiLU(),
                AttentionPool2d(
                    (image_size // ds), ch, num_head_channels, out_channels
                ),
            )
        elif pool == "spatial":
            self.out = nn.Sequential(
                nn.Linear(self._feature_size, 2048),
                nn.ReLU(),
                nn.Linear(2048, self.out_channels),
            )
        elif pool == "spatial_v2":
            self.out = nn.Sequential(
                nn.Linear(self._feature_size, 2048),
                normalization(2048),
                nn.SiLU(),
                nn.Linear(2048, self.out_channels),
            )
        else:
            raise NotImplementedError(f"Unexpected {pool} pooling")

    def convert_to_fp16(self):
        """
        Convert the torso of the model to float16.
        """
        self.input_blocks.apply(convert_module_to_f16)
        self.middle_block.apply(convert_module_to_f16)

    def convert_to_fp32(self):
        """
        Convert the torso of the model to float32.
        """
        self.input_blocks.apply(convert_module_to_f32)
        self.middle_block.apply(convert_module_to_f32)

    def forward(self, x, timesteps):
        """
        Apply the model to an input batch.
        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :return: an [N x K] Tensor of outputs.
        """
        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))

        results = []
        h = x.type(self.dtype)
        for module in self.input_blocks:
            h = module(h, emb)
            if self.pool.startswith("spatial"):
                results.append(h.type(x.dtype).mean(dim=(2, 3)))
        h = self.middle_block(h, emb)
        if self.pool.startswith("spatial"):
            results.append(h.type(x.dtype).mean(dim=(2, 3)))
            h = th.cat(results, axis=-1)
            return self.out(h)
        else:
            h = h.type(x.dtype)
            return self.out(h)


================================================
FILE: ldm/modules/diffusionmodules/upscaling.py
================================================
import torch
import torch.nn as nn
import numpy as np
from functools import partial

from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
from ldm.util import default


class AbstractLowScaleModel(nn.Module):
    # for concatenating a downsampled image to the latent representation
    def __init__(self, noise_schedule_config=None):
        super(AbstractLowScaleModel, self).__init__()
        if noise_schedule_config is not None:
            self.register_schedule(**noise_schedule_config)

    def register_schedule(
        self,
        beta_schedule="linear",
        timesteps=1000,
        linear_start=1e-4,
        linear_end=2e-2,
        cosine_s=8e-3,
    ):
        betas = make_beta_schedule(
            beta_schedule,
            timesteps,
            linear_start=linear_start,
            linear_end=linear_end,
            cosine_s=cosine_s,
        )
        alphas = 1.0 - betas
        alphas_cumprod = np.cumprod(alphas, axis=0)
        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])

        (timesteps,) = betas.shape
        self.num_timesteps = int(timesteps)
        self.linear_start = linear_start
        self.linear_end = linear_end
        assert (
            alphas_cumprod.shape[0] == self.num_timesteps
        ), "alphas have to be defined for each timestep"

        to_torch = partial(torch.tensor, dtype=torch.float32)

        self.register_buffer("betas", to_torch(betas))
        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
        self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
        self.register_buffer(
            "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
        )
        self.register_buffer(
            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
        )
        self.register_buffer(
            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
        )
        self.register_buffer(
            "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
        )

    def q_sample(self, x_start, t, noise=None):
        noise = default(noise, lambda: torch.randn_like(x_start))
        return (
            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
            + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
            * noise
        )

    def forward(self, x):
        return x, None

    def decode(self, x):
        return x


class SimpleImageConcat(AbstractLowScaleModel):
    # no noise level conditioning
    def __init__(self):
        super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
        self.max_noise_level = 0

    def forward(self, x):
        # fix to constant noise level
        return x, torch.zeros(x.shape[0], device=x.device).long()


class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
    def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
        super().__init__(noise_schedule_config=noise_schedule_config)
        self.max_noise_level = max_noise_level

    def forward(self, x, noise_level=None):
        if noise_level is None:
            noise_level = torch.randint(
                0, self.max_noise_level, (x.shape[0],), device=x.device
            ).long()
        else:
            assert isinstance(noise_level, torch.Tensor)
        z = self.q_sample(x, noise_level)
        return z, noise_level


================================================
FILE: ldm/modules/diffusionmodules/util.py
================================================
# adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
#
# thanks!


import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat

from ldm.util import instantiate_from_config


def make_beta_schedule(
    schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3
):
    if schedule == "linear":
        betas = (
            torch.linspace(
                linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64
            )
            ** 2
        )

    elif schedule == "cosine":
        timesteps = (
            torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
        )
        alphas = timesteps / (1 + cosine_s) * np.pi / 2
        alphas = torch.cos(alphas).pow(2)
        alphas = alphas / alphas[0]
        betas = 1 - alphas[1:] / alphas[:-1]
        betas = np.clip(betas, a_min=0, a_max=0.999)

    elif schedule == "sqrt_linear":
        betas = torch.linspace(
            linear_start, linear_end, n_timestep, dtype=torch.float64
        )
    elif schedule == "sqrt":
        betas = (
            torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
            ** 0.5
        )
    else:
        raise ValueError(f"schedule '{schedule}' unknown.")
    return betas.numpy()


def make_ddim_timesteps(
    ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True
):
    if ddim_discr_method == "uniform":
        c = num_ddpm_timesteps // num_ddim_timesteps
        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
    elif ddim_discr_method == "quad":
        ddim_timesteps = (
            (np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2
        ).astype(int)
    else:
        raise NotImplementedError(
            f'There is no ddim discretization method called "{ddim_discr_method}"'
        )

    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
    # add one to get the final alpha values right (the ones from first scale to data during sampling)
    # steps_out = ddim_timesteps + 1 # removed due to some issues when reaching 1000
    steps_out = np.where(ddim_timesteps != 999, ddim_timesteps + 1, ddim_timesteps)
    if verbose:
        print(f"Selected timesteps for ddim sampler: {steps_out}")
    return steps_out


def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
    # select alphas for computing the variance schedule
    alphas = alphacums[ddim_timesteps]
    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())

    # according the the formula provided in https://arxiv.org/abs/2010.02502
    sigmas = eta * np.sqrt(
        (1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)
    )
    if verbose:
        print(
            f"Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}"
        )
        print(
            f"For the chosen value of eta, which is {eta}, "
            f"this results in the following sigma_t schedule for ddim sampler {sigmas}"
        )
    return sigmas, alphas, alphas_prev


def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
    """
    Create a beta schedule that discretizes the given alpha_t_bar function,
    which defines the cumulative product of (1-beta) over time from t = [0,1].
    :param num_diffusion_timesteps: the number of betas to produce.
    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
                      produces the cumulative product of (1-beta) up to that
                      part of the diffusion process.
    :param max_beta: the maximum beta to use; use values lower than 1 to
                     prevent singularities.
    """
    betas = []
    for i in range(num_diffusion_timesteps):
        t1 = i / num_diffusion_timesteps
        t2 = (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
    return np.array(betas)


def extract_into_tensor(a, t, x_shape):
    b, *_ = t.shape
    out = a.gather(-1, t)
    return out.reshape(b, *((1,) * (len(x_shape) - 1)))


def checkpoint(func, inputs, params, flag):
    """
    Evaluate a function without caching intermediate activations, allowing for
    reduced memory at the expense of extra compute in the backward pass.
    :param func: the function to evaluate.
    :param inputs: the argument sequence to pass to `func`.
    :param params: a sequence of parameters `func` depends on but does not
                   explicitly take as arguments.
    :param flag: if False, disable gradient checkpointing.
    """
    if flag:
        args = tuple(inputs) + tuple(params)
        return CheckpointFunction.apply(func, len(inputs), *args)
    else:
        return func(*inputs)


class CheckpointFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, run_function, length, *args):
        ctx.run_function = run_function
        ctx.input_tensors = list(args[:length])
        ctx.input_params = list(args[length:])

        with torch.no_grad():
            output_tensors = ctx.run_function(*ctx.input_tensors)
        return output_tensors

    @staticmethod
    def backward(ctx, *output_grads):
        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
        with torch.enable_grad():
            # Fixes a bug where the first op in run_function modifies the
            # Tensor storage in place, which is not allowed for detach()'d
            # Tensors.
            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
            output_tensors = ctx.run_function(*shallow_copies)
        input_grads = torch.autograd.grad(
            output_tensors,
            ctx.input_tensors + ctx.input_params,
            output_grads,
            allow_unused=True,
        )
        del ctx.input_tensors
        del ctx.input_params
        del output_tensors
        return (None, None) + input_grads


def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
    """
    Create sinusoidal timestep embeddings.
    :param timesteps: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an [N x dim] Tensor of positional embeddings.
    """
    if not repeat_only:
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period)
            * torch.arange(start=0, end=half, dtype=torch.float32)
            / half
        ).to(device=timesteps.device)
        args = timesteps[:, None].float() * freqs[None]
        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        if dim % 2:
            embedding = torch.cat(
                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
            )
    else:
        embedding = repeat(timesteps, "b -> b d", d=dim)
    return embedding


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


def scale_module(module, scale):
    """
    Scale the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().mul_(scale)
    return module


def mean_flat(tensor):
    """
    Take the mean over all non-batch dimensions.
    """
    return tensor.mean(dim=list(range(1, len(tensor.shape))))


def normalization(channels):
    """
    Make a standard normalization layer.
    :param channels: number of input channels.
    :return: an nn.Module for normalization.
    """
    return GroupNorm32(32, channels)


# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
    def forward(self, x):
        return x * torch.sigmoid(x)


class GroupNorm32(nn.GroupNorm):
    def forward(self, x):
        return super().forward(x.float()).type(x.dtype)


def conv_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D convolution module.
    """
    if dims == 1:
        return nn.Conv1d(*args, **kwargs)
    elif dims == 2:
        return nn.Conv2d(*args, **kwargs)
    elif dims == 3:
        return nn.Conv3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


def linear(*args, **kwargs):
    """
    Create a linear module.
    """
    return nn.Linear(*args, **kwargs)


def avg_pool_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D average pooling module.
    """
    if dims == 1:
        return nn.AvgPool1d(*args, **kwargs)
    elif dims == 2:
        return nn.AvgPool2d(*args, **kwargs)
    elif dims == 3:
        return nn.AvgPool3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


class HybridConditioner(nn.Module):
    def __init__(self, c_concat_config, c_crossattn_config):
        super().__init__()
        self.concat_conditioner = instantiate_from_config(c_concat_config)
        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)

    def forward(self, c_concat, c_crossattn):
        c_concat = self.concat_conditioner(c_concat)
        c_crossattn = self.crossattn_conditioner(c_crossattn)
        return {"c_concat": [c_concat], "c_crossattn": [c_crossattn]}


def noise_like(shape, device, repeat=False):
    def repeat_noise():
        return torch.randn((1, *shape[1:]), device=device).repeat(
            shape[0], *((1,) * (len(shape) - 1))
        )

    def noise():
        return torch.randn(shape, device=device)

    return repeat_noise() if repeat else noise()


================================================
FILE: ldm/modules/distributions/__init__.py
================================================


================================================
FILE: ldm/modules/distributions/distributions.py
================================================
import torch
import numpy as np


class AbstractDistribution:
    def sample(self):
        raise NotImplementedError()

    def mode(self):
        raise NotImplementedError()


class DiracDistribution(AbstractDistribution):
    def __init__(self, value):
        self.value = value

    def sample(self):
        return self.value

    def mode(self):
        return self.value


class DiagonalGaussianDistribution(object):
    def __init__(self, parameters, deterministic=False):
        self.parameters = parameters
        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
        self.deterministic = deterministic
        self.std = torch.exp(0.5 * self.logvar)
        self.var = torch.exp(self.logvar)
        if self.deterministic:
            self.var = self.std = torch.zeros_like(self.mean).to(
                device=self.parameters.device
            )

    def sample(self):
        x = self.mean + self.std * torch.randn(self.mean.shape).to(
            device=self.parameters.device
        )
        return x

    def kl(self, other=None):
        if self.deterministic:
            return torch.Tensor([0.0])
        else:
            if other is None:
                return 0.5 * torch.sum(
                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
                    dim=[1, 2, 3],
                )
            else:
                return 0.5 * torch.sum(
                    torch.pow(self.mean - other.mean, 2) / other.var
                    + self.var / other.var
                    - 1.0
                    - self.logvar
                    + other.logvar,
                    dim=[1, 2, 3],
                )

    def nll(self, sample, dims=[1, 2, 3]):
        if self.deterministic:
            return torch.Tensor([0.0])
        logtwopi = np.log(2.0 * np.pi)
        return 0.5 * torch.sum(
            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
            dim=dims,
        )

    def mode(self):
        return self.mean


def normal_kl(mean1, logvar1, mean2, logvar2):
    """
    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
    Compute the KL divergence between two gaussians.
    Shapes are automatically broadcasted, so batches can be compared to
    scalars, among other use cases.
    """
    tensor = None
    for obj in (mean1, logvar1, mean2, logvar2):
        if isinstance(obj, torch.Tensor):
            tensor = obj
            break
    assert tensor is not None, "at least one argument must be a Tensor"

    # Force variances to be Tensors. Broadcasting helps convert scalars to
    # Tensors, but it does not work for torch.exp().
    logvar1, logvar2 = [
        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
        for x in (logvar1, logvar2)
    ]

    return 0.5 * (
        -1.0
        + logvar2
        - logvar1
        + torch.exp(logvar1 - logvar2)
        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
    )


================================================
FILE: ldm/modules/ema.py
================================================
import torch
from torch import nn


class LitEma(nn.Module):
    def __init__(self, model, decay=0.9999, use_num_upates=True):
        super().__init__()
        if decay < 0.0 or decay > 1.0:
            raise ValueError("Decay must be between 0 and 1")

        self.m_name2s_name = {}
        self.register_buffer("decay", torch.tensor(decay, dtype=torch.float32))
        self.register_buffer(
            "num_updates",
            torch.tensor(0, dtype=torch.int)
            if use_num_upates
            else torch.tensor(-1, dtype=torch.int),
        )

        for name, p in model.named_parameters():
            if p.requires_grad:
                # remove as '.'-character is not allowed in buffers
                s_name = name.replace(".", "")
                self.m_name2s_name.update({name: s_name})
                self.register_buffer(s_name, p.clone().detach().data)

        self.collected_params = []

    def forward(self, model):
        decay = self.decay

        if self.num_updates >= 0:
            self.num_updates += 1
            decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))

        one_minus_decay = 1.0 - decay

        with torch.no_grad():
            m_param = dict(model.named_parameters())
            shadow_params = dict(self.named_buffers())

            for key in m_param:
                if m_param[key].requires_grad:
                    sname = self.m_name2s_name[key]
                    shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
                    shadow_params[sname].sub_(
                        one_minus_decay * (shadow_params[sname] - m_param[key])
                    )
                else:
                    assert key not in self.m_name2s_name

    def copy_to(self, model):
        m_param = dict(model.named_parameters())
        shadow_params = dict(self.named_buffers())
        for key in m_param:
            if m_param[key].requires_grad:
                m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
            else:
                assert key not in self.m_name2s_name

    def store(self, parameters):
        """
        Save the current parameters for restoring later.
        Args:
          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
            temporarily stored.
        """
        self.collected_params = [param.clone() for param in parameters]

    def restore(self, parameters):
        """
        Restore the parameters stored with the `store` method.
        Useful to validate the model with EMA parameters without affecting the
        original optimization process. Store the parameters before the
        `copy_to` method. After validation (or model saving), use this to
        restore the former parameters.
        Args:
          parameters: Iterable of `torch.nn.Parameter`; the parameters to be
            updated with the stored parameters.
        """
        for c_param, param in zip(self.collected_params, parameters):
            param.data.copy_(c_param.data)


================================================
FILE: ldm/modules/embedding_manager.py
================================================
import torch
from torch import nn


from ldm.data.personalized import per_img_token_list
from functools import partial

DEFAULT_PLACEHOLDER_TOKEN = ["*"]

PROGRESSIVE_SCALE = 2000


def get_clip_token_for_string(tokenizer, string):
    batch_encoding = tokenizer(
        string,
        truncation=True,
        max_length=77,
        return_length=True,
        return_overflowing_tokens=False,
        padding="max_length",
        return_tensors="pt",
    )
    tokens = batch_encoding["input_ids"]
    """ assert (
        torch.count_nonzero(tokens - 49407) == 2
    ), f"String '{string}' maps to more than a single token. Please use another string" """

    return tokens[0, 1]


def get_bert_token_for_string(tokenizer, string):
    token = tokenizer(string)
    # assert torch.count_nonzero(token) == 3, f"String '{string}' maps to more than a single token. Please use another string"

    token = token[0, 1]

    return token


def get_embedding_for_clip_token(embedder, token):
    return embedder(token.unsqueeze(0))[0, 0]


class EmbeddingManager(nn.Module):
    def __init__(
        self,
        embedder,
        placeholder_strings=None,
        initializer_words=None,
        per_image_tokens=False,
        num_vectors_per_token=1,
        progressive_words=False,
        **kwargs,
    ):
        super().__init__()

        self.embedder = embedder
        device = embedder.device

        self.string_to_token_dict = {}
        self.string_to_param_dict = nn.ParameterDict()

        self.initial_embeddings = nn.ParameterDict()  # These should not be optimized

        self.progressive_words = progressive_words
        self.progressive_counter = 0

        self.max_vectors_per_token = num_vectors_per_token

        if hasattr(embedder, "tokenizer"):  # using Stable Diffusion's CLIP encoder
            self.is_clip = True
            get_token_for_string = partial(
                get_clip_token_for_string, embedder.tokenizer
            )
            get_embedding_for_tkn = partial(
                get_embedding_for_clip_token,
                embedder.transformer.text_model.embeddings,
            )
            # per bug report #572
            # token_dim = 1280
            token_dim = 768
        else:  # using LDM's BERT encoder
            self.is_clip = False
            get_token_for_string = partial(get_bert_token_for_string, embedder.tknz_fn)
            get_embedding_for_tkn = embedder.transformer.token_emb
            token_dim = 1280

        if per_image_tokens:
            placeholder_strings.extend(per_img_token_list)

        for idx, placeholder_string in enumerate(placeholder_strings):
            token = get_token_for_string(placeholder_string)

            if initializer_words and idx < len(initializer_words):
                init_word_token = get_token_for_string(initializer_words[idx])

                with torch.no_grad():
                    init_word_embedding = get_embedding_for_tkn(
                        init_word_token.to(device)
                    )

                token_params = torch.nn.Parameter(
                    init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1),
                    requires_grad=True,
                )
                self.initial_embeddings[placeholder_string] = torch.nn.Parameter(
                    init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1),
                    requires_grad=False,
                )
            else:
                token_params = torch.nn.Parameter(
                    torch.rand(
                        size=(num_vectors_per_token, token_dim),
                        requires_grad=True,
                    )
                )

            self.string_to_token_dict[placeholder_string] = token
            self.string_to_param_dict[placeholder_string] = token_params

    def forward(
        self,
        tokenized_text,
        embedded_text,
    ):
        b, n, device = *tokenized_text.shape, tokenized_text.device

        for (
            placeholder_string,
            placeholder_token,
        ) in self.string_to_token_dict.items():
            placeholder_embedding = self.string_to_param_dict[placeholder_string].to(
                device
            )

            if (
                self.max_vectors_per_token == 1
            ):  # If there's only one vector per token, we can do a simple replacement
                placeholder_idx = torch.where(
                    tokenized_text == placeholder_token.to(device)
                )
                embedded_text[placeholder_idx] = placeholder_embedding
            else:  # otherwise, need to insert and keep track of changing indices
                if self.progressive_words:
                    self.progressive_counter += 1
                    max_step_tokens = 1 + self.progressive_counter // PROGRESSIVE_SCALE
                else:
                    max_step_tokens = self.max_vectors_per_token

                num_vectors_for_token = min(
                    placeholder_embedding.shape[0], max_step_tokens
                )

                placeholder_rows, placeholder_cols = torch.where(
                    tokenized_text == placeholder_token.to(device)
                )

                if placeholder_rows.nelement() == 0:
                    continue

                sorted_cols, sort_idx = torch.sort(placeholder_cols, descending=True)
                sorted_rows = placeholder_rows[sort_idx]

                for idx in range(len(sorted_rows)):
                    row = sorted_rows[idx]
                    col = sorted_cols[idx]

                    new_token_row = torch.cat(
                        [
                            tokenized_text[row][:col],
                            placeholder_token.repeat(num_vectors_for_token).to(device),
                            tokenized_text[row][col + 1 :],
                        ],
                        axis=0,
                    )[:n]
                    new_embed_row = torch.cat(
                        [
                            embedded_text[row][:col],
                            placeholder_embedding[:num_vectors_for_token],
                            embedded_text[row][col + 1 :],
                        ],
                        axis=0,
                    )[:n]

                    embedded_text[row] = new_embed_row
                    tokenized_text[row] = new_token_row

        return embedded_text

    def save(self, ckpt_path):
        torch.save(
            {
                "string_to_token": self.string_to_token_dict,
                "string_to_param": self.string_to_param_dict,
            },
            ckpt_path,
        )

    def load(self, ckpt_path, full=True):
        ckpt = torch.load(ckpt_path, map_location="cpu")

        # Handle .pt textual inversion files
        if "string_to_token" in ckpt and "string_to_param" in ckpt:
            self.string_to_token_dict = ckpt["string_to_token"]
            self.string_to_param_dict = ckpt["string_to_param"]

        # Handle .bin textual inversion files from Huggingface Concepts
        # https://huggingface.co/sd-concepts-library
        else:
            for token_str in list(ckpt.keys()):
                token = get_clip_token_for_string(self.embedder.tokenizer, token_str)
                self.string_to_token_dict[token_str] = token
                ckpt[token_str] = torch.nn.Parameter(ckpt[token_str])

            self.string_to_param_dict.update(ckpt)

        if not full:
            for key, value in self.string_to_param_dict.items():
                self.string_to_param_dict[key] = torch.nn.Parameter(value.half())

    def get_embedding_norms_squared(self):
        all_params = torch.cat(
            list(self.string_to_param_dict.values()), axis=0
        )  # num_placeholders x embedding_dim
        param_norm_squared = (all_params * all_params).sum(axis=-1)  # num_placeholders

        return param_norm_squared

    def embedding_parameters(self):
        return self.string_to_param_dict.parameters()

    def embedding_to_coarse_loss(self):
        loss = 0.0
        num_embeddings = len(self.initial_embeddings)

        for key in self.initial_embeddings:
            optimized = self.string_to_param_dict[key]
            coarse = self.initial_embeddings[key].clone().to(optimized.device)

            loss = loss + (optimized - coarse) @ (optimized - coarse).T / num_embeddings

        return loss


================================================
FILE: ldm/modules/encoders/__init__.py
================================================


================================================
FILE: ldm/modules/encoders/modules.py
================================================
import torch
import torch.nn as nn
from functools import partial
import clip
from einops import repeat
from transformers import CLIPTokenizer, CLIPTextModel
import kornia
import os
from ldm.modules.x_transformer import (
    Encoder,
    TransformerWrapper,
)  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test


class AbstractEncoder(nn.Module):
    def __init__(self):
        super().__init__()

    def encode(self, *args, **kwargs):
        raise NotImplementedError


class ClassEmbedder(nn.Module):
    def __init__(self, embed_dim, n_classes=1000, key="class"):
        super().__init__()
        self.key = key
        self.embedding = nn.Embedding(n_classes, embed_dim)

    def forward(self, batch, key=None):
        if key is None:
            key = self.key
        # this is for use in crossattn
        c = batch[key][:, None]
        c = self.embedding(c)
        return c


class TransformerEmbedder(AbstractEncoder):
    """Some transformer encoder layers"""

    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
        super().__init__()
        self.device = device
        self.transformer = TransformerWrapper(
            num_tokens=vocab_size,
            max_seq_len=max_seq_len,
            attn_layers=Encoder(dim=n_embed, depth=n_layer),
        )

    def forward(self, tokens):
        tokens = tokens.to(self.device)  # meh
        z = self.transformer(tokens, return_embeddings=True)
        return z

    def encode(self, x):
        return self(x)


class BERTTokenizer(AbstractEncoder):
    """Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""

    def __init__(self, device="cuda", vq_interface=True, max_length=77):
        super().__init__()
        from transformers import BertTokenizerFast  # TODO: add to reuquirements

        self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
        self.device = device
        self.vq_interface = vq_interface
        self.max_length = max_length

    def forward(self, text):
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_length=True,
            return_overflowing_tokens=False,
            padding="max_length",
            return_tensors="pt",
        )
        tokens = batch_encoding["input_ids"].to(self.device)
        return tokens

    @torch.no_grad()
    def encode(self, text):
        tokens = self(text)
        if not self.vq_interface:
            return tokens
        return None, None, [None, None, tokens]

    def decode(self, text):
        return text


class BERTEmbedder(AbstractEncoder):
    """Uses the BERT tokenizr model and add some transformer encoder layers"""

    def __init__(
        self,
        n_embed,
        n_layer,
        vocab_size=30522,
        max_seq_len=77,
        device="cuda",
        use_tokenizer=True,
        embedding_dropout=0.0,
    ):
        super().__init__()
        self.use_tknz_fn = use_tokenizer
        if self.use_tknz_fn:
            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
        self.device = device
        self.transformer = TransformerWrapper(
            num_tokens=vocab_size,
            max_seq_len=max_seq_len,
            attn_layers=Encoder(dim=n_embed, depth=n_layer),
            emb_dropout=embedding_dropout,
        )

    def forward(self, text):
        if self.use_tknz_fn:
            tokens = self.tknz_fn(text)  # .to(self.device)
        else:
            tokens = text
        z = self.transformer(tokens, return_embeddings=True)
        return z

    def encode(self, text):
        # output of length 77
        return self(text)


class SpatialRescaler(nn.Module):
    def __init__(
        self,
        n_stages=1,
        method="bilinear",
        multiplier=0.5,
        in_channels=3,
        out_channels=None,
        bias=False,
    ):
        super().__init__()
        self.n_stages = n_stages
        assert self.n_stages >= 0
        assert method in [
            "nearest",
            "linear",
            "bilinear",
            "trilinear",
            "bicubic",
            "area",
        ]
        self.multiplier = multiplier
        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
        self.remap_output = out_channels is not None
        if self.remap_output:
            print(
                f"Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing."
            )
            self.channel_mapper = nn.Conv2d(in_channels, out_channels, 1, bias=bias)

    def forward(self, x):
        for stage in range(self.n_stages):
            x = self.interpolator(x, scale_factor=self.multiplier)

        if self.remap_output:
            x = self.channel_mapper(x)
        return x

    def encode(self, x):
        return self(x)


class FrozenCLIPEmbedder(AbstractEncoder):
    """Uses the CLIP transformer encoder for text (from Hugging Face)"""

    def __init__(
        self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77
    ):
        super().__init__()
        if os.path.exists("models/clip-vit-large-patch14"):
            self.tokenizer = CLIPTokenizer.from_pretrained(
                "models/clip-vit-large-patch14"
            )
            self.transformer = CLIPTextModel.from_pretrained(
                "models/clip-vit-large-patch14"
            )
        else:
            self.tokenizer = CLIPTokenizer.from_pretrained(version)
            self.transformer = CLIPTextModel.from_pretrained(version)
        self.device = device
        self.max_length = max_length
        self.freeze()

    def freeze(self):
        self.transformer = self.transformer.eval()
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_length=True,
            return_overflowing_tokens=False,
            padding="max_length",
            return_tensors="pt",
        )
        tokens = batch_encoding["input_ids"].to(self.device)
        outputs = self.transformer(input_ids=tokens)

        z = outputs.last_hidden_state
        return z

    def encode(self, text):
        return self(text)


class FrozenCLIPTextEmbedder(nn.Module):
    """
    Uses the CLIP transformer encoder for text.
    """

    def __init__(
        self,
        version="ViT-L/14",
        device="cuda",
        max_length=77,
        n_repeat=1,
        normalize=True,
    ):
        super().__init__()
        self.model, _ = clip.load(version, jit=False, device="cpu")
        self.device = device
        self.max_length = max_length
        self.n_repeat = n_repeat
        self.normalize = normalize

    def freeze(self):
        self.model = self.model.eval()
        for param in self.parameters():
            param.requires_grad = False

    def forward(self, text):
        tokens = clip.tokenize(text).to(self.device)
        z = self.model.encode_text(tokens)
        if self.normalize:
            z = z / torch.linalg.norm(z, dim=1, keepdim=True)
        return z

    def encode(self, text):
        z = self(text)
        if z.ndim == 2:
            z = z[:, None, :]
        z = repeat(z, "b 1 d -> b k d", k=self.n_repeat)
        return z


class FrozenClipImageEmbedder(nn.Module):
    """
    Uses the CLIP image encoder.
    """

    def __init__(
        self,
        model,
        jit=False,
        device="cuda" if torch.cuda.is_available() else "cpu",
        antialias=False,
    ):
        super().__init__()
        self.model, _ = clip.load(name=model, device=device, jit=jit)

        self.antialias = antialias

        self.register_buffer(
            "mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False
        )
        self.register_buffer(
            "std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False
        )

    def preprocess(self, x):
        # normalize to [0,1]
        x = kornia.geometry.resize(
            x,
            (224, 224),
            interpolation="bicubic",
            align_corners=True,
            antialias=self.antialias,
        )
        x = (x + 1.0) / 2.0
        # renormalize according to clip
        x = kornia.enhance.normalize(x, self.mean, self.std)
        return x

    def forward(self, x):
        # x is assumed to be in range [-1,1]
        return self.model.encode_image(self.preprocess(x))


if __name__ == "__main__":
    from ldm.util import count_params

    model = FrozenCLIPEmbedder()
    count_params(model, verbose=True)


================================================
FILE: ldm/modules/image_degradation/__init__.py
================================================


================================================
FILE: ldm/modules/image_degradation/bsrgan.py
================================================
# -*- coding: utf-8 -*-
"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""

import numpy as np
import cv2
import torch

from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations

import ldm.modules.image_degradation.utils_image as util


def modcrop_np(img, sf):
    """
    Args:
        img: numpy image, WxH or WxHxC
        sf: scale factor
    Return:
        cropped image
    """
    w, h = img.shape[:2]
    im = np.copy(img)
    return im[: w - w % sf, : h - h % sf, ...]


"""
# --------------------------------------------
# anisotropic Gaussian kernels
# --------------------------------------------
"""


def analytic_kernel(k):
    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
    k_size = k.shape[0]
    # Calculate the big kernels size
    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
    # Loop over the small kernel to fill the big one
    for r in range(k_size):
        for c in range(k_size):
            big_k[2 * r : 2 * r + k_size, 2 * c : 2 * c + k_size] += k[r, c] * k
    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
    crop = k_size // 2
    cropped_big_k = big_k[crop:-crop, crop:-crop]
    # Normalize to 1
    return cropped_big_k / cropped_big_k.sum()


def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
    """generate an anisotropic Gaussian kernel
    Args:
        ksize : e.g., 15, kernel size
        theta : [0,  pi], rotation angle range
        l1    : [0.1,50], scaling of eigenvalues
        l2    : [0.1,l1], scaling of eigenvalues
        If l1 = l2, will get an isotropic Gaussian kernel.
    Returns:
        k     : kernel
    """

    v = np.dot(
        np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]),
        np.array([1.0, 0.0]),
    )
    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
    D = np.array([[l1, 0], [0, l2]])
    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)

    return k


def gm_blur_kernel(mean, cov, size=15):
    center = size / 2.0 + 0.5
    k = np.zeros([size, size])
    for y in range(size):
        for x in range(size):
            cy = y - center + 1
            cx = x - center + 1
            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)

    k = k / np.sum(k)
    return k


def shift_pixel(x, sf, upper_left=True):
    """shift pixel for super-resolution with different scale factors
    Args:
        x: WxHxC or WxH
        sf: scale factor
        upper_left: shift direction
    """
    h, w = x.shape[:2]
    shift = (sf - 1) * 0.5
    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
    if upper_left:
        x1 = xv + shift
        y1 = yv + shift
    else:
        x1 = xv - shift
        y1 = yv - shift

    x1 = np.clip(x1, 0, w - 1)
    y1 = np.clip(y1, 0, h - 1)

    if x.ndim == 2:
        x = interp2d(xv, yv, x)(x1, y1)
    if x.ndim == 3:
        for i in range(x.shape[-1]):
            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)

    return x


def blur(x, k):
    """
    x: image, NxcxHxW
    k: kernel, Nx1xhxw
    """
    n, c = x.shape[:2]
    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode="replicate")
    k = k.repeat(1, c, 1, 1)
    k = k.view(-1, 1, k.shape[2], k.shape[3])
    x = x.view(1, -1, x.shape[2], x.shape[3])
    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
    x = x.view(n, c, x.shape[2], x.shape[3])

    return x


def gen_kernel(
    k_size=np.array([15, 15]),
    scale_factor=np.array([4, 4]),
    min_var=0.6,
    max_var=10.0,
    noise_level=0,
):
    """ "
    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
    # Kai Zhang
    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
    # max_var = 2.5 * sf
    """
    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
    theta = np.random.rand() * np.pi  # random theta
    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2

    # Set COV matrix using Lambdas and Theta
    LAMBDA = np.diag([lambda_1, lambda_2])
    Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
    SIGMA = Q @ LAMBDA @ Q.T
    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]

    # Set expectation position (shifting kernel for aligned image)
    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
    MU = MU[None, None, :, None]

    # Create meshgrid for Gaussian
    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
    Z = np.stack([X, Y], 2)[:, :, :, None]

    # Calcualte Gaussian for every pixel of the kernel
    ZZ = Z - MU
    ZZ_t = ZZ.transpose(0, 1, 3, 2)
    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)

    # shift the kernel so it will be centered
    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)

    # Normalize the kernel and return
    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
    kernel = raw_kernel / np.sum(raw_kernel)
    return kernel


def fspecial_gaussian(hsize, sigma):
    hsize = [hsize, hsize]
    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
    std = sigma
    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
    arg = -(x * x + y * y) / (2 * std * std)
    h = np.exp(arg)
    h[h < scipy.finfo(float).eps * h.max()] = 0
    sumh = h.sum()
    if sumh != 0:
        h = h / sumh
    return h


def fspecial_laplacian(alpha):
    alpha = max([0, min([alpha, 1])])
    h1 = alpha / (alpha + 1)
    h2 = (1 - alpha) / (alpha + 1)
    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
    h = np.array(h)
    return h


def fspecial(filter_type, *args, **kwargs):
    """
    python code from:
    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
    """
    if filter_type == "gaussian":
        return fspecial_gaussian(*args, **kwargs)
    if filter_type == "laplacian":
        return fspecial_laplacian(*args, **kwargs)


"""
# --------------------------------------------
# degradation models
# --------------------------------------------
"""


def bicubic_degradation(x, sf=3):
    """
    Args:
        x: HxWxC image, [0, 1]
        sf: down-scale factor
    Return:
        bicubicly downsampled LR image
    """
    x = util.imresize_np(x, scale=1 / sf)
    return x


def srmd_degradation(x, k, sf=3):
    """blur + bicubic downsampling
    Args:
        x: HxWxC image, [0, 1]
        k: hxw, double
        sf: down-scale factor
    Return:
        downsampled LR image
    Reference:
        @inproceedings{zhang2018learning,
          title={Learning a single convolutional super-resolution network for multiple degradations},
          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
          pages={3262--3271},
          year={2018}
        }
    """
    x = ndimage.filters.convolve(
        x, np.expand_dims(k, axis=2), mode="wrap"
    )  # 'nearest' | 'mirror'
    x = bicubic_degradation(x, sf=sf)
    return x


def dpsr_degradation(x, k, sf=3):
    """bicubic downsampling + blur
    Args:
        x: HxWxC image, [0, 1]
        k: hxw, double
        sf: down-scale factor
    Return:
        downsampled LR image
    Reference:
        @inproceedings{zhang2019deep,
          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
          pages={1671--1681},
          year={2019}
        }
    """
    x = bicubic_degradation(x, sf=sf)
    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap")
    return x


def classical_degradation(x, k, sf=3):
    """blur + downsampling
    Args:
        x: HxWxC image, [0, 1]/[0, 255]
        k: hxw, double
        sf: down-scale factor
    Return:
        downsampled LR image
    """
    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap")
    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
    st = 0
    return x[st::sf, st::sf, ...]


def add_sharpening(img, weight=0.5, radius=50, threshold=10):
    """USM sharpening. borrowed from real-ESRGAN
    Input image: I; Blurry image: B.
    1. K = I + weight * (I - B)
    2. Mask = 1 if abs(I - B) > threshold, else: 0
    3. Blur mask:
    4. Out = Mask * K + (1 - Mask) * I
    Args:
        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
        weight (float): Sharp weight. Default: 1.
        radius (float): Kernel size of Gaussian blur. Default: 50.
        threshold (int):
    """
    if radius % 2 == 0:
        radius += 1
    blur = cv2.GaussianBlur(img, (radius, radius), 0)
    residual = img - blur
    mask = np.abs(residual) * 255 > threshold
    mask = mask.astype("float32")
    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)

    K = img + weight * residual
    K = np.clip(K, 0, 1)
    return soft_mask * K + (1 - soft_mask) * img


def add_blur(img, sf=4):
    wd2 = 4.0 + sf
    wd = 2.0 + 0.2 * sf
    if random.random() < 0.5:
        l1 = wd2 * random.random()
        l2 = wd2 * random.random()
        k = anisotropic_Gaussian(
            ksize=2 * random.randint(2, 11) + 3,
            theta=random.random() * np.pi,
            l1=l1,
            l2=l2,
        )
    else:
        k = fspecial("gaussian", 2 * random.randint(2, 11) + 3, wd * random.random())
    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode="mirror")

    return img


def add_resize(img, sf=4):
    rnum = np.random.rand()
    if rnum > 0.8:  # up
        sf1 = random.uniform(1, 2)
    elif rnum < 0.7:  # down
        sf1 = random.uniform(0.5 / sf, 1)
    else:
        sf1 = 1.0
    img = cv2.resize(
        img,
        (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])),
        interpolation=random.choice([1, 2, 3]),
    )
    img = np.clip(img, 0.0, 1.0)

    return img


# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
#     noise_level = random.randint(noise_level1, noise_level2)
#     rnum = np.random.rand()
#     if rnum > 0.6:  # add color Gaussian noise
#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
#     elif rnum < 0.4:  # add grayscale Gaussian noise
#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
#     else:  # add  noise
#         L = noise_level2 / 255.
#         D = np.diag(np.random.rand(3))
#         U = orth(np.random.rand(3, 3))
#         conv = np.dot(np.dot(np.transpose(U), D), U)
#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
#     img = np.clip(img, 0.0, 1.0)
#     return img


def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
    noise_level = random.randint(noise_level1, noise_level2)
    rnum = np.random.rand()
    if rnum > 0.6:  # add color Gaussian noise
        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
            np.float32
        )
    elif rnum < 0.4:  # add grayscale Gaussian noise
        img = img + np.random.normal(
            0, noise_level / 255.0, (*img.shape[:2], 1)
        ).astype(np.float32)
    else:  # add  noise
        L = noise_level2 / 255.0
        D = np.diag(np.random.rand(3))
        U = orth(np.random.rand(3, 3))
        conv = np.dot(np.dot(np.transpose(U), D), U)
        img = img + np.random.multivariate_normal(
            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
        ).astype(np.float32)
    img = np.clip(img, 0.0, 1.0)
    return img


def add_speckle_noise(img, noise_level1=2, noise_level2=25):
    noise_level = random.randint(noise_level1, noise_level2)
    img = np.clip(img, 0.0, 1.0)
    rnum = random.random()
    if rnum > 0.6:
        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(
            np.float32
        )
    elif rnum < 0.4:
        img += img * np.random.normal(
            0, noise_level / 255.0, (*img.shape[:2], 1)
        ).astype(np.float32)
    else:
        L = noise_level2 / 255.0
        D = np.diag(np.random.rand(3))
        U = orth(np.random.rand(3, 3))
        conv = np.dot(np.dot(np.transpose(U), D), U)
        img += img * np.random.multivariate_normal(
            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
        ).astype(np.float32)
    img = np.clip(img, 0.0, 1.0)
    return img


def add_Poisson_noise(img):
    img = np.clip((img * 255.0).round(), 0, 255) / 255.0
    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
    if random.random() < 0.5:
        img = np.random.poisson(img * vals).astype(np.float32) / vals
    else:
        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
        noise_gray = (
            np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
        )
        img += noise_gray[:, :, np.newaxis]
    img = np.clip(img, 0.0, 1.0)
    return img


def add_JPEG_noise(img):
    quality_factor = random.randint(30, 95)
    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
    result, encimg = cv2.imencode(
        ".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
    )
    img = cv2.imdecode(encimg, 1)
    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
    return img


def random_crop(lq, hq, sf=4, lq_patchsize=64):
    h, w = lq.shape[:2]
    rnd_h = random.randint(0, h - lq_patchsize)
    rnd_w = random.randint(0, w - lq_patchsize)
    lq = lq[rnd_h : rnd_h + lq_patchsize, rnd_w : rnd_w + lq_patchsize, :]

    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
    hq = hq[
        rnd_h_H : rnd_h_H + lq_patchsize * sf, rnd_w_H : rnd_w_H + lq_patchsize * sf, :
    ]
    return lq, hq


def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
    """
    This is the degradation model of BSRGAN from the paper
    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
    ----------
    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
    sf: scale factor
    isp_model: camera ISP model
    Returns
    -------
    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
    """
    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
    sf_ori = sf

    h1, w1 = img.shape[:2]
    img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
    h, w = img.shape[:2]

    if h < lq_patchsize * sf or w < lq_patchsize * sf:
        raise ValueError(f"img size ({h1}X{w1}) is too small!")

    hq = img.copy()

    if sf == 4 and random.random() < scale2_prob:  # downsample1
        if np.random.rand() < 0.5:
            img = cv2.resize(
                img,
                (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
                interpolation=random.choice([1, 2, 3]),
            )
        else:
            img = util.imresize_np(img, 1 / 2, True)
        img = np.clip(img, 0.0, 1.0)
        sf = 2

    shuffle_order = random.sample(range(7), 7)
    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
    if idx1 > idx2:  # keep downsample3 last
        shuffle_order[idx1], shuffle_order[idx2] = (
            shuffle_order[idx2],
            shuffle_order[idx1],
        )

    for i in shuffle_order:
        if i == 0:
            img = add_blur(img, sf=sf)

        elif i == 1:
            img = add_blur(img, sf=sf)

        elif i == 2:
            a, b = img.shape[1], img.shape[0]
            # downsample2
            if random.random() < 0.75:
                sf1 = random.uniform(1, 2 * sf)
                img = cv2.resize(
                    img,
                    (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
                    interpolation=random.choice([1, 2, 3]),
                )
            else:
                k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
                k_shifted = shift_pixel(k, sf)
                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
                img = ndimage.filters.convolve(
                    img, np.expand_dims(k_shifted, axis=2), mode="mirror"
                )
                img = img[0::sf, 0::sf, ...]  # nearest downsampling
            img = np.clip(img, 0.0, 1.0)

        elif i == 3:
            # downsample3
            img = cv2.resize(
                img,
                (int(1 / sf * a), int(1 / sf * b)),
                interpolation=random.choice([1, 2, 3]),
            )
            img = np.clip(img, 0.0, 1.0)

        elif i == 4:
            # add Gaussian noise
            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)

        elif i == 5:
            # add JPEG noise
            if random.random() < jpeg_prob:
                img = add_JPEG_noise(img)

        elif i == 6:
            # add processed camera sensor noise
            if random.random() < isp_prob and isp_model is not None:
                with torch.no_grad():
                    img, hq = isp_model.forward(img.copy(), hq)

    # add final JPEG compression noise
    img = add_JPEG_noise(img)

    # random crop
    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)

    return img, hq


# todo no isp_model?
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
    """
    This is the degradation model of BSRGAN from the paper
    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
    ----------
    sf: scale factor
    isp_model: camera ISP model
    Returns
    -------
    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
    """
    image = util.uint2single(image)
    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25

    h1, w1 = image.shape[:2]
    image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
    h, w = image.shape[:2]

    image.copy()

    if sf == 4 and random.random() < scale2_prob:  # downsample1
        if np.random.rand() < 0.5:
            image = cv2.resize(
                image,
                (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
                interpolation=random.choice([1, 2, 3]),
            )
        else:
            image = util.imresize_np(image, 1 / 2, True)
        image = np.clip(image, 0.0, 1.0)
        sf = 2

    shuffle_order = random.sample(range(7), 7)
    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
    if idx1 > idx2:  # keep downsample3 last
        shuffle_order[idx1], shuffle_order[idx2] = (
            shuffle_order[idx2],
            shuffle_order[idx1],
        )

    for i in shuffle_order:
        if i == 0:
            image = add_blur(image, sf=sf)

        elif i == 1:
            image = add_blur(image, sf=sf)

        elif i == 2:
            a, b = image.shape[1], image.shape[0]
            # downsample2
            if random.random() < 0.75:
                sf1 = random.uniform(1, 2 * sf)
                image = cv2.resize(
                    image,
                    (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
                    interpolation=random.choice([1, 2, 3]),
                )
            else:
                k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
                k_shifted = shift_pixel(k, sf)
                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
                image = ndimage.filters.convolve(
                    image, np.expand_dims(k_shifted, axis=2), mode="mirror"
                )
                image = image[0::sf, 0::sf, ...]  # nearest downsampling
            image = np.clip(image, 0.0, 1.0)

        elif i == 3:
            # downsample3
            image = cv2.resize(
                image,
                (int(1 / sf * a), int(1 / sf * b)),
                interpolation=random.choice([1, 2, 3]),
            )
            image = np.clip(image, 0.0, 1.0)

        elif i == 4:
            # add Gaussian noise
            image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)

        elif i == 5:
            # add JPEG noise
            if random.random() < jpeg_prob:
                image = add_JPEG_noise(image)

        # elif i == 6:
        #     # add processed camera sensor noise
        #     if random.random() < isp_prob and isp_model is not None:
        #         with torch.no_grad():
        #             img, hq = isp_model.forward(img.copy(), hq)

    # add final JPEG compression noise
    image = add_JPEG_noise(image)
    image = util.single2uint(image)
    example = {"image": image}
    return example


# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
def degradation_bsrgan_plus(
    img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None
):
    """
    This is an extended degradation model by combining
    the degradation models of BSRGAN and Real-ESRGAN
    ----------
    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
    sf: scale factor
    use_shuffle: the degradation shuffle
    use_sharp: sharpening the img
    Returns
    -------
    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
    """

    h1, w1 = img.shape[:2]
    img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
    h, w = img.shape[:2]

    if h < lq_patchsize * sf or w < lq_patchsize * sf:
        raise ValueError(f"img size ({h1}X{w1}) is too small!")

    if use_sharp:
        img = add_sharpening(img)
    hq = img.copy()

    if random.random() < shuffle_prob:
        shuffle_order = random.sample(range(13), 13)
    else:
        shuffle_order = list(range(13))
        # local shuffle for noise, JPEG is always the last one
        shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
        shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))

    poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1

    for i in shuffle_order:
        if i == 0:
            img = add_blur(img, sf=sf)
        elif i == 1:
            img = add_resize(img, sf=sf)
        elif i == 2:
            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
        elif i == 3:
            if random.random() < poisson_prob:
                img = add_Poisson_noise(img)
        elif i == 4:
            if random.random() < speckle_prob:
                img = add_speckle_noise(img)
        elif i == 5:
            if random.random() < isp_prob and isp_model is not None:
                with torch.no_grad():
                    img, hq = isp_model.forward(img.copy(), hq)
        elif i == 6:
            img = add_JPEG_noise(img)
        elif i == 7:
            img = add_blur(img, sf=sf)
        elif i == 8:
            img = add_resize(img, sf=sf)
        elif i == 9:
            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
        elif i == 10:
            if random.random() < poisson_prob:
                img = add_Poisson_noise(img)
        elif i == 11:
            if random.random() < speckle_prob:
                img = add_speckle_noise(img)
        elif i == 12:
            if random.random() < isp_prob and isp_model is not None:
                with torch.no_grad():
                    img, hq = isp_model.forward(img.copy(), hq)
        else:
            print("check the shuffle!")

    # resize to desired size
    img = cv2.resize(
        img,
        (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
        interpolation=random.choice([1, 2, 3]),
    )

    # add final JPEG compression noise
    img = add_JPEG_noise(img)

    # random crop
    img, hq = random_crop(img, hq, sf, lq_patchsize)

    return img, hq


if __name__ == "__main__":
    print("hey")
    img = util.imread_uint("utils/test.png", 3)
    print(img)
    img = util.uint2single(img)
    print(img)
    img = img[:448, :448]
    h = img.shape[0] // 4
    print("resizing to", h)
    sf = 4
    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
    for i in range(20):
        print(i)
        img_lq = deg_fn(img)
        print(img_lq)
        img_lq_bicubic = albumentations.SmallestMaxSize(
            max_size=h, interpolation=cv2.INTER_CUBIC
        )(image=img)["image"]
        print(img_lq.shape)
        print("bicubic", img_lq_bicubic.shape)
        print(img_hq.shape)
        lq_nearest = cv2.resize(
            util.single2uint(img_lq),
            (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
            interpolation=0,
        )
        lq_bicubic_nearest = cv2.resize(
            util.single2uint(img_lq_bicubic),
            (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
            interpolation=0,
        )
        img_concat = np.concatenate(
            [lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
        )
        util.imsave(img_concat, str(i) + ".png")


================================================
FILE: ldm/modules/image_degradation/bsrgan_light.py
================================================
# -*- coding: utf-8 -*-
import numpy as np
import cv2
import torch

from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations

import ldm.modules.image_degradation.utils_image as util

"""
# --------------------------------------------
# Super-Resolution
# --------------------------------------------
#
# Kai Zhang (cskaizhang@gmail.com)
# https://github.com/cszn
# From 2019/03--2021/08
# --------------------------------------------
"""


def modcrop_np(img, sf):
    """
    Args:
        img: numpy image, WxH or WxHxC
        sf: scale factor
    Return:
        cropped image
    """
    w, h = img.shape[:2]
    im = np.copy(img)
    return im[: w - w % sf, : h - h % sf, ...]


"""
# --------------------------------------------
# anisotropic Gaussian kernels
# --------------------------------------------
"""


def analytic_kernel(k):
    """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
    k_size = k.shape[0]
    # Calculate the big kernels size
    big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
    # Loop over the small kernel to fill the big one
    for r in range(k_size):
        for c in range(k_size):
            big_k[2 * r : 2 * r + k_size, 2 * c : 2 * c + k_size] += k[r, c] * k
    # Crop the edges of the big kernel to ignore very small values and increase run time of SR
    crop = k_size // 2
    cropped_big_k = big_k[crop:-crop, crop:-crop]
    # Normalize to 1
    return cropped_big_k / cropped_big_k.sum()


def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
    """generate an anisotropic Gaussian kernel
    Args:
        ksize : e.g., 15, kernel size
        theta : [0,  pi], rotation angle range
        l1    : [0.1,50], scaling of eigenvalues
        l2    : [0.1,l1], scaling of eigenvalues
        If l1 = l2, will get an isotropic Gaussian kernel.
    Returns:
        k     : kernel
    """

    v = np.dot(
        np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]),
        np.array([1.0, 0.0]),
    )
    V = np.array([[v[0], v[1]], [v[1], -v[0]]])
    D = np.array([[l1, 0], [0, l2]])
    Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
    k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)

    return k


def gm_blur_kernel(mean, cov, size=15):
    center = size / 2.0 + 0.5
    k = np.zeros([size, size])
    for y in range(size):
        for x in range(size):
            cy = y - center + 1
            cx = x - center + 1
            k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)

    k = k / np.sum(k)
    return k


def shift_pixel(x, sf, upper_left=True):
    """shift pixel for super-resolution with different scale factors
    Args:
        x: WxHxC or WxH
        sf: scale factor
        upper_left: shift direction
    """
    h, w = x.shape[:2]
    shift = (sf - 1) * 0.5
    xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
    if upper_left:
        x1 = xv + shift
        y1 = yv + shift
    else:
        x1 = xv - shift
        y1 = yv - shift

    x1 = np.clip(x1, 0, w - 1)
    y1 = np.clip(y1, 0, h - 1)

    if x.ndim == 2:
        x = interp2d(xv, yv, x)(x1, y1)
    if x.ndim == 3:
        for i in range(x.shape[-1]):
            x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)

    return x


def blur(x, k):
    """
    x: image, NxcxHxW
    k: kernel, Nx1xhxw
    """
    n, c = x.shape[:2]
    p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
    x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode="replicate")
    k = k.repeat(1, c, 1, 1)
    k = k.view(-1, 1, k.shape[2], k.shape[3])
    x = x.view(1, -1, x.shape[2], x.shape[3])
    x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
    x = x.view(n, c, x.shape[2], x.shape[3])

    return x


def gen_kernel(
    k_size=np.array([15, 15]),
    scale_factor=np.array([4, 4]),
    min_var=0.6,
    max_var=10.0,
    noise_level=0,
):
    """ "
    # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
    # Kai Zhang
    # min_var = 0.175 * sf  # variance of the gaussian kernel will be sampled between min_var and max_var
    # max_var = 2.5 * sf
    """
    # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
    lambda_1 = min_var + np.random.rand() * (max_var - min_var)
    lambda_2 = min_var + np.random.rand() * (max_var - min_var)
    theta = np.random.rand() * np.pi  # random theta
    noise = -noise_level + np.random.rand(*k_size) * noise_level * 2

    # Set COV matrix using Lambdas and Theta
    LAMBDA = np.diag([lambda_1, lambda_2])
    Q = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
    SIGMA = Q @ LAMBDA @ Q.T
    INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]

    # Set expectation position (shifting kernel for aligned image)
    MU = k_size // 2 - 0.5 * (scale_factor - 1)  # - 0.5 * (scale_factor - k_size % 2)
    MU = MU[None, None, :, None]

    # Create meshgrid for Gaussian
    [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
    Z = np.stack([X, Y], 2)[:, :, :, None]

    # Calcualte Gaussian for every pixel of the kernel
    ZZ = Z - MU
    ZZ_t = ZZ.transpose(0, 1, 3, 2)
    raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)

    # shift the kernel so it will be centered
    # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)

    # Normalize the kernel and return
    # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
    kernel = raw_kernel / np.sum(raw_kernel)
    return kernel


def fspecial_gaussian(hsize, sigma):
    hsize = [hsize, hsize]
    siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
    std = sigma
    [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
    arg = -(x * x + y * y) / (2 * std * std)
    h = np.exp(arg)
    h[h < scipy.finfo(float).eps * h.max()] = 0
    sumh = h.sum()
    if sumh != 0:
        h = h / sumh
    return h


def fspecial_laplacian(alpha):
    alpha = max([0, min([alpha, 1])])
    h1 = alpha / (alpha + 1)
    h2 = (1 - alpha) / (alpha + 1)
    h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
    h = np.array(h)
    return h


def fspecial(filter_type, *args, **kwargs):
    """
    python code from:
    https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
    """
    if filter_type == "gaussian":
        return fspecial_gaussian(*args, **kwargs)
    if filter_type == "laplacian":
        return fspecial_laplacian(*args, **kwargs)


"""
# --------------------------------------------
# degradation models
# --------------------------------------------
"""


def bicubic_degradation(x, sf=3):
    """
    Args:
        x: HxWxC image, [0, 1]
        sf: down-scale factor
    Return:
        bicubicly downsampled LR image
    """
    x = util.imresize_np(x, scale=1 / sf)
    return x


def srmd_degradation(x, k, sf=3):
    """blur + bicubic downsampling
    Args:
        x: HxWxC image, [0, 1]
        k: hxw, double
        sf: down-scale factor
    Return:
        downsampled LR image
    Reference:
        @inproceedings{zhang2018learning,
          title={Learning a single convolutional super-resolution network for multiple degradations},
          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
          pages={3262--3271},
          year={2018}
        }
    """
    x = ndimage.filters.convolve(
        x, np.expand_dims(k, axis=2), mode="wrap"
    )  # 'nearest' | 'mirror'
    x = bicubic_degradation(x, sf=sf)
    return x


def dpsr_degradation(x, k, sf=3):
    """bicubic downsampling + blur
    Args:
        x: HxWxC image, [0, 1]
        k: hxw, double
        sf: down-scale factor
    Return:
        downsampled LR image
    Reference:
        @inproceedings{zhang2019deep,
          title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
          author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
          booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
          pages={1671--1681},
          year={2019}
        }
    """
    x = bicubic_degradation(x, sf=sf)
    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap")
    return x


def classical_degradation(x, k, sf=3):
    """blur + downsampling
    Args:
        x: HxWxC image, [0, 1]/[0, 255]
        k: hxw, double
        sf: down-scale factor
    Return:
        downsampled LR image
    """
    x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap")
    # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
    st = 0
    return x[st::sf, st::sf, ...]


def add_sharpening(img, weight=0.5, radius=50, threshold=10):
    """USM sharpening. borrowed from real-ESRGAN
    Input image: I; Blurry image: B.
    1. K = I + weight * (I - B)
    2. Mask = 1 if abs(I - B) > threshold, else: 0
    3. Blur mask:
    4. Out = Mask * K + (1 - Mask) * I
    Args:
        img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
        weight (float): Sharp weight. Default: 1.
        radius (float): Kernel size of Gaussian blur. Default: 50.
        threshold (int):
    """
    if radius % 2 == 0:
        radius += 1
    blur = cv2.GaussianBlur(img, (radius, radius), 0)
    residual = img - blur
    mask = np.abs(residual) * 255 > threshold
    mask = mask.astype("float32")
    soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)

    K = img + weight * residual
    K = np.clip(K, 0, 1)
    return soft_mask * K + (1 - soft_mask) * img


def add_blur(img, sf=4):
    wd2 = 4.0 + sf
    wd = 2.0 + 0.2 * sf

    wd2 = wd2 / 4
    wd = wd / 4

    if random.random() < 0.5:
        l1 = wd2 * random.random()
        l2 = wd2 * random.random()
        k = anisotropic_Gaussian(
            ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2
        )
    else:
        k = fspecial("gaussian", random.randint(2, 4) + 3, wd * random.random())
    img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode="mirror")

    return img


def add_resize(img, sf=4):
    rnum = np.random.rand()
    if rnum > 0.8:  # up
        sf1 = random.uniform(1, 2)
    elif rnum < 0.7:  # down
        sf1 = random.uniform(0.5 / sf, 1)
    else:
        sf1 = 1.0
    img = cv2.resize(
        img,
        (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])),
        interpolation=random.choice([1, 2, 3]),
    )
    img = np.clip(img, 0.0, 1.0)

    return img


# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
#     noise_level = random.randint(noise_level1, noise_level2)
#     rnum = np.random.rand()
#     if rnum > 0.6:  # add color Gaussian noise
#         img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
#     elif rnum < 0.4:  # add grayscale Gaussian noise
#         img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
#     else:  # add  noise
#         L = noise_level2 / 255.
#         D = np.diag(np.random.rand(3))
#         U = orth(np.random.rand(3, 3))
#         conv = np.dot(np.dot(np.transpose(U), D), U)
#         img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
#     img = np.clip(img, 0.0, 1.0)
#     return img


def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
    noise_level = random.randint(noise_level1, noise_level2)
    rnum = np.random.rand()
    if rnum > 0.6:  # add color Gaussian noise
        img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
            np.float32
        )
    elif rnum < 0.4:  # add grayscale Gaussian noise
        img = img + np.random.normal(
            0, noise_level / 255.0, (*img.shape[:2], 1)
        ).astype(np.float32)
    else:  # add  noise
        L = noise_level2 / 255.0
        D = np.diag(np.random.rand(3))
        U = orth(np.random.rand(3, 3))
        conv = np.dot(np.dot(np.transpose(U), D), U)
        img = img + np.random.multivariate_normal(
            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
        ).astype(np.float32)
    img = np.clip(img, 0.0, 1.0)
    return img


def add_speckle_noise(img, noise_level1=2, noise_level2=25):
    noise_level = random.randint(noise_level1, noise_level2)
    img = np.clip(img, 0.0, 1.0)
    rnum = random.random()
    if rnum > 0.6:
        img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(
            np.float32
        )
    elif rnum < 0.4:
        img += img * np.random.normal(
            0, noise_level / 255.0, (*img.shape[:2], 1)
        ).astype(np.float32)
    else:
        L = noise_level2 / 255.0
        D = np.diag(np.random.rand(3))
        U = orth(np.random.rand(3, 3))
        conv = np.dot(np.dot(np.transpose(U), D), U)
        img += img * np.random.multivariate_normal(
            [0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
        ).astype(np.float32)
    img = np.clip(img, 0.0, 1.0)
    return img


def add_Poisson_noise(img):
    img = np.clip((img * 255.0).round(), 0, 255) / 255.0
    vals = 10 ** (2 * random.random() + 2.0)  # [2, 4]
    if random.random() < 0.5:
        img = np.random.poisson(img * vals).astype(np.float32) / vals
    else:
        img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
        img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
        noise_gray = (
            np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
        )
        img += noise_gray[:, :, np.newaxis]
    img = np.clip(img, 0.0, 1.0)
    return img


def add_JPEG_noise(img):
    quality_factor = random.randint(80, 95)
    img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
    result, encimg = cv2.imencode(
        ".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
    )
    img = cv2.imdecode(encimg, 1)
    img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
    return img


def random_crop(lq, hq, sf=4, lq_patchsize=64):
    h, w = lq.shape[:2]
    rnd_h = random.randint(0, h - lq_patchsize)
    rnd_w = random.randint(0, w - lq_patchsize)
    lq = lq[rnd_h : rnd_h + lq_patchsize, rnd_w : rnd_w + lq_patchsize, :]

    rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
    hq = hq[
        rnd_h_H : rnd_h_H + lq_patchsize * sf, rnd_w_H : rnd_w_H + lq_patchsize * sf, :
    ]
    return lq, hq


def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
    """
    This is the degradation model of BSRGAN from the paper
    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
    ----------
    img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
    sf: scale factor
    isp_model: camera ISP model
    Returns
    -------
    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
    """
    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
    sf_ori = sf

    h1, w1 = img.shape[:2]
    img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
    h, w = img.shape[:2]

    if h < lq_patchsize * sf or w < lq_patchsize * sf:
        raise ValueError(f"img size ({h1}X{w1}) is too small!")

    hq = img.copy()

    if sf == 4 and random.random() < scale2_prob:  # downsample1
        if np.random.rand() < 0.5:
            img = cv2.resize(
                img,
                (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
                interpolation=random.choice([1, 2, 3]),
            )
        else:
            img = util.imresize_np(img, 1 / 2, True)
        img = np.clip(img, 0.0, 1.0)
        sf = 2

    shuffle_order = random.sample(range(7), 7)
    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
    if idx1 > idx2:  # keep downsample3 last
        shuffle_order[idx1], shuffle_order[idx2] = (
            shuffle_order[idx2],
            shuffle_order[idx1],
        )

    for i in shuffle_order:
        if i == 0:
            img = add_blur(img, sf=sf)

        elif i == 1:
            img = add_blur(img, sf=sf)

        elif i == 2:
            a, b = img.shape[1], img.shape[0]
            # downsample2
            if random.random() < 0.75:
                sf1 = random.uniform(1, 2 * sf)
                img = cv2.resize(
                    img,
                    (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
                    interpolation=random.choice([1, 2, 3]),
                )
            else:
                k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
                k_shifted = shift_pixel(k, sf)
                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
                img = ndimage.filters.convolve(
                    img, np.expand_dims(k_shifted, axis=2), mode="mirror"
                )
                img = img[0::sf, 0::sf, ...]  # nearest downsampling
            img = np.clip(img, 0.0, 1.0)

        elif i == 3:
            # downsample3
            img = cv2.resize(
                img,
                (int(1 / sf * a), int(1 / sf * b)),
                interpolation=random.choice([1, 2, 3]),
            )
            img = np.clip(img, 0.0, 1.0)

        elif i == 4:
            # add Gaussian noise
            img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)

        elif i == 5:
            # add JPEG noise
            if random.random() < jpeg_prob:
                img = add_JPEG_noise(img)

        elif i == 6:
            # add processed camera sensor noise
            if random.random() < isp_prob and isp_model is not None:
                with torch.no_grad():
                    img, hq = isp_model.forward(img.copy(), hq)

    # add final JPEG compression noise
    img = add_JPEG_noise(img)

    # random crop
    img, hq = random_crop(img, hq, sf_ori, lq_patchsize)

    return img, hq


# todo no isp_model?
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
    """
    This is the degradation model of BSRGAN from the paper
    "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
    ----------
    sf: scale factor
    isp_model: camera ISP model
    Returns
    -------
    img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
    hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
    """
    image = util.uint2single(image)
    isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25

    h1, w1 = image.shape[:2]
    image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...]  # mod crop
    h, w = image.shape[:2]

    image.copy()

    if sf == 4 and random.random() < scale2_prob:  # downsample1
        if np.random.rand() < 0.5:
            image = cv2.resize(
                image,
                (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
                interpolation=random.choice([1, 2, 3]),
            )
        else:
            image = util.imresize_np(image, 1 / 2, True)
        image = np.clip(image, 0.0, 1.0)
        sf = 2

    shuffle_order = random.sample(range(7), 7)
    idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
    if idx1 > idx2:  # keep downsample3 last
        shuffle_order[idx1], shuffle_order[idx2] = (
            shuffle_order[idx2],
            shuffle_order[idx1],
        )

    for i in shuffle_order:
        if i == 0:
            image = add_blur(image, sf=sf)

        # elif i == 1:
        #     image = add_blur(image, sf=sf)

        if i == 0:
            pass

        elif i == 2:
            a, b = image.shape[1], image.shape[0]
            # downsample2
            if random.random() < 0.8:
                sf1 = random.uniform(1, 2 * sf)
                image = cv2.resize(
                    image,
                    (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
                    interpolation=random.choice([1, 2, 3]),
                )
            else:
                k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
                k_shifted = shift_pixel(k, sf)
                k_shifted = k_shifted / k_shifted.sum()  # blur with shifted kernel
                image = ndimage.filters.convolve(
                    image, np.expand_dims(k_shifted, axis=2), mode="mirror"
                )
                image = image[0::sf, 0::sf, ...]  # nearest downsampling

            image = np.clip(image, 0.0, 1.0)

        elif i == 3:
            # downsample3
            image = cv2.resize(
                image,
                (int(1 / sf * a), int(1 / sf * b)),
                interpolation=random.choice([1, 2, 3]),
            )
            image = np.clip(image, 0.0, 1.0)

        elif i == 4:
            # add Gaussian noise
            image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)

        elif i == 5:
            # add JPEG noise
            if random.random() < jpeg_prob:
                image = add_JPEG_noise(image)
        #
        # elif i == 6:
        #     # add processed camera sensor noise
        #     if random.random() < isp_prob and isp_model is not None:
        #         with torch.no_grad():
        #             img, hq = isp_model.forward(img.copy(), hq)

    # add final JPEG compression noise
    image = add_JPEG_noise(image)
    image = util.single2uint(image)
    example = {"image": image}
    return example


if __name__ == "__main__":
    print("hey")
    img = util.imread_uint("utils/test.png", 3)
    img = img[:448, :448]
    h = img.shape[0] // 4
    print("resizing to", h)
    sf = 4
    deg_fn = partial(degradation_bsrgan_variant, sf=sf)
    for i in range(20):
        print(i)
        img_hq = img
        img_lq = deg_fn(img)["image"]
        img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
        print(img_lq)
        img_lq_bicubic = albumentations.SmallestMaxSize(
            max_size=h, interpolation=cv2.INTER_CUBIC
        )(image=img_hq)["image"]
        print(img_lq.shape)
        print("bicubic", img_lq_bicubic.shape)
        print(img_hq.shape)
        lq_nearest = cv2.resize(
            util.single2uint(img_lq),
            (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
            interpolation=0,
        )
        lq_bicubic_nearest = cv2.resize(
            util.single2uint(img_lq_bicubic),
            (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
            interpolation=0,
        )
        img_concat = np.concatenate(
            [lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
        )
        util.imsave(img_concat, str(i) + ".png")


================================================
FILE: ldm/modules/image_degradation/utils_image.py
================================================
import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime

# import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py


os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"


"""
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 03/Mar/2019
# --------------------------------------------
# https://github.com/twhui/SRGAN-pyTorch
# https://github.com/xinntao/BasicSR
# --------------------------------------------
"""


IMG_EXTENSIONS = [
    ".jpg",
    ".JPG",
    ".jpeg",
    ".JPEG",
    ".png",
    ".PNG",
    ".ppm",
    ".PPM",
    ".bmp",
    ".BMP",
    ".tif",
]


def is_image_file(filename):
    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)


def get_timestamp():
    return datetime.now().strftime("%y%m%d-%H%M%S")


def imshow(x, title=None, cbar=False, figsize=None):
    plt.figure(figsize=figsize)
    plt.imshow(np.squeeze(x), interpolation="nearest", cmap="gray")
    if title:
        plt.title(title)
    if cbar:
        plt.colorbar()
    plt.show()


def surf(Z, cmap="rainbow", figsize=None):
    plt.figure(figsize=figsize)
    ax3 = plt.axes(projection="3d")

    w, h = Z.shape[:2]
    xx = np.arange(0, w, 1)
    yy = np.arange(0, h, 1)
    X, Y = np.meshgrid(xx, yy)
    ax3.plot_surface(X, Y, Z, cmap=cmap)
    # ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
    plt.show()


"""
# --------------------------------------------
# get image pathes
# --------------------------------------------
"""


def get_image_paths(dataroot):
    paths = None  # return None if dataroot is None
    if dataroot is not None:
        paths = sorted(_get_paths_from_images(dataroot))
    return paths


def _get_paths_from_images(path):
    assert os.path.isdir(path), "{:s} is not a valid directory".format(path)
    images = []
    for dirpath, _, fnames in sorted(os.walk(path)):
        for fname in sorted(fnames):
            if is_image_file(fname):
                img_path = os.path.join(dirpath, fname)
                images.append(img_path)
    assert images, "{:s} has no valid image file".format(path)
    return images


"""
# --------------------------------------------
# split large images into small images
# --------------------------------------------
"""


def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
    w, h = img.shape[:2]
    patches = []
    if w > p_max and h > p_max:
        w1 = list(np.arange(0, w - p_size, p_size - p_overlap, dtype=np.int))
        h1 = list(np.arange(0, h - p_size, p_size - p_overlap, dtype=np.int))
        w1.append(w - p_size)
        h1.append(h - p_size)
        #        print(w1)
        #        print(h1)
        for i in w1:
            for j in h1:
                patches.append(img[i : i + p_size, j : j + p_size, :])
    else:
        patches.append(img)

    return patches


def imssave(imgs, img_path):
    """
    imgs: list, N images of size WxHxC
    """
    img_name, ext = os.path.splitext(os.path.basename(img_path))

    for i, img in enumerate(imgs):
        if img.ndim == 3:
            img = img[:, :, [2, 1, 0]]
        new_path = os.path.join(
            os.path.dirname(img_path), img_name + str("_s{:04d}".format(i)) + ".png"
        )
        cv2.imwrite(new_path, img)


def split_imageset(
    original_dataroot,
    taget_dataroot,
    n_channels=3,
    p_size=800,
    p_overlap=96,
    p_max=1000,
):
    """
    split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
    and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
    will be splitted.
    Args:
        original_dataroot:
        taget_dataroot:
        p_size: size of small images
        p_overlap: patch size in training is a good choice
        p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
    """
    paths = get_image_paths(original_dataroot)
    for img_path in paths:
        # img_name, ext = os.path.splitext(os.path.basename(img_path))
        img = imread_uint(img_path, n_channels=n_channels)
        patches = patches_from_image(img, p_size, p_overlap, p_max)
        imssave(patches, os.path.join(taget_dataroot, os.path.basename(img_path)))
        # if original_dataroot == taget_dataroot:
        # del img_path


"""
# --------------------------------------------
# makedir
# --------------------------------------------
"""


def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)


def mkdirs(paths):
    if isinstance(paths, str):
        mkdir(paths)
    else:
        for path in paths:
            mkdir(path)


def mkdir_and_rename(path):
    if os.path.exists(path):
        new_name = path + "_archived_" + get_timestamp()
        print("Path already exists. Rename it to [{:s}]".format(new_name))
        os.rename(path, new_name)
    os.makedirs(path)


"""
# --------------------------------------------
# read image from path
# opencv is fast, but read BGR numpy image
# --------------------------------------------
"""


# --------------------------------------------
# get uint8 image of size HxWxn_channles (RGB)
# --------------------------------------------
def imread_uint(path, n_channels=3):
    #  input: path
    # output: HxWx3(RGB or GGG), or HxWx1 (G)
    if n_channels == 1:
        img = cv2.imread(path, 0)  # cv2.IMREAD_GRAYSCALE
        img = np.expand_dims(img, axis=2)  # HxWx1
    elif n_channels == 3:
        img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # BGR or G
        if img.ndim == 2:
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # GGG
        else:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # RGB
    return img


# --------------------------------------------
# matlab's imwrite
# --------------------------------------------
def imsave(img, img_path):
    img = np.squeeze(img)
    if img.ndim == 3:
        img = img[:, :, [2, 1, 0]]
    cv2.imwrite(img_path, img)


def imwrite(img, img_path):
    img = np.squeeze(img)
    if img.ndim == 3:
        img = img[:, :, [2, 1, 0]]
    cv2.imwrite(img_path, img)


# --------------------------------------------
# get single image of size HxWxn_channles (BGR)
# --------------------------------------------
def read_img(path):
    # read image by cv2
    # return: Numpy float32, HWC, BGR, [0,1]
    img = cv2.imread(path, cv2.IMREAD_UNCHANGED)  # cv2.IMREAD_GRAYSCALE
    img = img.astype(np.float32) / 255.0
    if img.ndim == 2:
        img = np.expand_dims(img, axis=2)
    # some images have 4 channels
    if img.shape[2] > 3:
        img = img[:, :, :3]
    return img


"""
# --------------------------------------------
# image format conversion
# --------------------------------------------
# numpy(single) <--->  numpy(unit)
# numpy(single) <--->  tensor
# numpy(unit)   <--->  tensor
# --------------------------------------------
"""


# --------------------------------------------
# numpy(single) [0, 1] <--->  numpy(unit)
# --------------------------------------------


def uint2single(img):
    return np.float32(img / 255.0)


def single2uint(img):
    return np.uint8((img.clip(0, 1) * 255.0).round())


def uint162single(img):
    return np.float32(img / 65535.0)


def single2uint16(img):
    return np.uint16((img.clip(0, 1) * 65535.0).round())


# --------------------------------------------
# numpy(unit) (HxWxC or HxW) <--->  tensor
# --------------------------------------------


# convert uint to 4-dimensional torch tensor
def uint2tensor4(img):
    if img.ndim == 2:
        img = np.expand_dims(img, axis=2)
    return (
        torch.from_numpy(np.ascontiguousarray(img))
        .permute(2, 0, 1)
        .float()
        .div(255.0)
        .unsqueeze(0)
    )


# convert uint to 3-dimensional torch tensor
def uint2tensor3(img):
    if img.ndim == 2:
        img = np.expand_dims(img, axis=2)
    return (
        torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.0)
    )


# convert 2/3/4-dimensional torch tensor to uint
def tensor2uint(img):
    img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
    if img.ndim == 3:
        img = np.transpose(img, (1, 2, 0))
    return np.uint8((img * 255.0).round())


# --------------------------------------------
# numpy(single) (HxWxC) <--->  tensor
# --------------------------------------------


# convert single (HxWxC) to 3-dimensional torch tensor
def single2tensor3(img):
    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()


# convert single (HxWxC) to 4-dimensional torch tensor
def single2tensor4(img):
    return (
        torch.from_numpy(np.ascontiguousarray(img))
        .permute(2, 0, 1)
        .float()
        .unsqueeze(0)
    )


# convert torch tensor to single
def tensor2single(img):
    img = img.data.squeeze().float().cpu().numpy()
    if img.ndim == 3:
        img = np.transpose(img, (1, 2, 0))

    return img


# convert torch tensor to single
def tensor2single3(img):
    img = img.data.squeeze().float().cpu().numpy()
    if img.ndim == 3:
        img = np.transpose(img, (1, 2, 0))
    elif img.ndim == 2:
        img = np.expand_dims(img, axis=2)
    return img


def single2tensor5(img):
    return (
        torch.from_numpy(np.ascontiguousarray(img))
        .permute(2, 0, 1, 3)
        .float()
        .unsqueeze(0)
    )


def single32tensor5(img):
    return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)


def single42tensor4(img):
    return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()


# from skimage.io import imread, imsave
def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
    """
    Converts a torch Tensor into an image Numpy array of BGR channel order
    Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
    Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
    """
    tensor = (
        tensor.squeeze().float().cpu().clamp_(*min_max)
    )  # squeeze first, then clamp
    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
    n_dim = tensor.dim()
    if n_dim == 4:
        n_img = len(tensor)
        img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
    elif n_dim == 3:
        img_np = tensor.numpy()
        img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
    elif n_dim == 2:
        img_np = tensor.numpy()
    else:
        raise TypeError(
            "Only support 4D, 3D and 2D tensor. But received with dimension: {:d}".format(
                n_dim
            )
        )
    if out_type == np.uint8:
        img_np = (img_np * 255.0).round()
        # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
    return img_np.astype(out_type)


"""
# --------------------------------------------
# Augmentation, flipe and/or rotate
# --------------------------------------------
# The following two are enough.
# (1) augmet_img: numpy image of WxHxC or WxH
# (2) augment_img_tensor4: tensor image 1xCxWxH
# --------------------------------------------
"""


def augment_img(img, mode=0):
    """Kai Zhang (github: https://github.com/cszn)"""
    if mode == 0:
        return img
    elif mode == 1:
        return np.flipud(np.rot90(img))
    elif mode == 2:
        return np.flipud(img)
    elif mode == 3:
        return np.rot90(img, k=3)
    elif mode == 4:
        return np.flipud(np.rot90(img, k=2))
    elif mode == 5:
        return np.rot90(img)
    elif mode == 6:
        return np.rot90(img, k=2)
    elif mode == 7:
        return np.flipud(np.rot90(img, k=3))


def augment_img_tensor4(img, mode=0):
    """Kai Zhang (github: https://github.com/cszn)"""
    if mode == 0:
        return img
    elif mode == 1:
        return img.rot90(1, [2, 3]).flip([2])
    elif mode == 2:
        return img.flip([2])
    elif mode == 3:
        return img.rot90(3, [2, 3])
    elif mode == 4:
        return img.rot90(2, [2, 3]).flip([2])
    elif mode == 5:
        return img.rot90(1, [2, 3])
    elif mode == 6:
        return img.rot90(2, [2, 3])
    elif mode == 7:
        return img.rot90(3, [2, 3]).flip([2])


def augment_img_tensor(img, mode=0):
    """Kai Zhang (github: https://github.com/cszn)"""
    img_size = img.size()
    img_np = img.data.cpu().numpy()
    if len(img_size) == 3:
        img_np = np.transpose(img_np, (1, 2, 0))
    elif len(img_size) == 4:
        img_np = np.transpose(img_np, (2, 3, 1, 0))
    img_np = augment_img(img_np, mode=mode)
    img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
    if len(img_size) == 3:
        img_tensor = img_tensor.permute(2, 0, 1)
    elif len(img_size) == 4:
        img_tensor = img_tensor.permute(3, 2, 0, 1)

    return img_tensor.type_as(img)


def augment_img_np3(img, mode=0):
    if mode == 0:
        return img
    elif mode == 1:
        return img.transpose(1, 0, 2)
    elif mode == 2:
        return img[::-1, :, :]
    elif mode == 3:
        img = img[::-1, :, :]
        img = img.transpose(1, 0, 2)
        return img
    elif mode == 4:
        return img[:, ::-1, :]
    elif mode == 5:
        img = img[:, ::-1, :]
        img = img.transpose(1, 0, 2)
        return img
    elif mode == 6:
        img = img[:, ::-1, :]
        img = img[::-1, :, :]
        return img
    elif mode == 7:
        img = img[:, ::-1, :]
        img = img[::-1, :, :]
        img = img.transpose(1, 0, 2)
        return img


def augment_imgs(img_list, hflip=True, rot=True):
    # horizontal flip OR rotate
    hflip = hflip and random.random() < 0.5
    vflip = rot and random.random() < 0.5
    rot90 = rot and random.random() < 0.5

    def _augment(img):
        if hflip:
            img = img[:, ::-1, :]
        if vflip:
            img = img[::-1, :, :]
        if rot90:
            img = img.transpose(1, 0, 2)
        return img

    return [_augment(img) for img in img_list]


"""
# --------------------------------------------
# modcrop and shave
# --------------------------------------------
"""


def modcrop(img_in, scale):
    # img_in: Numpy, HWC or HW
    img = np.copy(img_in)
    if img.ndim == 2:
        H, W = img.shape
        H_r, W_r = H % scale, W % scale
        img = img[: H - H_r, : W - W_r]
    elif img.ndim == 3:
        H, W, C = img.shape
        H_r, W_r = H % scale, W % scale
        img = img[: H - H_r, : W - W_r, :]
    else:
        raise ValueError("Wrong img ndim: [{:d}].".format(img.ndim))
    return img


def shave(img_in, border=0):
    # img_in: Numpy, HWC or HW
    img = np.copy(img_in)
    h, w = img.shape[:2]
    img = img[border : h - border, border : w - border]
    return img


"""
# --------------------------------------------
# image processing process on numpy image
# channel_convert(in_c, tar_type, img_list):
# rgb2ycbcr(img, only_y=True):
# bgr2ycbcr(img, only_y=True):
# ycbcr2rgb(img):
# --------------------------------------------
"""


def rgb2ycbcr(img, only_y=True):
    """same as matlab rgb2ycbcr
    only_y: only return Y channel
    Input:
        uint8, [0, 255]
        float, [0, 1]
    """
    in_img_type = img.dtype
    img.astype(np.float32)
    if in_img_type != np.uint8:
        img *= 255.0
    # convert
    if only_y:
        rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
    else:
        rlt = np.matmul(
            img,
            [
                [65.481, -37.797, 112.0],
                [128.553, -74.203, -93.786],
                [24.966, 112.0, -18.214],
            ],
        ) / 255.0 + [16, 128, 128]
    if in_img_type == np.uint8:
        rlt = rlt.round()
    else:
        rlt /= 255.0
    return rlt.astype(in_img_type)


def ycbcr2rgb(img):
    """same as matlab ycbcr2rgb
    Input:
        uint8, [0, 255]
        float, [0, 1]
    """
    in_img_type = img.dtype
    img.astype(np.float32)
    if in_img_type != np.uint8:
        img *= 255.0
    # convert
    rlt = np.matmul(
        img,
        [
            [0.00456621, 0.00456621, 0.00456621],
            [0, -0.00153632, 0.00791071],
            [0.00625893, -0.00318811, 0],
        ],
    ) * 255.0 + [-222.921, 135.576, -276.836]
    if in_img_type == np.uint8:
        rlt = rlt.round()
    else:
        rlt /= 255.0
    return rlt.astype(in_img_type)


def bgr2ycbcr(img, only_y=True):
    """bgr version of rgb2ycbcr
    only_y: only return Y channel
    Input:
        uint8, [0, 255]
        float, [0, 1]
    """
    in_img_type = img.dtype
    img.astype(np.float32)
    if in_img_type != np.uint8:
        img *= 255.0
    # convert
    if only_y:
        rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
    else:
        rlt = np.matmul(
            img,
            [
                [24.966, 112.0, -18.214],
                [128.553, -74.203, -93.786],
                [65.481, -37.797, 112.0],
            ],
        ) / 255.0 + [16, 128, 128]
    if in_img_type == np.uint8:
        rlt = rlt.round()
    else:
        rlt /= 255.0
    return rlt.astype(in_img_type)


def channel_convert(in_c, tar_type, img_list):
    # conversion among BGR, gray and y
    if in_c == 3 and tar_type == "gray":  # BGR to gray
        gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
        return [np.expand_dims(img, axis=2) for img in gray_list]
    elif in_c == 3 and tar_type == "y":  # BGR to y
        y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
        return [np.expand_dims(img, axis=2) for img in y_list]
    elif in_c == 1 and tar_type == "RGB":  # gray/y to BGR
        return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
    else:
        return img_list


"""
# --------------------------------------------
# metric, PSNR and SSIM
# --------------------------------------------
"""


# --------------------------------------------
# PSNR
# --------------------------------------------
def calculate_psnr(img1, img2, border=0):
    # img1 and img2 have range [0, 255]
    # img1 = img1.squeeze()
    # img2 = img2.squeeze()
    if not img1.shape == img2.shape:
        raise ValueError("Input images must have the same dimensions.")
    h, w = img1.shape[:2]
    img1 = img1[border : h - border, border : w - border]
    img2 = img2[border : h - border, border : w - border]

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    mse = np.mean((img1 - img2) ** 2)
    if mse == 0:
        return float("inf")
    return 20 * math.log10(255.0 / math.sqrt(mse))


# --------------------------------------------
# SSIM
# --------------------------------------------
def calculate_ssim(img1, img2, border=0):
    """calculate SSIM
    the same outputs as MATLAB's
    img1, img2: [0, 255]
    """
    # img1 = img1.squeeze()
    # img2 = img2.squeeze()
    if not img1.shape == img2.shape:
        raise ValueError("Input images must have the same dimensions.")
    h, w = img1.shape[:2]
    img1 = img1[border : h - border, border : w - border]
    img2 = img2[border : h - border, border : w - border]

    if img1.ndim == 2:
        return ssim(img1, img2)
    elif img1.ndim == 3:
        if img1.shape[2] == 3:
            ssims = []
            for i in range(3):
                ssims.append(ssim(img1[:, :, i], img2[:, :, i]))
            return np.array(ssims).mean()
        elif img1.shape[2] == 1:
            return ssim(np.squeeze(img1), np.squeeze(img2))
    else:
        raise ValueError("Wrong input image dimensions.")


def ssim(img1, img2):
    C1 = (0.01 * 255) ** 2
    C2 = (0.03 * 255) ** 2

    img1 = img1.astype(np.float64)
    img2 = img2.astype(np.float64)
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())

    mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
    mu1_sq = mu1**2
    mu2_sq = mu2**2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
    sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
        (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
    )
    return ssim_map.mean()


"""
# --------------------------------------------
# matlab's bicubic imresize (numpy and torch) [0, 1]
# --------------------------------------------
"""


# matlab 'imresize' function, now only support 'bicubic'
def cubic(x):
    absx = torch.abs(x)
    absx2 = absx**2
    absx3 = absx**3
    return (1.5 * absx3 - 2.5 * absx2 + 1) * ((absx <= 1).type_as(absx)) + (
        -0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2
    ) * (((absx > 1) * (absx <= 2)).type_as(absx))


def calculate_weights_indices(
    in_length, out_length, scale, kernel, kernel_width, antialiasing
):
    if (scale < 1) and (antialiasing):
        # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
        kernel_width = kernel_width / scale

    # Output-space coordinates
    x = torch.linspace(1, out_length, out_length)

    # Input-space coordinates. Calculate the inverse mapping such that 0.5
    # in output space maps to 0.5 in input space, and 0.5+scale in output
    # space maps to 1.5 in input space.
    u = x / scale + 0.5 * (1 - 1 / scale)

    # What is the left-most pixel that can be involved in the computation?
    left = torch.floor(u - kernel_width / 2)

    # What is the maximum number of pixels that can be involved in the
    # computation?  Note: it's OK to use an extra pixel here; if the
    # corresponding weights are all zero, it will be eliminated at the end
    # of this function.
    P = math.ceil(kernel_width) + 2

    # The indices of the input pixels involved in computing the k-th output
    # pixel are in row k of the indices matrix.
    indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(
        0, P - 1, P
    ).view(1, P).expand(out_length, P)

    # The weights used to compute the k-th output pixel are in row k of the
    # weights matrix.
    distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
    # apply cubic kernel
    if (scale < 1) and (antialiasing):
        weights = scale * cubic(distance_to_center * scale)
    else:
        weights = cubic(distance_to_center)
    # Normalize the weights matrix so that each row sums to 1.
    weights_sum = torch.sum(weights, 1).view(out_length, 1)
    weights = weights / weights_sum.expand(out_length, P)

    # If a column in weights is all zero, get rid of it. only consider the first and last column.
    weights_zero_tmp = torch.sum((weights == 0), 0)
    if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
        indices = indices.narrow(1, 1, P - 2)
        weights = weights.narrow(1, 1, P - 2)
    if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
        indices = indices.narrow(1, 0, P - 2)
        weights = weights.narrow(1, 0, P - 2)
    weights = weights.contiguous()
    indices = indices.contiguous()
    sym_len_s = -indices.min() + 1
    sym_len_e = indices.max() - in_length
    indices = indices + sym_len_s - 1
    return weights, indices, int(sym_len_s), int(sym_len_e)


# --------------------------------------------
# imresize for tensor image [0, 1]
# --------------------------------------------
def imresize(img, scale, antialiasing=True):
    # Now the scale should be the same for H and W
    # input: img: pytorch tensor, CHW or HW [0,1]
    # output: CHW or HW [0,1] w/o round
    need_squeeze = True if img.dim() == 2 else False
    if need_squeeze:
        img.unsqueeze_(0)
    in_C, in_H, in_W = img.size()
    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
    kernel_width = 4
    kernel = "cubic"

    # Return the desired dimension order for performing the resize.  The
    # strategy is to perform the resize first along the dimension with the
    # smallest scale factor.
    # Now we do not support this.

    # get weights and indices
    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
        in_H, out_H, scale, kernel, kernel_width, antialiasing
    )
    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
        in_W, out_W, scale, kernel, kernel_width, antialiasing
    )
    # process H dimension
    # symmetric copying
    img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
    img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)

    sym_patch = img[:, :sym_len_Hs, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)

    sym_patch = img[:, -sym_len_He:, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)

    out_1 = torch.FloatTensor(in_C, out_H, in_W)
    kernel_width = weights_H.size(1)
    for i in range(out_H):
        idx = int(indices_H[i][0])
        for j in range(out_C):
            out_1[j, i, :] = (
                img_aug[j, idx : idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
            )

    # process W dimension
    # symmetric copying
    out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
    out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)

    sym_patch = out_1[:, :, :sym_len_Ws]
    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(2, inv_idx)
    out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)

    sym_patch = out_1[:, :, -sym_len_We:]
    inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(2, inv_idx)
    out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)

    out_2 = torch.FloatTensor(in_C, out_H, out_W)
    kernel_width = weights_W.size(1)
    for i in range(out_W):
        idx = int(indices_W[i][0])
        for j in range(out_C):
            out_2[j, :, i] = out_1_aug[j, :, idx : idx + kernel_width].mv(weights_W[i])
    if need_squeeze:
        out_2.squeeze_()
    return out_2


# --------------------------------------------
# imresize for numpy image [0, 1]
# --------------------------------------------
def imresize_np(img, scale, antialiasing=True):
    # Now the scale should be the same for H and W
    # input: img: Numpy, HWC or HW [0,1]
    # output: HWC or HW [0,1] w/o round
    img = torch.from_numpy(img)
    need_squeeze = True if img.dim() == 2 else False
    if need_squeeze:
        img.unsqueeze_(2)

    in_H, in_W, in_C = img.size()
    out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
    kernel_width = 4
    kernel = "cubic"

    # Return the desired dimension order for performing the resize.  The
    # strategy is to perform the resize first along the dimension with the
    # smallest scale factor.
    # Now we do not support this.

    # get weights and indices
    weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
        in_H, out_H, scale, kernel, kernel_width, antialiasing
    )
    weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
        in_W, out_W, scale, kernel, kernel_width, antialiasing
    )
    # process H dimension
    # symmetric copying
    img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
    img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)

    sym_patch = img[:sym_len_Hs, :, :]
    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(0, inv_idx)
    img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)

    sym_patch = img[-sym_len_He:, :, :]
    inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(0, inv_idx)
    img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)

    out_1 = torch.FloatTensor(out_H, in_W, in_C)
    kernel_width = weights_H.size(1)
    for i in range(out_H):
        idx = int(indices_H[i][0])
        for j in range(out_C):
            out_1[i, :, j] = (
                img_aug[idx : idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
            )

    # process W dimension
    # symmetric copying
    out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
    out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)

    sym_patch = out_1[:, :sym_len_Ws, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)

    sym_patch = out_1[:, -sym_len_We:, :]
    inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
    sym_patch_inv = sym_patch.index_select(1, inv_idx)
    out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)

    out_2 = torch.FloatTensor(out_H, out_W, in_C)
    kernel_width = weights_W.size(1)
    for i in range(out_W):
        idx = int(indices_W[i][0])
        for j in range(out_C):
            out_2[:, i, j] = out_1_aug[:, idx : idx + kernel_width, j].mv(weights_W[i])
    if need_squeeze:
        out_2.squeeze_()

    return out_2.numpy()


if __name__ == "__main__":
    print("---")
#    img = imread_uint('test.bmp', 3)
#    img = uint2single(img)
#    img_bicubic = imresize_np(img, 1/4)


================================================
FILE: ldm/modules/losses/__init__.py
================================================


================================================
FILE: ldm/modules/losses/contperceptual.py
================================================
import torch
import torch.nn as nn

from taming.modules.losses.vqperceptual import *  # TODO: taming dependency yes/no?


class LPIPSWithDiscriminator(nn.Module):
    def __init__(
        self,
        disc_start,
        logvar_init=0.0,
        kl_weight=1.0,
        pixelloss_weight=1.0,
        disc_num_layers=3,
        disc_in_channels=3,
        disc_factor=1.0,
        disc_weight=1.0,
        perceptual_weight=1.0,
        use_actnorm=False,
        disc_conditional=False,
        disc_loss="hinge",
    ):
        super().__init__()
        assert disc_loss in ["hinge", "vanilla"]
        self.kl_weight = kl_weight
        self.pixel_weight = pixelloss_weight
        self.perceptual_loss = LPIPS().eval()
        self.perceptual_weight = perceptual_weight
        # output log variance
        self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)

        self.discriminator = NLayerDiscriminator(
            input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=use_actnorm
        ).apply(weights_init)
        self.discriminator_iter_start = disc_start
        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
        self.disc_factor = disc_factor
        self.discriminator_weight = disc_weight
        self.disc_conditional = disc_conditional

    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
        if last_layer is not None:
            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
        else:
            nll_grads = torch.autograd.grad(
                nll_loss, self.last_layer[0], retain_graph=True
            )[0]
            g_grads = torch.autograd.grad(
                g_loss, self.last_layer[0], retain_graph=True
            )[0]

        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
        d_weight = d_weight * self.discriminator_weight
        return d_weight

    def forward(
        self,
        inputs,
        reconstructions,
        posteriors,
        optimizer_idx,
        global_step,
        last_layer=None,
        cond=None,
        split="train",
        weights=None,
    ):
        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
        if self.perceptual_weight > 0:
            p_loss = self.perceptual_loss(
                inputs.contiguous(), reconstructions.contiguous()
            )
            rec_loss = rec_loss + self.perceptual_weight * p_loss

        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
        weighted_nll_loss = nll_loss
        if weights is not None:
            weighted_nll_loss = weights * nll_loss
        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
        kl_loss = posteriors.kl()
        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]

        # now the GAN part
        if optimizer_idx == 0:
            # generator update
            if cond is None:
                assert not self.disc_conditional
                logits_fake = self.discriminator(reconstructions.contiguous())
            else:
                assert self.disc_conditional
                logits_fake = self.discriminator(
                    torch.cat((reconstructions.contiguous(), cond), dim=1)
                )
            g_loss = -torch.mean(logits_fake)

            if self.disc_factor > 0.0:
                try:
                    d_weight = self.calculate_adaptive_weight(
                        nll_loss, g_loss, last_layer=last_layer
                    )
                except RuntimeError:
                    assert not self.training
                    d_weight = torch.tensor(0.0)
            else:
                d_weight = torch.tensor(0.0)

            disc_factor = adopt_weight(
                self.disc_factor, global_step, threshold=self.discriminator_iter_start
            )
            loss = (
                weighted_nll_loss
                + self.kl_weight * kl_loss
                + d_weight * disc_factor * g_loss
            )

            log = {
                "{}/total_loss".format(split): loss.clone().detach().mean(),
                "{}/logvar".format(split): self.logvar.detach(),
                "{}/kl_loss".format(split): kl_loss.detach().mean(),
                "{}/nll_loss".format(split): nll_loss.detach().mean(),
                "{}/rec_loss".format(split): rec_loss.detach().mean(),
                "{}/d_weight".format(split): d_weight.detach(),
                "{}/disc_factor".format(split): torch.tensor(disc_factor),
                "{}/g_loss".format(split): g_loss.detach().mean(),
            }
            return loss, log

        if optimizer_idx == 1:
            # second pass for discriminator update
            if cond is None:
                logits_real = self.discriminator(inputs.contiguous().detach())
                logits_fake = self.discriminator(reconstructions.contiguous().detach())
            else:
                logits_real = self.discriminator(
                    torch.cat((inputs.contiguous().detach(), cond), dim=1)
                )
                logits_fake = self.discriminator(
                    torch.cat((reconstructions.contiguous().detach(), cond), dim=1)
                )

            disc_factor = adopt_weight(
                self.disc_factor, global_step, threshold=self.discriminator_iter_start
            )
            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)

            log = {
                "{}/disc_loss".format(split): d_loss.clone().detach().mean(),
                "{}/logits_real".format(split): logits_real.detach().mean(),
                "{}/logits_fake".format(split): logits_fake.detach().mean(),
            }
            return d_loss, log


================================================
FILE: ldm/modules/losses/vqperceptual.py
================================================
import torch
from torch import nn
import torch.nn.functional as F

from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
from taming.modules.losses.lpips import LPIPS
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss


def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
    assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
    loss_real = torch.mean(F.relu(1.0 - logits_real), dim=[1, 2, 3])
    loss_fake = torch.mean(F.relu(1.0 + logits_fake), dim=[1, 2, 3])
    loss_real = (weights * loss_real).sum() / weights.sum()
    loss_fake = (weights * loss_fake).sum() / weights.sum()
    d_loss = 0.5 * (loss_real + loss_fake)
    return d_loss


def adopt_weight(weight, global_step, threshold=0, value=0.0):
    if global_step < threshold:
        weight = value
    return weight


def measure_perplexity(predicted_indices, n_embed):
    # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
    # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
    encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
    avg_probs = encodings.mean(0)
    perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
    cluster_use = torch.sum(avg_probs > 0)
    return perplexity, cluster_use


def l1(x, y):
    return torch.abs(x - y)


def l2(x, y):
    return torch.pow((x - y), 2)


class VQLPIPSWithDiscriminator(nn.Module):
    def __init__(
        self,
        disc_start,
        codebook_weight=1.0,
        pixelloss_weight=1.0,
        disc_num_layers=3,
        disc_in_channels=3,
        disc_factor=1.0,
        disc_weight=1.0,
        perceptual_weight=1.0,
        use_actnorm=False,
        disc_conditional=False,
        disc_ndf=64,
        disc_loss="hinge",
        n_classes=None,
        perceptual_loss="lpips",
        pixel_loss="l1",
    ):
        super().__init__()
        assert disc_loss in ["hinge", "vanilla"]
        assert perceptual_loss in ["lpips", "clips", "dists"]
        assert pixel_loss in ["l1", "l2"]
        self.codebook_weight = codebook_weight
        self.pixel_weight = pixelloss_weight
        if perceptual_loss == "lpips":
            print(f"{self.__class__.__name__}: Running with LPIPS.")
            self.perceptual_loss = LPIPS().eval()
        else:
            raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<")
        self.perceptual_weight = perceptual_weight

        if pixel_loss == "l1":
            self.pixel_loss = l1
        else:
            self.pixel_loss = l2

        self.discriminator = NLayerDiscriminator(
            input_nc=disc_in_channels,
            n_layers=disc_num_layers,
            use_actnorm=use_actnorm,
            ndf=disc_ndf,
        ).apply(weights_init)
        self.discriminator_iter_start = disc_start
        if disc_loss == "hinge":
            self.disc_loss = hinge_d_loss
        elif disc_loss == "vanilla":
            self.disc_loss = vanilla_d_loss
        else:
            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
        print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.")
        self.disc_factor = disc_factor
        self.discriminator_weight = disc_weight
        self.disc_conditional = disc_conditional
        self.n_classes = n_classes

    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
        if last_layer is not None:
            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
        else:
            nll_grads = torch.autograd.grad(
                nll_loss, self.last_layer[0], retain_graph=True
            )[0]
            g_grads = torch.autograd.grad(
                g_loss, self.last_layer[0], retain_graph=True
            )[0]

        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
        d_weight = d_weight * self.discriminator_weight
        return d_weight

    def forward(
        self,
        codebook_loss,
        inputs,
        reconstructions,
        optimizer_idx,
        global_step,
        last_layer=None,
        cond=None,
        split="train",
        predicted_indices=None,
    ):
        if not exists(codebook_loss):
            codebook_loss = torch.tensor([0.0]).to(inputs.device)
        # rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
        rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous())
        if self.perceptual_weight > 0:
            p_loss = self.perceptual_loss(
                inputs.contiguous(), reconstructions.contiguous()
            )
            rec_loss = rec_loss + self.perceptual_weight * p_loss
        else:
            p_loss = torch.tensor([0.0])

        nll_loss = rec_loss
        # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
        nll_loss = torch.mean(nll_loss)

        # now the GAN part
        if optimizer_idx == 0:
            # generator update
            if cond is None:
                assert not self.disc_conditional
                logits_fake = self.discriminator(reconstructions.contiguous())
            else:
                assert self.disc_conditional
                logits_fake = self.discriminator(
                    torch.cat((reconstructions.contiguous(), cond), dim=1)
                )
            g_loss = -torch.mean(logits_fake)

            try:
                d_weight = self.calculate_adaptive_weight(
                    nll_loss, g_loss, last_layer=last_layer
                )
            except RuntimeError:
                assert not self.training
                d_weight = torch.tensor(0.0)

            disc_factor = adopt_weight(
                self.disc_factor, global_step, threshold=self.discriminator_iter_start
            )
            loss = (
                nll_loss
                + d_weight * disc_factor * g_loss
                + self.codebook_weight * codebook_loss.mean()
            )

            log = {
                "{}/total_loss".format(split): loss.clone().detach().mean(),
                "{}/quant_loss".format(split): codebook_loss.detach().mean(),
                "{}/nll_loss".format(split): nll_loss.detach().mean(),
                "{}/rec_loss".format(split): rec_loss.detach().mean(),
                "{}/p_loss".format(split): p_loss.detach().mean(),
                "{}/d_weight".format(split): d_weight.detach(),
                "{}/disc_factor".format(split): torch.tensor(disc_factor),
                "{}/g_loss".format(split): g_loss.detach().mean(),
            }
            if predicted_indices is not None:
                assert self.n_classes is not None
                with torch.no_grad():
                    perplexity, cluster_usage = measure_perplexity(
                        predicted_indices, self.n_classes
                    )
                log[f"{split}/perplexity"] = perplexity
                log[f"{split}/cluster_usage"] = cluster_usage
            return loss, log

        if optimizer_idx == 1:
            # second pass for discriminator update
            if cond is None:
                logits_real = self.discriminator(inputs.contiguous().detach())
                logits_fake = self.discriminator(reconstructions.contiguous().detach())
            else:
                logits_real = self.discriminator(
                    torch.cat((inputs.contiguous().detach(), cond), dim=1)
                )
                logits_fake = self.discriminator(
                    torch.cat((reconstructions.contiguous().detach(), cond), dim=1)
                )

            disc_factor = adopt_weight(
                self.disc_factor, global_step, threshold=self.discriminator_iter_start
            )
            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)

            log = {
                "{}/disc_loss".format(split): d_loss.clone().detach().mean(),
                "{}/logits_real".format(split): logits_real.detach().mean(),
                "{}/logits_fake".format(split): logits_fake.detach().mean(),
            }
            return d_loss, log


================================================
FILE: ldm/modules/midas/api.py
================================================
# based on https://github.com/isl-org/MiDaS

import cv2
import torch
import torch.nn as nn
from torchvision.transforms import Compose

from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
from ldm.modules.midas.midas.midas_net import MidasNet
from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet


ISL_PATHS = {
    "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
    "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
    "midas_v21": "",
    "midas_v21_small": "",
}


def disabled_train(self, mode=True):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


def load_midas_transform(model_type):
    # https://github.com/isl-org/MiDaS/blob/master/run.py
    # load transform only
    if model_type == "dpt_large":  # DPT-Large
        net_w, net_h = 384, 384
        resize_mode = "minimal"
        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])

    elif model_type == "dpt_hybrid":  # DPT-Hybrid
        net_w, net_h = 384, 384
        resize_mode = "minimal"
        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])

    elif model_type == "midas_v21":
        net_w, net_h = 384, 384
        resize_mode = "upper_bound"
        normalization = NormalizeImage(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
        )

    elif model_type == "midas_v21_small":
        net_w, net_h = 256, 256
        resize_mode = "upper_bound"
        normalization = NormalizeImage(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
        )

    else:
        assert (
            False
        ), f"model_type '{model_type}' not implemented, use: --model_type large"

    transform = Compose(
        [
            Resize(
                net_w,
                net_h,
                resize_target=None,
                keep_aspect_ratio=True,
                ensure_multiple_of=32,
                resize_method=resize_mode,
                image_interpolation_method=cv2.INTER_CUBIC,
            ),
            normalization,
            PrepareForNet(),
        ]
    )

    return transform


def load_model(model_type):
    # https://github.com/isl-org/MiDaS/blob/master/run.py
    # load network
    model_path = ISL_PATHS[model_type]
    if model_type == "dpt_large":  # DPT-Large
        model = DPTDepthModel(
            path=model_path,
            backbone="vitl16_384",
            non_negative=True,
        )
        net_w, net_h = 384, 384
        resize_mode = "minimal"
        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])

    elif model_type == "dpt_hybrid":  # DPT-Hybrid
        model = DPTDepthModel(
            path=model_path,
            backbone="vitb_rn50_384",
            non_negative=True,
        )
        net_w, net_h = 384, 384
        resize_mode = "minimal"
        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])

    elif model_type == "midas_v21":
        model = MidasNet(model_path, non_negative=True)
        net_w, net_h = 384, 384
        resize_mode = "upper_bound"
        normalization = NormalizeImage(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
        )

    elif model_type == "midas_v21_small":
        model = MidasNet_small(
            model_path,
            features=64,
            backbone="efficientnet_lite3",
            exportable=True,
            non_negative=True,
            blocks={"expand": True},
        )
        net_w, net_h = 256, 256
        resize_mode = "upper_bound"
        normalization = NormalizeImage(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
        )

    else:
        print(f"model_type '{model_type}' not implemented, use: --model_type large")
        assert False

    transform = Compose(
        [
            Resize(
                net_w,
                net_h,
                resize_target=None,
                keep_aspect_ratio=True,
                ensure_multiple_of=32,
                resize_method=resize_mode,
                image_interpolation_method=cv2.INTER_CUBIC,
            ),
            normalization,
            PrepareForNet(),
        ]
    )

    return model.eval(), transform


class MiDaSInference(nn.Module):
    MODEL_TYPES_TORCH_HUB = ["DPT_Large", "DPT_Hybrid", "MiDaS_small"]
    MODEL_TYPES_ISL = [
        "dpt_large",
        "dpt_hybrid",
        "midas_v21",
        "midas_v21_small",
    ]

    def __init__(self, model_type):
        super().__init__()
        assert model_type in self.MODEL_TYPES_ISL
        model, _ = load_model(model_type)
        self.model = model
        self.model.train = disabled_train

    def forward(self, x):
        # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
        # NOTE: we expect that the correct transform has been called during dataloading.
        with torch.no_grad():
            prediction = self.model(x)
            prediction = torch.nn.functional.interpolate(
                prediction.unsqueeze(1),
                size=x.shape[2:],
                mode="bicubic",
                align_corners=False,
            )
        assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
        return prediction


================================================
FILE: ldm/modules/midas/midas/__init__.py
================================================


================================================
FILE: ldm/modules/midas/midas/base_model.py
================================================
import torch


class BaseModel(torch.nn.Module):
    def load(self, path):
        """Load model from file.

        Args:
            path (str): file path
        """
        parameters = torch.load(path, map_location=torch.device("cpu"))

        if "optimizer" in parameters:
            parameters = parameters["model"]

        self.load_state_dict(parameters)


================================================
FILE: ldm/modules/midas/midas/blocks.py
================================================
import torch
import torch.nn as nn

from .vit import (
    _make_pretrained_vitb_rn50_384,
    _make_pretrained_vitl16_384,
    _make_pretrained_vitb16_384,
)


def _make_encoder(
    backbone,
    features,
    use_pretrained,
    groups=1,
    expand=False,
    exportable=True,
    hooks=None,
    use_vit_only=False,
    use_readout="ignore",
):
    if backbone == "vitl16_384":
        pretrained = _make_pretrained_vitl16_384(
            use_pretrained, hooks=hooks, use_readout=use_readout
        )
        scratch = _make_scratch(
            [256, 512, 1024, 1024], features, groups=groups, expand=expand
        )  # ViT-L/16 - 85.0% Top1 (backbone)
    elif backbone == "vitb_rn50_384":
        pretrained = _make_pretrained_vitb_rn50_384(
            use_pretrained,
            hooks=hooks,
            use_vit_only=use_vit_only,
            use_readout=use_readout,
        )
        scratch = _make_scratch(
            [256, 512, 768, 768], features, groups=groups, expand=expand
        )  # ViT-H/16 - 85.0% Top1 (backbone)
    elif backbone == "vitb16_384":
        pretrained = _make_pretrained_vitb16_384(
            use_pretrained, hooks=hooks, use_readout=use_readout
        )
        scratch = _make_scratch(
            [96, 192, 384, 768], features, groups=groups, expand=expand
        )  # ViT-B/16 - 84.6% Top1 (backbone)
    elif backbone == "resnext101_wsl":
        pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
        scratch = _make_scratch(
            [256, 512, 1024, 2048], features, groups=groups, expand=expand
        )  # efficientnet_lite3
    elif backbone == "efficientnet_lite3":
        pretrained = _make_pretrained_efficientnet_lite3(
            use_pretrained, exportable=exportable
        )
        scratch = _make_scratch(
            [32, 48, 136, 384], features, groups=groups, expand=expand
        )  # efficientnet_lite3
    else:
        print(f"Backbone '{backbone}' not implemented")
        assert False

    return pretrained, scratch


def _make_scratch(in_shape, out_shape, groups=1, expand=False):
    scratch = nn.Module()

    out_shape1 = out_shape
    out_shape2 = out_shape
    out_shape3 = out_shape
    out_shape4 = out_shape
    if expand is True:
        out_shape1 = out_shape
        out_shape2 = out_shape * 2
        out_shape3 = out_shape * 4
        out_shape4 = out_shape * 8

    scratch.layer1_rn = nn.Conv2d(
        in_shape[0],
        out_shape1,
        kernel_size=3,
        stride=1,
        padding=1,
        bias=False,
        groups=groups,
    )
    scratch.layer2_rn = nn.Conv2d(
        in_shape[1],
        out_shape2,
        kernel_size=3,
        stride=1,
        padding=1,
        bias=False,
        groups=groups,
    )
    scratch.layer3_rn = nn.Conv2d(
        in_shape[2],
        out_shape3,
        kernel_size=3,
        stride=1,
        padding=1,
        bias=False,
        groups=groups,
    )
    scratch.layer4_rn = nn.Conv2d(
        in_shape[3],
        out_shape4,
        kernel_size=3,
        stride=1,
        padding=1,
        bias=False,
        groups=groups,
    )

    return scratch


def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
    efficientnet = torch.hub.load(
        "rwightman/gen-efficientnet-pytorch",
        "tf_efficientnet_lite3",
        pretrained=use_pretrained,
        exportable=exportable,
    )
    return _make_efficientnet_backbone(efficientnet)


def _make_efficientnet_backbone(effnet):
    pretrained = nn.Module()

    pretrained.layer1 = nn.Sequential(
        effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
    )
    pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
    pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
    pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])

    return pretrained


def _make_resnet_backbone(resnet):
    pretrained = nn.Module()
    pretrained.layer1 = nn.Sequential(
        resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
    )

    pretrained.layer2 = resnet.layer2
    pretrained.layer3 = resnet.layer3
    pretrained.layer4 = resnet.layer4

    return pretrained


def _make_pretrained_resnext101_wsl(use_pretrained):
    resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
    return _make_resnet_backbone(resnet)


class Interpolate(nn.Module):
    """Interpolation module."""

    def __init__(self, scale_factor, mode, align_corners=False):
        """Init.

        Args:
            scale_factor (float): scaling
            mode (str): interpolation mode
        """
        super(Interpolate, self).__init__()

        self.interp = nn.functional.interpolate
        self.scale_factor = scale_factor
        self.mode = mode
        self.align_corners = align_corners

    def forward(self, x):
        """Forward pass.

        Args:
            x (tensor): input

        Returns:
            tensor: interpolated data
        """

        x = self.interp(
            x,
            scale_factor=self.scale_factor,
            mode=self.mode,
            align_corners=self.align_corners,
        )

        return x


class ResidualConvUnit(nn.Module):
    """Residual convolution module."""

    def __init__(self, features):
        """Init.

        Args:
            features (int): number of features
        """
        super().__init__()

        self.conv1 = nn.Conv2d(
            features, features, kernel_size=3, stride=1, padding=1, bias=True
        )

        self.conv2 = nn.Conv2d(
            features, features, kernel_size=3, stride=1, padding=1, bias=True
        )

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        """Forward pass.

        Args:
            x (tensor): input

        Returns:
            tensor: output
        """
        out = self.relu(x)
        out = self.conv1(out)
        out = self.relu(out)
        out = self.conv2(out)

        return out + x


class FeatureFusionBlock(nn.Module):
    """Feature fusion block."""

    def __init__(self, features):
        """Init.

        Args:
            features (int): number of features
        """
        super(FeatureFusionBlock, self).__init__()

        self.resConfUnit1 = ResidualConvUnit(features)
        self.resConfUnit2 = ResidualConvUnit(features)

    def forward(self, *xs):
        """Forward pass.

        Returns:
            tensor: output
        """
        output = xs[0]

        if len(xs) == 2:
            output += self.resConfUnit1(xs[1])

        output = self.resConfUnit2(output)

        output = nn.functional.interpolate(
            output, scale_factor=2, mode="bilinear", align_corners=True
        )

        return output


class ResidualConvUnit_custom(nn.Module):
    """Residual convolution module."""

    def __init__(self, features, activation, bn):
        """Init.

        Args:
            features (int): number of features
        """
        super().__init__()

        self.bn = bn

        self.groups = 1

        self.conv1 = nn.Conv2d(
            features,
            features,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=True,
            groups=self.groups,
        )

        self.conv2 = nn.Conv2d(
            features,
            features,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=True,
            groups=self.groups,
        )

        if self.bn is True:
            self.bn1 = nn.BatchNorm2d(features)
            self.bn2 = nn.BatchNorm2d(features)

        self.activation = activation

        self.skip_add = nn.quantized.FloatFunctional()

    def forward(self, x):
        """Forward pass.

        Args:
            x (tensor): input

        Returns:
            tensor: output
        """

        out = self.activation(x)
        out = self.conv1(out)
        if self.bn is True:
            out = self.bn1(out)

        out = self.activation(out)
        out = self.conv2(out)
        if self.bn is True:
            out = self.bn2(out)

        if self.groups > 1:
            out = self.conv_merge(out)

        return self.skip_add.add(out, x)

        # return out + x


class FeatureFusionBlock_custom(nn.Module):
    """Feature fusion block."""

    def __init__(
        self,
        features,
        activation,
        deconv=False,
        bn=False,
        expand=False,
        align_corners=True,
    ):
        """Init.

        Args:
            features (int): number of features
        """
        super(FeatureFusionBlock_custom, self).__init__()

        self.deconv = deconv
        self.align_corners = align_corners

        self.groups = 1

        self.expand = expand
        out_features = features
        if self.expand is True:
            out_features = features // 2

        self.out_conv = nn.Conv2d(
            features,
            out_features,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=True,
            groups=1,
        )

        self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
        self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)

        self.skip_add = nn.quantized.FloatFunctional()

    def forward(self, *xs):
        """Forward pass.

        Returns:
            tensor: output
        """
        output = xs[0]

        if len(xs) == 2:
            res = self.resConfUnit1(xs[1])
            output = self.skip_add.add(output, res)
            # output += res

        output = self.resConfUnit2(output)

        output = nn.functional.interpolate(
            output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
        )

        output = self.out_conv(output)

        return output


================================================
FILE: ldm/modules/midas/midas/dpt_depth.py
================================================
import torch
import torch.nn as nn

from .base_model import BaseModel
from .blocks import (
    FeatureFusionBlock_custom,
    Interpolate,
    _make_encoder,
    forward_vit,
)


def _make_fusion_block(features, use_bn):
    return FeatureFusionBlock_custom(
        features,
        nn.ReLU(False),
        deconv=False,
        bn=use_bn,
        expand=False,
        align_corners=True,
    )


class DPT(BaseModel):
    def __init__(
        self,
        head,
        features=256,
        backbone="vitb_rn50_384",
        readout="project",
        channels_last=False,
        use_bn=False,
    ):
        super(DPT, self).__init__()

        self.channels_last = channels_last

        hooks = {
            "vitb_rn50_384": [0, 1, 8, 11],
            "vitb16_384": [2, 5, 8, 11],
            "vitl16_384": [5, 11, 17, 23],
        }

        # Instantiate backbone and reassemble blocks
        self.pretrained, self.scratch = _make_encoder(
            backbone,
            features,
            False,  # Set to true of you want to train from scratch, uses ImageNet weights
            groups=1,
            expand=False,
            exportable=False,
            hooks=hooks[backbone],
            use_readout=readout,
        )

        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)

        self.scratch.output_conv = head

    def forward(self, x):
        if self.channels_last is True:
            x.contiguous(memory_format=torch.channels_last)

        layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)

        layer_1_rn = self.scratch.layer1_rn(layer_1)
        layer_2_rn = self.scratch.layer2_rn(layer_2)
        layer_3_rn = self.scratch.layer3_rn(layer_3)
        layer_4_rn = self.scratch.layer4_rn(layer_4)

        path_4 = self.scratch.refinenet4(layer_4_rn)
        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)

        out = self.scratch.output_conv(path_1)

        return out


class DPTDepthModel(DPT):
    def __init__(self, path=None, non_negative=True, **kwargs):
        features = kwargs["features"] if "features" in kwargs else 256

        head = nn.Sequential(
            nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
            Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
            nn.ReLU(True) if non_negative else nn.Identity(),
            nn.Identity(),
        )

        super().__init__(head, **kwargs)

        if path is not None:
            self.load(path)

    def forward(self, x):
        return super().forward(x).squeeze(dim=1)


================================================
FILE: ldm/modules/midas/midas/midas_net.py
================================================
"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn

from .base_model import BaseModel
from .blocks import FeatureFusionBlock, Interpolate, _make_encoder


class MidasNet(BaseModel):
    """Network for monocular depth estimation."""

    def __init__(self, path=None, features=256, non_negative=True):
        """Init.

        Args:
            path (str, optional): Path to saved model. Defaults to None.
            features (int, optional): Number of features. Defaults to 256.
            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
        """
        print("Loading weights: ", path)

        super(MidasNet, self).__init__()

        use_pretrained = False if path is None else True

        self.pretrained, self.scratch = _make_encoder(
            backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained
        )

        self.scratch.refinenet4 = FeatureFusionBlock(features)
        self.scratch.refinenet3 = FeatureFusionBlock(features)
        self.scratch.refinenet2 = FeatureFusionBlock(features)
        self.scratch.refinenet1 = FeatureFusionBlock(features)

        self.scratch.output_conv = nn.Sequential(
            nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
            Interpolate(scale_factor=2, mode="bilinear"),
            nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
            nn.ReLU(True) if non_negative else nn.Identity(),
        )

        if path:
            self.load(path)

    def forward(self, x):
        """Forward pass.

        Args:
            x (tensor): input data (image)

        Returns:
            tensor: depth
        """

        layer_1 = self.pretrained.layer1(x)
        layer_2 = self.pretrained.layer2(layer_1)
        layer_3 = self.pretrained.layer3(layer_2)
        layer_4 = self.pretrained.layer4(layer_3)

        layer_1_rn = self.scratch.layer1_rn(layer_1)
        layer_2_rn = self.scratch.layer2_rn(layer_2)
        layer_3_rn = self.scratch.layer3_rn(layer_3)
        layer_4_rn = self.scratch.layer4_rn(layer_4)

        path_4 = self.scratch.refinenet4(layer_4_rn)
        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)

        out = self.scratch.output_conv(path_1)

        return torch.squeeze(out, dim=1)


================================================
FILE: ldm/modules/midas/midas/midas_net_custom.py
================================================
"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn

from .base_model import BaseModel
from .blocks import FeatureFusionBlock_custom, Interpolate, _make_encoder


class MidasNet_small(BaseModel):
    """Network for monocular depth estimation."""

    def __init__(
        self,
        path=None,
        features=64,
        backbone="efficientnet_lite3",
        non_negative=True,
        exportable=True,
        channels_last=False,
        align_corners=True,
        blocks={"expand": True},
    ):
        """Init.

        Args:
            path (str, optional): Path to saved model. Defaults to None.
            features (int, optional): Number of features. Defaults to 256.
            backbone (str, optional): Backbone network for encoder. Defaults to resnet50
        """
        print("Loading weights: ", path)

        super(MidasNet_small, self).__init__()

        use_pretrained = False if path else True

        self.channels_last = channels_last
        self.blocks = blocks
        self.backbone = backbone

        self.groups = 1

        features1 = features
        features2 = features
        features3 = features
        features4 = features
        self.expand = False
        if "expand" in self.blocks and self.blocks["expand"] is True:
            self.expand = True
            features1 = features
            features2 = features * 2
            features3 = features * 4
            features4 = features * 8

        self.pretrained, self.scratch = _make_encoder(
            self.backbone,
            features,
            use_pretrained,
            groups=self.groups,
            expand=self.expand,
            exportable=exportable,
        )

        self.scratch.activation = nn.ReLU(False)

        self.scratch.refinenet4 = FeatureFusionBlock_custom(
            features4,
            self.scratch.activation,
            deconv=False,
            bn=False,
            expand=self.expand,
            align_corners=align_corners,
        )
        self.scratch.refinenet3 = FeatureFusionBlock_custom(
            features3,
            self.scratch.activation,
            deconv=False,
            bn=False,
            expand=self.expand,
            align_corners=align_corners,
        )
        self.scratch.refinenet2 = FeatureFusionBlock_custom(
            features2,
            self.scratch.activation,
            deconv=False,
            bn=False,
            expand=self.expand,
            align_corners=align_corners,
        )
        self.scratch.refinenet1 = FeatureFusionBlock_custom(
            features1,
            self.scratch.activation,
            deconv=False,
            bn=False,
            align_corners=align_corners,
        )

        self.scratch.output_conv = nn.Sequential(
            nn.Conv2d(
                features,
                features // 2,
                kernel_size=3,
                stride=1,
                padding=1,
                groups=self.groups,
            ),
            Interpolate(scale_factor=2, mode="bilinear"),
            nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
            self.scratch.activation,
            nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
            nn.ReLU(True) if non_negative else nn.Identity(),
            nn.Identity(),
        )

        if path:
            self.load(path)

    def forward(self, x):
        """Forward pass.

        Args:
            x (tensor): input data (image)

        Returns:
            tensor: depth
        """
        if self.channels_last is True:
            print("self.channels_last = ", self.channels_last)
            x.contiguous(memory_format=torch.channels_last)

        layer_1 = self.pretrained.layer1(x)
        layer_2 = self.pretrained.layer2(layer_1)
        layer_3 = self.pretrained.layer3(layer_2)
        layer_4 = self.pretrained.layer4(layer_3)

        layer_1_rn = self.scratch.layer1_rn(layer_1)
        layer_2_rn = self.scratch.layer2_rn(layer_2)
        layer_3_rn = self.scratch.layer3_rn(layer_3)
        layer_4_rn = self.scratch.layer4_rn(layer_4)

        path_4 = self.scratch.refinenet4(layer_4_rn)
        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)

        out = self.scratch.output_conv(path_1)

        return torch.squeeze(out, dim=1)


def fuse_model(m):
    prev_previous_type = nn.Identity()
    prev_previous_name = ""
    previous_type = nn.Identity()
    previous_name = ""
    for name, module in m.named_modules():
        if (
            prev_previous_type == nn.Conv2d
            and previous_type == nn.BatchNorm2d
            and type(module) == nn.ReLU
        ):
            # print("FUSED ", prev_previous_name, previous_name, name)
            torch.quantization.fuse_modules(
                m, [prev_previous_name, previous_name, name], inplace=True
            )
        elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
            # print("FUSED ", prev_previous_name, previous_name)
            torch.quantization.fuse_modules(
                m, [prev_previous_name, previous_name], inplace=True
            )
        # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
        #    print("FUSED ", previous_name, name)
        #    torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)

        prev_previous_type = previous_type
        prev_previous_name = previous_name
        previous_type = type(module)
        previous_name = name


================================================
FILE: ldm/modules/midas/midas/transforms.py
================================================
import numpy as np
import cv2
import math


def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
    """Rezise the sample to ensure the given size. Keeps aspect ratio.

    Args:
        sample (dict): sample
        size (tuple): image size

    Returns:
        tuple: new size
    """
    shape = list(sample["disparity"].shape)

    if shape[0] >= size[0] and shape[1] >= size[1]:
        return sample

    scale = [0, 0]
    scale[0] = size[0] / shape[0]
    scale[1] = size[1] / shape[1]

    scale = max(scale)

    shape[0] = math.ceil(scale * shape[0])
    shape[1] = math.ceil(scale * shape[1])

    # resize
    sample["image"] = cv2.resize(
        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
    )

    sample["disparity"] = cv2.resize(
        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
    )
    sample["mask"] = cv2.resize(
        sample["mask"].astype(np.float32),
        tuple(shape[::-1]),
        interpolation=cv2.INTER_NEAREST,
    )
    sample["mask"] = sample["mask"].astype(bool)

    return tuple(shape)


class Resize(object):
    """Resize sample to given size (width, height)."""

    def __init__(
        self,
        width,
        height,
        resize_target=True,
        keep_aspect_ratio=False,
        ensure_multiple_of=1,
        resize_method="lower_bound",
        image_interpolation_method=cv2.INTER_AREA,
    ):
        """Init.

        Args:
            width (int): desired output width
            height (int): desired output height
            resize_target (bool, optional):
                True: Resize the full sample (image, mask, target).
                False: Resize image only.
                Defaults to True.
            keep_aspect_ratio (bool, optional):
                True: Keep the aspect ratio of the input sample.
                Output sample might not have the given width and height, and
                resize behaviour depends on the parameter 'resize_method'.
                Defaults to False.
            ensure_multiple_of (int, optional):
                Output width and height is constrained to be multiple of this parameter.
                Defaults to 1.
            resize_method (str, optional):
                "lower_bound": Output will be at least as large as the given size.
                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
                Defaults to "lower_bound".
        """
        self.__width = width
        self.__height = height

        self.__resize_target = resize_target
        self.__keep_aspect_ratio = keep_aspect_ratio
        self.__multiple_of = ensure_multiple_of
        self.__resize_method = resize_method
        self.__image_interpolation_method = image_interpolation_method

    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)

        if max_val is not None and y > max_val:
            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)

        if y < min_val:
            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)

        return y

    def get_size(self, width, height):
        # determine new height and width
        scale_height = self.__height / height
        scale_width = self.__width / width

        if self.__keep_aspect_ratio:
            if self.__resize_method == "lower_bound":
                # scale such that output size is lower bound
                if scale_width > scale_height:
                    # fit width
                    scale_height = scale_width
                else:
                    # fit height
                    scale_width = scale_height
            elif self.__resize_method == "upper_bound":
                # scale such that output size is upper bound
                if scale_width < scale_height:
                    # fit width
                    scale_height = scale_width
                else:
                    # fit height
                    scale_width = scale_height
            elif self.__resize_method == "minimal":
                # scale as least as possbile
                if abs(1 - scale_width) < abs(1 - scale_height):
                    # fit width
                    scale_height = scale_width
                else:
                    # fit height
                    scale_width = scale_height
            else:
                raise ValueError(
                    f"resize_method {self.__resize_method} not implemented"
                )

        if self.__resize_method == "lower_bound":
            new_height = self.constrain_to_multiple_of(
                scale_height * height, min_val=self.__height
            )
            new_width = self.constrain_to_multiple_of(
                scale_width * width, min_val=self.__width
            )
        elif self.__resize_method == "upper_bound":
            new_height = self.constrain_to_multiple_of(
                scale_height * height, max_val=self.__height
            )
            new_width = self.constrain_to_multiple_of(
                scale_width * width, max_val=self.__width
            )
        elif self.__resize_method == "minimal":
            new_height = self.constrain_to_multiple_of(scale_height * height)
            new_width = self.constrain_to_multiple_of(scale_width * width)
        else:
            raise ValueError(f"resize_method {self.__resize_method} not implemented")

        return (new_width, new_height)

    def __call__(self, sample):
        width, height = self.get_size(
            sample["image"].shape[1], sample["image"].shape[0]
        )

        # resize sample
        sample["image"] = cv2.resize(
            sample["image"],
            (width, height),
            interpolation=self.__image_interpolation_method,
        )

        if self.__resize_target:
            if "disparity" in sample:
                sample["disparity"] = cv2.resize(
                    sample["disparity"],
                    (width, height),
                    interpolation=cv2.INTER_NEAREST,
                )

            if "depth" in sample:
                sample["depth"] = cv2.resize(
                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
                )

            sample["mask"] = cv2.resize(
                sample["mask"].astype(np.float32),
                (width, height),
                interpolation=cv2.INTER_NEAREST,
            )
            sample["mask"] = sample["mask"].astype(bool)

        return sample


class NormalizeImage(object):
    """Normlize image by given mean and std."""

    def __init__(self, mean, std):
        self.__mean = mean
        self.__std = std

    def __call__(self, sample):
        sample["image"] = (sample["image"] - self.__mean) / self.__std

        return sample


class PrepareForNet(object):
    """Prepare sample for usage as network input."""

    def __init__(self):
        pass

    def __call__(self, sample):
        image = np.transpose(sample["image"], (2, 0, 1))
        sample["image"] = np.ascontiguousarray(image).astype(np.float32)

        if "mask" in sample:
            sample["mask"] = sample["mask"].astype(np.float32)
            sample["mask"] = np.ascontiguousarray(sample["mask"])

        if "disparity" in sample:
            disparity = sample["disparity"].astype(np.float32)
            sample["disparity"] = np.ascontiguousarray(disparity)

        if "depth" in sample:
            depth = sample["depth"].astype(np.float32)
            sample["depth"] = np.ascontiguousarray(depth)

        return sample


================================================
FILE: ldm/modules/midas/midas/vit.py
================================================
import torch
import torch.nn as nn
import timm
import types
import math
import torch.nn.functional as F


class Slice(nn.Module):
    def __init__(self, start_index=1):
        super(Slice, self).__init__()
        self.start_index = start_index

    def forward(self, x):
        return x[:, self.start_index :]


class AddReadout(nn.Module):
    def __init__(self, start_index=1):
        super(AddReadout, self).__init__()
        self.start_index = start_index

    def forward(self, x):
        if self.start_index == 2:
            readout = (x[:, 0] + x[:, 1]) / 2
        else:
            readout = x[:, 0]
        return x[:, self.start_index :] + readout.unsqueeze(1)


class ProjectReadout(nn.Module):
    def __init__(self, in_features, start_index=1):
        super(ProjectReadout, self).__init__()
        self.start_index = start_index

        self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())

    def forward(self, x):
        readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
        features = torch.cat((x[:, self.start_index :], readout), -1)

        return self.project(features)


class Transpose(nn.Module):
    def __init__(self, dim0, dim1):
        super(Transpose, self).__init__()
        self.dim0 = dim0
        self.dim1 = dim1

    def forward(self, x):
        x = x.transpose(self.dim0, self.dim1)
        return x


def forward_vit(pretrained, x):
    b, c, h, w = x.shape

    pretrained.model.forward_flex(x)

    layer_1 = pretrained.activations["1"]
    layer_2 = pretrained.activations["2"]
    layer_3 = pretrained.activations["3"]
    layer_4 = pretrained.activations["4"]

    layer_1 = pretrained.act_postprocess1[0:2](layer_1)
    layer_2 = pretrained.act_postprocess2[0:2](layer_2)
    layer_3 = pretrained.act_postprocess3[0:2](layer_3)
    layer_4 = pretrained.act_postprocess4[0:2](layer_4)

    unflatten = nn.Sequential(
        nn.Unflatten(
            2,
            torch.Size(
                [
                    h // pretrained.model.patch_size[1],
                    w // pretrained.model.patch_size[0],
                ]
            ),
        )
    )

    if layer_1.ndim == 3:
        layer_1 = unflatten(layer_1)
    if layer_2.ndim == 3:
        layer_2 = unflatten(layer_2)
    if layer_3.ndim == 3:
        layer_3 = unflatten(layer_3)
    if layer_4.ndim == 3:
        layer_4 = unflatten(layer_4)

    layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
    layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
    layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
    layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)

    return layer_1, layer_2, layer_3, layer_4


def _resize_pos_embed(self, posemb, gs_h, gs_w):
    posemb_tok, posemb_grid = (
        posemb[:, : self.start_index],
        posemb[0, self.start_index :],
    )

    gs_old = int(math.sqrt(len(posemb_grid)))

    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
    posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)

    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)

    return posemb


def forward_flex(self, x):
    b, c, h, w = x.shape

    pos_embed = self._resize_pos_embed(
        self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
    )

    B = x.shape[0]

    if hasattr(self.patch_embed, "backbone"):
        x = self.patch_embed.backbone(x)
        if isinstance(x, (list, tuple)):
            x = x[-1]  # last feature if backbone outputs list/tuple of features

    x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)

    if getattr(self, "dist_token", None) is not None:
        cls_tokens = self.cls_token.expand(
            B, -1, -1
        )  # stole cls_tokens impl from Phil Wang, thanks
        dist_token = self.dist_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, dist_token, x), dim=1)
    else:
        cls_tokens = self.cls_token.expand(
            B, -1, -1
        )  # stole cls_tokens impl from Phil Wang, thanks
        x = torch.cat((cls_tokens, x), dim=1)

    x = x + pos_embed
    x = self.pos_drop(x)

    for blk in self.blocks:
        x = blk(x)

    x = self.norm(x)

    return x


activations = {}


def get_activation(name):
    def hook(model, input, output):
        activations[name] = output

    return hook


def get_readout_oper(vit_features, features, use_readout, start_index=1):
    if use_readout == "ignore":
        readout_oper = [Slice(start_index)] * len(features)
    elif use_readout == "add":
        readout_oper = [AddReadout(start_index)] * len(features)
    elif use_readout == "project":
        readout_oper = [
            ProjectReadout(vit_features, start_index) for out_feat in features
        ]
    else:
        assert (
            False
        ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"

    return readout_oper


def _make_vit_b16_backbone(
    model,
    features=[96, 192, 384, 768],
    size=[384, 384],
    hooks=[2, 5, 8, 11],
    vit_features=768,
    use_readout="ignore",
    start_index=1,
):
    pretrained = nn.Module()

    pretrained.model = model
    pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
    pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))

    pretrained.activations = activations

    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)

    # 32, 48, 136, 384
    pretrained.act_postprocess1 = nn.Sequential(
        readout_oper[0],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[0],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
        nn.ConvTranspose2d(
            in_channels=features[0],
            out_channels=features[0],
            kernel_size=4,
            stride=4,
            padding=0,
            bias=True,
            dilation=1,
            groups=1,
        ),
    )

    pretrained.act_postprocess2 = nn.Sequential(
        readout_oper[1],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[1],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
        nn.ConvTranspose2d(
            in_channels=features[1],
            out_channels=features[1],
            kernel_size=2,
            stride=2,
            padding=0,
            bias=True,
            dilation=1,
            groups=1,
        ),
    )

    pretrained.act_postprocess3 = nn.Sequential(
        readout_oper[2],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[2],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
    )

    pretrained.act_postprocess4 = nn.Sequential(
        readout_oper[3],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[3],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
        nn.Conv2d(
            in_channels=features[3],
            out_channels=features[3],
            kernel_size=3,
            stride=2,
            padding=1,
        ),
    )

    pretrained.model.start_index = start_index
    pretrained.model.patch_size = [16, 16]

    # We inject this function into the VisionTransformer instances so that
    # we can use it with interpolated position embeddings without modifying the library source.
    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
    pretrained.model._resize_pos_embed = types.MethodType(
        _resize_pos_embed, pretrained.model
    )

    return pretrained


def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
    model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)

    hooks = [5, 11, 17, 23] if hooks is None else hooks
    return _make_vit_b16_backbone(
        model,
        features=[256, 512, 1024, 1024],
        hooks=hooks,
        vit_features=1024,
        use_readout=use_readout,
    )


def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
    model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)

    hooks = [2, 5, 8, 11] if hooks is None else hooks
    return _make_vit_b16_backbone(
        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
    )


def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
    model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)

    hooks = [2, 5, 8, 11] if hooks is None else hooks
    return _make_vit_b16_backbone(
        model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
    )


def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
    model = timm.create_model(
        "vit_deit_base_distilled_patch16_384", pretrained=pretrained
    )

    hooks = [2, 5, 8, 11] if hooks is None else hooks
    return _make_vit_b16_backbone(
        model,
        features=[96, 192, 384, 768],
        hooks=hooks,
        use_readout=use_readout,
        start_index=2,
    )


def _make_vit_b_rn50_backbone(
    model,
    features=[256, 512, 768, 768],
    size=[384, 384],
    hooks=[0, 1, 8, 11],
    vit_features=768,
    use_vit_only=False,
    use_readout="ignore",
    start_index=1,
):
    pretrained = nn.Module()

    pretrained.model = model

    if use_vit_only is True:
        pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
        pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
    else:
        pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
            get_activation("1")
        )
        pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
            get_activation("2")
        )

    pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
    pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))

    pretrained.activations = activations

    readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)

    if use_vit_only is True:
        pretrained.act_postprocess1 = nn.Sequential(
            readout_oper[0],
            Transpose(1, 2),
            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
            nn.Conv2d(
                in_channels=vit_features,
                out_channels=features[0],
                kernel_size=1,
                stride=1,
                padding=0,
            ),
            nn.ConvTranspose2d(
                in_channels=features[0],
                out_channels=features[0],
                kernel_size=4,
                stride=4,
                padding=0,
                bias=True,
                dilation=1,
                groups=1,
            ),
        )

        pretrained.act_postprocess2 = nn.Sequential(
            readout_oper[1],
            Transpose(1, 2),
            nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
            nn.Conv2d(
                in_channels=vit_features,
                out_channels=features[1],
                kernel_size=1,
                stride=1,
                padding=0,
            ),
            nn.ConvTranspose2d(
                in_channels=features[1],
                out_channels=features[1],
                kernel_size=2,
                stride=2,
                padding=0,
                bias=True,
                dilation=1,
                groups=1,
            ),
        )
    else:
        pretrained.act_postprocess1 = nn.Sequential(
            nn.Identity(), nn.Identity(), nn.Identity()
        )
        pretrained.act_postprocess2 = nn.Sequential(
            nn.Identity(), nn.Identity(), nn.Identity()
        )

    pretrained.act_postprocess3 = nn.Sequential(
        readout_oper[2],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[2],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
    )

    pretrained.act_postprocess4 = nn.Sequential(
        readout_oper[3],
        Transpose(1, 2),
        nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
        nn.Conv2d(
            in_channels=vit_features,
            out_channels=features[3],
            kernel_size=1,
            stride=1,
            padding=0,
        ),
        nn.Conv2d(
            in_channels=features[3],
            out_channels=features[3],
            kernel_size=3,
            stride=2,
            padding=1,
        ),
    )

    pretrained.model.start_index = start_index
    pretrained.model.patch_size = [16, 16]

    # We inject this function into the VisionTransformer instances so that
    # we can use it with interpolated position embeddings without modifying the library source.
    pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)

    # We inject this function into the VisionTransformer instances so that
    # we can use it with interpolated position embeddings without modifying the library source.
    pretrained.model._resize_pos_embed = types.MethodType(
        _resize_pos_embed, pretrained.model
    )

    return pretrained


def _make_pretrained_vitb_rn50_384(
    pretrained, use_readout="ignore", hooks=None, use_vit_only=False
):
    model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)

    hooks = [0, 1, 8, 11] if hooks is None else hooks
    return _make_vit_b_rn50_backbone(
        model,
        features=[256, 512, 768, 768],
        size=[384, 384],
        hooks=hooks,
        use_vit_only=use_vit_only,
        use_readout=use_readout,
    )


================================================
FILE: ldm/modules/midas/utils.py
================================================
"""Utils for monoDepth."""
import sys
import re
import numpy as np
import cv2
import torch


def read_pfm(path):
    """Read pfm file.

    Args:
        path (str): path to file

    Returns:
        tuple: (data, scale)
    """
    with open(path, "rb") as file:
        color = None
        width = None
        height = None
        scale = None
        endian = None

        header = file.readline().rstrip()
        if header.decode("ascii") == "PF":
            color = True
        elif header.decode("ascii") == "Pf":
            color = False
        else:
            raise Exception("Not a PFM file: " + path)

        dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
        if dim_match:
            width, height = list(map(int, dim_match.groups()))
        else:
            raise Exception("Malformed PFM header.")

        scale = float(file.readline().decode("ascii").rstrip())
        if scale < 0:
            # little-endian
            endian = "<"
            scale = -scale
        else:
            # big-endian
            endian = ">"

        data = np.fromfile(file, endian + "f")
        shape = (height, width, 3) if color else (height, width)

        data = np.reshape(data, shape)
        data = np.flipud(data)

        return data, scale


def write_pfm(path, image, scale=1):
    """Write pfm file.

    Args:
        path (str): pathto file
        image (array): data
        scale (int, optional): Scale. Defaults to 1.
    """

    with open(path, "wb") as file:
        color = None

        if image.dtype.name != "float32":
            raise Exception("Image dtype must be float32.")

        image = np.flipud(image)

        if len(image.shape) == 3 and image.shape[2] == 3:  # color image
            color = True
        elif (
            len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
        ):  # greyscale
            color = False
        else:
            raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")

        file.write("PF\n" if color else "Pf\n".encode())
        file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))

        endian = image.dtype.byteorder

        if endian == "<" or endian == "=" and sys.byteorder == "little":
            scale = -scale

        file.write("%f\n".encode() % scale)

        image.tofile(file)


def read_image(path):
    """Read image and output RGB image (0-1).

    Args:
        path (str): path to file

    Returns:
        array: RGB image (0-1)
    """
    img = cv2.imread(path)

    if img.ndim == 2:
        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0

    return img


def resize_image(img):
    """Resize image and make it fit for network.

    Args:
        img (array): image

    Returns:
        tensor: data ready for network
    """
    height_orig = img.shape[0]
    width_orig = img.shape[1]

    if width_orig > height_orig:
        scale = width_orig / 384
    else:
        scale = height_orig / 384

    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)

    img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)

    img_resized = (
        torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
    )
    img_resized = img_resized.unsqueeze(0)

    return img_resized


def resize_depth(depth, width, height):
    """Resize depth map and bring to CPU (numpy).

    Args:
        depth (tensor): depth
        width (int): image width
        height (int): image height

    Returns:
        array: processed depth
    """
    depth = torch.squeeze(depth[0, :, :, :]).to("cpu")

    depth_resized = cv2.resize(
        depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
    )

    return depth_resized


def write_depth(path, depth, bits=1):
    """Write depth map to pfm and png file.

    Args:
        path (str): filepath without extension
        depth (array): depth
    """
    write_pfm(path + ".pfm", depth.astype(np.float32))

    depth_min = depth.min()
    depth_max = depth.max()

    max_val = (2 ** (8 * bits)) - 1

    if depth_max - depth_min > np.finfo("float").eps:
        out = max_val * (depth - depth_min) / (depth_max - depth_min)
    else:
        out = np.zeros(depth.shape, dtype=depth.type)

    if bits == 1:
        cv2.imwrite(path + ".png", out.astype("uint8"))
    elif bits == 2:
        cv2.imwrite(path + ".png", out.astype("uint16"))

    return


================================================
FILE: ldm/modules/x_transformer.py
================================================
"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat

# constants

DEFAULT_DIM_HEAD = 64

Intermediates = namedtuple("Intermediates", ["pre_softmax_attn", "post_softmax_attn"])

LayerIntermediates = namedtuple("Intermediates", ["hiddens", "attn_intermediates"])


class AbsolutePositionalEmbedding(nn.Module):
    def __init__(self, dim, max_seq_len):
        super().__init__()
        self.emb = nn.Embedding(max_seq_len, dim)
        self.init_()

    def init_(self):
        nn.init.normal_(self.emb.weight, std=0.02)

    def forward(self, x):
        n = torch.arange(x.shape[1], device=x.device)
        return self.emb(n)[None, :, :]


class FixedPositionalEmbedding(nn.Module):
    def __init__(self, dim):
        super().__init__()
        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq)

    def forward(self, x, seq_dim=1, offset=0):
        t = (
            torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq)
            + offset
        )
        sinusoid_inp = torch.einsum("i , j -> i j", t, self.inv_freq)
        emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
        return emb[None, :, :]


# helpers


def exists(val):
    return val is not None


def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d


def always(val):
    def inner(*args, **kwargs):
        return val

    return inner


def not_equals(val):
    def inner(x):
        return x != val

    return inner


def equals(val):
    def inner(x):
        return x == val

    return inner


def max_neg_value(tensor):
    return -torch.finfo(tensor.dtype).max


# keyword argument helpers


def pick_and_pop(keys, d):
    values = list(map(lambda key: d.pop(key), keys))
    return dict(zip(keys, values))


def group_dict_by_key(cond, d):
    return_val = [dict(), dict()]
    for key in d.keys():
        match = bool(cond(key))
        ind = int(not match)
        return_val[ind][key] = d[key]
    return (*return_val,)


def string_begins_with(prefix, str):
    return str.startswith(prefix)


def group_by_key_prefix(prefix, d):
    return group_dict_by_key(partial(string_begins_with, prefix), d)


def groupby_prefix_and_trim(prefix, d):
    kwargs_with_prefix, kwargs = group_dict_by_key(
        partial(string_begins_with, prefix), d
    )
    kwargs_without_prefix = dict(
        map(lambda x: (x[0][len(prefix) :], x[1]), tuple(kwargs_with_prefix.items()))
    )
    return kwargs_without_prefix, kwargs


# classes
class Scale(nn.Module):
    def __init__(self, value, fn):
        super().__init__()
        self.value = value
        self.fn = fn

    def forward(self, x, **kwargs):
        x, *rest = self.fn(x, **kwargs)
        return (x * self.value, *rest)


class Rezero(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn
        self.g = nn.Parameter(torch.zeros(1))

    def forward(self, x, **kwargs):
        x, *rest = self.fn(x, **kwargs)
        return (x * self.g, *rest)


class ScaleNorm(nn.Module):
    def __init__(self, dim, eps=1e-5):
        super().__init__()
        self.scale = dim**-0.5
        self.eps = eps
        self.g = nn.Parameter(torch.ones(1))

    def forward(self, x):
        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
        return x / norm.clamp(min=self.eps) * self.g


class RMSNorm(nn.Module):
    def __init__(self, dim, eps=1e-8):
        super().__init__()
        self.scale = dim**-0.5
        self.eps = eps
        self.g = nn.Parameter(torch.ones(dim))

    def forward(self, x):
        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
        return x / norm.clamp(min=self.eps) * self.g


class Residual(nn.Module):
    def forward(self, x, residual):
        return x + residual


class GRUGating(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.gru = nn.GRUCell(dim, dim)

    def forward(self, x, residual):
        gated_output = self.gru(
            rearrange(x, "b n d -> (b n) d"), rearrange(residual, "b n d -> (b n) d")
        )

        return gated_output.reshape_as(x)


# feedforward


class GEGLU(nn.Module):
    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

    def forward(self, x):
        x, gate = self.proj(x).chunk(2, dim=-1)
        return x * F.gelu(gate)


class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = default(dim_out, dim)
        project_in = (
            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
            if not glu
            else GEGLU(dim, inner_dim)
        )

        self.net = nn.Sequential(
            project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
        )

    def forward(self, x):
        return self.net(x)


# attention.
class Attention(nn.Module):
    def __init__(
        self,
        dim,
        dim_head=DEFAULT_DIM_HEAD,
        heads=8,
        causal=False,
        mask=None,
        talking_heads=False,
        sparse_topk=None,
        use_entmax15=False,
        num_mem_kv=0,
        dropout=0.0,
        on_attn=False,
    ):
        super().__init__()
        if use_entmax15:
            raise NotImplementedError(
                "Check out entmax activation instead of softmax activation!"
            )
        self.scale = dim_head**-0.5
        self.heads = heads
        self.causal = causal
        self.mask = mask

        inner_dim = dim_head * heads

        self.to_q = nn.Linear(dim, inner_dim, bias=False)
        self.to_k = nn.Linear(dim, inner_dim, bias=False)
        self.to_v = nn.Linear(dim, inner_dim, bias=False)
        self.dropout = nn.Dropout(dropout)

        # talking heads
        self.talking_heads = talking_heads
        if talking_heads:
            self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
            self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))

        # explicit topk sparse attention
        self.sparse_topk = sparse_topk

        # entmax
        # self.attn_fn = entmax15 if use_entmax15 else F.softmax
        self.attn_fn = F.softmax

        # add memory key / values
        self.num_mem_kv = num_mem_kv
        if num_mem_kv > 0:
            self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
            self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))

        # attention on attention
        self.attn_on_attn = on_attn
        self.to_out = (
            nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU())
            if on_attn
            else nn.Linear(inner_dim, dim)
        )

    def forward(
        self,
        x,
        context=None,
        mask=None,
        context_mask=None,
        rel_pos=None,
        sinusoidal_emb=None,
        prev_attn=None,
        mem=None,
    ):
        b, n, _, h, talking_heads, device = (
            *x.shape,
            self.heads,
            self.talking_heads,
            x.device,
        )
        kv_input = default(context, x)

        q_input = x
        k_input = kv_input
        v_input = kv_input

        if exists(mem):
            k_input = torch.cat((mem, k_input), dim=-2)
            v_input = torch.cat((mem, v_input), dim=-2)

        if exists(sinusoidal_emb):
            # in shortformer, the query would start at a position offset depending on the past cached memory
            offset = k_input.shape[-2] - q_input.shape[-2]
            q_input = q_input + sinusoidal_emb(q_input, offset=offset)
            k_input = k_input + sinusoidal_emb(k_input)

        q = self.to_q(q_input)
        k = self.to_k(k_input)
        v = self.to_v(v_input)

        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))

        input_mask = None
        if any(map(exists, (mask, context_mask))):
            q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
            k_mask = q_mask if not exists(context) else context_mask
            k_mask = default(
                k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool()
            )
            q_mask = rearrange(q_mask, "b i -> b () i ()")
            k_mask = rearrange(k_mask, "b j -> b () () j")
            input_mask = q_mask * k_mask

        if self.num_mem_kv > 0:
            mem_k, mem_v = map(
                lambda t: repeat(t, "h n d -> b h n d", b=b), (self.mem_k, self.mem_v)
            )
            k = torch.cat((mem_k, k), dim=-2)
            v = torch.cat((mem_v, v), dim=-2)
            if exists(input_mask):
                input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)

        dots = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
        mask_value = max_neg_value(dots)

        if exists(prev_attn):
            dots = dots + prev_attn

        pre_softmax_attn = dots

        if talking_heads:
            dots = einsum(
                "b h i j, h k -> b k i j", dots, self.pre_softmax_proj
            ).contiguous()

        if exists(rel_pos):
            dots = rel_pos(dots)

        if exists(input_mask):
            dots.masked_fill_(~input_mask, mask_value)
            del input_mask

        if self.causal:
            i, j = dots.shape[-2:]
            r = torch.arange(i, device=device)
            mask = rearrange(r, "i -> () () i ()") < rearrange(r, "j -> () () () j")
            mask = F.pad(mask, (j - i, 0), value=False)
            dots.masked_fill_(mask, mask_value)
            del mask

        if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
            top, _ = dots.topk(self.sparse_topk, dim=-1)
            vk = top[..., -1].unsqueeze(-1).expand_as(dots)
            mask = dots < vk
            dots.masked_fill_(mask, mask_value)
            del mask

        attn = self.attn_fn(dots, dim=-1)
        post_softmax_attn = attn

        attn = self.dropout(attn)

        if talking_heads:
            attn = einsum(
                "b h i j, h k -> b k i j", attn, self.post_softmax_proj
            ).contiguous()

        out = einsum("b h i j, b h j d -> b h i d", attn, v)
        out = rearrange(out, "b h n d -> b n (h d)")

        intermediates = Intermediates(
            pre_softmax_attn=pre_softmax_attn, post_softmax_attn=post_softmax_attn
        )

        return self.to_out(out), intermediates


class AttentionLayers(nn.Module):
    def __init__(
        self,
        dim,
        depth,
        heads=8,
        causal=False,
        cross_attend=False,
        only_cross=False,
        use_scalenorm=False,
        use_rmsnorm=False,
        use_rezero=False,
        rel_pos_num_buckets=32,
        rel_pos_max_distance=128,
        position_infused_attn=False,
        custom_layers=None,
        sandwich_coef=None,
        par_ratio=None,
        residual_attn=False,
        cross_residual_attn=False,
        macaron=False,
        pre_norm=True,
        gate_residual=False,
        **kwargs,
    ):
        super().__init__()
        ff_kwargs, kwargs = groupby_prefix_and_trim("ff_", kwargs)
        attn_kwargs, _ = groupby_prefix_and_trim("attn_", kwargs)

        attn_kwargs.get("dim_head", DEFAULT_DIM_HEAD)

        self.dim = dim
        self.depth = depth
        self.layers = nn.ModuleList([])

        self.has_pos_emb = position_infused_attn
        self.pia_pos_emb = (
            FixedPositionalEmbedding(dim) if position_infused_attn else None
        )
        self.rotary_pos_emb = always(None)

        assert (
            rel_pos_num_buckets <= rel_pos_max_distance
        ), "number of relative position buckets must be less than the relative position max distance"
        self.rel_pos = None

        self.pre_norm = pre_norm

        self.residual_attn = residual_attn
        self.cross_residual_attn = cross_residual_attn

        norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
        norm_class = RMSNorm if use_rmsnorm else norm_class
        norm_fn = partial(norm_class, dim)

        norm_fn = nn.Identity if use_rezero else norm_fn
        branch_fn = Rezero if use_rezero else None

        if cross_attend and not only_cross:
            default_block = ("a", "c", "f")
        elif cross_attend and only_cross:
            default_block = ("c", "f")
        else:
            default_block = ("a", "f")

        if macaron:
            default_block = ("f",) + default_block

        if exists(custom_layers):
            layer_types = custom_layers
        elif exists(par_ratio):
            par_depth = depth * len(default_block)
            assert 1 < par_ratio <= par_depth, "par ratio out of range"
            default_block = tuple(filter(not_equals("f"), default_block))
            par_attn = par_depth // par_ratio
            depth_cut = (
                par_depth * 2 // 3
            )  # 2 / 3 attention layer cutoff suggested by PAR paper
            par_width = (depth_cut + depth_cut // par_attn) // par_attn
            assert (
                len(default_block) <= par_width
            ), "default block is too large for par_ratio"
            par_block = default_block + ("f",) * (par_width - len(default_block))
            par_head = par_block * par_attn
            layer_types = par_head + ("f",) * (par_depth - len(par_head))
        elif exists(sandwich_coef):
            assert (
                sandwich_coef > 0 and sandwich_coef <= depth
            ), "sandwich coefficient should be less than the depth"
            layer_types = (
                ("a",) * sandwich_coef
                + default_block * (depth - sandwich_coef)
                + ("f",) * sandwich_coef
            )
        else:
            layer_types = default_block * depth

        self.layer_types = layer_types
        self.num_attn_layers = len(list(filter(equals("a"), layer_types)))

        for layer_type in self.layer_types:
            if layer_type == "a":
                layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
            elif layer_type == "c":
                layer = Attention(dim, heads=heads, **attn_kwargs)
            elif layer_type == "f":
                layer = FeedForward(dim, **ff_kwargs)
                layer = layer if not macaron else Scale(0.5, layer)
            else:
                raise Exception(f"invalid layer type {layer_type}")

            if isinstance(layer, Attention) and exists(branch_fn):
                layer = branch_fn(layer)

            if gate_residual:
                residual_fn = GRUGating(dim)
            else:
                residual_fn = Residual()

            self.layers.append(nn.ModuleList([norm_fn(), layer, residual_fn]))

    def forward(
        self,
        x,
        context=None,
        mask=None,
        context_mask=None,
        mems=None,
        return_hiddens=False,
    ):
        hiddens = []
        intermediates = []
        prev_attn = None
        prev_cross_attn = None

        mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers

        for ind, (layer_type, (norm, block, residual_fn)) in enumerate(
            zip(self.layer_types, self.layers)
        ):
            is_last = ind == (len(self.layers) - 1)

            if layer_type == "a":
                hiddens.append(x)
                layer_mem = mems.pop(0)

            residual = x

            if self.pre_norm:
                x = norm(x)

            if layer_type == "a":
                out, inter = block(
                    x,
                    mask=mask,
                    sinusoidal_emb=self.pia_pos_emb,
                    rel_pos=self.rel_pos,
                    prev_attn=prev_attn,
                    mem=layer_mem,
                )
            elif layer_type == "c":
                out, inter = block(
                    x,
                    context=context,
                    mask=mask,
                    context_mask=context_mask,
                    prev_attn=prev_cross_attn,
                )
            elif layer_type == "f":
                out = block(x)

            x = residual_fn(out, residual)

            if layer_type in ("a", "c"):
                intermediates.append(inter)

            if layer_type == "a" and self.residual_attn:
                prev_attn = inter.pre_softmax_attn
            elif layer_type == "c" and self.cross_residual_attn:
                prev_cross_attn = inter.pre_softmax_attn

            if not self.pre_norm and not is_last:
                x = norm(x)

        if return_hiddens:
            intermediates = LayerIntermediates(
                hiddens=hiddens, attn_intermediates=intermediates
            )

            return x, intermediates

        return x


class Encoder(AttentionLayers):
    def __init__(self, **kwargs):
        assert "causal" not in kwargs, "cannot set causality on encoder"
        super().__init__(causal=False, **kwargs)


class TransformerWrapper(nn.Module):
    def __init__(
        self,
        *,
        num_tokens,
        max_seq_len,
        attn_layers,
        emb_dim=None,
        max_mem_len=0.0,
        emb_dropout=0.0,
        num_memory_tokens=None,
        tie_embedding=False,
        use_pos_emb=True,
    ):
        super().__init__()
        assert isinstance(
            attn_layers, AttentionLayers
        ), "attention layers must be one of Encoder or Decoder"

        dim = attn_layers.dim
        emb_dim = default(emb_dim, dim)

        self.max_seq_len = max_seq_len
        self.max_mem_len = max_mem_len
        self.num_tokens = num_tokens

        self.token_emb = nn.Embedding(num_tokens, emb_dim)
        self.pos_emb = (
            AbsolutePositionalEmbedding(emb_dim, max_seq_len)
            if (use_pos_emb and not attn_layers.has_pos_emb)
            else always(0)
        )
        self.emb_dropout = nn.Dropout(emb_dropout)

        self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
        self.attn_layers = attn_layers
        self.norm = nn.LayerNorm(dim)

        self.init_()

        self.to_logits = (
            nn.Linear(dim, num_tokens)
            if not tie_embedding
            else lambda t: t @ self.token_emb.weight.t()
        )

        # memory tokens (like [cls]) from Memory Transformers paper
        num_memory_tokens = default(num_memory_tokens, 0)
        self.num_memory_tokens = num_memory_tokens
        if num_memory_tokens > 0:
            self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))

            # let funnel encoder know number of memory tokens, if specified
            if hasattr(attn_layers, "num_memory_tokens"):
                attn_layers.num_memory_tokens = num_memory_tokens

    def init_(self):
        nn.init.normal_(self.token_emb.weight, std=0.02)

    def forward(
        self,
        x,
        return_embeddings=False,
        mask=None,
        return_mems=False,
        return_attn=False,
        mems=None,
        **kwargs,
    ):
        b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
        x = self.token_emb(x)
        x += self.pos_emb(x)
        x = self.emb_dropout(x)

        x = self.project_emb(x)

        if num_mem > 0:
            mem = repeat(self.memory_tokens, "n d -> b n d", b=b)
            x = torch.cat((mem, x), dim=1)

            # auto-handle masking after appending memory tokens
            if exists(mask):
                mask = F.pad(mask, (num_mem, 0), value=True)

        x, intermediates = self.attn_layers(
            x, mask=mask, mems=mems, return_hiddens=True, **kwargs
        )
        x = self.norm(x)

        mem, x = x[:, :num_mem], x[:, num_mem:]

        out = self.to_logits(x) if not return_embeddings else x

        if return_mems:
            hiddens = intermediates.hiddens
            new_mems = (
                list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens)))
                if exists(mems)
                else hiddens
            )
            new_mems = list(
                map(lambda t: t[..., -self.max_mem_len :, :].detach(), new_mems)
            )
            return out, new_mems

        if return_attn:
            attn_maps = list(
                map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)
            )
            return out, attn_maps

        return out


================================================
FILE: ldm/util.py
================================================
import importlib

import torch
import numpy as np
from collections import abc

import multiprocessing as mp
from threading import Thread
from queue import Queue

from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont


def log_txt_as_img(wh, xc, size=10):
    # wh a tuple of (width, height)
    # xc a list of captions to plot
    b = len(xc)
    txts = list()
    for bi in range(b):
        txt = Image.new("RGB", wh, color="white")
        draw = ImageDraw.Draw(txt)
        font = ImageFont.truetype("data/DejaVuSans.ttf", size=size)
        nc = int(40 * (wh[0] / 256))
        lines = "\n".join(
            xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)
        )

        try:
            draw.text((0, 0), lines, fill="black", font=font)
        except UnicodeEncodeError:
            print("Cant encode string for logging. Skipping.")

        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
        txts.append(txt)
    txts = np.stack(txts)
    txts = torch.tensor(txts)
    return txts


def ismap(x):
    if not isinstance(x, torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] > 3)


def isimage(x):
    if not isinstance(x, torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)


def exists(x):
    return x is not None


def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d


def mean_flat(tensor):
    """
    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
    Take the mean over all non-batch dimensions.
    """
    return tensor.mean(dim=list(range(1, len(tensor.shape))))


def count_params(model, verbose=False):
    total_params = sum(p.numel() for p in model.parameters())
    if verbose:
        print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
    return total_params


def instantiate_from_config(config):
    if "target" not in config:
        if config == "__is_first_stage__":
            return None
        elif config == "__is_unconditional__":
            return None
        raise KeyError("Expected key `target` to instantiate.")
    return get_obj_from_str(config["target"])(**config.get("params", dict()))


def get_obj_from_str(string, reload=False):
    module, cls = string.rsplit(".", 1)
    if reload:
        module_imp = importlib.import_module(module)
        importlib.reload(module_imp)
    return getattr(importlib.import_module(module, package=None), cls)


def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
    # create dummy dataset instance

    # run prefetching
    if idx_to_fn:
        res = func(data, worker_id=idx)
    else:
        res = func(data)
    Q.put([idx, res])
    Q.put("Done")


def parallel_data_prefetch(
    func: callable,
    data,
    n_proc,
    target_data_type="ndarray",
    cpu_intensive=True,
    use_worker_id=False,
):
    # if target_data_type not in ["ndarray", "list"]:
    #     raise ValueError(
    #         "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
    #     )
    if isinstance(data, np.ndarray) and target_data_type == "list":
        raise ValueError("list expected but function got ndarray.")
    elif isinstance(data, abc.Iterable):
        if isinstance(data, dict):
            print(
                'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
            )
            data = list(data.values())
        if target_data_type == "ndarray":
            data = np.asarray(data)
        else:
            data = list(data)
    else:
        raise TypeError(
            f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
        )

    if cpu_intensive:
        Q = mp.Queue(1000)
        proc = mp.Process
    else:
        Q = Queue(1000)
        proc = Thread
    # spawn processes
    if target_data_type == "ndarray":
        arguments = [
            [func, Q, part, i, use_worker_id]
            for i, part in enumerate(np.array_split(data, n_proc))
        ]
    else:
        step = (
            int(len(data) / n_proc + 1)
            if len(data) % n_proc != 0
            else int(len(data) / n_proc)
        )
        arguments = [
            [func, Q, part, i, use_worker_id]
            for i, part in enumerate(
                [data[i : i + step] for i in range(0, len(data), step)]
            )
        ]
    processes = []
    for i in range(n_proc):
        p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
        processes += [p]

    # start processes
    print("Start prefetching...")
    import time

    start = time.time()
    gather_res = [[] for _ in range(n_proc)]
    try:
        for p in processes:
            p.start()

        k = 0
        while k < n_proc:
            # get result
            res = Q.get()
            if res == "Done":
                k += 1
            else:
                gather_res[res[0]] = res[1]

    except Exception as e:
        print("Exception: ", e)
        for p in processes:
            p.terminate()

        raise e
    finally:
        for p in processes:
            p.join()
        print(f"Prefetching complete. [{time.time() - start} sec.]")

    if target_data_type == "ndarray":
        if not isinstance(gather_res[0], np.ndarray):
            return np.concatenate([np.asarray(r) for r in gather_res], axis=0)

        # order outputs
        return np.concatenate(gather_res, axis=0)
    elif target_data_type == "list":
        out = []
        for r in gather_res:
            out.extend(r)
        return out
    else:
        return gather_res


================================================
FILE: optimizedSD/ddpm.py
================================================
"""
wild mixture of
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
https://github.com/CompVis/taming-transformers
-- merci
"""

from tqdm.auto import trange, tqdm
import torch
from einops import rearrange
from tqdm import tqdm
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface
import numpy as np
import pytorch_lightning as pl
from functools import partial
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import exists, default, instantiate_from_config
from ldm.modules.diffusionmodules.util import make_beta_schedule
from ldm.modules.diffusionmodules.util import (
    make_ddim_sampling_parameters,
    make_ddim_timesteps,
    noise_like,
)
from ldm.modules.diffusionmodules.util import (
    make_beta_schedule,
    extract_into_tensor,
    noise_like,
)
from .samplers import (
    CompVisDenoiser,
    get_ancestral_step,
    to_d,
    append_dims,
    linear_multistep_coeff,
)


def disabled_train(self):
    """Overwrite model.train with this function to make sure train/eval mode
    does not change anymore."""
    return self


class DDPM(pl.LightningModule):
    # classic DDPM with Gaussian diffusion, in image space
    def __init__(
        self,
        timesteps=1000,
        beta_schedule="linear",
        ckpt_path=None,
        ignore_keys=[],
        load_only_unet=False,
        monitor="val/loss",
        use_ema=True,
        first_stage_key="image",
        image_size=256,
        channels=3,
        log_every_t=100,
        clip_denoised=True,
        linear_start=1e-4,
        linear_end=2e-2,
        cosine_s=8e-3,
        given_betas=None,
        original_elbo_weight=0.0,
        v_posterior=0.0,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
        l_simple_weight=1.0,
        conditioning_key=None,
        parameterization="eps",  # all assuming fixed variance schedules
        scheduler_config=None,
        use_positional_encodings=False,
    ):
        super().__init__()
        assert parameterization in [
            "eps",
            "x0",
        ], 'currently only supporting "eps" and "x0"'
        self.parameterization = parameterization
        print(
            f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode"
        )
        self.cond_stage_model = None
        self.clip_denoised = clip_denoised
        self.log_every_t = log_every_t
        self.first_stage_key = first_stage_key
        self.image_size = image_size  # try conv?
        self.channels = channels
        self.use_positional_encodings = use_positional_encodings
        self.use_scheduler = scheduler_config is not None
        if self.use_scheduler:
            self.scheduler_config = scheduler_config

        self.v_posterior = v_posterior
        self.original_elbo_weight = original_elbo_weight
        self.l_simple_weight = l_simple_weight

        if monitor is not None:
            self.monitor = monitor
        if ckpt_path is not None:
            self.init_from_ckpt(
                ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet
            )
        self.register_schedule(
            given_betas=given_betas,
            beta_schedule=beta_schedule,
            timesteps=timesteps,
            linear_start=linear_start,
            linear_end=linear_end,
            cosine_s=cosine_s,
        )

    def register_schedule(
        self,
        given_betas=None,
        beta_schedule="linear",
        timesteps=1000,
        linear_start=1e-4,
        linear_end=2e-2,
        cosine_s=8e-3,
    ):
        if exists(given_betas):
            betas = given_betas
        else:
            betas = make_beta_schedule(
                beta_schedule,
                timesteps,
                linear_start=linear_start,
                linear_end=linear_end,
                cosine_s=cosine_s,
            )
        alphas = 1.0 - betas
        alphas_cumprod = np.cumprod(alphas, axis=0)

        (timesteps,) = betas.shape
        self.num_timesteps = int(timesteps)
        self.linear_start = linear_start
        self.linear_end = linear_end
        assert (
            alphas_cumprod.shape[0] == self.num_timesteps
        ), "alphas have to be defined for each timestep"

        to_torch = partial(torch.tensor, dtype=torch.float32)

        self.register_buffer("betas", to_torch(betas))
        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))


class FirstStage(DDPM):
    """main class"""

    def __init__(
        self,
        first_stage_config,
        num_timesteps_cond=None,
        cond_stage_key="image",
        cond_stage_trainable=False,
        concat_mode=True,
        cond_stage_forward=None,
        conditioning_key=None,
        scale_factor=1.0,
        scale_by_std=False,
        *args,
        **kwargs,
    ):
        self.num_timesteps_cond = default(num_timesteps_cond, 1)
        self.scale_by_std = scale_by_std
        assert self.num_timesteps_cond <= kwargs["timesteps"]
        # for backwards compatibility after implementation of DiffusionWrapper
        if conditioning_key is None:
            conditioning_key = "concat" if concat_mode else "crossattn"
        ckpt_path = kwargs.pop("ckpt_path", None)
        ignore_keys = kwargs.pop("ignore_keys", [])
        super().__init__()
        self.concat_mode = concat_mode
        self.cond_stage_trainable = cond_stage_trainable
        self.cond_stage_key = cond_stage_key
        try:
            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
        except:
            self.num_downs = 0
        if not scale_by_std:
            self.scale_factor = scale_factor
        self.instantiate_first_stage(first_stage_config)
        self.cond_stage_forward = cond_stage_forward
        self.clip_denoised = False
        self.bbox_tokenizer = None

        self.restarted_from_ckpt = False
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys)
            self.restarted_from_ckpt = True

    def instantiate_first_stage(self, config):
        model = instantiate_from_config(config)
        self.first_stage_model = model.eval()
        self.first_stage_model.train = disabled_train
        for param in self.first_stage_model.parameters():
            param.requires_grad = False

    def get_first_stage_encoding(self, encoder_posterior):
        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
            z = encoder_posterior.sample()
        elif isinstance(encoder_posterior, torch.Tensor):
            z = encoder_posterior
        else:
            raise NotImplementedError(
                f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented"
            )
        return self.scale_factor * z

    @torch.no_grad()
    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
        if predict_cids:
            if z.dim() == 4:
                z = torch.argmax(z.exp(), dim=1).long()
            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
            z = rearrange(z, "b h w c -> b c h w").contiguous()

        z = 1.0 / self.scale_factor * z

        if hasattr(self, "split_input_params"):
            if isinstance(self.first_stage_model, VQModelInterface):
                return self.first_stage_model.decode(
                    z, force_not_quantize=predict_cids or force_not_quantize
                )
            else:
                return self.first_stage_model.decode(z)

        else:
            if isinstance(self.first_stage_model, VQModelInterface):
                return self.first_stage_model.decode(
                    z, force_not_quantize=predict_cids or force_not_quantize
                )
            else:
                return self.first_stage_model.decode(z)

    @torch.no_grad()
    def encode_first_stage(self, x):
        if hasattr(self, "split_input_params"):
            if self.split_input_params["patch_distributed_vq"]:
                ks = self.split_input_params["ks"]  # eg. (128, 128)
                stride = self.split_input_params["stride"]  # eg. (64, 64)
                df = self.split_input_params["vqf"]
                self.split_input_params["original_image_size"] = x.shape[-2:]
                bs, nc, h, w = x.shape
                if ks[0] > h or ks[1] > w:
                    ks = (min(ks[0], h), min(ks[1], w))
                    print("reducing Kernel")

                if stride[0] > h or stride[1] > w:
                    stride = (min(stride[0], h), min(stride[1], w))
                    print("reducing stride")

                fold, unfold, normalization, weighting = self.get_fold_unfold(
                    x, ks, stride, df=df
                )
                z = unfold(x)  # (bn, nc * prod(**ks), L)
                # Reshape to img shape
                z = z.view(
                    (z.shape[0], -1, ks[0], ks[1], z.shape[-1])
                )  # (bn, nc, ks[0], ks[1], L )

                output_list = [
                    self.first_stage_model.encode(z[:, :, :, :, i])
                    for i in range(z.shape[-1])
                ]

                o = torch.stack(output_list, axis=-1)
                o = o * weighting

                # Reverse reshape to img shape
                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
                # stitch crops together
                decoded = fold(o)
                decoded = decoded / normalization
                return decoded

            else:
                return self.first_stage_model.encode(x)
        else:
            return self.first_stage_model.encode(x)


class CondStage(DDPM):
    """main class"""

    def __init__(
        self,
        cond_stage_config,
        num_timesteps_cond=None,
        cond_stage_key="image",
        cond_stage_trainable=False,
        concat_mode=True,
        cond_stage_forward=None,
        conditioning_key=None,
        scale_factor=1.0,
        scale_by_std=False,
        *args,
        **kwargs,
    ):
        self.num_timesteps_cond = default(num_timesteps_cond, 1)
        self.scale_by_std = scale_by_std
        assert self.num_timesteps_cond <= kwargs["timesteps"]
        # for backwards compatibility after implementation of DiffusionWrapper
        if conditioning_key is None:
            conditioning_key = "concat" if concat_mode else "crossattn"
        if cond_stage_config == "__is_unconditional__":
            conditioning_key = None
        ckpt_path = kwargs.pop("ckpt_path", None)
        ignore_keys = kwargs.pop("ignore_keys", [])
        super().__init__()
        self.concat_mode = concat_mode
        self.cond_stage_trainable = cond_stage_trainable
        self.cond_stage_key = cond_stage_key
        self.num_downs = 0
        if not scale_by_std:
            self.scale_factor = scale_factor
        self.instantiate_cond_stage(cond_stage_config)
        self.cond_stage_forward = cond_stage_forward
        self.clip_denoised = False
        self.bbox_tokenizer = None

        self.restarted_from_ckpt = False
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys)
            self.restarted_from_ckpt = True

    def instantiate_cond_stage(self, config):
        if not self.cond_stage_trainable:
            if config == "__is_first_stage__":
                print("Using first stage also as cond stage.")
                self.cond_stage_model = self.first_stage_model
            elif config == "__is_unconditional__":
                print(f"Training {self.__class__.__name__} as an unconditional model.")
                self.cond_stage_model = None
                # self.be_unconditional = True
            else:
                model = instantiate_from_config(config)
                self.cond_stage_model = model.eval()
                self.cond_stage_model.train = disabled_train
                for param in self.cond_stage_model.parameters():
                    param.requires_grad = False
        else:
            assert config != "__is_first_stage__"
            assert config != "__is_unconditional__"
            model = instantiate_from_config(config)
            self.cond_stage_model = model

    def get_learned_conditioning(self, c):
        if self.cond_stage_forward is None:
            if hasattr(self.cond_stage_model, "encode") and callable(
                self.cond_stage_model.encode
            ):
                c = self.cond_stage_model.encode(c)
                if isinstance(c, DiagonalGaussianDistribution):
                    c = c.mode()
            else:
                c = self.cond_stage_model(c)
        else:
            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
        return c


class DiffusionWrapper(pl.LightningModule):
    def __init__(self, diff_model_config):
        super().__init__()
        self.diffusion_model = instantiate_from_config(diff_model_config)

    def forward(self, x, t, cc):
        out = self.diffusion_model(x, t, context=cc)
        return out


class DiffusionWrapperOut(pl.LightningModule):
    def __init__(self, diff_model_config):
        super().__init__()
        self.diffusion_model = instantiate_from_config(diff_model_config)

    def forward(self, h, emb, tp, hs, cc):
        return self.diffusion_model(h, emb, tp, hs, context=cc)


class UNet(DDPM):
    """main class"""

    def __init__(
        self,
        unetConfigEncode,
        unetConfigDecode,
        num_timesteps_cond=None,
        cond_stage_key="image",
        cond_stage_trainable=False,
        concat_mode=True,
        cond_stage_forward=None,
        conditioning_key=None,
        scale_factor=1.0,
        unet_bs=1,
        scale_by_std=False,
        *args,
        **kwargs,
    ):
        self.num_timesteps_cond = default(num_timesteps_cond, 1)
        self.scale_by_std = scale_by_std
        assert self.num_timesteps_cond <= kwargs["timesteps"]
        # for backwards compatibility after implementation of DiffusionWrapper
        if conditioning_key is None:
            conditioning_key = "concat" if concat_mode else "crossattn"
        ckpt_path = kwargs.pop("ckpt_path", None)
        ignore_keys = kwargs.pop("ignore_keys", [])
        super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
        self.concat_mode = concat_mode
        self.cond_stage_trainable = cond_stage_trainable
        self.cond_stage_key = cond_stage_key
        self.num_downs = 0
        self.cdevice = "cuda"
        self.unetConfigEncode = unetConfigEncode
        self.unetConfigDecode = unetConfigDecode
        if not scale_by_std:
            self.scale_factor = scale_factor
        else:
            self.register_buffer("scale_factor", torch.tensor(scale_factor))
        self.cond_stage_forward = cond_stage_forward
        self.clip_denoised = False
        self.bbox_tokenizer = None
        self.model1 = DiffusionWrapper(self.unetConfigEncode)
        self.model2 = DiffusionWrapperOut(self.unetConfigDecode)
        self.model1.eval()
        self.model2.eval()
        self.turbo = False
        self.unet_bs = unet_bs
        self.restarted_from_ckpt = False
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys)
            self.restarted_from_ckpt = True

    def make_cond_schedule(
        self,
    ):
        self.cond_ids = torch.full(
            size=(self.num_timesteps,),
            fill_value=self.num_timesteps - 1,
            dtype=torch.long,
        )
        ids = torch.round(
            torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
        ).long()
        self.cond_ids[: self.num_timesteps_cond] = ids

    @rank_zero_only
    @torch.no_grad()
    def on_train_batch_start(self, batch, batch_idx):
        # only for very first batch
        if (
            self.scale_by_std
            and self.current_epoch == 0
            and self.global_step == 0
            and batch_idx == 0
            and not self.restarted_from_ckpt
        ):
            assert (
                self.scale_factor == 1.0
            ), "rather not use custom rescaling and std-rescaling simultaneously"
            # set rescale weight to 1./std of encodings
            print("### USING STD-RESCALING ###")
            x = super().get_input(batch, self.first_stage_key)
            x = x.to(self.cdevice)
            encoder_posterior = self.encode_first_stage(x)
            z = self.get_first_stage_encoding(encoder_posterior).detach()
            del self.scale_factor
            self.register_buffer("scale_factor", 1.0 / z.flatten().std())
            print(f"setting self.scale_factor to {self.scale_factor}")
            print("### USING STD-RESCALING ###")

    def apply_model(self, x_noisy, t, cond, return_ids=False):
        if not self.turbo:
            self.model1.to(self.cdevice)

        step = self.unet_bs
        h, emb, hs = self.model1(x_noisy[0:step], t[:step], cond[:step])
        bs = cond.shape[0]

        # assert bs%2 == 0
        lenhs = len(hs)

        for i in range(step, bs, step):
            h_temp, emb_temp, hs_temp = self.model1(
                x_noisy[i : i + step], t[i : i + step], cond[i : i + step]
            )
            h = torch.cat((h, h_temp))
            emb = torch.cat((emb, emb_temp))
            for j in range(lenhs):
                hs[j] = torch.cat((hs[j], hs_temp[j]))

        if not self.turbo:
            self.model1.to("cpu")
            self.model2.to(self.cdevice)

        hs_temp = [hs[j][:step] for j in range(lenhs)]
        x_recon = self.model2(h[:step], emb[:step], x_noisy.dtype, hs_temp, cond[:step])

        for i in range(step, bs, step):
            hs_temp = [hs[j][i : i + step] for j in range(lenhs)]
            x_recon1 = self.model2(
                h[i : i + step],
                emb[i : i + step],
                x_noisy.dtype,
                hs_temp,
                cond[i : i + step],
            )
            x_recon = torch.cat((x_recon, x_recon1))

        if not self.turbo:
            self.model2.to("cpu")

        if isinstance(x_recon, tuple) and not return_ids:
            return x_recon[0]
        else:
            return x_recon

    def register_buffer1(self, name, attr):
        if type(attr) == torch.Tensor:
            if attr.device != torch.device(self.cdevice):
                attr = attr.to(torch.device(self.cdevice))
        setattr(self, name, attr)

    def make_schedule(
        self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0.0, verbose=True
    ):
        self.ddim_timesteps = make_ddim_timesteps(
            ddim_discr_method=ddim_discretize,
            num_ddim_timesteps=ddim_num_steps,
            num_ddpm_timesteps=self.num_timesteps,
            verbose=verbose,
        )

        assert (
            self.alphas_cumprod.shape[0] == self.num_timesteps
        ), "alphas have to be defined for each timestep"

        def to_torch(x):
            return x.to(self.cdevice)

        self.register_buffer1("betas", to_torch(self.betas))
        self.register_buffer1("alphas_cumprod", to_torch(self.alphas_cumprod))
        # ddim sampling parameters
        ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(
            alphacums=self.alphas_cumprod.cpu(),
            ddim_timesteps=self.ddim_timesteps,
            eta=ddim_eta,
            verbose=verbose,
        )
        self.register_buffer1("ddim_sigmas", ddim_sigmas)
        self.register_buffer1("ddim_alphas", ddim_alphas)
        self.register_buffer1("ddim_alphas_prev", ddim_alphas_prev)
        self.register_buffer1("ddim_sqrt_one_minus_alphas", np.sqrt(1.0 - ddim_alphas))

    @torch.no_grad()
    def sample(
        self,
        S,
        conditioning,
        x0=None,
        shape=None,
        seed=1234,
        callback=None,
        img_callback=None,
        quantize_x0=False,
        eta=0.0,
        mask=None,
        sampler="plms",
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        verbose=True,
        x_T=None,
        log_every_t=100,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
    ):
        if self.turbo:
            self.model1.to(self.cdevice)
            self.model2.to(self.cdevice)

        if x0 is None:
            batch_size, b1, b2, b3 = shape
            img_shape = (1, b1, b2, b3)
            tens = []
            print("seeds used = ", [seed + s for s in range(batch_size)])
            for _ in range(batch_size):
                torch.manual_seed(seed)
                tens.append(torch.randn(img_shape, device=self.cdevice))
                seed += 1
            noise = torch.cat(tens)
            del tens

        x_latent = noise if x0 is None else x0
        # sampling

        if sampler == "plms":
            self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
            print(f"Data shape for PLMS sampling is {shape}")
            samples = self.plms_sampling(
                conditioning,
                batch_size,
                x_latent,
                callback=callback,
                img_callback=img_callback,
                quantize_denoised=quantize_x0,
                mask=mask,
                x0=x0,
                ddim_use_original_steps=False,
                noise_dropout=noise_dropout,
                temperature=temperature,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs,
                log_every_t=log_every_t,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
            )

        elif sampler == "ddim":
            samples = self.ddim_sampling(
                x_latent,
                conditioning,
                S,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
                mask=mask,
                init_latent=x_T,
                use_original_steps=False,
            )

        elif sampler == "euler":
            self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
            samples = self.euler_sampling(
                self.alphas_cumprod,
                x_latent,
                S,
                conditioning,
                unconditional_conditioning=unconditional_conditioning,
                unconditional_guidance_scale=unconditional_guidance_scale,
            )
        elif sampler == "euler_a":
            self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
            samples = self.euler_ancestral_sampling(
                self.alphas_cumprod,
                x_latent,
                S,
                conditioning,
                unconditional_conditioning=unconditional_conditioning,
                unconditional_guidance_scale=unconditional_guidance_scale,
            )

        elif sampler == "dpm2":
            samples = self.dpm_2_sampling(
                self.alphas_cumprod,
                x_latent,
                S,
                conditioning,
                unconditional_conditioning=unconditional_conditioning,
                unconditional_guidance_scale=unconditional_guidance_scale,
            )
        elif sampler == "heun":
            samples = self.heun_sampling(
                self.alphas_cumprod,
                x_latent,
                S,
                conditioning,
                unconditional_conditioning=unconditional_conditioning,
                unconditional_guidance_scale=unconditional_guidance_scale,
            )

        elif sampler == "dpm2_a":
            samples = self.dpm_2_ancestral_sampling(
                self.alphas_cumprod,
                x_latent,
                S,
                conditioning,
                unconditional_conditioning=unconditional_conditioning,
                unconditional_guidance_scale=unconditional_guidance_scale,
            )

        elif sampler == "lms":
            samples = self.lms_sampling(
                self.alphas_cumprod,
                x_latent,
                S,
                conditioning,
                unconditional_conditioning=unconditional_conditioning,
                unconditional_guidance_scale=unconditional_guidance_scale,
            )

        if self.turbo:
            self.model1.to("cpu")
            self.model2.to("cpu")

        return samples

    @torch.no_grad()
    def plms_sampling(
        self,
        cond,
        b,
        img,
        ddim_use_original_steps=False,
        callback=None,
        quantize_denoised=False,
        mask=None,
        x0=None,
        img_callback=None,
        log_every_t=100,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
    ):
        device = self.betas.device
        timesteps = self.ddim_timesteps
        time_range = np.flip(timesteps)
        total_steps = timesteps.shape[0]
        print(f"Running PLMS Sampling with {total_steps} timesteps")

        iterator = tqdm(time_range, desc="PLMS Sampler", total=total_steps)
        old_eps = []

        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full((b,), step, device=device, dtype=torch.long)
            ts_next = torch.full(
                (b,),
                time_range[min(i + 1, len(time_range) - 1)],
                device=device,
                dtype=torch.long,
            )

            if mask is not None:
                assert x0 is not None
                img_orig = self.q_sample(x0, ts)  # TODO: deterministic forward pass?
                img = img_orig * mask + (1.0 - mask) * img

            outs = self.p_sample_plms(
                img,
                cond,
                ts,
                index=index,
                use_original_steps=ddim_use_original_steps,
                quantize_denoised=quantize_denoised,
                temperature=temperature,
                noise_dropout=noise_dropout,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
                old_eps=old_eps,
                t_next=ts_next,
            )
            img, pred_x0, e_t = outs
            old_eps.append(e_t)
            if len(old_eps) >= 4:
                old_eps.pop(0)
            if callback:
                callback(i)
            if img_callback:
                img_callback(pred_x0, i)

        return img

    @torch.no_grad()
    def p_sample_plms(
        self,
        x,
        c,
        t,
        index,
        repeat_noise=False,
        use_original_steps=False,
        quantize_denoised=False,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        old_eps=None,
        t_next=None,
    ):
        b, *_, device = *x.shape, x.device

        def get_model_output(x, t):
            if (
                unconditional_conditioning is None
                or unconditional_guidance_scale == 1.0
            ):
                e_t = self.apply_model(x, t, c)
            else:
                x_in = torch.cat([x] * 2)
                t_in = torch.cat([t] * 2)
                c_in = torch.cat([unconditional_conditioning, c])
                e_t_uncond, e_t = self.apply_model(x_in, t_in, c_in).chunk(2)
                e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

            if score_corrector is not None:
                assert self.parameterization == "eps"
                e_t = score_corrector.modify_score(
                    self.model, e_t, x, t, c, **corrector_kwargs
                )

            return e_t

        alphas = self.ddim_alphas
        alphas_prev = self.ddim_alphas_prev
        sqrt_one_minus_alphas = self.ddim_sqrt_one_minus_alphas
        sigmas = self.ddim_sigmas

        def get_x_prev_and_pred_x0(e_t, index):
            # select parameters corresponding to the currently considered timestep
            a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
            a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
            sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
            sqrt_one_minus_at = torch.full(
                (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
            )

            # current prediction for x_0
            pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
            if quantize_denoised:
                pred_x0, _, *_ = self.first_stage_model.quantize(pred_x0)
            # direction pointing to x_t
            dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
            noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
            if noise_dropout > 0.0:
                noise = torch.nn.functional.dropout(noise, p=noise_dropout)
            x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
            return x_prev, pred_x0

        e_t = get_model_output(x, t)
        if len(old_eps) == 0:
            # Pseudo Improved Euler (2nd order)
            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
            e_t_next = get_model_output(x_prev, t_next)
            e_t_prime = (e_t + e_t_next) / 2
        elif len(old_eps) == 1:
            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
            e_t_prime = (3 * e_t - old_eps[-1]) / 2
        elif len(old_eps) == 2:
            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
        elif len(old_eps) >= 3:
            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
            e_t_prime = (
                55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]
            ) / 24

        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)

        return x_prev, pred_x0, e_t

    @torch.no_grad()
    def stochastic_encode(
        self, x0, t, seed, ddim_eta, ddim_steps, use_original_steps=False, noise=None
    ):
        # fast, but does not allow for exact reconstruction
        # t serves as an index to gather the correct alphas
        self.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=ddim_eta, verbose=False)
        sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)

        if noise is None:
            b0, b1, b2, b3 = x0.shape
            img_shape = (1, b1, b2, b3)
            tens = []
            print("seeds used = ", [seed + s for s in range(b0)])
            for _ in range(b0):
                torch.manual_seed(seed)
                tens.append(torch.randn(img_shape, device=x0.device))
                seed += 1
            noise = torch.cat(tens)
            del tens
        return (
            extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
            + extract_into_tensor(self.ddim_sqrt_one_minus_alphas, t, x0.shape) * noise
        )

    @torch.no_grad()
    def add_noise(self, x0, t):
        sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
        noise = torch.randn(x0.shape, device=x0.device)

        # print(extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape),
        #       extract_into_tensor(self.ddim_sqrt_one_minus_alphas, t, x0.shape))
        return (
            extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
            + extract_into_tensor(self.ddim_sqrt_one_minus_alphas, t, x0.shape) * noise
        )

    @torch.no_grad()
    def ddim_sampling(
        self,
        x_latent,
        cond,
        t_start,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
        mask=None,
        init_latent=None,
        use_original_steps=False,
    ):
        timesteps = self.ddim_timesteps
        timesteps = timesteps[:t_start]
        time_range = np.flip(timesteps)
        total_steps = timesteps.shape[0]
        print(f"Running DDIM Sampling with {total_steps} timesteps")

        iterator = tqdm(time_range, desc="Decoding image", total=total_steps)
        x_dec = x_latent
        x0 = init_latent
        for i, step in enumerate(iterator):
            index = total_steps - i - 1
            ts = torch.full(
                (x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long
            )

            if mask is not None:
                # x0_noisy = self.add_noise(mask, torch.tensor([index] * x0.shape[0]).to(self.cdevice))
                x0_noisy = x0
                x_dec = x0_noisy * mask + (1.0 - mask) * x_dec

            x_dec = self.p_sample_ddim(
                x_dec,
                cond,
                ts,
                index=index,
                use_original_steps=use_original_steps,
                unconditional_guidance_scale=unconditional_guidance_scale,
                unconditional_conditioning=unconditional_conditioning,
            )

        if mask is not None:
            return x0 * mask + (1.0 - mask) * x_dec

        return x_dec

    @torch.no_grad()
    def p_sample_ddim(
        self,
        x,
        c,
        t,
        index,
        repeat_noise=False,
        use_original_steps=False,
        quantize_denoised=False,
        temperature=1.0,
        noise_dropout=0.0,
        score_corrector=None,
        corrector_kwargs=None,
        unconditional_guidance_scale=1.0,
        unconditional_conditioning=None,
    ):
        b, *_, device = *x.shape, x.device

        if unconditional_conditioning is None or unconditional_guidance_scale == 1.0:
            e_t = self.apply_model(x, t, c)
        else:
            x_in = torch.cat([x] * 2)
            t_in = torch.cat([t] * 2)
            c_in = torch.cat([unconditional_conditioning, c])
            e_t_uncond, e_t = self.apply_model(x_in, t_in, c_in).chunk(2)
            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

        if score_corrector is not None:
            assert self.model.parameterization == "eps"
            e_t = score_corrector.modify_score(
                self.model, e_t, x, t, c, **corrector_kwargs
            )

        alphas = self.ddim_alphas
        alphas_prev = self.ddim_alphas_prev
        sqrt_one_minus_alphas = self.ddim_sqrt_one_minus_alphas
        sigmas = self.ddim_sigmas
        # select parameters corresponding to the currently considered timestep
        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
        sqrt_one_minus_at = torch.full(
            (b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
        )

        # current prediction for x_0
        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
        if quantize_denoised:
            pred_x0, _, *_ = self.first_stage_model.quantize(pred_x0)
        # direction pointing to x_t
        dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
        if noise_dropout > 0.0:
            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
        return x_prev

    @torch.no_grad()
    def euler_sampling(
        self,
        ac,
        x,
        S,
        cond,
        unconditional_conditioning=None,
        unconditional_guidance_scale=1,
        extra_args=None,
        callback=None,
        disable=None,
        s_churn=0.0,
        s_tmin=0.0,
        s_tmax=float("inf"),
        s_noise=1.0,
    ):
        """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
        extra_args = {} if extra_args is None else extra_args
        cvd = CompVisDenoiser(ac)
        sigmas = cvd.get_sigmas(S)
        x = x * sigmas[0]

        s_in = x.new_ones([x.shape[0]]).half()
        for i in trange(len(sigmas) - 1, disable=disable):
            gamma = (
                min(s_churn / (len(sigmas) - 1), 2**0.5 - 1)
                if s_tmin <= sigmas[i] <= s_tmax
                else 0.0
            )
            eps = torch.randn_like(x) * s_noise
            sigma_hat = (sigmas[i] * (gamma + 1)).half()
            if gamma > 0:
                x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5

            s_i = sigma_hat * s_in
            x_in = torch.cat([x] * 2)
            t_in = torch.cat([s_i] * 2)
            cond_in = torch.cat([unconditional_conditioning, cond])
            c_out, c_in = [
                append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)
            ]
            eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
            e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
            denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

            d = to_d(x, sigma_hat, denoised)
            if callback is not None:
                callback(
                    {
                        "x": x,
                        "i": i,
                        "sigma": sigmas[i],
                        "sigma_hat": sigma_hat,
                        "denoised": denoised,
                    }
                )
            dt = sigmas[i + 1] - sigma_hat
            # Euler method
            x = x + d * dt
        return x

    @torch.no_grad()
    def euler_ancestral_sampling(
        self,
        ac,
        x,
        S,
        cond,
        unconditional_conditioning=None,
        unconditional_guidance_scale=1,
        extra_args=None,
        callback=None,
        disable=None,
    ):
        """Ancestral sampling with Euler method steps."""
        extra_args = {} if extra_args is None else extra_args

        cvd = CompVisDenoiser(ac)
        sigmas = cvd.get_sigmas(S)
        x = x * sigmas[0]

        s_in = x.new_ones([x.shape[0]]).half()
        for i in trange(len(sigmas) - 1, disable=disable):
            s_i = sigmas[i] * s_in
            x_in = torch.cat([x] * 2)
            t_in = torch.cat([s_i] * 2)
            cond_in = torch.cat([unconditional_conditioning, cond])
            c_out, c_in = [
                append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)
            ]
            eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
            e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
            denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

            sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
            if callback is not None:
                callback(
                    {
                        "x": x,
                        "i": i,
                        "sigma": sigmas[i],
                        "sigma_hat": sigmas[i],
                        "denoised": denoised,
                    }
                )
            d = to_d(x, sigmas[i], denoised)
            # Euler method
            dt = sigma_down - sigmas[i]
            x = x + d * dt
            x = x + torch.randn_like(x) * sigma_up
        return x

    @torch.no_grad()
    def heun_sampling(
        self,
        ac,
        x,
        S,
        cond,
        unconditional_conditioning=None,
        unconditional_guidance_scale=1,
        extra_args=None,
        callback=None,
        disable=None,
        s_churn=0.0,
        s_tmin=0.0,
        s_tmax=float("inf"),
        s_noise=1.0,
    ):
        """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
        extra_args = {} if extra_args is None else extra_args

        cvd = CompVisDenoiser(alphas_cumprod=ac)
        sigmas = cvd.get_sigmas(S)
        x = x * sigmas[0]

        s_in = x.new_ones([x.shape[0]]).half()
        for i in trange(len(sigmas) - 1, disable=disable):
            gamma = (
                min(s_churn / (len(sigmas) - 1), 2**0.5 - 1)
                if s_tmin <= sigmas[i] <= s_tmax
                else 0.0
            )
            eps = torch.randn_like(x) * s_noise
            sigma_hat = (sigmas[i] * (gamma + 1)).half()
            if gamma > 0:
                x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5

            s_i = sigma_hat * s_in
            x_in = torch.cat([x] * 2)
            t_in = torch.cat([s_i] * 2)
            cond_in = torch.cat([unconditional_conditioning, cond])
            c_out, c_in = [
                append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)
            ]
            eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
            e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
            denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

            d = to_d(x, sigma_hat, denoised)
            if callback is not None:
                callback(
                    {
                        "x": x,
                        "i": i,
                        "sigma": sigmas[i],
                        "sigma_hat": sigma_hat,
                        "denoised": denoised,
                    }
                )
            dt = sigmas[i + 1] - sigma_hat
            if sigmas[i + 1] == 0:
                # Euler method
                x = x + d * dt
            else:
                # Heun's method
                x_2 = x + d * dt
                s_i = sigmas[i + 1] * s_in
                x_in = torch.cat([x_2] * 2)
                t_in = torch.cat([s_i] * 2)
                cond_in = torch.cat([unconditional_conditioning, cond])
                c_out, c_in = [
                    append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)
                ]
                eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
                e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
                denoised_2 = e_t_uncond + unconditional_guidance_scale * (
                    e_t - e_t_uncond
                )

                d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
                d_prime = (d + d_2) / 2
                x = x + d_prime * dt
        return x

    @torch.no_grad()
    def dpm_2_sampling(
        self,
        ac,
        x,
        S,
        cond,
        unconditional_conditioning=None,
        unconditional_guidance_scale=1,
        extra_args=None,
        callback=None,
        disable=None,
        s_churn=0.0,
        s_tmin=0.0,
        s_tmax=float("inf"),
        s_noise=1.0,
    ):
        """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
        extra_args = {} if extra_args is None else extra_args

        cvd = CompVisDenoiser(ac)
        sigmas = cvd.get_sigmas(S)
        x = x * sigmas[0]

        s_in = x.new_ones([x.shape[0]]).half()
        for i in trange(len(sigmas) - 1, disable=disable):
            gamma = (
                min(s_churn / (len(sigmas) - 1), 2**0.5 - 1)
                if s_tmin <= sigmas[i] <= s_tmax
                else 0.0
            )
            eps = torch.randn_like(x) * s_noise
            sigma_hat = sigmas[i] * (gamma + 1)
            if gamma > 0:
                x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5

            s_i = sigma_hat * s_in
            x_in = torch.cat([x] * 2)
            t_in = torch.cat([s_i] * 2)
            cond_in = torch.cat([unconditional_conditioning, cond])
            c_out, c_in = [
                append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)
            ]
            eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
            e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
            denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

            d = to_d(x, sigma_hat, denoised)
            # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
            sigma_mid = ((sigma_hat ** (1 / 3) + sigmas[i + 1] ** (1 / 3)) / 2) ** 3
            dt_1 = sigma_mid - sigma_hat
            dt_2 = sigmas[i + 1] - sigma_hat
            x_2 = x + d * dt_1

            s_i = sigma_mid * s_in
            x_in = torch.cat([x_2] * 2)
            t_in = torch.cat([s_i] * 2)
            cond_in = torch.cat([unconditional_conditioning, cond])
            c_out, c_in = [
                append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)
            ]
            eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
            e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
            denoised_2 = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

            d_2 = to_d(x_2, sigma_mid, denoised_2)
            x = x + d_2 * dt_2
        return x

    @torch.no_grad()
    def dpm_2_ancestral_sampling(
        self,
        ac,
        x,
        S,
        cond,
        unconditional_conditioning=None,
        unconditional_guidance_scale=1,
        extra_args=None,
        callback=None,
        disable=None,
    ):
        """Ancestral sampling with DPM-Solver inspired second-order steps."""
        extra_args = {} if extra_args is None else extra_args

        cvd = CompVisDenoiser(ac)
        sigmas = cvd.get_sigmas(S)
        x = x * sigmas[0]

        s_in = x.new_ones([x.shape[0]]).half()
        for i in trange(len(sigmas) - 1, disable=disable):
            s_i = sigmas[i] * s_in
            x_in = torch.cat([x] * 2)
            t_in = torch.cat([s_i] * 2)
            cond_in = torch.cat([unconditional_conditioning, cond])
            c_out, c_in = [
                append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)
            ]
            eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
            e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
            denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

            sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
            if callback is not None:
                callback(
                    {
                        "x": x,
                        "i": i,
                        "sigma": sigmas[i],
                        "sigma_hat": sigmas[i],
                        "denoised": denoised,
                    }
                )
            d = to_d(x, sigmas[i], denoised)
            # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
            sigma_mid = ((sigmas[i] ** (1 / 3) + sigma_down ** (1 / 3)) / 2) ** 3
            dt_1 = sigma_mid - sigmas[i]
            dt_2 = sigma_down - sigmas[i]
            x_2 = x + d * dt_1

            s_i = sigma_mid * s_in
            x_in = torch.cat([x_2] * 2)
            t_in = torch.cat([s_i] * 2)
            cond_in = torch.cat([unconditional_conditioning, cond])
            c_out, c_in = [
                append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)
            ]
            eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
            e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
            denoised_2 = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

            d_2 = to_d(x_2, sigma_mid, denoised_2)
            x = x + d_2 * dt_2
            x = x + torch.randn_like(x) * sigma_up
        return x

    @torch.no_grad()
    def lms_sampling(
        self,
        ac,
        x,
        S,
        cond,
        unconditional_conditioning=None,
        unconditional_guidance_scale=1,
        extra_args=None,
        callback=None,
        disable=None,
        order=4,
    ):
        extra_args = {} if extra_args is None else extra_args
        s_in = x.new_ones([x.shape[0]])

        cvd = CompVisDenoiser(ac)
        sigmas = cvd.get_sigmas(S)
        x = x * sigmas[0]

        ds = []
        for i in trange(len(sigmas) - 1, disable=disable):
            s_i = sigmas[i] * s_in
            x_in = torch.cat([x] * 2)
            t_in = torch.cat([s_i] * 2)
            cond_in = torch.cat([unconditional_conditioning, cond])
            c_out, c_in = [
                append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)
            ]
            eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
            e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
            denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)

            d = to_d(x, sigmas[i], denoised)
            ds.append(d)
            if len(ds) > order:
                ds.pop(0)
            if callback is not None:
                callback(
                    {
                        "x": x,
                        "i": i,
                        "sigma": sigmas[i],
                        "sigma_hat": sigmas[i],
                        "denoised": denoised,
                    }
                )
            cur_order = min(i + 1, order)
            coeffs = [
                linear_multistep_coeff(cur_order, sigmas.cpu(), i, j)
                for j in range(cur_order)
            ]
            x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
        return x


================================================
FILE: optimizedSD/diffusers_txt2img.py
================================================
import torch
from diffusers import LDMTextToImagePipeline

pipe = LDMTextToImagePipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-3-diffusers", use_auth_token=True
)

prompt = "19th Century wooden engraving of Elon musk"

seed = torch.manual_seed(1024)
images = pipe(
    [prompt],
    batch_size=1,
    num_inference_steps=50,
    guidance_scale=7,
    generator=seed,
    torch_device="cpu",
)["sample"]

# save images
for idx, image in enumerate(images):
    image.save(f"image-{idx}.png")


================================================
FILE: optimizedSD/openaimodelSplit.py
================================================
from abc import abstractmethod
import math
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
    checkpoint,
    conv_nd,
    linear,
    avg_pool_nd,
    zero_module,
    normalization,
    timestep_embedding,
)
from .splitAttention import SpatialTransformer


class AttentionPool2d(nn.Module):
    """
    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
    """

    def __init__(
        self,
        spacial_dim: int,
        embed_dim: int,
        num_heads_channels: int,
        output_dim: int = None,
    ):
        super().__init__()
        self.positional_embedding = nn.Parameter(
            th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5
        )
        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
        self.num_heads = embed_dim // num_heads_channels
        self.attention = QKVAttention(self.num_heads)

    def forward(self, x):
        b, c, *_spatial = x.shape
        x = x.reshape(b, c, -1)  # NC(HW)
        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
        x = self.qkv_proj(x)
        x = self.attention(x)
        x = self.c_proj(x)
        return x[:, :, 0]


class TimestepBlock(nn.Module):
    """
    Any module where forward() takes timestep embeddings as a second argument.
    """

    @abstractmethod
    def forward(self, x, emb):
        """
        Apply the module to `x` given `emb` timestep embeddings.
        """


class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    """
    A sequential module that passes timestep embeddings to the children that
    support it as an extra input.
    """

    def forward(self, x, emb, context=None):
        for layer in self:
            if isinstance(layer, TimestepBlock):
                x = layer(x, emb)
            elif isinstance(layer, SpatialTransformer):
                x = layer(x, context)
            else:
                x = layer(x)
        return x


class Upsample(nn.Module):
    """
    An upsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 upsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        if use_conv:
            self.conv = conv_nd(
                dims, self.channels, self.out_channels, 3, padding=padding
            )

    def forward(self, x):
        assert x.shape[1] == self.channels
        if self.dims == 3:
            x = F.interpolate(
                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
            )
        else:
            x = F.interpolate(x, scale_factor=2, mode="nearest")
        if self.use_conv:
            x = self.conv(x)
        return x


class TransposedUpsample(nn.Module):
    "Learned 2x upsampling without padding"

    def __init__(self, channels, out_channels=None, ks=5):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels

        self.up = nn.ConvTranspose2d(
            self.channels, self.out_channels, kernel_size=ks, stride=2
        )

    def forward(self, x):
        return self.up(x)


class Downsample(nn.Module):
    """
    A downsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 downsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        stride = 2 if dims != 3 else (1, 2, 2)
        if use_conv:
            self.op = conv_nd(
                dims,
                self.channels,
                self.out_channels,
                3,
                stride=stride,
                padding=padding,
            )
        else:
            assert self.channels == self.out_channels
            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)

    def forward(self, x):
        assert x.shape[1] == self.channels
        return self.op(x)


class ResBlock(TimestepBlock):
    """
    A residual block that can optionally change the number of channels.
    :param channels: the number of input channels.
    :param emb_channels: the number of timestep embedding channels.
    :param dropout: the rate of dropout.
    :param out_channels: if specified, the number of out channels.
    :param use_conv: if True and out_channels is specified, use a spatial
        convolution instead of a smaller 1x1 convolution to change the
        channels in the skip connection.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param use_checkpoint: if True, use gradient checkpointing on this module.
    :param up: if True, use this block for upsampling.
    :param down: if True, use this block for downsampling.
    """

    def __init__(
        self,
        channels,
        emb_channels,
        dropout,
        out_channels=None,
        use_conv=False,
        use_scale_shift_norm=False,
        dims=2,
        use_checkpoint=False,
        up=False,
        down=False,
    ):
        super().__init__()
        self.channels = channels
        self.emb_channels = emb_channels
        self.dropout = dropout
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.use_checkpoint = use_checkpoint
        self.use_scale_shift_norm = use_scale_shift_norm

        self.in_layers = nn.Sequential(
            normalization(channels),
            nn.SiLU(),
            conv_nd(dims, channels, self.out_channels, 3, padding=1),
        )

        self.updown = up or down

        if up:
            self.h_upd = Upsample(channels, False, dims)
            self.x_upd = Upsample(channels, False, dims)
        elif down:
            self.h_upd = Downsample(channels, False, dims)
            self.x_upd = Downsample(channels, False, dims)
        else:
            self.h_upd = self.x_upd = nn.Identity()

        self.emb_layers = nn.Sequential(
            nn.SiLU(),
            linear(
                emb_channels,
                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
            ),
        )
        self.out_layers = nn.Sequential(
            normalization(self.out_channels),
            nn.SiLU(),
            nn.Dropout(p=dropout),
            zero_module(
                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
            ),
        )

        if self.out_channels == channels:
            self.skip_connection = nn.Identity()
        elif use_conv:
            self.skip_connection = conv_nd(
                dims, channels, self.out_channels, 3, padding=1
            )
        else:
            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)

    def forward(self, x, emb):
        """
        Apply the block to a Tensor, conditioned on a timestep embedding.
        :param x: an [N x C x ...] Tensor of features.
        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
        :return: an [N x C x ...] Tensor of outputs.
        """
        return checkpoint(
            self._forward, (x, emb), self.parameters(), self.use_checkpoint
        )

    def _forward(self, x, emb):
        if self.updown:
            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
            h = in_rest(x)
            h = self.h_upd(h)
            x = self.x_upd(x)
            h = in_conv(h)
        else:
            h = self.in_layers(x)
        emb_out = self.emb_layers(emb).type(h.dtype)
        while len(emb_out.shape) < len(h.shape):
            emb_out = emb_out[..., None]
        if self.use_scale_shift_norm:
            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
            scale, shift = th.chunk(emb_out, 2, dim=1)
            h = out_norm(h) * (1 + scale) + shift
            h = out_rest(h)
        else:
            h = h + emb_out
            h = self.out_layers(h)
        return self.skip_connection(x) + h


class AttentionBlock(nn.Module):
    """
    An attention block that allows spatial positions to attend to each other.
    Originally ported from here, but adapted to the N-d case.
    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
    """

    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        use_checkpoint=False,
        use_new_attention_order=False,
    ):
        super().__init__()
        self.channels = channels
        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        self.use_checkpoint = use_checkpoint
        self.norm = normalization(channels)
        self.qkv = conv_nd(1, channels, channels * 3, 1)
        if use_new_attention_order:
            # split qkv before split heads
            self.attention = QKVAttention(self.num_heads)
        else:
            # split heads before split qkv
            self.attention = QKVAttentionLegacy(self.num_heads)

        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))

    def forward(self, x):
        return checkpoint(
            self._forward, (x,), self.parameters(), True
        )  # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
        # return pt_checkpoint(self._forward, x)  # pytorch

    def _forward(self, x):
        b, c, *spatial = x.shape
        x = x.reshape(b, c, -1)
        qkv = self.qkv(self.norm(x))
        h = self.attention(qkv)
        h = self.proj_out(h)
        return (x + h).reshape(b, c, *spatial)


def count_flops_attn(model, _x, y):
    """
    A counter for the `thop` package to count the operations in an
    attention operation.
    Meant to be used like:
        macs, params = thop.profile(
            model,
            inputs=(inputs, timestamps),
            custom_ops={QKVAttention: QKVAttention.count_flops},
        )
    """
    b, c, *spatial = y[0].shape
    num_spatial = int(np.prod(spatial))
    # We perform two matmuls with the same number of ops.
    # The first computes the weight matrix, the second computes
    # the combination of the value vectors.
    matmul_ops = 2 * b * (num_spatial**2) * c
    model.total_ops += th.DoubleTensor([matmul_ops])


class QKVAttentionLegacy(nn.Module):
    """
    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv):
        """
        Apply QKV attention.
        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts", q * scale, k * scale
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v)
        return a.reshape(bs, -1, length)

    @staticmethod
    def count_flops(model, _x, y):
        return count_flops_attn(model, _x, y)


class QKVAttention(nn.Module):
    """
    A module which performs QKV attention and splits in a different order.
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv):
        """
        Apply QKV attention.
        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.chunk(3, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts",
            (q * scale).view(bs * self.n_heads, ch, length),
            (k * scale).view(bs * self.n_heads, ch, length),
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
        return a.reshape(bs, -1, length)

    @staticmethod
    def count_flops(model, _x, y):
        return count_flops_attn(model, _x, y)


class UNetModelEncode(nn.Module):
    def __init__(
        self,
        image_size,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        attention_resolutions,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=2,
        num_classes=None,
        use_checkpoint=False,
        use_fp16=False,
        num_heads=-1,
        num_head_channels=-1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        resblock_updown=False,
        use_new_attention_order=False,
        use_spatial_transformer=False,  # custom transformer support
        transformer_depth=1,  # custom transformer support
        context_dim=None,  # custom transformer support
        n_embed=None,  # custom support for prediction of discrete ids into codebook of first stage vq model
        legacy=True,
    ):
        super().__init__()
        if use_spatial_transformer:
            assert (
                context_dim is not None
            ), "Fool!! You forgot to include the dimension of your cross-attention conditioning..."

        if context_dim is not None:
            assert (
                use_spatial_transformer
            ), "Fool!! You forgot to use the spatial transformer for your cross-attention conditioning..."
            from omegaconf.listconfig import ListConfig

            if type(context_dim) == ListConfig:
                context_dim = list(context_dim)

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        if num_heads == -1:
            assert (
                num_head_channels != -1
            ), "Either num_heads or num_head_channels has to be set"

        if num_head_channels == -1:
            assert (
                num_heads != -1
            ), "Either num_heads or num_head_channels has to be set"

        self.image_size = image_size
        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        self.num_res_blocks = num_res_blocks
        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.num_classes = num_classes
        self.use_checkpoint = use_checkpoint
        self.dtype = th.float16 if use_fp16 else th.float32
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample
        self.predict_codebook_ids = n_embed is not None

        time_embed_dim = model_channels * 4
        self.time_embed = nn.Sequential(
            linear(model_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )

        if self.num_classes is not None:
            self.label_emb = nn.Embedding(num_classes, time_embed_dim)

        self.input_blocks = nn.ModuleList(
            [
                TimestepEmbedSequential(
                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
                )
            ]
        )
        self._feature_size = model_channels
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1
        for level, mult in enumerate(channel_mult):
            for _ in range(num_res_blocks):
                layers = [
                    ResBlock(
                        ch,
                        time_embed_dim,
                        dropout,
                        out_channels=mult * model_channels,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = mult * model_channels
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        # num_heads = 1
                        dim_head = (
                            ch // num_heads
                            if use_spatial_transformer
                            else num_head_channels
                        )
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads,
                            num_head_channels=dim_head,
                            use_new_attention_order=use_new_attention_order,
                        )
                        if not use_spatial_transformer
                        else SpatialTransformer(
                            ch,
                            num_heads,
                            dim_head,
                            depth=transformer_depth,
                            context_dim=context_dim,
                        )
                    )
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            down=True,
                        )
                        if resblock_updown
                        else Downsample(
                            ch, conv_resample, dims=dims, out_channels=out_ch
                        )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2
                self._feature_size += ch

        if num_head_channels == -1:
            dim_head = ch // num_heads
        else:
            num_heads = ch // num_head_channels
            dim_head = num_head_channels
        if legacy:
            # num_heads = 1
            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
        self.middle_block = TimestepEmbedSequential(
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
            AttentionBlock(
                ch,
                use_checkpoint=use_checkpoint,
                num_heads=num_heads,
                num_head_channels=dim_head,
                use_new_attention_order=use_new_attention_order,
            )
            if not use_spatial_transformer
            else SpatialTransformer(
                ch,
                num_heads,
                dim_head,
                depth=transformer_depth,
                context_dim=context_dim,
            ),
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
        )
        self._feature_size += ch

    def forward(self, x, timesteps=None, context=None, y=None):
        """
        Apply the model to an input batch.
        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :param context: conditioning plugged in via crossattn
        :param y: an [N] Tensor of labels, if class-conditional.
        :return: an [N x C x ...] Tensor of outputs.
        """
        assert (y is not None) == (
            self.num_classes is not None
        ), "must specify y if and only if the model is class-conditional"
        hs = []
        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
        emb = self.time_embed(t_emb)

        if self.num_classes is not None:
            assert y.shape == (x.shape[0],)
            emb = emb + self.label_emb(y)

        h = x.type(self.dtype)
        for module in self.input_blocks:
            h = module(h, emb, context)
            hs.append(h)
        h = self.middle_block(h, emb, context)

        return h, emb, hs


class UNetModelDecode(nn.Module):
    def __init__(
        self,
        image_size,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        attention_resolutions,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=2,
        num_classes=None,
        use_checkpoint=False,
        use_fp16=False,
        num_heads=-1,
        num_head_channels=-1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        resblock_updown=False,
        use_new_attention_order=False,
        use_spatial_transformer=False,  # custom transformer support
        transformer_depth=1,  # custom transformer support
        context_dim=None,  # custom transformer support
        n_embed=None,  # custom support for prediction of discrete ids into codebook of first stage vq model
        legacy=True,
    ):
        super().__init__()
        if use_spatial_transformer:
            assert (
                context_dim is not None
            ), "Fool!! You forgot to include the dimension of your cross-attention conditioning..."

        if context_dim is not None:
            assert (
                use_spatial_transformer
            ), "Fool!! You forgot to use the spatial transformer for your cross-attention conditioning..."
            from omegaconf.listconfig import ListConfig

            if type(context_dim) == ListConfig:
                context_dim = list(context_dim)

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        if num_heads == -1:
            assert (
                num_head_channels != -1
            ), "Either num_heads or num_head_channels has to be set"

        if num_head_channels == -1:
            assert (
                num_heads != -1
            ), "Either num_heads or num_head_channels has to be set"

        self.image_size = image_size
        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        self.num_res_blocks = num_res_blocks
        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.num_classes = num_classes
        self.use_checkpoint = use_checkpoint
        self.dtype = th.float16 if use_fp16 else th.float32
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample
        self.predict_codebook_ids = n_embed is not None

        time_embed_dim = model_channels * 4

        self._feature_size = model_channels
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1
        for level, mult in enumerate(channel_mult):
            for _ in range(num_res_blocks):
                ch = mult * model_channels
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        # num_heads = 1
                        dim_head = (
                            ch // num_heads
                            if use_spatial_transformer
                            else num_head_channels
                        )

                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch

                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2
                self._feature_size += ch

        if num_head_channels == -1:
            dim_head = ch // num_heads
        else:
            num_heads = ch // num_head_channels
            dim_head = num_head_channels
        if legacy:
            # num_heads = 1
            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels

        self._feature_size += ch

        self.output_blocks = nn.ModuleList([])
        for level, mult in list(enumerate(channel_mult))[::-1]:
            for i in range(num_res_blocks + 1):
                ich = input_block_chans.pop()
                layers = [
                    ResBlock(
                        ch + ich,
                        time_embed_dim,
                        dropout,
                        out_channels=model_channels * mult,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = model_channels * mult
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        # num_heads = 1
                        dim_head = (
                            ch // num_heads
                            if use_spatial_transformer
                            else num_head_channels
                        )
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads_upsample,
                            num_head_channels=dim_head,
                            use_new_attention_order=use_new_attention_order,
                        )
                        if not use_spatial_transformer
                        else SpatialTransformer(
                            ch,
                            num_heads,
                            dim_head,
                            depth=transformer_depth,
                            context_dim=context_dim,
                        )
                    )
                if level and i == num_res_blocks:
                    out_ch = ch
                    layers.append(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            up=True,
                        )
                        if resblock_updown
                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
                    )
                    ds //= 2
                self.output_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch

        self.out = nn.Sequential(
            normalization(ch),
            nn.SiLU(),
            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
        )
        if self.predict_codebook_ids:
            self.id_predictor = nn.Sequential(
                normalization(ch),
                conv_nd(dims, model_channels, n_embed, 1),
                # nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
            )

    def forward(self, h, emb, tp, hs, context=None, y=None):
        """
        Apply the model to an input batch.
        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :param context: conditioning plugged in via crossattn
        :param y: an [N] Tensor of labels, if class-conditional.
        :return: an [N x C x ...] Tensor of outputs.
        """

        for module in self.output_blocks:
            h = th.cat([h, hs.pop()], dim=1)
            h = module(h, emb, context)
        h = h.type(tp)
        if self.predict_codebook_ids:
            return self.id_predictor(h)
        else:
            return self.out(h)


================================================
FILE: optimizedSD/optimUtils.py
================================================
import os
import pandas as pd


def split_weighted_subprompts(text):
    """
    grabs all text up to the first occurrence of ':'
    uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
    if ':' has no value defined, defaults to 1.0
    repeats until no text remaining
    """
    remaining = len(text)
    prompts = []
    weights = []
    while remaining > 0:
        if ":" in text:
            idx = text.index(":")  # first occurrence from start
            # grab up to index as sub-prompt
            prompt = text[:idx]
            remaining -= idx
            # remove from main text
            text = text[idx + 1 :]
            # find value for weight
            if " " in text:
                idx = text.index(" ")  # first occurence
            else:  # no space, read to end
                idx = len(text)
            if idx != 0:
                try:
                    weight = float(text[:idx])
                except:  # couldn't treat as float
                    print(
                        f"Warning: '{text[:idx]}' is not a value, are you missing a space?"
                    )
                    weight = 1.0
            else:  # no value found
                weight = 1.0
            # remove from main text
            remaining -= idx
            text = text[idx + 1 :]
            # append the sub-prompt and its weight
            prompts.append(prompt)
            weights.append(weight)
        else:  # no : found
            if len(text) > 0:  # there is still text though
                # take remainder as weight 1
                prompts.append(text)
                weights.append(1.0)
            remaining = 0
    return prompts, weights


def logger(params, log_csv):
    os.makedirs("logs", exist_ok=True)
    cols = [arg for arg, _ in params.items()]
    if not os.path.exists(log_csv):
        df = pd.DataFrame(columns=cols)
        df.to_csv(log_csv, index=False)

    df = pd.read_csv(log_csv)
    for arg in cols:
        if arg not in df.columns:
            df[arg] = ""
    df.to_csv(log_csv, index=False)

    li = {}
    cols = [col for col in df.columns]
    data = {arg: value for arg, value in params.items()}
    for col in cols:
        if col in data:
            li[col] = data[col]
        else:
            li[col] = ""

    df = pd.DataFrame(li, index=[0])
    df.to_csv(log_csv, index=False, mode="a", header=False)


================================================
FILE: optimizedSD/optimized_img2img.py
================================================
import argparse, os, re
import torch
import numpy as np
from random import randint
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
import time
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import nullcontext
from einops import rearrange, repeat
from ldm.util import instantiate_from_config
from optimUtils import split_weighted_subprompts, logger
from transformers import logging

logging.set_verbosity_error()


def chunk(it, size):
    it = iter(it)
    return iter(lambda: tuple(islice(it, size)), ())


def load_model_from_config(ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    return sd


def load_img(path, h0, w0):
    image = Image.open(path).convert("RGB")
    w, h = image.size

    print(f"loaded input image of size ({w}, {h}) from {path}")
    if h0 is not None and w0 is not None:
        h, w = h0, w0

    w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 32

    print(f"New image size ({w}, {h})")
    image = image.resize((w, h), resample=Image.LANCZOS)
    image = np.array(image).astype(np.float32) / 255.0
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    return 2.0 * image - 1.0


config = "optimizedSD/v1-inference.yaml"
ckpt = "models/ldm/stable-diffusion-v1/model.ckpt"

parser = argparse.ArgumentParser()

parser.add_argument(
    "--prompt",
    type=str,
    nargs="?",
    default="a painting of a virus monster playing guitar",
    help="the prompt to render",
)
parser.add_argument(
    "--outdir",
    type=str,
    nargs="?",
    help="dir to write results to",
    default="outputs/img2img-samples",
)
parser.add_argument("--init-img", type=str, nargs="?", help="path to the input image")

parser.add_argument(
    "--skip_grid",
    action="store_true",
    help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
    "--skip_save",
    action="store_true",
    help="do not save individual samples. For speed measurements.",
)
parser.add_argument(
    "--ddim_steps",
    type=int,
    default=50,
    help="number of ddim sampling steps",
)

parser.add_argument(
    "--ddim_eta",
    type=float,
    default=0.0,
    help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
    "--n_iter",
    type=int,
    default=1,
    help="sample this often",
)
parser.add_argument(
    "--H",
    type=int,
    default=None,
    help="image height, in pixel space",
)
parser.add_argument(
    "--W",
    type=int,
    default=None,
    help="image width, in pixel space",
)
parser.add_argument(
    "--strength",
    type=float,
    default=0.75,
    help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image",
)
parser.add_argument(
    "--n_samples",
    type=int,
    default=5,
    help="how many samples to produce for each given prompt. A.k.a. batch size",
)
parser.add_argument(
    "--n_rows",
    type=int,
    default=0,
    help="rows in the grid (default: n_samples)",
)
parser.add_argument(
    "--scale",
    type=float,
    default=7.5,
    help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
    "--from-file",
    type=str,
    help="if specified, load prompts from this file",
)
parser.add_argument(
    "--seed",
    type=int,
    default=None,
    help="the seed (for reproducible sampling)",
)
parser.add_argument(
    "--device",
    type=str,
    default="cuda",
    help="CPU or GPU (cuda/cuda:0/cuda:1/...)",
)
parser.add_argument(
    "--unet_bs",
    type=int,
    default=1,
    help="Slightly reduces inference time at the expense of high VRAM (value > 1 not recommended )",
)
parser.add_argument(
    "--turbo",
    action="store_true",
    help="Reduces inference time on the expense of 1GB VRAM",
)
parser.add_argument(
    "--precision",
    type=str,
    help="evaluate at this precision",
    choices=["full", "autocast"],
    default="autocast",
)
parser.add_argument(
    "--format",
    type=str,
    help="output image format",
    choices=["jpg", "png"],
    default="png",
)
parser.add_argument(
    "--sampler",
    type=str,
    help="sampler",
    choices=["ddim"],
    default="ddim",
)
opt = parser.parse_args()

tic = time.time()
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
grid_count = len(os.listdir(outpath)) - 1

if opt.seed is None:
    opt.seed = randint(0, 1000000)
seed_everything(opt.seed)

# Logging
logger(vars(opt), log_csv="logs/img2img_logs.csv")

sd = load_model_from_config(f"{ckpt}")
li, lo = [], []
for key, value in sd.items():
    sp = key.split(".")
    if (sp[0]) == "model":
        if "input_blocks" in sp:
            li.append(key)
        elif "middle_block" in sp:
            li.append(key)
        elif "time_embed" in sp:
            li.append(key)
        else:
            lo.append(key)
for key in li:
    sd["model1." + key[6:]] = sd.pop(key)
for key in lo:
    sd["model2." + key[6:]] = sd.pop(key)

config = OmegaConf.load(f"{config}")

assert os.path.isfile(opt.init_img)
init_image = load_img(opt.init_img, opt.H, opt.W).to(opt.device)

model = instantiate_from_config(config.modelUNet)
_, _ = model.load_state_dict(sd, strict=False)
model.eval()
model.cdevice = opt.device
model.unet_bs = opt.unet_bs
model.turbo = opt.turbo

modelCS = instantiate_from_config(config.modelCondStage)
_, _ = modelCS.load_state_dict(sd, strict=False)
modelCS.eval()
modelCS.cond_stage_model.device = opt.device

modelFS = instantiate_from_config(config.modelFirstStage)
_, _ = modelFS.load_state_dict(sd, strict=False)
modelFS.eval()
del sd
if opt.device != "cpu" and opt.precision == "autocast":
    model.half()
    modelCS.half()
    modelFS.half()
    init_image = init_image.half()

batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
    assert opt.prompt is not None
    prompt = opt.prompt
    data = [batch_size * [prompt]]

else:
    print(f"reading prompts from {opt.from_file}")
    with open(opt.from_file, "r") as f:
        data = f.read().splitlines()
        data = batch_size * list(data)
        data = list(chunk(sorted(data), batch_size))

modelFS.to(opt.device)

init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
init_latent = modelFS.get_first_stage_encoding(
    modelFS.encode_first_stage(init_image)
)  # move to latent space

if opt.device != "cpu":
    mem = torch.cuda.memory_allocated(device=opt.device) / 1e6
    modelFS.to("cpu")
    while torch.cuda.memory_allocated(device=opt.device) / 1e6 >= mem:
        time.sleep(1)


assert 0.0 <= opt.strength <= 1.0, "can only work with strength in [0.0, 1.0]"
t_enc = int(opt.strength * opt.ddim_steps)
print(f"target t_enc is {t_enc} steps")


if opt.precision == "autocast" and opt.device != "cpu":
    precision_scope = autocast
else:
    precision_scope = nullcontext

seeds = ""
with torch.no_grad():
    all_samples = list()
    for n in trange(opt.n_iter, desc="Sampling"):
        for prompts in tqdm(data, desc="data"):
            sample_path = os.path.join(outpath, "_".join(re.split(":| ", prompts[0])))[
                :150
            ]
            os.makedirs(sample_path, exist_ok=True)
            base_count = len(os.listdir(sample_path))

            with precision_scope("cuda"):
                modelCS.to(opt.device)
                uc = None
                if opt.scale != 1.0:
                    uc = modelCS.get_learned_conditioning(batch_size * [""])
                if isinstance(prompts, tuple):
                    prompts = list(prompts)

                subprompts, weights = split_weighted_subprompts(prompts[0])
                if len(subprompts) > 1:
                    c = torch.zeros_like(uc)
                    totalWeight = sum(weights)
                    # normalize each "sub prompt" and add it
                    for i in range(len(subprompts)):
                        weight = weights[i]
                        # if not skip_normalize:
                        weight = weight / totalWeight
                        c = torch.add(
                            c,
                            modelCS.get_learned_conditioning(subprompts[i]),
                            alpha=weight,
                        )
                else:
                    c = modelCS.get_learned_conditioning(prompts)

                if opt.device != "cpu":
                    mem = torch.cuda.memory_allocated(device=opt.device) / 1e6
                    modelCS.to("cpu")
                    while torch.cuda.memory_allocated(device=opt.device) / 1e6 >= mem:
                        time.sleep(1)

                # encode (scaled latent)
                z_enc = model.stochastic_encode(
                    init_latent,
                    torch.tensor([t_enc] * batch_size).to(opt.device),
                    opt.seed,
                    opt.ddim_eta,
                    opt.ddim_steps,
                )
                # decode it
                samples_ddim = model.sample(
                    t_enc,
                    c,
                    z_enc,
                    unconditional_guidance_scale=opt.scale,
                    unconditional_conditioning=uc,
                    sampler=opt.sampler,
                )

                modelFS.to(opt.device)
                print("saving images")
                for i in range(batch_size):
                    x_samples_ddim = modelFS.decode_first_stage(
                        samples_ddim[i].unsqueeze(0)
                    )
                    x_sample = torch.clamp(
                        (x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0
                    )
                    x_sample = 255.0 * rearrange(
                        x_sample[0].cpu().numpy(), "c h w -> h w c"
                    )
                    Image.fromarray(x_sample.astype(np.uint8)).save(
                        os.path.join(
                            sample_path,
                            "seed_"
                            + str(opt.seed)
                            + "_"
                            + f"{base_count:05}.{opt.format}",
                        )
                    )
                    seeds += str(opt.seed) + ","
                    opt.seed += 1
                    base_count += 1

                if opt.device != "cpu":
                    mem = torch.cuda.memory_allocated(device=opt.device) / 1e6
                    modelFS.to("cpu")
                    while torch.cuda.memory_allocated(device=opt.device) / 1e6 >= mem:
                        time.sleep(1)

                del samples_ddim
                print(
                    "memory_final = ",
                    torch.cuda.memory_allocated(device=opt.device) / 1e6,
                )

toc = time.time()

time_taken = (toc - tic) / 60.0

print(
    (
        "Samples finished in {0:.2f} minutes and exported to "
        + sample_path
        + "\n Seeds used = "
        + seeds[:-1]
    ).format(time_taken)
)


================================================
FILE: optimizedSD/optimized_txt2img.py
================================================
import argparse, os, re
import torch
import numpy as np
from random import randint
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
import time
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import nullcontext
from ldm.util import instantiate_from_config
from optimUtils import split_weighted_subprompts, logger
from transformers import logging

# from samplers import CompVisDenoiser
logging.set_verbosity_error()


def chunk(it, size):
    it = iter(it)
    return iter(lambda: tuple(islice(it, size)), ())


def load_model_from_config(ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    return sd


config = "optimizedSD/v1-inference.yaml"
DEFAULT_CKPT = "models/ldm/stable-diffusion-v1/model.ckpt"

parser = argparse.ArgumentParser()

parser.add_argument(
    "--prompt",
    type=str,
    nargs="?",
    default="a painting of a virus monster playing guitar",
    help="the prompt to render",
)
parser.add_argument(
    "--outdir",
    type=str,
    nargs="?",
    help="dir to write results to",
    default="outputs/txt2img-samples",
)
parser.add_argument(
    "--skip_grid",
    action="store_true",
    help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
    "--skip_save",
    action="store_true",
    help="do not save individual samples. For speed measurements.",
)
parser.add_argument(
    "--ddim_steps",
    type=int,
    default=50,
    help="number of ddim sampling steps",
)

parser.add_argument(
    "--fixed_code",
    action="store_true",
    help="if enabled, uses the same starting code across samples ",
)
parser.add_argument(
    "--ddim_eta",
    type=float,
    default=0.0,
    help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
    "--n_iter",
    type=int,
    default=1,
    help="sample this often",
)
parser.add_argument(
    "--H",
    type=int,
    default=512,
    help="image height, in pixel space",
)
parser.add_argument(
    "--W",
    type=int,
    default=512,
    help="image width, in pixel space",
)
parser.add_argument(
    "--C",
    type=int,
    default=4,
    help="latent channels",
)
parser.add_argument(
    "--f",
    type=int,
    default=8,
    help="downsampling factor",
)
parser.add_argument(
    "--n_samples",
    type=int,
    default=5,
    help="how many samples to produce for each given prompt. A.k.a. batch size",
)
parser.add_argument(
    "--n_rows",
    type=int,
    default=0,
    help="rows in the grid (default: n_samples)",
)
parser.add_argument(
    "--scale",
    type=float,
    default=7.5,
    help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
    "--device",
    type=str,
    default="cuda",
    help="specify GPU (cuda/cuda:0/cuda:1/...)",
)
parser.add_argument(
    "--from-file",
    type=str,
    help="if specified, load prompts from this file",
)
parser.add_argument(
    "--seed",
    type=int,
    default=None,
    help="the seed (for reproducible sampling)",
)
parser.add_argument(
    "--unet_bs",
    type=int,
    default=1,
    help="Slightly reduces inference time at the expense of high VRAM (value > 1 not recommended )",
)
parser.add_argument(
    "--turbo",
    action="store_true",
    help="Reduces inference time on the expense of 1GB VRAM",
)
parser.add_argument(
    "--precision",
    type=str,
    help="evaluate at this precision",
    choices=["full", "autocast"],
    default="autocast",
)
parser.add_argument(
    "--format",
    type=str,
    help="output image format",
    choices=["jpg", "png"],
    default="png",
)
parser.add_argument(
    "--sampler",
    type=str,
    help="sampler",
    choices=["ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"],
    default="plms",
)
parser.add_argument(
    "--ckpt",
    type=str,
    help="path to checkpoint of model",
    default=DEFAULT_CKPT,
)
opt = parser.parse_args()

tic = time.time()
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
grid_count = len(os.listdir(outpath)) - 1

if opt.seed is None:
    opt.seed = randint(0, 1000000)
seed_everything(opt.seed)

# Logging
logger(vars(opt), log_csv="logs/txt2img_logs.csv")

sd = load_model_from_config(f"{opt.ckpt}")
li, lo = [], []
for key, value in sd.items():
    sp = key.split(".")
    if (sp[0]) == "model":
        if "input_blocks" in sp:
            li.append(key)
        elif "middle_block" in sp:
            li.append(key)
        elif "time_embed" in sp:
            li.append(key)
        else:
            lo.append(key)
for key in li:
    sd["model1." + key[6:]] = sd.pop(key)
for key in lo:
    sd["model2." + key[6:]] = sd.pop(key)

config = OmegaConf.load(f"{config}")

model = instantiate_from_config(config.modelUNet)
_, _ = model.load_state_dict(sd, strict=False)
model.eval()
model.unet_bs = opt.unet_bs
model.cdevice = opt.device
model.turbo = opt.turbo

modelCS = instantiate_from_config(config.modelCondStage)
_, _ = modelCS.load_state_dict(sd, strict=False)
modelCS.eval()
modelCS.cond_stage_model.device = opt.device

modelFS = instantiate_from_config(config.modelFirstStage)
_, _ = modelFS.load_state_dict(sd, strict=False)
modelFS.eval()
del sd

if opt.device != "cpu" and opt.precision == "autocast":
    model.half()
    modelCS.half()

start_code = None
if opt.fixed_code:
    start_code = torch.randn(
        [opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=opt.device
    )


batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
    assert opt.prompt is not None
    prompt = opt.prompt
    print(f"Using prompt: {prompt}")
    data = [batch_size * [prompt]]

else:
    print(f"reading prompts from {opt.from_file}")
    with open(opt.from_file, "r") as f:
        text = f.read()
        print(f"Using prompt: {text.strip()}")
        data = text.splitlines()
        data = batch_size * list(data)
        data = list(chunk(sorted(data), batch_size))


if opt.precision == "autocast" and opt.device != "cpu":
    precision_scope = autocast
else:
    precision_scope = nullcontext

seeds = ""
with torch.no_grad():
    all_samples = list()
    for n in trange(opt.n_iter, desc="Sampling"):
        for prompts in tqdm(data, desc="data"):
            sample_path = os.path.join(outpath, "_".join(re.split(":| ", prompts[0])))[
                :150
            ]
            os.makedirs(sample_path, exist_ok=True)
            base_count = len(os.listdir(sample_path))

            with precision_scope("cuda"):
                modelCS.to(opt.device)
                uc = None
                if opt.scale != 1.0:
                    uc = modelCS.get_learned_conditioning(batch_size * [""])
                if isinstance(prompts, tuple):
                    prompts = list(prompts)

                subprompts, weights = split_weighted_subprompts(prompts[0])
                if len(subprompts) > 1:
                    c = torch.zeros_like(uc)
                    totalWeight = sum(weights)
                    # normalize each "sub prompt" and add it
                    for i in range(len(subprompts)):
                        weight = weights[i]
                        # if not skip_normalize:
                        weight = weight / totalWeight
                        c = torch.add(
                            c,
                            modelCS.get_learned_conditioning(subprompts[i]),
                            alpha=weight,
                        )
                else:
                    c = modelCS.get_learned_conditioning(prompts)

                shape = [opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f]

                if opt.device != "cpu":
                    mem = torch.cuda.memory_allocated() / 1e6
                    modelCS.to("cpu")
                    while torch.cuda.memory_allocated() / 1e6 >= mem:
                        time.sleep(1)

                samples_ddim = model.sample(
                    S=opt.ddim_steps,
                    conditioning=c,
                    seed=opt.seed,
                    shape=shape,
                    verbose=False,
                    unconditional_guidance_scale=opt.scale,
                    unconditional_conditioning=uc,
                    eta=opt.ddim_eta,
                    x_T=start_code,
                    sampler=opt.sampler,
                )

                modelFS.to(opt.device)

                print(samples_ddim.shape)
                print("saving images")
                for i in range(batch_size):
                    x_samples_ddim = modelFS.decode_first_stage(
                        samples_ddim[i].unsqueeze(0)
                    )
                    x_sample = torch.clamp(
                        (x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0
                    )
                    x_sample = 255.0 * rearrange(
                        x_sample[0].cpu().numpy(), "c h w -> h w c"
                    )
                    Image.fromarray(x_sample.astype(np.uint8)).save(
                        os.path.join(
                            sample_path,
                            "seed_"
                            + str(opt.seed)
                            + "_"
                            + f"{base_count:05}.{opt.format}",
                        )
                    )
                    seeds += str(opt.seed) + ","
                    opt.seed += 1
                    base_count += 1

                if opt.device != "cpu":
                    mem = torch.cuda.memory_allocated() / 1e6
                    modelFS.to("cpu")
                    while torch.cuda.memory_allocated() / 1e6 >= mem:
                        time.sleep(1)
                del samples_ddim
                print("memory_final = ", torch.cuda.memory_allocated() / 1e6)

toc = time.time()

time_taken = (toc - tic) / 60.0

print(
    (
        "Samples finished in {0:.2f} minutes and exported to "
        + sample_path
        + "\n Seeds used = "
        + seeds[:-1]
    ).format(time_taken)
)


================================================
FILE: optimizedSD/samplers.py
================================================
from scipy import integrate
import torch
from tqdm.auto import trange
import torch.nn as nn


def append_zero(x):
    return torch.cat([x, x.new_zeros([1])])


def append_dims(x, target_dims):
    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
    dims_to_append = target_dims - x.ndim
    if dims_to_append < 0:
        raise ValueError(
            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
        )
    return x[(...,) + (None,) * dims_to_append]


def get_ancestral_step(sigma_from, sigma_to):
    """Calculates the noise level (sigma_down) to step down to and the amount
    of noise to add (sigma_up) when doing an ancestral sampling step."""
    sigma_up = (
        sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2
    ) ** 0.5
    sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
    return sigma_down, sigma_up


class DiscreteSchedule(nn.Module):
    """A mapping between continuous noise levels (sigmas) and a list of discrete noise
    levels."""

    def __init__(self, sigmas, quantize):
        super().__init__()
        self.register_buffer("sigmas", sigmas)
        self.quantize = quantize

    def get_sigmas(self, n=None):
        if n is None:
            return append_zero(self.sigmas.flip(0))
        t_max = len(self.sigmas) - 1
        t = torch.linspace(t_max, 0, n, device=self.sigmas.device)
        return append_zero(self.t_to_sigma(t))

    def sigma_to_t(self, sigma, quantize=None):
        quantize = self.quantize if quantize is None else quantize
        dists = torch.abs(sigma - self.sigmas[:, None])
        if quantize:
            return torch.argmin(dists, dim=0).view(sigma.shape)
        low_idx, high_idx = torch.sort(
            torch.topk(dists, dim=0, k=2, largest=False).indices, dim=0
        )[0]
        low, high = self.sigmas[low_idx], self.sigmas[high_idx]
        w = (low - sigma) / (low - high)
        w = w.clamp(0, 1)
        t = (1 - w) * low_idx + w * high_idx
        return t.view(sigma.shape)

    def t_to_sigma(self, t):
        t = t.float()
        low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
        # print(low_idx, high_idx, w )
        return (1 - w) * self.sigmas[low_idx] + w * self.sigmas[high_idx]


class DiscreteEpsDDPMDenoiser(DiscreteSchedule):
    """A wrapper for discrete schedule DDPM models that output eps (the predicted
    noise)."""

    def __init__(self, alphas_cumprod, quantize):
        super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize)
        self.sigma_data = 1.0

    def get_scalings(self, sigma):
        c_out = -sigma
        c_in = 1 / (sigma**2 + self.sigma_data**2) ** 0.5
        return c_out, c_in

    def get_eps(self, *args, **kwargs):
        return self.inner_model(*args, **kwargs)

    def forward(self, input, sigma, **kwargs):
        c_out, c_in = [append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
        eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
        return input + eps * c_out


class CompVisDenoiser(DiscreteEpsDDPMDenoiser):
    """A wrapper for CompVis diffusion models."""

    def __init__(self, alphas_cumprod, quantize=False, device="cpu"):
        super().__init__(alphas_cumprod, quantize=quantize)

    def get_eps(self, *args, **kwargs):
        return self.inner_model.apply_model(*args, **kwargs)


def to_d(x, sigma, denoised):
    """Converts a denoiser output to a Karras ODE derivative."""
    return (x - denoised) / append_dims(sigma, x.ndim)


def get_ancestral_step(sigma_from, sigma_to):
    """Calculates the noise level (sigma_down) to step down to and the amount
    of noise to add (sigma_up) when doing an ancestral sampling step."""
    sigma_up = (
        sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2
    ) ** 0.5
    sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
    return sigma_down, sigma_up


@torch.no_grad()
def sample_euler(
    model,
    x,
    sigmas,
    extra_args=None,
    callback=None,
    disable=None,
    s_churn=0.0,
    s_tmin=0.0,
    s_tmax=float("inf"),
    s_noise=1.0,
):
    """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    for i in trange(len(sigmas) - 1, disable=disable):
        gamma = (
            min(s_churn / (len(sigmas) - 1), 2**0.5 - 1)
            if s_tmin <= sigmas[i] <= s_tmax
            else 0.0
        )
        eps = torch.randn_like(x) * s_noise
        sigma_hat = sigmas[i] * (gamma + 1)
        if gamma > 0:
            x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
        denoised = model(x, sigma_hat * s_in, **extra_args)
        d = to_d(x, sigma_hat, denoised)
        if callback is not None:
            callback(
                {
                    "x": x,
                    "i": i,
                    "sigma": sigmas[i],
                    "sigma_hat": sigma_hat,
                    "denoised": denoised,
                }
            )
        dt = sigmas[i + 1] - sigma_hat
        # Euler method
        x = x + d * dt
    return x


@torch.no_grad()
def sample_euler_ancestral(
    model, x, sigmas, extra_args=None, callback=None, disable=None
):
    """Ancestral sampling with Euler method steps."""
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    for i in trange(len(sigmas) - 1, disable=disable):
        denoised = model(x, sigmas[i] * s_in, **extra_args)
        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
        if callback is not None:
            callback(
                {
                    "x": x,
                    "i": i,
                    "sigma": sigmas[i],
                    "sigma_hat": sigmas[i],
                    "denoised": denoised,
                }
            )
        d = to_d(x, sigmas[i], denoised)
        # Euler method
        dt = sigma_down - sigmas[i]
        x = x + d * dt
        x = x + torch.randn_like(x) * sigma_up
    return x


@torch.no_grad()
def sample_heun(
    model,
    x,
    sigmas,
    extra_args=None,
    callback=None,
    disable=None,
    s_churn=0.0,
    s_tmin=0.0,
    s_tmax=float("inf"),
    s_noise=1.0,
):
    """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    for i in trange(len(sigmas) - 1, disable=disable):
        gamma = (
            min(s_churn / (len(sigmas) - 1), 2**0.5 - 1)
            if s_tmin <= sigmas[i] <= s_tmax
            else 0.0
        )
        eps = torch.randn_like(x) * s_noise
        sigma_hat = sigmas[i] * (gamma + 1)
        if gamma > 0:
            x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
        denoised = model(x, sigma_hat * s_in, **extra_args)
        d = to_d(x, sigma_hat, denoised)
        if callback is not None:
            callback(
                {
                    "x": x,
                    "i": i,
                    "sigma": sigmas[i],
                    "sigma_hat": sigma_hat,
                    "denoised": denoised,
                }
            )
        dt = sigmas[i + 1] - sigma_hat
        if sigmas[i + 1] == 0:
            # Euler method
            x = x + d * dt
        else:
            # Heun's method
            x_2 = x + d * dt
            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
            d_prime = (d + d_2) / 2
            x = x + d_prime * dt
    return x


@torch.no_grad()
def sample_dpm_2(
    model,
    x,
    sigmas,
    extra_args=None,
    callback=None,
    disable=None,
    s_churn=0.0,
    s_tmin=0.0,
    s_tmax=float("inf"),
    s_noise=1.0,
):
    """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    for i in trange(len(sigmas) - 1, disable=disable):
        gamma = (
            min(s_churn / (len(sigmas) - 1), 2**0.5 - 1)
            if s_tmin <= sigmas[i] <= s_tmax
            else 0.0
        )
        eps = torch.randn_like(x) * s_noise
        sigma_hat = sigmas[i] * (gamma + 1)
        if gamma > 0:
            x = x + eps * (sigma_hat**2 - sigmas[i] ** 2) ** 0.5
        denoised = model(x, sigma_hat * s_in, **extra_args)
        d = to_d(x, sigma_hat, denoised)
        if callback is not None:
            callback(
                {
                    "x": x,
                    "i": i,
                    "sigma": sigmas[i],
                    "sigma_hat": sigma_hat,
                    "denoised": denoised,
                }
            )
        # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
        sigma_mid = ((sigma_hat ** (1 / 3) + sigmas[i + 1] ** (1 / 3)) / 2) ** 3
        dt_1 = sigma_mid - sigma_hat
        dt_2 = sigmas[i + 1] - sigma_hat
        x_2 = x + d * dt_1
        denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
        d_2 = to_d(x_2, sigma_mid, denoised_2)
        x = x + d_2 * dt_2
    return x


@torch.no_grad()
def sample_dpm_2_ancestral(
    model, x, sigmas, extra_args=None, callback=None, disable=None
):
    """Ancestral sampling with DPM-Solver inspired second-order steps."""
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    for i in trange(len(sigmas) - 1, disable=disable):
        denoised = model(x, sigmas[i] * s_in, **extra_args)
        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
        if callback is not None:
            callback(
                {
                    "x": x,
                    "i": i,
                    "sigma": sigmas[i],
                    "sigma_hat": sigmas[i],
                    "denoised": denoised,
                }
            )
        d = to_d(x, sigmas[i], denoised)
        # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
        sigma_mid = ((sigmas[i] ** (1 / 3) + sigma_down ** (1 / 3)) / 2) ** 3
        dt_1 = sigma_mid - sigmas[i]
        dt_2 = sigma_down - sigmas[i]
        x_2 = x + d * dt_1
        denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
        d_2 = to_d(x_2, sigma_mid, denoised_2)
        x = x + d_2 * dt_2
        x = x + torch.randn_like(x) * sigma_up
    return x


def linear_multistep_coeff(order, t, i, j):
    if order - 1 > i:
        raise ValueError(f"Order {order} too high for step {i}")

    def fn(tau):
        prod = 1.0
        for k in range(order):
            if j == k:
                continue
            prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
        return prod

    return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]


@torch.no_grad()
def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4):
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    ds = []
    for i in trange(len(sigmas) - 1, disable=disable):
        denoised = model(x, sigmas[i] * s_in, **extra_args)
        d = to_d(x, sigmas[i], denoised)
        ds.append(d)
        if len(ds) > order:
            ds.pop(0)
        if callback is not None:
            callback(
                {
                    "x": x,
                    "i": i,
                    "sigma": sigmas[i],
                    "sigma_hat": sigmas[i],
                    "denoised": denoised,
                }
            )
        cur_order = min(i + 1, order)
        coeffs = [
            linear_multistep_coeff(cur_order, sigmas.cpu(), i, j)
            for j in range(cur_order)
        ]
        x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
    return x


================================================
FILE: optimizedSD/splitAttention.py
================================================
from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange

from ldm.modules.diffusionmodules.util import checkpoint


def exists(val):
    return val is not None


def uniq(arr):
    return {el: True for el in arr}.keys()


def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d


def max_neg_value(t):
    return -torch.finfo(t.dtype).max


def init_(tensor):
    dim = tensor.shape[-1]
    std = 1 / math.sqrt(dim)
    tensor.uniform_(-std, std)
    return tensor


# feedforward
class GEGLU(nn.Module):
    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

    def forward(self, x):
        x, gate = self.proj(x).chunk(2, dim=-1)
        return x * F.gelu(gate)


class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = default(dim_out, dim)
        project_in = (
            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
            if not glu
            else GEGLU(dim, inner_dim)
        )

        self.net = nn.Sequential(
            project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
        )

    def forward(self, x):
        return self.net(x)


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


def Normalize(in_channels):
    return torch.nn.GroupNorm(
        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
    )


class LinearAttention(nn.Module):
    def __init__(self, dim, heads=4, dim_head=32):
        super().__init__()
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
        self.to_out = nn.Conv2d(hidden_dim, dim, 1)

    def forward(self, x):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x)
        q, k, v = rearrange(
            qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
        )
        k = k.softmax(dim=-1)
        context = torch.einsum("bhdn,bhen->bhde", k, v)
        out = torch.einsum("bhde,bhdn->bhen", context, q)
        out = rearrange(
            out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
        )
        return self.to_out(out)


class SpatialSelfAttention(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.k = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.v = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.proj_out = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q.shape
        q = rearrange(q, "b c h w -> b (h w) c")
        k = rearrange(k, "b c h w -> b c (h w)")
        w_ = torch.einsum("bij,bjk->bik", q, k)

        w_ = w_ * (int(c) ** (-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = rearrange(v, "b c h w -> b c (h w)")
        w_ = rearrange(w_, "b i j -> b j i")
        h_ = torch.einsum("bij,bjk->bik", v, w_)
        h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
        h_ = self.proj_out(h_)

        return x + h_


class CrossAttention(nn.Module):
    def __init__(
        self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, att_step=1
    ):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.scale = dim_head**-0.5
        self.heads = heads
        self.att_step = att_step

        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
        )

    def forward(self, x, context=None, mask=None):
        h = self.heads

        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
        v = self.to_v(context)
        del context, x

        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))

        limit = k.shape[0]
        att_step = self.att_step
        q_chunks = list(torch.tensor_split(q, limit // att_step, dim=0))
        k_chunks = list(torch.tensor_split(k, limit // att_step, dim=0))
        v_chunks = list(torch.tensor_split(v, limit // att_step, dim=0))

        q_chunks.reverse()
        k_chunks.reverse()
        v_chunks.reverse()
        sim = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
        del k, q, v
        for i in range(0, limit, att_step):
            q_buffer = q_chunks.pop()
            k_buffer = k_chunks.pop()
            v_buffer = v_chunks.pop()
            sim_buffer = (
                einsum("b i d, b j d -> b i j", q_buffer, k_buffer) * self.scale
            )

            del k_buffer, q_buffer
            # attention, what we cannot get enough of, by chunks

            sim_buffer = sim_buffer.softmax(dim=-1)

            sim_buffer = einsum("b i j, b j d -> b i d", sim_buffer, v_buffer)
            del v_buffer
            sim[i : i + att_step, :, :] = sim_buffer

            del sim_buffer
        sim = rearrange(sim, "(b h) n d -> b n (h d)", h=h)
        return self.to_out(sim)


class BasicTransformerBlock(nn.Module):
    def __init__(
        self,
        dim,
        n_heads,
        d_head,
        dropout=0.0,
        context_dim=None,
        gated_ff=True,
        checkpoint=True,
    ):
        super().__init__()
        self.attn1 = CrossAttention(
            query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
        )  # is a self-attention
        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.attn2 = CrossAttention(
            query_dim=dim,
            context_dim=context_dim,
            heads=n_heads,
            dim_head=d_head,
            dropout=dropout,
        )  # is self-attn if context is none
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
        self.norm3 = nn.LayerNorm(dim)
        self.checkpoint = checkpoint

    def forward(self, x, context=None):
        return checkpoint(
            self._forward, (x, context), self.parameters(), self.checkpoint
        )

    def _forward(self, x, context=None):
        x = self.attn1(self.norm1(x)) + x
        x = self.attn2(self.norm2(x), context=context) + x
        x = self.ff(self.norm3(x)) + x
        return x


class SpatialTransformer(nn.Module):
    """
    Transformer block for image-like data.
    First, project the input (aka embedding)
    and reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    """

    def __init__(
        self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None
    ):
        super().__init__()
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = Normalize(in_channels)

        self.proj_in = nn.Conv2d(
            in_channels, inner_dim, kernel_size=1, stride=1, padding=0
        )

        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim
                )
                for d in range(depth)
            ]
        )

        self.proj_out = zero_module(
            nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
        )

    def forward(self, x, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        b, c, h, w = x.shape
        x_in = x
        x = self.norm(x)
        x = self.proj_in(x)
        x = rearrange(x, "b c h w -> b (h w) c")
        for block in self.transformer_blocks:
            x = block(x, context=context)
        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
        x = self.proj_out(x)
        return x + x_in


================================================
FILE: optimizedSD/v1-inference.yaml
================================================
modelUNet:
  base_learning_rate: 1.0e-04
  target: optimizedSD.ddpm.UNet
  params:
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 64
    channels: 4
    cond_stage_trainable: false # Note: different from the one we trained before
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    use_ema: False

    unetConfigEncode:
      target: optimizedSD.openaimodelSplit.UNetModelEncode
      params:
        image_size: 32 # unused
        in_channels: 4
        out_channels: 4
        model_channels: 320
        attention_resolutions: [4, 2, 1]
        num_res_blocks: 2
        channel_mult: [1, 2, 4, 4]
        num_heads: 8
        use_spatial_transformer: True
        transformer_depth: 1
        context_dim: 768
        use_checkpoint: True
        legacy: False

    unetConfigDecode:
      target: optimizedSD.openaimodelSplit.UNetModelDecode
      params:
        image_size: 32 # unused
        in_channels: 4
        out_channels: 4
        model_channels: 320
        attention_resolutions: [4, 2, 1]
        num_res_blocks: 2
        channel_mult: [1, 2, 4, 4]
        num_heads: 8
        use_spatial_transformer: True
        transformer_depth: 1
        context_dim: 768
        use_checkpoint: True
        legacy: False

modelFirstStage:
  target: optimizedSD.ddpm.FirstStage
  params:
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 64
    channels: 4
    cond_stage_trainable: false # Note: different from the one we trained before
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    use_ema: False
    first_stage_config:
      target: ldm.models.autoencoder.AutoencoderKL
      params:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
          double_z: true
          z_channels: 4
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult:
            - 1
            - 2
            - 4
            - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity

modelCondStage:
  target: optimizedSD.ddpm.CondStage
  params:
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: "jpg"
    cond_stage_key: "txt"
    image_size: 64
    channels: 4
    cond_stage_trainable: false # Note: different from the one we trained before
    conditioning_key: crossattn
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
    use_ema: False
    cond_stage_config:
      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
      params:
        device: cpu


================================================
FILE: package.json
================================================
{
  "name": "docs",
  "version": "0.0.0",
  "private": true,
  "scripts": {
    "docusaurus": "docusaurus",
    "start": "docusaurus start",
    "build": "docusaurus build",
    "swizzle": "docusaurus swizzle",
    "deploy": "docusaurus deploy",
    "clear": "docusaurus clear",
    "serve": "docusaurus serve",
    "write-translations": "docusaurus write-translations",
    "write-heading-ids": "docusaurus write-heading-ids"
  },
  "dependencies": {
    "@cmfcmf/docusaurus-search-local": "^0.11.0",
    "@docusaurus/core": "2.2.0",
    "@docusaurus/preset-classic": "2.2.0",
    "@mdx-js/react": "^1.6.22",
    "clsx": "^1.2.1",
    "create-docusaurus": "^2.2.0",
    "prism-react-renderer": "^1.3.5",
    "react": "^17.0.2",
    "react-dom": "^17.0.2"
  },
  "devDependencies": {
    "@docusaurus/module-type-aliases": "2.2.0"
  },
  "browserslist": {
    "production": [
      ">0.5%",
      "not dead",
      "not op_mini all"
    ],
    "development": [
      "last 1 chrome version",
      "last 1 firefox version",
      "last 1 safari version"
    ]
  },
  "engines": {
    "node": ">=16.14"
  }
}


================================================
FILE: requirements.txt
================================================
-e .

# See: https://github.com/CompVis/taming-transformers/issues/176
# -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers # required by ldm
# Note: taming package needs to be installed with -e option
-e git+https://github.com/CompVis/taming-transformers#egg=taming-transformers
invisible-watermark==0.1.5
taming-transformers-rom1504==0.0.6  # required by ldm

# Note: K-diffusion brings in CLIP 1.0 as a dependency automatically; will create a dependency resolution conflict when explicitly specified together
git+https://github.com/openai/CLIP.git@main#egg=clip

git+https://github.com/crowsonkb/k-diffusion.git
# git+https://github.com/hlky/k-diffusion-sd#egg=k_diffusion

# Dependencies required for Stable Diffusion UI
pynvml==11.4.1
omegaconf==2.2.3

# Note: Jinja2 3.x major version required due to breaking changes found in markupsafe==2.1.1; 2.0.1 is incompatible with other upstream dependencies
# see https://github.com/pallets/markupsafe/issues/304
Jinja2==3.1.2  # Jinja2 is required by Gradio

# Environment Dependencies for WebUI (gradio)
gradio==3.4.1

# Environment Dependencies for WebUI (streamlit)
streamlit==1.14.0
streamlit-on-Hover-tabs==1.0.1
streamlit-option-menu==0.3.2
streamlit_nested_layout==0.1.1
streamlit-server-state==0.15.0
streamlit-tensorboard==0.0.2
streamlit-elements==0.1.* # used for the draggable dashboard and new UI design (WIP)
streamlit-ace==0.1.1 # used to replace the text area on the prompt and also for the code editor tool.
#streamlit-base-extras # used for logging, thread spawning, thread locking and page routing. For now we are using a modified local version which we have to change to use the proper version from pypi.
hydralit==1.0.14
hydralit_components==1.0.10
stqdm==0.0.4
uvicorn
fastapi
jsonmerge==1.8.
matplotlib==3.6.
resize-right==0.0.2
torchdiffeq==0.2.3
barfi==0.7.0
altair<5

# Environment Dependencies for WebUI (flet)

# txt2vid
diffusers==0.7.2
librosa==0.9.2

# img2img inpainting
streamlit-drawable-canvas==0.9.2

# Img2text
ftfy==6.1.1
fairscale==0.4.4
regex
timm==0.6.7
tqdm==4.64.0
tensorboard==2.10.1

# Other
retry==0.9.2  # used by sd_utils
python-slugify==6.1.2  # used by sd_utils
piexif==1.1.3  # used by sd_utils
pywebview==3.6.3 # used by streamlit_webview.py
shutup==0.2.0 # remove all the annoying warnings

accelerate==0.12.0
albumentations==0.4.3
einops==0.3.1
facexlib>=0.2.3
imageio-ffmpeg==0.4.2
imageio==2.9.0
kornia==0.6
loguru
opencv-python-headless==4.6.0.66
open-clip-torch==2.0.2
pandas==1.4.3
pudb==2019.2
pytorch-lightning==1.7.7
realesrgan==0.3.0
test-tube>=0.7.5
timm==0.6.7
torch-fidelity==0.3.0
transformers==4.19.2 # do not change
torchmetrics==0.11.4
wget

# Optional packages commonly used with Stable Diffusion workflow

# Upscalers
basicsr==1.4.2  # required by RealESRGAN
gfpgan==1.3.8  # GFPGAN
realesrgan==0.3.0  # RealESRGAN brings in GFPGAN as a requirement
-e git+https://github.com/devilismyfriend/latent-diffusion#egg=latent-diffusion

## for monocular depth estimation
tensorflow==2.10.0

# Unused Packages: No current usage but will be used in the future.


# Orphaned Packages:  No usage found


================================================
FILE: runpod_entrypoint.sh
================================================
#!/bin/bash
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
# Starts the webserver inside the docker container
#

# set -x

SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
cd $SCRIPT_DIR
export PYTHONPATH=$SCRIPT_DIR

if [[ $PUBLIC_KEY ]]
then
    mkdir -p ~/.ssh
    chmod 700 ~/.ssh
    cd ~/.ssh
    echo $PUBLIC_KEY >> authorized_keys
    chmod 700 -R ~/.ssh
    cd /
    service ssh start
    echo "SSH Service Started"
fi

cd $SCRIPT_DIR
launch_command="streamlit run ${SCRIPT_DIR}/scripts/webui_streamlit.py"

$launch_command

sleep infinity


================================================
FILE: scripts/APIServer.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
# from sd_utils import *
from sd_utils import st

# streamlit imports

# streamlit components section

# other imports
# from fastapi import FastAPI
# import uvicorn

# Temp imports

# end of imports
# ---------------------------------------------------------------------------------------------------------------


def layout():
    st.info("Under Construction. :construction_worker:")


================================================
FILE: scripts/ModelManager.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import st, logger

# streamlit imports


# other imports
import os, requests
from requests.auth import HTTPBasicAuth
from requests import HTTPError
from stqdm import stqdm

# Temp imports


# end of imports
# ---------------------------------------------------------------------------------------------------------------
def download_file(file_name, file_path, file_url):
    if not os.path.exists(file_path):
        os.makedirs(file_path)

    if not os.path.exists(os.path.join(file_path, file_name)):
        print("Downloading " + file_name + "...")
        # TODO - add progress bar in streamlit
        # download file with `requests``
        if file_name == "Stable Diffusion v1.5":
            if (
                "huggingface_token" not in st.session_state
                or st.session_state["defaults"].general.huggingface_token == "None"
            ):
                if "progress_bar_text" in st.session_state:
                    st.session_state["progress_bar_text"].error(
                        "You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token."
                    )
                raise OSError(
                    "You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token."
                )

        try:
            with requests.get(
                file_url,
                auth=HTTPBasicAuth(
                    "token", st.session_state.defaults.general.huggingface_token
                )
                if "huggingface.co" in file_url
                else None,
                stream=True,
            ) as r:
                r.raise_for_status()
                with open(os.path.join(file_path, file_name), "wb") as f:
                    for chunk in stqdm(
                        r.iter_content(chunk_size=8192), backend=True, unit="kb"
                    ):
                        f.write(chunk)
        except HTTPError as e:
            if "huggingface.co" in file_url:
                if "resolve" in file_url:
                    repo_url = file_url.split("resolve")[0]

                    st.session_state["progress_bar_text"].error(
                        f"You need to accept the license for the model in order to be able to download it. "
                        f"Please visit {repo_url} and accept the lincense there, then try again to download the model."
                    )

            logger.error(e)

    else:
        print(file_name + " already exists.")


def download_model(models, model_name):
    """Download all files from model_list[model_name]"""
    for file in models[model_name]:
        download_file(file["file_name"], file["file_path"], file["file_url"])
    return


def layout():
    # search = st.text_input(label="Search", placeholder="Type the name of the model you want to search for.", help="")

    colms = st.columns((1, 3, 3, 5, 5))
    columns = ["№", "Model Name", "Save Location", "Download", "Download Link"]

    models = st.session_state["defaults"].model_manager.models

    for col, field_name in zip(colms, columns):
        # table header
        col.write(field_name)

    for x, model_name in enumerate(models):
        col1, col2, col3, col4, col5 = st.columns((1, 3, 3, 3, 6))
        col1.write(x)  # index
        col2.write(models[model_name]["model_name"])
        col3.write(models[model_name]["save_location"])
        with col4:
            files_exist = 0
            for file in models[model_name]["files"]:
                if "save_location" in models[model_name]["files"][file]:
                    os.path.exists(
                        os.path.join(
                            models[model_name]["files"][file]["save_location"],
                            models[model_name]["files"][file]["file_name"],
                        )
                    )
                    files_exist += 1
                elif os.path.exists(
                    os.path.join(
                        models[model_name]["save_location"],
                        models[model_name]["files"][file]["file_name"],
                    )
                ):
                    files_exist += 1
            files_needed = []
            for file in models[model_name]["files"]:
                if "save_location" in models[model_name]["files"][file]:
                    if not os.path.exists(
                        os.path.join(
                            models[model_name]["files"][file]["save_location"],
                            models[model_name]["files"][file]["file_name"],
                        )
                    ):
                        files_needed.append(file)
                elif not os.path.exists(
                    os.path.join(
                        models[model_name]["save_location"],
                        models[model_name]["files"][file]["file_name"],
                    )
                ):
                    files_needed.append(file)
            if len(files_needed) > 0:
                if st.button(
                    "Download",
                    key=models[model_name]["model_name"],
                    help="Download " + models[model_name]["model_name"],
                ):
                    for file in files_needed:
                        if "save_location" in models[model_name]["files"][file]:
                            download_file(
                                models[model_name]["files"][file]["file_name"],
                                models[model_name]["files"][file]["save_location"],
                                models[model_name]["files"][file]["download_link"],
                            )
                        else:
                            download_file(
                                models[model_name]["files"][file]["file_name"],
                                models[model_name]["save_location"],
                                models[model_name]["files"][file]["download_link"],
                            )
                    st.experimental_rerun()
                else:
                    st.empty()
            else:
                st.write("✅")

        #


================================================
FILE: scripts/Settings.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import st, custom_models_available, logger, human_readable_size

# streamlit imports

# streamlit components section
from streamlit_server_state import server_state

# other imports
from omegaconf import OmegaConf
import torch
import os, toml

# end of imports
# ---------------------------------------------------------------------------------------------------------------


@logger.catch(reraise=True)
def layout():
    # st.header("Settings")

    with st.form("Settings"):
        (
            general_tab,
            txt2img_tab,
            img2img_tab,
            img2txt_tab,
            txt2vid_tab,
            image_processing,
            textual_inversion_tab,
            concepts_library_tab,
        ) = st.tabs(
            [
                "General",
                "Text-To-Image",
                "Image-To-Image",
                "Image-To-Text",
                "Text-To-Video",
                "Image processing",
                "Textual Inversion",
                "Concepts Library",
            ]
        )

        with general_tab:
            col1, col2, col3, col4, col5 = st.columns(5, gap="large")

            device_list = []
            device_properties = [
                (i, torch.cuda.get_device_properties(i))
                for i in range(torch.cuda.device_count())
            ]
            for device in device_properties:
                id = device[0]
                name = device[1].name
                total_memory = device[1].total_memory

                device_list.append(
                    f"{id}: {name} ({human_readable_size(total_memory, decimal_places=0)})"
                )

            with col1:
                st.title("General")
                st.session_state["defaults"].general.gpu = int(
                    st.selectbox(
                        "GPU",
                        device_list,
                        index=st.session_state["defaults"].general.gpu,
                        help=f"Select which GPU to use. Default: {device_list[0]}",
                    ).split(":")[0]
                )

                st.session_state["defaults"].general.outdir = str(
                    st.text_input(
                        "Output directory",
                        value=st.session_state["defaults"].general.outdir,
                        help="Relative directory on which the output images after a generation will be placed. Default: 'outputs'",
                    )
                )

                # If we have custom models available on the "models/custom"
                # folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
                custom_models_available()

                if server_state["CustomModel_available"]:
                    st.session_state.defaults.general.default_model = st.selectbox(
                        "Default Model:",
                        server_state["custom_models"],
                        index=server_state["custom_models"].index(
                            st.session_state["defaults"].general.default_model
                        ),
                        help="Select the model you want to use. If you have placed custom models \
                                                                  on your 'models/custom' folder they will be shown here as well. The model name that will be shown here \
                                                                  is the same as the name the file for the model has on said folder, \
                                                                  it is recommended to give the .ckpt file a name that \
                                                                  will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4",
                    )
                else:
                    st.session_state.defaults.general.default_model = st.selectbox(
                        "Default Model:",
                        [st.session_state["defaults"].general.default_model],
                        help="Select the model you want to use. If you have placed custom models \
                                                                  on your 'models/custom' folder they will be shown here as well. \
                                                                  The model name that will be shown here is the same as the name\
                                                                  the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
                                                                  will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4",
                    )

                st.session_state[
                    "defaults"
                ].general.default_model_config = st.text_input(
                    "Default Model Config",
                    value=st.session_state["defaults"].general.default_model_config,
                    help="Default model config file for inference. Default: 'configs/stable-diffusion/v1-inference.yaml'",
                )

                st.session_state["defaults"].general.default_model_path = st.text_input(
                    "Default Model Config",
                    value=st.session_state["defaults"].general.default_model_path,
                    help="Default model path. Default: 'models/ldm/stable-diffusion-v1/model.ckpt'",
                )

                st.session_state["defaults"].general.GFPGAN_dir = st.text_input(
                    "Default GFPGAN directory",
                    value=st.session_state["defaults"].general.GFPGAN_dir,
                    help="Default GFPGAN directory. Default: './models/gfpgan'",
                )

                st.session_state["defaults"].general.RealESRGAN_dir = st.text_input(
                    "Default RealESRGAN directory",
                    value=st.session_state["defaults"].general.RealESRGAN_dir,
                    help="Default GFPGAN directory. Default: './models/realesrgan'",
                )

                RealESRGAN_model_list = [
                    "RealESRGAN_x4plus",
                    "RealESRGAN_x4plus_anime_6B",
                ]
                st.session_state["defaults"].general.RealESRGAN_model = st.selectbox(
                    "RealESRGAN model",
                    RealESRGAN_model_list,
                    index=RealESRGAN_model_list.index(
                        st.session_state["defaults"].general.RealESRGAN_model
                    ),
                    help="Default RealESRGAN model. Default: 'RealESRGAN_x4plus'",
                )
                Upscaler_list = ["RealESRGAN", "LDSR"]
                st.session_state["defaults"].general.upscaling_method = st.selectbox(
                    "Upscaler",
                    Upscaler_list,
                    index=Upscaler_list.index(
                        st.session_state["defaults"].general.upscaling_method
                    ),
                    help="Default upscaling method. Default: 'RealESRGAN'",
                )

            with col2:
                st.title("Performance")

                st.session_state["defaults"].general.gfpgan_cpu = st.checkbox(
                    "GFPGAN - CPU",
                    value=st.session_state["defaults"].general.gfpgan_cpu,
                    help="Run GFPGAN on the cpu. Default: False",
                )

                st.session_state["defaults"].general.esrgan_cpu = st.checkbox(
                    "ESRGAN - CPU",
                    value=st.session_state["defaults"].general.esrgan_cpu,
                    help="Run ESRGAN on the cpu. Default: False",
                )

                st.session_state["defaults"].general.extra_models_cpu = st.checkbox(
                    "Extra Models - CPU",
                    value=st.session_state["defaults"].general.extra_models_cpu,
                    help="Run extra models (GFGPAN/ESRGAN) on cpu. Default: False",
                )

                st.session_state["defaults"].general.extra_models_gpu = st.checkbox(
                    "Extra Models - GPU",
                    value=st.session_state["defaults"].general.extra_models_gpu,
                    help="Run extra models (GFGPAN/ESRGAN) on gpu. \
                                                                                    Check and save in order to be able to select the GPU that each model will use. Default: False",
                )
                if st.session_state["defaults"].general.extra_models_gpu:
                    st.session_state["defaults"].general.gfpgan_gpu = int(
                        st.selectbox(
                            "GFGPAN GPU",
                            device_list,
                            index=st.session_state["defaults"].general.gfpgan_gpu,
                            help=f"Select which GPU to use. Default: {device_list[st.session_state['defaults'].general.gfpgan_gpu]}",
                            key="gfpgan_gpu",
                        ).split(":")[0]
                    )

                    st.session_state["defaults"].general.esrgan_gpu = int(
                        st.selectbox(
                            "ESRGAN - GPU",
                            device_list,
                            index=st.session_state["defaults"].general.esrgan_gpu,
                            help=f"Select which GPU to use. Default: {device_list[st.session_state['defaults'].general.esrgan_gpu]}",
                            key="esrgan_gpu",
                        ).split(":")[0]
                    )

                st.session_state["defaults"].general.no_half = st.checkbox(
                    "No Half",
                    value=st.session_state["defaults"].general.no_half,
                    help="DO NOT switch the model to 16-bit floats. Default: False",
                )

                st.session_state["defaults"].general.use_cudnn = st.checkbox(
                    "Use cudnn",
                    value=st.session_state["defaults"].general.use_cudnn,
                    help="Switch the pytorch backend to use cudnn, this should help with fixing Nvidia 16xx cards getting"
                    "a black or green image. Default: False",
                )

                st.session_state["defaults"].general.use_float16 = st.checkbox(
                    "Use float16",
                    value=st.session_state["defaults"].general.use_float16,
                    help="Switch the model to 16-bit floats. Default: False",
                )

                precision_list = ["full", "autocast"]
                st.session_state["defaults"].general.precision = st.selectbox(
                    "Precision",
                    precision_list,
                    index=precision_list.index(
                        st.session_state["defaults"].general.precision
                    ),
                    help="Evaluates at this precision. Default: autocast",
                )

                st.session_state["defaults"].general.optimized = st.checkbox(
                    "Optimized Mode",
                    value=st.session_state["defaults"].general.optimized,
                    help="Loads the model onto the device piecemeal instead of all at once to reduce VRAM usage\
																		   at the cost of performance. Default: False",
                )

                st.session_state["defaults"].general.optimized_turbo = st.checkbox(
                    "Optimized Turbo Mode",
                    value=st.session_state["defaults"].general.optimized_turbo,
                    help="Alternative optimization mode that does not save as much VRAM but \
                                                                                   runs siginificantly faster. Default: False",
                )

                st.session_state["defaults"].general.optimized_config = st.text_input(
                    "Optimized Config",
                    value=st.session_state["defaults"].general.optimized_config,
                    help="Loads alternative optimized configuration for inference. \
                                                                                      Default: optimizedSD/v1-inference.yaml",
                )

                st.session_state[
                    "defaults"
                ].general.enable_attention_slicing = st.checkbox(
                    "Enable Attention Slicing",
                    value=st.session_state["defaults"].general.enable_attention_slicing,
                    help="Enable sliced attention computation. When this option is enabled, the attention module will \
                                                                                            split the input tensor in slices, to compute attention in several steps. This is useful to save some \
                                                                                            memory in exchange for a small speed decrease. Only works the txt2vid tab right now. Default: False",
                )

                st.session_state[
                    "defaults"
                ].general.enable_minimal_memory_usage = st.checkbox(
                    "Enable Minimal Memory Usage",
                    value=st.session_state[
                        "defaults"
                    ].general.enable_minimal_memory_usage,
                    help="Moves only unet to fp16 and to CUDA, while keepping lighter models on CPUs \
                                                                                               (Not properly implemented and currently not working, check this \
                                                                                               link 'https://github.com/huggingface/diffusers/pull/537' for more information on it ). Default: False",
                )

                # st.session_state["defaults"].general.update_preview = st.checkbox("Update Preview Image", value=st.session_state['defaults'].general.update_preview,
                # help="Enables the preview image to be updated and shown to the user on the UI during the generation.\
                # If checked, once you save the settings an option to specify the frequency at which the image is updated\
                # in steps will be shown, this is helpful to reduce the negative effect this option has on performance. \
                # Default: True")
                st.session_state["defaults"].general.update_preview = True
                st.session_state[
                    "defaults"
                ].general.update_preview_frequency = st.number_input(
                    "Update Preview Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].general.update_preview_frequency,
                    help="Specify the frequency at which the image is updated in steps, this is helpful to reduce the \
                                                                                                negative effect updating the preview image has on performance. Default: 10",
                )

            with col3:
                st.title("Others")
                st.session_state[
                    "defaults"
                ].general.use_sd_concepts_library = st.checkbox(
                    "Use the Concepts Library",
                    value=st.session_state["defaults"].general.use_sd_concepts_library,
                    help="Use the embeds Concepts Library, if checked, once the settings are saved an option will\
                                                                                           appear to specify the directory where the concepts are stored. Default: True)",
                )

                if st.session_state["defaults"].general.use_sd_concepts_library:
                    st.session_state[
                        "defaults"
                    ].general.sd_concepts_library_folder = st.text_input(
                        "Concepts Library Folder",
                        value=st.session_state[
                            "defaults"
                        ].general.sd_concepts_library_folder,
                        help="Relative folder on which the concepts library embeds are stored. \
                                                                                                    Default: 'models/custom/sd-concepts-library'",
                    )

                st.session_state["defaults"].general.LDSR_dir = st.text_input(
                    "LDSR Folder",
                    value=st.session_state["defaults"].general.LDSR_dir,
                    help="Folder where LDSR is located. Default: './models/ldsr'",
                )

                st.session_state["defaults"].general.save_metadata = st.checkbox(
                    "Save Metadata",
                    value=st.session_state["defaults"].general.save_metadata,
                    help="Save metadata on the output image. Default: True",
                )
                save_format_list = ["png", "jpg", "jpeg", "webp"]
                st.session_state["defaults"].general.save_format = st.selectbox(
                    "Save Format",
                    save_format_list,
                    index=save_format_list.index(
                        st.session_state["defaults"].general.save_format
                    ),
                    help="Format that will be used whens saving the output images. Default: 'png'",
                )

                st.session_state["defaults"].general.skip_grid = st.checkbox(
                    "Skip Grid",
                    value=st.session_state["defaults"].general.skip_grid,
                    help="Skip saving the grid output image. Default: False",
                )
                if not st.session_state["defaults"].general.skip_grid:
                    st.session_state["defaults"].general.grid_quality = st.number_input(
                        "Grid Quality",
                        value=st.session_state["defaults"].general.grid_quality,
                        help="Format for saving the grid output image. Default: 95",
                    )

                st.session_state["defaults"].general.skip_save = st.checkbox(
                    "Skip Save",
                    value=st.session_state["defaults"].general.skip_save,
                    help="Skip saving the output image. Default: False",
                )

                st.session_state["defaults"].general.n_rows = st.number_input(
                    "Number of Grid Rows",
                    value=st.session_state["defaults"].general.n_rows,
                    help="Number of rows the grid wil have when saving the grid output image. Default: '-1'",
                )

                st.session_state["defaults"].general.no_verify_input = st.checkbox(
                    "Do not Verify Input",
                    value=st.session_state["defaults"].general.no_verify_input,
                    help="Do not verify input to check if it's too long. Default: False",
                )

                st.session_state[
                    "defaults"
                ].general.show_percent_in_tab_title = st.checkbox(
                    "Show Percent in tab title",
                    value=st.session_state[
                        "defaults"
                    ].general.show_percent_in_tab_title,
                    help="Add the progress percent value to the page title on the tab on your browser. "
                    "This is useful in case you need to know how the generation is going while doign something else"
                    "in another tab on your browser. Default: True",
                )

                st.session_state["defaults"].general.enable_suggestions = st.checkbox(
                    "Enable Suggestions Box",
                    value=st.session_state["defaults"].general.enable_suggestions,
                    help="Adds a suggestion box under the prompt when clicked. Default: True",
                )

                st.session_state[
                    "defaults"
                ].daisi_app.running_on_daisi_io = st.checkbox(
                    "Running on Daisi.io?",
                    value=st.session_state["defaults"].daisi_app.running_on_daisi_io,
                    help="Specify if we are running on app.Daisi.io . Default: False",
                )

            with col4:
                st.title("Streamlit Config")

                default_theme_list = ["light", "dark"]
                st.session_state["defaults"].general.default_theme = st.selectbox(
                    "Default Theme",
                    default_theme_list,
                    index=default_theme_list.index(
                        st.session_state["defaults"].general.default_theme
                    ),
                    help="Defaut theme to use as base for streamlit. Default: dark",
                )
                st.session_state["streamlit_config"]["theme"][
                    "base"
                ] = st.session_state["defaults"].general.default_theme

                if not st.session_state["defaults"].admin.hide_server_setting:
                    with st.expander("Server", True):
                        st.session_state["streamlit_config"]["server"][
                            "headless"
                        ] = st.checkbox(
                            "Run Headless",
                            help="If false, will attempt to open a browser window on start.  \
                                                                                                 Default: false unless (1) we are on a Linux box where DISPLAY is unset, \
                                                                                                 or (2) we are running in the Streamlit Atom plugin.",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "port"
                        ] = st.number_input(
                            "Port",
                            value=st.session_state["streamlit_config"]["server"][
                                "port"
                            ],
                            help="The port where the server will listen for browser connections. Default: 8501",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "baseUrlPath"
                        ] = st.text_input(
                            "Base Url Path",
                            value=st.session_state["streamlit_config"]["server"][
                                "baseUrlPath"
                            ],
                            help="The base path for the URL where Streamlit should be served from. Default: '' ",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "enableCORS"
                        ] = st.checkbox(
                            "Enable CORS",
                            value=st.session_state["streamlit_config"]["server"][
                                "enableCORS"
                            ],
                            help="Enables support for Cross-Origin Request Sharing (CORS) protection, for added security. \
                                                                                                   Due to conflicts between CORS and XSRF, if `server.enableXsrfProtection` is on and `server.enableCORS` \
                                                                                                   is off at the same time, we will prioritize `server.enableXsrfProtection`. Default: true",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "enableXsrfProtection"
                        ] = st.checkbox(
                            "Enable Xsrf Protection",
                            value=st.session_state["streamlit_config"]["server"][
                                "enableXsrfProtection"
                            ],
                            help="Enables support for Cross-Site Request Forgery (XSRF) protection, \
                                                                                                             for added security. Due to conflicts between CORS and XSRF, \
                                                                                                             if `server.enableXsrfProtection` is on and `server.enableCORS` is off at \
                                                                                                             the same time, we will prioritize `server.enableXsrfProtection`. Default: true",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "maxUploadSize"
                        ] = st.number_input(
                            "Max Upload Size",
                            value=st.session_state["streamlit_config"]["server"][
                                "maxUploadSize"
                            ],
                            help="Max size, in megabytes, for files uploaded with the file_uploader. Default: 200",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "maxMessageSize"
                        ] = st.number_input(
                            "Max Message Size",
                            value=st.session_state["streamlit_config"]["server"][
                                "maxUploadSize"
                            ],
                            help="Max size, in megabytes, of messages that can be sent via the WebSocket connection. Default: 200",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "enableWebsocketCompression"
                        ] = st.checkbox(
                            "Enable Websocket Compression",
                            value=st.session_state["streamlit_config"]["server"][
                                "enableWebsocketCompression"
                            ],
                            help=" Enables support for websocket compression. Default: false",
                        )
                if not st.session_state["defaults"].admin.hide_browser_setting:
                    with st.expander("Browser", expanded=True):
                        st.session_state["streamlit_config"]["browser"][
                            "serverAddress"
                        ] = st.text_input(
                            "Server Address",
                            value=st.session_state["streamlit_config"]["browser"][
                                "serverAddress"
                            ]
                            if "serverAddress" in st.session_state["streamlit_config"]
                            else "localhost",
                            help="Internet address where users should point their browsers in order \
                                                                                                       to connect to the app. Can be IP address or DNS name and path.\
                                                                                                       This is used to: - Set the correct URL for CORS and XSRF protection purposes. \
                                                                                                       - Show the URL on the terminal - Open the browser. Default: 'localhost'",
                        )

                        st.session_state[
                            "defaults"
                        ].general.streamlit_telemetry = st.checkbox(
                            "Enable Telemetry",
                            value=st.session_state[
                                "defaults"
                            ].general.streamlit_telemetry,
                            help="Enables or Disables streamlit telemetry. Default: False",
                        )
                        st.session_state["streamlit_config"]["browser"][
                            "gatherUsageStats"
                        ] = st.session_state["defaults"].general.streamlit_telemetry

                        st.session_state["streamlit_config"]["browser"][
                            "serverPort"
                        ] = st.number_input(
                            "Server Port",
                            value=st.session_state["streamlit_config"]["browser"][
                                "serverPort"
                            ],
                            help="Port where users should point their browsers in order to connect to the app. \
                                                                                                 This is used to: - Set the correct URL for CORS and XSRF protection purposes. \
                                                                                                 - Show the URL on the terminal - Open the browser \
                                                                                                 Default: whatever value is set in server.port.",
                        )

            with col5:
                st.title("Huggingface")
                st.session_state["defaults"].general.huggingface_token = st.text_input(
                    "Huggingface Token",
                    value=st.session_state["defaults"].general.huggingface_token,
                    type="password",
                    help="Your Huggingface Token, it's used to download the model for the diffusers library which \
                                                                                       is used on the Text To Video tab. This token will be saved to your user config file\
                                                                                       and WILL NOT be share with us or anyone. You can get your access token \
                                                                                       at https://huggingface.co/settings/tokens. Default: None",
                )

                st.title("Stable Horde")
                st.session_state["defaults"].general.stable_horde_api = st.text_input(
                    "Stable Horde Api",
                    value=st.session_state["defaults"].general.stable_horde_api,
                    type="password",
                    help="First Register an account at https://stablehorde.net/register which will generate for you \
                                                                                      an API key. Store that key somewhere safe. \n \
                                                                                      If you do not want to register, you can use `0000000000` as api_key to connect anonymously.\
                                                                                      However anonymous accounts have the lowest priority when there's too many concurrent requests! \
                                                                                      To increase your priority you will need a unique API key and then to increase your Kudos \
                                                                                      read more about them at https://dbzer0.com/blog/the-kudos-based-economy-for-the-koboldai-horde/.",
                )

        with txt2img_tab:
            col1, col2, col3, col4, col5 = st.columns(5, gap="medium")

            with col1:
                st.title("Slider Parameters")

                # Width
                st.session_state["defaults"].txt2img.width.value = st.number_input(
                    "Default Image Width",
                    value=st.session_state["defaults"].txt2img.width.value,
                    help="Set the default width for the generated image. Default is: 512",
                )

                st.session_state["defaults"].txt2img.width.min_value = st.number_input(
                    "Minimum Image Width",
                    value=st.session_state["defaults"].txt2img.width.min_value,
                    help="Set the default minimum value for the width slider. Default is: 64",
                )

                st.session_state["defaults"].txt2img.width.max_value = st.number_input(
                    "Maximum Image Width",
                    value=st.session_state["defaults"].txt2img.width.max_value,
                    help="Set the default maximum value for the width slider. Default is: 2048",
                )

                # Height
                st.session_state["defaults"].txt2img.height.value = st.number_input(
                    "Default Image Height",
                    value=st.session_state["defaults"].txt2img.height.value,
                    help="Set the default height for the generated image. Default is: 512",
                )

                st.session_state["defaults"].txt2img.height.min_value = st.number_input(
                    "Minimum Image Height",
                    value=st.session_state["defaults"].txt2img.height.min_value,
                    help="Set the default minimum value for the height slider. Default is: 64",
                )

                st.session_state["defaults"].txt2img.height.max_value = st.number_input(
                    "Maximum Image Height",
                    value=st.session_state["defaults"].txt2img.height.max_value,
                    help="Set the default maximum value for the height slider. Default is: 2048",
                )

                with col2:
                    # CFG
                    st.session_state[
                        "defaults"
                    ].txt2img.cfg_scale.value = st.number_input(
                        "Default CFG Scale",
                        value=st.session_state["defaults"].txt2img.cfg_scale.value,
                        help="Set the default value for the CFG Scale. Default is: 7.5",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2img.cfg_scale.min_value = st.number_input(
                        "Minimum CFG Scale Value",
                        value=st.session_state["defaults"].txt2img.cfg_scale.min_value,
                        help="Set the default minimum value for the CFG scale slider. Default is: 1",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2img.cfg_scale.step = st.number_input(
                        "CFG Slider Steps",
                        value=st.session_state["defaults"].txt2img.cfg_scale.step,
                        help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5",
                    )
                    # Sampling Steps
                    st.session_state[
                        "defaults"
                    ].txt2img.sampling_steps.value = st.number_input(
                        "Default Sampling Steps",
                        value=st.session_state["defaults"].txt2img.sampling_steps.value,
                        help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2img.sampling_steps.min_value = st.number_input(
                        "Minimum Sampling Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2img.sampling_steps.min_value,
                        help="Set the default minimum value for the sampling steps slider. Default is: 1",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2img.sampling_steps.step = st.number_input(
                        "Sampling Slider Steps",
                        value=st.session_state["defaults"].txt2img.sampling_steps.step,
                        help="Set the default value for the number of steps on the sampling steps slider. Default is: 10",
                    )

            with col3:
                st.title("General Parameters")

                # Batch Count
                st.session_state[
                    "defaults"
                ].txt2img.batch_count.value = st.number_input(
                    "Batch count",
                    value=st.session_state["defaults"].txt2img.batch_count.value,
                    help="How many iterations or batches of images to generate in total.",
                )

                st.session_state["defaults"].txt2img.batch_size.value = st.number_input(
                    "Batch size",
                    value=st.session_state.defaults.txt2img.batch_size.value,
                    help="How many images are at once in a batch.\
                                                                                        It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
                                                                                        takes to finish generation as more images are generated at once.\
                                                                                        Default: 1",
                )

                default_sampler_list = [
                    "k_lms",
                    "k_euler",
                    "k_euler_a",
                    "k_dpm_2",
                    "k_dpm_2_a",
                    "k_heun",
                    "PLMS",
                    "DDIM",
                ]
                st.session_state["defaults"].txt2img.default_sampler = st.selectbox(
                    "Default Sampler",
                    default_sampler_list,
                    index=default_sampler_list.index(
                        st.session_state["defaults"].txt2img.default_sampler
                    ),
                    help="Defaut sampler to use for txt2img. Default: k_euler",
                )

                st.session_state["defaults"].txt2img.seed = st.text_input(
                    "Default Seed",
                    value=st.session_state["defaults"].txt2img.seed,
                    help="Default seed.",
                )

            with col4:
                st.session_state["defaults"].txt2img.separate_prompts = st.checkbox(
                    "Separate Prompts",
                    value=st.session_state["defaults"].txt2img.separate_prompts,
                    help="Separate Prompts. Default: False",
                )

                st.session_state[
                    "defaults"
                ].txt2img.normalize_prompt_weights = st.checkbox(
                    "Normalize Prompt Weights",
                    value=st.session_state["defaults"].txt2img.normalize_prompt_weights,
                    help="Choose to normalize prompt weights. Default: True",
                )

                st.session_state[
                    "defaults"
                ].txt2img.save_individual_images = st.checkbox(
                    "Save Individual Images",
                    value=st.session_state["defaults"].txt2img.save_individual_images,
                    help="Choose to save individual images. Default: True",
                )

                st.session_state["defaults"].txt2img.save_grid = st.checkbox(
                    "Save Grid Images",
                    value=st.session_state["defaults"].txt2img.save_grid,
                    help="Choose to save the grid images. Default: True",
                )

                st.session_state["defaults"].txt2img.group_by_prompt = st.checkbox(
                    "Group By Prompt",
                    value=st.session_state["defaults"].txt2img.group_by_prompt,
                    help="Choose to save images grouped by their prompt. Default: False",
                )

                st.session_state["defaults"].txt2img.save_as_jpg = st.checkbox(
                    "Save As JPG",
                    value=st.session_state["defaults"].txt2img.save_as_jpg,
                    help="Choose to save images as jpegs. Default: False",
                )

                st.session_state["defaults"].txt2img.write_info_files = st.checkbox(
                    "Write Info Files For Images",
                    value=st.session_state["defaults"].txt2img.write_info_files,
                    help="Choose to write the info files along with the generated images. Default: True",
                )

                st.session_state["defaults"].txt2img.use_GFPGAN = st.checkbox(
                    "Use GFPGAN",
                    value=st.session_state["defaults"].txt2img.use_GFPGAN,
                    help="Choose to use GFPGAN. Default: False",
                )

                st.session_state["defaults"].txt2img.use_upscaling = st.checkbox(
                    "Use Upscaling",
                    value=st.session_state["defaults"].txt2img.use_upscaling,
                    help="Choose to turn on upscaling by default. Default: False",
                )

                st.session_state["defaults"].txt2img.update_preview = True
                st.session_state[
                    "defaults"
                ].txt2img.update_preview_frequency = st.number_input(
                    "Preview Image Update Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].txt2img.update_preview_frequency,
                    help="Set the default value for the frrquency of the preview image updates. Default is: 10",
                )

            with col5:
                st.title("Variation Parameters")

                st.session_state[
                    "defaults"
                ].txt2img.variant_amount.value = st.number_input(
                    "Default Variation Amount",
                    value=st.session_state["defaults"].txt2img.variant_amount.value,
                    help="Set the default variation to use. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2img.variant_amount.min_value = st.number_input(
                    "Minimum Variation Amount",
                    value=st.session_state["defaults"].txt2img.variant_amount.min_value,
                    help="Set the default minimum value for the variation slider. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2img.variant_amount.max_value = st.number_input(
                    "Maximum Variation Amount",
                    value=st.session_state["defaults"].txt2img.variant_amount.max_value,
                    help="Set the default maximum value for the variation slider. Default is: 1.0",
                )

                st.session_state[
                    "defaults"
                ].txt2img.variant_amount.step = st.number_input(
                    "Variation Slider Steps",
                    value=st.session_state["defaults"].txt2img.variant_amount.step,
                    help="Set the default value for the number of steps on the variation slider. Default is: 1",
                )

                st.session_state["defaults"].txt2img.variant_seed = st.text_input(
                    "Default Variation Seed",
                    value=st.session_state["defaults"].txt2img.variant_seed,
                    help="Default variation seed.",
                )

        with img2img_tab:
            col1, col2, col3, col4, col5 = st.columns(5, gap="medium")

            with col1:
                st.title("Image Editing")

                # Denoising
                st.session_state[
                    "defaults"
                ].img2img.denoising_strength.value = st.number_input(
                    "Default Denoising Amount",
                    value=st.session_state["defaults"].img2img.denoising_strength.value,
                    help="Set the default denoising to use. Default is: 0.75",
                )

                st.session_state[
                    "defaults"
                ].img2img.denoising_strength.min_value = st.number_input(
                    "Minimum Denoising Amount",
                    value=st.session_state[
                        "defaults"
                    ].img2img.denoising_strength.min_value,
                    help="Set the default minimum value for the denoising slider. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].img2img.denoising_strength.max_value = st.number_input(
                    "Maximum Denoising Amount",
                    value=st.session_state[
                        "defaults"
                    ].img2img.denoising_strength.max_value,
                    help="Set the default maximum value for the denoising slider. Default is: 1.0",
                )

                st.session_state[
                    "defaults"
                ].img2img.denoising_strength.step = st.number_input(
                    "Denoising Slider Steps",
                    value=st.session_state["defaults"].img2img.denoising_strength.step,
                    help="Set the default value for the number of steps on the denoising slider. Default is: 0.01",
                )

                # Masking
                st.session_state["defaults"].img2img.mask_mode = st.number_input(
                    "Default Mask Mode",
                    value=st.session_state["defaults"].img2img.mask_mode,
                    help="Set the default mask mode to use. 0 = Keep Masked Area, 1 = Regenerate Masked Area. Default is: 0",
                )

                st.session_state["defaults"].img2img.mask_restore = st.checkbox(
                    "Default Mask Restore",
                    value=st.session_state["defaults"].img2img.mask_restore,
                    help="Mask Restore. Default: False",
                )

                st.session_state["defaults"].img2img.resize_mode = st.number_input(
                    "Default Resize Mode",
                    value=st.session_state["defaults"].img2img.resize_mode,
                    help="Set the default resizing mode. 0 = Just Resize, 1 = Crop and Resize, 3 = Resize and Fill. Default is: 0",
                )

            with col2:
                st.title("Slider Parameters")

                # Width
                st.session_state["defaults"].img2img.width.value = st.number_input(
                    "Default Outputted Image Width",
                    value=st.session_state["defaults"].img2img.width.value,
                    help="Set the default width for the generated image. Default is: 512",
                )

                st.session_state["defaults"].img2img.width.min_value = st.number_input(
                    "Minimum Outputted Image Width",
                    value=st.session_state["defaults"].img2img.width.min_value,
                    help="Set the default minimum value for the width slider. Default is: 64",
                )

                st.session_state["defaults"].img2img.width.max_value = st.number_input(
                    "Maximum Outputted Image Width",
                    value=st.session_state["defaults"].img2img.width.max_value,
                    help="Set the default maximum value for the width slider. Default is: 2048",
                )

                # Height
                st.session_state["defaults"].img2img.height.value = st.number_input(
                    "Default Outputted Image Height",
                    value=st.session_state["defaults"].img2img.height.value,
                    help="Set the default height for the generated image. Default is: 512",
                )

                st.session_state["defaults"].img2img.height.min_value = st.number_input(
                    "Minimum Outputted Image Height",
                    value=st.session_state["defaults"].img2img.height.min_value,
                    help="Set the default minimum value for the height slider. Default is: 64",
                )

                st.session_state["defaults"].img2img.height.max_value = st.number_input(
                    "Maximum Outputted Image Height",
                    value=st.session_state["defaults"].img2img.height.max_value,
                    help="Set the default maximum value for the height slider. Default is: 2048",
                )

                # CFG
                st.session_state["defaults"].img2img.cfg_scale.value = st.number_input(
                    "Default Img2Img CFG Scale",
                    value=st.session_state["defaults"].img2img.cfg_scale.value,
                    help="Set the default value for the CFG Scale. Default is: 7.5",
                )

                st.session_state[
                    "defaults"
                ].img2img.cfg_scale.min_value = st.number_input(
                    "Minimum Img2Img CFG Scale Value",
                    value=st.session_state["defaults"].img2img.cfg_scale.min_value,
                    help="Set the default minimum value for the CFG scale slider. Default is: 1",
                )

                with col3:
                    st.session_state[
                        "defaults"
                    ].img2img.cfg_scale.step = st.number_input(
                        "Img2Img CFG Slider Steps",
                        value=st.session_state["defaults"].img2img.cfg_scale.step,
                        help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5",
                    )

                    # Sampling Steps
                    st.session_state[
                        "defaults"
                    ].img2img.sampling_steps.value = st.number_input(
                        "Default Img2Img Sampling Steps",
                        value=st.session_state["defaults"].img2img.sampling_steps.value,
                        help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)",
                    )

                    st.session_state[
                        "defaults"
                    ].img2img.sampling_steps.min_value = st.number_input(
                        "Minimum Img2Img Sampling Steps",
                        value=st.session_state[
                            "defaults"
                        ].img2img.sampling_steps.min_value,
                        help="Set the default minimum value for the sampling steps slider. Default is: 1",
                    )

                    st.session_state[
                        "defaults"
                    ].img2img.sampling_steps.step = st.number_input(
                        "Img2Img Sampling Slider Steps",
                        value=st.session_state["defaults"].img2img.sampling_steps.step,
                        help="Set the default value for the number of steps on the sampling steps slider. Default is: 10",
                    )

                    # Batch Count
                    st.session_state[
                        "defaults"
                    ].img2img.batch_count.value = st.number_input(
                        "Img2img Batch count",
                        value=st.session_state["defaults"].img2img.batch_count.value,
                        help="How many iterations or batches of images to generate in total.",
                    )

                    st.session_state[
                        "defaults"
                    ].img2img.batch_size.value = st.number_input(
                        "Img2img Batch size",
                        value=st.session_state["defaults"].img2img.batch_size.value,
                        help="How many images are at once in a batch.\
                                                                                            It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
                                                                                            takes to finish generation as more images are generated at once.\
                                                                                            Default: 1",
                    )
                    with col4:
                        # Inference Steps
                        st.session_state[
                            "defaults"
                        ].img2img.num_inference_steps.value = st.number_input(
                            "Default Inference Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.num_inference_steps.value,
                            help="Set the default number of inference steps to use. Default is: 200",
                        )

                        st.session_state[
                            "defaults"
                        ].img2img.num_inference_steps.min_value = st.number_input(
                            "Minimum Sampling Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.num_inference_steps.min_value,
                            help="Set the default minimum value for the inference steps slider. Default is: 10",
                        )

                        st.session_state[
                            "defaults"
                        ].img2img.num_inference_steps.max_value = st.number_input(
                            "Maximum Sampling Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.num_inference_steps.max_value,
                            help="Set the default maximum value for the inference steps slider. Default is: 500",
                        )

                        st.session_state[
                            "defaults"
                        ].img2img.num_inference_steps.step = st.number_input(
                            "Inference Slider Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.num_inference_steps.step,
                            help="Set the default value for the number of steps on the inference steps slider.\
                                                                                                        Default is: 10",
                        )

                        # Find Noise Steps
                        st.session_state[
                            "defaults"
                        ].img2img.find_noise_steps.value = st.number_input(
                            "Default Find Noise Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.find_noise_steps.value,
                            help="Set the default number of find noise steps to use. Default is: 100",
                        )

                        st.session_state[
                            "defaults"
                        ].img2img.find_noise_steps.min_value = st.number_input(
                            "Minimum Find Noise Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.find_noise_steps.min_value,
                            help="Set the default minimum value for the find noise steps slider. Default is: 0",
                        )

                        st.session_state[
                            "defaults"
                        ].img2img.find_noise_steps.step = st.number_input(
                            "Find Noise Slider Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.find_noise_steps.step,
                            help="Set the default value for the number of steps on the find noise steps slider. \
                                                                                                     Default is: 100",
                        )

            with col5:
                st.title("General Parameters")

                default_sampler_list = [
                    "k_lms",
                    "k_euler",
                    "k_euler_a",
                    "k_dpm_2",
                    "k_dpm_2_a",
                    "k_heun",
                    "PLMS",
                    "DDIM",
                ]
                st.session_state["defaults"].img2img.sampler_name = st.selectbox(
                    "Default Img2Img Sampler",
                    default_sampler_list,
                    index=default_sampler_list.index(
                        st.session_state["defaults"].img2img.sampler_name
                    ),
                    help="Defaut sampler to use for img2img. Default: k_euler",
                )

                st.session_state["defaults"].img2img.seed = st.text_input(
                    "Default Img2Img Seed",
                    value=st.session_state["defaults"].img2img.seed,
                    help="Default seed.",
                )

                st.session_state["defaults"].img2img.separate_prompts = st.checkbox(
                    "Separate Img2Img Prompts",
                    value=st.session_state["defaults"].img2img.separate_prompts,
                    help="Separate Prompts. Default: False",
                )

                st.session_state[
                    "defaults"
                ].img2img.normalize_prompt_weights = st.checkbox(
                    "Normalize Img2Img Prompt Weights",
                    value=st.session_state["defaults"].img2img.normalize_prompt_weights,
                    help="Choose to normalize prompt weights. Default: True",
                )

                st.session_state[
                    "defaults"
                ].img2img.save_individual_images = st.checkbox(
                    "Save Individual Img2Img Images",
                    value=st.session_state["defaults"].img2img.save_individual_images,
                    help="Choose to save individual images. Default: True",
                )

                st.session_state["defaults"].img2img.save_grid = st.checkbox(
                    "Save Img2Img Grid Images",
                    value=st.session_state["defaults"].img2img.save_grid,
                    help="Choose to save the grid images. Default: True",
                )

                st.session_state["defaults"].img2img.group_by_prompt = st.checkbox(
                    "Group By Img2Img Prompt",
                    value=st.session_state["defaults"].img2img.group_by_prompt,
                    help="Choose to save images grouped by their prompt. Default: False",
                )

                st.session_state["defaults"].img2img.save_as_jpg = st.checkbox(
                    "Save Img2Img As JPG",
                    value=st.session_state["defaults"].img2img.save_as_jpg,
                    help="Choose to save images as jpegs. Default: False",
                )

                st.session_state["defaults"].img2img.write_info_files = st.checkbox(
                    "Write Info Files For Img2Img Images",
                    value=st.session_state["defaults"].img2img.write_info_files,
                    help="Choose to write the info files along with the generated images. Default: True",
                )

                st.session_state["defaults"].img2img.use_GFPGAN = st.checkbox(
                    "Img2Img Use GFPGAN",
                    value=st.session_state["defaults"].img2img.use_GFPGAN,
                    help="Choose to use GFPGAN. Default: False",
                )

                st.session_state["defaults"].img2img.use_RealESRGAN = st.checkbox(
                    "Img2Img Use RealESRGAN",
                    value=st.session_state["defaults"].img2img.use_RealESRGAN,
                    help="Choose to use RealESRGAN. Default: False",
                )

                st.session_state["defaults"].img2img.update_preview = True
                st.session_state[
                    "defaults"
                ].img2img.update_preview_frequency = st.number_input(
                    "Img2Img Preview Image Update Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].img2img.update_preview_frequency,
                    help="Set the default value for the frrquency of the preview image updates. Default is: 10",
                )

                st.title("Variation Parameters")

                st.session_state["defaults"].img2img.variant_amount = st.number_input(
                    "Default Img2Img Variation Amount",
                    value=st.session_state["defaults"].img2img.variant_amount,
                    help="Set the default variation to use. Default is: 0.0",
                )

                # I THINK THESE ARE MISSING FROM THE CONFIG FILE
                # st.session_state["defaults"].img2img.variant_amount.min_value = st.number_input("Minimum Img2Img Variation Amount",
                # value=st.session_state['defaults'].img2img.variant_amount.min_value, help="Set the default minimum value for the variation slider. Default is: 0.0"))

                # st.session_state["defaults"].img2img.variant_amount.max_value = st.number_input("Maximum Img2Img Variation Amount",
                # value=st.session_state['defaults'].img2img.variant_amount.max_value, help="Set the default maximum value for the variation slider. Default is: 1.0"))

                # st.session_state["defaults"].img2img.variant_amount.step = st.number_input("Img2Img Variation Slider Steps",
                # value=st.session_state['defaults'].img2img.variant_amount.step, help="Set the default value for the number of steps on the variation slider. Default is: 1"))

                st.session_state["defaults"].img2img.variant_seed = st.text_input(
                    "Default Img2Img Variation Seed",
                    value=st.session_state["defaults"].img2img.variant_seed,
                    help="Default variation seed.",
                )

        with img2txt_tab:
            col1 = st.columns(1, gap="large")

            st.title("Image-To-Text")

            st.session_state["defaults"].img2txt.batch_size = st.number_input(
                "Default Img2Txt Batch Size",
                value=st.session_state["defaults"].img2txt.batch_size,
                help="Set the default batch size for Img2Txt. Default is: 420?",
            )

            st.session_state["defaults"].img2txt.blip_image_eval_size = st.number_input(
                "Default Blip Image Size Evaluation",
                value=st.session_state["defaults"].img2txt.blip_image_eval_size,
                help="Set the default value for the blip image evaluation size. Default is: 512",
            )

        with txt2vid_tab:
            col1, col2, col3, col4, col5 = st.columns(5, gap="medium")

            with col1:
                st.title("Slider Parameters")

                # Width
                st.session_state["defaults"].txt2vid.width.value = st.number_input(
                    "Default txt2vid Image Width",
                    value=st.session_state["defaults"].txt2vid.width.value,
                    help="Set the default width for the generated image. Default is: 512",
                )

                st.session_state["defaults"].txt2vid.width.min_value = st.number_input(
                    "Minimum txt2vid Image Width",
                    value=st.session_state["defaults"].txt2vid.width.min_value,
                    help="Set the default minimum value for the width slider. Default is: 64",
                )

                st.session_state["defaults"].txt2vid.width.max_value = st.number_input(
                    "Maximum txt2vid Image Width",
                    value=st.session_state["defaults"].txt2vid.width.max_value,
                    help="Set the default maximum value for the width slider. Default is: 2048",
                )

                # Height
                st.session_state["defaults"].txt2vid.height.value = st.number_input(
                    "Default txt2vid Image Height",
                    value=st.session_state["defaults"].txt2vid.height.value,
                    help="Set the default height for the generated image. Default is: 512",
                )

                st.session_state["defaults"].txt2vid.height.min_value = st.number_input(
                    "Minimum txt2vid Image Height",
                    value=st.session_state["defaults"].txt2vid.height.min_value,
                    help="Set the default minimum value for the height slider. Default is: 64",
                )

                st.session_state["defaults"].txt2vid.height.max_value = st.number_input(
                    "Maximum txt2vid Image Height",
                    value=st.session_state["defaults"].txt2vid.height.max_value,
                    help="Set the default maximum value for the height slider. Default is: 2048",
                )

                # CFG
                st.session_state["defaults"].txt2vid.cfg_scale.value = st.number_input(
                    "Default txt2vid CFG Scale",
                    value=st.session_state["defaults"].txt2vid.cfg_scale.value,
                    help="Set the default value for the CFG Scale. Default is: 7.5",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.cfg_scale.min_value = st.number_input(
                    "Minimum txt2vid CFG Scale Value",
                    value=st.session_state["defaults"].txt2vid.cfg_scale.min_value,
                    help="Set the default minimum value for the CFG scale slider. Default is: 1",
                )

                st.session_state["defaults"].txt2vid.cfg_scale.step = st.number_input(
                    "txt2vid CFG Slider Steps",
                    value=st.session_state["defaults"].txt2vid.cfg_scale.step,
                    help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5",
                )

                with col2:
                    # Sampling Steps
                    st.session_state[
                        "defaults"
                    ].txt2vid.sampling_steps.value = st.number_input(
                        "Default txt2vid Sampling Steps",
                        value=st.session_state["defaults"].txt2vid.sampling_steps.value,
                        help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2vid.sampling_steps.min_value = st.number_input(
                        "Minimum txt2vid Sampling Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2vid.sampling_steps.min_value,
                        help="Set the default minimum value for the sampling steps slider. Default is: 1",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2vid.sampling_steps.step = st.number_input(
                        "txt2vid Sampling Slider Steps",
                        value=st.session_state["defaults"].txt2vid.sampling_steps.step,
                        help="Set the default value for the number of steps on the sampling steps slider. Default is: 10",
                    )

                    # Batch Count
                    st.session_state[
                        "defaults"
                    ].txt2vid.batch_count.value = st.number_input(
                        "txt2vid Batch count",
                        value=st.session_state["defaults"].txt2vid.batch_count.value,
                        help="How many iterations or batches of images to generate in total.",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2vid.batch_size.value = st.number_input(
                        "txt2vid Batch size",
                        value=st.session_state.defaults.txt2vid.batch_size.value,
                        help="How many images are at once in a batch.\
                                                                                            It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
                                                                                            takes to finish generation as more images are generated at once.\
                                                                                            Default: 1",
                    )

                    # Inference Steps
                    st.session_state[
                        "defaults"
                    ].txt2vid.num_inference_steps.value = st.number_input(
                        "Default Txt2Vid Inference Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2vid.num_inference_steps.value,
                        help="Set the default number of inference steps to use. Default is: 200",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2vid.num_inference_steps.min_value = st.number_input(
                        "Minimum Txt2Vid Sampling Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2vid.num_inference_steps.min_value,
                        help="Set the default minimum value for the inference steps slider. Default is: 10",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2vid.num_inference_steps.max_value = st.number_input(
                        "Maximum Txt2Vid Sampling Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2vid.num_inference_steps.max_value,
                        help="Set the default maximum value for the inference steps slider. Default is: 500",
                    )
                    st.session_state[
                        "defaults"
                    ].txt2vid.num_inference_steps.step = st.number_input(
                        "Txt2Vid Inference Slider Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2vid.num_inference_steps.step,
                        help="Set the default value for the number of steps on the inference steps slider. Default is: 10",
                    )

            with col3:
                st.title("General Parameters")

                st.session_state["defaults"].txt2vid.default_model = st.text_input(
                    "Default Txt2Vid Model",
                    value=st.session_state["defaults"].txt2vid.default_model,
                    help="Default: CompVis/stable-diffusion-v1-4",
                )

                # INSERT CUSTOM_MODELS_LIST HERE

                default_sampler_list = [
                    "k_lms",
                    "k_euler",
                    "k_euler_a",
                    "k_dpm_2",
                    "k_dpm_2_a",
                    "k_heun",
                    "PLMS",
                    "DDIM",
                ]
                st.session_state["defaults"].txt2vid.default_sampler = st.selectbox(
                    "Default txt2vid Sampler",
                    default_sampler_list,
                    index=default_sampler_list.index(
                        st.session_state["defaults"].txt2vid.default_sampler
                    ),
                    help="Defaut sampler to use for txt2vid. Default: k_euler",
                )

                st.session_state["defaults"].txt2vid.seed = st.text_input(
                    "Default txt2vid Seed",
                    value=st.session_state["defaults"].txt2vid.seed,
                    help="Default seed.",
                )

                st.session_state["defaults"].txt2vid.scheduler_name = st.text_input(
                    "Default Txt2Vid Scheduler",
                    value=st.session_state["defaults"].txt2vid.scheduler_name,
                    help="Default scheduler.",
                )

                st.session_state["defaults"].txt2vid.separate_prompts = st.checkbox(
                    "Separate txt2vid Prompts",
                    value=st.session_state["defaults"].txt2vid.separate_prompts,
                    help="Separate Prompts. Default: False",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.normalize_prompt_weights = st.checkbox(
                    "Normalize txt2vid Prompt Weights",
                    value=st.session_state["defaults"].txt2vid.normalize_prompt_weights,
                    help="Choose to normalize prompt weights. Default: True",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.save_individual_images = st.checkbox(
                    "Save Individual txt2vid Images",
                    value=st.session_state["defaults"].txt2vid.save_individual_images,
                    help="Choose to save individual images. Default: True",
                )

                st.session_state["defaults"].txt2vid.save_video = st.checkbox(
                    "Save Txt2Vid Video",
                    value=st.session_state["defaults"].txt2vid.save_video,
                    help="Choose to save the Txt2Vid video. Default: True",
                )

                st.session_state["defaults"].txt2vid.save_video_on_stop = st.checkbox(
                    "Save video on Stop",
                    value=st.session_state["defaults"].txt2vid.save_video_on_stop,
                    help="Save a video with all the images generated as frames when we hit the stop button \
																					  during a generation.",
                )

                st.session_state["defaults"].txt2vid.group_by_prompt = st.checkbox(
                    "Group By txt2vid Prompt",
                    value=st.session_state["defaults"].txt2vid.group_by_prompt,
                    help="Choose to save images grouped by their prompt. Default: False",
                )

                st.session_state["defaults"].txt2vid.save_as_jpg = st.checkbox(
                    "Save txt2vid As JPG",
                    value=st.session_state["defaults"].txt2vid.save_as_jpg,
                    help="Choose to save images as jpegs. Default: False",
                )

                # Need more info for the Help dialog...
                st.session_state["defaults"].txt2vid.do_loop = st.checkbox(
                    "Loop Generations",
                    value=st.session_state["defaults"].txt2vid.do_loop,
                    help="Choose to loop or something, IDK.... Default: False",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.max_duration_in_seconds = st.number_input(
                    "Txt2Vid Max Duration in Seconds",
                    value=st.session_state["defaults"].txt2vid.max_duration_in_seconds,
                    help="Set the default value for the max duration in seconds for the video generated. Default is: 30",
                )

                st.session_state["defaults"].txt2vid.write_info_files = st.checkbox(
                    "Write Info Files For txt2vid Images",
                    value=st.session_state["defaults"].txt2vid.write_info_files,
                    help="Choose to write the info files along with the generated images. Default: True",
                )

                st.session_state["defaults"].txt2vid.use_GFPGAN = st.checkbox(
                    "txt2vid Use GFPGAN",
                    value=st.session_state["defaults"].txt2vid.use_GFPGAN,
                    help="Choose to use GFPGAN. Default: False",
                )

                st.session_state["defaults"].txt2vid.use_RealESRGAN = st.checkbox(
                    "txt2vid Use RealESRGAN",
                    value=st.session_state["defaults"].txt2vid.use_RealESRGAN,
                    help="Choose to use RealESRGAN. Default: False",
                )

                st.session_state["defaults"].txt2vid.update_preview = True
                st.session_state[
                    "defaults"
                ].txt2vid.update_preview_frequency = st.number_input(
                    "txt2vid Preview Image Update Frequency",
                    value=st.session_state["defaults"].txt2vid.update_preview_frequency,
                    help="Set the default value for the frrquency of the preview image updates. Default is: 10",
                )

            with col4:
                st.title("Variation Parameters")

                st.session_state[
                    "defaults"
                ].txt2vid.variant_amount.value = st.number_input(
                    "Default txt2vid Variation Amount",
                    value=st.session_state["defaults"].txt2vid.variant_amount.value,
                    help="Set the default variation to use. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.variant_amount.min_value = st.number_input(
                    "Minimum txt2vid Variation Amount",
                    value=st.session_state["defaults"].txt2vid.variant_amount.min_value,
                    help="Set the default minimum value for the variation slider. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.variant_amount.max_value = st.number_input(
                    "Maximum txt2vid Variation Amount",
                    value=st.session_state["defaults"].txt2vid.variant_amount.max_value,
                    help="Set the default maximum value for the variation slider. Default is: 1.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.variant_amount.step = st.number_input(
                    "txt2vid Variation Slider Steps",
                    value=st.session_state["defaults"].txt2vid.variant_amount.step,
                    help="Set the default value for the number of steps on the variation slider. Default is: 1",
                )

                st.session_state["defaults"].txt2vid.variant_seed = st.text_input(
                    "Default txt2vid Variation Seed",
                    value=st.session_state["defaults"].txt2vid.variant_seed,
                    help="Default variation seed.",
                )

            with col5:
                st.title("Beta Parameters")

                # Beta Start
                st.session_state["defaults"].txt2vid.beta_start.value = st.number_input(
                    "Default txt2vid Beta Start Value",
                    value=st.session_state["defaults"].txt2vid.beta_start.value,
                    help="Set the default variation to use. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.beta_start.min_value = st.number_input(
                    "Minimum txt2vid Beta Start Amount",
                    value=st.session_state["defaults"].txt2vid.beta_start.min_value,
                    help="Set the default minimum value for the variation slider. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.beta_start.max_value = st.number_input(
                    "Maximum txt2vid Beta Start Amount",
                    value=st.session_state["defaults"].txt2vid.beta_start.max_value,
                    help="Set the default maximum value for the variation slider. Default is: 1.0",
                )

                st.session_state["defaults"].txt2vid.beta_start.step = st.number_input(
                    "txt2vid Beta Start Slider Steps",
                    value=st.session_state["defaults"].txt2vid.beta_start.step,
                    help="Set the default value for the number of steps on the variation slider. Default is: 1",
                )

                st.session_state["defaults"].txt2vid.beta_start.format = st.text_input(
                    "Default txt2vid Beta Start Format",
                    value=st.session_state["defaults"].txt2vid.beta_start.format,
                    help="Set the default Beta Start Format. Default is: %.5\f",
                )

                # Beta End
                st.session_state["defaults"].txt2vid.beta_end.value = st.number_input(
                    "Default txt2vid Beta End Value",
                    value=st.session_state["defaults"].txt2vid.beta_end.value,
                    help="Set the default variation to use. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.beta_end.min_value = st.number_input(
                    "Minimum txt2vid Beta End Amount",
                    value=st.session_state["defaults"].txt2vid.beta_end.min_value,
                    help="Set the default minimum value for the variation slider. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.beta_end.max_value = st.number_input(
                    "Maximum txt2vid Beta End Amount",
                    value=st.session_state["defaults"].txt2vid.beta_end.max_value,
                    help="Set the default maximum value for the variation slider. Default is: 1.0",
                )

                st.session_state["defaults"].txt2vid.beta_end.step = st.number_input(
                    "txt2vid Beta End Slider Steps",
                    value=st.session_state["defaults"].txt2vid.beta_end.step,
                    help="Set the default value for the number of steps on the variation slider. Default is: 1",
                )

                st.session_state["defaults"].txt2vid.beta_end.format = st.text_input(
                    "Default txt2vid Beta End Format",
                    value=st.session_state["defaults"].txt2vid.beta_start.format,
                    help="Set the default Beta Start Format. Default is: %.5\f",
                )

        with image_processing:
            col1, col2, col3, col4, col5 = st.columns(5, gap="large")

            with col1:
                st.title("GFPGAN")

                st.session_state["defaults"].gfpgan.strength = st.number_input(
                    "Default Img2Txt Batch Size",
                    value=st.session_state["defaults"].gfpgan.strength,
                    help="Set the default global strength for GFPGAN. Default is: 100",
                )
            with col2:
                st.title("GoBig")
            with col3:
                st.title("RealESRGAN")
            with col4:
                st.title("LDSR")
            with col5:
                st.title("GoLatent")

        with textual_inversion_tab:
            st.title("Textual Inversion")

            st.session_state[
                "defaults"
            ].textual_inversion.pretrained_model_name_or_path = st.text_input(
                "Default Textual Inversion Model Path",
                value=st.session_state[
                    "defaults"
                ].textual_inversion.pretrained_model_name_or_path,
                help="Default: models/ldm/stable-diffusion-v1-4",
            )

            st.session_state[
                "defaults"
            ].textual_inversion.tokenizer_name = st.text_input(
                "Default Img2Img Variation Seed",
                value=st.session_state["defaults"].textual_inversion.tokenizer_name,
                help="Default tokenizer seed.",
            )

        with concepts_library_tab:
            st.title("Concepts Library")
            # st.info("Under Construction. :construction_worker:")
            col1, col2, col3, col4, col5 = st.columns(5, gap="large")
            with col1:
                st.session_state[
                    "defaults"
                ].concepts_library.concepts_per_page = st.number_input(
                    "Concepts Per Page",
                    value=st.session_state[
                        "defaults"
                    ].concepts_library.concepts_per_page,
                    help="Number of concepts per page to show on the Concepts Library. Default: '12'",
                )

        # add space for the buttons at the bottom
        st.markdown("---")

        # We need a submit button to save the Settings
        # as well as one to reset them to the defaults, just in case.
        _, _, save_button_col, reset_button_col, _, _ = st.columns(
            [1, 1, 1, 1, 1, 1], gap="large"
        )
        with save_button_col:
            save_button = st.form_submit_button("Save")

        with reset_button_col:
            reset_button = st.form_submit_button("Reset")

        if save_button:
            OmegaConf.save(
                config=st.session_state.defaults,
                f="configs/webui/userconfig_streamlit.yaml",
            )
            loaded = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")
            assert st.session_state.defaults == loaded

            #
            if os.path.exists(".streamlit/config.toml"):
                with open(".streamlit/config.toml", "w") as toml_file:
                    toml.dump(st.session_state["streamlit_config"], toml_file)

        if reset_button:
            st.session_state["defaults"] = OmegaConf.load(
                "configs/webui/webui_streamlit.yaml"
            )
            st.experimental_rerun()


================================================
FILE: scripts/__init__.py
================================================


================================================
FILE: scripts/barfi_baklavajs.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sandbox-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
# from sd_utils import *
from sd_utils import st

# streamlit imports

# streamlit components section

# other imports
from barfi import st_barfi, barfi_schemas, Block

# Temp imports

# end of imports
# ---------------------------------------------------------------------------------------------------------------


def layout():
    # st.info("Under Construction. :construction_worker:")

    # from barfi import st_barfi, Block

    # add = Block(name='Addition')
    # sub = Block(name='Subtraction')
    # mul = Block(name='Multiplication')
    # div = Block(name='Division')

    # barfi_result = st_barfi(base_blocks= [add, sub, mul, div])
    # or if you want to use a category to organise them in the frontend sub-menu
    # barfi_result = st_barfi(base_blocks= {'Op 1': [add, sub], 'Op 2': [mul, div]})

    col1, col2, col3 = st.columns([1, 8, 1])

    with col2:
        feed = Block(name="Feed")
        feed.add_output()

        def feed_func(self):
            self.set_interface(name="Output 1", value=4)

        feed.add_compute(feed_func)

        splitter = Block(name="Splitter")
        splitter.add_input()
        splitter.add_output()
        splitter.add_output()

        def splitter_func(self):
            in_1 = self.get_interface(name="Input 1")
            value = in_1 / 2
            self.set_interface(name="Output 1", value=value)
            self.set_interface(name="Output 2", value=value)

        splitter.add_compute(splitter_func)

        mixer = Block(name="Mixer")
        mixer.add_input()
        mixer.add_input()
        mixer.add_output()

        def mixer_func(self):
            in_1 = self.get_interface(name="Input 1")
            in_2 = self.get_interface(name="Input 2")
            value = in_1 + in_2
            self.set_interface(name="Output 1", value=value)

        mixer.add_compute(mixer_func)

        result = Block(name="Result")
        result.add_input()

        def result_func(self):
            self.get_interface(name="Input 1")

        result.add_compute(result_func)

        load_schema = st.selectbox("Select a saved schema:", barfi_schemas())

        compute_engine = st.checkbox("Activate barfi compute engine", value=False)

        barfi_result = st_barfi(
            base_blocks=[feed, result, mixer, splitter],
            compute_engine=compute_engine,
            load_schema=load_schema,
        )

        if barfi_result:
            st.write(barfi_result)


================================================
FILE: scripts/bridgeData_template.py
================================================
# The horde url
horde_url = "https://stablehorde.net"
# Give a cool name to your instance
horde_name = "My Awesome Instance"
# The api_key identifies a unique user in the horde
# Visit https://stablehorde.net/register to create one before you can join
horde_api_key = "0000000000"
# Put other users whose prompts you want to prioritize.
# The owner's username is always included so you don't need to add it here, unless you want it to have lower priority than another user
horde_priority_usernames = []
# The amount of power your system can handle
# 8 means 512*512. Each increase increases the possible resoluion by 64 pixes
# So if you put this to 2 (the minimum, your SD can only generate 64x64 pixels
# If you put this to 32, it is equivalent to 1024x1024 pixels
horde_max_power = 8
# Set this to false, if you do not want your worker to receive requests for NSFW generations
horde_nsfw = True
# A list of words which you do not want to your worker to accept
horde_blacklist = []
# A list of words for which you always want to allow the NSFW censor filter, even when this worker is in NSFW mode
horde_censorlist = []


================================================
FILE: scripts/clip_interrogator.py
================================================
# @title Setup
#!pip3 install ftfy regex tqdm transformers==4.15.0 timm==0.4.12 fairscale==0.4.4
#!pip3 install git+https://github.com/openai/CLIP.git
#!git clone https://github.com/pharmapsychotic/clip-interrogator.git
#!git clone https://github.com/salesforce/BLIP
# %cd /content/BLIP

import clip
import gc

# import numpy as np
import os
import pandas as pd
import requests
import torch

# import torchvision.transforms as T
# import torchvision.transforms.functional as TF

from IPython.display import display
from PIL import Image

# from torch import nn
# from torch.nn import functional as F
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from ldm.models.blip import blip_decoder

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

blip_image_eval_size = 384
blip_model_url = "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth"
blip_model = blip_decoder(
    pretrained=blip_model_url, image_size=blip_image_eval_size, vit="base"
)
blip_model.eval()
blip_model = blip_model.to(device)


def generate_caption(pil_image):
    gpu_image = (
        transforms.Compose(
            [
                transforms.Resize(
                    (blip_image_eval_size, blip_image_eval_size),
                    interpolation=InterpolationMode.BICUBIC,
                ),
                transforms.ToTensor(),
                transforms.Normalize(
                    (0.48145466, 0.4578275, 0.40821073),
                    (0.26862954, 0.26130258, 0.27577711),
                ),
            ]
        )(image)
        .unsqueeze(0)
        .to(device)
    )

    with torch.no_grad():
        caption = blip_model.generate(
            gpu_image, sample=False, num_beams=3, max_length=20, min_length=5
        )
    return caption[0]


def load_list(filename):
    with open(filename, "r", encoding="utf-8", errors="replace") as f:
        items = [line.strip() for line in f.readlines()]
    return items


def rank(model, image_features, text_array, top_count=1):
    top_count = min(top_count, len(text_array))
    text_tokens = clip.tokenize([text for text in text_array]).cuda()
    with torch.no_grad():
        text_features = model.encode_text(text_tokens).float()
    text_features /= text_features.norm(dim=-1, keepdim=True)

    similarity = torch.zeros((1, len(text_array))).to(device)
    for i in range(image_features.shape[0]):
        similarity += (
            100.0 * image_features[i].unsqueeze(0) @ text_features.T
        ).softmax(dim=-1)
    similarity /= image_features.shape[0]

    top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
    return [
        (text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy() * 100))
        for i in range(top_count)
    ]


def interrogate(image, models):
    caption = generate_caption(image)
    if len(models) == 0:
        print(f"\n\n{caption}")
        return

    table = []
    bests = [[("", 0)]] * 5
    for model_name in models:
        print(f"Interrogating with {model_name}...")
        model, preprocess = clip.load(model_name)
        model.cuda().eval()

        images = preprocess(image).unsqueeze(0).cuda()
        with torch.no_grad():
            image_features = model.encode_image(images).float()
        image_features /= image_features.norm(dim=-1, keepdim=True)

        ranks = [
            rank(model, image_features, mediums),
            rank(model, image_features, ["by " + artist for artist in artists]),
            rank(model, image_features, trending_list),
            rank(model, image_features, movements),
            rank(model, image_features, flavors, top_count=3),
        ]

        for i in range(len(ranks)):
            confidence_sum = 0
            for ci in range(len(ranks[i])):
                confidence_sum += ranks[i][ci][1]
            if confidence_sum > sum(bests[i][t][1] for t in range(len(bests[i]))):
                bests[i] = ranks[i]

        row = [model_name]
        for r in ranks:
            row.append(", ".join([f"{x[0]} ({x[1]:0.1f}%)" for x in r]))

        table.append(row)

        del model
        gc.collect()
    display(
        pd.DataFrame(
            table,
            columns=["Model", "Medium", "Artist", "Trending", "Movement", "Flavors"],
        )
    )

    flaves = ", ".join([f"{x[0]}" for x in bests[4]])
    medium = bests[0][0][0]
    if caption.startswith(medium):
        print(
            f"\n\n{caption} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}"
        )
    else:
        print(
            f"\n\n{caption}, {medium} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}"
        )


data_path = "../clip-interrogator/data/"

artists = load_list(os.path.join(data_path, "artists.txt"))
flavors = load_list(os.path.join(data_path, "flavors.txt"))
mediums = load_list(os.path.join(data_path, "mediums.txt"))
movements = load_list(os.path.join(data_path, "movements.txt"))

sites = [
    "Artstation",
    "behance",
    "cg society",
    "cgsociety",
    "deviantart",
    "dribble",
    "flickr",
    "instagram",
    "pexels",
    "pinterest",
    "pixabay",
    "pixiv",
    "polycount",
    "reddit",
    "shutterstock",
    "tumblr",
    "unsplash",
    "zbrush central",
]
trending_list = [site for site in sites]
trending_list.extend(["trending on " + site for site in sites])
trending_list.extend(["featured on " + site for site in sites])
trending_list.extend([site + " contest winner" for site in sites])

# @title Interrogate!

# @markdown

# @markdown #####**Image:**

image_path_or_url = "https://i.redd.it/e2e8gimigjq91.jpg"  # @param {type:"string"}

# @markdown

# @markdown #####**CLIP models:**

# @markdown For [StableDiffusion](https://stability.ai/blog/stable-diffusion-announcement) you can just use ViTL14<br>
# @markdown For [DiscoDiffusion](https://colab.research.google.com/github/alembics/disco-diffusion/blob/main/Disco_Diffusion.ipynb) and
# @markdown [JAX](https://colab.research.google.com/github/huemin-art/jax-guided-diffusion/blob/v2.7/Huemin_Jax_Diffusion_2_7.ipynb) enable all the same models here as you intend to use when generating your images

ViTB32 = True  # @param{type:"boolean"}
ViTB16 = True  # @param{type:"boolean"}
ViTL14 = False  # @param{type:"boolean"}
ViTL14_336px = False  # @param{type:"boolean"}
RN101 = False  # @param{type:"boolean"}
RN50 = True  # @param{type:"boolean"}
RN50x4 = False  # @param{type:"boolean"}
RN50x16 = False  # @param{type:"boolean"}
RN50x64 = False  # @param{type:"boolean"}

models = []
if ViTB32:
    models.append("ViT-B/32")
if ViTB16:
    models.append("ViT-B/16")
if ViTL14:
    models.append("ViT-L/14")
if ViTL14_336px:
    models.append("ViT-L/14@336px")
if RN101:
    models.append("RN101")
if RN50:
    models.append("RN50")
if RN50x4:
    models.append("RN50x4")
if RN50x16:
    models.append("RN50x16")
if RN50x64:
    models.append("RN50x64")

if str(image_path_or_url).startswith("http://") or str(image_path_or_url).startswith(
    "https://"
):
    image = Image.open(requests.get(image_path_or_url, stream=True).raw).convert("RGB")
else:
    image = Image.open(image_path_or_url).convert("RGB")

thumb = image.copy()
thumb.thumbnail([blip_image_eval_size, blip_image_eval_size])
display(thumb)

interrogate(image, models=models)


================================================
FILE: scripts/convert_original_stable_diffusion_to_diffusers.py
================================================
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Conversion script for the LDM checkpoints. """

import argparse
import os

import torch


try:
    from omegaconf import OmegaConf
except ImportError:
    raise ImportError(
        "OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`."
    )

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    # DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LDMTextToImagePipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNet2DConditionModel,
)
from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import (
    LDMBertConfig,
    LDMBertModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from transformers import (
    AutoFeatureExtractor,
    BertTokenizerFast,
    CLIPTextModel,
    CLIPTokenizer,
)


def shave_segments(path, n_shave_prefix_segments=1):
    """
    Removes segments. Positive values shave the first segments, negative shave the last segments.
    """
    if n_shave_prefix_segments >= 0:
        return ".".join(path.split(".")[n_shave_prefix_segments:])
    else:
        return ".".join(path.split(".")[:n_shave_prefix_segments])


def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
    """
    Updates paths inside resnets to the new naming scheme (local renaming)
    """
    mapping = []
    for old_item in old_list:
        new_item = old_item.replace("in_layers.0", "norm1")
        new_item = new_item.replace("in_layers.2", "conv1")

        new_item = new_item.replace("out_layers.0", "norm2")
        new_item = new_item.replace("out_layers.3", "conv2")

        new_item = new_item.replace("emb_layers.1", "time_emb_proj")
        new_item = new_item.replace("skip_connection", "conv_shortcut")

        new_item = shave_segments(
            new_item, n_shave_prefix_segments=n_shave_prefix_segments
        )

        mapping.append({"old": old_item, "new": new_item})

    return mapping


def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
    """
    Updates paths inside resnets to the new naming scheme (local renaming)
    """
    mapping = []
    for old_item in old_list:
        new_item = old_item

        new_item = new_item.replace("nin_shortcut", "conv_shortcut")
        new_item = shave_segments(
            new_item, n_shave_prefix_segments=n_shave_prefix_segments
        )

        mapping.append({"old": old_item, "new": new_item})

    return mapping


def renew_attention_paths(old_list, n_shave_prefix_segments=0):
    """
    Updates paths inside attentions to the new naming scheme (local renaming)
    """
    mapping = []
    for old_item in old_list:
        new_item = old_item

        #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
        #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

        #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
        #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

        #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

        mapping.append({"old": old_item, "new": new_item})

    return mapping


def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
    """
    Updates paths inside attentions to the new naming scheme (local renaming)
    """
    mapping = []
    for old_item in old_list:
        new_item = old_item

        new_item = new_item.replace("norm.weight", "group_norm.weight")
        new_item = new_item.replace("norm.bias", "group_norm.bias")

        new_item = new_item.replace("q.weight", "query.weight")
        new_item = new_item.replace("q.bias", "query.bias")

        new_item = new_item.replace("k.weight", "key.weight")
        new_item = new_item.replace("k.bias", "key.bias")

        new_item = new_item.replace("v.weight", "value.weight")
        new_item = new_item.replace("v.bias", "value.bias")

        new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
        new_item = new_item.replace("proj_out.bias", "proj_attn.bias")

        new_item = shave_segments(
            new_item, n_shave_prefix_segments=n_shave_prefix_segments
        )

        mapping.append({"old": old_item, "new": new_item})

    return mapping


def assign_to_checkpoint(
    paths,
    checkpoint,
    old_checkpoint,
    attention_paths_to_split=None,
    additional_replacements=None,
    config=None,
):
    """
    This does the final conversion step: take locally converted weights and apply a global renaming
    to them. It splits attention layers, and takes into account additional replacements
    that may arise.

    Assigns the weights to the new checkpoint.
    """
    assert isinstance(
        paths, list
    ), "Paths should be a list of dicts containing 'old' and 'new' keys."

    # Splits the attention layers into three variables.
    if attention_paths_to_split is not None:
        for path, path_map in attention_paths_to_split.items():
            old_tensor = old_checkpoint[path]
            channels = old_tensor.shape[0] // 3

            target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)

            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3

            old_tensor = old_tensor.reshape(
                (num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]
            )
            query, key, value = old_tensor.split(channels // num_heads, dim=1)

            checkpoint[path_map["query"]] = query.reshape(target_shape)
            checkpoint[path_map["key"]] = key.reshape(target_shape)
            checkpoint[path_map["value"]] = value.reshape(target_shape)

    for path in paths:
        new_path = path["new"]

        # These have already been assigned
        if (
            attention_paths_to_split is not None
            and new_path in attention_paths_to_split
        ):
            continue

        # Global renaming happens here
        new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
        new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
        new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")

        if additional_replacements is not None:
            for replacement in additional_replacements:
                new_path = new_path.replace(replacement["old"], replacement["new"])

        # proj_attn.weight has to be converted from conv 1D to linear
        if "proj_attn.weight" in new_path:
            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
        else:
            checkpoint[new_path] = old_checkpoint[path["old"]]


def conv_attn_to_linear(checkpoint):
    keys = list(checkpoint.keys())
    attn_keys = ["query.weight", "key.weight", "value.weight"]
    for key in keys:
        if ".".join(key.split(".")[-2:]) in attn_keys:
            if checkpoint[key].ndim > 2:
                checkpoint[key] = checkpoint[key][:, :, 0, 0]
        elif "proj_attn.weight" in key:
            if checkpoint[key].ndim > 2:
                checkpoint[key] = checkpoint[key][:, :, 0]


def create_unet_diffusers_config(original_config):
    """
    Creates a config for the diffusers based on the config of the LDM model.
    """
    unet_params = original_config.model.params.unet_config.params

    block_out_channels = [
        unet_params.model_channels * mult for mult in unet_params.channel_mult
    ]

    down_block_types = []
    resolution = 1
    for i in range(len(block_out_channels)):
        block_type = (
            "CrossAttnDownBlock2D"
            if resolution in unet_params.attention_resolutions
            else "DownBlock2D"
        )
        down_block_types.append(block_type)
        if i != len(block_out_channels) - 1:
            resolution *= 2

    up_block_types = []
    for i in range(len(block_out_channels)):
        block_type = (
            "CrossAttnUpBlock2D"
            if resolution in unet_params.attention_resolutions
            else "UpBlock2D"
        )
        up_block_types.append(block_type)
        resolution //= 2

    config = dict(
        sample_size=unet_params.image_size,
        in_channels=unet_params.in_channels,
        out_channels=unet_params.out_channels,
        down_block_types=tuple(down_block_types),
        up_block_types=tuple(up_block_types),
        block_out_channels=tuple(block_out_channels),
        layers_per_block=unet_params.num_res_blocks,
        cross_attention_dim=unet_params.context_dim,
        attention_head_dim=unet_params.num_heads,
    )

    return config


def create_vae_diffusers_config(original_config):
    """
    Creates a config for the diffusers based on the config of the LDM model.
    """
    vae_params = original_config.model.params.first_stage_config.params.ddconfig
    _ = original_config.model.params.first_stage_config.params.embed_dim

    block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
    down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
    up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)

    config = dict(
        sample_size=vae_params.resolution,
        in_channels=vae_params.in_channels,
        out_channels=vae_params.out_ch,
        down_block_types=tuple(down_block_types),
        up_block_types=tuple(up_block_types),
        block_out_channels=tuple(block_out_channels),
        latent_channels=vae_params.z_channels,
        layers_per_block=vae_params.num_res_blocks,
    )
    return config


def create_diffusers_schedular(original_config):
    schedular = DDIMScheduler(
        num_train_timesteps=original_config.model.params.timesteps,
        beta_start=original_config.model.params.linear_start,
        beta_end=original_config.model.params.linear_end,
        beta_schedule="scaled_linear",
    )
    return schedular


def create_ldm_bert_config(original_config):
    bert_params = original_config.model.parms.cond_stage_config.params
    config = LDMBertConfig(
        d_model=bert_params.n_embed,
        encoder_layers=bert_params.n_layer,
        encoder_ffn_dim=bert_params.n_embed * 4,
    )
    return config


def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):
    """
    Takes a state dict and a config, and returns a converted checkpoint.
    """

    # extract state_dict for UNet
    unet_state_dict = {}
    keys = list(checkpoint.keys())

    unet_key = "model.diffusion_model."
    # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
    if sum(k.startswith("model_ema") for k in keys) > 100:
        print(f"Checkpoint {path} has both EMA and non-EMA weights.")
        if extract_ema:
            print(
                "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
                " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
            )
            for key in keys:
                if key.startswith("model.diffusion_model"):
                    flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
                    unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(
                        flat_ema_key
                    )
        else:
            print(
                "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
                " weights (usually better for inference), please make sure to add the `--extract_ema` flag."
            )

    for key in keys:
        if key.startswith(unet_key):
            unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)

    new_checkpoint = {}

    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict[
        "time_embed.0.weight"
    ]
    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict[
        "time_embed.0.bias"
    ]
    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict[
        "time_embed.2.weight"
    ]
    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict[
        "time_embed.2.bias"
    ]

    new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
    new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]

    new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
    new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
    new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
    new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]

    # Retrieves the keys for the input blocks only
    num_input_blocks = len(
        {
            ".".join(layer.split(".")[:2])
            for layer in unet_state_dict
            if "input_blocks" in layer
        }
    )
    input_blocks = {
        layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
        for layer_id in range(num_input_blocks)
    }

    # Retrieves the keys for the middle blocks only
    num_middle_blocks = len(
        {
            ".".join(layer.split(".")[:2])
            for layer in unet_state_dict
            if "middle_block" in layer
        }
    )
    middle_blocks = {
        layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
        for layer_id in range(num_middle_blocks)
    }

    # Retrieves the keys for the output blocks only
    num_output_blocks = len(
        {
            ".".join(layer.split(".")[:2])
            for layer in unet_state_dict
            if "output_blocks" in layer
        }
    )
    output_blocks = {
        layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
        for layer_id in range(num_output_blocks)
    }

    for i in range(1, num_input_blocks):
        block_id = (i - 1) // (config["layers_per_block"] + 1)
        layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)

        resnets = [
            key
            for key in input_blocks[i]
            if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
        ]
        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]

        if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
            new_checkpoint[
                f"down_blocks.{block_id}.downsamplers.0.conv.weight"
            ] = unet_state_dict.pop(f"input_blocks.{i}.0.op.weight")
            new_checkpoint[
                f"down_blocks.{block_id}.downsamplers.0.conv.bias"
            ] = unet_state_dict.pop(f"input_blocks.{i}.0.op.bias")

        paths = renew_resnet_paths(resnets)
        meta_path = {
            "old": f"input_blocks.{i}.0",
            "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}",
        }
        assign_to_checkpoint(
            paths,
            new_checkpoint,
            unet_state_dict,
            additional_replacements=[meta_path],
            config=config,
        )

        if len(attentions):
            paths = renew_attention_paths(attentions)
            meta_path = {
                "old": f"input_blocks.{i}.1",
                "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
            }
            assign_to_checkpoint(
                paths,
                new_checkpoint,
                unet_state_dict,
                additional_replacements=[meta_path],
                config=config,
            )

    resnet_0 = middle_blocks[0]
    attentions = middle_blocks[1]
    resnet_1 = middle_blocks[2]

    resnet_0_paths = renew_resnet_paths(resnet_0)
    assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)

    resnet_1_paths = renew_resnet_paths(resnet_1)
    assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)

    attentions_paths = renew_attention_paths(attentions)
    meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
    assign_to_checkpoint(
        attentions_paths,
        new_checkpoint,
        unet_state_dict,
        additional_replacements=[meta_path],
        config=config,
    )

    for i in range(num_output_blocks):
        block_id = i // (config["layers_per_block"] + 1)
        layer_in_block_id = i % (config["layers_per_block"] + 1)
        output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
        output_block_list = {}

        for layer in output_block_layers:
            layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(layer_name)
            else:
                output_block_list[layer_id] = [layer_name]

        if len(output_block_list) > 1:
            resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
            attentions = [
                key for key in output_blocks[i] if f"output_blocks.{i}.1" in key
            ]

            resnet_0_paths = renew_resnet_paths(resnets)
            paths = renew_resnet_paths(resnets)

            meta_path = {
                "old": f"output_blocks.{i}.0",
                "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}",
            }
            assign_to_checkpoint(
                paths,
                new_checkpoint,
                unet_state_dict,
                additional_replacements=[meta_path],
                config=config,
            )

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                index = list(output_block_list.values()).index(
                    ["conv.weight", "conv.bias"]
                )
                new_checkpoint[
                    f"up_blocks.{block_id}.upsamplers.0.conv.weight"
                ] = unet_state_dict[f"output_blocks.{i}.{index}.conv.weight"]
                new_checkpoint[
                    f"up_blocks.{block_id}.upsamplers.0.conv.bias"
                ] = unet_state_dict[f"output_blocks.{i}.{index}.conv.bias"]

                # Clear attentions as they have been attributed above.
                if len(attentions) == 2:
                    attentions = []

            if len(attentions):
                paths = renew_attention_paths(attentions)
                meta_path = {
                    "old": f"output_blocks.{i}.1",
                    "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                }
                assign_to_checkpoint(
                    paths,
                    new_checkpoint,
                    unet_state_dict,
                    additional_replacements=[meta_path],
                    config=config,
                )
        else:
            resnet_0_paths = renew_resnet_paths(
                output_block_layers, n_shave_prefix_segments=1
            )
            for path in resnet_0_paths:
                old_path = ".".join(["output_blocks", str(i), path["old"]])
                new_path = ".".join(
                    [
                        "up_blocks",
                        str(block_id),
                        "resnets",
                        str(layer_in_block_id),
                        path["new"],
                    ]
                )

                new_checkpoint[new_path] = unet_state_dict[old_path]

    return new_checkpoint


def convert_ldm_vae_checkpoint(checkpoint, config):
    # extract state dict for VAE
    vae_state_dict = {}
    vae_key = "first_stage_model."
    keys = list(checkpoint.keys())
    for key in keys:
        if key.startswith(vae_key):
            vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)

    new_checkpoint = {}

    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict[
        "encoder.conv_out.weight"
    ]
    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict[
        "encoder.norm_out.weight"
    ]
    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict[
        "encoder.norm_out.bias"
    ]

    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict[
        "decoder.conv_out.weight"
    ]
    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict[
        "decoder.norm_out.weight"
    ]
    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict[
        "decoder.norm_out.bias"
    ]

    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
    new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
    new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]

    # Retrieves the keys for the encoder down blocks only
    num_down_blocks = len(
        {
            ".".join(layer.split(".")[:3])
            for layer in vae_state_dict
            if "encoder.down" in layer
        }
    )
    down_blocks = {
        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key]
        for layer_id in range(num_down_blocks)
    }

    # Retrieves the keys for the decoder up blocks only
    num_up_blocks = len(
        {
            ".".join(layer.split(".")[:3])
            for layer in vae_state_dict
            if "decoder.up" in layer
        }
    )
    up_blocks = {
        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key]
        for layer_id in range(num_up_blocks)
    }

    for i in range(num_down_blocks):
        resnets = [
            key
            for key in down_blocks[i]
            if f"down.{i}" in key and f"down.{i}.downsample" not in key
        ]

        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
            new_checkpoint[
                f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"
            ] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.weight")
            new_checkpoint[
                f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"
            ] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.bias")

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
        assign_to_checkpoint(
            paths,
            new_checkpoint,
            vae_state_dict,
            additional_replacements=[meta_path],
            config=config,
        )

    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
    num_mid_res_blocks = 2
    for i in range(1, num_mid_res_blocks + 1):
        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
        assign_to_checkpoint(
            paths,
            new_checkpoint,
            vae_state_dict,
            additional_replacements=[meta_path],
            config=config,
        )

    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
    paths = renew_vae_attention_paths(mid_attentions)
    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
    assign_to_checkpoint(
        paths,
        new_checkpoint,
        vae_state_dict,
        additional_replacements=[meta_path],
        config=config,
    )
    conv_attn_to_linear(new_checkpoint)

    for i in range(num_up_blocks):
        block_id = num_up_blocks - 1 - i
        resnets = [
            key
            for key in up_blocks[block_id]
            if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
        ]

        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
            new_checkpoint[
                f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"
            ] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.weight"]
            new_checkpoint[
                f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"
            ] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.bias"]

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
        assign_to_checkpoint(
            paths,
            new_checkpoint,
            vae_state_dict,
            additional_replacements=[meta_path],
            config=config,
        )

    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
    num_mid_res_blocks = 2
    for i in range(1, num_mid_res_blocks + 1):
        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]

        paths = renew_vae_resnet_paths(resnets)
        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
        assign_to_checkpoint(
            paths,
            new_checkpoint,
            vae_state_dict,
            additional_replacements=[meta_path],
            config=config,
        )

    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
    paths = renew_vae_attention_paths(mid_attentions)
    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
    assign_to_checkpoint(
        paths,
        new_checkpoint,
        vae_state_dict,
        additional_replacements=[meta_path],
        config=config,
    )
    conv_attn_to_linear(new_checkpoint)
    return new_checkpoint


def convert_ldm_bert_checkpoint(checkpoint, config):
    def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
        hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
        hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
        hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight

        hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
        hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias

    def _copy_linear(hf_linear, pt_linear):
        hf_linear.weight = pt_linear.weight
        hf_linear.bias = pt_linear.bias

    def _copy_layer(hf_layer, pt_layer):
        # copy layer norms
        _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
        _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])

        # copy attn
        _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])

        # copy MLP
        pt_mlp = pt_layer[1][1]
        _copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
        _copy_linear(hf_layer.fc2, pt_mlp.net[2])

    def _copy_layers(hf_layers, pt_layers):
        for i, hf_layer in enumerate(hf_layers):
            if i != 0:
                i += i
            pt_layer = pt_layers[i : i + 2]
            _copy_layer(hf_layer, pt_layer)

    hf_model = LDMBertModel(config).eval()

    # copy  embeds
    hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
    hf_model.model.embed_positions.weight.data = (
        checkpoint.transformer.pos_emb.emb.weight
    )

    # copy layer norm
    _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)

    # copy hidden layers
    _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)

    _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)

    return hf_model


def convert_ldm_clip_checkpoint(checkpoint):
    text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")

    keys = list(checkpoint.keys())

    text_model_dict = {}

    for key in keys:
        if key.startswith("cond_stage_model.transformer"):
            text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[
                key
            ]

    try:
        text_model.load_state_dict(text_model_dict)
    except RuntimeError:
        pass

    return text_model


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--checkpoint_path",
        default=None,
        type=str,
        required=True,
        help="Path to the checkpoint to convert.",
    )
    # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
    parser.add_argument(
        "--original_config_file",
        default=None,
        type=str,
        help="The YAML config file corresponding to the original architecture.",
    )
    parser.add_argument(
        "--scheduler_type",
        default="pndm",
        type=str,
        help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancest', 'dpm']",
    )
    parser.add_argument(
        "--extract_ema",
        action="store_true",
        help=(
            "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
            " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
            " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
        ),
    )
    parser.add_argument(
        "--dump_path",
        default=None,
        type=str,
        required=True,
        help="Path to the output model.",
    )

    args = parser.parse_args()

    if args.original_config_file is None:
        os.system(
            "wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
        )
        args.original_config_file = "./v1-inference.yaml"

    original_config = OmegaConf.load(args.original_config_file)

    checkpoint = torch.load(args.checkpoint_path)
    checkpoint = checkpoint["state_dict"]

    num_train_timesteps = original_config.model.params.timesteps
    beta_start = original_config.model.params.linear_start
    beta_end = original_config.model.params.linear_end
    if args.scheduler_type == "pndm":
        scheduler = PNDMScheduler(
            beta_end=beta_end,
            beta_schedule="scaled_linear",
            beta_start=beta_start,
            num_train_timesteps=num_train_timesteps,
            skip_prk_steps=True,
        )
    elif args.scheduler_type == "lms":
        scheduler = LMSDiscreteScheduler(
            beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
        )
    elif args.scheduler_type == "euler":
        scheduler = EulerDiscreteScheduler(
            beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
        )
    elif args.scheduler_type == "euler-ancestral":
        scheduler = EulerAncestralDiscreteScheduler(
            beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
        )
    elif args.scheduler_type == "dpm":
        scheduler = DPMSolverMultistepScheduler(
            beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
        )
    elif args.scheduler_type == "ddim":
        scheduler = DDIMScheduler(
            beta_start=beta_start,
            beta_end=beta_end,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )
    else:
        raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!")

    # Convert the UNet2DConditionModel model.
    unet_config = create_unet_diffusers_config(original_config)
    converted_unet_checkpoint = convert_ldm_unet_checkpoint(
        checkpoint, unet_config, path=args.checkpoint_path, extract_ema=args.extract_ema
    )

    unet = UNet2DConditionModel(**unet_config)
    unet.load_state_dict(converted_unet_checkpoint)

    # Convert the VAE model.
    vae_config = create_vae_diffusers_config(original_config)
    converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)

    vae = AutoencoderKL(**vae_config)
    vae.load_state_dict(converted_vae_checkpoint)

    # Convert the text model.
    text_model_type = original_config.model.params.cond_stage_config.target.split(".")[
        -1
    ]
    if text_model_type == "FrozenCLIPEmbedder":
        text_model = convert_ldm_clip_checkpoint(checkpoint)
        tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
        safety_checker = StableDiffusionSafetyChecker.from_pretrained(
            "CompVis/stable-diffusion-safety-checker"
        )
        feature_extractor = AutoFeatureExtractor.from_pretrained(
            "CompVis/stable-diffusion-safety-checker"
        )
        pipe = StableDiffusionPipeline(
            vae=vae,
            text_encoder=text_model,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
    else:
        text_config = create_ldm_bert_config(original_config)
        text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
        tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
        pipe = LDMTextToImagePipeline(
            vqvae=vae,
            bert=text_model,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
        )

    pipe.save_pretrained(args.dump_path)


================================================
FILE: scripts/custom_components/draggable_number_input/__init__.py
================================================
import os
import streamlit.components.v1 as components


def load(pixel_per_step=50):
    parent_dir = os.path.dirname(os.path.abspath(__file__))
    file = os.path.join(parent_dir, "main.js")

    with open(file) as f:
        javascript_main = f.read()
        javascript_main = javascript_main.replace(
            "%%pixelPerStep%%", str(pixel_per_step)
        )
        components.html(f"<script>{javascript_main}</script>")


================================================
FILE: scripts/custom_components/draggable_number_input/main.js
================================================
// iframe parent
var parentDoc = window.parent.document

// check for mouse pointer locking support, not a requirement but improves the overall experience
var havePointerLock = 'pointerLockElement' in parentDoc ||
  'mozPointerLockElement' in parentDoc ||
  'webkitPointerLockElement' in parentDoc;

// the pointer locking exit function
parentDoc.exitPointerLock = parentDoc.exitPointerLock || parentDoc.mozExitPointerLock || parentDoc.webkitExitPointerLock;

// how far should the mouse travel for a step in pixel
var pixelPerStep = %%pixelPerStep%%;
// how many steps did the mouse move in as float
var movementDelta = 0.0;
// value when drag started
var lockedValue = 0.0;
// minimum value from field
var lockedMin = 0.0;
// maximum value from field
var lockedMax = 0.0;
// how big should the field steps be
var lockedStep = 0.0;
// the currently locked in field
var lockedField = null;

// lock box to just request pointer lock for one element
var lockBox = document.createElement("div");
lockBox.classList.add("lockbox");
parentDoc.body.appendChild(lockBox);
lockBox.requestPointerLock = lockBox.requestPointerLock || lockBox.mozRequestPointerLock || lockBox.webkitRequestPointerLock;

function Lock(field)
{
	var rect = field.getBoundingClientRect();
	lockBox.style.left = (rect.left-2.5)+"px";
	lockBox.style.top = (rect.top-2.5)+"px";

	lockBox.style.width = (rect.width+2.5)+"px";
	lockBox.style.height = (rect.height+5)+"px";

	lockBox.requestPointerLock();
}

function Unlock()
{
	parentDoc.exitPointerLock();
	lockBox.style.left = "0px";
	lockBox.style.top = "0px";

	lockBox.style.width = "0px";
	lockBox.style.height = "0px";
	lockedField.focus();
}

parentDoc.addEventListener('mousedown', (e) => {
	// if middle is down
	if(e.button === 1)
	{
		if(e.target.tagName === 'INPUT' && e.target.type === 'number')
		{
			e.preventDefault();
			var field = e.target;
			if(havePointerLock)
				Lock(field);

			// save current field
			lockedField = e.target;
			// add class for styling
			lockedField.classList.add("value-dragging");
			// reset movement delta
			movementDelta = 0.0;
			// set to 0 if field is empty
			if(lockedField.value === '')
				lockedField.value = 0.0;

			// save current field value
			lockedValue = parseFloat(lockedField.value);

			if(lockedField.min === '' || lockedField.min === '-Infinity')
				lockedMin = -99999999.0;
			else
				lockedMin = parseFloat(lockedField.min);

			if(lockedField.max === '' || lockedField.max === 'Infinity')
				lockedMax = 99999999.0;
			else
				lockedMax = parseFloat(lockedField.max);

			if(lockedField.step === '' || lockedField.step === 'Infinity')
				lockedStep = 1.0;
			else
				lockedStep = parseFloat(lockedField.step);

			// lock pointer if available
			if(havePointerLock)
				Lock(lockedField);

			// add drag event
			parentDoc.addEventListener("mousemove", onDrag, false);
		}
	}
});

function onDrag(e)
{
	if(lockedField !== null)
	{
		// add movement to delta
		movementDelta += e.movementX / pixelPerStep;
		if(lockedField === NaN)
			return;
		// set new value
		let value = lockedValue + Math.floor(Math.abs(movementDelta)) * lockedStep * Math.sign(movementDelta);
		lockedField.focus();
		lockedField.select();
		parentDoc.execCommand('insertText', false /*no UI*/, Math.min(Math.max(value, lockedMin), lockedMax));
	}
}

parentDoc.addEventListener('mouseup', (e) => {
	// if mouse is up
	if(e.button === 1)
	{
		// release pointer lock if available
		if(havePointerLock)
			Unlock();

		if(lockedField !== null && lockedField !== NaN)
		{
			// stop drag event
			parentDoc.removeEventListener("mousemove", onDrag, false);
			// remove class for styling
			lockedField.classList.remove("value-dragging");
			// remove reference
			lockedField = null;
		}
	}
});

// only execute once (even though multiple iframes exist)
if(!parentDoc.hasOwnProperty("dragableInitialized"))
{
	var parentCSS =
`
/* Make input-instruction not block mouse events */
.input-instructions,.input-instructions > *{
	pointer-events: none;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
}

.lockbox {
	background-color: transparent;
	position: absolute;
	pointer-events: none;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
	border-left: dotted 2px rgb(255,75,75);
	border-top: dotted 2px rgb(255,75,75);
	border-bottom: dotted 2px rgb(255,75,75);
	border-right: dotted 1px rgba(255,75,75,0.2);
	border-top-left-radius: 0.25rem;
	border-bottom-left-radius: 0.25rem;
	z-index: 1000;
}
`;

	// get parent document head
	var head = parentDoc.getElementsByTagName('head')[0];
	// add style tag
	var s = document.createElement('style');
    // set type attribute
	s.setAttribute('type', 'text/css');
    // add css forwarded from python
	if (s.styleSheet) {   // IE
        s.styleSheet.cssText = parentCSS;
    } else {                // the world
        s.appendChild(document.createTextNode(parentCSS));
    }
	// add style to head
    head.appendChild(s);
	// set flag so this only runs once
	parentDoc["dragableInitialized"] = true;
}


================================================
FILE: scripts/custom_components/sygil_suggestions/__init__.py
================================================
import os
import streamlit.components.v1 as components

# where to save the downloaded key_phrases
key_phrases_file = "data/tags/key_phrases.json"
# the loaded key phrase json as text
key_phrases_json = ""
# where to save the downloaded key_phrases
thumbnails_file = "data/tags/thumbnails.json"
# the loaded key phrase json as text
thumbnails_json = ""


def init():
    global key_phrases_json, thumbnails_json
    with open(key_phrases_file) as f:
        key_phrases_json = f.read()
    with open(thumbnails_file) as f:
        thumbnails_json = f.read()


def suggestion_area(placeholder):
    # get component path
    parent_dir = os.path.dirname(os.path.abspath(__file__))
    # get file paths
    javascript_file = os.path.join(parent_dir, "main.js")
    stylesheet_file = os.path.join(parent_dir, "main.css")
    parent_stylesheet_file = os.path.join(parent_dir, "parent.css")

    # load file texts
    with open(javascript_file) as f:
        javascript_main = f.read()
    with open(stylesheet_file) as f:
        stylesheet_main = f.read()
    with open(parent_stylesheet_file) as f:
        parent_stylesheet = f.read()

    # add suggestion area div box
    html = "<div id='scroll_area' class='st-bg'><div id='suggestion_area'>javascript failed</div></div>"
    # add loaded style
    html += f"<style>{stylesheet_main}</style>"
    # set default variables
    html += f"<script>var thumbnails = {thumbnails_json};\nvar keyPhrases = {key_phrases_json};\nvar parentCSS = `{parent_stylesheet}`;\nvar placeholder='{placeholder}';</script>"
    # add main java script
    html += f"\n<script>{javascript_main}</script>"
    # add component to site
    components.html(html, width=None, height=None, scrolling=True)


================================================
FILE: scripts/custom_components/sygil_suggestions/main.css
================================================
*
{
	padding: 0px;
	margin: 0px;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
}

body
{
	width: 100%;
	height: 100%;
	padding-left: calc( 1em - 1px );
	padding-top: calc( 1em - 1px );
	overflow: hidden;
}

/* width */
::-webkit-scrollbar {
  width: 7px;
}

/* Track */
::-webkit-scrollbar-track {
  background: rgb(10, 13, 19);
}

/* Handle */
::-webkit-scrollbar-thumb {
  background: #6c6e72;
  border-radius: 3px;
}

/* Handle on hover */
::-webkit-scrollbar-thumb:hover {
  background: #6c6e72;
}

#scroll_area
{
	display: flex;
	overflow-x: hidden;
	overflow-y: auto;
}

#suggestion_area
{
	overflow-x: hidden;
	width: calc( 100% - 2em - 2px );
	margin-bottom: calc( 1em + 13px );
	min-height: 50px;
}

span
{
	border: 1px solid rgba(250, 250, 250, 0.2);
	border-radius: 0.25rem;
	font-size: 1rem;
	font-family: "Source Sans Pro", sans-serif;

	background-color: rgb(38, 39, 48);
	color: white;
	display: inline-block;
	padding: 0.5rem;
	margin-right: 3px;
	cursor: pointer;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
}

span:hover
{
	color: rgb(255,75,75);
	border-color: rgb(255,75,75);
}


================================================
FILE: scripts/custom_components/sygil_suggestions/main.js
================================================

// parent document
var parentDoc = window.parent.document;
// iframe element in parent document
var frame = window.frameElement;
// the area to put the suggestions in
var suggestionArea = document.getElementById('suggestion_area');
var scrollArea = document.getElementById('scroll_area');
// button height is read when the first button gets created
var buttonHeight = -1;
// the maximum size of the iframe in buttons (3 x buttons height)
var maxHeightInButtons = 3;
// the prompt field connected to this iframe
var promptField = null;
// the category of suggestions
var activeCategory = contextCategory;

var conditionalButtons = null;

var contextCategory = "[context]";

var frameHeight = "calc( 3em - 3px + {} )";

var filterGroups = {nsfw_mild: "nsfw_mild", nsfw_basic: "nsfw_basic", nsfw_strict: "nsfw_strict", gore_mild: "gore_mild", gore_basic: "gore_basic", gore_strict: "gore_strict"};
var activeFilters = [filterGroups.nsfw_mild, filterGroups.nsfw_basic, filterGroups.gore_mild];

var triggers = {empty: "empty", nsfw: "nsfw", nude: "nude"};
var activeContext = [];

var triggerIndex = {};

var wordMap = {};
var tagMap = {};

// could pass in an array of specific stylesheets for optimization
function getAllCSSVariableNames(styleSheets = parentDoc.styleSheets){
   var cssVars = [];
   // loop each stylesheet
   for(var i = 0; i < styleSheets.length; i++){
      // loop stylesheet's cssRules
      try{ // try/catch used because 'hasOwnProperty' doesn't work
         for( var j = 0; j < styleSheets[i].cssRules.length; j++){
            try{
				//console.log(styleSheets[i].cssRules[j].selectorText);
               // loop stylesheet's cssRules' style (property names)
               for(var k = 0; k < styleSheets[i].cssRules[j].style.length; k++){
                  let name = styleSheets[i].cssRules[j].style[k];
                  // test name for css variable signiture and uniqueness
                  if(name.startsWith('--') && cssVars.indexOf(name) == -1){
                     cssVars.push(name);
                  }
               }
            } catch (error) {}
         }
      } catch (error) {}
   }
   return cssVars;
}

function currentFrameAbsolutePosition() {
  let currentWindow = window;
  let currentParentWindow;
  let positions = [];
  let rect;

  while (currentWindow !== window.top) {
    currentParentWindow = currentWindow.parent;
    for (let idx = 0; idx < currentParentWindow.frames.length; idx++)
      if (currentParentWindow.frames[idx] === currentWindow) {
        for (let frameElement of currentParentWindow.document.getElementsByTagName('iframe')) {
          if (frameElement.contentWindow === currentWindow) {
            rect = frameElement.getBoundingClientRect();
            positions.push({x: rect.x, y: rect.y});
          }
        }
        currentWindow = currentParentWindow;
        break;
      }
  }
  return positions.reduce((accumulator, currentValue) => {
    return {
      x: accumulator.x + currentValue.x,
      y: accumulator.y + currentValue.y
    };
  }, { x: 0, y: 0 });
}

// check if element is visible
function isVisible(e) {
    return !!( e.offsetWidth || e.offsetHeight || e.getClientRects().length );
}

// remove everything from the suggestion area
function ClearSuggestionArea(text = "")
{
	suggestionArea.innerHTML = text;
	conditionalButtons = [];
}

// update iframe size depending on button rows
function UpdateSize()
{
	// calculate maximum height
	var maxHeight = buttonHeight * maxHeightInButtons;

	var height = suggestionArea.lastChild.offsetTop + buttonHeight;
	// apply height to iframe
	frame.style.height = frameHeight.replace("{}", Math.min(height,maxHeight)+"px");
	scrollArea.style.height = frame.style.height;
}

// add a button to the suggestion area
function AddButton(label, action, dataTooltip = null, tooltipImage = null, pattern = null, data = null)
{
	// create span
	var button = document.createElement("span");
	// label it
	button.innerHTML = label;
	if(data != null)
	{
		// add category attribute to button, will be read on click
		button.setAttribute("data",data);
	}
	if(pattern != null)
	{
		// add category attribute to button, will be read on click
		button.setAttribute("pattern",pattern);
	}
	if(dataTooltip != null)
	{
		// add category attribute to button, will be read on click
		button.setAttribute("tooltip-text",dataTooltip);
	}
	if(tooltipImage != null)
	{
		// add category attribute to button, will be read on click
		button.setAttribute("tooltip-image",tooltipImage);
	}
	// add button function
	button.addEventListener('click', action, false);
	button.addEventListener('mouseover', ButtonHoverEnter);
	button.addEventListener('mouseout', ButtonHoverExit);
	// add button to suggestion area
	suggestionArea.appendChild(button);
	// get buttonHeight if not set
	if(buttonHeight < 0)
		buttonHeight = button.offsetHeight;
	return button;
}

// find visible prompt field to connect to this iframe
function GetPromptField()
{
	// get all prompt fields, the %% placeholder %% is set in python
	var all = parentDoc.querySelectorAll('textarea[placeholder="'+placeholder+'"]');
	// filter visible
	for(var i = 0; i < all.length; i++)
	{
		if(isVisible(all[i]))
		{
			promptField = all[i];
			promptField.addEventListener('input', OnChange, false);
			promptField.addEventListener('click', OnClick, false);
			promptField.addEventListener('keyup', OnKey, false);
			break;
		}
	}
}

function OnChange(e)
{
	ButtonConditions();
	ButtonUpdateContext(true);
}

function OnClick(e)
{
	ButtonUpdateContext(true);
}

function OnKey(e)
{
	if (e.keyCode == '37' || e.keyCode == '38' || e.keyCode == '39' || e.keyCode == '40') {
		ButtonUpdateContext(false);
	}
}

function getCaretPosition(ctrl) {
    // IE < 9 Support
    if (document.selection) {
        ctrl.focus();
        var range = document.selection.createRange();
        var rangelen = range.text.length;
        range.moveStart('character', -ctrl.value.length);
        var start = range.text.length - rangelen;
        return {
            'start': start,
            'end': start + rangelen
        };
    } // IE >=9 and other browsers
    else if (ctrl.selectionStart || ctrl.selectionStart == '0') {
        return {
            'start': ctrl.selectionStart,
            'end': ctrl.selectionEnd
        };
    } else {
        return {
            'start': 0,
            'end': 0
        };
    }
}

function setCaretPosition(ctrl, start, end) {
    // IE >= 9 and other browsers
    if (ctrl.setSelectionRange) {
        ctrl.focus();
        ctrl.setSelectionRange(start, end);
    }
    // IE < 9
    else if (ctrl.createTextRange) {
        var range = ctrl.createTextRange();
        range.collapse(true);
        range.moveEnd('character', end);
        range.moveStart('character', start);
        range.select();
    }
}

function isEmptyOrSpaces(str){
    return str === null || str.match(/^ *$/) !== null;
}

function ButtonUpdateContext(changeCategory)
{
	let targetCategory = contextCategory;
	let text = promptField.value;
	if(document.activeElement === promptField)
	{
		var pos = getCaretPosition(promptField).end;
		text = promptField.value.slice(0, pos);
	}

	activeContext = [];

	var parts = text.split(/[\.?!,]/);
	if(activeCategory == "Artists" && !isEmptyOrSpaces(parts[parts.length-1]))
	{
		return;
	}
	if(text == "")
	{
		activeContext.push(triggers.empty);
	}
	if(text.endsWith("by"))
	{
		changeCategory = true;
		targetCategory = "Artists";
		activeContext.push("Artists");
	}
	else
	{
		var parts = text.split(/[\.,!?;]/);
		parts = parts.reverse();

		parts.forEach( part =>
		{
			var words = part.split(" ");
			words = words.reverse();
			words.forEach( word =>
			{
				word = word.replace(/[^a-zA-Z0-9 \._\-]/g, '').trim().toLowerCase();
				word = WordToKey(word);
				if(wordMap.hasOwnProperty(word))
				{
					activeContext = activeContext.concat(wordMap[word]).unique();
				}
			});
		});
	}

	if(activeContext.length == 0)
	{
		if(activeCategory == contextCategory)
		{
			activeCategory = "";
			ShowMenu();
		}
	}
	else if(changeCategory)
	{
		activeCategory = targetCategory;
		ShowMenu();
	}
	else if(activeCategory == contextCategory)
		ShowMenu();
}

// when pressing a button, give the focus back to the prompt field
function KeepFocus(e)
{
	e.preventDefault();
	promptField.focus();
}

function selectCategory(e)
{
	KeepFocus(e);
	// set category from attribute
	activeCategory = e.target.getAttribute("data");
	// rebuild menu
	ShowMenu();
}

function leaveCategory(e)
{
	KeepFocus(e);
	activeCategory = "";
	// rebuild menu
	ShowMenu();
}

// [...]=block "..."=requirement ...=add {|}=cursor {}=insert .,!?;=start
// [{} {|}]
// [,by {}{|}]["by "* and by {}{|}]
// [, {}{|}]

function PatternWalk(text, pattern)
{
	var parts = text.split(/[\,!?;]/);
	var part = parts[parts.length - 1];

	var indent = 0;
	var outPattern = "";
	var requirement = ""
	var mode = "";
	var patternFailed = false;
	var partIndex = 0;
	for( let i = 0; i < pattern.length; i++)
	{
		if(mode == "")
		{
			if(pattern[i] == "[")
			{
				indent++;
				mode = "pattern";
				console.log("pattern start:");
			}
		}
		else if(indent > 0)
		{
			if(pattern[i] == "[")
			{
				indent++;
			}
			else if(mode == "pattern")
			{
				if(patternFailed)
				{
					if(pattern[i] == "]")
					{
						indent--;
						if(indent == 0)
						{
							mode = "";
							outPattern = "";
							partIndex = 0;
							patternFailed = false;
							part = parts[parts.length - 1];
						}
					}
					else
					{
					}
				}
				else
				{
					if(pattern[i] == "\"")
					{
						mode = "requirement";
					}
					else if(pattern[i] == "]")
					{
						indent--;
						if(indent == 0)
						{
							mode = "";
							return outPattern;
						}
					}
					else if(pattern[i] == "," || pattern[i] == "!" || pattern[i] == "?" || pattern[i] == ";" )
					{
						let textToCheck = (text+outPattern).trim();

						if(textToCheck.endsWith("and"))
						{
							outPattern += "{_}";
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("with"))
						{
							outPattern += "{_}";
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("of"))
						{
							outPattern += "{_}";
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("at"))
						{
							outPattern += "{_}";
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("and a"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("with a"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("of a"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("at a"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("and an"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("with an"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("of an"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("at an"))
						{
							part = "";
							partIndex = 0;
						}
						else if(!textToCheck.endsWith(pattern[i]))
						{
							outPattern += pattern[i];
							part = "";
							partIndex = 0;
						}
					}
					else if(pattern[i] == "{")
					{
						outPattern += pattern[i];
						mode = "write";
					}
					else if(pattern[i] == "." && pattern[i+1] == "*" || pattern[i] == "*")
					{
						let minLength = false;
						if(pattern[i] == "." && pattern[i+1] == "*")
						{
							minLength = true;
							i++;
						}
						var o = pattern.slice(i+1).search(/[^\w\s]/);
						var subpattern = pattern.slice(i+1,i+1+o);

						var index = part.lastIndexOf(subpattern);
						var subPatternIndex = subpattern.length;
						while(index == -1)
						{
							if(subPatternIndex <= 1)
							{
								patternFailed = true;
								break;
							}

							subPatternIndex--;
							var slice = subpattern.slice(0,subPatternIndex);
							index = part.lastIndexOf(slice);
						}
						if(!patternFailed)
						{
							if(minLength && index == 0)
							{
								patternFailed = true;
							}
							partIndex += index;
						}
						else
						{
						}
					}
					else
					{
						if(partIndex >= part.length)
						{
							outPattern += pattern[i];
						}
						else if(part[partIndex] == pattern[i])
						{
							partIndex++;
						}
						else
						{
							patternFailed = true;
						}
					}
				}
			}
			else if(mode == "requirement")
			{
				if(pattern[i] == "\"")
				{
					if(!part.includes(requirement))
					{
						patternFailed = true;
					}
					else
					{
						partIndex = part.indexOf(requirement)+requirement.length;
					}
					mode = "pattern";
					requirement = "";
				}
				else
				{
					requirement += pattern[i];
				}
			}
			else if(mode == "write")
			{
				if(pattern[i] == "}")
				{
					outPattern += pattern[i];
					mode = "pattern";
				}
				else
				{
					outPattern += pattern[i];
				}
			}
		}
		else if(pattern[i] == "[")
			indent++;
	}
	// fallback
	return ", {}";
}

function InsertPhrase(phrase, pattern)
{
	var text = promptField.value ?? "";
	if(document.activeElement === promptField)
	{
		var pos = getCaretPosition(promptField).end;
		text = promptField.value.slice(0, pos);
	}
	var insert = PatternWalk(text,pattern);
	insert = insert.replace('{}',phrase);

	let firstLetter = phrase.trim()[0];

	if(firstLetter == "a" || firstLetter == "e" || firstLetter == "i" || firstLetter == "o" || firstLetter == "u")
		insert = insert.replace('{_}',"an");
	else
		insert = insert.replace('{_}',"a");

	insert = insert.replace(/{[^|]/,"");
	insert = insert.replace(/[^|]}/,"");

	var caret = (text+insert).indexOf("{|}");
	insert = insert.replace('{|}',"");
	// inserting via execCommand is required, this triggers all native browser functionality as if the user wrote into the prompt field.
	parentDoc.execCommand('insertText', false, insert);
	setCaretPosition(promptField, caret, caret);
}

function SelectPhrase(e)
{
	KeepFocus(e);
	var pattern = e.target.getAttribute("pattern");
	var phrase = e.target.getAttribute("data");

	InsertPhrase(phrase,pattern);
}

function CheckButtonCondition(condition)
{
	var pos = getCaretPosition(promptField).end;
	var text = promptField.value.slice(0, pos);
	if(condition === "empty")
	{
		return text == "";
	}
}

function ButtonConditions()
{
	conditionalButtons.forEach(entry =>
	{
		let filtered = !CheckButtonCondition(entry.condition);

		if(entry.filterGroup != null)
		{
			entry.filterGroup.split(",").forEach( (group) =>
			{

				if(activeFilters.includes(group.trim().toLowerCase()))
				{
					filtered = filtered || true;
					return;
				}
			});
		}
		if(filtered)
			entry.element.style.display = "none";
		else
			entry.element.style.display = "inline-block";
	});
}

function ButtonHoverEnter(e)
{
	var text = e.target.getAttribute("tooltip-text");
	var image = e.target.getAttribute("tooltip-image");
	ShowTooltip(text, e.target, image)
}

function ButtonHoverExit(e)
{
	HideTooltip();
}

function ShowTooltip(text, target, image = "")
{
	var cleanedName = image == null? null : image.replace(/[^a-zA-Z0-9 \._\-]/g, '');
	if((text == "" || text == null) && (image == "" || image == null || thumbnails[cleanedName] === undefined))
		return;

	var currentFramePosition = currentFrameAbsolutePosition();
	var rect = target.getBoundingClientRect();
	var element = parentDoc["phraseTooltip"];
	element.innerText = text;
	if(image != "" && image != null && thumbnails[cleanedName] !== undefined)
	{

		var img = parentDoc.createElement('img');
		img.src = GetThumbnailURL(cleanedName);
		element.appendChild(img)
	}
	element.style.display = "flex";
	element.style.top = (rect.bottom+currentFramePosition.y)+"px";
	element.style.left = (rect.right+currentFramePosition.x)+"px";
	element.style.width = "inherit";
	element.style.height = "inherit";
}

function base64toBlob(base64Data, contentType) {
    contentType = contentType || '';
    var sliceSize = 1024;
    var byteCharacters = atob(base64Data);
    var bytesLength = byteCharacters.length;
    var slicesCount = Math.ceil(bytesLength / sliceSize);
    var byteArrays = new Array(slicesCount);

    for (var sliceIndex = 0; sliceIndex < slicesCount; ++sliceIndex) {
        var begin = sliceIndex * sliceSize;
        var end = Math.min(begin + sliceSize, bytesLength);

        var bytes = new Array(end - begin);
        for (var offset = begin, i = 0; offset < end; ++i, ++offset) {
            bytes[i] = byteCharacters[offset].charCodeAt(0);
        }
        byteArrays[sliceIndex] = new Uint8Array(bytes);
    }
    return new Blob(byteArrays, { type: contentType });
}

function GetThumbnailURL(image)
{
	if(parentDoc["keyPhraseSuggestionsLoadedBlobs"].hasOwnProperty(image))
	{
		return parentDoc["keyPhraseSuggestionsLoadedBlobs"][image];
	}
	else
	{
		let url = URL.createObjectURL(GetThumbnail(image));
		parentDoc["keyPhraseSuggestionsLoadedBlobs"][image] = url;
		return url;
	}
}

function GetThumbnail(image)
{
	return base64toBlob(thumbnails[image], 'image/webp');
}

function HideTooltip()
{
	var element = parentDoc["phraseTooltip"];
	element.style.display= "none";
	element.innerHTML = "";
	element.style.top = "0px";
	element.style.left = "0px";
	element.style.width = "0px";
	element.style.height = "0px";
}

function RemoveDouble(str, symbol)
{
	let doubleSymbole = symbol+symbol;
	while(str.includes(doubleSymbole))
	{
		str = str.replace(doubleSymbole, symbol);
	}
	return str;
}

function ReplaceAll(str, toReplace, seperator, symbol)
{
	toReplace.split(seperator).forEach( (replaceSymbol) =>
	{
		str = str.replace(replaceSymbol, symbol);
	});
	return str;
}

function WordToKey(word)
{
	if(word.endsWith("s"))
		word = word.slice(0, -1);
	word = word.replace("'", "");
	if(word.endsWith("s"))
		word = word.slice(0, -1);
	word = ReplaceAll(word, "sch;sh;ch;ll;gg;r;l;j;g", ';', 'h');
	word = ReplaceAll(word, "sw;ss;zz;qu;kk;k;z;q;s;x", ';','c');
	word = ReplaceAll(word, "pp;bb;tt;th;ff;p;t;b;f;v", ';','d');
	word = ReplaceAll(word, "yu;yo;oo;u;y;w", ';','o');
	word = ReplaceAll(word, "ee;ie;a;i", ';','e');
	word = ReplaceAll(word, "mm;nn;n", ';','n');
	word = RemoveDouble(word, "l");
	word = RemoveDouble(word, "c");
	word = RemoveDouble(word, "e");
	word = RemoveDouble(word, "m");
	word = RemoveDouble(word, "j");
	word = RemoveDouble(word, "o");
	word = RemoveDouble(word, "d");
	word = RemoveDouble(word, "f");
	return word;
}

Array.prototype.unique = function() {
    var a = this.concat();
    for(var i=0; i<a.length; ++i) {
        for(var j=i+1; j<a.length; ++j) {
            if(a[i] == a[j])
                a.splice(j--, 1);
        }
    }

    return a;
};


function BuildTriggerIndex()
{
	triggerIndex = {};
	wordMap = {};
	tagMap = {};
	for (let category in keyPhrases)
	{
		let count = keyPhrases[category]["entries"].length;
		for(let i = 0; i < count; i++)
		{
			let entry = keyPhrases[category]["entries"][i];
			if(entry["trigger"] != null && entry["trigger"] != "")
			{
				let entryTriggers = entry["trigger"].split(",");
				entryTriggers.forEach( trigger =>
				{
					trigger = trigger.replace(/[^a-zA-Z0-9 \._\-]/g, '').trim().toLowerCase();
					if(!triggers.hasOwnProperty(trigger))
					{
						trigger = WordToKey(trigger);
					}
					if(triggerIndex.hasOwnProperty(trigger))
					{
						triggerIndex[trigger].push( { category: category, index: i });
					}
					else
					{
						triggerIndex[trigger] = [];
						triggerIndex[trigger].push( { category: category, index: i });
					}
				});
			}

			/*let words = entry["phrase"].split(" ");
			let wordCount = words.length;
			for(let e = 0; e < wordCount; e++)
			{
				let wordKey = WordToKey(words[e].replace(/[^a-zA-Z0-9 \._\-]/g, '').trim().toLowerCase());

				if(wordKey.length < 2)
					continue;

				if(!wordMap.hasOwnProperty(wordKey))
				{
					wordMap[wordKey] = [];
				}

				let entrySearchTags = entry["search_tags"].split(",");
				entrySearchTags.push(category);
				entrySearchTags.forEach( search_tag =>
				{
					if(search_tag != null && search_tag != "")
					{
						if(search_tag.endsWith("'s"))
							search_tag = search_tag.slice(0, -2);
						if(search_tag.endsWith("s"))
							search_tag = search_tag.slice(0, -1);
						search_tag = search_tag.replace(/[^a-zA-Z0-9 \._\-]/g, '').trim().toLowerCase();
						wordMap[wordKey].push(search_tag);
						if(!tagMap.hasOwnProperty(search_tag))
						{
							tagMap[search_tag] = [];
						}
						tagMap[search_tag].push({ category: category, index: i });
						tagMap[search_tag] = tagMap[search_tag].unique();
					}
				});
				wordMap[wordKey] = wordMap[wordKey].unique();
			}*/
		}
	}
}

function ConditionalButton(entry, button)
{
	if(entry["show_if"] != "" || entry["filter_group"] != "")
		conditionalButtons.push({element:button,condition:entry["show_if"], filterGroup:entry["filter_group"]});
}

// generate menu in suggestion area
function ShowMenu()
{
	// clear all buttons from menu
	ClearSuggestionArea();
	HideTooltip();

	// if no chategory is selected
	if(activeCategory == "")
	{
		if(activeContext.length != 0)
		{
			AddButton("Context", selectCategory, "A dynamicly updating category based on the current prompt.", null, null, contextCategory);
		}
		for (var category in keyPhrases)
		{
			AddButton(category, selectCategory, keyPhrases[category]["description"], null, null, category);
		}
		// change iframe size after buttons have been added
		UpdateSize();
	}
	else if(activeCategory == contextCategory)
	{
		// add a button to leave the chategory
		var backbutton = AddButton("&#x2191; back", leaveCategory);
		activeContext.forEach( context =>
		{
			if(tagMap.hasOwnProperty(context))
			{
				var words = tagMap[context].unique();
				words.forEach( word =>
				{
					var entry = keyPhrases[word.category]["entries"][word.index];
					var tempPattern = keyPhrases[word.category]["pattern"];

					if(entry["pattern_override"] != "")
					{
						tempPattern = entry["pattern_override"];
					}

					var button = AddButton(entry["phrase"], SelectPhrase, entry["description"], entry["phrase"],tempPattern, entry["phrase"]);

					ConditionalButton(entry, button);
				});
			}
			if(triggerIndex.hasOwnProperty(context))
			{
				var triggered = triggerIndex[context];
				triggered.forEach( trigger =>
				{
					var entry = keyPhrases[trigger.category]["entries"][trigger.index];
					var tempPattern = keyPhrases[trigger.category]["pattern"];

					if(entry["pattern_override"] != "")
					{
						tempPattern = entry["pattern_override"];
					}

					var button = AddButton(entry["phrase"], SelectPhrase, entry["description"], entry["phrase"],tempPattern, entry["phrase"]);

					ConditionalButton(entry, button);
				});
			}
		});

		ButtonConditions();
		// change iframe size after buttons have been added
		UpdateSize();
	}
	// if a chategory is selected
	else
	{
		// add a button to leave the chategory
		var backbutton = AddButton("&#x2191; back", leaveCategory);
		var pattern = keyPhrases[activeCategory]["pattern"];
		keyPhrases[activeCategory]["entries"].forEach(entry =>
		{
			var tempPattern = pattern;
			if(entry["pattern_override"] != "")
			{
				tempPattern = entry["pattern_override"];
			}

			var button = AddButton(entry["phrase"], SelectPhrase, entry["description"], entry["phrase"],tempPattern, entry["phrase"]);

			ConditionalButton(entry, button);
		});
		ButtonConditions();
		// change iframe size after buttons have been added
		UpdateSize();
	}
}

// listen for clicks on the prompt field
parentDoc.addEventListener("click", (e) =>
{
	// skip if this frame is not visible
	if(!isVisible(frame))
		return;

	// if the iframes prompt field is not set, get it and set it
	if(promptField === null)
	{
		GetPromptField();
		ButtonUpdateContext(true);
	}

	// get the field with focus
	var target = parentDoc.activeElement;

	// if the field with focus is a prompt field, the %% placeholder %% is set in python
	if(	target.placeholder === placeholder)
	{
		// generate menu
		ShowMenu();
		frame.style.borderBottomWidth = '13px';
	}
	else
	{
		// else hide the iframe
		frame.style.height = "0px";
		frame.style.borderBottomWidth = '0px';
	}
});

function AppendStyle(targetDoc, id, content)
{
	  // get parent document head
	var head = targetDoc.getElementsByTagName('head')[0];

	// add style tag
	var style = targetDoc.createElement('style');
    // set type attribute
	style.setAttribute('type', 'text/css');
	style.id = id;
    // add css forwarded from python
	if (style.styleSheet) {   // IE
        style.styleSheet.cssText = content;
    } else {                // the world
        style.appendChild(parentDoc.createTextNode(content));
    }
	// add style to head
    head.appendChild(style);
}

// Transfer all styles
var head = document.getElementsByTagName("head")[0];
var parentStyle = parentDoc.getElementsByTagName("style");
for (var i = 0; i < parentStyle.length; i++)
	head.appendChild(parentStyle[i].cloneNode(true));
var parentLinks = parentDoc.querySelectorAll('link[rel="stylesheet"]');
for (var i = 0; i < parentLinks.length; i++)
	head.appendChild(parentLinks[i].cloneNode(true));

// add custom style to iframe
frame.classList.add("suggestion-frame");
// clear suggestion area to remove the "javascript failed" message
ClearSuggestionArea();
// collapse the iframe by default
frame.style.height = "0px";
frame.style.borderBottomWidth = '0px';

BuildTriggerIndex();

// only execute once (even though multiple iframes exist)
if(!parentDoc.hasOwnProperty('keyPhraseSuggestionsInitialized'))
{
	AppendStyle(parentDoc, "key-phrase-suggestions", parentCSS);

	var tooltip = parentDoc.createElement('div');
	tooltip.id = "phrase-tooltip";
	parentDoc.body.appendChild(tooltip);
	parentDoc["phraseTooltip"] = tooltip;
	// set flag so this only runs once
	parentDoc["keyPhraseSuggestionsLoadedBlobs"] = {};
	parentDoc["keyPhraseSuggestionsInitialized"] = true;

	var cssVars = getAllCSSVariableNames();
	computedStyle = getComputedStyle(parentDoc.documentElement);

	parentDoc["keyPhraseSuggestionsCSSvariables"] = ":root{";

	cssVars.forEach( (rule) =>
	{
		parentDoc["keyPhraseSuggestionsCSSvariables"] += rule+": "+computedStyle.getPropertyValue(rule)+";";
	});
	parentDoc["keyPhraseSuggestionsCSSvariables"] += "}";
}

AppendStyle(document, "variables", parentDoc["keyPhraseSuggestionsCSSvariables"]);


================================================
FILE: scripts/custom_components/sygil_suggestions/parent.css
================================================
.suggestion-frame
{
	position: absolute;

	/* make as small as possible */
	margin: 0px;
	padding: 0px;
	min-height: 0px;
	line-height: 0;

	/* animate transitions of the height property */
	-webkit-transition: height 1s;
	-moz-transition: height 1s;
	-ms-transition: height 1s;
	-o-transition: height 1s;
	transition: height 1s, border-bottom-width 1s;

	/* block selection */
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;

	z-index: 700;

	outline: 1px solid rgba(250, 250, 250, 0.2);
	outline-offset: 0px;
	border-radius: 0.25rem;
	background: rgb(14, 17, 23);

	box-sizing: border-box;
    -moz-box-sizing: border-box;
    -webkit-box-sizing: border-box;
	border-bottom: solid 13px rgb(14, 17, 23) !important;
	border-left: solid 13px rgb(14, 17, 23) !important;
}

#phrase-tooltip
{
	display: none;
	pointer-events: none;
	position: absolute;
	border-bottom-left-radius: 0.5rem;
	border-top-right-radius: 0.5rem;
	border-bottom-right-radius: 0.5rem;
	border: solid rgb(255,75,75) 2px;
	background-color: rgb(38, 39, 48);
	color: rgb(255,75,75);
	font-size: 1rem;
	font-family: "Source Sans Pro", sans-serif;
	padding: 0.5rem;

	cursor: default;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
	z-index: 1000;
}

#phrase-tooltip:has(img)
{
	transform: scale(1.25, 1.25);
	-ms-transform: scale(1.25, 1.25);
	-webkit-transform: scale(1.25, 1.25);
}

#phrase-tooltip>img
{
	pointer-events: none;
	border-bottom-left-radius: 0.5rem;
	border-top-right-radius: 0.5rem;
	border-bottom-right-radius: 0.5rem;

	cursor: default;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
	z-index: 1500;
}


================================================
FILE: scripts/diffusers_textual_inversion_2.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
import argparse
import itertools
import math
import os
import random
import datetime

import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.utils.data import Dataset

import PIL
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    LMSDiscreteScheduler,
    StableDiffusionPipeline,
    UNet2DConditionModel,
)
from diffusers.optimization import get_scheduler
from pipelines.stable_diffusion.no_check import NoCheck
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from slugify import slugify
import json
import os

logger = get_logger(__name__)


def parse_args():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    parser.add_argument(
        "--pretrained_model_name_or_path",
        type=str,
        default=None,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--train_data_dir",
        type=str,
        default=None,
        help="A folder containing the training data.",
    )
    parser.add_argument(
        "--placeholder_token",
        type=str,
        default=None,
        help="A token to use as a placeholder for the concept.",
    )
    parser.add_argument(
        "--initializer_token",
        type=str,
        default=None,
        help="A token to use as initializer word.",
    )
    parser.add_argument(
        "--learnable_property",
        type=str,
        default="object",
        help="Choose between 'object' and 'style'",
    )
    parser.add_argument(
        "--repeats",
        type=int,
        default=100,
        help="How many times to repeat the training data.",
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="text-inversion-model",
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    parser.add_argument(
        "--seed", type=int, default=None, help="A seed for reproducible training."
    )
    parser.add_argument(
        "--resolution",
        type=int,
        default=512,
        help=(
            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
            " resolution"
        ),
    )
    parser.add_argument(
        "--center_crop",
        action="store_true",
        help="Whether to center crop images before resizing to resolution",
    )
    parser.add_argument(
        "--train_batch_size",
        type=int,
        default=1,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument("--num_train_epochs", type=int, default=100)
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=5000,
        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=1e-4,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=True,
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="constant",
        help=(
            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
            ' "constant", "constant_with_warmup"]'
        ),
    )
    parser.add_argument(
        "--lr_warmup_steps",
        type=int,
        default=500,
        help="Number of steps for the warmup in the lr scheduler.",
    )
    parser.add_argument(
        "--adam_beta1",
        type=float,
        default=0.9,
        help="The beta1 parameter for the Adam optimizer.",
    )
    parser.add_argument(
        "--adam_beta2",
        type=float,
        default=0.999,
        help="The beta2 parameter for the Adam optimizer.",
    )
    parser.add_argument(
        "--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use."
    )
    parser.add_argument(
        "--adam_epsilon",
        type=float,
        default=1e-08,
        help="Epsilon value for the Adam optimizer",
    )
    parser.add_argument(
        "--mixed_precision",
        type=str,
        default="no",
        choices=["no", "fp16", "bf16"],
        help=(
            "Whether to use mixed precision. Choose"
            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
            "and an Nvidia Ampere GPU."
        ),
    )
    parser.add_argument(
        "--local_rank",
        type=int,
        default=-1,
        help="For distributed training: local_rank",
    )
    parser.add_argument(
        "--checkpoint_frequency",
        type=int,
        default=500,
        help="How often to save a checkpoint and sample image",
    )
    parser.add_argument(
        "--stable_sample_batches",
        type=int,
        default=0,
        help="Number of fixed seed sample batches to generate per checkpoint",
    )
    parser.add_argument(
        "--random_sample_batches",
        type=int,
        default=1,
        help="Number of random seed sample batches to generate per checkpoint",
    )
    parser.add_argument(
        "--sample_batch_size",
        type=int,
        default=1,
        help="Number of samples to generate per batch",
    )
    parser.add_argument(
        "--sample_steps",
        type=int,
        default=50,
        help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.",
    )
    parser.add_argument(
        "--custom_templates",
        type=str,
        default=None,
        help=(
            "A semicolon-delimited list of custom template to use for samples, using {} as a placeholder for the concept."
        ),
    )
    parser.add_argument(
        "--resume_from",
        type=str,
        default=None,
        help="Path to a directory to resume training from (ie, logs/token_name/2022-09-22T23-36-27)",
    )
    parser.add_argument(
        "--resume_checkpoint",
        type=str,
        default=None,
        help="Path to a specific checkpoint to resume training from (ie, logs/token_name/2022-09-22T23-36-27/checkpoints/something.bin).",
    )
    parser.add_argument(
        "--config",
        type=str,
        default=None,
        help="Path to a JSON configuration file containing arguments for invoking this script. If resume_from is given, its resume.json takes priority over this.",
    )

    args = parser.parse_args()
    if args.resume_from is not None:
        with open(f"{args.resume_from}/resume.json", "rt") as f:
            args = parser.parse_args(
                namespace=argparse.Namespace(**json.load(f)["args"])
            )
    elif args.config is not None:
        with open(args.config, "rt") as f:
            args = parser.parse_args(
                namespace=argparse.Namespace(**json.load(f)["args"])
            )

    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
    if env_local_rank != -1 and env_local_rank != args.local_rank:
        args.local_rank = env_local_rank

    if args.train_data_dir is None:
        raise ValueError("You must specify --train_data_dir")

    if args.pretrained_model_name_or_path is None:
        raise ValueError("You must specify --pretrained_model_name_or_path")

    if args.placeholder_token is None:
        raise ValueError("You must specify --placeholder_token")

    if args.initializer_token is None:
        raise ValueError("You must specify --initializer_token")

    if args.output_dir is None:
        raise ValueError("You must specify --output_dir")

    return args


imagenet_templates_small = [
    "a photo of a {}",
    "a rendering of a {}",
    "a cropped photo of the {}",
    "the photo of a {}",
    "a photo of a clean {}",
    "a photo of a dirty {}",
    "a dark photo of the {}",
    "a photo of my {}",
    "a photo of the cool {}",
    "a close-up photo of a {}",
    "a bright photo of the {}",
    "a cropped photo of a {}",
    "a photo of the {}",
    "a good photo of the {}",
    "a photo of one {}",
    "a close-up photo of the {}",
    "a rendition of the {}",
    "a photo of the clean {}",
    "a rendition of a {}",
    "a photo of a nice {}",
    "a good photo of a {}",
    "a photo of the nice {}",
    "a photo of the small {}",
    "a photo of the weird {}",
    "a photo of the large {}",
    "a photo of a cool {}",
    "a photo of a small {}",
]

imagenet_style_templates_small = [
    "a painting in the style of {}",
    "a rendering in the style of {}",
    "a cropped painting in the style of {}",
    "the painting in the style of {}",
    "a clean painting in the style of {}",
    "a dirty painting in the style of {}",
    "a dark painting in the style of {}",
    "a picture in the style of {}",
    "a cool painting in the style of {}",
    "a close-up painting in the style of {}",
    "a bright painting in the style of {}",
    "a cropped painting in the style of {}",
    "a good painting in the style of {}",
    "a close-up painting in the style of {}",
    "a rendition in the style of {}",
    "a nice painting in the style of {}",
    "a small painting in the style of {}",
    "a weird painting in the style of {}",
    "a large painting in the style of {}",
]


class TextualInversionDataset(Dataset):
    def __init__(
        self,
        data_root,
        tokenizer,
        learnable_property="object",  # [object, style]
        size=512,
        repeats=100,
        interpolation="bicubic",
        set="train",
        placeholder_token="*",
        center_crop=False,
        templates=None,
    ):
        self.data_root = data_root
        self.tokenizer = tokenizer
        self.learnable_property = learnable_property
        self.size = size
        self.placeholder_token = placeholder_token
        self.center_crop = center_crop

        self.image_paths = [
            os.path.join(self.data_root, file_path)
            for file_path in os.listdir(self.data_root)
            if file_path.lower().endswith((".png", ".jpg", ".jpeg"))
        ]

        self.num_images = len(self.image_paths)
        self._length = self.num_images

        if set == "train":
            self._length = self.num_images * repeats

        self.interpolation = {
            "linear": PIL.Image.LINEAR,
            "bilinear": PIL.Image.BILINEAR,
            "bicubic": PIL.Image.BICUBIC,
            "lanczos": PIL.Image.LANCZOS,
        }[interpolation]

        self.templates = templates
        self.cache = {}
        self.tokenized_templates = [
            self.tokenizer(
                text.format(self.placeholder_token),
                padding="max_length",
                truncation=True,
                max_length=self.tokenizer.model_max_length,
                return_tensors="pt",
            ).input_ids[0]
            for text in self.templates
        ]

    def __len__(self):
        return self._length

    def get_example(self, image_path, flipped):
        if image_path in self.cache:
            return self.cache[image_path]

        example = {}
        image = Image.open(image_path)

        if not image.mode == "RGB":
            image = image.convert("RGB")

        # default to score-sde preprocessing
        img = np.array(image).astype(np.uint8)
        if self.center_crop:
            crop = min(img.shape[0], img.shape[1])
            (
                h,
                w,
            ) = (
                img.shape[0],
                img.shape[1],
            )
            img = img[
                (h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2
            ]
        image = Image.fromarray(img)
        image = image.resize((self.size, self.size), resample=self.interpolation)
        image = transforms.RandomHorizontalFlip(p=1 if flipped else 0)(image)
        image = np.array(image).astype(np.uint8)
        image = (image / 127.5 - 1.0).astype(np.float32)
        example["key"] = "-".join([image_path, "-", str(flipped)])
        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)

        self.cache[image_path] = example
        return example

    def __getitem__(self, i):
        flipped = random.choice([False, True])
        example = self.get_example(self.image_paths[i % self.num_images], flipped)
        example["input_ids"] = random.choice(self.tokenized_templates)
        return example


def freeze_params(params):
    for param in params:
        param.requires_grad = False


def save_resume_file(basepath, args, extra={}):
    info = {"args": vars(args)}
    info["args"].update(extra)
    with open(f"{basepath}/resume.json", "w") as f:
        json.dump(info, f, indent=4)


class Checkpointer:
    def __init__(
        self,
        accelerator,
        vae,
        unet,
        tokenizer,
        placeholder_token,
        placeholder_token_id,
        templates,
        output_dir,
        random_sample_batches,
        sample_batch_size,
        stable_sample_batches,
        seed,
    ):
        self.accelerator = accelerator
        self.vae = vae
        self.unet = unet
        self.tokenizer = tokenizer
        self.placeholder_token = placeholder_token
        self.placeholder_token_id = placeholder_token_id
        self.templates = templates
        self.output_dir = output_dir
        self.seed = seed
        self.random_sample_batches = random_sample_batches
        self.sample_batch_size = sample_batch_size
        self.stable_sample_batches = stable_sample_batches

    @torch.no_grad()
    def checkpoint(self, step, text_encoder, save_samples=True, path=None):
        print("Saving checkpoint for step %d..." % step)
        with torch.autocast("cuda"):
            if path is None:
                checkpoints_path = f"{self.output_dir}/checkpoints"
                os.makedirs(checkpoints_path, exist_ok=True)

            unwrapped = self.accelerator.unwrap_model(text_encoder)

            # Save a checkpoint
            learned_embeds = unwrapped.get_input_embeddings().weight[
                self.placeholder_token_id
            ]
            learned_embeds_dict = {
                self.placeholder_token: learned_embeds.detach().cpu()
            }

            filename = "%s_%d.bin" % (slugify(self.placeholder_token), step)
            if path is not None:
                torch.save(learned_embeds_dict, path)
            else:
                torch.save(learned_embeds_dict, f"{checkpoints_path}/{filename}")
                torch.save(learned_embeds_dict, f"{checkpoints_path}/last.bin")
            del unwrapped
            del learned_embeds

    @torch.no_grad()
    def save_samples(
        self,
        step,
        text_encoder,
        height,
        width,
        guidance_scale,
        eta,
        num_inference_steps,
    ):
        samples_path = f"{self.output_dir}/samples"
        os.makedirs(samples_path, exist_ok=True)
        checker = NoCheck()

        unwrapped = self.accelerator.unwrap_model(text_encoder)
        # Save a sample image
        pipeline = StableDiffusionPipeline(
            text_encoder=unwrapped,
            vae=self.vae,
            unet=self.unet,
            tokenizer=self.tokenizer,
            scheduler=LMSDiscreteScheduler(
                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
            ),
            safety_checker=NoCheck(),
            feature_extractor=CLIPFeatureExtractor.from_pretrained(
                "openai/clip-vit-base-patch32"
            ),
        ).to("cuda")
        pipeline.enable_attention_slicing()

        if self.stable_sample_batches > 0:
            stable_latents = torch.randn(
                (
                    self.sample_batch_size,
                    pipeline.unet.in_channels,
                    height // 8,
                    width // 8,
                ),
                device=pipeline.device,
                generator=torch.Generator(device=pipeline.device).manual_seed(
                    self.seed
                ),
            )

            stable_prompts = [
                choice.format(self.placeholder_token)
                for choice in (self.templates * self.sample_batch_size)[
                    : self.sample_batch_size
                ]
            ]

            # Generate and save stable samples
            for i in range(0, self.stable_sample_batches):
                samples = pipeline(
                    prompt=stable_prompts,
                    height=384,
                    latents=stable_latents,
                    width=384,
                    guidance_scale=guidance_scale,
                    eta=eta,
                    num_inference_steps=num_inference_steps,
                    output_type="pil",
                )["sample"]
                for idx, im in enumerate(samples):
                    filename = "stable_sample_%d_%d_step_%d.png" % (
                        i + 1,
                        idx + 1,
                        step,
                    )
                    im.save(f"{samples_path}/{filename}")
                del samples
            del stable_latents

        prompts = [
            choice.format(self.placeholder_token)
            for choice in random.choices(self.templates, k=self.sample_batch_size)
        ]
        # Generate and save random samples
        for i in range(0, self.random_sample_batches):
            samples = pipeline(
                prompt=prompts,
                height=384,
                width=384,
                guidance_scale=guidance_scale,
                eta=eta,
                num_inference_steps=num_inference_steps,
                output_type="pil",
            )["sample"]
            for idx, im in enumerate(samples):
                filename = "step_%d_sample_%d_%d.png" % (step, i + 1, idx + 1)
                im.save(f"{samples_path}/{filename}")
            del samples

        del checker
        del unwrapped
        del pipeline
        torch.cuda.empty_cache()


def main():
    args = parse_args()

    global_step_offset = 0
    if args.resume_from is not None:
        basepath = f"{args.resume_from}"
        print("Resuming state from %s" % args.resume_from)
        with open(f"{basepath}/resume.json", "r") as f:
            state = json.load(f)
        global_step_offset = state["args"].get("global_step", 0)

        print("We've trained %d steps so far" % global_step_offset)
    else:
        now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
        basepath = f"{args.output_dir}/{slugify(args.placeholder_token)}/{now}"
        os.makedirs(basepath, exist_ok=True)

    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
    )

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed)

    # Load the tokenizer and add the placeholder token as a additional special token
    if args.tokenizer_name:
        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
    elif args.pretrained_model_name_or_path:
        tokenizer = CLIPTokenizer.from_pretrained(
            args.pretrained_model_name_or_path + "/tokenizer"
        )

    # Add the placeholder token in tokenizer
    num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
    if num_added_tokens == 0:
        raise ValueError(
            f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
            " `placeholder_token` that is not already in the tokenizer."
        )

    # Convert the initializer_token, placeholder_token to ids
    token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
    # Check if initializer_token is a single token or a sequence of tokens
    if len(token_ids) > 1:
        raise ValueError("The initializer token must be a single token.")

    initializer_token_id = token_ids[0]
    placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)

    # Load models and create wrapper for stable diffusion
    text_encoder = CLIPTextModel.from_pretrained(
        args.pretrained_model_name_or_path + "/text_encoder",
    )
    vae = AutoencoderKL.from_pretrained(
        args.pretrained_model_name_or_path + "/vae",
    )
    unet = UNet2DConditionModel.from_pretrained(
        args.pretrained_model_name_or_path + "/unet",
    )

    base_templates = (
        imagenet_style_templates_small
        if args.learnable_property == "style"
        else imagenet_templates_small
    )
    if args.custom_templates:
        templates = args.custom_templates.split(";")
    else:
        templates = base_templates

    slice_size = unet.config.attention_head_dim // 2
    unet.set_attention_slice(slice_size)

    # Resize the token embeddings as we are adding new special tokens to the tokenizer
    text_encoder.resize_token_embeddings(len(tokenizer))

    # Initialise the newly added placeholder token with the embeddings of the initializer token
    token_embeds = text_encoder.get_input_embeddings().weight.data

    if args.resume_checkpoint is not None:
        token_embeds[placeholder_token_id] = torch.load(args.resume_checkpoint)[
            args.placeholder_token
        ]
    else:
        token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]

    # Freeze vae and unet
    freeze_params(vae.parameters())
    freeze_params(unet.parameters())
    # Freeze all parameters except for the token embeddings in text encoder
    params_to_freeze = itertools.chain(
        text_encoder.text_model.encoder.parameters(),
        text_encoder.text_model.final_layer_norm.parameters(),
        text_encoder.text_model.embeddings.position_embedding.parameters(),
    )
    freeze_params(params_to_freeze)

    checkpointer = Checkpointer(
        accelerator=accelerator,
        vae=vae,
        unet=unet,
        tokenizer=tokenizer,
        placeholder_token=args.placeholder_token,
        placeholder_token_id=placeholder_token_id,
        templates=templates,
        output_dir=basepath,
        sample_batch_size=args.sample_batch_size,
        random_sample_batches=args.random_sample_batches,
        stable_sample_batches=args.stable_sample_batches,
        seed=args.seed,
    )

    if args.scale_lr:
        args.learning_rate = (
            args.learning_rate
            * args.gradient_accumulation_steps
            * args.train_batch_size
            * accelerator.num_processes
        )

    # Initialize the optimizer
    optimizer = torch.optim.AdamW(
        text_encoder.get_input_embeddings().parameters(),  # only optimize the embeddings
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )

    # TODO (patil-suraj): laod scheduler using args
    noise_scheduler = DDPMScheduler(
        beta_start=0.00085,
        beta_end=0.012,
        beta_schedule="scaled_linear",
        num_train_timesteps=1000,
        tensor_format="pt",
    )

    train_dataset = TextualInversionDataset(
        data_root=args.train_data_dir,
        tokenizer=tokenizer,
        size=args.resolution,
        placeholder_token=args.placeholder_token,
        repeats=args.repeats,
        learnable_property=args.learnable_property,
        center_crop=args.center_crop,
        set="train",
        templates=templates,
    )
    train_dataloader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.train_batch_size, shuffle=True
    )

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(
        len(train_dataloader) / args.gradient_accumulation_steps
    )
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
    )

    text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        text_encoder, optimizer, train_dataloader, lr_scheduler
    )

    # Move vae and unet to device
    vae.to(accelerator.device)
    unet.to(accelerator.device)

    # Keep vae and unet in eval mode as we don't train these
    vae.eval()
    unet.eval()

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(
        len(train_dataloader) / args.gradient_accumulation_steps
    )
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if accelerator.is_main_process:
        accelerator.init_trackers("textual_inversion", config=vars(args))

    # Train!
    total_batch_size = (
        args.train_batch_size
        * accelerator.num_processes
        * args.gradient_accumulation_steps
    )

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
    logger.info(
        f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
    )
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")
    # Only show the progress bar once on each machine.
    progress_bar = tqdm(
        range(args.max_train_steps), disable=not accelerator.is_local_main_process
    )
    progress_bar.set_description("Steps")
    global_step = 0
    encoded_pixel_values_cache = {}

    try:
        for epoch in range(args.num_train_epochs):
            text_encoder.train()
            for step, batch in enumerate(train_dataloader):
                with accelerator.accumulate(text_encoder):
                    # Convert images to latent space
                    key = "|".join(batch["key"])
                    if encoded_pixel_values_cache.get(key, None) is None:
                        encoded_pixel_values_cache[key] = vae.encode(
                            batch["pixel_values"]
                        ).latent_dist
                    latents = (
                        encoded_pixel_values_cache[key].sample().detach().half()
                        * 0.18215
                    )

                    # Sample noise that we'll add to the latents
                    noise = torch.randn(latents.shape).to(latents.device)
                    bsz = latents.shape[0]
                    # Sample a random timestep for each image
                    timesteps = torch.randint(
                        0,
                        noise_scheduler.num_train_timesteps,
                        (bsz,),
                        device=latents.device,
                    ).long()

                    # Add noise to the latents according to the noise magnitude at each timestep
                    # (this is the forward diffusion process)
                    noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

                    # Get the text embedding for conditioning
                    encoder_hidden_states = text_encoder(batch["input_ids"])[0]

                    # Predict the noise residual
                    noise_pred = unet(
                        noisy_latents, timesteps, encoder_hidden_states
                    ).sample

                    loss = (
                        F.mse_loss(noise_pred, noise, reduction="none")
                        .mean([1, 2, 3])
                        .mean()
                    )
                    accelerator.backward(loss)

                    # Zero out the gradients for all token embeddings except the newly added
                    # embeddings for the concept, as we only want to optimize the concept embeddings
                    if accelerator.num_processes > 1:
                        grads = text_encoder.module.get_input_embeddings().weight.grad
                    else:
                        grads = text_encoder.get_input_embeddings().weight.grad
                    # Get the index for tokens that we want to zero the grads for
                    index_grads_to_zero = (
                        torch.arange(len(tokenizer)) != placeholder_token_id
                    )
                    grads.data[index_grads_to_zero, :] = grads.data[
                        index_grads_to_zero, :
                    ].fill_(0)

                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

                # Checks if the accelerator has performed an optimization step behind the scenes
                if accelerator.sync_gradients:
                    progress_bar.update(1)
                    global_step += 1

                    if (
                        global_step % args.checkpoint_frequency == 0
                        and global_step > 0
                        and accelerator.is_main_process
                    ):
                        checkpointer.checkpoint(
                            global_step + global_step_offset, text_encoder
                        )
                        save_resume_file(
                            basepath,
                            args,
                            {
                                "global_step": global_step + global_step_offset,
                                "resume_checkpoint": f"{basepath}/checkpoints/last.bin",
                            },
                        )
                        checkpointer.save_samples(
                            global_step + global_step_offset,
                            text_encoder,
                            args.resolution,
                            args.resolution,
                            7.5,
                            0.0,
                            args.sample_steps,
                        )

                logs = {
                    "loss": loss.detach().item(),
                    "lr": lr_scheduler.get_last_lr()[0],
                }
                progress_bar.set_postfix(**logs)
                # accelerator.log(logs, step=global_step)

                if global_step >= args.max_train_steps:
                    break

            accelerator.wait_for_everyone()

        # Create the pipeline using using the trained modules and save it.
        if accelerator.is_main_process:
            print("Finished! Saving final checkpoint and resume state.")
            checkpointer.checkpoint(
                global_step + global_step_offset,
                text_encoder,
                path=f"{basepath}/learned_embeds.bin",
            )

            save_resume_file(
                basepath,
                args,
                {
                    "global_step": global_step + global_step_offset,
                    "resume_checkpoint": f"{basepath}/checkpoints/last.bin",
                },
            )

            accelerator.end_training()

    except KeyboardInterrupt:
        if accelerator.is_main_process:
            print("Interrupted, saving checkpoint and resume state...")
            checkpointer.checkpoint(global_step + global_step_offset, text_encoder)
            save_resume_file(
                basepath,
                args,
                {
                    "global_step": global_step + global_step_offset,
                    "resume_checkpoint": f"{basepath}/checkpoints/last.bin",
                },
            )
        quit()


if __name__ == "__main__":
    main()


================================================
FILE: scripts/home.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from webui_streamlit import st
from sd_utils import *

# streamlit imports


# other imports

# Temp imports


# end of imports
# ---------------------------------------------------------------------------------------------------------------

import os
from PIL import Image

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging

    logging.set_verbosity_error()
except:
    pass


class plugin_info:
    plugname = "home"
    description = "Home"
    isTab = True
    displayPriority = 0


def getLatestGeneratedImagesFromPath():
    # get the latest images from the generated images folder
    # get the path to the generated images folder
    generatedImagesPath = os.path.join(os.getcwd(), "outputs")
    # get all the files from the folders and subfolders
    files = []
    # get the latest 10 images from the output folder without walking the subfolders
    for r, d, f in os.walk(generatedImagesPath):
        for file in f:
            if ".png" in file:
                files.append(os.path.join(r, file))
    # sort the files by date
    files.sort(reverse=True, key=os.path.getmtime)
    latest = files[:90]
    latest.reverse()

    # reverse the list so the latest images are first and truncate to
    # a reasonable number of images, 10 pages worth
    return [Image.open(f) for f in latest]


def get_images_from_lexica():
    # scrape images from lexica.art
    # get the html from the page
    # get the html with cookies and javascript
    apiEndpoint = r"https://lexica.art/api/trpc/prompts.infinitePrompts?batch=1&input=%7B%220%22%3A%7B%22json%22%3A%7B%22limit%22%3A10%2C%22text%22%3A%22%22%2C%22cursor%22%3A10%7D%7D%7D"
    # REST API call
    #
    from requests_html import HTMLSession

    session = HTMLSession()

    response = session.get(apiEndpoint)
    # req = requests.Session()
    # req.headers['user-agent'] = 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'
    # response = req.get(apiEndpoint)
    print(response.status_code)
    print(response.text)
    # get the json from the response
    # json = response.json()
    # get the prompts from the json
    print(response)
    # session = requests.Session()
    # parseEndpointJson = session.get(apiEndpoint,headers=headers,verify=False)
    # print(parseEndpointJson)
    # print('test2')
    # page = requests.get("https://lexica.art/", headers={'User-Agent': 'Mozilla/5.0'})
    # parse the html
    # soup = BeautifulSoup(page.content, 'html.parser')
    # find all the images
    # print(soup)
    # images = soup.find_all('alt-image')
    # create a list to store the image urls
    image_urls = []
    # loop through the images
    for image in images:
        # get the url
        image_url = image["src"]
        # add it to the list
        image_urls.append("http://www.lexica.art/" + image_url)
    # return the list
    print(image_urls)
    return image_urls


def layout():
    # streamlit home page layout
    # center the title
    st.markdown(
        "<h1 style='text-align: center; color: white;'>Welcome, let's make some 🎨</h1>",
        unsafe_allow_html=True,
    )
    # make a gallery of images
    # st.markdown("<h2 style='text-align: center; color: white;'>Gallery</h2>", unsafe_allow_html=True)
    # create a gallery of images using columns
    # col1, col2, col3 = st.columns(3)
    # load the images
    # create 3 columns
    # create a tab for the gallery
    # st.markdown("<h2 style='text-align: center; color: white;'>Gallery</h2>", unsafe_allow_html=True)
    # st.markdown("<h2 style='text-align: center; color: white;'>Gallery</h2>", unsafe_allow_html=True)

    history_tab, discover_tabs = st.tabs(["History", "Discover"])

    latestImages = getLatestGeneratedImagesFromPath()
    st.session_state["latestImages"] = latestImages

    with history_tab:
        ##---------------------------------------------------------
        ## image slideshow test
        ## Number of entries per screen
        # slideshow_N = 9
        # slideshow_page_number = 0
        # slideshow_last_page = len(latestImages) // slideshow_N

        ## Add a next button and a previous button

        # slideshow_prev, slideshow_image_col , slideshow_next = st.columns([1, 10, 1])

        # with slideshow_image_col:
        # slideshow_image = st.empty()

        # slideshow_image.image(st.session_state['latestImages'][0])

        # current_image = 0

        # if slideshow_next.button("Next", key=1):
        ##print (current_image+1)
        # current_image = current_image+1
        # slideshow_image.image(st.session_state['latestImages'][current_image+1])
        # if slideshow_prev.button("Previous", key=0):
        ##print ([current_image-1])
        # current_image = current_image-1
        # slideshow_image.image(st.session_state['latestImages'][current_image - 1])

        # ---------------------------------------------------------

        # image gallery
        # Number of entries per screen
        gallery_N = 9
        if "galleryPage" not in st.session_state:
            st.session_state["galleryPage"] = 0
        gallery_last_page = len(latestImages) // gallery_N

        # Add a next button and a previous button

        gallery_prev, gallery_refresh, gallery_pagination, gallery_next = st.columns(
            [2, 2, 8, 1]
        )

        # the pagination doesnt work for now so its better to disable the buttons.

        if gallery_refresh.button("Refresh", key=4):
            st.session_state["galleryPage"] = 0

        if gallery_next.button("Next", key=3):
            if st.session_state["galleryPage"] + 1 > gallery_last_page:
                st.session_state["galleryPage"] = 0
            else:
                st.session_state["galleryPage"] += 1

        if gallery_prev.button("Previous", key=2):
            if st.session_state["galleryPage"] - 1 < 0:
                st.session_state["galleryPage"] = gallery_last_page
            else:
                st.session_state["galleryPage"] -= 1

        print(st.session_state["galleryPage"])
        # Get start and end indices of the next page of the dataframe
        gallery_start_idx = st.session_state["galleryPage"] * gallery_N
        (1 + st.session_state["galleryPage"]) * gallery_N

        # ---------------------------------------------------------

        placeholder = st.empty()

        # populate the 3 images per column
        with placeholder.container():
            col1, col2, col3 = st.columns(3)
            col1_cont = st.container()
            col2_cont = st.container()
            col3_cont = st.container()

            print(len(st.session_state["latestImages"]))
            images = list(reversed(st.session_state["latestImages"]))[
                gallery_start_idx : (gallery_start_idx + gallery_N)
            ]

            with col1_cont:
                with col1:
                    [
                        st.image(images[index])
                        for index in [0, 3, 6]
                        if index < len(images)
                    ]
            with col2_cont:
                with col2:
                    [
                        st.image(images[index])
                        for index in [1, 4, 7]
                        if index < len(images)
                    ]
            with col3_cont:
                with col3:
                    [
                        st.image(images[index])
                        for index in [2, 5, 8]
                        if index < len(images)
                    ]

        st.session_state["historyTab"] = [
            history_tab,
            col1,
            col2,
            col3,
            placeholder,
            col1_cont,
            col2_cont,
            col3_cont,
        ]

    with discover_tabs:
        st.markdown(
            "<h1 style='text-align: center; color: white;'>Soon :)</h1>",
            unsafe_allow_html=True,
        )

    # display the images
    # add a button to the gallery
    # st.markdown("<h2 style='text-align: center; color: white;'>Try it out</h2>", unsafe_allow_html=True)
    # create a button to the gallery
    # if st.button("Try it out"):
    # if the button is clicked, go to the gallery
    # st.experimental_rerun()


================================================
FILE: scripts/hydrus_api/__init__.py
================================================
# Copyright (C) 2021 cryzed
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

import enum
import json
import os
import typing as T
from collections import abc

import requests

__version__ = "4.0.0"

DEFAULT_API_URL = "http://127.0.0.1:45869/"
HYDRUS_METADATA_ENCODING = "utf-8"
AUTHENTICATION_TIMEOUT_CODE = 419


class HydrusAPIException(Exception):
    pass


class ConnectionError(HydrusAPIException, requests.ConnectTimeout):
    pass


class APIError(HydrusAPIException):
    def __init__(self, response: requests.Response):
        super().__init__(response.text)
        self.response = response


class MissingParameter(APIError):
    pass


class InsufficientAccess(APIError):
    pass


class DatabaseLocked(APIError):
    pass


class ServerError(APIError):
    pass


# Customize IntEnum, so we can just do str(Enum.member) to get the string representation of its value unmodified,
# without users having to access .value explicitly
class StringableIntEnum(enum.IntEnum):
    def __str__(self):
        return str(self.value)


@enum.unique
class Permission(StringableIntEnum):
    IMPORT_URLS = 0
    IMPORT_FILES = 1
    ADD_TAGS = 2
    SEARCH_FILES = 3
    MANAGE_PAGES = 4
    MANAGE_COOKIES = 5
    MANAGE_DATABASE = 6
    ADD_NOTES = 7


@enum.unique
class URLType(StringableIntEnum):
    POST_URL = 0
    FILE_URL = 2
    GALLERY_URL = 3
    WATCHABLE_URL = 4
    UNKNOWN_URL = 5


@enum.unique
class ImportStatus(StringableIntEnum):
    IMPORTABLE = 0
    SUCCESS = 1
    EXISTS = 2
    PREVIOUSLY_DELETED = 3
    FAILED = 4
    VETOED = 7


@enum.unique
class TagAction(StringableIntEnum):
    ADD = 0
    DELETE = 1
    PEND = 2
    RESCIND_PENDING = 3
    PETITION = 4
    RESCIND_PETITION = 5


@enum.unique
class TagStatus(StringableIntEnum):
    CURRENT = 0
    PENDING = 1
    DELETED = 2
    PETITIONED = 3


@enum.unique
class PageType(StringableIntEnum):
    GALLERY_DOWNLOADER = 1
    SIMPLE_DOWNLOADER = 2
    HARD_DRIVE_IMPORT = 3
    PETITIONS = 5
    FILE_SEARCH = 6
    URL_DOWNLOADER = 7
    DUPLICATES = 8
    THREAD_WATCHER = 9
    PAGE_OF_PAGES = 10


@enum.unique
class FileSortType(StringableIntEnum):
    FILE_SIZE = 0
    DURATION = 1
    IMPORT_TIME = 2
    FILE_TYPE = 3
    RANDOM = 4
    WIDTH = 5
    HEIGHT = 6
    RATIO = 7
    NUMBER_OF_PIXELS = 8
    NUMBER_OF_TAGS = 9
    NUMBER_OF_MEDIA_VIEWS = 10
    TOTAL_MEDIA_VIEWTIME = 11
    APPROXIMATE_BITRATE = 12
    HAS_AUDIO = 13
    MODIFIED_TIME = 14
    FRAMERATE = 15
    NUMBER_OF_FRAMES = 16


class BinaryFileLike(T.Protocol):
    def read(self):
        ...


# The client should accept all objects that either support the iterable or mapping protocol. We must ensure that objects
# are either lists or dicts, so Python's json module can handle them
class JSONEncoder(json.JSONEncoder):
    def default(self, object_: T.Any):
        if isinstance(object_, abc.Mapping):
            return dict(object_)
        if isinstance(object_, abc.Iterable):
            return list(object_)
        return super().default(object_)


class Client:
    VERSION = 31

    # Access Management
    _GET_API_VERSION_PATH = "/api_version"
    _REQUEST_NEW_PERMISSIONS_PATH = "/request_new_permissions"
    _GET_SESSION_KEY_PATH = "/session_key"
    _VERIFY_ACCESS_KEY_PATH = "/verify_access_key"
    _GET_SERVICES_PATH = "/get_services"

    # Adding Files
    _ADD_FILE_PATH = "/add_files/add_file"
    _DELETE_FILES_PATH = "/add_files/delete_files"
    _UNDELETE_FILES_PATH = "/add_files/undelete_files"
    _ARCHIVE_FILES_PATH = "/add_files/archive_files"
    _UNARCHIVE_FILES_PATH = "/add_files/unarchive_files"

    # Adding Tags
    _CLEAN_TAGS_PATH = "/add_tags/clean_tags"
    _SEARCH_TAGS_PATH = "/add_tags/search_tags"
    _ADD_TAGS_PATH = "/add_tags/add_tags"

    # Adding URLs
    _GET_URL_FILES_PATH = "/add_urls/get_url_files"
    _GET_URL_INFO_PATH = "/add_urls/get_url_info"
    _ADD_URL_PATH = "/add_urls/add_url"
    _ASSOCIATE_URL_PATH = "/add_urls/associate_url"

    # Adding Notes
    _SET_NOTES_PATH = "/add_notes/set_notes"
    _DELETE_NOTES_PATH = "/add_notes/delete_notes"

    # Managing Cookies and HTTP Headers
    _GET_COOKIES_PATH = "/manage_cookies/get_cookies"
    _SET_COOKIES_PATH = "/manage_cookies/set_cookies"
    _SET_USER_AGENT_PATH = "/manage_headers/set_user_agent"

    # Managing Pages
    _GET_PAGES_PATH = "/manage_pages/get_pages"
    _GET_PAGE_INFO_PATH = "/manage_pages/get_page_info"
    _ADD_FILES_TO_PAGE_PATH = "/manage_pages/add_files"
    _FOCUS_PAGE_PATH = "/manage_pages/focus_page"

    # Searching and Fetching Files
    _SEARCH_FILES_PATH = "/get_files/search_files"
    _GET_FILE_METADATA_PATH = "/get_files/file_metadata"
    _GET_FILE_PATH = "/get_files/file"
    _GET_THUMBNAIL_PATH = "/get_files/thumbnail"

    # Managing the Database
    _LOCK_DATABASE_PATH = "/manage_database/lock_on"
    _UNLOCK_DATABASE_PATH = "/manage_database/lock_off"
    _MR_BONES_PATH = "/manage_database/mr_bones"

    def __init__(
        self,
        access_key=None,
        api_url: str = DEFAULT_API_URL,
        session=None,
    ):
        """
        See https://hydrusnetwork.github.io/hydrus/help/client_api.html for documentation.
        """

        self.access_key = access_key
        self.api_url = api_url.rstrip("/")
        self.session = session or requests.Session()

    def _api_request(self, method: str, path: str, **kwargs: T.Any):
        if self.access_key is not None:
            kwargs.setdefault("headers", {}).update(
                {"Hydrus-Client-API-Access-Key": self.access_key}
            )

        # Make sure we use our custom JSONEncoder that can serialize all objects that implement the iterable or mapping
        # protocol
        json_data = kwargs.pop("json", None)
        if json_data is not None:
            kwargs["data"] = json.dumps(json_data, cls=JSONEncoder)
            # Since we aren't using the json keyword-argument, we have to set the Content-Type manually
            kwargs["headers"]["Content-Type"] = "application/json"

        try:
            response = self.session.request(method, self.api_url + path, **kwargs)
        except requests.RequestException as error:
            # Re-raise connection and timeout errors as hydrus.ConnectionErrors so these are more easy to handle for
            # client applications
            raise ConnectionError(*error.args)

        try:
            response.raise_for_status()
        except requests.HTTPError:
            if response.status_code == requests.codes.bad_request:
                raise MissingParameter(response)
            elif response.status_code in {
                requests.codes.unauthorized,
                requests.codes.forbidden,
                AUTHENTICATION_TIMEOUT_CODE,
            }:
                raise InsufficientAccess(response)
            elif response.status_code == requests.codes.service_unavailable:
                raise DatabaseLocked(response)
            elif response.status_code == requests.codes.server_error:
                raise ServerError(response)
            raise APIError(response)

        return response

    def get_api_version(self):
        response = self._api_request("GET", self._GET_API_VERSION_PATH)
        return response.json()

    def request_new_permissions(self, name, permissions):
        response = self._api_request(
            "GET",
            self._REQUEST_NEW_PERMISSIONS_PATH,
            params={
                "name": name,
                "basic_permissions": json.dumps(permissions, cls=JSONEncoder),
            },
        )
        return response.json()["access_key"]

    def get_session_key(self):
        response = self._api_request("GET", self._GET_SESSION_KEY_PATH)
        return response.json()["session_key"]

    def verify_access_key(self):
        response = self._api_request("GET", self._VERIFY_ACCESS_KEY_PATH)
        return response.json()

    def get_services(self):
        response = self._api_request("GET", self._GET_SERVICES_PATH)
        return response.json()

    def add_file(self, path_or_file: T.Union[str, os.PathLike, BinaryFileLike]):
        if isinstance(path_or_file, (str, os.PathLike)):
            response = self._api_request(
                "POST", self._ADD_FILE_PATH, json={"path": os.fspath(path_or_file)}
            )
        else:
            response = self._api_request(
                "POST",
                self._ADD_FILE_PATH,
                data=path_or_file.read(),
                headers={"Content-Type": "application/octet-stream"},
            )

        return response.json()

    def delete_files(
        self,
        hashes=None,
        file_ids=None,
        file_service_name=None,
        file_service_key=None,
        reason=None,
    ):
        if hashes is None and file_ids is None:
            raise ValueError("At least one of hashes, file_ids is required")
        if file_service_name is not None and file_service_key is not None:
            raise ValueError(
                "Exactly one of file_service_name, file_service_key is required"
            )

        payload: dict[str, T.Any] = {}
        if hashes is not None:
            payload["hashes"] = hashes
        if file_ids is not None:
            payload["file_ids"] = file_ids
        if file_service_name is not None:
            payload["file_service_name"] = file_service_name
        if file_service_key is not None:
            payload["file_service_key"] = file_service_key
        if reason is not None:
            payload["reason"] = reason

        self._api_request("POST", self._DELETE_FILES_PATH, json=payload)

    def undelete_files(
        self,
        hashes=None,
        file_ids=None,
        file_service_name=None,
        file_service_key=None,
    ):
        if hashes is None and file_ids is None:
            raise ValueError("At least one of hashes, file_ids is required")
        if file_service_name is not None and file_service_key is not None:
            raise ValueError(
                "Exactly one of file_service_name, file_service_key is required"
            )

        payload: dict[str, T.Any] = {}
        if hashes is not None:
            payload["hashes"] = hashes
        if file_ids is not None:
            payload["file_ids"] = file_ids
        if file_service_name is not None:
            payload["file_service_name"] = file_service_name
        if file_service_key is not None:
            payload["file_service_key"] = file_service_key

        self._api_request("POST", self._UNDELETE_FILES_PATH, json=payload)

    def archive_files(self, hashes=None, file_ids=None):
        if hashes is None and file_ids is None:
            raise ValueError("At least one of hashes, file_ids is required")

        payload: dict[str, T.Any] = {}
        if hashes is not None:
            payload["hashes"] = hashes
        if file_ids is not None:
            payload["file_ids"] = file_ids

        self._api_request("POST", self._ARCHIVE_FILES_PATH, json=payload)

    def unarchive_files(self, hashes=None, file_ids=None):
        if hashes is None and file_ids is None:
            raise ValueError("At least one of hashes, file_ids is required")

        payload: dict[str, T.Any] = {}
        if hashes is not None:
            payload["hashes"] = hashes
        if file_ids is not None:
            payload["file_ids"] = file_ids

        self._api_request("POST", self._UNARCHIVE_FILES_PATH, json=payload)

    def clean_tags(self, tags):
        response = self._api_request(
            "GET",
            self._CLEAN_TAGS_PATH,
            params={"tags": json.dumps(tags, cls=JSONEncoder)},
        )
        return response.json()["tags"]

    def search_tags(self, search: str, tag_service_key=None, tag_service_name=None):
        if tag_service_name is not None and tag_service_key is not None:
            raise ValueError(
                "Exactly one of tag_service_name, tag_service_key is required"
            )

        payload: dict[str, T.Any] = {"search": search}
        if tag_service_key is not None:
            payload["tag_service_key"] = tag_service_key
        if tag_service_name is not None:
            payload["tag_service_name"] = tag_service_name

        response = self._api_request("GET", self._SEARCH_TAGS_PATH, params=payload)
        return response.json()["tags"]

    def add_tags(
        self,
        hashes=None,
        file_ids=None,
        service_names_to_tags=None,
        service_keys_to_tags=None,
        service_names_to_actions_to_tags=None,
        service_keys_to_actions_to_tags=None,
    ):
        if hashes is None and file_ids is None:
            raise ValueError("At least one of hashes, file_ids is required")
        if (
            service_names_to_tags is None
            and service_keys_to_tags is None
            and service_names_to_actions_to_tags is None
            and service_keys_to_actions_to_tags is None
        ):
            raise ValueError(
                "At least one of service_names_to_tags, service_keys_to_tags, service_names_to_actions_to_tags or "
                "service_keys_to_actions_to_tags is required"
            )

        payload: dict[str, T.Any] = {}
        if hashes is not None:
            payload["hashes"] = hashes
        if file_ids is not None:
            payload["file_ids"] = file_ids
        if service_names_to_tags is not None:
            payload["service_names_to_tags"] = service_names_to_tags
        if service_keys_to_tags is not None:
            payload["service_keys_to_tags"] = service_keys_to_tags
        if service_names_to_actions_to_tags is not None:
            payload[
                "service_names_to_actions_to_tags"
            ] = service_names_to_actions_to_tags
        if service_keys_to_actions_to_tags is not None:
            payload["service_keys_to_actions_to_tags"] = service_keys_to_actions_to_tags

        self._api_request("POST", self._ADD_TAGS_PATH, json=payload)

    def get_url_files(self, url: str):
        response = self._api_request(
            "GET", self._GET_URL_FILES_PATH, params={"url": url}
        )
        return response.json()

    def get_url_info(self, url: str):
        response = self._api_request(
            "GET", self._GET_URL_INFO_PATH, params={"url": url}
        )
        return response.json()

    def add_url(
        self,
        url: str,
        destination_page_key=None,
        destination_page_name=None,
        show_destination_page=None,
        service_names_to_additional_tags=None,
        service_keys_to_additional_tags=None,
        filterable_tags=None,
    ):
        if destination_page_key is not None and destination_page_name is not None:
            raise ValueError(
                "Exactly one of destination_page_key, destination_page_name is required"
            )

        payload: dict[str, T.Any] = {"url": url}
        if destination_page_key is not None:
            payload["destination_page_key"] = destination_page_key
        if destination_page_name is not None:
            payload["destination_page_name"] = destination_page_name
        if show_destination_page is not None:
            payload["show_destination_page"] = show_destination_page
        if service_names_to_additional_tags is not None:
            payload[
                "service_names_to_additional_tags"
            ] = service_names_to_additional_tags
        if service_keys_to_additional_tags is not None:
            payload["service_keys_to_additional_tags"] = service_keys_to_additional_tags
        if filterable_tags is not None:
            payload["filterable_tags"] = filterable_tags

        response = self._api_request("POST", self._ADD_URL_PATH, json=payload)
        return response.json()

    def associate_url(
        self,
        hashes=None,
        file_ids=None,
        urls_to_add=None,
        urls_to_delete=None,
    ):
        if hashes is None and file_ids is None:
            raise ValueError("At least one of hashes, file_ids is required")
        if urls_to_add is None and urls_to_delete is None:
            raise ValueError("At least one of urls_to_add, urls_to_delete is required")

        payload: dict[str, T.Any] = {}
        if hashes is not None:
            payload["hashes"] = hashes
        if file_ids is not None:
            payload["file_ids"] = file_ids
        if urls_to_add is not None:
            urls_to_add = urls_to_add
            payload["urls_to_add"] = urls_to_add
        if urls_to_delete is not None:
            urls_to_delete = urls_to_delete
            payload["urls_to_delete"] = urls_to_delete

        self._api_request("POST", self._ASSOCIATE_URL_PATH, json=payload)

    def set_notes(self, notes, hash_=None, file_id=None):
        if (hash_ is None and file_id is None) or (
            hash_ is not None and file_id is not None
        ):
            raise ValueError("Exactly one of hash_, file_id is required")

        payload: dict[str, T.Any] = {"notes": notes}
        if hash_ is not None:
            payload["hash"] = hash_
        if file_id is not None:
            payload["file_id"] = file_id

        self._api_request("POST", self._SET_NOTES_PATH, json=payload)

    def delete_notes(self, note_names, hash_=None, file_id=None):
        if (hash_ is None and file_id is None) or (
            hash_ is not None and file_id is not None
        ):
            raise ValueError("Exactly one of hash_, file_id is required")

        payload: dict[str, T.Any] = {"note_names": note_names}
        if hash_ is not None:
            payload["hash"] = hash_
        if file_id is not None:
            payload["file_id"] = file_id

        self._api_request("POST", self._DELETE_NOTES_PATH, json=payload)

    def get_cookies(self, domain: str):
        response = self._api_request(
            "GET", self._GET_COOKIES_PATH, params={"domain": domain}
        )
        return response.json()["cookies"]

    def set_cookies(self, cookies):
        self._api_request("POST", self._SET_COOKIES_PATH, json={"cookies": cookies})

    def set_user_agent(self, user_agent: str):
        self._api_request(
            "POST", self._SET_USER_AGENT_PATH, json={"user-agent": user_agent}
        )

    def get_pages(self):
        response = self._api_request("GET", self._GET_PAGES_PATH)
        return response.json()["pages"]

    def get_page_info(self, page_key: str, simple=None):
        parameters = {"page_key": page_key}
        if simple is not None:
            parameters["simple"] = json.dumps(simple, cls=JSONEncoder)

        response = self._api_request("GET", self._GET_PAGE_INFO_PATH, params=parameters)
        return response.json()["page_info"]

    def add_files_to_page(self, page_key: str, file_ids=None, hashes=None):
        if file_ids is None and hashes is None:
            raise ValueError("At least one of file_ids, hashes is required")

        payload: dict[str, T.Any] = {"page_key": page_key}
        if file_ids is not None:
            payload["file_ids"] = file_ids
        if hashes is not None:
            payload["hashes"] = hashes

        self._api_request("POST", self._ADD_FILES_TO_PAGE_PATH, json=payload)

    def focus_page(self, page_key: str):
        self._api_request("POST", self._FOCUS_PAGE_PATH, json={"page_key": page_key})

    def search_files(
        self,
        tags,
        file_service_name=None,
        file_service_key=None,
        tag_service_name=None,
        tag_service_key=None,
        file_sort_type=None,
        file_sort_asc=None,
        return_hashes=None,
    ):
        if file_service_name is not None and file_service_key is not None:
            raise ValueError(
                "Exactly one of file_service_name, file_service_key is required"
            )
        if tag_service_name is not None and tag_service_key is not None:
            raise ValueError(
                "Exactly one of tag_service_name, tag_service_key is required"
            )

        parameters: dict[str, T.Union[str, int]] = {
            "tags": json.dumps(tags, cls=JSONEncoder)
        }
        if file_service_name is not None:
            parameters["file_service_name"] = file_service_name
        if file_service_key is not None:
            parameters["file_service_key"] = file_service_key

        if tag_service_name is not None:
            parameters["tag_service_name"] = tag_service_name
        if tag_service_key is not None:
            parameters["tag_service_key"] = tag_service_key

        if file_sort_type is not None:
            parameters["file_sort_type"] = file_sort_type
        if file_sort_asc is not None:
            parameters["file_sort_asc"] = json.dumps(file_sort_asc, cls=JSONEncoder)
        if return_hashes is not None:
            parameters["return_hashes"] = json.dumps(return_hashes, cls=JSONEncoder)

        response = self._api_request("GET", self._SEARCH_FILES_PATH, params=parameters)
        return response.json()["hashes" if return_hashes else "file_ids"]

    def get_file_metadata(
        self,
        hashes=None,
        file_ids=None,
        create_new_file_ids=None,
        only_return_identifiers=None,
        only_return_basic_information=None,
        detailed_url_information=None,
        hide_service_name_tags=None,
        include_notes=None,
    ):
        if hashes is None and file_ids is None:
            raise ValueError("At least one of hashes, file_ids is required")

        parameters = {}
        if hashes is not None:
            parameters["hashes"] = json.dumps(hashes, cls=JSONEncoder)
        if file_ids is not None:
            parameters["file_ids"] = json.dumps(file_ids, cls=JSONEncoder)

        if create_new_file_ids is not None:
            parameters["create_new_file_ids"] = json.dumps(
                create_new_file_ids, cls=JSONEncoder
            )
        if only_return_identifiers is not None:
            parameters["only_return_identifiers"] = json.dumps(
                only_return_identifiers, cls=JSONEncoder
            )
        if only_return_basic_information is not None:
            parameters["only_return_basic_information"] = json.dumps(
                only_return_basic_information, cls=JSONEncoder
            )
        if detailed_url_information is not None:
            parameters["detailed_url_information"] = json.dumps(
                detailed_url_information, cls=JSONEncoder
            )
        if hide_service_name_tags is not None:
            parameters["hide_service_name_tags"] = json.dumps(
                hide_service_name_tags, cls=JSONEncoder
            )
        if include_notes is not None:
            parameters["include_notes"] = json.dumps(include_notes, cls=JSONEncoder)

        response = self._api_request(
            "GET", self._GET_FILE_METADATA_PATH, params=parameters
        )
        return response.json()["metadata"]

    def get_file(self, hash_=None, file_id=None):
        if (hash_ is None and file_id is None) or (
            hash_ is not None and file_id is not None
        ):
            raise ValueError("Exactly one of hash_, file_id is required")

        parameters: dict[str, T.Union[str, int]] = {}
        if hash_ is not None:
            parameters["hash"] = hash_
        if file_id is not None:
            parameters["file_id"] = file_id

        return self._api_request(
            "GET", self._GET_FILE_PATH, params=parameters, stream=True
        )

    def get_thumbnail(self, hash_=None, file_id=None):
        if (hash_ is None and file_id is None) or (
            hash_ is not None and file_id is not None
        ):
            raise ValueError("Exactly one of hash_, file_id is required")

        parameters: dict[str, T.Union[str, int]] = {}
        if hash_ is not None:
            parameters["hash"] = hash_
        if file_id is not None:
            parameters["file_id"] = file_id

        return self._api_request(
            "GET", self._GET_THUMBNAIL_PATH, params=parameters, stream=True
        )

    def lock_database(self):
        self._api_request("POST", self._LOCK_DATABASE_PATH)

    def unlock_database(self):
        self._api_request("POST", self._UNLOCK_DATABASE_PATH)

    def get_mr_bones(self):
        return self._api_request("GET", self._MR_BONES_PATH).json()["boned_stats"]

    def add_and_tag_files(
        self,
        paths_or_files,
        tags,
        service_names=None,
        service_keys=None,
    ):
        """Convenience method to add and tag multiple files at the same time.

        If service_names and service_keys aren't specified, the default service name "my tags" will be used. If a file
        already exists in Hydrus, it will also be tagged.

        Returns:
            list[dict[str, T.Any]]: Returns results of all `Client.add_file()` calls, matching the order of the
            paths_or_files iterable
        """
        if service_names is None and service_keys is None:
            service_names = ("my tags",)

        results = []
        hashes = set()
        for path_or_file in paths_or_files:
            result = self.add_file(path_or_file)
            results.append(result)
            if result["status"] != ImportStatus.FAILED:
                hashes.add(result["hash"])

        service_names_to_tags = (
            {name: tags for name in service_names}
            if service_names is not None
            else None
        )
        service_keys_to_tags = (
            {key: tags for key in service_keys} if service_keys is not None else None
        )
        # Ignore type, we know that hashes only contains strings
        self.add_tags(hashes, service_names_to_tags=service_names_to_tags, service_keys_to_tags=service_keys_to_tags)  # type: ignore
        return results

    def get_page_list(self):
        """Convenience method that returns a flattened version of the page tree from `Client.get_pages()`.

        Returns:
            list[dict[str, T.Any]]: A list of every "pages" value in the page tree in pre-order (NLR)
        """
        tree = self.get_pages()
        pages = []

        def walk_tree(page: dict[str, T.Any]):
            pages.append(page)
            # Ignore type, we know that pages is always a list
            for sub_page in page.get("pages", ()):  # type: ignore
                # Ignore type, we know that sub_page is always a dict
                walk_tree(sub_page)  # type: ignore

        walk_tree(tree)
        return pages


__all__ = [
    "__version__",
    "DEFAULT_API_URL",
    "HYDRUS_METADATA_ENCODING",
    "HydrusAPIException",
    "ConnectionError",
    "APIError",
    "MissingParameter",
    "InsufficientAccess",
    "DatabaseLocked",
    "ServerError",
    "Permission",
    "URLType",
    "ImportStatus",
    "TagAction",
    "TagStatus",
    "PageType",
    "FileSortType",
    "Client",
]


================================================
FILE: scripts/hydrus_api/utils.py
================================================
# Copyright (C) 2021 cryzed
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

import collections
import os
import typing as T
from collections import abc

from hydrus_api import DEFAULT_API_URL, HYDRUS_METADATA_ENCODING, Client, Permission

X = T.TypeVar("X")


class TextFileLike(T.Protocol):
    def read(self) -> str:
        pass


def verify_permissions(
    client: Client,
    permissions: abc.Iterable[T.Union[int, Permission]],
    exact: bool = False,
) -> bool:
    granted_permissions = set(client.verify_access_key()["basic_permissions"])
    return (
        granted_permissions == set(permissions)
        if exact
        else granted_permissions.issuperset(permissions)
    )


def cli_request_api_key(
    name: str,
    permissions: abc.Iterable[T.Union[int, Permission]],
    verify: bool = True,
    exact: bool = False,
    api_url: str = DEFAULT_API_URL,
) -> str:
    while True:
        input(
            'Navigate to "services->review services->local->client api" in the Hydrus client and click "add->from api '
            'request". Then press enter to continue...'
        )
        access_key = Client(api_url=api_url).request_new_permissions(name, permissions)
        input(
            "Press OK and then apply in the Hydrus client dialog. Then press enter to continue..."
        )

        client = Client(access_key, api_url)
        if verify and not verify_permissions(client, permissions, exact):
            granted = client.verify_access_key()["basic_permissions"]
            print(
                f"The granted permissions ({granted}) differ from the requested permissions ({permissions}), please "
                "grant all requested permissions."
            )
            continue

        return access_key


def parse_hydrus_metadata(
    text: str,
) -> collections.defaultdict[T.Optional[str], set[str]]:
    namespaces = collections.defaultdict(set)
    for line in (line.strip() for line in text.splitlines()):
        if not line:
            continue

        parts = line.split(":", 1)
        namespace, tag = (None, line) if len(parts) == 1 else parts
        namespaces[namespace].add(tag)

    # Ignore type, mypy has trouble figuring out that tag isn't optional
    return namespaces  # type: ignore


def parse_hydrus_metadata_file(
    path_or_file: T.Union[str, os.PathLike, TextFileLike]
) -> collections.defaultdict[T.Optional[str], set[str]]:
    if isinstance(path_or_file, (str, os.PathLike)):
        with open(path_or_file, encoding=HYDRUS_METADATA_ENCODING) as file:
            return parse_hydrus_metadata(file.read())

    return parse_hydrus_metadata(path_or_file.read())


# Useful for splitting up requests to get_file_metadata()
def yield_chunks(
    sequence: T.Sequence[X], chunk_size: int, offset: int = 0
) -> T.Generator[T.Sequence[X], None, None]:
    while offset < len(sequence):
        yield sequence[offset : offset + chunk_size]
        offset += chunk_size


__all__ = [
    "verify_permissions",
    "cli_request_api_key",
    "parse_hydrus_metadata",
    "parse_hydrus_metadata_file",
    "yield_chunks",
]


================================================
FILE: scripts/img2img.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import (
    st,
    server_state,
    no_rerun,
    generation_callback,
    process_images,
    KDiffusionSampler,
    custom_models_available,
    RealESRGAN_available,
    GFPGAN_available,
    LDSR_available,
    load_models,
    hc,
    seed_to_int,
    logger,
    resize_image,
    get_matched_noise,
    CFGMaskedDenoiser,
    ImageFilter,
    set_page_title,
)

# streamlit imports
from streamlit.runtime.scriptrunner import StopException

# other imports
import cv2
from PIL import Image, ImageOps
import torch
import k_diffusion as K
import numpy as np
import time
import torch
import skimage
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler

# streamlit components
from custom_components import sygil_suggestions
from streamlit_drawable_canvas import st_canvas

# Temp imports


# end of imports
# ---------------------------------------------------------------------------------------------------------------

sygil_suggestions.init()

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging

    logging.set_verbosity_error()
except:
    pass


def img2img(
    prompt: str = "",
    init_info: any = None,
    init_info_mask: any = None,
    mask_mode: int = 0,
    mask_blur_strength: int = 3,
    mask_restore: bool = False,
    ddim_steps: int = 50,
    sampler_name: str = "DDIM",
    n_iter: int = 1,
    cfg_scale: float = 7.5,
    denoising_strength: float = 0.8,
    seed: int = -1,
    noise_mode: int = 0,
    find_noise_steps: str = "",
    height: int = 512,
    width: int = 512,
    resize_mode: int = 0,
    fp=None,
    variant_amount: float = 0.0,
    variant_seed: int = None,
    ddim_eta: float = 0.0,
    write_info_files: bool = True,
    separate_prompts: bool = False,
    normalize_prompt_weights: bool = True,
    save_individual_images: bool = True,
    save_grid: bool = True,
    group_by_prompt: bool = True,
    save_as_jpg: bool = True,
    use_GFPGAN: bool = True,
    GFPGAN_model: str = "GFPGANv1.4",
    use_RealESRGAN: bool = True,
    RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B",
    use_LDSR: bool = True,
    LDSR_model: str = "model",
    loopback: bool = False,
    random_seed_loopback: bool = False,
):
    outpath = st.session_state["defaults"].general.outdir_img2img
    seed = seed_to_int(seed)

    batch_size = 1

    if sampler_name == "PLMS":
        sampler = PLMSSampler(server_state["model"])
    elif sampler_name == "DDIM":
        sampler = DDIMSampler(server_state["model"])
    elif sampler_name == "k_dpm_2_a":
        sampler = KDiffusionSampler(server_state["model"], "dpm_2_ancestral")
    elif sampler_name == "k_dpm_2":
        sampler = KDiffusionSampler(server_state["model"], "dpm_2")
    elif sampler_name == "k_dpmpp_2m":
        sampler = KDiffusionSampler(server_state["model"], "dpmpp_2m")
    elif sampler_name == "k_euler_a":
        sampler = KDiffusionSampler(server_state["model"], "euler_ancestral")
    elif sampler_name == "k_euler":
        sampler = KDiffusionSampler(server_state["model"], "euler")
    elif sampler_name == "k_heun":
        sampler = KDiffusionSampler(server_state["model"], "heun")
    elif sampler_name == "k_lms":
        sampler = KDiffusionSampler(server_state["model"], "lms")
    else:
        raise Exception("Unknown sampler: " + sampler_name)

    def process_init_mask(init_mask: Image):
        if init_mask.mode == "RGBA":
            init_mask = init_mask.convert("RGBA")
            background = Image.new("RGBA", init_mask.size, (0, 0, 0))
            init_mask = Image.alpha_composite(background, init_mask)
            init_mask = init_mask.convert("RGB")
        return init_mask

    init_img = init_info
    init_mask = None
    if mask_mode == 0:
        if init_info_mask:
            init_mask = process_init_mask(init_info_mask)
    elif mask_mode == 1:
        if init_info_mask:
            init_mask = process_init_mask(init_info_mask)
            init_mask = ImageOps.invert(init_mask)
    elif mask_mode == 2:
        init_img_transparency = init_img.split()[-1].convert(
            "L"
        )  # .point(lambda x: 255 if x > 0 else 0, mode='1')
        init_mask = init_img_transparency
        init_mask = init_mask.convert("RGB")
        init_mask = resize_image(resize_mode, init_mask, width, height)
        init_mask = init_mask.convert("RGB")

    assert 0.0 <= denoising_strength <= 1.0, "can only work with strength in [0.0, 1.0]"
    t_enc = int(denoising_strength * ddim_steps)

    if (
        init_mask is not None
        and (noise_mode == 2 or noise_mode == 3)
        and init_img is not None
    ):
        noise_q = 0.99
        color_variation = 0.0
        mask_blend_factor = 1.0

        np_init = (np.asarray(init_img.convert("RGB")) / 255.0).astype(
            np.float64
        )  # annoyingly complex mask fixing
        np_mask_rgb = 1.0 - (
            np.asarray(ImageOps.invert(init_mask).convert("RGB")) / 255.0
        ).astype(np.float64)
        np_mask_rgb -= np.min(np_mask_rgb)
        np_mask_rgb /= np.max(np_mask_rgb)
        np_mask_rgb = 1.0 - np_mask_rgb
        np_mask_rgb_hardened = 1.0 - (np_mask_rgb < 0.99).astype(np.float64)
        blurred = skimage.filters.gaussian(
            np_mask_rgb_hardened[:], sigma=16.0, channel_axis=2, truncate=32.0
        )
        blurred2 = skimage.filters.gaussian(
            np_mask_rgb_hardened[:], sigma=16.0, channel_axis=2, truncate=32.0
        )
        # np_mask_rgb_dilated = np_mask_rgb + blurred  # fixup mask todo: derive magic constants
        # np_mask_rgb = np_mask_rgb + blurred
        np_mask_rgb_dilated = np.clip((np_mask_rgb + blurred2) * 0.7071, 0.0, 1.0)
        np_mask_rgb = np.clip((np_mask_rgb + blurred) * 0.7071, 0.0, 1.0)

        noise_rgb = get_matched_noise(np_init, np_mask_rgb, noise_q, color_variation)
        blend_mask_rgb = np.clip(np_mask_rgb_dilated, 0.0, 1.0) ** (mask_blend_factor)
        noised = noise_rgb[:]
        blend_mask_rgb **= 2.0
        noised = np_init[:] * (1.0 - blend_mask_rgb) + noised * blend_mask_rgb

        np_mask_grey = np.sum(np_mask_rgb, axis=2) / 3.0
        ref_mask = np_mask_grey < 1e-3

        all_mask = np.ones((height, width), dtype=bool)
        noised[all_mask, :] = skimage.exposure.match_histograms(
            noised[all_mask, :] ** 1.0, noised[ref_mask, :], channel_axis=1
        )

        init_img = Image.fromarray(
            np.clip(noised * 255.0, 0.0, 255.0).astype(np.uint8), mode="RGB"
        )
        st.session_state["editor_image"].image(init_img)  # debug

    def init():
        image = init_img.convert("RGB")
        image = np.array(image).astype(np.float32) / 255.0
        image = image[None].transpose(0, 3, 1, 2)
        image = torch.from_numpy(image)

        mask_channel = None
        if init_mask:
            alpha = resize_image(resize_mode, init_mask, width // 8, height // 8)
            mask_channel = alpha.split()[-1]

        mask = None
        if mask_channel is not None:
            mask = np.array(mask_channel).astype(np.float32) / 255.0
            mask = 1 - mask
            mask = np.tile(mask, (4, 1, 1))
            mask = mask[None].transpose(0, 1, 2, 3)
            mask = torch.from_numpy(mask).to(server_state["device"])

        if st.session_state["defaults"].general.optimized:
            server_state["modelFS"].to(server_state["device"])

        init_image = 2.0 * image - 1.0
        init_image = init_image.to(server_state["device"])
        init_latent = (
            server_state["model"]
            if not st.session_state["defaults"].general.optimized
            else server_state["modelFS"]
        ).get_first_stage_encoding(
            (
                server_state["model"]
                if not st.session_state["defaults"].general.optimized
                else server_state["modelFS"]
            ).encode_first_stage(init_image)
        )  # move to latent space

        if st.session_state["defaults"].general.optimized:
            mem = torch.cuda.memory_allocated() / 1e6
            server_state["modelFS"].to("cpu")
            while torch.cuda.memory_allocated() / 1e6 >= mem:
                time.sleep(1)

        return (
            init_latent,
            mask,
        )

    def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
        t_enc_steps = t_enc
        obliterate = False
        if ddim_steps == t_enc_steps:
            t_enc_steps = t_enc_steps - 1
            obliterate = True

        if sampler_name != "DDIM":
            x0, z_mask = init_data

            sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
            noise = x * sigmas[ddim_steps - t_enc_steps - 1]

            xi = x0 + noise

            # Obliterate masked image
            if z_mask is not None and obliterate:
                random = torch.randn(z_mask.shape, device=xi.device)
                xi = (z_mask * noise) + ((1 - z_mask) * xi)

            sigma_sched = sigmas[ddim_steps - t_enc_steps - 1 :]
            model_wrap_cfg = CFGMaskedDenoiser(sampler.model_wrap)
            samples_ddim = K.sampling.__dict__[f"sample_{sampler.get_sampler_name()}"](
                model_wrap_cfg,
                xi,
                sigma_sched,
                extra_args={
                    "cond": conditioning,
                    "uncond": unconditional_conditioning,
                    "cond_scale": cfg_scale,
                    "mask": z_mask,
                    "x0": x0,
                    "xi": xi,
                },
                disable=False,
                callback=generation_callback if not server_state["bridge"] else None,
            )
        else:
            x0, z_mask = init_data

            sampler.make_schedule(
                ddim_num_steps=ddim_steps, ddim_eta=0.0, verbose=False
            )
            z_enc = sampler.stochastic_encode(
                x0, torch.tensor([t_enc_steps] * batch_size).to(server_state["device"])
            )

            # Obliterate masked image
            if z_mask is not None and obliterate:
                random = torch.randn(z_mask.shape, device=z_enc.device)
                z_enc = (z_mask * random) + ((1 - z_mask) * z_enc)

                # decode it
            samples_ddim = sampler.decode(
                z_enc,
                conditioning,
                t_enc_steps,
                unconditional_guidance_scale=cfg_scale,
                unconditional_conditioning=unconditional_conditioning,
                z_mask=z_mask,
                x0=x0,
            )
        return samples_ddim

    if loopback:
        output_images, info = None, None
        history = []
        initial_seed = None

        do_color_correction = False
        try:
            from skimage import exposure

            do_color_correction = True
        except:
            logger.error("Install scikit-image to perform color correction on loopback")

        for i in range(n_iter):
            if do_color_correction and i == 0:
                correction_target = cv2.cvtColor(
                    np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB
                )

            # RealESRGAN can only run on the final iteration
            is_final_iteration = i == n_iter - 1

            output_images, seed, info, stats = process_images(
                outpath=outpath,
                func_init=init,
                func_sample=sample,
                prompt=prompt,
                seed=seed,
                sampler_name=sampler_name,
                save_grid=save_grid,
                batch_size=1,
                n_iter=1,
                steps=ddim_steps,
                cfg_scale=cfg_scale,
                width=width,
                height=height,
                prompt_matrix=separate_prompts,
                use_GFPGAN=use_GFPGAN,
                GFPGAN_model=GFPGAN_model,
                use_RealESRGAN=use_RealESRGAN
                and is_final_iteration,  # Forcefully disable upscaling when using loopback
                realesrgan_model_name=RealESRGAN_model,
                use_LDSR=use_LDSR,
                LDSR_model_name=LDSR_model,
                normalize_prompt_weights=normalize_prompt_weights,
                save_individual_images=save_individual_images,
                init_img=init_img,
                init_mask=init_mask,
                mask_blur_strength=mask_blur_strength,
                mask_restore=mask_restore,
                denoising_strength=denoising_strength,
                noise_mode=noise_mode,
                find_noise_steps=find_noise_steps,
                resize_mode=resize_mode,
                uses_loopback=loopback,
                uses_random_seed_loopback=random_seed_loopback,
                sort_samples=group_by_prompt,
                write_info_files=write_info_files,
                jpg_sample=save_as_jpg,
            )

            if initial_seed is None:
                initial_seed = seed

            input_image = init_img
            init_img = output_images[0]

            if do_color_correction and correction_target is not None:
                init_img = Image.fromarray(
                    cv2.cvtColor(
                        exposure.match_histograms(
                            cv2.cvtColor(np.asarray(init_img), cv2.COLOR_RGB2LAB),
                            correction_target,
                            channel_axis=2,
                        ),
                        cv2.COLOR_LAB2RGB,
                    ).astype("uint8")
                )
                if mask_restore is True and init_mask is not None:
                    color_mask = init_mask.filter(
                        ImageFilter.GaussianBlur(mask_blur_strength)
                    )
                    color_mask = color_mask.convert("L")
                    source_image = input_image.convert("RGB")
                    target_image = init_img.convert("RGB")

                    init_img = Image.composite(source_image, target_image, color_mask)

            if not random_seed_loopback:
                seed = seed + 1
            else:
                seed = seed_to_int(None)

            denoising_strength = max(denoising_strength * 0.95, 0.1)
            history.append(init_img)

        output_images = history
        seed = initial_seed

    else:
        output_images, seed, info, stats = process_images(
            outpath=outpath,
            func_init=init,
            func_sample=sample,
            prompt=prompt,
            seed=seed,
            sampler_name=sampler_name,
            save_grid=save_grid,
            batch_size=batch_size,
            n_iter=n_iter,
            steps=ddim_steps,
            cfg_scale=cfg_scale,
            width=width,
            height=height,
            prompt_matrix=separate_prompts,
            use_GFPGAN=use_GFPGAN,
            GFPGAN_model=GFPGAN_model,
            use_RealESRGAN=use_RealESRGAN,
            realesrgan_model_name=RealESRGAN_model,
            use_LDSR=use_LDSR,
            LDSR_model_name=LDSR_model,
            normalize_prompt_weights=normalize_prompt_weights,
            save_individual_images=save_individual_images,
            init_img=init_img,
            init_mask=init_mask,
            mask_blur_strength=mask_blur_strength,
            denoising_strength=denoising_strength,
            noise_mode=noise_mode,
            find_noise_steps=find_noise_steps,
            mask_restore=mask_restore,
            resize_mode=resize_mode,
            uses_loopback=loopback,
            sort_samples=group_by_prompt,
            write_info_files=write_info_files,
            jpg_sample=save_as_jpg,
        )

    del sampler

    return output_images, seed, info, stats


#
def layout():
    with st.form("img2img-inputs"):
        st.session_state["generation_mode"] = "img2img"

        img2img_input_col, img2img_generate_col = st.columns([10, 1])
        with img2img_input_col:
            # prompt = st.text_area("Input Text","")
            placeholder = "A corgi wearing a top hat as an oil painting."
            prompt = st.text_area("Input Text", "", placeholder=placeholder, height=54)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].general.enable_suggestions:
                    sygil_suggestions.suggestion_area(placeholder)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].admin.global_negative_prompt:
                    prompt += f"### {st.session_state['defaults'].admin.global_negative_prompt}"

        # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
        img2img_generate_col.write("")
        img2img_generate_col.write("")
        generate_button = img2img_generate_col.form_submit_button("Generate")

        # creating the page layout using columns
        col1_img2img_layout, col2_img2img_layout, col3_img2img_layout = st.columns(
            [2, 4, 4], gap="medium"
        )

        with col1_img2img_layout:
            # If we have custom models available on the "models/custom"
            # folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
            custom_models_available()
            if server_state["CustomModel_available"]:
                st.session_state["custom_model"] = st.selectbox(
                    "Custom Model:",
                    server_state["custom_models"],
                    index=server_state["custom_models"].index(
                        st.session_state["defaults"].general.default_model
                    ),
                    help="Select the model you want to use. This option is only available if you have custom models \
							    on your 'models/custom' folder. The model name that will be shown here is the same as the name\
							    the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
							    will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.5",
                )
            else:
                st.session_state["custom_model"] = "Stable Diffusion v1.5"

            st.session_state["sampling_steps"] = st.number_input(
                "Sampling Steps",
                value=st.session_state["defaults"].img2img.sampling_steps.value,
                min_value=st.session_state["defaults"].img2img.sampling_steps.min_value,
                step=st.session_state["defaults"].img2img.sampling_steps.step,
            )

            sampler_name_list = [
                "k_lms",
                "k_euler",
                "k_euler_a",
                "k_dpm_2",
                "k_dpm_2_a",
                "k_dpmpp_2m",
                "k_heun",
                "PLMS",
                "DDIM",
            ]
            st.session_state["sampler_name"] = st.selectbox(
                "Sampling method",
                sampler_name_list,
                index=sampler_name_list.index(
                    st.session_state["defaults"].img2img.sampler_name
                ),
                help="Sampling method to use.",
            )

            width = st.slider(
                "Width:",
                min_value=st.session_state["defaults"].img2img.width.min_value,
                max_value=st.session_state["defaults"].img2img.width.max_value,
                value=st.session_state["defaults"].img2img.width.value,
                step=st.session_state["defaults"].img2img.width.step,
            )
            height = st.slider(
                "Height:",
                min_value=st.session_state["defaults"].img2img.height.min_value,
                max_value=st.session_state["defaults"].img2img.height.max_value,
                value=st.session_state["defaults"].img2img.height.value,
                step=st.session_state["defaults"].img2img.height.step,
            )
            seed = st.text_input(
                "Seed:",
                value=st.session_state["defaults"].img2img.seed,
                help=" The seed to use, if left blank a random seed will be generated.",
            )

            cfg_scale = st.number_input(
                "CFG (Classifier Free Guidance Scale):",
                min_value=st.session_state["defaults"].img2img.cfg_scale.min_value,
                value=st.session_state["defaults"].img2img.cfg_scale.value,
                step=st.session_state["defaults"].img2img.cfg_scale.step,
                help="How strongly the image should follow the prompt.",
            )

            st.session_state["denoising_strength"] = st.slider(
                "Denoising Strength:",
                value=st.session_state["defaults"].img2img.denoising_strength.value,
                min_value=st.session_state[
                    "defaults"
                ].img2img.denoising_strength.min_value,
                max_value=st.session_state[
                    "defaults"
                ].img2img.denoising_strength.max_value,
                step=st.session_state["defaults"].img2img.denoising_strength.step,
            )

            mask_expander = st.empty()
            with mask_expander.expander("Inpainting/Outpainting"):
                mask_mode_list = ["Outpainting", "Inpainting", "Image alpha"]
                mask_mode = st.selectbox(
                    "Painting Mode",
                    mask_mode_list,
                    index=st.session_state["defaults"].img2img.mask_mode,
                    help='Select how you want your image to be masked/painted."Inpainting" modifies the image where the mask is white.\n\
							 "Inverted mask" modifies the image where the mask is black. "Image alpha" modifies the image where the image is transparent.',
                )
                mask_mode = mask_mode_list.index(mask_mode)

                noise_mode_list = [
                    "Seed",
                    "Find Noise",
                    "Matched Noise",
                    "Find+Matched Noise",
                ]
                noise_mode = st.selectbox(
                    "Noise Mode",
                    noise_mode_list,
                    index=noise_mode_list.index(
                        st.session_state["defaults"].img2img.noise_mode
                    ),
                    help="",
                )
                # noise_mode = noise_mode_list.index(noise_mode)
                find_noise_steps = st.number_input(
                    "Find Noise Steps",
                    value=st.session_state["defaults"].img2img.find_noise_steps.value,
                    min_value=st.session_state[
                        "defaults"
                    ].img2img.find_noise_steps.min_value,
                    step=st.session_state["defaults"].img2img.find_noise_steps.step,
                )

                # Specify canvas parameters in application
                drawing_mode = st.selectbox(
                    "Drawing tool:",
                    (
                        "freedraw",
                        "transform",
                        # "line",
                        "rect",
                        "circle",
                        # "polygon",
                    ),
                )

                stroke_width = st.slider("Stroke width: ", 1, 100, 50)
                stroke_color = st.color_picker("Stroke color hex: ", value="#EEEEEE")
                bg_color = st.color_picker("Background color hex: ", "#7B6E6E")

                display_toolbar = st.checkbox("Display toolbar", True)
                # realtime_update = st.checkbox("Update in realtime", True)

            with st.expander("Batch Options"):
                st.session_state["batch_count"] = st.number_input(
                    "Batch count.",
                    value=st.session_state["defaults"].img2img.batch_count.value,
                    help="How many iterations or batches of images to generate in total.",
                )

                st.session_state["batch_size"] = st.number_input(
                    "Batch size",
                    value=st.session_state.defaults.img2img.batch_size.value,
                    help="How many images are at once in a batch.\
										 It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
										 Default: 1",
                )

            with st.expander("Preview Settings"):
                st.session_state["update_preview"] = st.session_state[
                    "defaults"
                ].general.update_preview
                st.session_state["update_preview_frequency"] = st.number_input(
                    "Update Image Preview Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].img2img.update_preview_frequency,
                    help="Frequency in steps at which the the preview image is updated. By default the frequency \
											       is set to 1 step.",
                )
            #
            with st.expander("Advanced"):
                with st.expander("Output Settings"):
                    separate_prompts = st.checkbox(
                        "Create Prompt Matrix.",
                        value=st.session_state["defaults"].img2img.separate_prompts,
                        help="Separate multiple prompts using the `|` character, and get all combinations of them.",
                    )
                    normalize_prompt_weights = st.checkbox(
                        "Normalize Prompt Weights.",
                        value=st.session_state[
                            "defaults"
                        ].img2img.normalize_prompt_weights,
                        help="Ensure the sum of all weights add up to 1.0",
                    )
                    loopback = st.checkbox(
                        "Loopback.",
                        value=st.session_state["defaults"].img2img.loopback,
                        help="Use images from previous batch when creating next batch.",
                    )
                    st.checkbox(
                        "Random loopback seed.",
                        value=st.session_state["defaults"].img2img.random_seed_loopback,
                        help="Random loopback seed",
                    )
                    img2img_mask_restore = st.checkbox(
                        "Only modify regenerated parts of image",
                        value=st.session_state["defaults"].img2img.mask_restore,
                        help="Enable to restore the unmasked parts of the image with the input, may not blend as well but preserves detail",
                    )
                    save_individual_images = st.checkbox(
                        "Save individual images.",
                        value=st.session_state[
                            "defaults"
                        ].img2img.save_individual_images,
                        help="Save each image generated before any filter or enhancement is applied.",
                    )
                    save_grid = st.checkbox(
                        "Save grid",
                        value=st.session_state["defaults"].img2img.save_grid,
                        help="Save a grid with all the images generated into a single image.",
                    )
                    group_by_prompt = st.checkbox(
                        "Group results by prompt",
                        value=st.session_state["defaults"].img2img.group_by_prompt,
                        help="Saves all the images with the same prompt into the same folder. \
						                      When using a prompt matrix each prompt combination will have its own folder.",
                    )
                    write_info_files = st.checkbox(
                        "Write Info file",
                        value=st.session_state["defaults"].img2img.write_info_files,
                        help="Save a file next to the image with informartion about the generation.",
                    )
                    save_as_jpg = st.checkbox(
                        "Save samples as jpg",
                        value=st.session_state["defaults"].img2img.save_as_jpg,
                        help="Saves the images as jpg instead of png.",
                    )

                #
                # check if GFPGAN, RealESRGAN and LDSR are available.
                if "GFPGAN_available" not in st.session_state:
                    GFPGAN_available()

                if "RealESRGAN_available" not in st.session_state:
                    RealESRGAN_available()

                if "LDSR_available" not in st.session_state:
                    LDSR_available()

                if (
                    st.session_state["GFPGAN_available"]
                    or st.session_state["RealESRGAN_available"]
                    or st.session_state["LDSR_available"]
                ):
                    with st.expander("Post-Processing"):
                        face_restoration_tab, upscaling_tab = st.tabs(
                            ["Face Restoration", "Upscaling"]
                        )
                        with face_restoration_tab:
                            # GFPGAN used for face restoration
                            if st.session_state["GFPGAN_available"]:
                                # with st.expander("Face Restoration"):
                                # if st.session_state["GFPGAN_available"]:
                                # with st.expander("GFPGAN"):
                                st.session_state["use_GFPGAN"] = st.checkbox(
                                    "Use GFPGAN",
                                    value=st.session_state[
                                        "defaults"
                                    ].img2img.use_GFPGAN,
                                    help="Uses the GFPGAN model to improve faces after the generation.\
																							 This greatly improve the quality and consistency of faces but uses\
																							 extra VRAM. Disable if you need the extra VRAM.",
                                )

                                st.session_state["GFPGAN_model"] = st.selectbox(
                                    "GFPGAN model",
                                    st.session_state["GFPGAN_models"],
                                    index=st.session_state["GFPGAN_models"].index(
                                        st.session_state[
                                            "defaults"
                                        ].general.GFPGAN_model
                                    ),
                                )

                                # st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')

                            else:
                                st.session_state["use_GFPGAN"] = False

                        with upscaling_tab:
                            st.session_state["us_upscaling"] = st.checkbox(
                                "Use Upscaling",
                                value=st.session_state[
                                    "defaults"
                                ].img2img.use_upscaling,
                            )

                            # RealESRGAN and LDSR used for upscaling.
                            if (
                                st.session_state["RealESRGAN_available"]
                                or st.session_state["LDSR_available"]
                            ):
                                upscaling_method_list = []
                                if st.session_state["RealESRGAN_available"]:
                                    upscaling_method_list.append("RealESRGAN")
                                if st.session_state["LDSR_available"]:
                                    upscaling_method_list.append("LDSR")

                                st.session_state["upscaling_method"] = st.selectbox(
                                    "Upscaling Method",
                                    upscaling_method_list,
                                    index=upscaling_method_list.index(
                                        st.session_state[
                                            "defaults"
                                        ].general.upscaling_method
                                    )
                                    if st.session_state[
                                        "defaults"
                                    ].general.upscaling_method
                                    in upscaling_method_list
                                    else 0,
                                )

                                if st.session_state["RealESRGAN_available"]:
                                    with st.expander("RealESRGAN"):
                                        if (
                                            st.session_state["upscaling_method"]
                                            == "RealESRGAN"
                                            and st.session_state["us_upscaling"]
                                        ):
                                            st.session_state["use_RealESRGAN"] = True
                                        else:
                                            st.session_state["use_RealESRGAN"] = False

                                        st.session_state[
                                            "RealESRGAN_model"
                                        ] = st.selectbox(
                                            "RealESRGAN model",
                                            st.session_state["RealESRGAN_models"],
                                            index=st.session_state[
                                                "RealESRGAN_models"
                                            ].index(
                                                st.session_state[
                                                    "defaults"
                                                ].general.RealESRGAN_model
                                            ),
                                        )
                                else:
                                    st.session_state["use_RealESRGAN"] = False
                                    st.session_state[
                                        "RealESRGAN_model"
                                    ] = "RealESRGAN_x4plus"

                                #
                                if st.session_state["LDSR_available"]:
                                    with st.expander("LDSR"):
                                        if (
                                            st.session_state["upscaling_method"]
                                            == "LDSR"
                                            and st.session_state["us_upscaling"]
                                        ):
                                            st.session_state["use_LDSR"] = True
                                        else:
                                            st.session_state["use_LDSR"] = False

                                        st.session_state["LDSR_model"] = st.selectbox(
                                            "LDSR model",
                                            st.session_state["LDSR_models"],
                                            index=st.session_state["LDSR_models"].index(
                                                st.session_state[
                                                    "defaults"
                                                ].general.LDSR_model
                                            ),
                                        )

                                        st.session_state[
                                            "ldsr_sampling_steps"
                                        ] = st.number_input(
                                            "Sampling Steps",
                                            value=st.session_state[
                                                "defaults"
                                            ].img2img.LDSR_config.sampling_steps,
                                            help="",
                                        )

                                        st.session_state[
                                            "preDownScale"
                                        ] = st.number_input(
                                            "PreDownScale",
                                            value=st.session_state[
                                                "defaults"
                                            ].img2img.LDSR_config.preDownScale,
                                            help="",
                                        )

                                        st.session_state[
                                            "postDownScale"
                                        ] = st.number_input(
                                            "postDownScale",
                                            value=st.session_state[
                                                "defaults"
                                            ].img2img.LDSR_config.postDownScale,
                                            help="",
                                        )

                                        downsample_method_list = ["Nearest", "Lanczos"]
                                        st.session_state[
                                            "downsample_method"
                                        ] = st.selectbox(
                                            "Downsample Method",
                                            downsample_method_list,
                                            index=downsample_method_list.index(
                                                st.session_state[
                                                    "defaults"
                                                ].img2img.LDSR_config.downsample_method
                                            ),
                                        )

                                else:
                                    st.session_state["use_LDSR"] = False
                                    st.session_state["LDSR_model"] = "model"

                with st.expander("Variant"):
                    variant_amount = st.slider(
                        "Variant Amount:",
                        value=st.session_state["defaults"].img2img.variant_amount,
                        min_value=0.0,
                        max_value=1.0,
                        step=0.01,
                    )
                    variant_seed = st.text_input(
                        "Variant Seed:",
                        value=st.session_state["defaults"].img2img.variant_seed,
                        help="The seed to use when generating a variant, if left blank a random seed will be generated.",
                    )

        with col2_img2img_layout:
            st.tabs(["Editor"])

            editor_image = st.empty()
            st.session_state["editor_image"] = editor_image

            st.form_submit_button("Refresh")

            # if "canvas" not in st.session_state:
            st.session_state["canvas"] = st.empty()

            st.empty()
            st.empty()

            uploaded_images = st.file_uploader(
                "Upload Image",
                accept_multiple_files=False,
                type=["png", "jpg", "jpeg", "webp", "jfif"],
                help="Upload an image which will be used for the image to image generation.",
            )
            if uploaded_images:
                image = Image.open(uploaded_images).convert("RGB")
                new_img = image.resize((width, height))
                # image_holder.image(new_img)

            # mask_holder = st.empty()

            # uploaded_masks = st.file_uploader(
            # "Upload Mask", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp", 'jfif'],
            # help="Upload an mask image which will be used for masking the image to image generation.",
            # )

            #
            # Create a canvas component
            with st.session_state["canvas"]:
                st.session_state["uploaded_masks"] = st_canvas(
                    fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
                    stroke_width=stroke_width,
                    stroke_color=stroke_color,
                    background_color=bg_color,
                    background_image=image if uploaded_images else None,
                    update_streamlit=True,
                    width=width,
                    height=height,
                    drawing_mode=drawing_mode,
                    initial_drawing=st.session_state["uploaded_masks"].json_data
                    if "uploaded_masks" in st.session_state
                    else None,
                    display_toolbar=display_toolbar,
                    key="full_app",
                )

                # try:
                ##print (type(st.session_state["uploaded_masks"]))
                # if st.session_state["uploaded_masks"] != None:
                # mask_expander.expander("Mask", expanded=True)
                # mask = Image.fromarray(st.session_state["uploaded_masks"].image_data)

                # st.image(mask)

                # if mask.mode == "RGBA":
                # mask = mask.convert('RGBA')
                # background = Image.new('RGBA', mask.size, (0, 0, 0))
                # mask = Image.alpha_composite(background, mask)
                # mask = mask.resize((width, height))
                # except AttributeError:
                # pass

            with col3_img2img_layout:
                st.tabs(["Result"])

                # create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
                preview_image = st.empty()
                st.session_state["preview_image"] = preview_image

                # st.session_state["loading"] = st.empty()

                st.session_state["progress_bar_text"] = st.empty()
                st.session_state["progress_bar"] = st.empty()

                message = st.empty()

                # if uploaded_images:
                # image = Image.open(uploaded_images).convert('RGB')
                ##img_array = np.array(image) # if you want to pass it to OpenCV
                # new_img = image.resize((width, height))
                # st.image(new_img, use_column_width=True)

        if generate_button:
            # print("Loading models")
            # load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
            with col3_img2img_layout:
                with no_rerun:
                    with hc.HyLoader(
                        "Loading Models...", hc.Loaders.standard_loaders, index=[0]
                    ):
                        load_models(
                            use_LDSR=st.session_state["use_LDSR"],
                            LDSR_model=st.session_state["LDSR_model"],
                            use_GFPGAN=st.session_state["use_GFPGAN"],
                            GFPGAN_model=st.session_state["GFPGAN_model"],
                            use_RealESRGAN=st.session_state["use_RealESRGAN"],
                            RealESRGAN_model=st.session_state["RealESRGAN_model"],
                            CustomModel_available=server_state["CustomModel_available"],
                            custom_model=st.session_state["custom_model"],
                        )

            if uploaded_images:
                # image = Image.fromarray(image).convert('RGBA')
                # new_img = image.resize((width, height))
                ###img_array = np.array(image) # if you want to pass it to OpenCV
                # image_holder.image(new_img)
                new_mask = None

                if st.session_state["uploaded_masks"]:
                    mask = Image.fromarray(
                        st.session_state["uploaded_masks"].image_data
                    )
                    new_mask = mask.resize((width, height))

                    # masked_image_holder.image(new_mask)
                try:
                    output_images, seed, info, stats = img2img(
                        prompt=prompt,
                        init_info=new_img,
                        init_info_mask=new_mask,
                        mask_mode=mask_mode,
                        mask_restore=img2img_mask_restore,
                        ddim_steps=st.session_state["sampling_steps"],
                        sampler_name=st.session_state["sampler_name"],
                        n_iter=st.session_state["batch_count"],
                        cfg_scale=cfg_scale,
                        denoising_strength=st.session_state["denoising_strength"],
                        variant_seed=variant_seed,
                        seed=seed,
                        noise_mode=noise_mode,
                        find_noise_steps=find_noise_steps,
                        width=width,
                        height=height,
                        variant_amount=variant_amount,
                        ddim_eta=st.session_state.defaults.img2img.ddim_eta,
                        write_info_files=write_info_files,
                        separate_prompts=separate_prompts,
                        normalize_prompt_weights=normalize_prompt_weights,
                        save_individual_images=save_individual_images,
                        save_grid=save_grid,
                        group_by_prompt=group_by_prompt,
                        save_as_jpg=save_as_jpg,
                        use_GFPGAN=st.session_state["use_GFPGAN"],
                        GFPGAN_model=st.session_state["GFPGAN_model"],
                        use_RealESRGAN=st.session_state["use_RealESRGAN"],
                        RealESRGAN_model=st.session_state["RealESRGAN_model"],
                        use_LDSR=st.session_state["use_LDSR"],
                        LDSR_model=st.session_state["LDSR_model"],
                        loopback=loopback,
                    )

                    # show a message when the generation is complete.
                    message.success(
                        "Render Complete: " + info + "; Stats: " + stats, icon="✅"
                    )

                except (
                    StopException,
                    # KeyError
                ):
                    logger.info("Received Streamlit StopException")
                    # reset the page title so the percent doesnt stay on it confusing the user.
                    set_page_title("Stable Diffusion Playground")

                # this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
                # use the current col2 first tab to show the preview_img and update it as its generated.
                # preview_image.image(output_images, width=750)


# on import run init


================================================
FILE: scripts/img2txt.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

# ---------------------------------------------------------------------------------------------------------------------------------------------------
"""
CLIP Interrogator made by @pharmapsychotic modified to work with our WebUI.

# CLIP Interrogator by @pharmapsychotic
Twitter: https://twitter.com/pharmapsychotic
Github: https://github.com/pharmapsychotic/clip-interrogator

Description:
What do the different OpenAI CLIP models see in an image? What might be a good text prompt to create similar images using CLIP guided diffusion
or another text to image model? The CLIP Interrogator is here to get you answers!

Please consider buying him a coffee via [ko-fi](https://ko-fi.com/pharmapsychotic) or following him on [twitter](https://twitter.com/pharmapsychotic).

And if you're looking for more Ai art tools check out my [Ai generative art tools list](https://pharmapsychotic.com/tools.html).

"""
# ---------------------------------------------------------------------------------------------------------------------------------------------------

# base webui import and utils.
from sd_utils import st, logger, server_state, server_state_lock, random

# streamlit imports

# streamlit components section

# other imports

import clip
import open_clip
import gc
import os
import pandas as pd

# import requests
import torch
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from ldm.models.blip import blip_decoder

# import hashlib

# end of imports
# ---------------------------------------------------------------------------------------------------------------

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
blip_image_eval_size = 512

st.session_state["log"] = []


def load_blip_model():
    logger.info("Loading BLIP Model")
    if "log" not in st.session_state:
        st.session_state["log"] = []

    st.session_state["log"].append("Loading BLIP Model")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )

    if "blip_model" not in server_state:
        with server_state_lock["blip_model"]:
            server_state["blip_model"] = blip_decoder(
                pretrained="models/blip/model__base_caption.pth",
                image_size=blip_image_eval_size,
                vit="base",
                med_config="configs/blip/med_config.json",
            )

            server_state["blip_model"] = server_state["blip_model"].eval()

            server_state["blip_model"] = server_state["blip_model"].to(device).half()

            logger.info("BLIP Model Loaded")
            st.session_state["log"].append("BLIP Model Loaded")
            st.session_state["log_message"].code(
                "\n".join(st.session_state["log"]), language=""
            )
    else:
        logger.info("BLIP Model already loaded")
        st.session_state["log"].append("BLIP Model already loaded")
        st.session_state["log_message"].code(
            "\n".join(st.session_state["log"]), language=""
        )


def generate_caption(pil_image):
    load_blip_model()

    gpu_image = (
        transforms.Compose(
            [  # type: ignore
                transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),  # type: ignore
                transforms.ToTensor(),  # type: ignore
                transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),  # type: ignore
            ]
        )(pil_image)
        .unsqueeze(0)
        .to(device)
        .half()
    )

    with torch.no_grad():
        caption = server_state["blip_model"].generate(
            gpu_image, sample=False, num_beams=3, max_length=20, min_length=5
        )

    return caption[0]


def load_list(filename):
    with open(filename, "r", encoding="utf-8", errors="replace") as f:
        items = [line.strip() for line in f.readlines()]
        return items


def rank(model, image_features, text_array, top_count=1):
    top_count = min(top_count, len(text_array))
    text_tokens = clip.tokenize([text for text in text_array]).cuda()
    with torch.no_grad():
        text_features = model.encode_text(text_tokens).float()
    text_features /= text_features.norm(dim=-1, keepdim=True)

    similarity = torch.zeros((1, len(text_array))).to(device)
    for i in range(image_features.shape[0]):
        similarity += (
            100.0 * image_features[i].unsqueeze(0) @ text_features.T
        ).softmax(dim=-1)
    similarity /= image_features.shape[0]

    top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
    return [
        (text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy() * 100))
        for i in range(top_count)
    ]


def clear_cuda():
    torch.cuda.empty_cache()
    gc.collect()


def batch_rank(
    model,
    image_features,
    text_array,
    batch_size=st.session_state["defaults"].img2txt.batch_size,
):
    batch_size = min(batch_size, len(text_array))
    batch_count = int(len(text_array) / batch_size)
    batches = [
        text_array[i * batch_size : (i + 1) * batch_size] for i in range(batch_count)
    ]
    ranks = []
    for batch in batches:
        ranks += rank(model, image_features, batch)
    return ranks


def interrogate(image, models):
    load_blip_model()

    logger.info("Generating Caption")
    st.session_state["log"].append("Generating Caption")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )
    caption = generate_caption(image)

    if st.session_state["defaults"].general.optimized:
        del server_state["blip_model"]
        clear_cuda()

    logger.info("Caption Generated")
    st.session_state["log"].append("Caption Generated")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )

    if len(models) == 0:
        logger.info(f"\n\n{caption}")
        return

    table = []
    bests = [[("", 0)]] * 7

    logger.info("Ranking Text")
    st.session_state["log"].append("Ranking Text")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )

    for model_name in models:
        with torch.no_grad(), torch.autocast("cuda", dtype=torch.float16):
            logger.info(f"Interrogating with {model_name}...")
            st.session_state["log"].append(f"Interrogating with {model_name}...")
            st.session_state["log_message"].code(
                "\n".join(st.session_state["log"]), language=""
            )

            if model_name not in server_state["clip_models"]:
                if not st.session_state["defaults"].img2txt.keep_all_models_loaded:
                    model_to_delete = []
                    for model in server_state["clip_models"]:
                        if model != model_name:
                            model_to_delete.append(model)
                    for model in model_to_delete:
                        del server_state["clip_models"][model]
                        del server_state["preprocesses"][model]
                        clear_cuda()
                if model_name == "ViT-H-14":
                    (
                        server_state["clip_models"][model_name],
                        _,
                        server_state["preprocesses"][model_name],
                    ) = open_clip.create_model_and_transforms(
                        model_name,
                        pretrained="laion2b_s32b_b79k",
                        cache_dir="models/clip",
                    )
                elif model_name == "ViT-g-14":
                    (
                        server_state["clip_models"][model_name],
                        _,
                        server_state["preprocesses"][model_name],
                    ) = open_clip.create_model_and_transforms(
                        model_name,
                        pretrained="laion2b_s12b_b42k",
                        cache_dir="models/clip",
                    )
                else:
                    (
                        server_state["clip_models"][model_name],
                        server_state["preprocesses"][model_name],
                    ) = clip.load(
                        model_name, device=device, download_root="models/clip"
                    )
                server_state["clip_models"][model_name] = (
                    server_state["clip_models"][model_name].cuda().eval()
                )

            images = server_state["preprocesses"][model_name](image).unsqueeze(0).cuda()

            image_features = (
                server_state["clip_models"][model_name].encode_image(images).float()
            )

            image_features /= image_features.norm(dim=-1, keepdim=True)

            if st.session_state["defaults"].general.optimized:
                clear_cuda()

            ranks = []
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["mediums"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    ["by " + artist for artist in server_state["artists"]],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["trending_list"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["movements"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["flavors"],
                )
            )
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["domains"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["subreddits"]))
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["techniques"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["tags"],
                )
            )

            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["genres"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["styles"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["subjects"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["colors"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["moods"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["themes"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["keywords"]))

            # print (bests)
            # print (ranks)

            for i in range(len(ranks)):
                confidence_sum = 0
                for ci in range(len(ranks[i])):
                    confidence_sum += ranks[i][ci][1]
                if confidence_sum > sum(bests[i][t][1] for t in range(len(bests[i]))):
                    bests[i] = ranks[i]

            for best in bests:
                best.sort(key=lambda x: x[1], reverse=True)
                # prune to 3
                best = best[:3]

            row = [model_name]

            for r in ranks:
                row.append(", ".join([f"{x[0]} ({x[1]:0.1f}%)" for x in r]))

            # for rank in ranks:
            #    rank.sort(key=lambda x: x[1], reverse=True)
            #    row.append(f'{rank[0][0]} {rank[0][1]:.2f}%')

            table.append(row)

            if st.session_state["defaults"].general.optimized:
                del server_state["clip_models"][model_name]
                gc.collect()

    st.session_state["prediction_table"][
        st.session_state["processed_image_count"]
    ].dataframe(
        pd.DataFrame(
            table,
            columns=[
                "Model",
                "Medium",
                "Artist",
                "Trending",
                "Movement",
                "Flavors",
                "Techniques",
                "Tags",
            ],
        )
    )

    medium = bests[0][0][0]
    artist = bests[1][0][0]
    trending = bests[2][0][0]
    movement = bests[3][0][0]
    flavors = bests[4][0][0]
    # domains = bests[5][0][0]
    # subreddits = bests[6][0][0]
    techniques = bests[5][0][0]
    tags = bests[6][0][0]

    if caption.startswith(medium):
        st.session_state["text_result"][st.session_state["processed_image_count"]].code(
            f"\n\n{caption} {artist}, {trending}, {movement}, {techniques}, {flavors}, {tags}",
            language="",
        )
    else:
        st.session_state["text_result"][st.session_state["processed_image_count"]].code(
            f"\n\n{caption}, {medium} {artist}, {trending}, {movement}, {techniques}, {flavors}, {tags}",
            language="",
        )

    logger.info("Finished Interrogating.")
    st.session_state["log"].append("Finished Interrogating.")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )


def img2txt():
    models = []

    if st.session_state["ViT-L/14"]:
        models.append("ViT-L/14")
    if st.session_state["ViT-H-14"]:
        models.append("ViT-H-14")
    if st.session_state["ViT-g-14"]:
        models.append("ViT-g-14")

    if st.session_state["ViTB32"]:
        models.append("ViT-B/32")
    if st.session_state["ViTB16"]:
        models.append("ViT-B/16")

    if st.session_state["ViTL14_336px"]:
        models.append("ViT-L/14@336px")
    if st.session_state["RN101"]:
        models.append("RN101")
    if st.session_state["RN50"]:
        models.append("RN50")
    if st.session_state["RN50x4"]:
        models.append("RN50x4")
    if st.session_state["RN50x16"]:
        models.append("RN50x16")
    if st.session_state["RN50x64"]:
        models.append("RN50x64")

    # if str(image_path_or_url).startswith('http://') or str(image_path_or_url).startswith('https://'):
    # image = Image.open(requests.get(image_path_or_url, stream=True).raw).convert('RGB')
    # else:
    # image = Image.open(image_path_or_url).convert('RGB')

    # thumb = st.session_state["uploaded_image"].image.copy()
    # thumb.thumbnail([blip_image_eval_size, blip_image_eval_size])
    # display(thumb)

    st.session_state["processed_image_count"] = 0

    for i in range(len(st.session_state["uploaded_image"])):
        interrogate(st.session_state["uploaded_image"][i].pil_image, models=models)
        # increase counter.
        st.session_state["processed_image_count"] += 1


#


def layout():
    # set_page_title("Image-to-Text - Stable Diffusion WebUI")
    # st.info("Under Construction. :construction_worker:")
    #
    if "clip_models" not in server_state:
        server_state["clip_models"] = {}
    if "preprocesses" not in server_state:
        server_state["preprocesses"] = {}
    data_path = "data/"
    if "artists" not in server_state:
        server_state["artists"] = load_list(
            os.path.join(data_path, "img2txt", "artists.txt")
        )
    if "flavors" not in server_state:
        server_state["flavors"] = random.choices(
            load_list(os.path.join(data_path, "img2txt", "flavors.txt")), k=2000
        )
    if "mediums" not in server_state:
        server_state["mediums"] = load_list(
            os.path.join(data_path, "img2txt", "mediums.txt")
        )
    if "movements" not in server_state:
        server_state["movements"] = load_list(
            os.path.join(data_path, "img2txt", "movements.txt")
        )
    if "sites" not in server_state:
        server_state["sites"] = load_list(
            os.path.join(data_path, "img2txt", "sites.txt")
        )
    # server_state["domains"] = load_list(os.path.join(data_path, 'img2txt', 'domains.txt'))
    # server_state["subreddits"] = load_list(os.path.join(data_path, 'img2txt', 'subreddits.txt'))
    if "techniques" not in server_state:
        server_state["techniques"] = load_list(
            os.path.join(data_path, "img2txt", "techniques.txt")
        )
    if "tags" not in server_state:
        server_state["tags"] = load_list(os.path.join(data_path, "img2txt", "tags.txt"))
    # server_state["genres"] = load_list(os.path.join(data_path, 'img2txt', 'genres.txt'))
    # server_state["styles"] = load_list(os.path.join(data_path, 'img2txt', 'styles.txt'))
    # server_state["subjects"] = load_list(os.path.join(data_path, 'img2txt', 'subjects.txt'))
    if "trending_list" not in server_state:
        server_state["trending_list"] = [site for site in server_state["sites"]]
        server_state["trending_list"].extend(
            ["trending on " + site for site in server_state["sites"]]
        )
        server_state["trending_list"].extend(
            ["featured on " + site for site in server_state["sites"]]
        )
        server_state["trending_list"].extend(
            [site + " contest winner" for site in server_state["sites"]]
        )
    with st.form("img2txt-inputs"):
        st.session_state["generation_mode"] = "img2txt"

        # st.write("---")
        # creating the page layout using columns
        col1, col2 = st.columns([1, 4], gap="large")

        with col1:
            st.session_state["uploaded_image"] = st.file_uploader(
                "Input Image",
                type=["png", "jpg", "jpeg", "jfif", "webp"],
                accept_multiple_files=True,
            )

            with st.expander("CLIP models", expanded=True):
                st.session_state["ViT-L/14"] = st.checkbox(
                    "ViT-L/14", value=True, help="ViT-L/14 model."
                )
                st.session_state["ViT-H-14"] = st.checkbox(
                    "ViT-H-14", value=False, help="ViT-H-14 model."
                )
                st.session_state["ViT-g-14"] = st.checkbox(
                    "ViT-g-14", value=False, help="ViT-g-14 model."
                )

            with st.expander("Others"):
                st.info(
                    "For DiscoDiffusion and JAX enable all the same models here as you intend to use when generating your images."
                )

                st.session_state["ViTL14_336px"] = st.checkbox(
                    "ViTL14_336px", value=False, help="ViTL14_336px model."
                )
                st.session_state["ViTB16"] = st.checkbox(
                    "ViTB16", value=False, help="ViTB16 model."
                )
                st.session_state["ViTB32"] = st.checkbox(
                    "ViTB32", value=False, help="ViTB32 model."
                )
                st.session_state["RN50"] = st.checkbox(
                    "RN50", value=False, help="RN50 model."
                )
                st.session_state["RN50x4"] = st.checkbox(
                    "RN50x4", value=False, help="RN50x4 model."
                )
                st.session_state["RN50x16"] = st.checkbox(
                    "RN50x16", value=False, help="RN50x16 model."
                )
                st.session_state["RN50x64"] = st.checkbox(
                    "RN50x64", value=False, help="RN50x64 model."
                )
                st.session_state["RN101"] = st.checkbox(
                    "RN101", value=False, help="RN101 model."
                )

            #
            # st.subheader("Logs:")

            st.session_state["log_message"] = st.empty()
            st.session_state["log_message"].code("", language="")

        with col2:
            st.subheader("Image")

            image_col1, image_col2 = st.columns([10, 25])
            with image_col1:
                st.form_submit_button(
                    "Update Preview Image",
                    help="Refresh the image preview to show your uploaded image instead of the default placeholder.",
                )

            if st.session_state["uploaded_image"]:
                # print (type(st.session_state["uploaded_image"]))
                # if len(st.session_state["uploaded_image"]) == 1:
                st.session_state["input_image_preview"] = []
                st.session_state["input_image_preview_container"] = []
                st.session_state["prediction_table"] = []
                st.session_state["text_result"] = []

                for i in range(len(st.session_state["uploaded_image"])):
                    st.session_state["input_image_preview_container"].append(i)
                    st.session_state["input_image_preview_container"][i] = st.empty()

                    with st.session_state["input_image_preview_container"][
                        i
                    ].container():
                        col1_output, col2_output = st.columns([2, 10], gap="medium")
                        with col1_output:
                            st.session_state["input_image_preview"].append(i)
                            st.session_state["input_image_preview"][i] = st.empty()
                            st.session_state["uploaded_image"][
                                i
                            ].pil_image = Image.open(
                                st.session_state["uploaded_image"][i]
                            ).convert(
                                "RGB"
                            )

                            st.session_state["input_image_preview"][i].image(
                                st.session_state["uploaded_image"][i].pil_image,
                                use_column_width=True,
                                clamp=True,
                            )

                    with st.session_state["input_image_preview_container"][
                        i
                    ].container():
                        with col2_output:
                            st.session_state["prediction_table"].append(i)
                            st.session_state["prediction_table"][i] = st.empty()
                            st.session_state["prediction_table"][i].table()

                            st.session_state["text_result"].append(i)
                            st.session_state["text_result"][i] = st.empty()
                            st.session_state["text_result"][i].code("", language="")

            else:
                # st.session_state["input_image_preview"].code('', language="")
                st.image("images/streamlit/img2txt_placeholder.png", clamp=True)

        with image_col2:
            #
            # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
            # generate_col1.title("")
            # generate_col1.title("")
            generate_button = st.form_submit_button(
                "Generate!",
                help="Start interrogating the images to generate a prompt from each of the selected images",
            )

    if generate_button:
        # if model, pipe, RealESRGAN or GFPGAN is in st.session_state remove the model and pipe form session_state so that they are reloaded.
        if "model" in server_state and st.session_state["defaults"].general.optimized:
            del server_state["model"]
        if "pipe" in server_state and st.session_state["defaults"].general.optimized:
            del server_state["pipe"]
        if (
            "RealESRGAN" in server_state
            and st.session_state["defaults"].general.optimized
        ):
            del server_state["RealESRGAN"]
        if "GFPGAN" in server_state and st.session_state["defaults"].general.optimized:
            del server_state["GFPGAN"]

        # run clip interrogator
        img2txt()


================================================
FILE: scripts/imglab.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import *

# home plugin
import os
from PIL import Image

# from bs4 import BeautifulSoup
from streamlit.runtime.in_memory_file_manager import in_memory_file_manager
from streamlit.elements import image as STImage

# Temp imports


# end of imports
# ---------------------------------------------------------------------------------------------------------------

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging

    logging.set_verbosity_error()
except:
    pass


class plugin_info:
    plugname = "imglab"
    description = "Image Lab"
    isTab = True
    displayPriority = 3


def getLatestGeneratedImagesFromPath():
    # get the latest images from the generated images folder
    # get the path to the generated images folder
    generatedImagesPath = os.path.join(os.getcwd(), "outputs")
    # get all the files from the folders and subfolders
    files = []
    # get the laest 10 images from the output folder without walking the subfolders
    for r, d, f in os.walk(generatedImagesPath):
        for file in f:
            if ".png" in file:
                files.append(os.path.join(r, file))
    # sort the files by date
    files.sort(key=os.path.getmtime)
    # reverse the list so the latest images are first
    for f in files:
        img = Image.open(f)
        files[files.index(f)] = img
    # get the latest 10 files
    # get all the files with the .png or .jpg extension
    # sort files by date
    # get the latest 10 files
    latestFiles = files[-10:]
    # reverse the list
    latestFiles.reverse()
    return latestFiles


def getImagesFromLexica():
    # scrape images from lexica.art
    # get the html from the page
    # get the html with cookies and javascript
    apiEndpoint = r"https://lexica.art/api/trpc/prompts.infinitePrompts?batch=1&input=%7B%220%22%3A%7B%22json%22%3A%7B%22limit%22%3A10%2C%22text%22%3A%22%22%2C%22cursor%22%3A10%7D%7D%7D"
    # REST API call
    #
    from requests_html import HTMLSession

    session = HTMLSession()

    response = session.get(apiEndpoint)
    # req = requests.Session()
    # req.headers['user-agent'] = 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'
    # response = req.get(apiEndpoint)
    print(response.status_code)
    print(response.text)
    # get the json from the response
    # json = response.json()
    # get the prompts from the json
    print(response)
    # session = requests.Session()
    # parseEndpointJson = session.get(apiEndpoint,headers=headers,verify=False)
    # print(parseEndpointJson)
    # print('test2')
    # page = requests.get("https://lexica.art/", headers={'User-Agent': 'Mozilla/5.0'})
    # parse the html
    # soup = BeautifulSoup(page.content, 'html.parser')
    # find all the images
    # print(soup)
    # images = soup.find_all('alt-image')
    # create a list to store the image urls
    image_urls = []
    # loop through the images
    for image in images:
        # get the url
        image_url = image["src"]
        # add it to the list
        image_urls.append("http://www.lexica.art/" + image_url)
    # return the list
    print(image_urls)
    return image_urls


def changeImage():
    # change the image in the image holder
    # check if the file is not empty
    if len(st.session_state["uploaded_file"]) > 0:
        # read the file
        print("test2")
        uploaded = st.session_state["uploaded_file"][0].read()
        # show the image in the image holder
        st.session_state["previewImg"].empty()
        st.session_state["previewImg"].image(uploaded, use_column_width=True)


def createHTMLGallery(images):
    html3 = """
        <div class="gallery-history" style="
    display: flex;
    flex-wrap: wrap;
	align-items: flex-start;">
        """
    mkdwn_array = []
    for i in images:
        bImg = i.read()
        i = Image.save(bImg, "PNG")
        width, height = i.size
        # get random number for the id
        image_id = "%s" % (str(images.index(i)))
        (data, mimetype) = STImage._normalize_to_bytes(bImg.getvalue(), width, "auto")
        this_file = in_memory_file_manager.add(data, mimetype, image_id)
        img_str = this_file.url
        # img_str = 'data:image/png;base64,' + b64encode(image_io.getvalue()).decode('ascii')
        # get image size

        # make sure the image is not bigger then 150px but keep the aspect ratio
        if width > 150:
            height = int(height * (150 / width))
            width = 150
        if height > 150:
            width = int(width * (150 / height))
            height = 150

        # mkdwn = f"""<img src="{img_str}" alt="Image" with="200" height="200" />"""
        mkdwn = f"""<div class="gallery" style="margin: 3px;" >
<a href="{img_str}">
<img src="{img_str}" alt="Image" width="{width}" height="{height}">
</a>
</div>
"""
        mkdwn_array.append(mkdwn)
    html3 += "".join(mkdwn_array)
    html3 += "</div>"
    return html3


def layout():
    col1, col2 = st.columns(2)
    with col1:
        st.session_state["uploaded_file"] = st.file_uploader(
            "Choose an image or images",
            type=["png", "jpg", "jpeg", "webp"],
            accept_multiple_files=True,
            on_change=changeImage,
        )
        if "previewImg" not in st.session_state:
            st.session_state["previewImg"] = st.empty()
        else:
            if len(st.session_state["uploaded_file"]) > 0:
                st.session_state["previewImg"].empty()
                st.session_state["previewImg"].image(
                    st.session_state["uploaded_file"][0], use_column_width=True
                )
            else:
                st.session_state["previewImg"] = st.empty()


================================================
FILE: scripts/logger.py
================================================
import sys
from functools import partialmethod
from loguru import logger

STDOUT_LEVELS = ["GENERATION", "PROMPT"]
INIT_LEVELS = ["INIT", "INIT_OK", "INIT_WARN", "INIT_ERR"]
MESSAGE_LEVELS = ["MESSAGE"]
# By default we're at error level or higher
verbosity = 20
quiet = 0


def set_logger_verbosity(count):
    global verbosity
    # The count comes reversed. So count = 0 means minimum verbosity
    # While count 5 means maximum verbosity
    # So the more count we have, the lowe we drop the versbosity maximum
    verbosity = 20 - (count * 10)


def quiesce_logger(count):
    global quiet
    # The bigger the count, the more silent we want our logger
    quiet = count * 10


def is_stdout_log(record):
    if record["level"].name not in STDOUT_LEVELS:
        return False
    if record["level"].no < verbosity + quiet:
        return False
    return True


def is_init_log(record):
    if record["level"].name not in INIT_LEVELS:
        return False
    if record["level"].no < verbosity + quiet:
        return False
    return True


def is_msg_log(record):
    if record["level"].name not in MESSAGE_LEVELS:
        return False
    if record["level"].no < verbosity + quiet:
        return False
    return True


def is_stderr_log(record):
    if record["level"].name in STDOUT_LEVELS + INIT_LEVELS + MESSAGE_LEVELS:
        return False
    if record["level"].no < verbosity + quiet:
        return False
    return True


def test_logger():
    logger.generation(
        "This is a generation message\nIt is typically multiline\nThee Lines".encode(
            "unicode_escape"
        ).decode("utf-8")
    )
    logger.prompt("This is a prompt message")
    logger.debug("Debug Message")
    logger.info("Info Message")
    logger.warning("Info Warning")
    logger.error("Error Message")
    logger.critical("Critical Message")
    logger.init("This is an init message", status="Starting")
    logger.init_ok("This is an init message", status="OK")
    logger.init_warn("This is an init message", status="Warning")
    logger.init_err("This is an init message", status="Error")
    logger.message("This is user message")
    sys.exit()


logfmt = "<level>{level: <10}</level> @ <green>{time:YYYY-MM-DD HH:mm:ss}</green> | <green>{name}</green>:<green>{function}</green>:<green>{line}</green> - <level>{message}</level>"
genfmt = "<level>{level: <10}</level> @ <green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{message}</level>"
initfmt = "<magenta>INIT      </magenta> | <level>{extra[status]: <10}</level> | <magenta>{message}</magenta>"
msgfmt = "<level>{level: <10}</level> | <level>{message}</level>"

try:
    logger.level("GENERATION", no=24, color="<cyan>")
    logger.level("PROMPT", no=23, color="<yellow>")
    logger.level("INIT", no=31, color="<white>")
    logger.level("INIT_OK", no=31, color="<green>")
    logger.level("INIT_WARN", no=31, color="<yellow>")
    logger.level("INIT_ERR", no=31, color="<red>")
    # Messages contain important information without which this application might not be able to be used
    # As such, they have the highest priority
    logger.level("MESSAGE", no=61, color="<green>")
except TypeError:
    pass


logger.__class__.generation = partialmethod(logger.__class__.log, "GENERATION")
logger.__class__.prompt = partialmethod(logger.__class__.log, "PROMPT")
logger.__class__.init = partialmethod(logger.__class__.log, "INIT")
logger.__class__.init_ok = partialmethod(logger.__class__.log, "INIT_OK")
logger.__class__.init_warn = partialmethod(logger.__class__.log, "INIT_WARN")
logger.__class__.init_err = partialmethod(logger.__class__.log, "INIT_ERR")
logger.__class__.message = partialmethod(logger.__class__.log, "MESSAGE")

config = {
    "handlers": [
        {
            "sink": sys.stderr,
            "format": logfmt,
            "colorize": True,
            "filter": is_stderr_log,
        },
        {
            "sink": sys.stdout,
            "format": genfmt,
            "level": "PROMPT",
            "colorize": True,
            "filter": is_stdout_log,
        },
        {
            "sink": sys.stdout,
            "format": initfmt,
            "level": "INIT",
            "colorize": True,
            "filter": is_init_log,
        },
        {
            "sink": sys.stdout,
            "format": msgfmt,
            "level": "MESSAGE",
            "colorize": True,
            "filter": is_msg_log,
        },
    ],
}
logger.configure(**config)

logger.add(
    "logs/log_{time:MM-DD-YYYY!UTC}.log",
    rotation="8 MB",
    compression="zip",
    level="INFO",
)  # Once the file is too old, it's rotated


================================================
FILE: scripts/merge.py
================================================
import torch
import tkinter as tk


window = tk.Tk()
window.title(string="Model Merger")
tk.Label(text="Model Merger", font=("Arial", 25)).pack()
tk.Label(text="GUI by antrobot1234").pack()

frame1 = tk.Frame()
frame2 = tk.Frame()
frame3 = tk.Frame()

frameSlider = tk.Frame()
frameButton = tk.Frame()

tk.Label(frame1, text="File 1:").pack(side="left")
file1text = tk.Entry(frame1, width=40)
file1text.pack(side="left")

tk.Label(frame2, text="File 2:").pack(side="left")
file2text = tk.Entry(frame2, width=40)
file2text.pack(side="left")

tk.Label(frame3, text="File Out:").pack(side="left")
fileOtext = tk.Entry(frame3, width=38)
fileOtext.pack(side="left")

tk.Label(frameSlider, text="Weight of file 1").pack(side="left")
scale = tk.Scale(
    frameSlider, from_=0, to=100, orient="horizontal", tickinterval=10, length=450
)
scale.pack(side="left")


goButton = tk.Button(frameButton, text="RUN", height=2, width=20, bg="green")


def merge(file1, file2, out, a):
    alpha = (a) / 100
    if not (file1.endswith(".ckpt")):
        file1 += ".ckpt"
    if not (file2.endswith(".ckpt")):
        file2 += ".ckpt"
    if not (out.endswith(".ckpt")):
        out += ".ckpt"
    # Load Models
    model_0 = torch.load(file1)
    model_1 = torch.load(file2)
    theta_0 = model_0["state_dict"]
    theta_1 = model_1["state_dict"]

    for key in theta_0.keys():
        if "model" in key and key in theta_1:
            theta_0[key] = (alpha) * theta_0[key] + (1 - alpha) * theta_1[key]

    goButton.config(bg="red", text="RUNNING...\n(STAGE 2)")
    window.update()

    for key in theta_1.keys():
        if "model" in key and key not in theta_0:
            theta_0[key] = theta_1[key]
    torch.save(model_0, out)


def handleClick(event):
    goButton.config(bg="red", text="RUNNING...\n(STAGE 1)")
    window.update()
    merge(file1text.get(), file2text.get(), fileOtext.get(), scale.get())
    goButton.config(bg="green", text="RUN")


goButton.pack()
goButton.bind("<Button-1>", handleClick)


frame1.pack()
frame2.pack()
frame3.pack()
frameSlider.pack()
frameButton.pack()

window.mainloop()


================================================
FILE: scripts/modeldownload.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
import os
import os.path as op


def updateModels():
    if op.exists("models/ldm/stable-diffusion-v1/model.ckpt"):
        pass
    else:
        os.system(
            "wget https://www.googleapis.com/storage/v1/b/aai-blog-files/o/sd-v1-4.ckpt?alt=media -o models/ldm/stable-diffusion-v1/model.ckpt"
        )
        # os.rename('models/ldm/stable-diffusion-v1/sd-v1-4.ckpt?alt=media','models/ldm/stable-diffusion-v1/model.ckpt')
        # os.system('wget https://cdn-lfs.huggingface.co/repos/ab/41/ab41ccb635cd5bd124c8eac1b5796b4f64049c9453c4e50d51819468ca69ceb8/14749efc0ae8ef0329391ad4436feb781b402f4fece4883c7ad8d10556d8a36a?response-content-disposition=attachment%3B%20filename%3D%22modelfull.ckpt%22 -o models/ldm/stable-diffusion-v1/model.ckpt')
        # os.rename('models/ldm/stable-diffusion-v1/modelfull.ckpt','models/ldm/stable-diffusion-v1/model.ckpt')

    if op.exists("models/realesrgan/RealESRGAN_x4plus.pth") and op.exists(
        "models/realesrgan/RealESRGAN_x4plus_anime_6B.pth"
    ):
        pass
    else:
        os.system(
            "wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P models/realesrgan"
        )
        os.system(
            "wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P models/realesrgan"
        )

    if op.exists("models/gfpgan/GFPGANv1.3.pth"):
        pass
    else:
        os.system(
            "wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P models/gfpgan"
        )

    if op.exists("models/ldsr"):
        pass
    else:
        os.system("git clone https://github.com/devilismyfriend/latent-diffusion.git")
        os.system("mv latent-diffusion models/ldsr")

    if op.exists("models/ldsr/model.ckpt"):
        pass
    else:
        os.mkdir("models/ldsr/experiments")
        os.mkdir("models/ldsr")
        os.system(
            "wget https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1 -o models/ldsr/project.yaml"
        )
        # os.rename('models/ldsr/index.html?dl=1', 'models/ldsr/project.yaml')
        os.system(
            "wget https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1 -o models/ldsr/model.ckpt"
        )
        # os.rename('models/ldsr/index.html?dl=1', 'models/ldsr/model.ckpt')


================================================
FILE: scripts/perlin.py
================================================
import numpy as np


def perlin(x, y, seed=0):
    # permutation table
    np.random.seed(seed)
    p = np.arange(256, dtype=int)
    np.random.shuffle(p)
    p = np.stack([p, p]).flatten()
    # coordinates of the top-left
    xi, yi = x.astype(int), y.astype(int)
    # internal coordinates
    xf, yf = x - xi, y - yi
    # fade factors
    u, v = fade(xf), fade(yf)
    # noise components
    n00 = gradient(p[p[xi] + yi], xf, yf)
    n01 = gradient(p[p[xi] + yi + 1], xf, yf - 1)
    n11 = gradient(p[p[xi + 1] + yi + 1], xf - 1, yf - 1)
    n10 = gradient(p[p[xi + 1] + yi], xf - 1, yf)
    # combine noises
    x1 = lerp(n00, n10, u)
    x2 = lerp(n01, n11, u)  # FIX1: I was using n10 instead of n01
    return lerp(x1, x2, v)  # FIX2: I also had to reverse x1 and x2 here


def lerp(a, b, x):
    "linear interpolation"
    return a + x * (b - a)


def fade(t):
    "6t^5 - 15t^4 + 10t^3"
    return 6 * t**5 - 15 * t**4 + 10 * t**3


def gradient(h, x, y):
    "grad converts h to the right gradient vector and return the dot product with (x,y)"
    vectors = np.array([[0, 1], [0, -1], [1, 0], [-1, 0]])
    g = vectors[h % 4]
    return g[:, :, 0] * x + g[:, :, 1] * y


lin = np.linspace(0, 5, 100, endpoint=False)
x, y = np.meshgrid(lin, lin)


def perlinNoise(height, width, octavesx=5, octavesy=5, seed=None):
    linx = np.linspace(0, octavesx, width, endpoint=False)
    liny = np.linspace(0, octavesy, height, endpoint=False)
    x, y = np.meshgrid(linx, liny)
    return perlin(x, y, seed=seed)


================================================
FILE: scripts/pipelines/stable_diffusion/no_check.py
================================================
from diffusers import ModelMixin
import torch


class NoCheck(ModelMixin):
    """Can be used in place of safety checker. Use responsibly and at your own risk."""

    def __init__(self):
        super().__init__()
        self.register_parameter(name="asdf", param=torch.nn.Parameter(torch.randn(3)))

    def forward(self, images=None, **kwargs):
        return images, [False]


================================================
FILE: scripts/post_processing.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sandbox-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
# from sd_utils import *
from sd_utils import (
    st,
    server_state,
    torch_gc,
    RealESRGAN_available,
    GFPGAN_available,
    LDSR_available,
    load_models,
    logger,
    load_GFPGAN,
    load_RealESRGAN,
    load_LDSR,
)

# streamlit imports

# streamlit components section
import hydralit_components as hc

# other imports
import os
from PIL import Image
import torch

# Temp imports

# end of imports
# ---------------------------------------------------------------------------------------------------------------


def post_process(
    use_GFPGAN=True,
    GFPGAN_model="",
    use_RealESRGAN=False,
    realesrgan_model_name="",
    use_LDSR=False,
    LDSR_model_name="",
):
    for i in range(len(st.session_state["uploaded_image"])):
        # st.session_state["uploaded_image"][i].pil_image

        if (
            use_GFPGAN
            and server_state["GFPGAN"] is not None
            and not use_RealESRGAN
            and not use_LDSR
        ):
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    "Running GFPGAN on image %d of %d..."
                    % (i + 1, len(st.session_state["uploaded_image"]))
                )

            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    int(
                        100
                        * float(
                            i + 1
                            if i + 1 < len(st.session_state["uploaded_image"])
                            else len(st.session_state["uploaded_image"])
                        )
                        / float(len(st.session_state["uploaded_image"]))
                    )
                )

            if server_state["GFPGAN"].name != GFPGAN_model:
                load_models(
                    use_LDSR=use_LDSR,
                    LDSR_model=LDSR_model_name,
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            torch_gc()

            with torch.autocast("cuda"):
                cropped_faces, restored_faces, restored_img = server_state[
                    "GFPGAN"
                ].enhance(
                    st.session_state["uploaded_image"][i].pil_image,
                    has_aligned=False,
                    only_center_face=False,
                    paste_back=True,
                )

            gfpgan_sample = restored_img[:, :, ::-1]
            gfpgan_image = Image.fromarray(gfpgan_sample)

            # if st.session_state["GFPGAN_strenght"]:
            # gfpgan_sample = Image.blend(image, gfpgan_image, st.session_state["GFPGAN_strenght"])

            gfpgan_filename = (
                st.session_state["uploaded_image"][i].name.split(".")[0] + "-gfpgan"
            )

            gfpgan_image.save(
                os.path.join(
                    st.session_state["defaults"].post_processing.outdir_post_processing,
                    f"{gfpgan_filename}.png",
                )
            )

        #
        elif (
            use_RealESRGAN and server_state["RealESRGAN"] is not None and not use_GFPGAN
        ):
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    "Running RealESRGAN on image %d of %d..."
                    % (i + 1, len(st.session_state["uploaded_image"]))
                )

            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    int(
                        100
                        * float(
                            i + 1
                            if i + 1 < len(st.session_state["uploaded_image"])
                            else len(st.session_state["uploaded_image"])
                        )
                        / float(len(st.session_state["uploaded_image"]))
                    )
                )

            torch_gc()

            if server_state["RealESRGAN"].model.name != realesrgan_model_name:
                # try_loading_RealESRGAN(realesrgan_model_name)
                load_models(
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            output, img_mode = server_state["RealESRGAN"].enhance(
                st.session_state["uploaded_image"][i].pil_image
            )
            esrgan_filename = (
                st.session_state["uploaded_image"][i].name.split(".")[0] + "-esrgan4x"
            )
            esrgan_sample = output[:, :, ::-1]
            esrgan_image = Image.fromarray(esrgan_sample)

            esrgan_image.save(
                os.path.join(
                    st.session_state["defaults"].post_processing.outdir_post_processing,
                    f"{esrgan_filename}.png",
                )
            )

        #
        elif use_LDSR and "LDSR" in server_state and not use_GFPGAN:
            logger.info(
                "Running LDSR on image %d of %d..."
                % (i + 1, len(st.session_state["uploaded_image"]))
            )
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    "Running LDSR on image %d of %d..."
                    % (i + 1, len(st.session_state["uploaded_image"]))
                )
            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    int(
                        100
                        * float(
                            i + 1
                            if i + 1 < len(st.session_state["uploaded_image"])
                            else len(st.session_state["uploaded_image"])
                        )
                        / float(len(st.session_state["uploaded_image"]))
                    )
                )

            torch_gc()

            if server_state["LDSR"].name != LDSR_model_name:
                # try_loading_RealESRGAN(realesrgan_model_name)
                load_models(
                    use_LDSR=use_LDSR,
                    LDSR_model=LDSR_model_name,
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            result = server_state["LDSR"].superResolution(
                st.session_state["uploaded_image"][i].pil_image,
                ddimSteps=st.session_state["ldsr_sampling_steps"],
                preDownScale=st.session_state["preDownScale"],
                postDownScale=st.session_state["postDownScale"],
                downsample_method=st.session_state["downsample_method"],
            )

            ldsr_filename = (
                st.session_state["uploaded_image"][i].name.split(".")[0] + "-ldsr4x"
            )

            result.save(
                os.path.join(
                    st.session_state["defaults"].post_processing.outdir_post_processing,
                    f"{ldsr_filename}.png",
                )
            )

        #
        elif (
            use_LDSR
            and "LDSR" in server_state
            and use_GFPGAN
            and "GFPGAN" in server_state
        ):
            logger.info(
                "Running GFPGAN+LDSR on image %d of %d..."
                % (i + 1, len(st.session_state["uploaded_image"]))
            )
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    "Running GFPGAN+LDSR on image %d of %d..."
                    % (i + 1, len(st.session_state["uploaded_image"]))
                )

            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    int(
                        100
                        * float(
                            i + 1
                            if i + 1 < len(st.session_state["uploaded_image"])
                            else len(st.session_state["uploaded_image"])
                        )
                        / float(len(st.session_state["uploaded_image"]))
                    )
                )

            if server_state["GFPGAN"].name != GFPGAN_model:
                load_models(
                    use_LDSR=use_LDSR,
                    LDSR_model=LDSR_model_name,
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            torch_gc()
            cropped_faces, restored_faces, restored_img = server_state[
                "GFPGAN"
            ].enhance(
                st.session_state["uploaded_image"][i].pil_image,
                has_aligned=False,
                only_center_face=False,
                paste_back=True,
            )

            gfpgan_sample = restored_img[:, :, ::-1]
            gfpgan_image = Image.fromarray(gfpgan_sample)

            if server_state["LDSR"].name != LDSR_model_name:
                # try_loading_RealESRGAN(realesrgan_model_name)
                load_models(
                    use_LDSR=use_LDSR,
                    LDSR_model=LDSR_model_name,
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            # LDSR.superResolution(gfpgan_image, ddimSteps=100, preDownScale='None', postDownScale='None', downsample_method="Lanczos")
            result = server_state["LDSR"].superResolution(
                gfpgan_image,
                ddimSteps=st.session_state["ldsr_sampling_steps"],
                preDownScale=st.session_state["preDownScale"],
                postDownScale=st.session_state["postDownScale"],
                downsample_method=st.session_state["downsample_method"],
            )

            ldsr_filename = (
                st.session_state["uploaded_image"][i].name.split(".")[0]
                + "-gfpgan-ldsr2x"
            )

            result.save(
                os.path.join(
                    st.session_state["defaults"].post_processing.outdir_post_processing,
                    f"{ldsr_filename}.png",
                )
            )

        elif (
            use_RealESRGAN
            and server_state["RealESRGAN"] is not None
            and use_GFPGAN
            and server_state["GFPGAN"] is not None
        ):
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    "Running GFPGAN+RealESRGAN on image %d of %d..."
                    % (i + 1, len(st.session_state["uploaded_image"]))
                )

            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    int(
                        100
                        * float(
                            i + 1
                            if i + 1 < len(st.session_state["uploaded_image"])
                            else len(st.session_state["uploaded_image"])
                        )
                        / float(len(st.session_state["uploaded_image"]))
                    )
                )

            torch_gc()
            cropped_faces, restored_faces, restored_img = server_state[
                "GFPGAN"
            ].enhance(
                st.session_state["uploaded_image"][i].pil_image,
                has_aligned=False,
                only_center_face=False,
                paste_back=True,
            )
            gfpgan_sample = restored_img[:, :, ::-1]

            if server_state["RealESRGAN"].model.name != realesrgan_model_name:
                # try_loading_RealESRGAN(realesrgan_model_name)
                load_models(
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            output, img_mode = server_state["RealESRGAN"].enhance(
                gfpgan_sample[:, :, ::-1]
            )
            gfpgan_esrgan_filename = (
                st.session_state["uploaded_image"][i].name.split(".")[0]
                + "-gfpgan-esrgan4x"
            )
            gfpgan_esrgan_sample = output[:, :, ::-1]
            gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample)

            gfpgan_esrgan_image.save(
                os.path.join(
                    st.session_state["defaults"].post_processing.outdir_post_processing,
                    f"{gfpgan_esrgan_filename}.png",
                )
            )


def layout():
    # st.info("Under Construction. :construction_worker:")
    st.session_state["progress_bar_text"] = st.empty()
    # st.session_state["progress_bar_text"].info("Nothing but crickets here, try generating something first.")

    st.session_state["progress_bar"] = st.empty()

    with st.form("post-processing-inputs"):
        # creating the page layout using columns
        col1, col2 = st.columns([1, 4], gap="medium")

        with col1:
            st.session_state["uploaded_image"] = st.file_uploader(
                "Input Image",
                type=["png", "jpg", "jpeg", "jfif", "webp"],
                accept_multiple_files=True,
            )

            # check if GFPGAN, RealESRGAN and LDSR are available.
            # if "GFPGAN_available" not in st.session_state:
            GFPGAN_available()

            # if "RealESRGAN_available" not in st.session_state:
            RealESRGAN_available()

            # if "LDSR_available" not in st.session_state:
            LDSR_available()

            if (
                st.session_state["GFPGAN_available"]
                or st.session_state["RealESRGAN_available"]
                or st.session_state["LDSR_available"]
            ):
                face_restoration_tab, upscaling_tab = st.tabs(
                    ["Face Restoration", "Upscaling"]
                )
                with face_restoration_tab:
                    # GFPGAN used for face restoration
                    if st.session_state["GFPGAN_available"]:
                        # with st.expander("Face Restoration"):
                        # if st.session_state["GFPGAN_available"]:
                        # with st.expander("GFPGAN"):
                        st.session_state["use_GFPGAN"] = st.checkbox(
                            "Use GFPGAN",
                            value=st.session_state["defaults"].txt2img.use_GFPGAN,
                            help="Uses the GFPGAN model to improve faces after the generation.\
                                                                         This greatly improve the quality and consistency of faces but uses\
                                                                         extra VRAM. Disable if you need the extra VRAM.",
                        )

                        st.session_state["GFPGAN_model"] = st.selectbox(
                            "GFPGAN model",
                            st.session_state["GFPGAN_models"],
                            index=st.session_state["GFPGAN_models"].index(
                                st.session_state["defaults"].general.GFPGAN_model
                            ),
                        )

                    # st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')

                    else:
                        st.session_state["use_GFPGAN"] = False

                with upscaling_tab:
                    st.session_state["use_upscaling"] = st.checkbox(
                        "Use Upscaling",
                        value=st.session_state["defaults"].txt2img.use_upscaling,
                    )

                    # RealESRGAN and LDSR used for upscaling.
                    if (
                        st.session_state["RealESRGAN_available"]
                        or st.session_state["LDSR_available"]
                    ):
                        upscaling_method_list = []
                        if st.session_state["RealESRGAN_available"]:
                            upscaling_method_list.append("RealESRGAN")
                        if st.session_state["LDSR_available"]:
                            upscaling_method_list.append("LDSR")

                        # print (st.session_state["RealESRGAN_available"])
                        st.session_state["upscaling_method"] = st.selectbox(
                            "Upscaling Method",
                            upscaling_method_list,
                            index=upscaling_method_list.index(
                                st.session_state["defaults"].general.upscaling_method
                            )
                            if st.session_state["defaults"].general.upscaling_method
                            in upscaling_method_list
                            else 0,
                        )

                        if st.session_state["RealESRGAN_available"]:
                            with st.expander("RealESRGAN"):
                                if (
                                    st.session_state["upscaling_method"] == "RealESRGAN"
                                    and st.session_state["use_upscaling"]
                                ):
                                    st.session_state["use_RealESRGAN"] = True
                                else:
                                    st.session_state["use_RealESRGAN"] = False

                                st.session_state["RealESRGAN_model"] = st.selectbox(
                                    "RealESRGAN model",
                                    st.session_state["RealESRGAN_models"],
                                    index=st.session_state["RealESRGAN_models"].index(
                                        st.session_state[
                                            "defaults"
                                        ].general.RealESRGAN_model
                                    ),
                                )
                        else:
                            st.session_state["use_RealESRGAN"] = False
                            st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"

                        #
                        if st.session_state["LDSR_available"]:
                            with st.expander("LDSR"):
                                if (
                                    st.session_state["upscaling_method"] == "LDSR"
                                    and st.session_state["use_upscaling"]
                                ):
                                    st.session_state["use_LDSR"] = True
                                else:
                                    st.session_state["use_LDSR"] = False

                                st.session_state["LDSR_model"] = st.selectbox(
                                    "LDSR model",
                                    st.session_state["LDSR_models"],
                                    index=st.session_state["LDSR_models"].index(
                                        st.session_state["defaults"].general.LDSR_model
                                    ),
                                )

                                st.session_state[
                                    "ldsr_sampling_steps"
                                ] = st.number_input(
                                    "Sampling Steps",
                                    value=st.session_state[
                                        "defaults"
                                    ].txt2img.LDSR_config.sampling_steps,
                                    help="",
                                )

                                st.session_state["preDownScale"] = st.number_input(
                                    "PreDownScale",
                                    value=st.session_state[
                                        "defaults"
                                    ].txt2img.LDSR_config.preDownScale,
                                    help="",
                                )

                                st.session_state["postDownScale"] = st.number_input(
                                    "postDownScale",
                                    value=st.session_state[
                                        "defaults"
                                    ].txt2img.LDSR_config.postDownScale,
                                    help="",
                                )

                                downsample_method_list = ["Nearest", "Lanczos"]
                                st.session_state["downsample_method"] = st.selectbox(
                                    "Downsample Method",
                                    downsample_method_list,
                                    index=downsample_method_list.index(
                                        st.session_state[
                                            "defaults"
                                        ].txt2img.LDSR_config.downsample_method
                                    ),
                                )

                        else:
                            st.session_state["use_LDSR"] = False
                            st.session_state["LDSR_model"] = "model"

            # process = st.form_submit_button("Process Images", help="")

            #
            with st.expander("Output Settings", True):
                # st.session_state['defaults'].post_processing.save_original_images = st.checkbox("Save input images.", value=st.session_state['defaults'].post_processing.save_original_images,
                # help="Save each original/input image next to the Post Processed image. "
                # "This might be helpful for comparing the before and after images.")

                st.session_state[
                    "defaults"
                ].post_processing.outdir_post_processing = st.text_input(
                    "Output Dir",
                    value=st.session_state[
                        "defaults"
                    ].post_processing.outdir_post_processing,
                    help="Folder where the images will be saved after post processing.",
                )

        with col2:
            st.subheader("Image")

            image_col1, image_col2, image_col3 = st.columns([2, 2, 2], gap="small")
            with image_col1:
                st.form_submit_button(
                    "Refresh",
                    help="Refresh the image preview to show your uploaded image.",
                )

            if st.session_state["uploaded_image"]:
                # print (type(st.session_state["uploaded_image"]))
                # if len(st.session_state["uploaded_image"]) == 1:
                st.session_state["input_image_preview"] = []
                st.session_state["input_image_caption"] = []
                st.session_state["output_image_preview"] = []
                st.session_state["output_image_caption"] = []
                st.session_state["input_image_preview_container"] = []
                st.session_state["prediction_table"] = []
                st.session_state["text_result"] = []

                for i in range(len(st.session_state["uploaded_image"])):
                    st.session_state["input_image_preview_container"].append(i)
                    st.session_state["input_image_preview_container"][i] = st.empty()

                    with st.session_state["input_image_preview_container"][
                        i
                    ].container():
                        col1_output, col2_output, col3_output = st.columns(
                            [2, 2, 2], gap="medium"
                        )
                        with col1_output:
                            st.session_state["output_image_caption"].append(i)
                            st.session_state["output_image_caption"][i] = st.empty()
                            # st.session_state["output_image_caption"][i] = st.session_state["uploaded_image"][i].name

                            st.session_state["input_image_caption"].append(i)
                            st.session_state["input_image_caption"][i] = st.empty()
                            # st.session_state["input_image_caption"][i].caption(")

                            st.session_state["input_image_preview"].append(i)
                            st.session_state["input_image_preview"][i] = st.empty()
                            st.session_state["uploaded_image"][
                                i
                            ].pil_image = Image.open(
                                st.session_state["uploaded_image"][i]
                            ).convert(
                                "RGB"
                            )

                            st.session_state["input_image_preview"][i].image(
                                st.session_state["uploaded_image"][i].pil_image,
                                use_column_width=True,
                                clamp=True,
                            )

                        with col2_output:
                            st.session_state["output_image_preview"].append(i)
                            st.session_state["output_image_preview"][i] = st.empty()

                            st.session_state["output_image_preview"][i].image(
                                st.session_state["uploaded_image"][i].pil_image,
                                use_column_width=True,
                                clamp=True,
                            )

                    with st.session_state["input_image_preview_container"][
                        i
                    ].container():
                        with col3_output:
                            # st.session_state["prediction_table"].append(i)
                            # st.session_state["prediction_table"][i] = st.empty()
                            # st.session_state["prediction_table"][i].table(pd.DataFrame(columns=["Model", "Filename", "Progress"]))

                            st.session_state["text_result"].append(i)
                            st.session_state["text_result"][i] = st.empty()
                            st.session_state["text_result"][i].code("", language="")

            # else:
            ##st.session_state["input_image_preview"].code('', language="")
            # st.image("images/streamlit/img2txt_placeholder.png", clamp=True)

        with image_col3:
            # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
            process = st.form_submit_button("Process Images!")

        if process:
            with hc.HyLoader(
                "Loading Models...", hc.Loaders.standard_loaders, index=[0]
            ):
                # load_models(use_LDSR=st.session_state["use_LDSR"], LDSR_model=st.session_state["LDSR_model"],
                # use_GFPGAN=st.session_state["use_GFPGAN"], GFPGAN_model=st.session_state["GFPGAN_model"] ,
                # use_RealESRGAN=st.session_state["use_RealESRGAN"], RealESRGAN_model=st.session_state["RealESRGAN_model"])

                if st.session_state["use_GFPGAN"]:
                    load_GFPGAN(model_name=st.session_state["GFPGAN_model"])

                if st.session_state["use_RealESRGAN"]:
                    load_RealESRGAN(st.session_state["RealESRGAN_model"])

                if st.session_state["use_LDSR"]:
                    load_LDSR(st.session_state["LDSR_model"])

            post_process(
                use_GFPGAN=st.session_state["use_GFPGAN"],
                GFPGAN_model=st.session_state["GFPGAN_model"],
                use_RealESRGAN=st.session_state["use_RealESRGAN"],
                realesrgan_model_name=st.session_state["RealESRGAN_model"],
                use_LDSR=st.session_state["use_LDSR"],
                LDSR_model_name=st.session_state["LDSR_model"],
            )


================================================
FILE: scripts/prune-ckpt.py
================================================
import os
import torch
import argparse


parser = argparse.ArgumentParser(description="Pruning")
parser.add_argument("--ckpt", type=str, default=None, help="path to model ckpt")
args = parser.parse_args()
ckpt = args.ckpt


def prune_it(p, keep_only_ema=False):
    print(f"prunin' in path: {p}")
    size_initial = os.path.getsize(p)
    nsd = dict()
    sd = torch.load(p, map_location="cpu")
    print(sd.keys())
    for k in sd.keys():
        if k != "optimizer_states":
            nsd[k] = sd[k]
    else:
        print(f"removing optimizer states for path {p}")
    if "global_step" in sd:
        print(f"This is global step {sd['global_step']}.")
    if keep_only_ema:
        sd = nsd["state_dict"].copy()
        # infer ema keys
        ema_keys = {
            k: "model_ema." + k[6:].replace(".", ".")
            for k in sd.keys()
            if k.startswith("model.")
        }
        new_sd = dict()

        for k in sd:
            if k in ema_keys:
                new_sd[k] = sd[ema_keys[k]].half()
            elif not k.startswith("model_ema.") or k in [
                "model_ema.num_updates",
                "model_ema.decay",
            ]:
                new_sd[k] = sd[k].half()

        assert len(new_sd) == len(sd) - len(ema_keys)
        nsd["state_dict"] = new_sd
    else:
        sd = nsd["state_dict"].copy()
        new_sd = dict()
        for k in sd:
            new_sd[k] = sd[k].half()
        nsd["state_dict"] = new_sd

    fn = (
        f"{os.path.splitext(p)[0]}-pruned.ckpt"
        if not keep_only_ema
        else f"{os.path.splitext(p)[0]}-ema-pruned.ckpt"
    )
    print(f"saving pruned checkpoint at: {fn}")
    torch.save(nsd, fn)
    newsize = os.path.getsize(fn)
    MSG = (
        f"New ckpt size: {newsize*1e-9:.2f} GB. "
        + f"Saved {(size_initial - newsize)*1e-9:.2f} GB by removing optimizer states"
    )
    if keep_only_ema:
        MSG += " and non-EMA weights"
    print(MSG)


if __name__ == "__main__":
    prune_it(ckpt)


================================================
FILE: scripts/relauncher.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
import os, time, argparse

# USER CHANGABLE ARGUMENTS

# Change to `True` if you wish to enable these common arguments

# Run upscaling models on the CPU
extra_models_cpu = False

# Automatically open a new browser window or tab on first launch
open_in_browser = False

# Run Stable Diffusion in Optimized Mode - Only requires 4Gb of VRAM, but is significantly slower
optimized = False

# Run in Optimized Turbo Mode - Needs more VRAM than regular optimized mode, but is faster
optimized_turbo = False

# Creates a public xxxxx.gradio.app share link to allow others to use your interface (requires properly forwarded ports to work correctly)
share = False

# Generate tiling images
tiling = False
# Enter other `--arguments` you wish to use - Must be entered as a `--argument ` syntax
additional_arguments = ""

parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
    "-v",
    "--verbosity",
    action="count",
    default=0,
    help="The default logging level is ERROR or higher. This value increases the amount of logging seen in your screen",
)
parser.add_argument(
    "-n",
    "--horde_name",
    action="store",
    required=False,
    type=str,
    help="The server name for the Horde. It will be shown to the world and there can be only one.",
)
parser.add_argument(
    "--bridge",
    action="store_true",
    required=False,
    default=False,
    help="When specified, start the stable horde bridge instead of the webui.",
)
args = parser.parse_args()

if args.bridge:
    additional_arguments += " --bridge"
    if args.horde_name:
        additional_arguments += f' --horde_name "{args.horde_name}"'
    if args.verbosity:
        for iter in range(args.verbosity):
            additional_arguments += " -v"
    print(f"Additional args: {additional_arguments}")


# BEGIN RELAUNCHER PYTHON CODE

common_arguments = ""

if extra_models_cpu is True:
    common_arguments += "--extra-models-cpu "
if optimized_turbo is True:
    common_arguments += "--optimized-turbo "
if optimized is True:
    common_arguments += "--optimized "
if tiling is True:
    common_arguments += "--tiling "
if share is True:
    common_arguments += "--share "

if open_in_browser is True:
    inbrowser_argument = "--inbrowser "
else:
    inbrowser_argument = ""

n = 0
while True:
    if n == 0:
        print("Relauncher: Launching...")
        os.system(
            f"python scripts/webui.py {common_arguments} {inbrowser_argument} {additional_arguments}"
        )

    else:
        print(f"\tRelaunch count: {n}")
        print("Relauncher: Launching...")
        os.system(f"python scripts/webui.py {common_arguments} {additional_arguments}")

    n += 1
    if n > 100:
        print("Too many relaunch attempts. Aborting...")
        break
    print("Relauncher: Process is ending. Relaunching in 1s...")
    time.sleep(1)


================================================
FILE: scripts/scn2img.py
================================================
import argparse, os, sys, re, time
import collections
import yaml
import math
import random
from typing import List, Union, Dict, Callable, Type, Tuple

import numba

import numpy as np
import cv2
from PIL import Image, ImageFilter, ImageChops

import torch

from frontend.job_manager import JobInfo
from frontend.image_metadata import ImageMetadata

scn2img_cache = {"seed": None, "cache": {}}

monocular_depth_estimation = None


def try_loading_monocular_depth_estimation(
    monocular_depth_estimation_dir="./src/monocular-depth-estimation/",
):
    global monocular_depth_estimation
    if os.path.exists(monocular_depth_estimation_dir):
        import tensorflow as tf

        gpus = tf.config.experimental.list_physical_devices("GPU")
        if gpus:
            # Restrict TensorFlow to only allocate 1GB of memory on the first GPU
            try:
                tf.config.experimental.set_virtual_device_configuration(
                    gpus[0],
                    [
                        tf.config.experimental.VirtualDeviceConfiguration(
                            memory_limit=1024
                        )
                    ],
                )
            except Exception:
                import traceback

                print(
                    "Exception during tf.config.experimental.set_virtual_device_configuration:",
                    file=sys.stderr,
                )
                print(traceback.format_exc(), file=sys.stderr)
        try:
            from tensorflow.keras.layers import Layer, InputSpec
            import tensorflow.keras

            # from huggingface_hub import from_pretrained_keras
            # https://stackoverflow.com/a/63631510/798588

            from tensorflow.python.keras.utils import conv_utils

            def normalize_data_format(value):
                if value is None:
                    value = tensorflow.keras.backend.image_data_format()
                data_format = value.lower()
                if data_format not in {"channels_first", "channels_last"}:
                    raise ValueError(
                        "The `data_format` argument must be one of "
                        '"channels_first", "channels_last". Received: ' + str(value)
                    )
                return data_format

            class BilinearUpSampling2D(Layer):
                def __init__(self, size=(2, 2), data_format=None, **kwargs):
                    super(BilinearUpSampling2D, self).__init__(**kwargs)
                    self.data_format = normalize_data_format(data_format)
                    self.size = conv_utils.normalize_tuple(size, 2, "size")
                    self.input_spec = InputSpec(ndim=4)

                def compute_output_shape(self, input_shape):
                    if self.data_format == "channels_first":
                        height = (
                            self.size[0] * input_shape[2]
                            if input_shape[2] is not None
                            else None
                        )
                        width = (
                            self.size[1] * input_shape[3]
                            if input_shape[3] is not None
                            else None
                        )
                        return (input_shape[0], input_shape[1], height, width)
                    elif self.data_format == "channels_last":
                        height = (
                            self.size[0] * input_shape[1]
                            if input_shape[1] is not None
                            else None
                        )
                        width = (
                            self.size[1] * input_shape[2]
                            if input_shape[2] is not None
                            else None
                        )
                        return (input_shape[0], height, width, input_shape[3])

                def call(self, inputs):
                    input_shape = tensorflow.keras.backend.shape(inputs)
                    if self.data_format == "channels_first":
                        height = (
                            self.size[0] * input_shape[2]
                            if input_shape[2] is not None
                            else None
                        )
                        width = (
                            self.size[1] * input_shape[3]
                            if input_shape[3] is not None
                            else None
                        )
                    elif self.data_format == "channels_last":
                        height = (
                            self.size[0] * input_shape[1]
                            if input_shape[1] is not None
                            else None
                        )
                        width = (
                            self.size[1] * input_shape[2]
                            if input_shape[2] is not None
                            else None
                        )

                    return tf.image.resize(
                        inputs, [height, width], method=tf.image.ResizeMethod.BILINEAR
                    )

                def get_config(self):
                    config = {"size": self.size, "data_format": self.data_format}
                    base_config = super(BilinearUpSampling2D, self).get_config()
                    return dict(list(base_config.items()) + list(config.items()))

            custom_objects = {
                "BilinearUpSampling2D": BilinearUpSampling2D,
                "depth_loss_function": None,
            }
            monocular_depth_estimation = tf.keras.models.load_model(
                monocular_depth_estimation_dir,
                custom_objects=custom_objects,
                compile=False,
            )
            # todo: load model from pretrained keras into user .cache folder like transformers lib is doing it.
            #
            # custom_objects = {'BilinearUpSampling2D': BilinearUpSampling2D, 'depth_loss_function': None}
            # custom_objects = {'depth_loss_function': None}
            # monocular_depth_estimation = from_pretrained_keras(
            # "keras-io/monocular-depth-estimation",
            # custom_objects=custom_objects, compile=False
            # )
            # monocular_depth_estimation = from_pretrained_keras("keras-io/monocular-depth-estimation")
            print("monocular_depth_estimation loaded")
        except Exception:
            import traceback

            print("Error loading monocular_depth_estimation:", file=sys.stderr)
            print(traceback.format_exc(), file=sys.stderr)
    else:
        print(
            f"monocular_depth_estimation not found at path, please make sure you have cloned \n the repository https://huggingface.co/keras-io/monocular-depth-estimation to {monocular_depth_estimation_dir}"
        )


midas_depth_estimation = None
midas_transforms = None
midas_transform = None


def try_loading_midas_depth_estimation(use_large_model=True):
    global midas_depth_estimation
    global midas_transforms
    global midas_transform
    try:
        if use_large_model:
            midas_depth_estimation = torch.hub.load("intel-isl/MiDaS", "MiDaS")
        else:
            midas_depth_estimation = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")

        device = "cpu"
        midas_depth_estimation.to(device)

        midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")

        if use_large_model:
            midas_transform = midas_transforms.default_transform
        else:
            midas_transform = midas_transforms.small_transform
    except Exception:
        import traceback

        print("Error loading midas_depth_estimation:", file=sys.stderr)
        print(traceback.format_exc(), file=sys.stderr)


def try_many(fs, *args, **kwargs):
    for f in fs:
        try:
            return f(*args, **kwargs)
        except:
            pass
    raise Exception("")


def scn2img_define_args():
    parse_arg = {}
    parse_arg["str"] = lambda x: str(x)
    parse_arg["int"] = int
    parse_arg["float"] = float
    parse_arg["bool"] = lambda s: (s.strip() == str(bool(s)))
    parse_arg["tuple"] = (lambda s: tuple(s.split(",")),)
    parse_arg["int_tuple"] = lambda s: tuple(map(int, s.split(",")))
    parse_arg["float_tuple"] = lambda s: tuple(map(float, s.split(",")))
    parse_arg["degrees"] = lambda s: float(s) * math.pi / 180
    parse_arg["color"] = lambda s: try_many(
        [parse_arg["int_tuple"], parse_arg["str"]], s
    )
    parse_arg["anything"] = lambda s: try_many(
        [
            parse_arg["int_tuple"],
            parse_arg["float_tuple"],
            parse_arg["int"],
            parse_arg["float"],
            parse_arg["tuple"],
            parse_arg["color"],
            parse_arg["str"],
        ],
        s,
    )
    function_args = {
        "img2img": {
            "prompt": "str",
            "image_editor_mode": "str",
            "mask_mode": "int",
            "mask_blur_strength": "float",
            "mask_restore": "bool",
            "ddim_steps": "int",
            "sampler_name": "str",
            "toggles": "int_tuple",
            "realesrgan_model_name": "str",
            "n_iter": "int",
            "cfg_scale": "float",
            "denoising_strength": "float",
            "seed": "int",
            "height": "int",
            "width": "int",
            "resize_mode": "int",
        },
        "txt2img": {
            "prompt": "str",
            "ddim_steps": "int",
            "sampler_name": "str",
            "toggles": "int_tuple",
            "realesrgan_model_name": "str",
            "ddim_eta": "float",
            "n_iter": "int",
            "batch_size": "int",
            "cfg_scale": "float",
            "seed": "int",
            "height": "int",
            "width": "int",
            "variant_amount": "float",
            "variant_seed": "int",
        },
        "render_img2img": {
            "select": "int",
            "variation": "int",
        },
        "render_txt2img": {
            "select": "int",
            "variation": "int",
        },
        "image": {
            "size": "int_tuple",
            "crop": "int_tuple",
            "position": "float_tuple",
            "resize": "int_tuple",
            "rotation": "degrees",
            "color": "color",
            "blend": "str",
        },
        "render_mask": {
            "mask_value": "int",
            "mask_by_color": "color",
            "mask_by_color_space": "str",
            "mask_by_color_threshold": "int",
            "mask_by_color_at": "int_tuple",
            "mask_is_depth": "bool",
            "mask_depth": "bool",
            "mask_depth_normalize": "bool",
            "mask_depth_model": "int",
            "mask_depth_min": "float",
            "mask_depth_max": "float",
            "mask_depth_invert": "bool",
            "mask_open": "int",
            "mask_close": "int",
            "mask_blur": "float",
            "mask_grow": "int",
            "mask_shrink": "int",
            "mask_invert": "bool",
        },
        "render_3d": {
            "transform3d": "bool",
            "transform3d_depth_model": "int",
            "transform3d_depth_near": "float",
            "transform3d_depth_scale": "float",
            "transform3d_from_hfov": "degrees",
            "transform3d_from_pose": "float_tuple",
            "transform3d_to_hfov": "degrees",
            "transform3d_to_pose": "float_tuple",
            "transform3d_min_mask": "int",
            "transform3d_max_mask": "int",
            "transform3d_mask_invert": "bool",
            "transform3d_inpaint": "bool",
            "transform3d_inpaint_radius": "int",
            "transform3d_inpaint_method": "int",
            "transform3d_inpaint_restore_mask": "bool",
        },
        "object": {
            "initial_seed": "int",
        },
    }
    function_args_ext = {
        "image": ["object", "image", "render_mask", "render_3d"],
        "img2img": [
            "object",
            "render_img2img",
            "img2img",
            "image",
            "render_mask",
            "render_3d",
        ],
        "txt2img": [
            "object",
            "render_txt2img",
            "txt2img",
            "image",
            "render_mask",
            "render_3d",
        ],
    }
    return parse_arg, function_args, function_args_ext


def get_scn2img(
    MemUsageMonitor: Type,
    save_sample: Callable,
    get_next_sequence_number: Callable,
    seed_to_int: Callable,
    txt2img: Callable,
    txt2img_defaults: Dict,
    img2img: Callable,
    img2img_defaults: Dict,
    opt: argparse.Namespace = None,
):
    opt = opt or argparse.Namespace()

    def next_seed(s):
        return random.Random(seed_to_int(s)).randint(0, 2**32 - 1)

    class SeedGenerator:
        def __init__(self, seed):
            self._seed = seed_to_int(seed)

        def next_seed(self):
            seed = self._seed
            self._seed = next_seed(self._seed)
            return seed

        def peek_seed(self):
            return self._seed

    def scn2img(
        prompt: str,
        toggles: List[int],
        seed: Union[int, str, None],
        fp=None,
        job_info: JobInfo = None,
    ):
        global scn2img_cache
        outpath = opt.outdir_scn2img or opt.outdir or "outputs/scn2img-samples"
        seed = seed_to_int(seed)

        prompt = prompt or ""
        clear_cache = 0 in toggles
        output_intermediates = 1 in toggles
        skip_save = 2 not in toggles
        write_info_files = 3 in toggles
        write_sample_info_to_log_file = 4 in toggles
        jpg_sample = 5 in toggles

        os.makedirs(outpath, exist_ok=True)

        if clear_cache or scn2img_cache["seed"] != seed:
            scn2img_cache["seed"] = seed
            scn2img_cache["cache"] = {}

        comments = []
        print_log_lvl = 2

        def gen_log_lines(*args, **kwargs):
            yield (" ".join(map(str, args)))
            for k, v in kwargs.items():
                yield (f"{k} = {v}")

        def log(*args, **kwargs):
            lines = gen_log_lines(*args, **kwargs)
            for line in lines:
                comments.append(line)

        def log_lvl(lvl, *args, **kwargs):
            if lvl <= print_log_lvl:
                lines = gen_log_lines(*args, **kwargs)
                print("\n".join(lines))
            log(*args, **kwargs)

        def log_trace(*args, **kwargs):
            log_lvl(5, "[TRACE]", *args, **kwargs)

        def log_debug(*args, **kwargs):
            log_lvl(4, "[DEBUG]", *args, **kwargs)

        def log_info(*args, **kwargs):
            log_lvl(3, "[INFO]", *args, **kwargs)

        def log_warn(*args, **kwargs):
            log_lvl(2, "[WARN]", *args, **kwargs)

        def log_err(*args, **kwargs):
            log_lvl(1, "[ERROR]", *args, **kwargs)

        def log_exception(*args, **kwargs):
            log_lvl(0, "[EXCEPTION]", *args, **kwargs)
            import traceback

            log_lvl(0, traceback.format_exc())

        # cache = scn2img_cache["cache"]
        log_info("scn2img_cache")
        log_info(list(scn2img_cache["cache"].keys()))

        def is_seed_invalid(s):
            result = (type(s) != int) or (s == "") or (s is None)
            return result

        def is_seed_valid(s):
            result = not is_seed_invalid(s)
            return result

        def vary_seed(s, v):
            s = int(s)
            v = int(v)
            if v == 0:
                return s
            else:
                return next_seed(s + v)

        if job_info:
            output_images = job_info.images
        else:
            output_images = []

        class SceneObject:
            def __init__(self, func, title, args, depth, children):
                self.func = func
                self.title = title
                self.args = args or collections.OrderedDict()
                self.depth = depth
                self.children = children or []

            def __len__(self):
                return len(self.children)

            def __iter__(self):
                return iter(self.children)

            def __getitem__(self, key):
                if type(key) == int:
                    return self.children[key]
                elif str(key) in self.args:
                    return self.args[str(key)]
                else:
                    return None

            def __setitem__(self, key, value):
                if type(key) == int:
                    self.children[key] = value
                else:
                    self.args[str(key)] = value

            def __contains__(self, key):
                if type(key) == int:
                    return key < len(self.children)
                else:
                    return str(key) in self.args

            def __str__(self):
                return repr(self)

            def __repr__(self):
                args = collections.OrderedDict()
                if len(self.title) > 0:
                    args["title"] = self.title
                args.update(self.args)
                if len(self.children) > 0:
                    args["children"] = self.children
                args = ", ".join(
                    map(lambda kv: f"{str(kv[0])} = {repr(kv[1])}", args.items())
                )
                return f"{self.func}({args})"

            def cache_hash(
                self,
                seed=None,
                exclude_args=None,
                exclude_child_args=None,
                extra=None,
                child_extra=None,
            ):
                exclude_args = exclude_args or set()
                exclude_args = set(exclude_args)
                exclude_child_args = exclude_child_args or set()
                exclude_child_args = set(exclude_child_args)
                if None not in exclude_args:
                    exclude_args.add(None)
                return hash(
                    (
                        hash(seed),
                        hash(extra),
                        hash(self.func),
                        hash(
                            tuple(
                                [
                                    (k, v)
                                    for k, v in self.args.items()
                                    if k not in exclude_args
                                ]
                            )
                        ),
                        hash(
                            tuple(
                                [
                                    c.cache_hash(
                                        seed=seed,
                                        exclude_args=exclude_child_args,
                                        exclude_child_args=exclude_child_args,
                                        extra=child_extra,
                                        child_extra=child_extra,
                                    )
                                    for c in self.children
                                ]
                            )
                        ),
                    )
                )

        parse_arg, function_args, function_args_ext = scn2img_define_args()
        # log_debug("function_args", function_args)

        def parse_scene(prompt, log):
            parse_inline_comment = re.compile(
                r"(?m)//.+?$"
            )  # (?m): $ also matches at before \n
            parse_multiline_comment = re.compile(
                r"(?s)(^|[^/])/\*.+?\*/"
            )  # (?s): . matches \n
            parse_attr = re.compile(r"^\s*([\w_][\d\w_]*)\s*[:=\s]\s*(.+)\s*$")
            parse_heading = re.compile(r"^\s*(#+)([<]?)([>]?)\s*(.*)$")  #

            class Section:
                def __init__(self, depth=0, title="", content=None, children=None):
                    self.depth = depth
                    self.title = title
                    self.lines = []
                    self.content = content or collections.OrderedDict()
                    self.children = children or []
                    self.func = None

                def __repr__(self):
                    return str(self)

                def __str__(self):
                    return "\n".join(
                        [("#" * self.depth) + " " + self.title]
                        + [f"func={self.func}"]
                        + [f"{k}={v}" for k, v in self.content.items()]
                        + list(map(str, self.children))
                    )

            def strip_inline_comments(txt):
                while True:
                    txt, replaced = parse_inline_comment.subn("", txt)
                    if replaced == 0:
                        break
                return txt

            def strip_multiline_comments(txt):
                while True:
                    txt, replaced = parse_multiline_comment.subn("\1", txt)
                    if replaced == 0:
                        break
                return txt

            def strip_comments(txt):
                txt = strip_multiline_comments(txt)
                txt = strip_inline_comments(txt)
                return txt

            def parse_content(lines):
                content = collections.OrderedDict()
                for line in lines:
                    # line = strip_inline_comments(line)
                    m = parse_attr.match(line)
                    if m is None:
                        attr = None
                        value = line
                    else:
                        attr = m.group(1)
                        value = m.group(2)

                    is_multi_value = attr is None
                    if is_multi_value and attr in content:
                        content[attr].append(value)
                    elif is_multi_value and attr not in content:
                        content[attr] = [value]
                    elif attr not in content:
                        content[attr] = value
                    else:
                        log.append(
                            f"Warn: value for attr {attr} already exists. ignoring {line}."
                        )

                return content

            def parse_sections(lines):
                current_section = Section()
                bump_depth = 0
                for line in lines:
                    m = parse_heading.match(line)
                    if m is None:
                        current_section.lines.append(line)
                    else:
                        current_section.content = parse_content(current_section.lines)
                        yield current_section
                        current_section = Section(
                            depth=len(m.group(1)) + bump_depth, title=m.group(3)
                        )
                        # sections after this will have their depth bumped by number matched '>'.
                        # this allows deep trees while avoiding growing number of '#' by
                        # just using '#> example title' headings
                        bump_depth -= len(m.group(2))
                        bump_depth += len(m.group(3))

                current_section.content = parse_content(current_section.lines)
                yield current_section

            def to_trees(sections):
                stack = []
                roots = []

                def insert_section(section):
                    assert len(stack) == section.depth
                    if section.depth == 0:
                        roots.append(section)
                    if len(stack) > 0:
                        parent = stack[len(stack) - 1]
                        parent.children.append(section)
                    stack.append(section)

                for section in sections:
                    last_depth = len(stack) - 1

                    is_child = section.depth > last_depth
                    is_sibling = section.depth == last_depth
                    is_parental_sibling = section.depth < last_depth
                    if is_child:
                        for d in range(last_depth + 1, section.depth, 1):
                            intermediate = Section(depth=d)
                            insert_section(intermediate)

                    elif is_sibling or is_parental_sibling:
                        stack = stack[: section.depth]

                    insert_section(section)
                return roots

            def to_scene(trees, depth=0):
                if depth == 0:
                    return SceneObject(
                        func="scn2img",
                        title="",
                        args=None,
                        depth=depth,
                        children=[
                            SceneObject(
                                func="scene",
                                title="",
                                args=None,
                                depth=depth + 1,
                                children=[to_scene(tree, depth + 2)],
                            )
                            for tree in trees
                        ],
                    )
                else:
                    assert type(trees) == Section
                    section = trees
                    has_prompt = "prompt" in section.content
                    has_color = "color" in section.content
                    has_childs = len(section.children) > 0
                    has_input_img = has_childs or has_color
                    func = (
                        "img2img"
                        if (has_input_img and has_prompt)
                        else "txt2img"
                        if (has_prompt)
                        else "image"
                    )
                    return SceneObject(
                        func=func,
                        title=section.title,
                        args=section.content,
                        depth=depth,
                        children=[
                            to_scene(child, depth + 1) for child in section.children
                        ],
                    )

            def parse_scene_args(scene):
                function_args["image"]
                function_args[scene.func] if scene.func in function_args else {}
                extends = (
                    function_args_ext[scene.func]
                    if scene.func in function_args_ext
                    else []
                )
                for arg in scene.args.keys():
                    arg_type = "anything"
                    for ext in extends:
                        if arg in function_args[ext]:
                            arg_type = function_args[ext][arg]
                            break
                    try:
                        scene.args[arg] = parse_arg[arg_type](scene.args[arg])
                    except Exception as e:
                        value = scene.args[arg]
                        msg = f"Attribute parsing failed. Expected {arg_type}, got '{value}'."
                        log.append(f"{msg}. Exception: '{str(e)}'")
                for child in scene.children:
                    parse_scene_args(child)
                return scene

            prompt = strip_comments(prompt)
            lines = prompt.split("\n")
            sections = parse_sections(lines)
            sections = list(sections)
            trees = to_trees(sections)
            scene = to_scene(trees)
            parse_scene_args(scene)

            return scene

        def save_sample_scn2img(img, obj, name, seed):
            if img is None:
                return
            base_count = get_next_sequence_number(outpath)
            filename = "[SEED]_result"
            filename = f"{base_count:05}-" + filename
            filename = filename.replace("[SEED]", str(seed))
            wrapped = SceneObject(
                func=name,
                title=obj.title,
                args={"seed": seed},
                depth=obj.depth - 1,
                children=[obj],
            )
            info_dict = {"prompt": prompt, "scene_object": str(wrapped), "seed": seed}
            metadata = ImageMetadata(
                prompt=info_dict["scene_object"],
                seed=seed,
                width=img.size[0],
                height=img.size[0],
            )
            ImageMetadata.set_on_image(img, metadata)
            save_sample(
                img,
                outpath,
                filename,
                jpg_sample,
                None,
                None,
                None,
                None,
                None,
                False,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                False,
                False,
            )
            if write_info_files:
                filename_i = os.path.join(outpath, filename)
                with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
                    yaml.dump(info_dict, f, allow_unicode=True, width=10000)
            if write_sample_info_to_log_file:
                sample_log_path = os.path.join(outpath, "log.yaml")
                with open(sample_log_path, "a", encoding="utf8") as log_file:
                    yaml.dump(info_dict, log_file, allow_unicode=True, width=10000)
                    log_file.write(" \n")

        def render_scene(output_images, scene, seeds):
            def pose(pos, rotation, center):
                cs, sn = math.cos(rotation), math.sin(rotation)
                return x, y, cs, sn, cy, c

            def pose_mat3(pos=(0, 0), rotation=0, center=(0, 0)):
                x, y = pos or (0, 0)
                cs, sn = math.cos(rotation), math.sin(rotation)
                cx, cy = center or (0, 0)
                return np.array(
                    [  # coordinates in parent coordinates
                        [1, 0, x],
                        [0, 1, y],
                        [0, 0, 1],
                    ]
                ) @ np.array(
                    [  # rotated coordinates with center in origin
                        [cs, -sn, -cx],
                        [+sn, cs, -cy],
                        [0, 0, 1],
                    ]
                )  # coordinates in pose

            def get_rect(img):
                w, h = img.size
                return np.array(
                    [
                        [0, 0],  # TL
                        [0, h],  # BL
                        [w, h],  # BR
                        [w, 0],  # TR
                    ]
                )

            def transform_points(mat3, pts):
                rot = mat3[:2, :2]
                pos = mat3[:2, 2]
                # return rot @ pts.T + pos
                return pts @ rot.T + pos

            def create_image(size, color=None):
                # log_debug("")
                # log_debug("Creating image...", size = type(size), color = color)
                # log_debug("")
                if size is None:
                    return None
                if color is None:
                    color = (0, 0, 0, 0)
                return Image.new("RGBA", size, color)

            def resize_image(img, size, crop=None):
                if img is None:
                    return None
                if size is None:
                    return img if (crop is None) else img.crop(box=crop)
                # resize_is_upscaling = (size[0] > img.size[0]) or (size[1] > img.size[1])
                # todo: upscale with realesrgan
                return img.resize(size, box=crop)

            def blend_image_at(dst, img, pos, rotation, center, blend_mode):
                if img is None:
                    return dst
                assert blend_mode.lower() in [
                    "alpha",
                    "mask",
                    "add",
                    "add_modulo",
                    "darker",
                    "difference",
                    "lighter",
                    "logical_and",
                    "logical_or",
                    "logical_xor",
                    "multiply",
                    "soft_light",
                    "hard_light",
                    "overlay",
                    "screen",
                    "subtract",
                    "subtract_modulo",
                ]
                blend_mode = blend_mode.lower()
                # log_debug(f"blend_image_at({dst}, {img}, {pos}, {rotation}, {center})")
                center = center or (img.size[0] * 0.5, img.size[1] * 0.5)
                pos = pos or (
                    (dst.size[0] * 0.5, dst.size[1] * 0.5) if dst is not None else None
                )

                tf = pose_mat3((0, 0), rotation)
                rect_points = get_rect(img) - center
                rect_points = transform_points(tf, rect_points)
                min_x = min([p[0] for p in rect_points])
                min_y = min([p[1] for p in rect_points])
                max_x = max([p[0] for p in rect_points])
                max_y = max([p[1] for p in rect_points])
                new_w = max_x - min_x
                new_h = max_y - min_y
                new_size = (int(new_w), int(new_h))

                # default values for pos
                if pos is None and dst is not None:
                    # center img in dst
                    pos = (dst.size[0] * 0.5, dst.size[0] * 0.5)
                elif pos is None and dst is None:
                    # dst is None, choose pos so that it shows whole img
                    pos = (-min_x, -min_y)

                min_x += pos[0]
                min_y += pos[1]
                max_x += pos[0]
                max_y += pos[1]

                if rotation != 0:
                    img = img.rotate(
                        angle=-rotation * (180 / math.pi),
                        expand=True,
                        fillcolor=(0, 0, 0, 0),
                    )

                if (dst is None) and (img.size == new_size):
                    dst = img.copy()
                    # dst = img
                    return dst

                else:
                    if dst is None:
                        dst = create_image(new_size)
                    dx = int(min_x)
                    dy = int(min_y)
                    sx = -dx if (dx < 0) else 0
                    sy = -dy if (dy < 0) else 0
                    dx = max(0, dx)
                    dy = max(0, dy)
                    # log_debug(f"dest=({dx},{dy}), source=({sx},{sy})")
                    if blend_mode in ["alpha", "mask"]:
                        dst.alpha_composite(img, dest=(dx, dy), source=(sx, sy))
                    else:
                        w, h = img.size
                        img_crop = img.crop(box=(sx, sy, w - 1, h - 1))
                        w, h = img_crop.size
                        dst_crop = dst.crop(box=(dx, dy, dx + w, dy + h))
                        blend_func = getattr(ImageChops, blend_mode)
                        blended = blend_func(dst_crop, img_crop)
                        dst.paste(blended, box=(dx, dy))
                return dst

            def blend_objects(seeds, dst, objects):
                # log_debug("")
                # log_debug(f"blend_objects({dst}, {objects})")
                # log_debug("")
                for obj in reversed(objects):
                    img = render_object(seeds, obj)
                    # if img is None:
                    # log_debug("")
                    # log_debug(f"img is None after render_object in blend_objects({dst}, {objects})")
                    # log_debug("")
                    try:
                        dst = blend_image_at(
                            dst=dst,
                            img=img,
                            pos=obj["pos"] or obj["position"] or None,
                            rotation=obj["rotation"]
                            or obj["rotate"]
                            or obj["angle"]
                            or 0,
                            center=obj["center"] or None,
                            blend_mode=obj["blend"] if "blend" in obj else "alpha",
                        )
                    except Exception as e:
                        # log_debug("")
                        log_exception(f"Exception! blend_objects({dst}, {objects})")
                        log_err("obj", obj)
                        log_err("img", img)
                        log_err("")
                        raise e

                if dst is not None:
                    dst = dst.copy()
                return dst

            def render_mask(seeds, obj, img, input_mask=None):
                if img is None and input_mask is None:
                    return img

                mask = (
                    img.getchannel("A")
                    if img is not None and input_mask is None
                    else None
                )
                changed_mask = False

                def combine_masks(old_mask, new_mask, mode):
                    return new_mask

                if input_mask is not None:
                    mask = input_mask
                    changed_mask = True

                if "mask_value" in obj:
                    new_value = obj["mask_value"]
                    mask.paste(new_value, mask.getbbox())
                    changed_mask = True

                if (
                    "mask_by_color" in obj or "mask_by_color_at" in obj
                ) and img is not None:
                    img_arr = np.asarray(img.convert("RGB"))
                    color = obj["mask_by_color"]
                    color_at = obj["mask_by_color_at"] or None
                    if color_at is not None:
                        num_points = int(math.floor(len(color_at) / 2))
                        points = [
                            (color_at[k * 2], color_at[k * 2 + 1])
                            for k in range(num_points)
                        ]
                        if len(points) > 0:
                            colors = np.array([img_arr[y, x] for x, y in points])
                            color = tuple(
                                np.round(colors.mean(axis=0)).astype(np.uint8).flatten()
                            )
                    colorspace = obj["mask_by_color_space"] or "LAB"
                    threshold = obj["mask_by_color_threshold"] or 15
                    colorspace = colorspace.upper()
                    reference_color = "RGB"
                    if colorspace != "RGB":
                        cvts = {
                            "LAB": cv2.COLOR_RGB2Lab,
                            "LUV": cv2.COLOR_RGB2Luv,
                            "HSV": cv2.COLOR_RGB2HSV,
                            "HLS": cv2.COLOR_RGB2HLS,
                            "YUV": cv2.COLOR_RGB2YUV,
                            "GRAY": cv2.COLOR_RGB2GRAY,
                            "XYZ": cv2.COLOR_RGB2XYZ,
                            "YCrCb": cv2.COLOR_RGB2YCrCb,
                        }
                        rgb = Image.new("RGB", size=(1, 1), color=color)
                        rgb_arr = np.asarray(rgb)
                        cvt_arr = cv2.cvtColor(rgb_arr, cvts[colorspace])
                        img_arr = cv2.cvtColor(img_arr, cvts[colorspace])
                        reference_color = cvt_arr[0, 0]
                    img_arr = img_arr.astype(np.float32)
                    dist = np.max(np.abs(img_arr - reference_color), axis=2)
                    mask_arr = (dist < threshold).astype(np.uint8) * 255
                    mask = Image.fromarray(mask_arr)
                    changed_mask = True

                if obj["mask_depth"]:
                    mask_depth_min = obj["mask_depth_min"] or 0.2
                    mask_depth_max = obj["mask_depth_max"] or 0.8
                    mask_depth_invert = bool(obj["mask_depth_invert"]) or False
                    mask_is_depth = (
                        obj["mask_is_depth"] if "mask_is_depth" in obj else False
                    )
                    mask_depth_normalize = (
                        obj["mask_depth_normalize"]
                        if "mask_depth_normalize" in obj
                        else True
                    )
                    mask_depth_model = (
                        int(obj["mask_depth_model"]) if "mask_depth_model" in obj else 1
                    )
                    depth = run_depth_estimation(img, mask_depth_model)
                    res = run_depth_filter(
                        depth,
                        mask_depth_min,
                        mask_depth_max,
                        mask_depth_invert,
                        mask_depth_normalize,
                        mask_is_depth,
                    )
                    if res is not None:
                        mask = res.resize(img.size)
                        changed_mask = True

                if "mask_open" in obj:
                    mask = mask.filter(ImageFilter.MinFilter(obj["mask_open"]))
                    mask = mask.filter(ImageFilter.MaxFilter(obj["mask_open"]))
                    changed_mask = True

                if "mask_close" in obj:
                    mask = mask.filter(ImageFilter.MaxFilter(obj["mask_close"]))
                    mask = mask.filter(ImageFilter.MinFilter(obj["mask_close"]))
                    changed_mask = True

                if "mask_grow" in obj:
                    mask = mask.filter(ImageFilter.MaxFilter(obj["mask_grow"]))
                    changed_mask = True

                if "mask_shrink" in obj:
                    mask = mask.filter(ImageFilter.MinFilter(obj["mask_shrink"]))
                    changed_mask = True

                if "mask_blur" in obj:
                    mask = mask.filter(ImageFilter.GaussianBlur(obj["mask_blur"]))
                    changed_mask = True

                if obj["mask_invert"]:
                    mask = ImageChops.invert(mask)
                    changed_mask = True

                if changed_mask and img is not None and mask is not None:
                    img.putalpha(mask)

                if img is not None:
                    return img
                else:
                    return mask

            # remember output images, to avoid duplicates
            output_image_set = set()

            def output_img(img):
                if img is None:
                    return
                img_id = id(img)
                if img_id in output_image_set:
                    return img
                output_image_set.add(img_id)
                output_images.append(img)

            def render_intermediate(img, obj, name, seed):
                if output_intermediates:
                    output_img(img)
                if not skip_save:
                    save_sample_scn2img(img, obj, name, seed)
                return img

            def render_3d(img, obj):
                if img is None:
                    return img
                if obj["transform3d"] is True:
                    d2r = math.pi / 180.0
                    depth_model = (
                        obj["transform3d_depth_model"]
                        if "transform3d_depth_model" in obj
                        else 1
                    )
                    depth_near = (
                        obj["transform3d_depth_near"]
                        if "transform3d_depth_near" in obj
                        else 0.1
                    )
                    depth_scale = (
                        obj["transform3d_depth_scale"]
                        if "transform3d_depth_scale" in obj
                        else 1.0
                    )
                    from_hfov = (
                        obj["transform3d_from_hfov"]
                        if "transform3d_from_hfov" in obj
                        else (45 * d2r)
                    )
                    from_pose = (
                        obj["transform3d_from_pose"]
                        if "transform3d_from_pose" in obj
                        else (0, 0, 0, 0, 0, 0)
                    )
                    to_hfov = (
                        obj["transform3d_to_hfov"]
                        if "transform3d_to_hfov" in obj
                        else (45 * d2r)
                    )
                    to_pose = (
                        obj["transform3d_to_pose"]
                        if "transform3d_to_pose" in obj
                        else (0, 0, 0, 0, 0, 0)
                    )
                    min_mask = (
                        obj["transform3d_min_mask"]
                        if "transform3d_min_mask" in obj
                        else 128
                    )
                    max_mask = (
                        obj["transform3d_max_mask"]
                        if "transform3d_max_mask" in obj
                        else 255
                    )
                    mask_invert = (
                        obj["transform3d_mask_invert"]
                        if "transform3d_mask_invert" in obj
                        else False
                    )
                    inpaint = (
                        obj["transform3d_inpaint"]
                        if "transform3d_inpaint" in obj
                        else True
                    )
                    inpaint_radius = (
                        obj["transform3d_inpaint_radius"]
                        if "transform3d_inpaint_radius" in obj
                        else 5
                    )
                    inpaint_method = (
                        obj["transform3d_inpaint_method"]
                        if "transform3d_inpaint_method" in obj
                        else 0
                    )
                    inpaint_rmask = (
                        obj["transform3d_inpaint_restore_mask"]
                        if "transform3d_inpaint_restore_mask" in obj
                        else False
                    )
                    from_pose = list(from_pose)
                    to_pose = list(to_pose)
                    while len(from_pose) < 6:
                        from_pose.append(0)
                    while len(to_pose) < 6:
                        to_pose.append(0)
                    from_pos, from_rpy = from_pose[:3], from_pose[3:6]
                    to_pos, to_rpy = to_pose[:3], to_pose[3:6]
                    hfov0_rad, hfov1_rad = from_hfov, to_hfov
                    tf_world_cam0 = pose3d_rpy(
                        *from_pos, *(deg * d2r for deg in from_rpy)
                    )
                    tf_world_cam1 = pose3d_rpy(*to_pos, *(deg * d2r for deg in to_rpy))

                    depth = run_depth_estimation(img, depth_model)
                    img = run_transform_image_3d_simple(
                        img,
                        depth,
                        depth_near,
                        depth_scale,
                        hfov0_rad,
                        tf_world_cam0,
                        hfov1_rad,
                        tf_world_cam1,
                        min_mask,
                        max_mask,
                        mask_invert,
                    )
                    if inpaint:
                        mask = img.getchannel("A")
                        img_inpainted = cv2.inpaint(
                            np.asarray(img.convert("RGB")),
                            255 - np.asarray(mask),
                            inpaint_radius,
                            [cv2.INPAINT_TELEA, cv2.INPAINT_NS][inpaint_method],
                        )
                        img = Image.fromarray(img_inpainted).convert("RGBA")
                        if inpaint_rmask:
                            img.putalpha(mask)
                return img

            def render_image(seeds, obj):
                start_seed = seeds.peek_seed()
                img = create_image(obj["size"], obj["color"])
                img = blend_objects(seeds, img, obj.children)
                img = render_mask(seeds, obj, img)
                img = resize_image(img, obj["resize"], obj["crop"])
                # if img is None: log_warn(f"result of render_image({obj}) is None")
                img = render_3d(img, obj)
                img = render_intermediate(img, obj, "render_image", start_seed)
                return img

            def prepare_img2img_kwargs(seeds, obj, img):
                # log_trace(f"prepare_img2img_kwargs({obj}, {img})")
                img2img_kwargs = {}
                # img2img_kwargs.update(img2img_defaults)
                func_args = function_args["img2img"]
                for k, v in img2img_defaults.items():
                    if k in func_args:
                        img2img_kwargs[k] = v

                if "mask_mode" in img2img_kwargs:
                    img2img_kwargs["mask_mode"] = 1 - img2img_kwargs["mask_mode"]

                if "size" in obj:
                    img2img_kwargs["width"] = obj["size"][0]
                    img2img_kwargs["height"] = obj["size"][1]

                for k, v in func_args.items():
                    if k in obj:
                        img2img_kwargs[k] = obj[k]

                if "toggles" in img2img_kwargs:
                    img2img_kwargs["toggles"] = list(img2img_kwargs["toggles"])

                assert "seed" in img2img_kwargs
                if "seed" in img2img_kwargs:
                    s = img2img_kwargs["seed"]
                    if is_seed_valid(s):
                        img2img_kwargs["seed"] = int(s)
                    else:
                        img2img_kwargs["seed"] = seeds.next_seed()

                log_info('img2img_kwargs["seed"]', img2img_kwargs["seed"])

                if "variation" in obj:
                    v = obj["variation"]
                    if is_seed_valid(v):
                        s = int(img2img_kwargs["seed"])
                        v = int(v)
                        ns = vary_seed(s, v)
                        log_info(f"Using seed variation {v}: {ns}")
                        img2img_kwargs["seed"] = ns

                img2img_kwargs["job_info"] = job_info
                # img2img_kwargs["job_info"] = None
                img2img_kwargs["fp"] = fp
                img2img_kwargs["init_info"] = img
                if img2img_kwargs["image_editor_mode"] == "Mask":
                    img2img_kwargs["init_info_mask"] = {
                        "image": img.convert("RGB").convert("RGBA"),
                        "mask": img.getchannel("A"),
                    }
                    # render_intermediate(img2img_kwargs["init_info_mask"]["mask"].convert("RGBA"), obj, "img2img_init_info_mask", start_seed)
                log_info("img2img_kwargs")
                log_info(img2img_kwargs)

                return img2img_kwargs

            def prepare_txt2img_kwargs(seeds, obj):
                # log_trace(f"prepare_txt2img_kwargs({obj})")
                txt2img_kwargs = {}
                # txt2img_kwargs.update(txt2img_defaults)
                func_args = function_args["txt2img"]
                for k, v in txt2img_defaults.items():
                    if k in func_args:
                        txt2img_kwargs[k] = v

                if "size" in obj:
                    txt2img_kwargs["width"] = obj["size"][0]
                    txt2img_kwargs["height"] = obj["size"][1]

                for k, v in func_args.items():
                    if k in obj:
                        txt2img_kwargs[k] = obj[k]

                if "toggles" in txt2img_kwargs:
                    txt2img_kwargs["toggles"] = list(txt2img_kwargs["toggles"])

                assert "seed" in txt2img_kwargs
                if "seed" in txt2img_kwargs:
                    s = txt2img_kwargs["seed"]
                    if is_seed_valid(s):
                        txt2img_kwargs["seed"] = int(s)
                    else:
                        txt2img_kwargs["seed"] = seeds.next_seed()

                log_info('txt2img_kwargs["seed"]', txt2img_kwargs["seed"])

                if "variation" in obj:
                    v = obj["variation"]
                    if is_seed_valid(v):
                        s = int(txt2img_kwargs["seed"])
                        v = int(v)
                        ns = vary_seed(s, v)
                        log_info(f"Using seed variation {v}: {ns}")
                        txt2img_kwargs["seed"] = ns

                txt2img_kwargs["job_info"] = job_info
                # txt2img_kwargs["job_info"] = None
                txt2img_kwargs["fp"] = fp

                log_info("txt2img_kwargs")
                log_info(txt2img_kwargs)

                return txt2img_kwargs

            def render_img2img(seeds, obj):
                start_seed = seeds.peek_seed()
                global scn2img_cache
                if obj["size"] is None:
                    obj["size"] = (
                        img2img_defaults["width"],
                        img2img_defaults["height"],
                    )
                img = create_image(obj["size"], obj["color"])
                img = blend_objects(seeds, img, obj.children)
                img = render_mask(seeds, obj, img)
                img = render_intermediate(img, obj, "render_img2img_input", start_seed)

                img2img_kwargs = prepare_img2img_kwargs(seeds, obj, img)

                used_kwargs.append(("img2img", img2img_kwargs))

                # obj_hash = hash(str((img2img_kwargs["seed"],obj)))
                obj_hash = obj.cache_hash(
                    seed=img2img_kwargs["seed"],
                    exclude_args={"select", "pos", "rotation"},
                )
                if obj_hash not in scn2img_cache["cache"]:
                    if job_info:
                        count_images_before = len(job_info.images)
                    outputs, seed, info, stats = img2img(**img2img_kwargs)
                    if job_info:
                        # img2img will output into job_info.images.
                        # we want to cache only the new images.
                        # extract new images and remove them from job_info.images.
                        assert job_info.images == outputs
                        outputs = job_info.images[count_images_before:]
                        outputs = [img.convert("RGBA") for img in outputs]
                        num_new = len(outputs)
                        # use images.pop so that images list is modified inplace and stays the same object.
                        for k in range(num_new):
                            job_info.images.pop()
                    scn2img_cache["cache"][obj_hash] = outputs, seed, info, stats

                outputs, seed, info, stats = scn2img_cache["cache"][obj_hash]

                for img in outputs:
                    output_img(img)

                log_info("outputs", outputs)

                # select img from outputs
                if len(outputs) > 0:
                    select = obj["select"] or 0
                    img = outputs[select]
                else:
                    # no outputs, so we just use (the input) img without modifying it
                    # img = img
                    pass

                # img = render_mask(seeds, obj, img)
                img = resize_image(img, obj["resize"], obj["crop"])
                if img is None:
                    log_warn(f"result of render_img2img({obj}) is None")
                img = render_3d(img, obj)
                img = render_intermediate(img, obj, "render_img2img", start_seed)
                return img

            def render_txt2img(seeds, obj):
                start_seed = seeds.peek_seed()
                global scn2img_cache

                txt2img_kwargs = prepare_txt2img_kwargs(seeds, obj)

                used_kwargs.append(("txt2img", txt2img_kwargs))

                # obj_hash = hash(str((txt2img_kwargs["seed"],obj)))
                obj_hash = obj.cache_hash(
                    seed=txt2img_kwargs["seed"],
                    exclude_args={"select", "pos", "rotation"},
                )
                if obj_hash not in scn2img_cache["cache"]:
                    if job_info:
                        count_images_before = len(job_info.images)
                    outputs, seed, info, stats = txt2img(**txt2img_kwargs)
                    if job_info:
                        # txt2img will output into job_info.images.
                        # we want to cache only the new images.
                        # extract new images and remove them from job_info.images.
                        assert job_info.images == outputs
                        outputs = job_info.images[count_images_before:]
                        outputs = [img.convert("RGBA") for img in outputs]
                        num_new = len(outputs)
                        # use images.pop so that images list is modified inplace and stays the same object.
                        for k in range(num_new):
                            job_info.images.pop()
                    scn2img_cache["cache"][obj_hash] = outputs, seed, info, stats

                outputs, seed, info, stats = scn2img_cache["cache"][obj_hash]

                for img in outputs:
                    output_img(img)

                log_info("outputs", outputs)

                # select img from outputs
                if len(outputs) > 0:
                    select = obj["select"] or 0
                    img = outputs[select]
                else:
                    # no outputs, so we use None
                    img = None

                img = render_mask(seeds, obj, img)
                img = resize_image(img, obj["resize"], obj["crop"])
                if img is None:
                    log_warn(f"result of render_txt2img({obj}) is None")
                img = render_3d(img, obj)
                img = render_intermediate(img, obj, "render_txt2img", start_seed)
                return img

            def render_object(seeds, obj):
                # log_trace(f"render_object({str(obj)})")

                if "initial_seed" in obj:
                    # create new generator rather than resetting current generator,
                    # so that seeds generator from function argument is not changed.
                    seeds = SeedGenerator(obj["initial_seed"])

                if obj.func == "scene":
                    assert len(obj.children) == 1
                    return render_object(seeds, obj.children[0])
                elif obj.func == "image":
                    return render_image(seeds, obj)
                elif obj.func == "img2img":
                    return render_img2img(seeds, obj)
                elif obj.func == "txt2img":
                    return render_txt2img(seeds, obj)
                else:
                    msg = f"Got unexpected SceneObject type {obj.func}"
                    comments.append(msg)
                    return None

            def render_scn2img(seeds, obj):
                result = []

                if "initial_seed" in obj:
                    # create new generator rather than resetting current generator,
                    # so that seeds generator from function argument is not changed.
                    seeds = SeedGenerator(obj["initial_seed"])

                if obj.func == "scn2img":
                    # Note on seed generation and for-loops instead of
                    # list-comprehensions:
                    #
                    # For instead of list-comprehension to ensure order as
                    # list-comprehension order is not guaranteed. Seed generator must be
                    # used by children in deterministic order.
                    #
                    # This also applies elsewhere.
                    for child in obj.children:
                        result.append(render_object(seeds, child))
                else:
                    result.append(render_object(seeds, obj))
                return result

            start_seed = seeds.peek_seed()
            for img in render_scn2img(seeds, scene):
                if output_intermediates:
                    # img already in output, do nothing here
                    pass
                else:
                    output_img(img)

                if skip_save:
                    # individual image save was skipped,
                    # we need to save them now
                    save_sample_scn2img(img, scene, "render_scene", start_seed)

            return output_images

        start_time = time.time()

        mem_mon = MemUsageMonitor("MemMon")
        mem_mon.start()

        used_kwargs = []

        scene = parse_scene(prompt, comments)
        log_info("scene")
        log_info(scene)
        # log_info("comments", comments)

        render_scene(output_images, scene, SeedGenerator(seed))
        log_info("output_images", output_images)
        # log_info("comments", comments)

        # comments.append(str(scene))
        mem_max_used, mem_total = mem_mon.read_and_stop()
        time_diff = time.time() - start_time

        output_infos = []
        output_infos.append(("initial_seed", seed))
        excluded_args = set(["job_info", "fp", "init_info", "init_info_mask", "prompt"])
        if len(used_kwargs) > 0:
            for func, kwargs in used_kwargs:
                output_infos.append("\n")
                output_infos.append(("", func))
                output_infos.append(kwargs["prompt"])
                for arg, value in kwargs.items():
                    if arg in excluded_args:
                        continue
                    if value is None:
                        continue
                    if type(value) == dict:
                        continue
                    if type(value) == Image:
                        continue
                    output_infos.append((arg, value))

        full_string = ""
        entities = []
        for output_info in output_infos:
            if type(output_info) == str:
                full_string += output_info
            else:
                assert type(output_info) is tuple
                k, v = output_info
                label = f" {k}:" if len(k) > 0 else ""
                entity = {
                    "entity": str(v),
                    "start": len(full_string),
                    "end": len(full_string) + len(label),
                }
                entities.append(entity)
                full_string += label

        info = {"text": full_string, "entities": entities}
        num_prompts = 1
        stats = " ".join(
            [
                f"Took { round(time_diff, 2) }s total ({ round(time_diff/(num_prompts),2) }s per image)",
                f"Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%",
            ]
        )

        return output_images, seed, info, stats, repr(scene)

    return scn2img


def run_monocular_depth_estimation_multi(
    images, minDepth=10, maxDepth=1000, batch_size=2
):
    # https://huggingface.co/keras-io/monocular-depth-estimation
    # https://huggingface.co/spaces/atsantiago/Monocular_Depth_Filter
    global monocular_depth_estimation
    if images is None:
        return None
    if monocular_depth_estimation is None:
        try_loading_monocular_depth_estimation()
    if monocular_depth_estimation is None:
        return None
    if type(images) == Image:
        images = [images]
    loaded_images = []
    for image in images:
        # print("image", image)
        # print("type(image)", type(image))
        # if type(image) is Image:
        # image = np.asarray(image.convert("RGB"))
        try:
            image = image.convert("RGB")
            image = image.resize((640, 480))
        except:
            pass
        image = np.asarray(image)
        x = np.clip(image.reshape(480, 640, 3) / 255, 0, 1)
        loaded_images.append(x)
    loaded_images = np.stack(loaded_images, axis=0)
    images = loaded_images

    # Support multiple RGB(A)s, one RGB(A) image, even grayscale
    if len(images.shape) < 3:
        images = np.stack((images, images, images), axis=2)
    if len(images.shape) < 4:
        images = images.reshape((1, images.shape[0], images.shape[1], images.shape[2]))
    if images.shape[3] > 3:
        images = images[:, :, :, :3]

    # Compute predictions
    predictions = monocular_depth_estimation.predict(images, batch_size=batch_size)

    def depth_norm(x, maxDepth):
        return maxDepth / x

    # Put in expected range
    # print("Max Depth:", np.amax(predictions), maxDepth)
    # print("Min Depth:", np.amin(predictions), minDepth)
    depths = (
        np.clip(depth_norm(predictions, maxDepth=maxDepth), minDepth, maxDepth)
        / maxDepth
    )
    return depths


def run_monocular_depth_estimation_single(image, minDepth=10, maxDepth=1000):
    depth = run_monocular_depth_estimation_multi([image], minDepth, maxDepth)[0][
        :, :, 0
    ]
    return depth


def run_Monocular_Depth_Filter_multi(
    images,
    filter_min_depth: float,
    filter_max_depth: float,
    invert: bool,
    normalize_depth: bool,
    mask_is_depth: bool,
    **kwargs,
):
    # https://huggingface.co/spaces/atsantiago/Monocular_Depth_Filter
    depths = run_monocular_depth_estimation_multi(images, **kwargs)
    if depths is None:
        return None
    n, h, w, c = depths.shape
    # print("run_Monocular_Depth_Filter n,h,w,c", n,h,w,c)
    outputs = []
    for k in range(n):
        depth = depths[k][:, :, 0]
        mask = run_depth_filter(
            depth,
            filter_min_depth,
            filter_max_depth,
            invert,
            normalize_depth,
            mask_is_depth,
        )
        outputs.append(mask)
    return outputs


def run_Monocular_Depth_Filter_single(
    image,
    filter_min_depth: float,
    filter_max_depth: float,
    invert: bool,
    normalize_depth: bool,
    mask_is_depth: bool,
    **kwargs,
):
    depths = run_Monocular_Depth_Filter_multi(
        [image],
        filter_min_depth,
        filter_max_depth,
        invert,
        normalize_depth,
        mask_is_depth,
        **kwargs,
    )
    return depths[0]


def run_midas_depth_estimation(image):
    global midas_depth_estimation
    global midas_transform
    if image is None:
        return None
    if midas_depth_estimation is None or midas_transform is None:
        try_loading_midas_depth_estimation()
    if midas_depth_estimation is None or midas_transform is None:
        return None

    image = image.convert("RGB")
    image = np.asarray(image)

    device = "cpu"
    input_batch = midas_transform(image).to(device)
    with torch.no_grad():
        prediction = midas_depth_estimation(input_batch)

        prediction = torch.nn.functional.interpolate(
            prediction.unsqueeze(1),
            size=image.shape[:2],
            mode="bicubic",
            align_corners=False,
        ).squeeze()

    output = prediction.cpu().numpy()
    depth = 1 - output / np.max(output)
    return depth


def run_midas_depth_filter(
    image,
    filter_min_depth: float,
    filter_max_depth: float,
    invert: bool,
    normalize_depth: bool,
    mask_is_depth: bool,
):
    depth = run_midas_depth_estimation(image)

    return run_depth_filter(
        depth,
        filter_min_depth,
        filter_max_depth,
        invert,
        normalize_depth,
        mask_is_depth,
    )


def run_depth_filter(
    depth: np.ndarray,
    filter_min_depth: float,
    filter_max_depth: float,
    invert: bool,
    normalize_depth: bool,
    mask_is_depth: bool,
):
    if depth is None:
        return None

    if normalize_depth:
        depth = depth - np.min(depth)
        depth = depth / np.max(depth)

    if mask_is_depth:
        depth = (depth - filter_min_depth) * (
            1.0 / (filter_max_depth - filter_min_depth)
        )
        depth[depth < 0] = 0
        depth[depth > 1] = 1
        mask = (depth * 255).astype(np.uint8)
    else:
        filt_arr_min = depth > filter_min_depth
        filt_arr_max = depth < filter_max_depth
        mask = np.logical_and(filt_arr_min, filt_arr_max).astype(np.uint8) * 255

    if invert:
        mask = 255 - mask

    mask = Image.fromarray(mask, "L")

    return mask


def run_depth_estimation(image: Image, model_idx: int):
    funcs_depth_estimation = [
        run_monocular_depth_estimation_single,
        run_midas_depth_estimation,
    ]
    func_depth_estimation = funcs_depth_estimation[model_idx]
    depth = func_depth_estimation(image)
    return depth


@numba.jit
def depth_reprojection(
    xyz: np.ndarray,
    depth: np.ndarray,
    depth_scale: float,
    fx: float,
    fy: float,
    cx: float,
    cy: float,
):
    h, w = depth.shape[:2]
    for v in range(h):
        y = fy * (v - cy)
        for u in range(w):
            x = fx * (u - cx)
            z = depth[v, u] * depth_scale
            xyz[v, u, 0] = x * z
            xyz[v, u, 1] = y * z
            xyz[v, u, 2] = z


def run_3d_estimation(
    depth: np.ndarray, depth_scale: float = 1, hfov_rad: float = 60 * math.pi / 180
):
    pass
    h, w = depth.shape[:2]
    cam_info = CameraInfo((h, w), hfov_rad)
    xyz = np.empty(shape=(h, w, 3), dtype=np.float32)
    depth_reprojection(
        xyz, depth, depth_scale, cam_info.fx, cam_info.fy, cam_info.cx, cam_info.cy
    )
    return xyz


@numba.jit
def transform_image_3d(
    img_out: np.ndarray,
    img_in: np.ndarray,
    depth: np.ndarray,
    depth_near: float,
    depth_scale: float,
    fx0: float,
    fy0: float,
    cx0: float,
    cy0: float,
    fx1: float,
    fy1: float,
    cx1: float,
    cy1: float,
    rot_cam1_cam0: np.ndarray,
    offset_cam1_cam0: np.ndarray,
    min_mask: int,
    max_mask: int,
):
    # assert(img_in.shape[2] == 4)
    # assert(img_out.shape[2] == 4)
    # assert(len(depth.shape) == 2)
    # (u0,v0)  : 2d pixel position in img_in
    # pos_cam0 : 3d pixel position in cam0 coordinate system
    # pos_cam1 : 3d pixel position in cam1 coordinate system
    # (u1,v1)  : 2d pixel position in img_out
    m00 = rot_cam1_cam0[0, 0]
    m01 = rot_cam1_cam0[0, 1]
    m02 = rot_cam1_cam0[0, 2]
    m10 = rot_cam1_cam0[1, 0]
    m11 = rot_cam1_cam0[1, 1]
    m12 = rot_cam1_cam0[1, 2]
    m20 = rot_cam1_cam0[2, 0]
    m21 = rot_cam1_cam0[2, 1]
    m22 = rot_cam1_cam0[2, 2]
    h0 = int(depth.shape[0])
    w0 = int(depth.shape[1])
    h1 = int(img_out.shape[0])
    w1 = int(img_out.shape[1])
    for v0 in range(h0):
        y0_ = fy0 * (v0 - cy0)
        for u0 in range(w0):
            r, g, b, a = img_in[v0, u0]
            # img_out[v0,u0,0] = r
            # img_out[v0,u0,1] = g
            # img_out[v0,u0,2] = b
            # img_out[v0,u0,3] = a
            # continue
            # if not (min_mask <= a <= max_mask): continue
            x0_ = fx0 * (u0 - cx0)
            z0 = depth_near + depth[v0, u0] * depth_scale
            x0 = x0_ * z0
            y0 = y0_ * z0
            x1 = offset_cam1_cam0[0] + m00 * x0 + m01 * y0 + m02 * z0
            y1 = offset_cam1_cam0[1] + m10 * x0 + m11 * y0 + m12 * z0
            z1 = offset_cam1_cam0[2] + m20 * x0 + m21 * y0 + m22 * z0
            # pos_cam0 = (x0*z0,y0*z0,z0)
            # pos_cam1 = offset_cam1_cam0 + rot_cam1_cam0 @ pos_cam0
            # x1,y1,z1 = pos_cam1
            if z1 <= 0:
                continue
            u1 = int(0.5 + (x1 / (z1 * fx1)) + cx1)
            v1 = int(0.5 + (y1 / (z1 * fy1)) + cy1)
            if u1 < 0:
                u1 = 0
            if u1 >= w1:
                u1 = w1 - 1
            if v1 < 0:
                v1 = 0
            if v1 >= h1:
                v1 = h1 - 1
            # if not (0 <= u1 < w1): continue
            # if not (0 <= v1 < h1): continue
            img_out[v1, u1, 0] = r
            img_out[v1, u1, 1] = g
            img_out[v1, u1, 2] = b
            img_out[v1, u1, 3] = a


class CameraInfo:
    def __init__(
        self,
        image_size: Tuple[int, int],
        hfov_rad: float = 60 * math.pi / 180,
        pose: np.ndarray = None,
    ):
        self.width = image_size[0]
        self.height = image_size[1]
        self.aspect_ratio = self.width * (1.0 / self.height)
        self.hfov_rad = hfov_rad
        self.vfov_rad = self.hfov_rad / self.aspect_ratio
        half_width = self.width * 0.5
        half_height = self.width * 0.5
        self.fx = math.tan(self.hfov_rad * 0.5) / half_width
        self.fy = math.tan(self.vfov_rad * 0.5) / half_height
        self.cx = half_width
        self.cy = half_height
        self.pose = pose if pose is not None else np.eye(4)
        assert self.pose.shape == (4, 4)


def run_transform_image_3d(
    image: Image,
    depth: np.ndarray,
    depth_near: float,
    depth_scale: float,
    from_caminfo: CameraInfo,
    to_caminfo: CameraInfo,
    min_mask: int,
    max_mask: int,
    mask_invert: bool,
):
    if image is None:
        return None
    h, w = image.size
    image_in = np.asarray(image.convert("RGBA"))
    image_out = np.zeros(shape=(h, w, 4), dtype=np.uint8)
    tf_world_cam0 = from_caminfo.pose
    tf_world_cam1 = to_caminfo.pose
    tf_cam1_world = affine_inv(tf_world_cam1)
    tf_cam1_cam0 = tf_cam1_world @ tf_world_cam0
    rot_cam1_cam0 = tf_cam1_cam0[:3, :3]
    offset_cam1_cam0 = tf_cam1_cam0[:3, 3]
    # print("depth_scale", depth_scale)
    # print("from_caminfo.fx", from_caminfo.fx)
    # print("from_caminfo.fy", from_caminfo.fy)
    # print("from_caminfo.cx", from_caminfo.cx)
    # print("from_caminfo.cy", from_caminfo.cy)
    # print("to_caminfo.fx", to_caminfo.fx)
    # print("to_caminfo.fy", to_caminfo.fy)
    # print("to_caminfo.cx", to_caminfo.cx)
    # print("to_caminfo.cy", to_caminfo.cy)
    # print("rot_cam1_cam0", rot_cam1_cam0)
    # print("offset_cam1_cam0", offset_cam1_cam0)
    # print("min_mask", min_mask)
    # print("max_mask", max_mask)

    transform_image_3d(
        image_out,
        image_in,
        depth,
        depth_near,
        depth_scale,
        from_caminfo.fx,
        from_caminfo.fy,
        from_caminfo.cx,
        from_caminfo.cy,
        to_caminfo.fx,
        to_caminfo.fy,
        to_caminfo.cx,
        to_caminfo.cy,
        rot_cam1_cam0,
        offset_cam1_cam0,
        min_mask,
        max_mask,
    )
    if mask_invert:
        image_out[:, :, 3] = 255 - image_out[:, :, 3]
    return Image.fromarray(image_out, "RGBA")


def run_transform_image_3d_simple(
    image: Image,
    depth: np.ndarray,
    depth_near: float,
    depth_scale: float,
    hfov0_rad: float,
    tf_world_cam0: np.ndarray,
    hfov1_rad: float,
    tf_world_cam1: np.ndarray,
    min_mask: int,
    max_mask: int,
    mask_invert: bool,
):
    from_caminfo = CameraInfo(image.size, hfov0_rad, tf_world_cam0)
    to_caminfo = CameraInfo(image.size, hfov1_rad, tf_world_cam1)
    return run_transform_image_3d(
        image,
        depth,
        depth_near,
        depth_scale,
        from_caminfo,
        to_caminfo,
        min_mask,
        max_mask,
        mask_invert,
    )


def translation3d(x, y, z):
    return np.array(
        [
            [1, 0, 0, x],
            [0, 1, 0, y],
            [0, 0, 1, z],
            [0, 0, 0, 1],
        ]
    )


def rotation3d_x(angle):
    cs, sn = math.cos(angle), math.sin(angle)
    return np.array(
        [
            [1, 0, 0, 0],
            [0, cs, -sn, 0],
            [0, +sn, cs, 0],
            [0, 0, 0, 1],
        ]
    )


def rotation3d_y(angle):
    cs, sn = math.cos(angle), math.sin(angle)
    return np.array(
        [
            [cs, 0, +sn, 0],
            [0, 1, 0, 0],
            [-sn, 0, cs, 0],
            [0, 0, 0, 1],
        ]
    )


def rotation3d_z(angle):
    cs, sn = math.cos(angle), math.sin(angle)
    return np.array(
        [
            [cs, -sn, 0, 0],
            [+sn, cs, 0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1],
        ]
    )


def rotation3d_rpy(roll, pitch, yaw):
    # Diebel, J. (2006). Representing attitude: Euler angles, unit quaternions, and rotation vectors. Matrix, 58(15-16), 1-35.
    # (the paper uses inverse transformations to ours, i.e. transformations from world to body)
    # euler-1-2-3 scheme

    # transforms from body to world
    return rotation3d_z(yaw) @ rotation3d_y(pitch) @ rotation3d_x(roll)


def rpy_from_rotation3d(mat):
    # Diebel, J. (2006). Representing attitude: Euler angles, unit quaternions, and rotation vectors. Matrix, 58(15-16), 1-35.
    # (the paper uses inverse transformations to ours, i.e. transformations from world to body)
    # euler-1-2-3 scheme
    matT = mat.T
    roll = np.arctan2(matT[1, 2], matT[2, 2])
    pitch = -np.arcsin(matT[0, 2])
    yaw = np.arctan2(matT[0, 1], matT[0, 0])

    return np.array([roll, pitch, yaw])


def affine_inv(mat44):
    rot = mat44[:3, :3]
    trans = mat44[:3, 3]
    inv_rot = rot.T
    inv_trans = -inv_rot @ trans
    return pose3d(inv_rot, inv_trans)


def pose3d(rotation, translation):
    mat44 = np.zeros(shape=(4, 4), dtype=rotation.dtype)
    mat44[:3, :3] = rotation
    mat44[:3, 3] = translation
    return mat44


def pose3d_rpy(x, y, z, roll, pitch, yaw):
    """returns transformation matrix which transforms from pose to world"""
    return translation3d(x, y, z) @ rotation3d_rpy(roll, pitch, yaw)


================================================
FILE: scripts/sd_concept_library.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import st

# streamlit imports
import streamlit.components.v1 as components

# other imports

import os, math
from PIL import Image

# Temp imports
# from basicsr.utils.registry import ARCH_REGISTRY


# end of imports
# ---------------------------------------------------------------------------------------------------------------

# Init Vuejs component
_component_func = components.declare_component(
    "sd-concepts-browser", "./frontend/dists/concept-browser/dist"
)


def sdConceptsBrowser(concepts, key=None):
    component_value = _component_func(concepts=concepts, key=key, default="")
    return component_value


@st.experimental_memo(persist="disk", show_spinner=False, suppress_st_warning=True)
def getConceptsFromPath(page, conceptPerPage, searchText=""):
    # print("getConceptsFromPath", "page:", page, "conceptPerPage:", conceptPerPage, "searchText:", searchText)
    # get the path where the concepts are stored
    path = os.path.join(
        os.getcwd(), st.session_state["defaults"].general.sd_concepts_library_folder
    )
    acceptedExtensions = ("jpeg", "jpg", "png")
    concepts = []

    if os.path.exists(path):
        # List all folders (concepts) in the path
        folders = [f for f in os.listdir(path) if os.path.isdir(os.path.join(path, f))]
        filteredFolders = folders

        # Filter the folders by the search text
        if searchText != "":
            filteredFolders = [f for f in folders if searchText.lower() in f.lower()]
    else:
        filteredFolders = []

    conceptIndex = 1
    for folder in filteredFolders:
        # handle pagination
        if conceptIndex > (page * conceptPerPage):
            continue
        if conceptIndex <= ((page - 1) * conceptPerPage):
            conceptIndex += 1
            continue

        concept = {
            "name": folder,
            "token": "<" + folder + ">",
            "images": [],
            "type": "",
        }

        # type of concept is inside type_of_concept.txt
        typePath = os.path.join(path, folder, "type_of_concept.txt")
        binFile = os.path.join(path, folder, "learned_embeds.bin")

        # Continue if the concept is not valid or the download has failed (no type_of_concept.txt or no binFile)
        if not os.path.exists(typePath) or not os.path.exists(binFile):
            continue

        with open(typePath, "r") as f:
            concept["type"] = f.read()

        # List all files in the concept/concept_images folder
        files = [
            f
            for f in os.listdir(os.path.join(path, folder, "concept_images"))
            if os.path.isfile(os.path.join(path, folder, "concept_images", f))
        ]
        # Retrieve only the 4 first images
        for file in files:
            # Skip if we already have 4 images
            if len(concept["images"]) >= 4:
                break

            if file.endswith(acceptedExtensions):
                try:
                    # Add a copy of the image to avoid file locking
                    originalImage = Image.open(
                        os.path.join(path, folder, "concept_images", file)
                    )

                    # Maintain the aspect ratio (max 200x200)
                    resizedImage = originalImage.copy()
                    resizedImage.thumbnail((200, 200), Image.Resampling.LANCZOS)

                    # concept["images"].append(resizedImage)

                    concept["images"].append(imageToBase64(resizedImage))
                    # Close original image
                    originalImage.close()
                except:
                    print(
                        "Error while loading image",
                        file,
                        "in concept",
                        folder,
                        "(The file may be corrupted). Skipping it.",
                    )

        concepts.append(concept)
        conceptIndex += 1
    # print all concepts name
    # print("Results:", [c["name"] for c in concepts])
    return concepts


@st.cache(
    persist=True,
    allow_output_mutation=True,
    show_spinner=False,
    suppress_st_warning=True,
)
def imageToBase64(image):
    import io
    import base64

    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return img_str


@st.experimental_memo(persist="disk", show_spinner=False, suppress_st_warning=True)
def getTotalNumberOfConcepts(searchText=""):
    # get the path where the concepts are stored
    path = os.path.join(
        os.getcwd(), st.session_state["defaults"].general.sd_concepts_library_folder
    )

    if os.path.exists(path):
        # List all folders (concepts) in the path
        folders = [f for f in os.listdir(path) if os.path.isdir(os.path.join(path, f))]
        filteredFolders = folders

        # Filter the folders by the search text
        if searchText != "":
            filteredFolders = [f for f in folders if searchText.lower() in f.lower()]
    else:
        filteredFolders = []
    return len(filteredFolders)


def layout():
    # 2 tabs, one for Concept Library and one for the Download Manager
    tab_library, tab_downloader = st.tabs(["Library", "Download Manager"])

    # Concept Library
    with tab_library:
        downloaded_concepts_count = getTotalNumberOfConcepts()
        concepts_per_page = st.session_state[
            "defaults"
        ].concepts_library.concepts_per_page

        if "results" not in st.session_state:
            st.session_state["results"] = getConceptsFromPath(1, concepts_per_page, "")

        # Pagination controls
        if "cl_current_page" not in st.session_state:
            st.session_state["cl_current_page"] = 1

        # Search
        if "cl_search_text" not in st.session_state:
            st.session_state["cl_search_text"] = ""

        if "cl_search_results_count" not in st.session_state:
            st.session_state["cl_search_results_count"] = downloaded_concepts_count

        # Search bar
        _search_col, _refresh_col = st.columns([10, 2])
        with _search_col:
            search_text_input = st.text_input(
                "Search",
                "",
                placeholder=f"Search for a concept ({downloaded_concepts_count} available)",
                label_visibility="hidden",
            )
            if search_text_input != st.session_state["cl_search_text"]:
                # Search text has changed
                st.session_state["cl_search_text"] = search_text_input
                st.session_state["cl_current_page"] = 1
                st.session_state["cl_search_results_count"] = getTotalNumberOfConcepts(
                    st.session_state["cl_search_text"]
                )
                st.session_state["results"] = getConceptsFromPath(
                    1, concepts_per_page, st.session_state["cl_search_text"]
                )

        with _refresh_col:
            # Super weird fix to align the refresh button with the search bar ( Please streamlit, add css support..  )
            _refresh_col.write("")
            _refresh_col.write("")
            if st.button(
                "Refresh concepts",
                key="refresh_concepts",
                help="Refresh the concepts folders. Use this if you have added new concepts manually or deleted some.",
            ):
                getTotalNumberOfConcepts.clear()
                getConceptsFromPath.clear()
                st.experimental_rerun()

        # Show results
        results_empty = st.empty()

        # Pagination
        pagination_empty = st.empty()

        # Layouts
        with pagination_empty:
            with st.container():
                if len(st.session_state["results"]) > 0:
                    last_page = math.ceil(
                        st.session_state["cl_search_results_count"] / concepts_per_page
                    )
                    (
                        _1,
                        _2,
                        _3,
                        _4,
                        _previous_page,
                        _current_page,
                        _next_page,
                        _9,
                        _10,
                        _11,
                        _12,
                    ) = st.columns([1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1])

                    # Previous page
                    with _previous_page:
                        if st.button("Previous", key="cl_previous_page"):
                            st.session_state["cl_current_page"] -= 1
                            if st.session_state["cl_current_page"] <= 0:
                                st.session_state["cl_current_page"] = last_page
                            st.session_state["results"] = getConceptsFromPath(
                                st.session_state["cl_current_page"],
                                concepts_per_page,
                                st.session_state["cl_search_text"],
                            )

                    # Current page
                    with _current_page:
                        _current_page_container = st.empty()

                    # Next page
                    with _next_page:
                        if st.button("Next", key="cl_next_page"):
                            st.session_state["cl_current_page"] += 1
                            if st.session_state["cl_current_page"] > last_page:
                                st.session_state["cl_current_page"] = 1
                            st.session_state["results"] = getConceptsFromPath(
                                st.session_state["cl_current_page"],
                                concepts_per_page,
                                st.session_state["cl_search_text"],
                            )

                    # Current page
                    with _current_page_container:
                        st.markdown(
                            f'<p style="text-align: center">Page {st.session_state["cl_current_page"]} of {last_page}</p>',
                            unsafe_allow_html=True,
                        )
                        # st.write(f"Page {st.session_state['cl_current_page']} of {last_page}", key="cl_current_page")

        with results_empty:
            with st.container():
                if downloaded_concepts_count == 0:
                    st.write("You don't have any concepts in your library ")
                    st.markdown(
                        "To add concepts to your library, download some from the [sd-concepts-library](https://github.com/Sygil-Dev/sd-concepts-library) \
						repository and save the content of `sd-concepts-library` into ```./models/custom/sd-concepts-library``` or just create your own concepts :wink:.",
                        unsafe_allow_html=False,
                    )
                else:
                    if len(st.session_state["results"]) == 0:
                        st.write(
                            "No concept found in the library matching your search: "
                            + st.session_state["cl_search_text"]
                        )
                    else:
                        # display number of results
                        if st.session_state["cl_search_text"]:
                            st.write(
                                f"Found {st.session_state['cl_search_results_count']} {'concepts' if st.session_state['cl_search_results_count'] > 1 else 'concept' } matching your search"
                            )
                        sdConceptsBrowser(st.session_state["results"], key="results")

    with tab_downloader:
        st.write("Not implemented yet")

    return False


================================================
FILE: scripts/sd_concepts_library_downloader.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

import os, subprocess, shutil
from huggingface_hub import HfApi
from git import Repo, RemoteProgress


class CloneProgress(RemoteProgress):
    def update(self, op_code, cur_count, max_count=None, message=""):
        if message:
            print(message)


api = HfApi()
models_list = api.list_models(author="sd-concepts-library", sort="likes", direction=-1)
models = []

print("Downloading the sd concept library from the huggingface site.")
for model in models_list:
    model_content = {}
    model_id = model.id
    url = f"https://huggingface.co/{model_id}"
    try:
        if not os.path.exists(os.path.join("../models/custom", model_id)):
            subprocess.run(["git", "lfs", "install"], stdout=subprocess.DEVNULL)
            Repo.clone_from(
                url,
                os.path.join("../models/custom", model_id),
                progress=CloneProgress(),
            )
        # else:
        # repo = Repo(os.path.join("../models/custom", model_id))
        # repo.git.stash('save')
        # repo.git.pull()

        subprocess.run(
            ["git", "lfs", "uninstall"], stdout=subprocess.DEVNULL
        )  # uninstall LFS
        os.remove(
            os.path.join("../models/custom", model_id, ".gitattributes")
        ) if os.path.exists(
            os.path.join("../models/custom", model_id, ".gitattributes")
        ) else None  # remove the .gitattributes so files don't use LFS
        subprocess.run(
            ["rm", "-rf", os.path.join("../models/custom", model_id, ".git")]
        ) if os.path.exists(
            os.path.join("../models/custom", model_id, ".git")
        ) else None  # remove all the .git folders as we dont need them.
        # get the folder size and delete it if its larger than 100mb
        size = 0
        for ele in os.scandir(
            os.path.join("../models/custom", model_id)
        ):  # get folder size
            size += os.stat(ele).st_size
        if size > 100000000:  # if the folder is larger than 100mb delete it.
            shutil.rmtree(
                os.path.join("../models/custom", model_id), ignore_errors=True
            )
    except:
        pass


================================================
FILE: scripts/sd_utils/__init__.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
# from webui_streamlit import st
import hydralit as st
import hydralit_components as hc

# streamlit imports
from streamlit.runtime.scriptrunner import StopException

# from streamlit.runtime.scriptrunner import script_run_context

# streamlit components section
from streamlit_server_state import server_state, server_state_lock, no_rerun
import streamlit_nested_layout


# from streamlitextras.threader import lock, trigger_rerun, \
# streamlit_thread, get_thread, \
# last_trigger_time

# other imports

import warnings
import json

import cv2
import os, sys, re, random, datetime, time, math, toml
import gc
from PIL import Image, ImageFont, ImageDraw, ImageFilter
from PIL.PngImagePlugin import PngInfo
from scipy import integrate
import torch
from torchdiffeq import odeint
import k_diffusion as K
import math
import mimetypes
import numpy as np
import pynvml
import threading
import torch, torchvision
from torch import autocast
from torchvision import transforms
import torch.nn as nn
from omegaconf import OmegaConf
import yaml
from pathlib import Path
from contextlib import nullcontext
from einops import rearrange, repeat
from ldm.util import instantiate_from_config
from retry import retry
from slugify import slugify
import skimage
import piexif
import piexif.helper
from tqdm import trange
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import ismap

# from abc import ABC, abstractmethod
from packaging import version
from pathlib import Path
from huggingface_hub import hf_hub_download
import shutup

# import librosa
from logger import logger

# from loguru import logger

try:
    from realesrgan import RealESRGANer
    from basicsr.archs.rrdbnet_arch import RRDBNet
except ImportError:
    logger.error(
        "You tried to import realesrgan without having it installed properly. To install Real-ESRGAN, run:\n\n"
        "pip install realesrgan"
    )

# Temp imports
# from basicsr.utils.registry import ARCH_REGISTRY


# end of imports
# ---------------------------------------------------------------------------------------------------------------

# remove all the annoying python warnings.
shutup.please()

# the following lines should help fixing an issue with nvidia 16xx cards.
if "defaults" in st.session_state:
    if st.session_state["defaults"].general.use_cudnn:
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.enabled = True

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging

    logging.set_verbosity_error()
except:
    pass

# disable diffusers telemetry
os.environ["DISABLE_TELEMETRY"] = "YES"

# remove some annoying deprecation warnings that show every now and then.
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)

# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init()
mimetypes.add_type("application/javascript", ".js")

# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
opt_f = 8

# The model manager loads and unloads the SD models and has features to download them or find their location
# model_manager = ModelManager()


def load_configs():
    if "defaults" not in st.session_state:
        st.session_state["defaults"] = {}

    st.session_state["defaults"] = OmegaConf.load("configs/webui/webui_streamlit.yaml")

    if os.path.exists("configs/webui/userconfig_streamlit.yaml"):
        user_defaults = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")

        if "version" in user_defaults.general:
            if version.parse(user_defaults.general.version) < version.parse(
                st.session_state["defaults"].general.version
            ):
                logger.error(
                    "The version of the user config file is older than the version on the defaults config file. "
                    "This means there were big changes we made on the config."
                    "We are removing this file and recreating it from the defaults in order to make sure things work properly."
                )
                os.remove("configs/webui/userconfig_streamlit.yaml")
                st.experimental_rerun()
        else:
            logger.error(
                "The version of the user config file is older than the version on the defaults config file. "
                "This means there were big changes we made on the config."
                "We are removing this file and recreating it from the defaults in order to make sure things work properly."
            )
            os.remove("configs/webui/userconfig_streamlit.yaml")
            st.experimental_rerun()

        try:
            st.session_state["defaults"] = OmegaConf.merge(
                st.session_state["defaults"], user_defaults
            )
        except KeyError:
            st.experimental_rerun()
    else:
        OmegaConf.save(
            config=st.session_state.defaults,
            f="configs/webui/userconfig_streamlit.yaml",
        )
        loaded = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")
        assert st.session_state.defaults == loaded

    if os.path.exists(".streamlit/config.toml"):
        st.session_state["streamlit_config"] = toml.load(".streamlit/config.toml")

    if st.session_state["defaults"].daisi_app.running_on_daisi_io:
        if os.path.exists("scripts/modeldownload.py"):
            import modeldownload

            modeldownload.updateModels()

    if "keep_all_models_loaded" in st.session_state.defaults.general:
        with server_state_lock["keep_all_models_loaded"]:
            server_state["keep_all_models_loaded"] = st.session_state[
                "defaults"
            ].general.keep_all_models_loaded
    else:
        st.session_state["defaults"].general.keep_all_models_loaded = False
        with server_state_lock["keep_all_models_loaded"]:
            server_state["keep_all_models_loaded"] = st.session_state[
                "defaults"
            ].general.keep_all_models_loaded


load_configs()

#
# if st.session_state["defaults"].debug.enable_hydralit:
# navbar_theme = {'txc_inactive': '#FFFFFF','menu_background':'#0e1117','txc_active':'black','option_active':'red'}
# app = st.HydraApp(title='Stable Diffusion WebUI', favicon="", use_cookie_cache=False, sidebar_state="expanded", layout="wide", navbar_theme=navbar_theme,
# hide_streamlit_markers=False, allow_url_nav=True , clear_cross_app_sessions=False, use_loader=False)
# else:
# app = None

#
grid_format = st.session_state["defaults"].general.save_format
grid_lossless = False
grid_quality = st.session_state["defaults"].general.grid_quality
if grid_format == "png":
    grid_ext = "png"
    grid_format = "png"
elif grid_format in ["jpg", "jpeg"]:
    grid_quality = int(grid_format) if len(grid_format) > 1 else 100
    grid_ext = "jpg"
    grid_format = "jpeg"
elif grid_format[0] == "webp":
    grid_quality = int(grid_format) if len(grid_format) > 1 else 100
    grid_ext = "webp"
    grid_format = "webp"
    if grid_quality < 0:  # e.g. webp:-100 for lossless mode
        grid_lossless = True
        grid_quality = abs(grid_quality)

#
save_format = st.session_state["defaults"].general.save_format
save_lossless = False
save_quality = 100
if save_format == "png":
    save_ext = "png"
    save_format = "png"
elif save_format in ["jpg", "jpeg"]:
    save_quality = int(save_format) if len(save_format) > 1 else 100
    save_ext = "jpg"
    save_format = "jpeg"
elif save_format == "webp":
    save_quality = int(save_format) if len(save_format) > 1 else 100
    save_ext = "webp"
    save_format = "webp"
    if save_quality < 0:  # e.g. webp:-100 for lossless mode
        save_lossless = True
        save_quality = abs(save_quality)

# this should force GFPGAN and RealESRGAN onto the selected gpu as well
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"  # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = str(st.session_state["defaults"].general.gpu)


# functions to load css locally OR remotely starts here. Options exist for future flexibility. Called as st.markdown with unsafe_allow_html as css injection
# TODO, maybe look into async loading the file especially for remote fetching
def local_css(file_name):
    with open(file_name) as f:
        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)


def remote_css(url):
    st.markdown(f'<link href="{url}" rel="stylesheet">', unsafe_allow_html=True)


def load_css(isLocal, nameOrURL):
    if isLocal:
        local_css(nameOrURL)
    else:
        remote_css(nameOrURL)


def set_page_title(title):
    """
    Simple function to allows us to change the title dynamically.
    Normally you can use `st.set_page_config` to change the title but it can only be used once per app.
    """

    st.sidebar.markdown(
        unsafe_allow_html=True,
        body=f"""
                            <iframe height=0 srcdoc="<script>
                            const title = window.parent.document.querySelector('title') \

                            const oldObserver = window.parent.titleObserver
                            if (oldObserver) {{
                            oldObserver.disconnect()
                            }} \

                            const newObserver = new MutationObserver(function(mutations) {{
                            const target = mutations[0].target
                            if (target.text !== '{title}') {{
                            target.text = '{title}'
                            }}
                            }}) \

                            newObserver.observe(title, {{ childList: true }})
                            window.parent.titleObserver = newObserver \

                            title.text = '{title}'
                            </script>" />
                            """,
    )


def make_grid(n_items=5, n_cols=5):
    # Compute number of rows
    n_rows = 1 + n_items // int(n_cols)

    # Create rows
    rows = [st.container() for _ in range(n_rows)]

    # Create columns in each row
    cols_per_row = [r.columns(n_cols) for r in rows]
    cols = [column for row in cols_per_row for column in row]

    return cols


def merge(file1, file2, out, weight):
    if not (file1.endswith(".ckpt")):
        file1 += ".ckpt"
    if not (file2.endswith(".ckpt")):
        file2 += ".ckpt"
    if not (out.endswith(".ckpt")):
        out += ".ckpt"
    try:
        # Load Models
        model_0 = torch.load(file1)
        model_1 = torch.load(file2)
        theta_0 = model_0["state_dict"]
        theta_1 = model_1["state_dict"]
        alpha = (weight) / 100
        for key in theta_0.keys():
            if "model" in key and key in theta_1:
                theta_0[key] = (alpha) * theta_0[key] + (1 - alpha) * theta_1[key]
        logger.info("RUNNING...\n(STAGE 2)")
        for key in theta_1.keys():
            if "model" in key and key not in theta_0:
                theta_0[key] = theta_1[key]
        torch.save(model_0, out)
    except:
        logger.error("Error in merging")


def human_readable_size(size, decimal_places=3):
    """Return a human readable size from bytes."""
    for unit in ["B", "KB", "MB", "GB", "TB"]:
        if size < 1024.0:
            break
        size /= 1024.0
    return f"{size:.{decimal_places}f}{unit}"


def load_models(
    use_LDSR=False,
    LDSR_model="model",
    use_GFPGAN=False,
    GFPGAN_model="GFPGANv1.4",
    use_RealESRGAN=False,
    RealESRGAN_model="RealESRGAN_x4plus",
    CustomModel_available=False,
    custom_model="Stable Diffusion v1.5",
):
    """Load the different models. We also reuse the models that are already in memory to speed things up instead of loading them again."""

    # model_manager.init()

    logger.info("Loading models.")

    if "progress_bar_text" in st.session_state:
        st.session_state["progress_bar_text"].text("")

    # check what models we want to use and if the they are already loaded.
    with server_state_lock["LDSR"]:
        if use_LDSR:
            if "LDSR" in server_state and server_state["LDSR"].name == LDSR_model:
                logger.info("LDSR already loaded")
            else:
                if "LDSR" in server_state:
                    del server_state["LDSR"]

                # Load GFPGAN
                if os.path.exists(st.session_state["defaults"].general.LDSR_dir):
                    try:
                        server_state["LDSR"] = load_LDSR(model_name=LDSR_model)
                        logger.info("Loaded LDSR")
                    except Exception:
                        import traceback

                        logger.error("Error loading LDSR:", file=sys.stderr)
                        logger.error(traceback.format_exc(), file=sys.stderr)
        else:
            if "LDSR" in server_state and not server_state["keep_all_models_loaded"]:
                logger.debug(
                    "LDSR was in memory but we won't use it. Removing to save VRAM."
                )
                del server_state["LDSR"]

    with server_state_lock["GFPGAN"]:
        if use_GFPGAN:
            if "GFPGAN" in server_state and server_state["GFPGAN"].name == GFPGAN_model:
                logger.info("GFPGAN already loaded")
            else:
                if "GFPGAN" in server_state:
                    del server_state["GFPGAN"]

                # Load GFPGAN
                if os.path.exists(st.session_state["defaults"].general.GFPGAN_dir):
                    try:
                        server_state["GFPGAN"] = load_GFPGAN(GFPGAN_model)
                        logger.info(f"Loaded GFPGAN: {GFPGAN_model}")
                    except Exception:
                        import traceback

                        logger.error("Error loading GFPGAN:", file=sys.stderr)
                        logger.error(traceback.format_exc(), file=sys.stderr)
        else:
            if "GFPGAN" in server_state and not server_state["keep_all_models_loaded"]:
                del server_state["GFPGAN"]

    with server_state_lock["RealESRGAN"]:
        if use_RealESRGAN:
            if (
                "RealESRGAN" in server_state
                and server_state["RealESRGAN"].model.name == RealESRGAN_model
            ):
                logger.info("RealESRGAN already loaded")
            else:
                # Load RealESRGAN
                try:
                    # We first remove the variable in case it has something there,
                    # some errors can load the model incorrectly and leave things in memory.
                    del server_state["RealESRGAN"]
                except KeyError as e:
                    logger.error(e)
                    pass

                if os.path.exists(st.session_state["defaults"].general.RealESRGAN_dir):
                    # st.session_state is used for keeping the models in memory across multiple pages or runs.
                    server_state["RealESRGAN"] = load_RealESRGAN(RealESRGAN_model)
                    logger.info(
                        "Loaded RealESRGAN with model "
                        + server_state["RealESRGAN"].model.name
                    )

        else:
            if (
                "RealESRGAN" in server_state
                and not server_state["keep_all_models_loaded"]
            ):
                del server_state["RealESRGAN"]

    with server_state_lock["model"], server_state_lock["modelCS"], server_state_lock[
        "modelFS"
    ], server_state_lock["loaded_model"]:
        if "model" in server_state:
            if "model" in server_state and server_state["loaded_model"] == custom_model:
                # if the float16 or no_half options have changed since the last time the model was loaded then we need to reload the model.
                if (
                    (
                        "float16" in server_state
                        and server_state["float16"]
                        != st.session_state["defaults"].general.use_float16
                    )
                    or (
                        "no_half" in server_state
                        and server_state["no_half"]
                        != st.session_state["defaults"].general.no_half
                    )
                    or (
                        "optimized" in server_state
                        and server_state["optimized"]
                        != st.session_state["defaults"].general.optimized
                    )
                ):
                    logger.info(
                        "Model options changed, deleting the model from memory."
                    )

                    del server_state["float16"]
                    del server_state["no_half"]

                    del server_state["model"]
                    del server_state["modelCS"]
                    del server_state["modelFS"]
                    del server_state["loaded_model"]

                    del server_state["optimized"]

                    server_state["float16"] = st.session_state[
                        "defaults"
                    ].general.use_float16
                    server_state["no_half"] = st.session_state[
                        "defaults"
                    ].general.no_half
                    server_state["optimized"] = st.session_state[
                        "defaults"
                    ].general.optimized

                    load_models(
                        use_LDSR=st.session_state["use_LDSR"],
                        LDSR_model=st.session_state["LDSR_model"],
                        use_GFPGAN=st.session_state["use_GFPGAN"],
                        GFPGAN_model=st.session_state["GFPGAN_model"],
                        use_RealESRGAN=st.session_state["use_RealESRGAN"],
                        RealESRGAN_model=st.session_state["RealESRGAN_model"],
                        CustomModel_available=server_state["CustomModel_available"],
                        custom_model=st.session_state["custom_model"],
                    )
                else:
                    logger.info("Model already loaded")

                return
            else:
                try:
                    del server_state["model"]
                    del server_state["modelCS"]
                    del server_state["modelFS"]
                    del server_state["loaded_model"]

                except KeyError as e:
                    logger.error(e)
                    pass

        # if the model from txt2vid is in memory we need to remove it to improve performance.
        with server_state_lock["pipe"]:
            if "pipe" in server_state and not server_state["keep_all_models_loaded"]:
                del server_state["pipe"]

        if (
            "textual_inversion" in st.session_state
            and not server_state["keep_all_models_loaded"]
        ):
            del st.session_state["textual_inversion"]

        # At this point the model is either
        # not loaded yet or have been deleted from memory:
        # load new model into memory
        server_state["custom_model"] = custom_model

        config, device, model, modelCS, modelFS = load_sd_model(custom_model)

        server_state["device"] = device
        server_state["model"] = model

        server_state["modelCS"] = modelCS
        server_state["modelFS"] = modelFS
        server_state["loaded_model"] = custom_model

        server_state["float16"] = st.session_state["defaults"].general.use_float16
        server_state["no_half"] = st.session_state["defaults"].general.no_half
        server_state["optimized"] = st.session_state["defaults"].general.optimized

        # trying to disable multiprocessing as it makes it so streamlit cant stop when the
        # model is loaded in memory and you need to kill the process sometimes.

        try:
            server_state["model"].args.use_multiprocessing_for_evaluation = False
        except AttributeError:
            pass

        if st.session_state.defaults.general.enable_attention_slicing:
            server_state["model"].enable_attention_slicing()

        if st.session_state.defaults.general.enable_minimal_memory_usage:
            server_state["model"].enable_minimal_memory_usage()

        logger.info("Model loaded.")

    return True


def load_model_from_config(config, ckpt, verbose=False):
    logger.info(f"Loading model from {ckpt}")

    try:
        pl_sd = torch.load(ckpt, map_location="cpu")
        if "global_step" in pl_sd:
            logger.info(f"Global Step: {pl_sd['global_step']}")
        sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd
        try:
            model = instantiate_from_config(config.model, personalization_config="")
        except TypeError:
            model = instantiate_from_config(config.model)
        m, u = model.load_state_dict(sd, strict=False)
        if len(m) > 0 and verbose:
            logger.info("missing keys:")
            logger.info(m)
        if len(u) > 0 and verbose:
            logger.info("unexpected keys:")
            logger.info(u)

        model.cuda()
        model.eval()

        return model

    except FileNotFoundError:
        if "progress_bar_text" in st.session_state:
            st.session_state["progress_bar_text"].error(
                "You need to download the Stable Diffusion model in order to use the UI. Use the Model Manager page in order to download the model."
            )

        raise FileNotFoundError(
            "You need to download the Stable Diffusion model in order to use the UI. Use the Model Manager page in order to download the model."
        )


def load_sd_from_config(ckpt, verbose=False):
    logger.info(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        logger.info(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    return sd


class MemUsageMonitor(threading.Thread):
    stop_flag = False
    max_usage = 0
    total = -1

    def __init__(self, name):
        threading.Thread.__init__(self)
        self.name = name

    def run(self):
        try:
            pynvml.nvmlInit()
        except:
            logger.debug(
                f"[{self.name}] Unable to initialize NVIDIA management. No memory stats. \n"
            )
            return
        logger.info(f"[{self.name}] Recording memory usage...\n")
        # Missing context
        # handle = pynvml.nvmlDeviceGetHandleByIndex(st.session_state['defaults'].general.gpu)
        handle = pynvml.nvmlDeviceGetHandleByIndex(0)
        self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
        while not self.stop_flag:
            m = pynvml.nvmlDeviceGetMemoryInfo(handle)
            self.max_usage = max(self.max_usage, m.used)
            # logger.info(self.max_usage)
            time.sleep(0.1)
        logger.info(f"[{self.name}] Stopped recording.\n")
        pynvml.nvmlShutdown()

    def read(self):
        return self.max_usage, self.total

    def stop(self):
        self.stop_flag = True

    def read_and_stop(self):
        self.stop_flag = True
        return self.max_usage, self.total


class CFGMaskedDenoiser(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.inner_model = model

    def forward(self, x, sigma, uncond, cond, cond_scale, mask, x0, xi):
        x_in = x
        x_in = torch.cat([x_in] * 2)
        sigma_in = torch.cat([sigma] * 2)
        cond_in = torch.cat([uncond, cond])
        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
        denoised = uncond + (cond - uncond) * cond_scale

        if mask is not None:
            assert x0 is not None
            img_orig = x0
            mask_inv = 1.0 - mask
            denoised = (img_orig * mask_inv) + (mask * denoised)

        return denoised


class CFGDenoiser(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.inner_model = model

    def forward(self, x, sigma, uncond, cond, cond_scale):
        x_in = torch.cat([x] * 2)
        sigma_in = torch.cat([sigma] * 2)
        cond_in = torch.cat([uncond, cond])
        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
        return uncond + (cond - uncond) * cond_scale


def append_zero(x):
    return torch.cat([x, x.new_zeros([1])])


def append_dims(x, target_dims):
    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
    dims_to_append = target_dims - x.ndim
    if dims_to_append < 0:
        raise ValueError(
            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
        )
    return x[(...,) + (None,) * dims_to_append]


def get_sigmas_karras(n, sigma_min, sigma_max, rho=7.0, device="cpu"):
    """Constructs the noise schedule of Karras et al. (2022)."""
    ramp = torch.linspace(0, 1, n)
    min_inv_rho = sigma_min ** (1 / rho)
    max_inv_rho = sigma_max ** (1 / rho)
    sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return append_zero(sigmas).to(device)


#
# helper fft routines that keep ortho normalization and auto-shift before and after fft
def _fft2(data):
    if data.ndim > 2:  # has channels
        out_fft = np.zeros(
            (data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128
        )
        for c in range(data.shape[2]):
            c_data = data[:, :, c]
            out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
            out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
    else:  # one channel
        out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
        out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
        out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])

    return out_fft


def _ifft2(data):
    if data.ndim > 2:  # has channels
        out_ifft = np.zeros(
            (data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128
        )
        for c in range(data.shape[2]):
            c_data = data[:, :, c]
            out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
            out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
    else:  # one channel
        out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
        out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
        out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])

    return out_ifft


def _get_gaussian_window(width, height, std=3.14, mode=0):
    window_scale_x = float(width / min(width, height))
    window_scale_y = float(height / min(width, height))

    window = np.zeros((width, height))
    x = (np.arange(width) / width * 2.0 - 1.0) * window_scale_x
    for y in range(height):
        fy = (y / height * 2.0 - 1.0) * window_scale_y
        if mode == 0:
            window[:, y] = np.exp(-(x**2 + fy**2) * std)
        else:
            window[:, y] = (1 / ((x**2 + 1.0) * (fy**2 + 1.0))) ** (
                std / 3.14
            )  # hey wait a minute that's not gaussian

    return window


def _get_masked_window_rgb(np_mask_grey, hardness=1.0):
    np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
    if hardness != 1.0:
        hardened = np_mask_grey[:] ** hardness
    else:
        hardened = np_mask_grey[:]
    for c in range(3):
        np_mask_rgb[:, :, c] = hardened[:]
    return np_mask_rgb


def get_matched_noise(_np_src_image, np_mask_rgb, noise_q, color_variation):
    """
    Explanation:
    Getting good results in/out-painting with stable diffusion can be challenging.
    Although there are simpler effective solutions for in-painting, out-painting can be especially challenging because there is no color data
    in the masked area to help prompt the generator. Ideally, even for in-painting we'd like work effectively without that data as well.
    Provided here is my take on a potential solution to this problem.

    By taking a fourier transform of the masked src img we get a function that tells us the presence and orientation of each feature scale in the unmasked src.
    Shaping the init/seed noise for in/outpainting to the same distribution of feature scales, orientations, and positions increases output coherence
    by helping keep features aligned. This technique is applicable to any continuous generation task such as audio or video, each of which can
    be conceptualized as a series of out-painting steps where the last half of the input "frame" is erased. For multi-channel data such as color
    or stereo sound the "color tone" or histogram of the seed noise can be matched to improve quality (using scikit-image currently)
    This method is quite robust and has the added benefit of being fast independently of the size of the out-painted area.
    The effects of this method include things like helping the generator integrate the pre-existing view distance and camera angle.

    Carefully managing color and brightness with histogram matching is also essential to achieving good coherence.

    noise_q controls the exponent in the fall-off of the distribution can be any positive number, lower values means higher detail (range > 0, default 1.)
    color_variation controls how much freedom is allowed for the colors/palette of the out-painted area (range 0..1, default 0.01)
    This code is provided as is under the Unlicense (https://unlicense.org/)
    Although you have no obligation to do so, if you found this code helpful please find it in your heart to credit me [parlance-zz].

    Questions or comments can be sent to parlance@fifth-harmonic.com (https://github.com/parlance-zz/)
    This code is part of a new branch of a discord bot I am working on integrating with diffusers (https://github.com/parlance-zz/g-diffuser-bot)

    """

    global DEBUG_MODE
    global TMP_ROOT_PATH

    width = _np_src_image.shape[0]
    height = _np_src_image.shape[1]
    num_channels = _np_src_image.shape[2]

    np_src_image = _np_src_image[:] * (1.0 - np_mask_rgb)
    np_mask_grey = np.sum(np_mask_rgb, axis=2) / 3.0
    (np.sum(np_src_image, axis=2) / 3.0)
    np.ones((width, height), dtype=bool)
    img_mask = np_mask_grey > 1e-6
    ref_mask = np_mask_grey < 1e-3

    windowed_image = _np_src_image * (1.0 - _get_masked_window_rgb(np_mask_grey))
    windowed_image /= np.max(windowed_image)
    windowed_image += (
        np.average(_np_src_image) * np_mask_rgb
    )  # / (1.-np.average(np_mask_rgb))  # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
    # windowed_image += np.average(_np_src_image) * (np_mask_rgb * (1.- np_mask_rgb)) / (1.-np.average(np_mask_rgb)) # compensate for darkening across the mask transition area
    # _save_debug_img(windowed_image, "windowed_src_img")

    src_fft = _fft2(windowed_image)  # get feature statistics from masked src img
    src_dist = np.absolute(src_fft)
    src_phase = src_fft / src_dist
    # _save_debug_img(src_dist, "windowed_src_dist")

    noise_window = _get_gaussian_window(
        width, height, mode=1
    )  # start with simple gaussian noise
    noise_rgb = np.random.random_sample((width, height, num_channels))
    noise_grey = np.sum(noise_rgb, axis=2) / 3.0
    noise_rgb *= color_variation  # the colorfulness of the starting noise is blended to greyscale with a parameter
    for c in range(num_channels):
        noise_rgb[:, :, c] += (1.0 - color_variation) * noise_grey

    noise_fft = _fft2(noise_rgb)
    for c in range(num_channels):
        noise_fft[:, :, c] *= noise_window
    noise_rgb = np.real(_ifft2(noise_fft))
    shaped_noise_fft = _fft2(noise_rgb)
    shaped_noise_fft[:, :, :] = (
        np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist**noise_q) * src_phase
    )  # perform the actual shaping

    brightness_variation = 0.0  # color_variation # todo: temporarily tieing brightness variation to color variation for now
    contrast_adjusted_np_src = (
        _np_src_image[:] * (brightness_variation + 1.0) - brightness_variation * 2.0
    )

    # scikit-image is used for histogram matching, very convenient!
    shaped_noise = np.real(_ifft2(shaped_noise_fft))
    shaped_noise -= np.min(shaped_noise)
    shaped_noise /= np.max(shaped_noise)
    shaped_noise[img_mask, :] = skimage.exposure.match_histograms(
        shaped_noise[img_mask, :] ** 1.0,
        contrast_adjusted_np_src[ref_mask, :],
        channel_axis=1,
    )
    shaped_noise = _np_src_image[:] * (1.0 - np_mask_rgb) + shaped_noise * np_mask_rgb
    # _save_debug_img(shaped_noise, "shaped_noise")

    matched_noise = np.zeros((width, height, num_channels))
    matched_noise = shaped_noise[:]
    # matched_noise[all_mask,:] = skimage.exposure.match_histograms(shaped_noise[all_mask,:], _np_src_image[ref_mask,:], channel_axis=1)
    # matched_noise = _np_src_image[:] * (1. - np_mask_rgb) + matched_noise * np_mask_rgb

    # _save_debug_img(matched_noise, "matched_noise")

    """
    todo:
    color_variation doesnt have to be a single number, the overall color tone of the out-painted area could be param controlled
    """

    return np.clip(matched_noise, 0.0, 1.0)


#
def find_noise_for_image(
    model,
    device,
    init_image,
    prompt,
    steps=200,
    cond_scale=2.0,
    verbose=False,
    normalize=False,
    generation_callback=None,
):
    image = np.array(init_image).astype(np.float32) / 255.0
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    image = 2.0 * image - 1.0
    image = image.to(device)
    x = model.get_first_stage_encoding(model.encode_first_stage(image))

    uncond = model.get_learned_conditioning([""])
    cond = model.get_learned_conditioning([prompt])

    s_in = x.new_ones([x.shape[0]])
    dnw = K.external.CompVisDenoiser(model)
    sigmas = dnw.get_sigmas(steps).flip(0)

    if verbose:
        logger.info(sigmas)

    for i in trange(1, len(sigmas)):
        x_in = torch.cat([x] * 2)
        sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
        cond_in = torch.cat([uncond, cond])

        c_out, c_in = [
            K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)
        ]

        if i == 1:
            t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
        else:
            t = dnw.sigma_to_t(sigma_in)

        eps = model.apply_model(x_in * c_in, t, cond=cond_in)
        denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)

        denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cond_scale

        if i == 1:
            d = (x - denoised) / (2 * sigmas[i])
        else:
            d = (x - denoised) / sigmas[i - 1]

        if generation_callback is not None:
            generation_callback(x, i)

        dt = sigmas[i] - sigmas[i - 1]
        x = x + d * dt

    return x / sigmas[-1]


#
def folder_picker(
    label="Select:",
    value="",
    help="",
    folder_button_label="Select",
    folder_button_help="",
    folder_button_key="",
):
    """A folder picker that has a text_input field next to it and a button to select the folder.
    Returns the text_input field with the folder path."""
    import tkinter as tk
    from tkinter import filedialog

    # Set up tkinter
    root = tk.Tk()
    root.withdraw()

    # Make folder picker dialog appear on top of other windows
    root.wm_attributes("-topmost", 1)

    col1, col2 = st.columns([2, 1], gap="small")

    with col1:
        dirname = st.empty()
    with col2:
        st.write("")
        st.write("")
        folder_picker = st.empty()

    # Folder picker button
    # st.title('Folder Picker')
    # st.write('Please select a folder:')

    # Create a label and add a random number of invisible characters
    # to it so no two buttons inside a form are the same.
    # folder_button_label = ''.join(random.choice(f"{folder_button_label}") for _ in range(5))
    folder_button_label = f"{str(folder_button_label)}{'‎' * random.randint(1, 500)}"
    clicked = folder_button_key + "‎" * random.randint(5, 500)

    # try:
    # clicked = folder_picker.button(folder_button_label, help=folder_button_help, key=folder_button_key)
    # except StreamlitAPIException:
    clicked = folder_picker.form_submit_button(
        folder_button_label, help=folder_button_help
    )

    if clicked:
        dirname = dirname.text_input(
            label, filedialog.askdirectory(master=root), help=help
        )
    else:
        dirname = dirname.text_input(label, value, help=help)

    return dirname


def get_sigmas_exponential(n, sigma_min, sigma_max, device="cpu"):
    """Constructs an exponential noise schedule."""
    sigmas = torch.linspace(
        math.log(sigma_max), math.log(sigma_min), n, device=device
    ).exp()
    return append_zero(sigmas)


def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device="cpu"):
    """Constructs a continuous VP noise schedule."""
    t = torch.linspace(1, eps_s, n, device=device)
    sigmas = torch.sqrt(torch.exp(beta_d * t**2 / 2 + beta_min * t) - 1)
    return append_zero(sigmas)


def to_d(x, sigma, denoised):
    """Converts a denoiser output to a Karras ODE derivative."""
    return (x - denoised) / append_dims(sigma, x.ndim)


def linear_multistep_coeff(order, t, i, j):
    if order - 1 > i:
        raise ValueError(f"Order {order} too high for step {i}")

    def fn(tau):
        prod = 1.0
        for k in range(order):
            if j == k:
                continue
            prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
        return prod

    return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]


class KDiffusionSampler:
    def __init__(self, m, sampler):
        self.model = m
        self.model_wrap = K.external.CompVisDenoiser(m)
        self.schedule = sampler

    def get_sampler_name(self):
        return self.schedule

    def sample(
        self,
        S,
        conditioning,
        batch_size,
        shape,
        verbose,
        unconditional_guidance_scale,
        unconditional_conditioning,
        eta,
        x_T,
        img_callback=None,
        log_every_t=None,
    ):
        sigmas = self.model_wrap.get_sigmas(S)
        x = x_T * sigmas[0]
        model_wrap_cfg = CFGDenoiser(self.model_wrap)
        samples_ddim = None
        samples_ddim = K.sampling.__dict__[f"sample_{self.schedule}"](
            model_wrap_cfg,
            x,
            sigmas,
            extra_args={
                "cond": conditioning,
                "uncond": unconditional_conditioning,
                "cond_scale": unconditional_guidance_scale,
            },
            disable=False,
            callback=generation_callback,
        )
        #
        return samples_ddim, None


#
# create class LDSR
class LDSR:
    # init function
    def __init__(self, modelPath, yamlPath):
        self.modelPath = modelPath
        self.yamlPath = yamlPath
        # self.model = self.load_model_from_config()
        # print(self.load_model_from_config(OmegaConf.load(yamlPath), modelPath))
        # self.print_current_directory()

    # get currennt directory

    """
    def check_model_exists(self):
        #check if model and yaml exist
        path = self.pathInput + "/models/ldm/ld_sr".replace('\\',os.sep).replace('/',os.sep)
        model = self.modelName
        yaml = self.yamlName
        if os.path.exists(path):
            #check if yaml exists
            if os.path.exists(os.path.join(path,yaml)):
                print('YAML found')
                #check if ckpt exists
                if os.path.exists(os.path.join(path,model)):
                    print('Model found')
                    return os.path.join(path,model), os.path.join(path,yaml)
                else:
                    return False
        #return onlyfiles
    """

    def load_model_from_config(self):
        # print(f"Loading model from {self.modelPath}")
        pl_sd = torch.load(self.modelPath, map_location="cpu")
        pl_sd["global_step"]
        sd = pl_sd["state_dict"]
        config = OmegaConf.load(self.yamlPath)
        model = instantiate_from_config(config.model, personalization_config="")
        m, u = model.load_state_dict(sd, strict=False)
        model.cuda()
        model.eval()
        return {"model": model}  # , global_step

    """
    def get_model(self):
        check = self.check_model_exists()
        if check != False:
            path_ckpt = check[0]
            path_conf = check[1]
        else:
            print('Model not found, please run the bat file to download the model')
        config = OmegaConf.load(path_conf)
        model, step = self.load_model_from_config(config, path_ckpt)
        return model


    def get_custom_cond(mode):
        dest = "data/example_conditioning"

        if mode == "superresolution":
            uploaded_img = files.upload()
            filename = next(iter(uploaded_img))
            name, filetype = filename.split(".") # todo assumes just one dot in name !
            os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}")

        elif mode == "text_conditional":
            #w = widgets.Text(value='A cake with cream!', disabled=True)
            w = 'Empty Test'
            display.display(w)

            with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", 'w') as f:
                f.write(w.value)

        elif mode == "class_conditional":
            #w = widgets.IntSlider(min=0, max=1000)
            w = 1000
            display.display(w)
            with open(f"{dest}/{mode}/custom.txt", 'w') as f:
                f.write(w.value)

        else:
            raise NotImplementedError(f"cond not implemented for mode{mode}")
    """

    def get_cond_options(self, mode):
        path = "data/example_conditioning"
        path = os.path.join(path, mode)
        onlyfiles = [f for f in sorted(os.listdir(path))]
        return path, onlyfiles

    """
    def select_cond_path(mode):
        path = "data/example_conditioning"  # todo
        path = os.path.join(path, mode)
        onlyfiles = [f for f in sorted(os.listdir(path))]

        selected = widgets.RadioButtons(
            options=onlyfiles,
            description='Select conditioning:',
            disabled=False
        )
        display.display(selected)
        selected_path = os.path.join(path, selected.value)
        return selected_path
    """

    """
    # Google Collab stuff
    def visualize_cond_img(path):
        display.display(ipyimg(filename=path))
    """

    def run(
        self,
        model,
        selected_path,
        task,
        custom_steps,
        eta,
        resize_enabled=False,
        classifier_ckpt=None,
        global_step=None,
    ):
        def make_convolutional_sample(
            batch,
            model,
            mode="vanilla",
            custom_steps=None,
            eta=1.0,
            swap_mode=False,
            masked=False,
            invert_mask=True,
            quantize_x0=False,
            custom_schedule=None,
            decode_interval=1000,
            resize_enabled=False,
            custom_shape=None,
            temperature=1.0,
            noise_dropout=0.0,
            corrector=None,
            corrector_kwargs=None,
            x_T=None,
            save_intermediate_vid=False,
            make_progrow=True,
            ddim_use_x0_pred=False,
        ):
            log = dict()

            z, c, x, xrec, xc = model.get_input(
                batch,
                model.first_stage_key,
                return_first_stage_outputs=True,
                force_c_encode=not (
                    hasattr(model, "split_input_params")
                    and model.cond_stage_key == "coordinates_bbox"
                ),
                return_original_cond=True,
            )

            log_every_t = 1 if save_intermediate_vid else None

            if custom_shape is not None:
                z = torch.randn(custom_shape)
                # print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")

            z0 = None

            log["input"] = x
            log["reconstruction"] = xrec

            if ismap(xc):
                log["original_conditioning"] = model.to_rgb(xc)
                if hasattr(model, "cond_stage_key"):
                    log[model.cond_stage_key] = model.to_rgb(xc)

            else:
                log["original_conditioning"] = (
                    xc if xc is not None else torch.zeros_like(x)
                )
                if model.cond_stage_model:
                    log[model.cond_stage_key] = (
                        xc if xc is not None else torch.zeros_like(x)
                    )
                    if model.cond_stage_key == "class_label":
                        log[model.cond_stage_key] = xc[model.cond_stage_key]

            with model.ema_scope("Plotting"):
                t0 = time.time()
                img_cb = None

                sample, intermediates = convsample_ddim(
                    model,
                    c,
                    steps=custom_steps,
                    shape=z.shape,
                    eta=eta,
                    quantize_x0=quantize_x0,
                    img_callback=img_cb,
                    mask=None,
                    x0=z0,
                    temperature=temperature,
                    noise_dropout=noise_dropout,
                    score_corrector=corrector,
                    corrector_kwargs=corrector_kwargs,
                    x_T=x_T,
                    log_every_t=log_every_t,
                )
                t1 = time.time()

                if ddim_use_x0_pred:
                    sample = intermediates["pred_x0"][-1]

            x_sample = model.decode_first_stage(sample)

            try:
                x_sample_noquant = model.decode_first_stage(
                    sample, force_not_quantize=True
                )
                log["sample_noquant"] = x_sample_noquant
                log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
            except:
                logger.error("Error with LDSR")
                pass

            log["sample"] = x_sample
            log["time"] = t1 - t0

            return log

        def convsample_ddim(
            model,
            cond,
            steps,
            shape,
            eta=1.0,
            callback=None,
            normals_sequence=None,
            mask=None,
            x0=None,
            quantize_x0=False,
            img_callback=None,
            temperature=1.0,
            noise_dropout=0.0,
            score_corrector=None,
            corrector_kwargs=None,
            x_T=None,
            log_every_t=None,
        ):
            ddim = DDIMSampler(model)
            bs = shape[0]  # dont know where this comes from but wayne
            shape = shape[1:]  # cut batch dim
            logger.info(f"Sampling with eta = {eta}; steps: {steps}")
            samples, intermediates = ddim.sample(
                steps,
                batch_size=bs,
                shape=shape,
                conditioning=cond,
                callback=callback,
                normals_sequence=normals_sequence,
                quantize_x0=quantize_x0,
                eta=eta,
                mask=mask,
                x0=x0,
                temperature=temperature,
                verbose=False,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs,
                x_T=x_T,
            )

            return samples, intermediates

        # global stride
        def get_cond(mode, selected_path):
            example = dict()
            if mode == "superresolution":
                up_f = 4
                # visualize_cond_img(selected_path)

                c = selected_path.convert("RGB")
                c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
                c_up = torchvision.transforms.functional.resize(
                    c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True
                )
                c_up = rearrange(c_up, "1 c h w -> 1 h w c")
                c = rearrange(c, "1 c h w -> 1 h w c")
                c = 2.0 * c - 1.0

                c = c.to(torch.device("cuda"))
                example["LR_image"] = c
                example["image"] = c_up

            return example

        example = get_cond(task, selected_path)

        save_intermediate_vid = False
        n_runs = 1
        masked = False
        guider = None
        ckwargs = None
        mode = "ddim"
        ddim_use_x0_pred = False
        temperature = 1.0
        eta = eta
        make_progrow = True
        custom_shape = None

        height, width = example["image"].shape[1:3]
        split_input = height >= 128 and width >= 128

        if split_input:
            ks = 128
            stride = 64
            vqf = 4  #
            model.split_input_params = {
                "ks": (ks, ks),
                "stride": (stride, stride),
                "vqf": vqf,
                "patch_distributed_vq": True,
                "tie_braker": False,
                "clip_max_weight": 0.5,
                "clip_min_weight": 0.01,
                "clip_max_tie_weight": 0.5,
                "clip_min_tie_weight": 0.01,
            }
        else:
            if hasattr(model, "split_input_params"):
                delattr(model, "split_input_params")

        invert_mask = False

        x_T = None
        for n in range(n_runs):
            if custom_shape is not None:
                x_T = torch.randn(
                    1, custom_shape[1], custom_shape[2], custom_shape[3]
                ).to(model.device)
                x_T = repeat(x_T, "1 c h w -> b c h w", b=custom_shape[0])

            logs = make_convolutional_sample(
                example,
                model,
                mode=mode,
                custom_steps=custom_steps,
                eta=eta,
                swap_mode=False,
                masked=masked,
                invert_mask=invert_mask,
                quantize_x0=False,
                custom_schedule=None,
                decode_interval=10,
                resize_enabled=resize_enabled,
                custom_shape=custom_shape,
                temperature=temperature,
                noise_dropout=0.0,
                corrector=guider,
                corrector_kwargs=ckwargs,
                x_T=x_T,
                save_intermediate_vid=save_intermediate_vid,
                make_progrow=make_progrow,
                ddim_use_x0_pred=ddim_use_x0_pred,
            )
        return logs

    @torch.no_grad()
    @torch.no_grad()
    def superResolution(
        self,
        image,
        ddimSteps=100,
        preDownScale=1,
        postDownScale=1,
        downsample_method="Lanczos",
    ):
        """
        #Run settings

        diffusion_steps = int(ddimSteps) #@param [25, 50, 100, 250, 500, 1000]
        eta = 1.0 #@param  {type: 'raw'}
        stride = 0 #not working atm

        # ####Scaling options:
        # Downsampling to 256px first will often improve the final image and runs faster.

        # You can improve sharpness without upscaling by upscaling and then downsampling to the original size (i.e. Super Resolution)
        preDownScale: Values ['None', '2', '4']

        postDownScale: Values ['None', 'Original Size', '2', '4']

        # Nearest gives sharper results, but may look more pixellated. Lancoz is much higher quality, but result may be less crisp.
        downsample_method = 'Lanczos' #@param ['Nearest', 'Lanczos']
        """

        diffMode = "superresolution"
        model = self.load_model_from_config()

        # Run settings

        diffusion_steps = int(ddimSteps)  # @param [25, 50, 100, 250, 500, 1000]
        eta = 1.0  # @param  {type: 'raw'}

        # ####Scaling options:
        # Downsampling to 256px first will often improve the final image and runs faster.

        # You can improve sharpness without upscaling by upscaling and then downsampling to the original size (i.e. Super Resolution)

        post_downsample = postDownScale  # @param ['None', 'Original Size', '2', '4']

        # Nearest gives sharper results, but may look more pixellated. Lancoz is much higher quality, but result may be less crisp.
        # downsample_method = 'Lanczos' #@param ['Nearest', 'Lanczos']

        gc.collect()
        torch.cuda.empty_cache()

        im_og = image
        width_og, height_og = im_og.size

        # Downsample Pre

        downsample_rate = preDownScale

        # get system temp directory
        width_downsampled_pre = width_og // downsample_rate
        height_downsampled_pre = height_og // downsample_rate
        if downsample_rate != 1:
            logger.info(
                f"Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]"
            )
            im_og = im_og.resize(
                (width_downsampled_pre, height_downsampled_pre), Image.LANCZOS
            )

        logs = self.run(model["model"], im_og, diffMode, diffusion_steps, eta)

        sample = logs["sample"]
        sample = sample.detach().cpu()
        sample = torch.clamp(sample, -1.0, 1.0)
        sample = (sample + 1.0) / 2.0 * 255
        sample = sample.numpy().astype(np.uint8)
        sample = np.transpose(sample, (0, 2, 3, 1))

        a = Image.fromarray(sample[0])

        # Downsample Post
        downsample_rate = postDownScale

        width, height = a.size
        width_downsampled_post = width // downsample_rate
        height_downsampled_post = height // downsample_rate

        if downsample_method == "Lanczos":
            aliasing = Image.LANCZOS
        else:
            aliasing = Image.NEAREST

        if downsample_rate != 1:
            logger.info(
                f"Downsampling from [{width}, {height}] to [{width_downsampled_post}, {height_downsampled_post}]"
            )
            a = a.resize((width_downsampled_post, height_downsampled_post), aliasing)
        elif post_downsample == "Original Size":
            logger.info(
                f"Downsampling from [{width}, {height}] to Original Size [{width_og}, {height_og}]"
            )
            a = a.resize((width_og, height_og), aliasing)

        del model
        gc.collect()
        torch.cuda.empty_cache()

        logger.info("Processing finished!")
        return a


@torch.no_grad()
def log_likelihood(
    model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4
):
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    v = torch.randint_like(x, 2) * 2 - 1
    fevals = 0

    def ode_fn(sigma, x):
        nonlocal fevals
        with torch.enable_grad():
            x = x[0].detach().requires_grad_()
            denoised = model(x, sigma * s_in, **extra_args)
            d = to_d(x, sigma, denoised)
            fevals += 1
            grad = torch.autograd.grad((d * v).sum(), x)[0]
            d_ll = (v * grad).flatten(1).sum(1)
        return d.detach(), d_ll

    x_min = x, x.new_zeros([x.shape[0]])
    t = x.new_tensor([sigma_min, sigma_max])
    sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method="dopri5")
    latent, delta_ll = sol[0][-1], sol[1][-1]
    ll_prior = (
        torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1)
    )
    return ll_prior + delta_ll, {"fevals": fevals}


def create_random_tensors(shape, seeds):
    xs = []
    for seed in seeds:
        torch.manual_seed(seed)

        # randn results depend on device; gpu and cpu get different results for same seed;
        # the way I see it, it's better to do this on CPU, so that everyone gets same result;
        # but the original script had it like this so i do not dare change it for now because
        # it will break everyone's seeds.
        xs.append(torch.randn(shape, device=st.session_state["defaults"].general.gpu))
    x = torch.stack(xs)
    return x


def torch_gc():
    torch.cuda.empty_cache()
    torch.cuda.ipc_collect()


@retry(tries=5)
# @st.experimental_memo(persist="disk", show_spinner=False)
def load_GFPGAN(model_name="GFPGANv1.4"):
    # model_name = 'GFPGANv1.3'

    model_path = os.path.join(
        st.session_state["defaults"].general.GFPGAN_dir, model_name + ".pth"
    )

    # if not os.path.isfile(model_path):
    # model_path = os.path.join(st.session_state['defaults'].general.GFPGAN_dir, model_name + '.pth')

    if not os.path.isfile(model_path):
        raise Exception("GFPGAN model not found at path " + model_path)

    sys.path.append(os.path.abspath(st.session_state["defaults"].general.GFPGAN_dir))
    from gfpgan import GFPGANer

    with server_state_lock["GFPGAN"]:
        if (
            st.session_state["defaults"].general.gfpgan_cpu
            or st.session_state["defaults"].general.extra_models_cpu
        ):
            server_state["GFPGAN"] = GFPGANer(
                model_path=model_path,
                upscale=1,
                arch="clean",
                channel_multiplier=2,
                bg_upsampler=None,
                device=torch.device("cpu"),
            )

        elif st.session_state["defaults"].general.extra_models_gpu:
            server_state["GFPGAN"] = GFPGANer(
                model_path=model_path,
                upscale=1,
                arch="clean",
                channel_multiplier=2,
                bg_upsampler=None,
                device=torch.device(
                    f"cuda:{st.session_state['defaults'].general.gfpgan_gpu}"
                ),
            )
        else:
            server_state["GFPGAN"] = GFPGANer(
                model_path=model_path,
                upscale=1,
                arch="clean",
                channel_multiplier=2,
                bg_upsampler=None,
                device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"),
            )

        # Add the model_name to model loaded so we can later
        # check if its the same when we change it on the UI.
        server_state["GFPGAN"].name = model_name

    return server_state["GFPGAN"]


@retry(tries=5)
def load_RealESRGAN(model_name: str):
    from basicsr.archs.rrdbnet_arch import RRDBNet

    RealESRGAN_models = {
        "RealESRGAN_x4plus": RRDBNet(
            num_in_ch=3,
            num_out_ch=3,
            num_feat=64,
            num_block=23,
            num_grow_ch=32,
            scale=4,
        ),
        "RealESRGAN_x4plus_anime_6B": RRDBNet(
            num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4
        ),
    }

    model_path = os.path.join(
        st.session_state["defaults"].general.RealESRGAN_dir, model_name + ".pth"
    )

    if not os.path.isfile(model_path):
        model_path = os.path.join(
            st.session_state["defaults"].general.RealESRGAN_dir, model_name + ".pth"
        )

    if not os.path.exists(model_path):
        raise Exception(model_name + ".pth not found at path " + model_path)

    sys.path.append(
        os.path.abspath(st.session_state["defaults"].general.RealESRGAN_dir)
    )
    from realesrgan import RealESRGANer

    with server_state_lock["RealESRGAN"]:
        if (
            st.session_state["defaults"].general.esrgan_cpu
            or st.session_state["defaults"].general.extra_models_cpu
        ):
            server_state["RealESRGAN"] = RealESRGANer(
                scale=2,
                model_path=model_path,
                model=RealESRGAN_models[model_name],
                pre_pad=0,
                half=False,
            )  # cpu does not support half

            server_state["RealESRGAN"].device = torch.device("cpu")
            server_state["RealESRGAN"].model.to("cpu")

        elif st.session_state["defaults"].general.extra_models_gpu:
            server_state["RealESRGAN"] = RealESRGANer(
                scale=2,
                model_path=model_path,
                model=RealESRGAN_models[model_name],
                pre_pad=0,
                half=not st.session_state["defaults"].general.no_half,
                device=torch.device(
                    f"cuda:{st.session_state['defaults'].general.esrgan_gpu}"
                ),
            )
        else:
            server_state["RealESRGAN"] = RealESRGANer(
                scale=2,
                model_path=model_path,
                model=RealESRGAN_models[model_name],
                pre_pad=0,
                half=not st.session_state["defaults"].general.no_half,
                device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"),
            )

        # Add the model_name to model loaded so we can later
        # check if its the same when we change it on the UI.
        server_state["RealESRGAN"].model.name = model_name

    return server_state["RealESRGAN"]


#
class RealESRGANModel(nn.Module):
    def __init__(self, model_path, tile=0, tile_pad=10, pre_pad=0, fp32=False):
        super().__init__()
        try:
            from basicsr.archs.rrdbnet_arch import RRDBNet
            from realesrgan import RealESRGANer
        except ImportError:
            logger.error(
                "You tried to import realesrgan without having it installed properly. To install Real-ESRGAN, run:\n\n"
                "pip install realesrgan"
            )

        model = RRDBNet(
            num_in_ch=3,
            num_out_ch=3,
            num_feat=64,
            num_block=23,
            num_grow_ch=32,
            scale=4,
        )
        self.upsampler = RealESRGANer(
            scale=4,
            model_path=model_path,
            model=model,
            tile=tile,
            tile_pad=tile_pad,
            pre_pad=pre_pad,
            half=not fp32,
        )

    def forward(self, image, outscale=4, convert_to_pil=True):
        """Upsample an image array or path.
        Args:
            image (Union[np.ndarray, str]): Either a np array or an image path. np array is assumed to be in RGB format,
                and we convert it to BGR.
            outscale (int, optional): Amount to upscale the image. Defaults to 4.
            convert_to_pil (bool, optional): If True, return PIL image. Otherwise, return numpy array (BGR). Defaults to True.
        Returns:
            Union[np.ndarray, PIL.Image.Image]: An upsampled version of the input image.
        """
        if isinstance(image, (str, Path)):
            img = cv2.imread(image, cv2.IMREAD_UNCHANGED)
        else:
            img = image
            img = (img * 255).round().astype("uint8")
            img = img[:, :, ::-1]

        image, _ = self.upsampler.enhance(img, outscale=outscale)

        if convert_to_pil:
            image = Image.fromarray(image[:, :, ::-1])

        return image

    @classmethod
    def from_pretrained(cls, model_name_or_path="nateraw/real-esrgan"):
        """Initialize a pretrained Real-ESRGAN upsampler.
        Args:
            model_name_or_path (str, optional): The Hugging Face repo ID or path to local model. Defaults to 'nateraw/real-esrgan'.
        Returns:
            PipelineRealESRGAN: An instance of `PipelineRealESRGAN` instantiated from pretrained model.
        """
        # reuploaded form official ones mentioned here:
        # https://github.com/xinntao/Real-ESRGAN
        if Path(model_name_or_path).exists():
            file = model_name_or_path
        else:
            file = hf_hub_download(model_name_or_path, "RealESRGAN_x4plus.pth")
        return cls(file)

    def upsample_imagefolder(self, in_dir, out_dir, suffix="out", outfile_ext=".png"):
        in_dir, out_dir = Path(in_dir), Path(out_dir)
        if not in_dir.exists():
            raise FileNotFoundError(f"Provided input directory {in_dir} does not exist")

        out_dir.mkdir(exist_ok=True, parents=True)

        image_paths = [
            x for x in in_dir.glob("*") if x.suffix.lower() in [".png", ".jpg", ".jpeg"]
        ]
        for image in image_paths:
            im = self(str(image))
            out_filepath = out_dir / (image.stem + suffix + outfile_ext)
            im.save(out_filepath)


#
@retry(tries=5)
def load_LDSR(model_name="model", config="project", checking=False):
    # model_name = 'model'
    # yaml_name = 'project'

    model_path = os.path.join(
        st.session_state["defaults"].general.LDSR_dir, model_name + ".ckpt"
    )
    yaml_path = os.path.join(
        st.session_state["defaults"].general.LDSR_dir, config + ".yaml"
    )

    if not os.path.isfile(model_path):
        raise Exception("LDSR model not found at path " + model_path)
    if not os.path.isfile(yaml_path):
        raise Exception("LDSR model not found at path " + yaml_path)
    if checking is True:
        return True

    # sys.path.append(os.path.abspath(st.session_state['defaults'].general.LDSR_dir))
    # from LDSR import LDSR
    server_state["LDSR"] = LDSR(model_path, yaml_path)

    server_state["LDSR"].name = model_name

    return server_state["LDSR"]


#


@retry(tries=5)
# def try_loading_LDSR(model_name: str,checking=False):
##LDSR = None
##global LDSR
# if os.path.exists(st.session_state['defaults'].general.LDSR_dir):
# try:
# server_state["LDSR"] = load_LDSR(checking=True) # TODO: Should try to load both models before giving up
# if checking == True:
# print("Found LDSR")
# return True
# print("Latent Diffusion Super Sampling (LDSR) model loaded")
# except Exception:
# import traceback
# print("Error loading LDSR:", file=sys.stderr)
# print(traceback.format_exc(), file=sys.stderr)
# else:
# print("LDSR not found at path, please make sure you have cloned the LDSR repo to ./models/ldsr/")

# try_loading_LDSR('model',checking=True)


# @retry(tries=5)
def load_sd_model(model_name: str):
    """Loads Stable Diffusion model by name"""
    ckpt_path = st.session_state.defaults.general.default_model_path

    if model_name != st.session_state.defaults.general.base_model:
        ckpt_path = os.path.join("models", "custom", f"{model_name}.ckpt")

    if st.session_state.defaults.general.optimized:
        config = OmegaConf.load(st.session_state.defaults.general.optimized_config)

        sd = load_sd_from_config(ckpt_path)
        li, lo = [], []
        for key, v_ in sd.items():
            sp = key.split(".")
            if (sp[0]) == "model":
                if "input_blocks" in sp:
                    li.append(key)
                elif "middle_block" in sp:
                    li.append(key)
                elif "time_embed" in sp:
                    li.append(key)
                else:
                    lo.append(key)
        for key in li:
            sd["model1." + key[6:]] = sd.pop(key)
        for key in lo:
            sd["model2." + key[6:]] = sd.pop(key)

        device = (
            torch.device(f"cuda:{st.session_state.defaults.general.gpu}")
            if torch.cuda.is_available()
            else torch.device("cpu")
        )

        model = instantiate_from_config(config.modelUNet)
        _, _ = model.load_state_dict(sd, strict=False)
        model.cuda()
        model.eval()
        model.turbo = st.session_state.defaults.general.optimized_turbo

        modelCS = instantiate_from_config(config.modelCondStage)
        _, _ = modelCS.load_state_dict(sd, strict=False)
        modelCS.cond_stage_model.device = device
        modelCS.eval()

        modelFS = instantiate_from_config(config.modelFirstStage)
        _, _ = modelFS.load_state_dict(sd, strict=False)
        modelFS.eval()

        del sd

        if not st.session_state.defaults.general.no_half:
            model = model.half().to(device)
            modelCS = modelCS.half().to(device)
            modelFS = modelFS.half().to(device)

        return config, device, model, modelCS, modelFS
    else:
        if os.path.exists(ckpt_path.replace("ckpt", "yaml")):
            logger.info(f"Using config file from: {ckpt_path.replace('ckpt','yaml')}")
            config = OmegaConf.load(ckpt_path.replace("ckpt", "yaml"))
        else:
            config = OmegaConf.load(
                st.session_state.defaults.general.default_model_config
            )
        model = load_model_from_config(config, ckpt_path)

        device = (
            torch.device(f"cuda:{st.session_state.defaults.general.gpu}")
            if torch.cuda.is_available()
            else torch.device("cpu")
        )
        model = (
            model if st.session_state.defaults.general.no_half else model.half()
        ).to(device)

        return config, device, model, None, None


def ModelLoader(
    models, load=False, unload=False, imgproc_realesrgan_model_name="RealESRGAN_x4plus"
):
    # codedealer: No usages
    # get global variables
    global_vars = globals()
    # check if m is in globals
    if unload:
        for m in models:
            if m in global_vars:
                # if it is, delete it
                del global_vars[m]
                if st.session_state["defaults"].general.optimized:
                    if m == "model":
                        del global_vars[m + "FS"]
                        del global_vars[m + "CS"]
                if m == "model":
                    m = "Stable Diffusion"
                logger.info("Unloaded " + m)
    if load:
        for m in models:
            if (
                m not in global_vars
                or m in global_vars
                and type(global_vars[m]) == bool
            ):
                # if it isn't, load it
                if m == "GFPGAN":
                    global_vars[m] = load_GFPGAN()
                elif m == "model":
                    sdLoader = load_sd_from_config()
                    global_vars[m] = sdLoader[0]
                    if st.session_state["defaults"].general.optimized:
                        global_vars[m + "CS"] = sdLoader[1]
                        global_vars[m + "FS"] = sdLoader[2]
                elif m == "RealESRGAN":
                    global_vars[m] = load_RealESRGAN(imgproc_realesrgan_model_name)
                elif m == "LDSR":
                    global_vars[m] = load_LDSR()
                if m == "model":
                    m = "Stable Diffusion"
                logger.info("Loaded " + m)
    torch_gc()


#
@retry(tries=5)
def generation_callback(img, i=0):
    # try to do garbage collection before decoding the image
    torch_gc()

    if "update_preview_frequency" not in st.session_state:
        raise StopException

    try:
        if i == 0:
            if img["i"]:
                i = img["i"]
    except TypeError as e:
        logger.error(e)
        pass

    if (
        st.session_state.update_preview
        and int(st.session_state.update_preview_frequency) > 0
        and i % int(st.session_state.update_preview_frequency) == 0
        and i > 0
    ):
        # print (img)
        # print (type(img))
        # The following lines will convert the tensor we got on img to an actual image we can render on the UI.
        # It can probably be done in a better way for someone who knows what they're doing. I don't.
        # print (img,isinstance(img, torch.Tensor))
        if isinstance(img, torch.Tensor):
            x_samples_ddim = (
                (
                    server_state["model"].to("cuda")
                    if not st.session_state["defaults"].general.optimized
                    else server_state["modelFS"].to("cuda")
                )
                .decode_first_stage(img)
                .to("cuda")
            )
        else:
            # When using the k Diffusion samplers they return a dict instead of a tensor that look like this:
            # {'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}
            x_samples_ddim = (
                (
                    server_state["model"].to("cuda")
                    if not st.session_state["defaults"].general.optimized
                    else server_state["modelFS"].to("cuda")
                )
                .decode_first_stage(img["denoised"])
                .to("cuda")
            )

        x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

        if x_samples_ddim.ndimension() == 4:
            pil_images = [
                transforms.ToPILImage()(x.squeeze_(0)) for x in x_samples_ddim
            ]
            pil_image = image_grid(pil_images, 1)
        else:
            pil_image = transforms.ToPILImage()(x_samples_ddim.squeeze_(0))

        # update image on the UI so we can see the progress
        if "preview_image" in st.session_state:
            st.session_state["preview_image"].image(pil_image)

    # Show a progress bar so we can keep track of the progress even when the image progress is not been shown,
    # Dont worry, it doesnt affect the performance.
    if st.session_state["generation_mode"] == "txt2img":
        percent = int(
            100
            * float(
                i + 1
                if i + 1 < st.session_state.sampling_steps
                else st.session_state.sampling_steps
            )
            / float(st.session_state.sampling_steps)
        )

        if "progress_bar_text" in st.session_state:
            st.session_state["progress_bar_text"].text(
                f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps} {percent if percent < 100 else 100}%"
            )
    else:
        if st.session_state["generation_mode"] == "img2img":
            round_sampling_steps = round(
                st.session_state.sampling_steps * st.session_state["denoising_strength"]
            )
            percent = int(
                100
                * float(i + 1 if i + 1 < round_sampling_steps else round_sampling_steps)
                / float(round_sampling_steps)
            )

            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    f"""Running step: {i+1 if i+1 < round_sampling_steps else round_sampling_steps}/{round_sampling_steps} {percent if percent < 100 else 100}%"""
                )
        else:
            if st.session_state["generation_mode"] == "txt2vid":
                percent = int(
                    100
                    * float(
                        i + 1
                        if i + 1 < st.session_state.sampling_steps
                        else st.session_state.sampling_steps
                    )
                    / float(st.session_state.sampling_steps)
                )

                if "progress_bar_text" in st.session_state:
                    st.session_state["progress_bar_text"].text(
                        f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps}"
                        f"{percent if percent < 100 else 100}%"
                    )

    if "progress_bar" in st.session_state:
        try:
            st.session_state["progress_bar"].progress(percent if percent < 100 else 100)
            if st.session_state["defaults"].general.show_percent_in_tab_title:
                set_page_title(
                    f"({percent if percent < 100 else 100}%) Stable Diffusion Playground"
                )
        except UnboundLocalError:
            # logger.error(e)
            pass


prompt_parser = re.compile(
    """
    (?P<prompt>                # capture group for 'prompt'
    [^:]+                      # match one or more non ':' characters
    )                          # end 'prompt'
    (?:                        # non-capture group
    :+                         # match one or more ':' characters
    (?P<weight>                # capture group for 'weight'
    -?\\d+(?:\\.\\d+)?            # match positive or negative decimal number
    )?                         # end weight capture group, make optional
    \\s*                        # strip spaces after weight
    |                          # OR
    $                          # else, if no ':' then match end of line
    )                          # end non-capture group
""",
    re.VERBOSE,
)


def split_weighted_subprompts(input_string, normalize=True):
    # grabs all text up to the first occurrence of ':' as sub-prompt
    # takes the value following ':' as weight
    # if ':' has no value defined, defaults to 1.0
    # repeats until no text remaining
    parsed_prompts = [
        (match.group("prompt"), float(match.group("weight") or 1))
        for match in re.finditer(prompt_parser, input_string)
    ]
    if not normalize:
        return parsed_prompts
    # this probably still doesn't handle negative weights very well
    weight_sum = sum(map(lambda x: x[1], parsed_prompts))
    return [(x[0], x[1] / weight_sum) for x in parsed_prompts]


def slerp(device, t, v0: torch.Tensor, v1: torch.Tensor, DOT_THRESHOLD=0.9995):
    v0 = v0.detach().cpu().numpy()
    v1 = v1.detach().cpu().numpy()

    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
    if np.abs(dot) > DOT_THRESHOLD:
        v2 = (1 - t) * v0 + t * v1
    else:
        theta_0 = np.arccos(dot)
        sin_theta_0 = np.sin(theta_0)
        theta_t = theta_0 * t
        sin_theta_t = np.sin(theta_t)
        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
        s1 = sin_theta_t / sin_theta_0
        v2 = s0 * v0 + s1 * v1

    v2 = torch.from_numpy(v2).to(device)

    return v2


#
@st.experimental_memo(persist="disk", show_spinner=False)
def optimize_update_preview_frequency(
    current_chunk_speed,
    previous_chunk_speed_list,
    update_preview_frequency,
    update_preview_frequency_list,
):
    """Find the optimal update_preview_frequency value maximizing
    performance while minimizing the time between updates."""
    from statistics import mean

    previous_chunk_avg_speed = mean(previous_chunk_speed_list)

    previous_chunk_speed_list.append(current_chunk_speed)
    current_chunk_avg_speed = mean(previous_chunk_speed_list)

    if current_chunk_avg_speed >= previous_chunk_avg_speed:
        # print(f"{current_chunk_speed} >= {previous_chunk_speed}")
        update_preview_frequency_list.append(update_preview_frequency + 1)
    else:
        # print(f"{current_chunk_speed} <= {previous_chunk_speed}")
        update_preview_frequency_list.append(update_preview_frequency - 1)

    update_preview_frequency = round(mean(update_preview_frequency_list))

    return (
        current_chunk_speed,
        previous_chunk_speed_list,
        update_preview_frequency,
        update_preview_frequency_list,
    )


def get_font(fontsize):
    fonts = ["arial.ttf", "DejaVuSans.ttf"]
    for font_name in fonts:
        try:
            return ImageFont.truetype(font_name, fontsize)
        except OSError:
            pass

    # ImageFont.load_default() is practically unusable as it only supports
    # latin1, so raise an exception instead if no usable font was found
    raise Exception(f"No usable font found (tried {', '.join(fonts)})")


def load_embeddings(fp):
    if fp is not None and hasattr(server_state["model"], "embedding_manager"):
        server_state["model"].embedding_manager.load(fp["name"])


def load_learned_embed_in_clip(
    learned_embeds_path, text_encoder, tokenizer, token=None
):
    loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")

    # separate token and the embeds
    if learned_embeds_path.endswith(".pt"):
        # old format
        # token = * so replace with file directory name when converting
        trained_token = os.path.basename(learned_embeds_path)
        params_dict = {
            trained_token: torch.tensor(
                list(loaded_learned_embeds["string_to_param"].items())[0][1]
            )
        }
        learned_embeds_path = os.path.splitext(learned_embeds_path)[0] + ".bin"
        torch.save(params_dict, learned_embeds_path)
        loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
        trained_token = list(loaded_learned_embeds.keys())[0]
        embeds = loaded_learned_embeds[trained_token]

    elif learned_embeds_path.endswith(".bin"):
        trained_token = list(loaded_learned_embeds.keys())[0]
        embeds = loaded_learned_embeds[trained_token]

    embeds = loaded_learned_embeds[trained_token]
    # cast to dtype of text_encoder
    dtype = text_encoder.get_input_embeddings().weight.dtype
    embeds.to(dtype)

    # add the token in tokenizer
    token = token if token is not None else trained_token
    tokenizer.add_tokens(token)

    # resize the token embeddings
    text_encoder.resize_token_embeddings(len(tokenizer))

    # get the id for the token and assign the embeds
    token_id = tokenizer.convert_tokens_to_ids(token)
    text_encoder.get_input_embeddings().weight.data[token_id] = embeds
    return token


def image_grid(imgs, batch_size, force_n_rows=None, captions=None):
    # print (len(imgs))
    if force_n_rows is not None:
        rows = force_n_rows
    elif st.session_state["defaults"].general.n_rows > 0:
        rows = st.session_state["defaults"].general.n_rows
    elif st.session_state["defaults"].general.n_rows == 0:
        rows = batch_size
    else:
        rows = math.sqrt(len(imgs))
        rows = round(rows)

    cols = math.ceil(len(imgs) / rows)

    w, h = imgs[0].size
    grid = Image.new("RGB", size=(cols * w, rows * h), color="black")

    try:
        fnt = get_font(30)
    except Exception:
        pass

    for i, img in enumerate(imgs):
        grid.paste(img, box=(i % cols * w, i // cols * h))
        try:
            if captions and i < len(captions):
                d = ImageDraw.Draw(grid)
                size = d.textbbox(
                    (0, 0), captions[i], font=fnt, stroke_width=2, align="center"
                )
                d.multiline_text(
                    (i % cols * w + w / 2, i // cols * h + h - size[3]),
                    captions[i],
                    font=fnt,
                    fill=(255, 255, 255),
                    stroke_width=2,
                    stroke_fill=(0, 0, 0),
                    anchor="mm",
                    align="center",
                )
        except Exception:
            pass
    return grid


def seed_to_int(s):
    if type(s) is int:
        return s
    if s is None or s == "":
        return random.randint(0, 2**32 - 1)

    if "," in s:
        s = s.split(",")

    if type(s) is list:
        seed_list = []
        for seed in s:
            if seed is None or seed == "":
                seed_list.append(random.randint(0, 2**32 - 1))
            else:
                seed_list = s

        return seed_list

    n = abs(int(s) if s.isdigit() else random.Random(s).randint(0, 2**32 - 1))
    while n >= 2**32:
        n = n >> 32
    return n


#
def draw_prompt_matrix(im, width, height, all_prompts):
    def wrap(text, d, font, line_length):
        lines = [""]
        for word in text.split():
            line = f"{lines[-1]} {word}".strip()
            if d.textlength(line, font=font) <= line_length:
                lines[-1] = line
            else:
                lines.append(word)
        return "\n".join(lines)

    def draw_texts(pos, x, y, texts, sizes):
        for i, (text, size) in enumerate(zip(texts, sizes)):
            active = pos & (1 << i) != 0

            if not active:
                text = "\u0336".join(text) + "\u0336"

            d.multiline_text(
                (x, y + size[1] / 2),
                text,
                font=fnt,
                fill=color_active if active else color_inactive,
                anchor="mm",
                align="center",
            )

            y += size[1] + line_spacing

    fontsize = (width + height) // 25
    line_spacing = fontsize // 2
    fnt = get_font(fontsize)
    color_active = (0, 0, 0)
    color_inactive = (153, 153, 153)

    pad_top = height // 4
    pad_left = width * 3 // 4 if len(all_prompts) > 2 else 0

    cols = im.width // width
    rows = im.height // height

    prompts = all_prompts[1:]

    result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
    result.paste(im, (pad_left, pad_top))

    d = ImageDraw.Draw(result)

    boundary = math.ceil(len(prompts) / 2)
    prompts_horiz = [wrap(x, d, fnt, width) for x in prompts[:boundary]]
    prompts_vert = [wrap(x, d, fnt, pad_left) for x in prompts[boundary:]]

    sizes_hor = [
        (x[2] - x[0], x[3] - x[1])
        for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_horiz]
    ]
    sizes_ver = [
        (x[2] - x[0], x[3] - x[1])
        for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_vert]
    ]
    hor_text_height = sum([x[1] + line_spacing for x in sizes_hor]) - line_spacing
    ver_text_height = sum([x[1] + line_spacing for x in sizes_ver]) - line_spacing

    for col in range(cols):
        x = pad_left + width * col + width / 2
        y = pad_top / 2 - hor_text_height / 2

        draw_texts(col, x, y, prompts_horiz, sizes_hor)

    for row in range(rows):
        x = pad_left / 2
        y = pad_top + height * row + height / 2 - ver_text_height / 2

        draw_texts(row, x, y, prompts_vert, sizes_ver)

    return result


#
def enable_minimal_memory_usage(model):
    """Moves only unet to fp16 and to CUDA, while keepping lighter models on CPUs"""
    model.unet.to(torch.float16).to(torch.device("cuda"))
    model.enable_attention_slicing(1)

    torch.cuda.empty_cache()
    torch_gc()


def check_prompt_length(prompt, comments):
    """this function tests if prompt is too long, and if so, adds a message to comments"""

    tokenizer = (
        server_state["model"]
        if not st.session_state["defaults"].general.optimized
        else server_state["modelCS"]
    ).cond_stage_model.tokenizer
    max_length = (
        server_state["model"]
        if not st.session_state["defaults"].general.optimized
        else server_state["modelCS"]
    ).cond_stage_model.max_length

    info = (
        server_state["model"]
        if not st.session_state["defaults"].general.optimized
        else server_state["modelCS"]
    ).cond_stage_model.tokenizer(
        [prompt],
        truncation=True,
        max_length=max_length,
        return_overflowing_tokens=True,
        padding="max_length",
        return_tensors="pt",
    )
    ovf = info["overflowing_tokens"][0]
    overflowing_count = ovf.shape[0]
    if overflowing_count == 0:
        return

    vocab = {v: k for k, v in tokenizer.get_vocab().items()}
    overflowing_words = [vocab.get(int(x), "") for x in ovf]
    overflowing_text = tokenizer.convert_tokens_to_string("".join(overflowing_words))

    comments.append(
        f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n"
    )


#
def custom_models_available():
    with server_state_lock["custom_models"]:
        #
        # Allow for custom models to be used instead of the default one,
        # an example would be Waifu-Diffusion or any other fine tune of stable diffusion
        server_state["custom_models"]: sorted = []

        for root, dirs, files in os.walk(os.path.join("models", "custom")):
            for file in files:
                if os.path.splitext(file)[1] == ".ckpt":
                    server_state["custom_models"].append(os.path.splitext(file)[0])

        with server_state_lock["CustomModel_available"]:
            if len(server_state["custom_models"]) > 0:
                server_state["CustomModel_available"] = True
                server_state["custom_models"].append("Stable Diffusion v1.5")
            else:
                server_state["CustomModel_available"] = False


#
def GFPGAN_available():
    # with server_state_lock["GFPGAN_models"]:
    #

    st.session_state["GFPGAN_models"]: sorted = []
    model = st.session_state["defaults"].model_manager.models.gfpgan

    files_available = 0

    for file in model["files"]:
        if "save_location" in model["files"][file]:
            if os.path.exists(
                os.path.join(
                    model["files"][file]["save_location"],
                    model["files"][file]["file_name"],
                )
            ):
                files_available += 1

        elif os.path.exists(
            os.path.join(model["save_location"], model["files"][file]["file_name"])
        ):
            base_name = os.path.splitext(model["files"][file]["file_name"])[0]
            if "GFPGANv" in base_name:
                st.session_state["GFPGAN_models"].append(base_name)
            files_available += 1

    # we need to show the other models from previous verions that we have on the
    # same directory in case we want to see how they perform vs each other.
    for root, dirs, files in os.walk(st.session_state["defaults"].general.GFPGAN_dir):
        for file in files:
            if os.path.splitext(file)[1] == ".pth":
                if os.path.splitext(file)[0] not in st.session_state["GFPGAN_models"]:
                    st.session_state["GFPGAN_models"].append(os.path.splitext(file)[0])

    if len(st.session_state["GFPGAN_models"]) > 0 and files_available == len(
        model["files"]
    ):
        st.session_state["GFPGAN_available"] = True
    else:
        st.session_state["GFPGAN_available"] = False
        st.session_state["use_GFPGAN"] = False
        st.session_state["GFPGAN_model"] = "GFPGANv1.4"


#
def RealESRGAN_available():
    # with server_state_lock["RealESRGAN_models"]:
    #
    # Allow for custom models to be used instead of the default one,
    # an example would be Waifu-Diffusion or any other fine tune of stable diffusion
    st.session_state["RealESRGAN_models"]: sorted = []
    model = st.session_state["defaults"].model_manager.models.realesrgan
    for file in model["files"]:
        if os.path.exists(
            os.path.join(model["save_location"], model["files"][file]["file_name"])
        ):
            base_name = os.path.splitext(model["files"][file]["file_name"])[0]
            st.session_state["RealESRGAN_models"].append(base_name)

    if len(st.session_state["RealESRGAN_models"]) > 0:
        st.session_state["RealESRGAN_available"] = True
    else:
        st.session_state["RealESRGAN_available"] = False
        st.session_state["use_RealESRGAN"] = False
        st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"


#
def LDSR_available():
    # with server_state_lock["RealESRGAN_models"]:
    #
    # Allow for custom models to be used instead of the default one,
    # an example would be Waifu-Diffusion or any other fine tune of stable diffusion
    st.session_state["LDSR_models"]: sorted = []
    files_available = 0
    model = st.session_state["defaults"].model_manager.models.ldsr
    for file in model["files"]:
        if os.path.exists(
            os.path.join(model["save_location"], model["files"][file]["file_name"])
        ):
            base_name = os.path.splitext(model["files"][file]["file_name"])[0]
            extension = os.path.splitext(model["files"][file]["file_name"])[1]
            if extension == ".ckpt":
                st.session_state["LDSR_models"].append(base_name)
            files_available += 1
    if files_available == len(model["files"]):
        st.session_state["LDSR_available"] = True
    else:
        st.session_state["LDSR_available"] = False
        st.session_state["use_LDSR"] = False
        st.session_state["LDSR_model"] = "model"


def save_sample(
    image,
    sample_path_i,
    filename,
    jpg_sample,
    prompts,
    seeds,
    width,
    height,
    steps,
    cfg_scale,
    normalize_prompt_weights,
    use_GFPGAN,
    write_info_files,
    prompt_matrix,
    init_img,
    uses_loopback,
    uses_random_seed_loopback,
    save_grid,
    sort_samples,
    sampler_name,
    ddim_eta,
    n_iter,
    batch_size,
    i,
    denoising_strength,
    resize_mode,
    save_individual_images,
    model_name,
):
    filename_i = os.path.join(sample_path_i, filename)

    if "defaults" in st.session_state:
        if st.session_state["defaults"].general.save_metadata or write_info_files:
            # toggles differ for txt2img vs. img2img:
            offset = 0 if init_img is None else 2
            toggles = []
            if prompt_matrix:
                toggles.append(0)
            if normalize_prompt_weights:
                toggles.append(1)
            if init_img is not None:
                if uses_loopback:
                    toggles.append(2)
                if uses_random_seed_loopback:
                    toggles.append(3)
            if save_individual_images:
                toggles.append(2 + offset)
            if save_grid:
                toggles.append(3 + offset)
            if sort_samples:
                toggles.append(4 + offset)
            if write_info_files:
                toggles.append(5 + offset)
            if use_GFPGAN:
                toggles.append(6 + offset)
            metadata = dict(
                target="txt2img" if init_img is None else "img2img",
                prompt=prompts[i],
                ddim_steps=steps,
                toggles=toggles,
                sampler_name=sampler_name,
                ddim_eta=ddim_eta,
                n_iter=n_iter,
                batch_size=batch_size,
                cfg_scale=cfg_scale,
                seed=seeds[i],
                width=width,
                height=height,
                normalize_prompt_weights=normalize_prompt_weights,
                model_name=model_name,
            )
            # Not yet any use for these, but they bloat up the files:
            # info_dict["init_img"] = init_img
            # info_dict["init_mask"] = init_mask
            if init_img is not None:
                metadata["denoising_strength"] = str(denoising_strength)
                metadata["resize_mode"] = resize_mode

    if write_info_files:
        with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
            yaml.dump(metadata, f, allow_unicode=True, width=10000)

    if st.session_state["defaults"].general.save_metadata:
        # metadata = {
        # 	"SD:prompt": prompts[i],
        # 	"SD:seed": str(seeds[i]),
        # 	"SD:width": str(width),
        # 	"SD:height": str(height),
        # 	"SD:steps": str(steps),
        # 	"SD:cfg_scale": str(cfg_scale),
        # 	"SD:normalize_prompt_weights": str(normalize_prompt_weights),
        # }
        metadata = {"SD:" + k: v for (k, v) in metadata.items()}

        if save_ext == "png":
            mdata = PngInfo()
            for key in metadata:
                mdata.add_text(key, str(metadata[key]))
            image.save(f"{filename_i}.png", pnginfo=mdata)
        else:
            if jpg_sample:
                image.save(f"{filename_i}.jpg", quality=save_quality, optimize=True)
            elif save_ext == "webp":
                image.save(
                    f"{filename_i}.{save_ext}",
                    "webp",
                    quality=save_quality,
                    lossless=save_lossless,
                )
            else:
                # not sure what file format this is
                image.save(f"{filename_i}.{save_ext}", f"{save_ext}")
            try:
                exif_dict = piexif.load(f"{filename_i}.{save_ext}")
            except:
                exif_dict = {"Exif": dict()}
            exif_dict["Exif"][
                piexif.ExifIFD.UserComment
            ] = piexif.helper.UserComment.dump(json.dumps(metadata), encoding="unicode")
            piexif.insert(piexif.dump(exif_dict), f"{filename_i}.{save_ext}")


def get_next_sequence_number(path, prefix=""):
    """
    Determines and returns the next sequence number to use when saving an
    image in the specified directory.

    If a prefix is given, only consider files whose names start with that
    prefix, and strip the prefix from filenames before extracting their
    sequence number.

    The sequence starts at 0.
    """
    result = -1
    for p in Path(path).iterdir():
        if p.name.endswith((".png", ".jpg")) and p.name.startswith(prefix):
            tmp = p.name[len(prefix) :]
            try:
                result = max(int(tmp.split("-")[0]), result)
            except ValueError:
                pass
    return result + 1


def oxlamon_matrix(prompt, seed, n_iter, batch_size):
    pattern = re.compile(r"(,\s){2,}")

    class PromptItem:
        def __init__(self, text, parts, item):
            self.text = text
            self.parts = parts
            if item:
                self.parts.append(item)

    def clean(txt):
        return re.sub(pattern, ", ", txt)

    def getrowcount(txt):
        for data in re.finditer(".*?\\((.*?)\\).*", txt):
            if data:
                return len(data.group(1).split("|"))
            break
        return None

    def repliter(txt):
        for data in re.finditer(".*?\\((.*?)\\).*", txt):
            if data:
                r = data.span(1)
                for item in data.group(1).split("|"):
                    yield (
                        clean(txt[: r[0] - 1] + item.strip() + txt[r[1] + 1 :]),
                        item.strip(),
                    )
            break

    def iterlist(items):
        outitems = []
        for item in items:
            for newitem, newpart in repliter(item.text):
                outitems.append(PromptItem(newitem, item.parts.copy(), newpart))

        return outitems

    def getmatrix(prompt):
        dataitems = [PromptItem(prompt[1:].strip(), [], None)]
        while True:
            newdataitems = iterlist(dataitems)
            if len(newdataitems) == 0:
                return dataitems
            dataitems = newdataitems

    def classToArrays(items, seed, n_iter):
        texts = []
        parts = []
        seeds = []

        for item in items:
            itemseed = seed
            for i in range(n_iter):
                texts.append(item.text)
                parts.append(f"Seed: {itemseed}\n" + "\n".join(item.parts))
                seeds.append(itemseed)
                itemseed += 1

        return seeds, texts, parts

    all_seeds, all_prompts, prompt_matrix_parts = classToArrays(
        getmatrix(prompt), seed, n_iter
    )
    n_iter = math.ceil(len(all_prompts) / batch_size)

    needrows = getrowcount(prompt)
    if needrows:
        xrows = math.sqrt(len(all_prompts))
        xrows = round(xrows)
        # if columns is to much
        cols = math.ceil(len(all_prompts) / xrows)
        if cols > needrows * 4:
            needrows *= 2

    return all_seeds, n_iter, prompt_matrix_parts, all_prompts, needrows


#
def process_images(
    outpath,
    func_init,
    func_sample,
    prompt,
    seed,
    sampler_name,
    save_grid,
    batch_size,
    n_iter,
    steps,
    cfg_scale,
    width,
    height,
    prompt_matrix,
    use_GFPGAN: bool = True,
    GFPGAN_model: str = "GFPGANv1.4",
    use_RealESRGAN: bool = False,
    realesrgan_model_name: str = "RealESRGAN_x4plus",
    use_LDSR: bool = False,
    LDSR_model_name: str = "model",
    ddim_eta=0.0,
    normalize_prompt_weights=True,
    init_img=None,
    init_mask=None,
    mask_blur_strength=3,
    mask_restore=False,
    denoising_strength=0.75,
    noise_mode=0,
    find_noise_steps=1,
    resize_mode=None,
    uses_loopback=False,
    uses_random_seed_loopback=False,
    sort_samples=True,
    write_info_files=True,
    jpg_sample=False,
    variant_amount=0.0,
    variant_seed=None,
    save_individual_images: bool = True,
):
    """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""

    torch_gc()
    # start time after garbage collection (or before?)
    start_time = time.time()

    # We will use this date here later for the folder name, need to start_time if not need
    datetime.datetime.now()

    mem_mon = MemUsageMonitor("MemMon")
    mem_mon.start()

    if st.session_state.defaults.general.use_sd_concepts_library:
        prompt_tokens = re.findall("<([a-zA-Z0-9-]+)>", prompt)

        if prompt_tokens:
            # compviz
            tokenizer = (
                server_state["model"]
                if not st.session_state["defaults"].general.optimized
                else server_state["modelCS"]
            ).cond_stage_model.tokenizer
            text_encoder = (
                server_state["model"]
                if not st.session_state["defaults"].general.optimized
                else server_state["modelCS"]
            ).cond_stage_model.transformer

            # diffusers
            # tokenizer = pipe.tokenizer
            # text_encoder = pipe.text_encoder

            ext = ("pt", "bin")

            if len(prompt_tokens) > 1:
                for token_name in prompt_tokens:
                    embedding_path = os.path.join(
                        st.session_state["defaults"].general.sd_concepts_library_folder,
                        token_name,
                    )
                    if os.path.exists(embedding_path):
                        for files in os.listdir(embedding_path):
                            if files.endswith(ext):
                                load_learned_embed_in_clip(
                                    f"{os.path.join(embedding_path, files)}",
                                    text_encoder,
                                    tokenizer,
                                    f"<{token_name}>",
                                )
            else:
                embedding_path = os.path.join(
                    st.session_state["defaults"].general.sd_concepts_library_folder,
                    prompt_tokens[0],
                )
                if os.path.exists(embedding_path):
                    for files in os.listdir(embedding_path):
                        if files.endswith(ext):
                            load_learned_embed_in_clip(
                                f"{os.path.join(embedding_path, files)}",
                                text_encoder,
                                tokenizer,
                                f"<{prompt_tokens[0]}>",
                            )

        #

    os.makedirs(outpath, exist_ok=True)

    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)

    if "|" not in prompt and prompt.startswith("@"):
        prompt = prompt[1:]

    negprompt = ""
    if "###" in prompt:
        prompt, negprompt = prompt.split("###", 1)
        prompt = prompt.strip()
        negprompt = negprompt.strip()

    comments = []

    prompt_matrix_parts = []
    simple_templating = False

    if prompt_matrix:
        if prompt.startswith("@"):
            simple_templating = True
            all_seeds, n_iter, prompt_matrix_parts, all_prompts, frows = oxlamon_matrix(
                prompt, seed, n_iter, batch_size
            )
        else:
            all_prompts = []
            prompt_matrix_parts = prompt.split("|")
            combination_count = 2 ** (len(prompt_matrix_parts) - 1)
            for combination_num in range(combination_count):
                current = prompt_matrix_parts[0]

                for n, text in enumerate(prompt_matrix_parts[1:]):
                    if combination_num & (2**n) > 0:
                        current += ("" if text.strip().startswith(",") else ", ") + text

                all_prompts.append(current)

            n_iter = math.ceil(len(all_prompts) / batch_size)
            all_seeds = len(all_prompts) * [seed]

        logger.info(
            f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches."
        )
    else:
        if not st.session_state["defaults"].general.no_verify_input:
            try:
                check_prompt_length(prompt, comments)
            except:
                import traceback

                logger.info("Error verifying input:", file=sys.stderr)
                logger.info(traceback.format_exc(), file=sys.stderr)

        all_prompts = batch_size * n_iter * [prompt]
        all_seeds = [seed + x for x in range(len(all_prompts))]

    precision_scope = (
        autocast
        if st.session_state["defaults"].general.precision == "autocast"
        else nullcontext
    )
    output_images = []
    grid_captions = []
    stats = []
    with torch.no_grad(), precision_scope("cuda"), (
        server_state["model"].ema_scope()
        if not st.session_state["defaults"].general.optimized
        else nullcontext()
    ):
        init_data = func_init()
        time.time()

        # if variant_amount > 0.0 create noise from base seed
        base_x = None
        if variant_amount > 0.0:
            target_seed_randomizer = seed_to_int("")  # random seed
            torch.manual_seed(
                seed
            )  # this has to be the single starting seed (not per-iteration)
            base_x = create_random_tensors(
                [opt_C, height // opt_f, width // opt_f], seeds=[seed]
            )
            # we don't want all_seeds to be sequential from starting seed with variants,
            # since that makes the same variants each time,
            # so we add target_seed_randomizer as a random offset
            for si in range(len(all_seeds)):
                all_seeds[si] += target_seed_randomizer

        for n in range(n_iter):
            logger.info(f"Iteration: {n+1}/{n_iter}")
            prompts = all_prompts[n * batch_size : (n + 1) * batch_size]
            captions = prompt_matrix_parts[n * batch_size : (n + 1) * batch_size]
            seeds = all_seeds[n * batch_size : (n + 1) * batch_size]

            logger.info(prompt)

            if st.session_state["defaults"].general.optimized:
                server_state["modelCS"].to(st.session_state["defaults"].general.gpu)

            uc = (
                server_state["model"]
                if not st.session_state["defaults"].general.optimized
                else server_state["modelCS"]
            ).get_learned_conditioning(len(prompts) * [negprompt])

            if isinstance(prompts, tuple):
                prompts = list(prompts)

            # split the prompt if it has : for weighting
            # TODO for speed it might help to have this occur when all_prompts filled??
            weighted_subprompts = split_weighted_subprompts(
                prompts[0], normalize_prompt_weights
            )

            # sub-prompt weighting used if more than 1
            if len(weighted_subprompts) > 1:
                c = torch.zeros_like(
                    uc
                )  # i dont know if this is correct.. but it works
                for i in range(0, len(weighted_subprompts)):
                    # note if alpha negative, it functions same as torch.sub
                    c = torch.add(
                        c,
                        (
                            server_state["model"]
                            if not st.session_state["defaults"].general.optimized
                            else server_state["modelCS"]
                        ).get_learned_conditioning(weighted_subprompts[i][0]),
                        alpha=weighted_subprompts[i][1],
                    )
            else:  # just behave like usual
                c = (
                    server_state["model"]
                    if not st.session_state["defaults"].general.optimized
                    else server_state["modelCS"]
                ).get_learned_conditioning(prompts)

            shape = [opt_C, height // opt_f, width // opt_f]

            if st.session_state["defaults"].general.optimized:
                mem = torch.cuda.memory_allocated() / 1e6
                server_state["modelCS"].to("cpu")
                while torch.cuda.memory_allocated() / 1e6 >= mem:
                    time.sleep(1)

            if noise_mode == 1 or noise_mode == 3:
                # TODO params for find_noise_to_image
                x = torch.cat(
                    batch_size
                    * [
                        find_noise_for_image(
                            server_state["model"],
                            server_state["device"],
                            init_img.convert("RGB"),
                            "",
                            find_noise_steps,
                            0.0,
                            normalize=True,
                            generation_callback=generation_callback,
                        )
                    ],
                    dim=0,
                )
            else:
                # we manually generate all input noises because each one should have a specific seed
                x = create_random_tensors(shape, seeds=seeds)

            if variant_amount > 0.0:  # we are making variants
                # using variant_seed as sneaky toggle,
                # when not None or '' use the variant_seed
                # otherwise use seeds
                if variant_seed is not None and variant_seed != "":
                    specified_variant_seed = seed_to_int(variant_seed)
                    torch.manual_seed(specified_variant_seed)
                    seeds = [specified_variant_seed]
                # finally, slerp base_x noise to target_x noise for creating a variant
                x = slerp(
                    st.session_state["defaults"].general.gpu,
                    max(0.0, min(1.0, variant_amount)),
                    base_x,
                    x,
                )

            samples_ddim = func_sample(
                init_data=init_data,
                x=x,
                conditioning=c,
                unconditional_conditioning=uc,
                sampler_name=sampler_name,
            )

            if st.session_state["defaults"].general.optimized:
                server_state["modelFS"].to(st.session_state["defaults"].general.gpu)

            x_samples_ddim = (
                server_state["model"]
                if not st.session_state["defaults"].general.optimized
                else server_state["modelFS"]
            ).decode_first_stage(samples_ddim)
            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

            run_images = []
            for i, x_sample in enumerate(x_samples_ddim):
                sanitized_prompt = slugify(prompts[i])

                percent = i / len(x_samples_ddim)

                if "progress_bar" in st.session_state:
                    st.session_state["progress_bar"].progress(
                        percent if percent < 100 else 100
                    )

                if sort_samples:
                    full_path = os.path.join(os.getcwd(), sample_path, sanitized_prompt)

                    sanitized_prompt = sanitized_prompt[: 120 - len(full_path)]
                    sample_path_i = os.path.join(sample_path, sanitized_prompt)

                    # print(f"output folder length: {len(os.path.join(os.getcwd(), sample_path_i))}")
                    # print(os.path.join(os.getcwd(), sample_path_i))

                    os.makedirs(sample_path_i, exist_ok=True)
                    base_count = get_next_sequence_number(sample_path_i)
                    filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}"
                else:
                    full_path = os.path.join(os.getcwd(), sample_path)
                    sample_path_i = sample_path
                    base_count = get_next_sequence_number(sample_path_i)
                    filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}_{sanitized_prompt}"[
                        : 120 - len(full_path)
                    ]  # same as before

                x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c")
                x_sample = x_sample.astype(np.uint8)
                image = Image.fromarray(x_sample)
                original_filename = filename

                if "preview_image" in st.session_state:
                    st.session_state["preview_image"].image(image)

                #
                if (
                    use_GFPGAN
                    and server_state["GFPGAN"] is not None
                    and not use_RealESRGAN
                    and not use_LDSR
                ):
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running GFPGAN on image %d of %d..."
                            % (i + 1, len(x_samples_ddim))
                        )

                    if server_state["GFPGAN"].name != GFPGAN_model:
                        load_models(
                            use_LDSR=use_LDSR,
                            LDSR_model=LDSR_model_name,
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    torch_gc()

                    with torch.autocast("cuda"):
                        cropped_faces, restored_faces, restored_img = server_state[
                            "GFPGAN"
                        ].enhance(
                            x_sample[:, :, ::-1],
                            has_aligned=False,
                            only_center_face=False,
                            paste_back=True,
                        )

                    gfpgan_sample = restored_img[:, :, ::-1]
                    gfpgan_image = Image.fromarray(gfpgan_sample)

                    # if st.session_state["GFPGAN_strenght"]:
                    # gfpgan_sample = Image.blend(image, gfpgan_image, st.session_state["GFPGAN_strenght"])

                    gfpgan_filename = original_filename + "-gfpgan"

                    save_sample(
                        gfpgan_image,
                        sample_path_i,
                        gfpgan_filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        use_GFPGAN,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        False,
                        server_state["loaded_model"],
                    )

                    output_images.append(gfpgan_image)  # 287
                    run_images.append(gfpgan_image)

                    if simple_templating:
                        grid_captions.append(captions[i] + "\ngfpgan")

                #
                elif (
                    use_RealESRGAN
                    and server_state["RealESRGAN"] is not None
                    and not use_GFPGAN
                ):
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running RealESRGAN on image %d of %d..."
                            % (i + 1, len(x_samples_ddim))
                        )
                    # skip_save = True # #287 >_>
                    torch_gc()

                    if server_state["RealESRGAN"].model.name != realesrgan_model_name:
                        # try_loading_RealESRGAN(realesrgan_model_name)
                        load_models(
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    output, img_mode = server_state["RealESRGAN"].enhance(
                        x_sample[:, :, ::-1]
                    )
                    esrgan_filename = original_filename + "-esrgan4x"
                    esrgan_sample = output[:, :, ::-1]
                    esrgan_image = Image.fromarray(esrgan_sample)

                    # save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
                    # normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
                    # save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)

                    save_sample(
                        esrgan_image,
                        sample_path_i,
                        esrgan_filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        use_GFPGAN,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        False,
                        server_state["loaded_model"],
                    )

                    output_images.append(esrgan_image)  # 287
                    run_images.append(esrgan_image)

                    if simple_templating:
                        grid_captions.append(captions[i] + "\nesrgan")

                #
                elif use_LDSR and "LDSR" in server_state and not use_GFPGAN:
                    logger.info(
                        "Running LDSR on image %d of %d..."
                        % (i + 1, len(x_samples_ddim))
                    )
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running LDSR on image %d of %d..."
                            % (i + 1, len(x_samples_ddim))
                        )
                    # skip_save = True # #287 >_>
                    torch_gc()

                    if server_state["LDSR"].name != LDSR_model_name:
                        # try_loading_RealESRGAN(realesrgan_model_name)
                        load_models(
                            use_LDSR=use_LDSR,
                            LDSR_model=LDSR_model_name,
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    result = server_state["LDSR"].superResolution(
                        image,
                        ddimSteps=st.session_state["ldsr_sampling_steps"],
                        preDownScale=st.session_state["preDownScale"],
                        postDownScale=st.session_state["postDownScale"],
                        downsample_method=st.session_state["downsample_method"],
                    )

                    ldsr_filename = original_filename + "-ldsr4x"
                    # ldsr_sample = result[:,:,::-1]
                    # ldsr_image = Image.fromarray(ldsr_sample)

                    # save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
                    # normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
                    # save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)

                    save_sample(
                        result,
                        sample_path_i,
                        ldsr_filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        use_GFPGAN,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        False,
                        server_state["loaded_model"],
                    )

                    output_images.append(result)  # 287
                    run_images.append(result)

                    if simple_templating:
                        grid_captions.append(captions[i] + "\nldsr")

                #
                elif (
                    use_LDSR
                    and "LDSR" in server_state
                    and use_GFPGAN
                    and "GFPGAN" in server_state
                ):
                    logger.info(
                        "Running GFPGAN+LDSR on image %d of %d..."
                        % (i + 1, len(x_samples_ddim))
                    )
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running GFPGAN+LDSR on image %d of %d..."
                            % (i + 1, len(x_samples_ddim))
                        )

                    if server_state["GFPGAN"].name != GFPGAN_model:
                        load_models(
                            use_LDSR=use_LDSR,
                            LDSR_model=LDSR_model_name,
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    torch_gc()
                    cropped_faces, restored_faces, restored_img = server_state[
                        "GFPGAN"
                    ].enhance(
                        x_sample[:, :, ::-1],
                        has_aligned=False,
                        only_center_face=False,
                        paste_back=True,
                    )

                    gfpgan_sample = restored_img[:, :, ::-1]
                    gfpgan_image = Image.fromarray(gfpgan_sample)

                    if server_state["LDSR"].name != LDSR_model_name:
                        # try_loading_RealESRGAN(realesrgan_model_name)
                        load_models(
                            use_LDSR=use_LDSR,
                            LDSR_model=LDSR_model_name,
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    # LDSR.superResolution(gfpgan_image, ddimSteps=100, preDownScale='None', postDownScale='None', downsample_method="Lanczos")
                    result = server_state["LDSR"].superResolution(
                        gfpgan_image,
                        ddimSteps=st.session_state["ldsr_sampling_steps"],
                        preDownScale=st.session_state["preDownScale"],
                        postDownScale=st.session_state["postDownScale"],
                        downsample_method=st.session_state["downsample_method"],
                    )

                    ldsr_filename = original_filename + "-gfpgan-ldsr2x"
                    # ldsr_sample = result[:,:,::-1]
                    # ldsr_image = Image.fromarray(result)

                    # save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
                    # normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
                    # save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)

                    save_sample(
                        result,
                        sample_path_i,
                        ldsr_filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        use_GFPGAN,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        False,
                        server_state["loaded_model"],
                    )

                    output_images.append(result)  # 287
                    run_images.append(result)

                    if simple_templating:
                        grid_captions.append(captions[i] + "\ngfpgan-ldsr")

                elif (
                    use_RealESRGAN
                    and server_state["RealESRGAN"] is not None
                    and use_GFPGAN
                    and server_state["GFPGAN"] is not None
                ):
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running GFPGAN+RealESRGAN on image %d of %d..."
                            % (i + 1, len(x_samples_ddim))
                        )
                    # skip_save = True # #287 >_>
                    torch_gc()
                    cropped_faces, restored_faces, restored_img = server_state[
                        "GFPGAN"
                    ].enhance(
                        x_sample[:, :, ::-1],
                        has_aligned=False,
                        only_center_face=False,
                        paste_back=True,
                    )
                    gfpgan_sample = restored_img[:, :, ::-1]

                    if server_state["RealESRGAN"].model.name != realesrgan_model_name:
                        # try_loading_RealESRGAN(realesrgan_model_name)
                        load_models(
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    output, img_mode = server_state["RealESRGAN"].enhance(
                        gfpgan_sample[:, :, ::-1]
                    )
                    gfpgan_esrgan_filename = original_filename + "-gfpgan-esrgan4x"
                    gfpgan_esrgan_sample = output[:, :, ::-1]
                    gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample)

                    save_sample(
                        gfpgan_esrgan_image,
                        sample_path_i,
                        gfpgan_esrgan_filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        False,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        False,
                        server_state["loaded_model"],
                    )

                    output_images.append(gfpgan_esrgan_image)  # 287
                    run_images.append(gfpgan_esrgan_image)

                    if simple_templating:
                        grid_captions.append(captions[i] + "\ngfpgan_esrgan")

                #

                else:
                    output_images.append(image)
                    run_images.append(image)

                if mask_restore and init_mask:
                    # init_mask = init_mask if keep_mask else ImageOps.invert(init_mask)
                    init_mask = init_mask.filter(
                        ImageFilter.GaussianBlur(mask_blur_strength)
                    )
                    init_mask = init_mask.convert("L")
                    init_img = init_img.convert("RGB")
                    image = image.convert("RGB")

                    if use_RealESRGAN and server_state["RealESRGAN"] is not None:
                        if (
                            server_state["RealESRGAN"].model.name
                            != realesrgan_model_name
                        ):
                            # try_loading_RealESRGAN(realesrgan_model_name)
                            load_models(
                                use_GFPGAN=use_GFPGAN,
                                use_RealESRGAN=use_RealESRGAN,
                                RealESRGAN_model=realesrgan_model_name,
                            )

                        output, img_mode = server_state["RealESRGAN"].enhance(
                            np.array(init_img, dtype=np.uint8)
                        )
                        init_img = Image.fromarray(output)
                        init_img = init_img.convert("RGB")

                        output, img_mode = server_state["RealESRGAN"].enhance(
                            np.array(init_mask, dtype=np.uint8)
                        )
                        init_mask = Image.fromarray(output)
                        init_mask = init_mask.convert("L")

                    image = Image.composite(init_img, image, init_mask)

                if save_individual_images:
                    save_sample(
                        image,
                        sample_path_i,
                        filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        use_GFPGAN,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        save_individual_images,
                        server_state["loaded_model"],
                    )

                    # if add_original_image or not simple_templating:
                    # output_images.append(image)
                    # if simple_templating:
                    # grid_captions.append( captions[i] )
                if "defaults" in st.session_state:
                    if st.session_state["defaults"].general.optimized:
                        mem = torch.cuda.memory_allocated() / 1e6
                        server_state["modelFS"].to("cpu")
                        while torch.cuda.memory_allocated() / 1e6 >= mem:
                            time.sleep(1)

            if len(run_images) > 1:
                preview_image = image_grid(run_images, n_iter)
            else:
                preview_image = run_images[0]

            # Constrain the final preview image to 1440x900 so we're not sending huge amounts of data
            # to the browser
            preview_image = constrain_image(preview_image, 1440, 900)
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text("Finished!")

            if "preview_image" in st.session_state:
                st.session_state["preview_image"].image(preview_image)

        if prompt_matrix or save_grid:
            if prompt_matrix:
                if simple_templating:
                    grid = image_grid(
                        output_images,
                        n_iter,
                        force_n_rows=frows,
                        captions=grid_captions,
                    )
                else:
                    grid = image_grid(
                        output_images,
                        n_iter,
                        force_n_rows=1 << ((len(prompt_matrix_parts) - 1) // 2),
                    )
                    try:
                        grid = draw_prompt_matrix(
                            grid, width, height, prompt_matrix_parts
                        )
                    except:
                        import traceback

                        logger.error(
                            "Error creating prompt_matrix text:", file=sys.stderr
                        )
                        logger.error(traceback.format_exc(), file=sys.stderr)
            else:
                grid = image_grid(output_images, batch_size)

            if grid and (batch_size > 1 or n_iter > 1):
                output_images.insert(0, grid)

            grid_count = get_next_sequence_number(outpath, "grid-")
            grid_file = f"grid-{grid_count:05}-{seed}_{slugify(prompts[i].replace(' ', '_')[:120-len(full_path)])}.{grid_ext}"
            grid.save(
                os.path.join(outpath, grid_file),
                grid_format,
                quality=grid_quality,
                lossless=grid_lossless,
                optimize=True,
            )

        time.time()

    mem_max_used, mem_total = mem_mon.read_and_stop()
    time_diff = time.time() - start_time

    info = f"""
            {prompt}
            Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', Denoising strength: '+str(denoising_strength) if init_img is not None else ''}{', GFPGAN' if use_GFPGAN and server_state["GFPGAN"] is not None else ''}{', '+realesrgan_model_name if use_RealESRGAN and server_state["RealESRGAN"] is not None else ''}{', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()
    stats = f"""
            Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
            Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%"""

    for comment in comments:
        info += "\n\n" + comment

    # mem_mon.stop()
    # del mem_mon
    torch_gc()

    return output_images, seed, info, stats


def resize_image(resize_mode, im, width, height):
    LANCZOS = (
        Image.Resampling.LANCZOS if hasattr(Image, "Resampling") else Image.LANCZOS
    )
    if resize_mode == 0:
        res = im.resize((width, height), resample=LANCZOS)
    elif resize_mode == 1:
        ratio = width / height
        src_ratio = im.width / im.height

        src_w = width if ratio > src_ratio else im.width * height // im.height
        src_h = height if ratio <= src_ratio else im.height * width // im.width

        resized = im.resize((src_w, src_h), resample=LANCZOS)
        res = Image.new("RGBA", (width, height))
        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
    else:
        ratio = width / height
        src_ratio = im.width / im.height

        src_w = width if ratio < src_ratio else im.width * height // im.height
        src_h = height if ratio >= src_ratio else im.height * width // im.width

        resized = im.resize((src_w, src_h), resample=LANCZOS)
        res = Image.new("RGBA", (width, height))
        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))

        if ratio < src_ratio:
            fill_height = height // 2 - src_h // 2
            res.paste(
                resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)
            )
            res.paste(
                resized.resize(
                    (width, fill_height), box=(0, resized.height, width, resized.height)
                ),
                box=(0, fill_height + src_h),
            )
        elif ratio > src_ratio:
            fill_width = width // 2 - src_w // 2
            res.paste(
                resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)
            )
            res.paste(
                resized.resize(
                    (fill_width, height), box=(resized.width, 0, resized.width, height)
                ),
                box=(fill_width + src_w, 0),
            )

    return res


def constrain_image(img, max_width, max_height):
    ratio = max(img.width / max_width, img.height / max_height)
    if ratio <= 1:
        return img
    resampler = (
        Image.Resampling.LANCZOS if hasattr(Image, "Resampling") else Image.LANCZOS
    )
    resized = img.resize(
        (int(img.width / ratio), int(img.height / ratio)), resample=resampler
    )
    return resized


def convert_pt_to_bin_and_load(input_file, text_encoder, tokenizer, placeholder_token):
    x = torch.load(input_file, map_location=torch.device("cpu"))

    params_dict = {
        placeholder_token: torch.tensor(list(x["string_to_param"].items())[0][1])
    }
    torch.save(params_dict, "learned_embeds.bin")
    load_learned_embed_in_clip(
        "learned_embeds.bin", text_encoder, tokenizer, placeholder_token
    )
    logger.info("loaded", placeholder_token)


#


================================================
FILE: scripts/sd_utils/bridge.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

# base webui import and utils.
# import streamlit as st

# We import hydralit like this to replace the previous stuff
# we had with native streamlit as it lets ur replace things 1:1
from sd_utils import logger, load_models

# streamlit imports

# streamlit components section

# other imports
import requests, time, json, base64
from io import BytesIO

# import custom components


# end of imports
# ---------------------------------------------------------------------------------------------------------------


@logger.catch(reraise=True)
def run_bridge(
    interval,
    api_key,
    horde_name,
    horde_url,
    priority_usernames,
    horde_max_pixels,
    horde_nsfw,
    horde_censor_nsfw,
    horde_blacklist,
    horde_censorlist,
):
    current_id = None
    current_payload = None
    loop_retry = 0
    # load the model for stable horde if its not in memory already
    # we should load it after we get the request from the API in
    # case the model is different from the loaded in memory but
    # for now we can load it here so its read right away.
    load_models(use_GFPGAN=True)
    while True:
        if loop_retry > 10 and current_id:
            logger.info(
                f"Exceeded retry count {loop_retry} for generation id {current_id}. Aborting generation!"
            )
            current_id = None
            current_payload = None
            current_generation = None
            loop_retry = 0
        elif current_id:
            logger.info(f"Retrying ({loop_retry}/10) for generation id {current_id}...")
        gen_dict = {
            "name": horde_name,
            "max_pixels": horde_max_pixels,
            "priority_usernames": priority_usernames,
            "nsfw": horde_nsfw,
            "blacklist": horde_blacklist,
            "models": ["stable_diffusion"],
        }
        headers = {"apikey": api_key}
        if current_id:
            loop_retry += 1
        else:
            try:
                pop_req = requests.post(
                    horde_url + "/api/v2/generate/pop", json=gen_dict, headers=headers
                )
            except requests.exceptions.ConnectionError:
                logger.warning(
                    f"Server {horde_url} unavailable during pop. Waiting 10 seconds..."
                )
                time.sleep(10)
                continue
            except requests.exceptions.JSONDecodeError():
                logger.warning(
                    f"Server {horde_url} unavailable during pop. Waiting 10 seconds..."
                )
                time.sleep(10)
                continue
            try:
                pop = pop_req.json()
            except json.decoder.JSONDecodeError:
                logger.warning(
                    f"Could not decode response from {horde_url} as json. Please inform its administrator!"
                )
                time.sleep(interval)
                continue
            if pop is None:
                logger.warning(
                    f"Something has gone wrong with {horde_url}. Please inform its administrator!"
                )
                time.sleep(interval)
                continue
            if not pop_req.ok:
                pop["message"]
                logger.warning(
                    f"During gen pop, server {horde_url} responded with status code {pop_req.status_code}: {pop['message']}. Waiting for 10 seconds..."
                )
                if "errors" in pop:
                    logger.debug(f"Detailed Request Errors: {pop['errors']}")
                time.sleep(10)
                continue
            if not pop.get("id"):
                skipped_info = pop.get("skipped")
                if skipped_info and len(skipped_info):
                    skipped_info = f" Skipped Info: {skipped_info}."
                else:
                    skipped_info = ""
                logger.info(
                    f"Server {horde_url} has no valid generations to do for us.{skipped_info}"
                )
                time.sleep(interval)
                continue
            current_id = pop["id"]
            logger.info(f"Request with id {current_id} picked up. Initiating work...")
            current_payload = pop["payload"]
            if "toggles" in current_payload and current_payload["toggles"] is None:
                logger.error(f"Received Bad payload: {pop}")
                current_id = None
                current_payload = None
                current_generation = None
                loop_retry = 0
                time.sleep(10)
                continue

        logger.debug(current_payload)
        current_payload["toggles"] = current_payload.get("toggles", [1, 4])
        # In bridge-mode, matrix is prepared on the horde and split in multiple nodes
        if 0 in current_payload["toggles"]:
            current_payload["toggles"].remove(0)
        if 8 not in current_payload["toggles"]:
            if horde_censor_nsfw and not horde_nsfw:
                current_payload["toggles"].append(8)
            elif any(word in current_payload["prompt"] for word in horde_censorlist):
                current_payload["toggles"].append(8)

        from txt2img import txt2img

        """{'prompt': 'Centred Husky, inside spiral with circular patterns, trending on dribbble, knotwork, spirals, key patterns,
        zoomorphics, ', 'ddim_steps': 30, 'n_iter': 1, 'sampler_name': 'DDIM', 'cfg_scale': 16.0, 'seed': '3405278433', 'height': 512, 'width': 512}"""

        # images, seed, info, stats = txt2img(**current_payload)
        images, seed, info, stats = txt2img(
            str(current_payload["prompt"]),
            int(current_payload["ddim_steps"]),
            str(current_payload["sampler_name"]),
            int(current_payload["n_iter"]),
            1,
            float(current_payload["cfg_scale"]),
            str(current_payload["seed"]),
            int(current_payload["height"]),
            int(current_payload["width"]),
            save_grid=False,
            group_by_prompt=False,
            save_individual_images=False,
            write_info_files=False,
        )

        buffer = BytesIO()
        # We send as WebP to avoid using all the horde bandwidth
        images[0].save(buffer, format="WebP", quality=90)
        # logger.info(info)
        submit_dict = {
            "id": current_id,
            "generation": base64.b64encode(buffer.getvalue()).decode("utf8"),
            "api_key": api_key,
            "seed": seed,
            "max_pixels": horde_max_pixels,
        }
        current_generation = seed
        while current_id and current_generation is not None:
            try:
                submit_req = requests.post(
                    horde_url + "/api/v2/generate/submit",
                    json=submit_dict,
                    headers=headers,
                )
                try:
                    submit = submit_req.json()
                except json.decoder.JSONDecodeError:
                    logger.error(
                        f"Something has gone wrong with {horde_url} during submit. Please inform its administrator!  (Retry {loop_retry}/10)"
                    )
                    time.sleep(interval)
                    continue
                if submit_req.status_code == 404:
                    logger.info(
                        "The generation we were working on got stale. Aborting!"
                    )
                elif not submit_req.ok:
                    logger.error(
                        f"During gen submit, server {horde_url} responded with status code {submit_req.status_code}: {submit['message']}. Waiting for 10 seconds...  (Retry {loop_retry}/10)"
                    )
                    if "errors" in submit:
                        logger.debug(f"Detailed Request Errors: {submit['errors']}")
                    time.sleep(10)
                    continue
                else:
                    logger.info(
                        f'Submitted generation with id {current_id} and contributed for {submit_req.json()["reward"]}'
                    )
                current_id = None
                current_payload = None
                current_generation = None
                loop_retry = 0
            except requests.exceptions.ConnectionError:
                logger.warning(
                    f"Server {horde_url} unavailable during submit. Waiting 10 seconds...  (Retry {loop_retry}/10)"
                )
                time.sleep(10)
                continue
        time.sleep(interval)


================================================
FILE: scripts/sd_utils_old.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
# from webui_streamlit import st
import hydralit as st

# streamlit imports
from streamlit import StopException

# from streamlit.runtime.scriptrunner import script_run_context

# streamlit components section
from streamlit_server_state import server_state, server_state_lock

# other imports

import warnings
import json

import base64, cv2
import os, sys, re, random, datetime, time, math, toml
import gc
from PIL import Image, ImageFont, ImageDraw, ImageFilter
from PIL.PngImagePlugin import PngInfo
from scipy import integrate
import torch
from torchdiffeq import odeint
import k_diffusion as K
import math, requests
import mimetypes
import numpy as np
import pynvml
import threading
import torch, torchvision
from torch import autocast
from torchvision import transforms
import torch.nn as nn
from omegaconf import OmegaConf
import yaml
from pathlib import Path
from contextlib import nullcontext
from einops import rearrange, repeat
from ldm.util import instantiate_from_config
from retry import retry
from slugify import slugify
import skimage
import piexif
import piexif.helper
from tqdm import trange
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import ismap

# from abc import ABC, abstractmethod
from io import BytesIO
from packaging import version
from pathlib import Path
from huggingface_hub import hf_hub_download

# import librosa
from logger import logger

# from loguru import logger

try:
    from realesrgan import RealESRGANer
    from basicsr.archs.rrdbnet_arch import RRDBNet
except ImportError:
    logger.error(
        "You tried to import realesrgan without having it installed properly. To install Real-ESRGAN, run:\n\n"
        "pip install realesrgan"
    )

# Temp imports
# from basicsr.utils.registry import ARCH_REGISTRY


# end of imports
# ---------------------------------------------------------------------------------------------------------------

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging

    logging.set_verbosity_error()
except:
    pass

# remove some annoying deprecation warnings that show every now and then.
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)

# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init()
mimetypes.add_type("application/javascript", ".js")

# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
opt_f = 8

# The model manager loads and unloads the SD models and has features to download them or find their location
# model_manager = ModelManager()


def load_configs():
    if "defaults" not in st.session_state:
        st.session_state["defaults"] = {}

    st.session_state["defaults"] = OmegaConf.load("configs/webui/webui_streamlit.yaml")

    if os.path.exists("configs/webui/userconfig_streamlit.yaml"):
        user_defaults = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")

        if "version" in user_defaults.general:
            if version.parse(user_defaults.general.version) < version.parse(
                st.session_state["defaults"].general.version
            ):
                logger.error(
                    "The version of the user config file is older than the version on the defaults config file. "
                    "This means there were big changes we made on the config."
                    "We are removing this file and recreating it from the defaults in order to make sure things work properly."
                )
                os.remove("configs/webui/userconfig_streamlit.yaml")
                st.experimental_rerun()
        else:
            logger.error(
                "The version of the user config file is older than the version on the defaults config file. "
                "This means there were big changes we made on the config."
                "We are removing this file and recreating it from the defaults in order to make sure things work properly."
            )
            os.remove("configs/webui/userconfig_streamlit.yaml")
            st.experimental_rerun()

        try:
            st.session_state["defaults"] = OmegaConf.merge(
                st.session_state["defaults"], user_defaults
            )
        except KeyError:
            st.experimental_rerun()
    else:
        OmegaConf.save(
            config=st.session_state.defaults,
            f="configs/webui/userconfig_streamlit.yaml",
        )
        loaded = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")
        assert st.session_state.defaults == loaded

    if os.path.exists(".streamlit/config.toml"):
        st.session_state["streamlit_config"] = toml.load(".streamlit/config.toml")

    if st.session_state["defaults"].daisi_app.running_on_daisi_io:
        if os.path.exists("scripts/modeldownload.py"):
            import modeldownload

            modeldownload.updateModels()

    if "keep_all_models_loaded" in st.session_state.defaults.general:
        with server_state_lock["keep_all_models_loaded"]:
            server_state["keep_all_models_loaded"] = st.session_state[
                "defaults"
            ].general.keep_all_models_loaded
    else:
        st.session_state["defaults"].general.keep_all_models_loaded = False
        with server_state_lock["keep_all_models_loaded"]:
            server_state["keep_all_models_loaded"] = st.session_state[
                "defaults"
            ].general.keep_all_models_loaded


load_configs()

#
# if st.session_state["defaults"].debug.enable_hydralit:
# navbar_theme = {'txc_inactive': '#FFFFFF','menu_background':'#0e1117','txc_active':'black','option_active':'red'}
# app = st.HydraApp(title='Stable Diffusion WebUI', favicon="", use_cookie_cache=False, sidebar_state="expanded", layout="wide", navbar_theme=navbar_theme,
# hide_streamlit_markers=False, allow_url_nav=True , clear_cross_app_sessions=False, use_loader=False)
# else:
# app = None

#
grid_format = st.session_state["defaults"].general.save_format
grid_lossless = False
grid_quality = st.session_state["defaults"].general.grid_quality
if grid_format == "png":
    grid_ext = "png"
    grid_format = "png"
elif grid_format in ["jpg", "jpeg"]:
    grid_quality = int(grid_format) if len(grid_format) > 1 else 100
    grid_ext = "jpg"
    grid_format = "jpeg"
elif grid_format[0] == "webp":
    grid_quality = int(grid_format) if len(grid_format) > 1 else 100
    grid_ext = "webp"
    grid_format = "webp"
    if grid_quality < 0:  # e.g. webp:-100 for lossless mode
        grid_lossless = True
        grid_quality = abs(grid_quality)

#
save_format = st.session_state["defaults"].general.save_format
save_lossless = False
save_quality = 100
if save_format == "png":
    save_ext = "png"
    save_format = "png"
elif save_format in ["jpg", "jpeg"]:
    save_quality = int(save_format) if len(save_format) > 1 else 100
    save_ext = "jpg"
    save_format = "jpeg"
elif save_format == "webp":
    save_quality = int(save_format) if len(save_format) > 1 else 100
    save_ext = "webp"
    save_format = "webp"
    if save_quality < 0:  # e.g. webp:-100 for lossless mode
        save_lossless = True
        save_quality = abs(save_quality)

# this should force GFPGAN and RealESRGAN onto the selected gpu as well
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"  # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = str(st.session_state["defaults"].general.gpu)


# functions to load css locally OR remotely starts here. Options exist for future flexibility. Called as st.markdown with unsafe_allow_html as css injection
# TODO, maybe look into async loading the file especially for remote fetching
def local_css(file_name):
    with open(file_name) as f:
        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)


def remote_css(url):
    st.markdown(f'<link href="{url}" rel="stylesheet">', unsafe_allow_html=True)


def load_css(isLocal, nameOrURL):
    if isLocal:
        local_css(nameOrURL)
    else:
        remote_css(nameOrURL)


def set_page_title(title):
    """
    Simple function to allows us to change the title dynamically.
    Normally you can use `st.set_page_config` to change the title but it can only be used once per app.
    """

    st.sidebar.markdown(
        unsafe_allow_html=True,
        body=f"""
                            <iframe height=0 srcdoc="<script>
                            const title = window.parent.document.querySelector('title') \

                            const oldObserver = window.parent.titleObserver
                            if (oldObserver) {{
                            oldObserver.disconnect()
                            }} \

                            const newObserver = new MutationObserver(function(mutations) {{
                            const target = mutations[0].target
                            if (target.text !== '{title}') {{
                            target.text = '{title}'
                            }}
                            }}) \

                            newObserver.observe(title, {{ childList: true }})
                            window.parent.titleObserver = newObserver \

                            title.text = '{title}'
                            </script>" />
                            """,
    )


def make_grid(n_items=5, n_cols=5):
    n_rows = 1 + n_items // int(n_cols)

    rows = [st.container() for _ in range(n_rows)]

    cols_per_row = [r.columns(n_cols) for r in rows]
    cols = [column for row in cols_per_row for column in row]

    return cols


def merge(file1, file2, out, weight):
    alpha = (weight) / 100
    if not (file1.endswith(".ckpt")):
        file1 += ".ckpt"
    if not (file2.endswith(".ckpt")):
        file2 += ".ckpt"
    if not (out.endswith(".ckpt")):
        out += ".ckpt"
    # Load Models
    model_0 = torch.load(file1)
    model_1 = torch.load(file2)
    theta_0 = model_0["state_dict"]
    theta_1 = model_1["state_dict"]

    for key in theta_0.keys():
        if "model" in key and key in theta_1:
            theta_0[key] = (alpha) * theta_0[key] + (1 - alpha) * theta_1[key]

    logger.info("RUNNING...\n(STAGE 2)")

    for key in theta_1.keys():
        if "model" in key and key not in theta_0:
            theta_0[key] = theta_1[key]
    torch.save(model_0, out)


def human_readable_size(size, decimal_places=3):
    """Return a human readable size from bytes."""
    for unit in ["B", "KB", "MB", "GB", "TB"]:
        if size < 1024.0:
            break
        size /= 1024.0
    return f"{size:.{decimal_places}f}{unit}"


def load_models(
    use_LDSR=False,
    LDSR_model="model",
    use_GFPGAN=False,
    GFPGAN_model="GFPGANv1.4",
    use_RealESRGAN=False,
    RealESRGAN_model="RealESRGAN_x4plus",
    CustomModel_available=False,
    custom_model="Stable Diffusion v1.5",
):
    """Load the different models. We also reuse the models that are already in memory to speed things up instead of loading them again."""

    # model_manager.init()

    logger.info("Loading models.")

    if "progress_bar_text" in st.session_state:
        st.session_state["progress_bar_text"].text("")

    # check what models we want to use and if the they are already loaded.
    with server_state_lock["LDSR"]:
        if use_LDSR:
            if "LDSR" in server_state and server_state["LDSR"].name == LDSR_model:
                logger.info("LDSR already loaded")
            else:
                if "LDSR" in server_state:
                    del server_state["LDSR"]

                # Load GFPGAN
                if os.path.exists(st.session_state["defaults"].general.LDSR_dir):
                    try:
                        server_state["LDSR"] = load_LDSR(model_name=LDSR_model)
                        logger.info("Loaded LDSR")
                    except Exception:
                        import traceback

                        logger.error("Error loading LDSR:", file=sys.stderr)
                        logger.error(traceback.format_exc(), file=sys.stderr)
        else:
            if "LDSR" in server_state and not server_state["keep_all_models_loaded"]:
                logger.debug(
                    "LDSR was in memory but we won't use it. Removing to save VRAM."
                )
                del server_state["LDSR"]

    with server_state_lock["GFPGAN"]:
        if use_GFPGAN:
            if "GFPGAN" in server_state and server_state["GFPGAN"].name == GFPGAN_model:
                logger.info("GFPGAN already loaded")
            else:
                if "GFPGAN" in server_state:
                    del server_state["GFPGAN"]

                # Load GFPGAN
                if os.path.exists(st.session_state["defaults"].general.GFPGAN_dir):
                    try:
                        server_state["GFPGAN"] = load_GFPGAN(GFPGAN_model)
                        logger.info(f"Loaded GFPGAN: {GFPGAN_model}")
                    except Exception:
                        import traceback

                        logger.error("Error loading GFPGAN:", file=sys.stderr)
                        logger.error(traceback.format_exc(), file=sys.stderr)
        else:
            if "GFPGAN" in server_state and not server_state["keep_all_models_loaded"]:
                del server_state["GFPGAN"]

    with server_state_lock["RealESRGAN"]:
        if use_RealESRGAN:
            if (
                "RealESRGAN" in server_state
                and server_state["RealESRGAN"].model.name == RealESRGAN_model
            ):
                logger.info("RealESRGAN already loaded")
            else:
                # Load RealESRGAN
                try:
                    # We first remove the variable in case it has something there,
                    # some errors can load the model incorrectly and leave things in memory.
                    del server_state["RealESRGAN"]
                except KeyError as e:
                    logger.error(e)
                    pass

                if os.path.exists(st.session_state["defaults"].general.RealESRGAN_dir):
                    # st.session_state is used for keeping the models in memory across multiple pages or runs.
                    server_state["RealESRGAN"] = load_RealESRGAN(RealESRGAN_model)
                    logger.info(
                        "Loaded RealESRGAN with model "
                        + server_state["RealESRGAN"].model.name
                    )

        else:
            if (
                "RealESRGAN" in server_state
                and not server_state["keep_all_models_loaded"]
            ):
                del server_state["RealESRGAN"]

    with server_state_lock["model"], server_state_lock["modelCS"], server_state_lock[
        "modelFS"
    ], server_state_lock["loaded_model"]:
        if "model" in server_state:
            if "model" in server_state and server_state["loaded_model"] == custom_model:
                # if the float16 or no_half options have changed since the last time the model was loaded then we need to reload the model.
                if (
                    (
                        "float16" in server_state
                        and server_state["float16"]
                        != st.session_state["defaults"].general.use_float16
                    )
                    or (
                        "no_half" in server_state
                        and server_state["no_half"]
                        != st.session_state["defaults"].general.no_half
                    )
                    or (
                        "optimized" in server_state
                        and server_state["optimized"]
                        != st.session_state["defaults"].general.optimized
                    )
                ):
                    logger.info(
                        "Model options changed, deleting the model from memory."
                    )

                    del server_state["float16"]
                    del server_state["no_half"]

                    del server_state["model"]
                    del server_state["modelCS"]
                    del server_state["modelFS"]
                    del server_state["loaded_model"]

                    del server_state["optimized"]

                    server_state["float16"] = st.session_state[
                        "defaults"
                    ].general.use_float16
                    server_state["no_half"] = st.session_state[
                        "defaults"
                    ].general.no_half
                    server_state["optimized"] = st.session_state[
                        "defaults"
                    ].general.optimized

                    load_models(
                        use_LDSR=st.session_state["use_LDSR"],
                        LDSR_model=st.session_state["LDSR_model"],
                        use_GFPGAN=st.session_state["use_GFPGAN"],
                        GFPGAN_model=st.session_state["GFPGAN_model"],
                        use_RealESRGAN=st.session_state["use_RealESRGAN"],
                        RealESRGAN_model=st.session_state["RealESRGAN_model"],
                        CustomModel_available=server_state["CustomModel_available"],
                        custom_model=st.session_state["custom_model"],
                    )
                else:
                    logger.info("Model already loaded")

                return
            else:
                try:
                    del server_state["model"]
                    del server_state["modelCS"]
                    del server_state["modelFS"]
                    del server_state["loaded_model"]

                except KeyError as e:
                    logger.error(e)
                    pass

        # if the model from txt2vid is in memory we need to remove it to improve performance.
        with server_state_lock["pipe"]:
            if "pipe" in server_state and not server_state["keep_all_models_loaded"]:
                del server_state["pipe"]

        if (
            "textual_inversion" in st.session_state
            and not server_state["keep_all_models_loaded"]
        ):
            del st.session_state["textual_inversion"]

        # At this point the model is either
        # not loaded yet or have been deleted from memory:
        # load new model into memory
        server_state["custom_model"] = custom_model

        config, device, model, modelCS, modelFS = load_sd_model(custom_model)

        server_state["device"] = device
        server_state["model"] = model

        server_state["modelCS"] = modelCS
        server_state["modelFS"] = modelFS
        server_state["loaded_model"] = custom_model

        server_state["float16"] = st.session_state["defaults"].general.use_float16
        server_state["no_half"] = st.session_state["defaults"].general.no_half
        server_state["optimized"] = st.session_state["defaults"].general.optimized

        # trying to disable multiprocessing as it makes it so streamlit cant stop when the
        # model is loaded in memory and you need to kill the process sometimes.

        try:
            server_state["model"].args.use_multiprocessing_for_evaluation = False
        except AttributeError:
            pass

        if st.session_state.defaults.general.enable_attention_slicing:
            server_state["model"].enable_attention_slicing()

        if st.session_state.defaults.general.enable_minimal_memory_usage:
            server_state["model"].enable_minimal_memory_usage()

        logger.info("Model loaded.")

    return True


def load_model_from_config(config, ckpt, verbose=False):
    logger.info(f"Loading model from {ckpt}")

    try:
        pl_sd = torch.load(ckpt, map_location="cpu")
        if "global_step" in pl_sd:
            logger.info(f"Global Step: {pl_sd['global_step']}")
        sd = pl_sd["state_dict"]
        model = instantiate_from_config(config.model)
        m, u = model.load_state_dict(sd, strict=False)
        if len(m) > 0 and verbose:
            logger.info("missing keys:")
            logger.info(m)
        if len(u) > 0 and verbose:
            logger.info("unexpected keys:")
            logger.info(u)

        model.cuda()
        model.eval()

        return model

    except FileNotFoundError:
        if "progress_bar_text" in st.session_state:
            st.session_state["progress_bar_text"].error(
                "You need to download the Stable Diffusion model in order to use the UI. Use the Model Manager page in order to download the model."
            )

        raise FileNotFoundError(
            "You need to download the Stable Diffusion model in order to use the UI. Use the Model Manager page in order to download the model."
        )


def load_sd_from_config(ckpt, verbose=False):
    logger.info(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        logger.info(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    return sd


class MemUsageMonitor(threading.Thread):
    stop_flag = False
    max_usage = 0
    total = -1

    def __init__(self, name):
        threading.Thread.__init__(self)
        self.name = name

    def run(self):
        try:
            pynvml.nvmlInit()
        except:
            logger.debug(
                f"[{self.name}] Unable to initialize NVIDIA management. No memory stats. \n"
            )
            return
        logger.info(f"[{self.name}] Recording memory usage...\n")
        # Missing context
        # handle = pynvml.nvmlDeviceGetHandleByIndex(st.session_state['defaults'].general.gpu)
        handle = pynvml.nvmlDeviceGetHandleByIndex(0)
        self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
        while not self.stop_flag:
            m = pynvml.nvmlDeviceGetMemoryInfo(handle)
            self.max_usage = max(self.max_usage, m.used)
            # logger.info(self.max_usage)
            time.sleep(0.1)
        logger.info(f"[{self.name}] Stopped recording.\n")
        pynvml.nvmlShutdown()

    def read(self):
        return self.max_usage, self.total

    def stop(self):
        self.stop_flag = True

    def read_and_stop(self):
        self.stop_flag = True
        return self.max_usage, self.total


class CFGMaskedDenoiser(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.inner_model = model

    def forward(self, x, sigma, uncond, cond, cond_scale, mask, x0, xi):
        x_in = x
        x_in = torch.cat([x_in] * 2)
        sigma_in = torch.cat([sigma] * 2)
        cond_in = torch.cat([uncond, cond])
        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
        denoised = uncond + (cond - uncond) * cond_scale

        if mask is not None:
            assert x0 is not None
            img_orig = x0
            mask_inv = 1.0 - mask
            denoised = (img_orig * mask_inv) + (mask * denoised)

        return denoised


class CFGDenoiser(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.inner_model = model

    def forward(self, x, sigma, uncond, cond, cond_scale):
        x_in = torch.cat([x] * 2)
        sigma_in = torch.cat([sigma] * 2)
        cond_in = torch.cat([uncond, cond])
        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
        return uncond + (cond - uncond) * cond_scale


def append_zero(x):
    return torch.cat([x, x.new_zeros([1])])


def append_dims(x, target_dims):
    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
    dims_to_append = target_dims - x.ndim
    if dims_to_append < 0:
        raise ValueError(
            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
        )
    return x[(...,) + (None,) * dims_to_append]


def get_sigmas_karras(n, sigma_min, sigma_max, rho=7.0, device="cpu"):
    """Constructs the noise schedule of Karras et al. (2022)."""
    ramp = torch.linspace(0, 1, n)
    min_inv_rho = sigma_min ** (1 / rho)
    max_inv_rho = sigma_max ** (1 / rho)
    sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return append_zero(sigmas).to(device)


#
# helper fft routines that keep ortho normalization and auto-shift before and after fft
def _fft2(data):
    if data.ndim > 2:  # has channels
        out_fft = np.zeros(
            (data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128
        )
        for c in range(data.shape[2]):
            c_data = data[:, :, c]
            out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
            out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
    else:  # one channel
        out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
        out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
        out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])

    return out_fft


def _ifft2(data):
    if data.ndim > 2:  # has channels
        out_ifft = np.zeros(
            (data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128
        )
        for c in range(data.shape[2]):
            c_data = data[:, :, c]
            out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
            out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
    else:  # one channel
        out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
        out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
        out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])

    return out_ifft


def _get_gaussian_window(width, height, std=3.14, mode=0):
    window_scale_x = float(width / min(width, height))
    window_scale_y = float(height / min(width, height))

    window = np.zeros((width, height))
    x = (np.arange(width) / width * 2.0 - 1.0) * window_scale_x
    for y in range(height):
        fy = (y / height * 2.0 - 1.0) * window_scale_y
        if mode == 0:
            window[:, y] = np.exp(-(x**2 + fy**2) * std)
        else:
            window[:, y] = (1 / ((x**2 + 1.0) * (fy**2 + 1.0))) ** (
                std / 3.14
            )  # hey wait a minute that's not gaussian

    return window


def _get_masked_window_rgb(np_mask_grey, hardness=1.0):
    np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
    if hardness != 1.0:
        hardened = np_mask_grey[:] ** hardness
    else:
        hardened = np_mask_grey[:]
    for c in range(3):
        np_mask_rgb[:, :, c] = hardened[:]
    return np_mask_rgb


def get_matched_noise(_np_src_image, np_mask_rgb, noise_q, color_variation):
    """
    Explanation:
    Getting good results in/out-painting with stable diffusion can be challenging.
    Although there are simpler effective solutions for in-painting, out-painting can be especially challenging because there is no color data
    in the masked area to help prompt the generator. Ideally, even for in-painting we'd like work effectively without that data as well.
    Provided here is my take on a potential solution to this problem.

    By taking a fourier transform of the masked src img we get a function that tells us the presence and orientation of each feature scale in the unmasked src.
    Shaping the init/seed noise for in/outpainting to the same distribution of feature scales, orientations, and positions increases output coherence
    by helping keep features aligned. This technique is applicable to any continuous generation task such as audio or video, each of which can
    be conceptualized as a series of out-painting steps where the last half of the input "frame" is erased. For multi-channel data such as color
    or stereo sound the "color tone" or histogram of the seed noise can be matched to improve quality (using scikit-image currently)
    This method is quite robust and has the added benefit of being fast independently of the size of the out-painted area.
    The effects of this method include things like helping the generator integrate the pre-existing view distance and camera angle.

    Carefully managing color and brightness with histogram matching is also essential to achieving good coherence.

    noise_q controls the exponent in the fall-off of the distribution can be any positive number, lower values means higher detail (range > 0, default 1.)
    color_variation controls how much freedom is allowed for the colors/palette of the out-painted area (range 0..1, default 0.01)
    This code is provided as is under the Unlicense (https://unlicense.org/)
    Although you have no obligation to do so, if you found this code helpful please find it in your heart to credit me [parlance-zz].

    Questions or comments can be sent to parlance@fifth-harmonic.com (https://github.com/parlance-zz/)
    This code is part of a new branch of a discord bot I am working on integrating with diffusers (https://github.com/parlance-zz/g-diffuser-bot)

    """

    global DEBUG_MODE
    global TMP_ROOT_PATH

    width = _np_src_image.shape[0]
    height = _np_src_image.shape[1]
    num_channels = _np_src_image.shape[2]

    np_src_image = _np_src_image[:] * (1.0 - np_mask_rgb)
    np_mask_grey = np.sum(np_mask_rgb, axis=2) / 3.0
    (np.sum(np_src_image, axis=2) / 3.0)
    np.ones((width, height), dtype=bool)
    img_mask = np_mask_grey > 1e-6
    ref_mask = np_mask_grey < 1e-3

    windowed_image = _np_src_image * (1.0 - _get_masked_window_rgb(np_mask_grey))
    windowed_image /= np.max(windowed_image)
    windowed_image += (
        np.average(_np_src_image) * np_mask_rgb
    )  # / (1.-np.average(np_mask_rgb))  # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
    # windowed_image += np.average(_np_src_image) * (np_mask_rgb * (1.- np_mask_rgb)) / (1.-np.average(np_mask_rgb)) # compensate for darkening across the mask transition area
    # _save_debug_img(windowed_image, "windowed_src_img")

    src_fft = _fft2(windowed_image)  # get feature statistics from masked src img
    src_dist = np.absolute(src_fft)
    src_phase = src_fft / src_dist
    # _save_debug_img(src_dist, "windowed_src_dist")

    noise_window = _get_gaussian_window(
        width, height, mode=1
    )  # start with simple gaussian noise
    noise_rgb = np.random.random_sample((width, height, num_channels))
    noise_grey = np.sum(noise_rgb, axis=2) / 3.0
    noise_rgb *= color_variation  # the colorfulness of the starting noise is blended to greyscale with a parameter
    for c in range(num_channels):
        noise_rgb[:, :, c] += (1.0 - color_variation) * noise_grey

    noise_fft = _fft2(noise_rgb)
    for c in range(num_channels):
        noise_fft[:, :, c] *= noise_window
    noise_rgb = np.real(_ifft2(noise_fft))
    shaped_noise_fft = _fft2(noise_rgb)
    shaped_noise_fft[:, :, :] = (
        np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist**noise_q) * src_phase
    )  # perform the actual shaping

    brightness_variation = 0.0  # color_variation # todo: temporarily tieing brightness variation to color variation for now
    contrast_adjusted_np_src = (
        _np_src_image[:] * (brightness_variation + 1.0) - brightness_variation * 2.0
    )

    # scikit-image is used for histogram matching, very convenient!
    shaped_noise = np.real(_ifft2(shaped_noise_fft))
    shaped_noise -= np.min(shaped_noise)
    shaped_noise /= np.max(shaped_noise)
    shaped_noise[img_mask, :] = skimage.exposure.match_histograms(
        shaped_noise[img_mask, :] ** 1.0,
        contrast_adjusted_np_src[ref_mask, :],
        channel_axis=1,
    )
    shaped_noise = _np_src_image[:] * (1.0 - np_mask_rgb) + shaped_noise * np_mask_rgb
    # _save_debug_img(shaped_noise, "shaped_noise")

    matched_noise = np.zeros((width, height, num_channels))
    matched_noise = shaped_noise[:]
    # matched_noise[all_mask,:] = skimage.exposure.match_histograms(shaped_noise[all_mask,:], _np_src_image[ref_mask,:], channel_axis=1)
    # matched_noise = _np_src_image[:] * (1. - np_mask_rgb) + matched_noise * np_mask_rgb

    # _save_debug_img(matched_noise, "matched_noise")

    """
    todo:
    color_variation doesnt have to be a single number, the overall color tone of the out-painted area could be param controlled
    """

    return np.clip(matched_noise, 0.0, 1.0)


#
def find_noise_for_image(
    model,
    device,
    init_image,
    prompt,
    steps=200,
    cond_scale=2.0,
    verbose=False,
    normalize=False,
    generation_callback=None,
):
    image = np.array(init_image).astype(np.float32) / 255.0
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    image = 2.0 * image - 1.0
    image = image.to(device)
    x = model.get_first_stage_encoding(model.encode_first_stage(image))

    uncond = model.get_learned_conditioning([""])
    cond = model.get_learned_conditioning([prompt])

    s_in = x.new_ones([x.shape[0]])
    dnw = K.external.CompVisDenoiser(model)
    sigmas = dnw.get_sigmas(steps).flip(0)

    if verbose:
        logger.info(sigmas)

    for i in trange(1, len(sigmas)):
        x_in = torch.cat([x] * 2)
        sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
        cond_in = torch.cat([uncond, cond])

        c_out, c_in = [
            K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)
        ]

        if i == 1:
            t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
        else:
            t = dnw.sigma_to_t(sigma_in)

        eps = model.apply_model(x_in * c_in, t, cond=cond_in)
        denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)

        denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cond_scale

        if i == 1:
            d = (x - denoised) / (2 * sigmas[i])
        else:
            d = (x - denoised) / sigmas[i - 1]

        if generation_callback is not None:
            generation_callback(x, i)

        dt = sigmas[i] - sigmas[i - 1]
        x = x + d * dt

    return x / sigmas[-1]


#
def folder_picker(
    label="Select:",
    value="",
    help="",
    folder_button_label="Select",
    folder_button_help="",
    folder_button_key="",
):
    """A folder picker that has a text_input field next to it and a button to select the folder.
    Returns the text_input field with the folder path."""
    import tkinter as tk
    from tkinter import filedialog

    # Set up tkinter
    root = tk.Tk()
    root.withdraw()

    # Make folder picker dialog appear on top of other windows
    root.wm_attributes("-topmost", 1)

    col1, col2 = st.columns([2, 1], gap="small")

    with col1:
        dirname = st.empty()
    with col2:
        st.write("")
        st.write("")
        folder_picker = st.empty()

    # Folder picker button
    # st.title('Folder Picker')
    # st.write('Please select a folder:')

    # Create a label and add a random number of invisible characters
    # to it so no two buttons inside a form are the same.
    # folder_button_label = ''.join(random.choice(f"{folder_button_label}") for _ in range(5))
    folder_button_label = f"{str(folder_button_label)}{'‎' * random.randint(1, 500)}"
    clicked = folder_button_key + "‎" * random.randint(5, 500)

    # try:
    # clicked = folder_picker.button(folder_button_label, help=folder_button_help, key=folder_button_key)
    # except StreamlitAPIException:
    clicked = folder_picker.form_submit_button(
        folder_button_label, help=folder_button_help
    )

    if clicked:
        dirname = dirname.text_input(
            label, filedialog.askdirectory(master=root), help=help
        )
    else:
        dirname = dirname.text_input(label, value, help=help)

    return dirname


def get_sigmas_exponential(n, sigma_min, sigma_max, device="cpu"):
    """Constructs an exponential noise schedule."""
    sigmas = torch.linspace(
        math.log(sigma_max), math.log(sigma_min), n, device=device
    ).exp()
    return append_zero(sigmas)


def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device="cpu"):
    """Constructs a continuous VP noise schedule."""
    t = torch.linspace(1, eps_s, n, device=device)
    sigmas = torch.sqrt(torch.exp(beta_d * t**2 / 2 + beta_min * t) - 1)
    return append_zero(sigmas)


def to_d(x, sigma, denoised):
    """Converts a denoiser output to a Karras ODE derivative."""
    return (x - denoised) / append_dims(sigma, x.ndim)


def linear_multistep_coeff(order, t, i, j):
    if order - 1 > i:
        raise ValueError(f"Order {order} too high for step {i}")

    def fn(tau):
        prod = 1.0
        for k in range(order):
            if j == k:
                continue
            prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
        return prod

    return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]


class KDiffusionSampler:
    def __init__(self, m, sampler):
        self.model = m
        self.model_wrap = K.external.CompVisDenoiser(m)
        self.schedule = sampler

    def get_sampler_name(self):
        return self.schedule

    def sample(
        self,
        S,
        conditioning,
        batch_size,
        shape,
        verbose,
        unconditional_guidance_scale,
        unconditional_conditioning,
        eta,
        x_T,
        img_callback=None,
        log_every_t=None,
    ):
        sigmas = self.model_wrap.get_sigmas(S)
        x = x_T * sigmas[0]
        model_wrap_cfg = CFGDenoiser(self.model_wrap)
        samples_ddim = None
        samples_ddim = K.sampling.__dict__[f"sample_{self.schedule}"](
            model_wrap_cfg,
            x,
            sigmas,
            extra_args={
                "cond": conditioning,
                "uncond": unconditional_conditioning,
                "cond_scale": unconditional_guidance_scale,
            },
            disable=False,
            callback=generation_callback,
        )
        #
        return samples_ddim, None


#
# create class LDSR
class LDSR:
    # init function
    def __init__(self, modelPath, yamlPath):
        self.modelPath = modelPath
        self.yamlPath = yamlPath
        # self.model = self.load_model_from_config()
        # print(self.load_model_from_config(OmegaConf.load(yamlPath), modelPath))
        # self.print_current_directory()

    # get currennt directory

    """
    def check_model_exists(self):
        #check if model and yaml exist
        path = self.pathInput + "/models/ldm/ld_sr".replace('\\',os.sep).replace('/',os.sep)
        model = self.modelName
        yaml = self.yamlName
        if os.path.exists(path):
            #check if yaml exists
            if os.path.exists(os.path.join(path,yaml)):
                print('YAML found')
                #check if ckpt exists
                if os.path.exists(os.path.join(path,model)):
                    print('Model found')
                    return os.path.join(path,model), os.path.join(path,yaml)
                else:
                    return False
        #return onlyfiles
    """

    def load_model_from_config(self):
        # print(f"Loading model from {self.modelPath}")
        pl_sd = torch.load(self.modelPath, map_location="cpu")
        pl_sd["global_step"]
        sd = pl_sd["state_dict"]
        config = OmegaConf.load(self.yamlPath)
        model = instantiate_from_config(config.model)
        m, u = model.load_state_dict(sd, strict=False)
        model.cuda()
        model.eval()
        return {"model": model}  # , global_step

    """
    def get_model(self):
        check = self.check_model_exists()
        if check != False:
            path_ckpt = check[0]
            path_conf = check[1]
        else:
            print('Model not found, please run the bat file to download the model')
        config = OmegaConf.load(path_conf)
        model, step = self.load_model_from_config(config, path_ckpt)
        return model


    def get_custom_cond(mode):
        dest = "data/example_conditioning"

        if mode == "superresolution":
            uploaded_img = files.upload()
            filename = next(iter(uploaded_img))
            name, filetype = filename.split(".") # todo assumes just one dot in name !
            os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}")

        elif mode == "text_conditional":
            #w = widgets.Text(value='A cake with cream!', disabled=True)
            w = 'Empty Test'
            display.display(w)

            with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", 'w') as f:
                f.write(w.value)

        elif mode == "class_conditional":
            #w = widgets.IntSlider(min=0, max=1000)
            w = 1000
            display.display(w)
            with open(f"{dest}/{mode}/custom.txt", 'w') as f:
                f.write(w.value)

        else:
            raise NotImplementedError(f"cond not implemented for mode{mode}")
    """

    def get_cond_options(self, mode):
        path = "data/example_conditioning"
        path = os.path.join(path, mode)
        onlyfiles = [f for f in sorted(os.listdir(path))]
        return path, onlyfiles

    """
    def select_cond_path(mode):
        path = "data/example_conditioning"  # todo
        path = os.path.join(path, mode)
        onlyfiles = [f for f in sorted(os.listdir(path))]

        selected = widgets.RadioButtons(
            options=onlyfiles,
            description='Select conditioning:',
            disabled=False
        )
        display.display(selected)
        selected_path = os.path.join(path, selected.value)
        return selected_path
    """

    """
    # Google Collab stuff
    def visualize_cond_img(path):
        display.display(ipyimg(filename=path))
    """

    def run(
        self,
        model,
        selected_path,
        task,
        custom_steps,
        eta,
        resize_enabled=False,
        classifier_ckpt=None,
        global_step=None,
    ):
        def make_convolutional_sample(
            batch,
            model,
            mode="vanilla",
            custom_steps=None,
            eta=1.0,
            swap_mode=False,
            masked=False,
            invert_mask=True,
            quantize_x0=False,
            custom_schedule=None,
            decode_interval=1000,
            resize_enabled=False,
            custom_shape=None,
            temperature=1.0,
            noise_dropout=0.0,
            corrector=None,
            corrector_kwargs=None,
            x_T=None,
            save_intermediate_vid=False,
            make_progrow=True,
            ddim_use_x0_pred=False,
        ):
            log = dict()

            z, c, x, xrec, xc = model.get_input(
                batch,
                model.first_stage_key,
                return_first_stage_outputs=True,
                force_c_encode=not (
                    hasattr(model, "split_input_params")
                    and model.cond_stage_key == "coordinates_bbox"
                ),
                return_original_cond=True,
            )

            log_every_t = 1 if save_intermediate_vid else None

            if custom_shape is not None:
                z = torch.randn(custom_shape)
                # print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")

            z0 = None

            log["input"] = x
            log["reconstruction"] = xrec

            if ismap(xc):
                log["original_conditioning"] = model.to_rgb(xc)
                if hasattr(model, "cond_stage_key"):
                    log[model.cond_stage_key] = model.to_rgb(xc)

            else:
                log["original_conditioning"] = (
                    xc if xc is not None else torch.zeros_like(x)
                )
                if model.cond_stage_model:
                    log[model.cond_stage_key] = (
                        xc if xc is not None else torch.zeros_like(x)
                    )
                    if model.cond_stage_key == "class_label":
                        log[model.cond_stage_key] = xc[model.cond_stage_key]

            with model.ema_scope("Plotting"):
                t0 = time.time()
                img_cb = None

                sample, intermediates = convsample_ddim(
                    model,
                    c,
                    steps=custom_steps,
                    shape=z.shape,
                    eta=eta,
                    quantize_x0=quantize_x0,
                    img_callback=img_cb,
                    mask=None,
                    x0=z0,
                    temperature=temperature,
                    noise_dropout=noise_dropout,
                    score_corrector=corrector,
                    corrector_kwargs=corrector_kwargs,
                    x_T=x_T,
                    log_every_t=log_every_t,
                )
                t1 = time.time()

                if ddim_use_x0_pred:
                    sample = intermediates["pred_x0"][-1]

            x_sample = model.decode_first_stage(sample)

            try:
                x_sample_noquant = model.decode_first_stage(
                    sample, force_not_quantize=True
                )
                log["sample_noquant"] = x_sample_noquant
                log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
            except:
                logger.error("Error with LDSR")
                pass

            log["sample"] = x_sample
            log["time"] = t1 - t0

            return log

        def convsample_ddim(
            model,
            cond,
            steps,
            shape,
            eta=1.0,
            callback=None,
            normals_sequence=None,
            mask=None,
            x0=None,
            quantize_x0=False,
            img_callback=None,
            temperature=1.0,
            noise_dropout=0.0,
            score_corrector=None,
            corrector_kwargs=None,
            x_T=None,
            log_every_t=None,
        ):
            ddim = DDIMSampler(model)
            bs = shape[0]  # dont know where this comes from but wayne
            shape = shape[1:]  # cut batch dim
            logger.info(f"Sampling with eta = {eta}; steps: {steps}")
            samples, intermediates = ddim.sample(
                steps,
                batch_size=bs,
                shape=shape,
                conditioning=cond,
                callback=callback,
                normals_sequence=normals_sequence,
                quantize_x0=quantize_x0,
                eta=eta,
                mask=mask,
                x0=x0,
                temperature=temperature,
                verbose=False,
                score_corrector=score_corrector,
                corrector_kwargs=corrector_kwargs,
                x_T=x_T,
            )

            return samples, intermediates

        # global stride
        def get_cond(mode, selected_path):
            example = dict()
            if mode == "superresolution":
                up_f = 4
                # visualize_cond_img(selected_path)

                c = selected_path.convert("RGB")
                c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
                c_up = torchvision.transforms.functional.resize(
                    c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True
                )
                c_up = rearrange(c_up, "1 c h w -> 1 h w c")
                c = rearrange(c, "1 c h w -> 1 h w c")
                c = 2.0 * c - 1.0

                c = c.to(torch.device("cuda"))
                example["LR_image"] = c
                example["image"] = c_up

            return example

        example = get_cond(task, selected_path)

        save_intermediate_vid = False
        n_runs = 1
        masked = False
        guider = None
        ckwargs = None
        mode = "ddim"
        ddim_use_x0_pred = False
        temperature = 1.0
        eta = eta
        make_progrow = True
        custom_shape = None

        height, width = example["image"].shape[1:3]
        split_input = height >= 128 and width >= 128

        if split_input:
            ks = 128
            stride = 64
            vqf = 4  #
            model.split_input_params = {
                "ks": (ks, ks),
                "stride": (stride, stride),
                "vqf": vqf,
                "patch_distributed_vq": True,
                "tie_braker": False,
                "clip_max_weight": 0.5,
                "clip_min_weight": 0.01,
                "clip_max_tie_weight": 0.5,
                "clip_min_tie_weight": 0.01,
            }
        else:
            if hasattr(model, "split_input_params"):
                delattr(model, "split_input_params")

        invert_mask = False

        x_T = None
        for n in range(n_runs):
            if custom_shape is not None:
                x_T = torch.randn(
                    1, custom_shape[1], custom_shape[2], custom_shape[3]
                ).to(model.device)
                x_T = repeat(x_T, "1 c h w -> b c h w", b=custom_shape[0])

            logs = make_convolutional_sample(
                example,
                model,
                mode=mode,
                custom_steps=custom_steps,
                eta=eta,
                swap_mode=False,
                masked=masked,
                invert_mask=invert_mask,
                quantize_x0=False,
                custom_schedule=None,
                decode_interval=10,
                resize_enabled=resize_enabled,
                custom_shape=custom_shape,
                temperature=temperature,
                noise_dropout=0.0,
                corrector=guider,
                corrector_kwargs=ckwargs,
                x_T=x_T,
                save_intermediate_vid=save_intermediate_vid,
                make_progrow=make_progrow,
                ddim_use_x0_pred=ddim_use_x0_pred,
            )
        return logs

    @torch.no_grad()
    @torch.no_grad()
    def superResolution(
        self,
        image,
        ddimSteps=100,
        preDownScale=1,
        postDownScale=1,
        downsample_method="Lanczos",
    ):
        """
        #Run settings

        diffusion_steps = int(ddimSteps) #@param [25, 50, 100, 250, 500, 1000]
        eta = 1.0 #@param  {type: 'raw'}
        stride = 0 #not working atm

        # ####Scaling options:
        # Downsampling to 256px first will often improve the final image and runs faster.

        # You can improve sharpness without upscaling by upscaling and then downsampling to the original size (i.e. Super Resolution)
        preDownScale: Values ['None', '2', '4']

        postDownScale: Values ['None', 'Original Size', '2', '4']

        # Nearest gives sharper results, but may look more pixellated. Lancoz is much higher quality, but result may be less crisp.
        downsample_method = 'Lanczos' #@param ['Nearest', 'Lanczos']
        """

        diffMode = "superresolution"
        model = self.load_model_from_config()

        # Run settings

        diffusion_steps = int(ddimSteps)  # @param [25, 50, 100, 250, 500, 1000]
        eta = 1.0  # @param  {type: 'raw'}

        # ####Scaling options:
        # Downsampling to 256px first will often improve the final image and runs faster.

        # You can improve sharpness without upscaling by upscaling and then downsampling to the original size (i.e. Super Resolution)

        post_downsample = postDownScale  # @param ['None', 'Original Size', '2', '4']

        # Nearest gives sharper results, but may look more pixellated. Lancoz is much higher quality, but result may be less crisp.
        # downsample_method = 'Lanczos' #@param ['Nearest', 'Lanczos']

        gc.collect()
        torch.cuda.empty_cache()

        im_og = image
        width_og, height_og = im_og.size

        # Downsample Pre

        downsample_rate = preDownScale

        # get system temp directory
        width_downsampled_pre = width_og // downsample_rate
        height_downsampled_pre = height_og // downsample_rate
        if downsample_rate != 1:
            logger.info(
                f"Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]"
            )
            im_og = im_og.resize(
                (width_downsampled_pre, height_downsampled_pre), Image.LANCZOS
            )

        logs = self.run(model["model"], im_og, diffMode, diffusion_steps, eta)

        sample = logs["sample"]
        sample = sample.detach().cpu()
        sample = torch.clamp(sample, -1.0, 1.0)
        sample = (sample + 1.0) / 2.0 * 255
        sample = sample.numpy().astype(np.uint8)
        sample = np.transpose(sample, (0, 2, 3, 1))

        a = Image.fromarray(sample[0])

        # Downsample Post
        downsample_rate = postDownScale

        width, height = a.size
        width_downsampled_post = width // downsample_rate
        height_downsampled_post = height // downsample_rate

        if downsample_method == "Lanczos":
            aliasing = Image.LANCZOS
        else:
            aliasing = Image.NEAREST

        if downsample_rate != 1:
            logger.info(
                f"Downsampling from [{width}, {height}] to [{width_downsampled_post}, {height_downsampled_post}]"
            )
            a = a.resize((width_downsampled_post, height_downsampled_post), aliasing)
        elif post_downsample == "Original Size":
            logger.info(
                f"Downsampling from [{width}, {height}] to Original Size [{width_og}, {height_og}]"
            )
            a = a.resize((width_og, height_og), aliasing)

        del model
        gc.collect()
        torch.cuda.empty_cache()

        logger.info("Processing finished!")
        return a


@torch.no_grad()
def log_likelihood(
    model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4
):
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    v = torch.randint_like(x, 2) * 2 - 1
    fevals = 0

    def ode_fn(sigma, x):
        nonlocal fevals
        with torch.enable_grad():
            x = x[0].detach().requires_grad_()
            denoised = model(x, sigma * s_in, **extra_args)
            d = to_d(x, sigma, denoised)
            fevals += 1
            grad = torch.autograd.grad((d * v).sum(), x)[0]
            d_ll = (v * grad).flatten(1).sum(1)
        return d.detach(), d_ll

    x_min = x, x.new_zeros([x.shape[0]])
    t = x.new_tensor([sigma_min, sigma_max])
    sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method="dopri5")
    latent, delta_ll = sol[0][-1], sol[1][-1]
    ll_prior = (
        torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1)
    )
    return ll_prior + delta_ll, {"fevals": fevals}


def create_random_tensors(shape, seeds):
    xs = []
    for seed in seeds:
        torch.manual_seed(seed)

        # randn results depend on device; gpu and cpu get different results for same seed;
        # the way I see it, it's better to do this on CPU, so that everyone gets same result;
        # but the original script had it like this so i do not dare change it for now because
        # it will break everyone's seeds.
        xs.append(torch.randn(shape, device=st.session_state["defaults"].general.gpu))
    x = torch.stack(xs)
    return x


def torch_gc():
    torch.cuda.empty_cache()
    torch.cuda.ipc_collect()


@retry(tries=5)
# @st.experimental_memo(persist="disk", show_spinner=False, suppress_st_warning=True)
def load_GFPGAN(model_name="GFPGANv1.4"):
    # model_name = 'GFPGANv1.3'

    model_path = os.path.join(
        st.session_state["defaults"].general.GFPGAN_dir, model_name + ".pth"
    )

    # if not os.path.isfile(model_path):
    # model_path = os.path.join(st.session_state['defaults'].general.GFPGAN_dir, model_name + '.pth')

    if not os.path.isfile(model_path):
        raise Exception("GFPGAN model not found at path " + model_path)

    sys.path.append(os.path.abspath(st.session_state["defaults"].general.GFPGAN_dir))
    from gfpgan import GFPGANer

    with server_state_lock["GFPGAN"]:
        if (
            st.session_state["defaults"].general.gfpgan_cpu
            or st.session_state["defaults"].general.extra_models_cpu
        ):
            server_state["GFPGAN"] = GFPGANer(
                model_path=model_path,
                upscale=1,
                arch="clean",
                channel_multiplier=2,
                bg_upsampler=None,
                device=torch.device("cpu"),
            )

        elif st.session_state["defaults"].general.extra_models_gpu:
            server_state["GFPGAN"] = GFPGANer(
                model_path=model_path,
                upscale=1,
                arch="clean",
                channel_multiplier=2,
                bg_upsampler=None,
                device=torch.device(
                    f"cuda:{st.session_state['defaults'].general.gfpgan_gpu}"
                ),
            )
        else:
            server_state["GFPGAN"] = GFPGANer(
                model_path=model_path,
                upscale=1,
                arch="clean",
                channel_multiplier=2,
                bg_upsampler=None,
                device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"),
            )

        # Add the model_name to model loaded so we can later
        # check if its the same when we change it on the UI.
        server_state["GFPGAN"].name = model_name

    return server_state["GFPGAN"]


@retry(tries=5)
def load_RealESRGAN(model_name: str):
    from basicsr.archs.rrdbnet_arch import RRDBNet

    RealESRGAN_models = {
        "RealESRGAN_x4plus": RRDBNet(
            num_in_ch=3,
            num_out_ch=3,
            num_feat=64,
            num_block=23,
            num_grow_ch=32,
            scale=4,
        ),
        "RealESRGAN_x4plus_anime_6B": RRDBNet(
            num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4
        ),
    }

    model_path = os.path.join(
        st.session_state["defaults"].general.RealESRGAN_dir, model_name + ".pth"
    )

    if not os.path.isfile(model_path):
        model_path = os.path.join(
            st.session_state["defaults"].general.RealESRGAN_dir, model_name + ".pth"
        )

    if not os.path.exists(model_path):
        raise Exception(model_name + ".pth not found at path " + model_path)

    sys.path.append(
        os.path.abspath(st.session_state["defaults"].general.RealESRGAN_dir)
    )
    from realesrgan import RealESRGANer

    with server_state_lock["RealESRGAN"]:
        if (
            st.session_state["defaults"].general.esrgan_cpu
            or st.session_state["defaults"].general.extra_models_cpu
        ):
            server_state["RealESRGAN"] = RealESRGANer(
                scale=2,
                model_path=model_path,
                model=RealESRGAN_models[model_name],
                pre_pad=0,
                half=False,
            )  # cpu does not support half

            server_state["RealESRGAN"].device = torch.device("cpu")
            server_state["RealESRGAN"].model.to("cpu")

        elif st.session_state["defaults"].general.extra_models_gpu:
            server_state["RealESRGAN"] = RealESRGANer(
                scale=2,
                model_path=model_path,
                model=RealESRGAN_models[model_name],
                pre_pad=0,
                half=not st.session_state["defaults"].general.no_half,
                device=torch.device(
                    f"cuda:{st.session_state['defaults'].general.esrgan_gpu}"
                ),
            )
        else:
            server_state["RealESRGAN"] = RealESRGANer(
                scale=2,
                model_path=model_path,
                model=RealESRGAN_models[model_name],
                pre_pad=0,
                half=not st.session_state["defaults"].general.no_half,
                device=torch.device(f"cuda:{st.session_state['defaults'].general.gpu}"),
            )

        # Add the model_name to model loaded so we can later
        # check if its the same when we change it on the UI.
        server_state["RealESRGAN"].model.name = model_name

    return server_state["RealESRGAN"]


#
class RealESRGANModel(nn.Module):
    def __init__(self, model_path, tile=0, tile_pad=10, pre_pad=0, fp32=False):
        super().__init__()
        try:
            from basicsr.archs.rrdbnet_arch import RRDBNet
            from realesrgan import RealESRGANer
        except ImportError:
            logger.error(
                "You tried to import realesrgan without having it installed properly. To install Real-ESRGAN, run:\n\n"
                "pip install realesrgan"
            )

        model = RRDBNet(
            num_in_ch=3,
            num_out_ch=3,
            num_feat=64,
            num_block=23,
            num_grow_ch=32,
            scale=4,
        )
        self.upsampler = RealESRGANer(
            scale=4,
            model_path=model_path,
            model=model,
            tile=tile,
            tile_pad=tile_pad,
            pre_pad=pre_pad,
            half=not fp32,
        )

    def forward(self, image, outscale=4, convert_to_pil=True):
        """Upsample an image array or path.
        Args:
            image (Union[np.ndarray, str]): Either a np array or an image path. np array is assumed to be in RGB format,
                and we convert it to BGR.
            outscale (int, optional): Amount to upscale the image. Defaults to 4.
            convert_to_pil (bool, optional): If True, return PIL image. Otherwise, return numpy array (BGR). Defaults to True.
        Returns:
            Union[np.ndarray, PIL.Image.Image]: An upsampled version of the input image.
        """
        if isinstance(image, (str, Path)):
            img = cv2.imread(image, cv2.IMREAD_UNCHANGED)
        else:
            img = image
            img = (img * 255).round().astype("uint8")
            img = img[:, :, ::-1]

        image, _ = self.upsampler.enhance(img, outscale=outscale)

        if convert_to_pil:
            image = Image.fromarray(image[:, :, ::-1])

        return image

    @classmethod
    def from_pretrained(cls, model_name_or_path="nateraw/real-esrgan"):
        """Initialize a pretrained Real-ESRGAN upsampler.
        Args:
            model_name_or_path (str, optional): The Hugging Face repo ID or path to local model. Defaults to 'nateraw/real-esrgan'.
        Returns:
            PipelineRealESRGAN: An instance of `PipelineRealESRGAN` instantiated from pretrained model.
        """
        # reuploaded form official ones mentioned here:
        # https://github.com/xinntao/Real-ESRGAN
        if Path(model_name_or_path).exists():
            file = model_name_or_path
        else:
            file = hf_hub_download(model_name_or_path, "RealESRGAN_x4plus.pth")
        return cls(file)

    def upsample_imagefolder(self, in_dir, out_dir, suffix="out", outfile_ext=".png"):
        in_dir, out_dir = Path(in_dir), Path(out_dir)
        if not in_dir.exists():
            raise FileNotFoundError(f"Provided input directory {in_dir} does not exist")

        out_dir.mkdir(exist_ok=True, parents=True)

        image_paths = [
            x for x in in_dir.glob("*") if x.suffix.lower() in [".png", ".jpg", ".jpeg"]
        ]
        for image in image_paths:
            im = self(str(image))
            out_filepath = out_dir / (image.stem + suffix + outfile_ext)
            im.save(out_filepath)


#
@retry(tries=5)
def load_LDSR(model_name="model", config="project", checking=False):
    # model_name = 'model'
    # yaml_name = 'project'

    model_path = os.path.join(
        st.session_state["defaults"].general.LDSR_dir, model_name + ".ckpt"
    )
    yaml_path = os.path.join(
        st.session_state["defaults"].general.LDSR_dir, config + ".yaml"
    )

    if not os.path.isfile(model_path):
        raise Exception("LDSR model not found at path " + model_path)
    if not os.path.isfile(yaml_path):
        raise Exception("LDSR model not found at path " + yaml_path)
    if checking is True:
        return True

    # sys.path.append(os.path.abspath(st.session_state['defaults'].general.LDSR_dir))
    # from LDSR import LDSR
    server_state["LDSR"] = LDSR(model_path, yaml_path)

    server_state["LDSR"].name = model_name

    return server_state["LDSR"]


#


@retry(tries=5)
# def try_loading_LDSR(model_name: str,checking=False):
##LDSR = None
##global LDSR
# if os.path.exists(st.session_state['defaults'].general.LDSR_dir):
# try:
# server_state["LDSR"] = load_LDSR(checking=True) # TODO: Should try to load both models before giving up
# if checking == True:
# print("Found LDSR")
# return True
# print("Latent Diffusion Super Sampling (LDSR) model loaded")
# except Exception:
# import traceback
# print("Error loading LDSR:", file=sys.stderr)
# print(traceback.format_exc(), file=sys.stderr)
# else:
# print("LDSR not found at path, please make sure you have cloned the LDSR repo to ./models/ldsr/")

# try_loading_LDSR('model',checking=True)


# @retry(tries=5)
def load_sd_model(model_name: str):
    """Loads Stable Diffusion model by name"""
    ckpt_path = st.session_state.defaults.general.default_model_path

    if model_name != st.session_state.defaults.general.default_model:
        ckpt_path = os.path.join("models", "custom", f"{model_name}.ckpt")

    if st.session_state.defaults.general.optimized:
        config = OmegaConf.load(st.session_state.defaults.general.optimized_config)

        sd = load_sd_from_config(ckpt_path)
        li, lo = [], []
        for key, v_ in sd.items():
            sp = key.split(".")
            if (sp[0]) == "model":
                if "input_blocks" in sp:
                    li.append(key)
                elif "middle_block" in sp:
                    li.append(key)
                elif "time_embed" in sp:
                    li.append(key)
                else:
                    lo.append(key)
        for key in li:
            sd["model1." + key[6:]] = sd.pop(key)
        for key in lo:
            sd["model2." + key[6:]] = sd.pop(key)

        device = (
            torch.device(f"cuda:{st.session_state.defaults.general.gpu}")
            if torch.cuda.is_available()
            else torch.device("cpu")
        )

        model = instantiate_from_config(config.modelUNet)
        _, _ = model.load_state_dict(sd, strict=False)
        model.cuda()
        model.eval()
        model.turbo = st.session_state.defaults.general.optimized_turbo

        modelCS = instantiate_from_config(config.modelCondStage)
        _, _ = modelCS.load_state_dict(sd, strict=False)
        modelCS.cond_stage_model.device = device
        modelCS.eval()

        modelFS = instantiate_from_config(config.modelFirstStage)
        _, _ = modelFS.load_state_dict(sd, strict=False)
        modelFS.eval()

        del sd

        if not st.session_state.defaults.general.no_half:
            model = model.half().to(device)
            modelCS = modelCS.half().to(device)
            modelFS = modelFS.half().to(device)

        return config, device, model, modelCS, modelFS
    else:
        config = OmegaConf.load(st.session_state.defaults.general.default_model_config)
        model = load_model_from_config(config, ckpt_path)

        device = (
            torch.device(f"cuda:{st.session_state.defaults.general.gpu}")
            if torch.cuda.is_available()
            else torch.device("cpu")
        )
        model = (
            model if st.session_state.defaults.general.no_half else model.half()
        ).to(device)

        return config, device, model, None, None


def ModelLoader(
    models, load=False, unload=False, imgproc_realesrgan_model_name="RealESRGAN_x4plus"
):
    # codedealer: No usages
    # get global variables
    global_vars = globals()
    # check if m is in globals
    if unload:
        for m in models:
            if m in global_vars:
                # if it is, delete it
                del global_vars[m]
                if st.session_state["defaults"].general.optimized:
                    if m == "model":
                        del global_vars[m + "FS"]
                        del global_vars[m + "CS"]
                if m == "model":
                    m = "Stable Diffusion"
                logger.info("Unloaded " + m)
    if load:
        for m in models:
            if (
                m not in global_vars
                or m in global_vars
                and type(global_vars[m]) == bool
            ):
                # if it isn't, load it
                if m == "GFPGAN":
                    global_vars[m] = load_GFPGAN()
                elif m == "model":
                    sdLoader = load_sd_from_config()
                    global_vars[m] = sdLoader[0]
                    if st.session_state["defaults"].general.optimized:
                        global_vars[m + "CS"] = sdLoader[1]
                        global_vars[m + "FS"] = sdLoader[2]
                elif m == "RealESRGAN":
                    global_vars[m] = load_RealESRGAN(imgproc_realesrgan_model_name)
                elif m == "LDSR":
                    global_vars[m] = load_LDSR()
                if m == "model":
                    m = "Stable Diffusion"
                logger.info("Loaded " + m)
    torch_gc()


#
@retry(tries=5)
def generation_callback(img, i=0):
    if "update_preview_frequency" not in st.session_state:
        raise StopException

    try:
        if i == 0:
            if img["i"]:
                i = img["i"]
    except TypeError as e:
        logger.error(e)
        pass

    if (
        st.session_state.update_preview
        and int(st.session_state.update_preview_frequency) > 0
        and i % int(st.session_state.update_preview_frequency) == 0
        and i > 0
    ):
        # print (img)
        # print (type(img))
        # The following lines will convert the tensor we got on img to an actual image we can render on the UI.
        # It can probably be done in a better way for someone who knows what they're doing. I don't.
        # print (img,isinstance(img, torch.Tensor))
        if isinstance(img, torch.Tensor):
            x_samples_ddim = (
                (
                    server_state["model"].to("cuda")
                    if not st.session_state["defaults"].general.optimized
                    else server_state["modelFS"].to("cuda")
                )
                .decode_first_stage(img)
                .to("cuda")
            )
        else:
            # When using the k Diffusion samplers they return a dict instead of a tensor that look like this:
            # {'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}
            x_samples_ddim = (
                (
                    server_state["model"].to("cuda")
                    if not st.session_state["defaults"].general.optimized
                    else server_state["modelFS"].to("cuda")
                )
                .decode_first_stage(img["denoised"])
                .to("cuda")
            )

        x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

        if x_samples_ddim.ndimension() == 4:
            pil_images = [
                transforms.ToPILImage()(x.squeeze_(0)) for x in x_samples_ddim
            ]
            pil_image = image_grid(pil_images, 1)
        else:
            pil_image = transforms.ToPILImage()(x_samples_ddim.squeeze_(0))

        # update image on the UI so we can see the progress
        if "preview_image" in st.session_state:
            st.session_state["preview_image"].image(pil_image)

    # Show a progress bar so we can keep track of the progress even when the image progress is not been shown,
    # Dont worry, it doesnt affect the performance.
    if st.session_state["generation_mode"] == "txt2img":
        percent = int(
            100
            * float(
                i + 1
                if i + 1 < st.session_state.sampling_steps
                else st.session_state.sampling_steps
            )
            / float(st.session_state.sampling_steps)
        )

        if "progress_bar_text" in st.session_state:
            st.session_state["progress_bar_text"].text(
                f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps} {percent if percent < 100 else 100}%"
            )
    else:
        if st.session_state["generation_mode"] == "img2img":
            round_sampling_steps = round(
                st.session_state.sampling_steps * st.session_state["denoising_strength"]
            )
            percent = int(
                100
                * float(i + 1 if i + 1 < round_sampling_steps else round_sampling_steps)
                / float(round_sampling_steps)
            )

            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    f"""Running step: {i+1 if i+1 < round_sampling_steps else round_sampling_steps}/{round_sampling_steps} {percent if percent < 100 else 100}%"""
                )
        else:
            if st.session_state["generation_mode"] == "txt2vid":
                percent = int(
                    100
                    * float(
                        i + 1
                        if i + 1 < st.session_state.sampling_steps
                        else st.session_state.sampling_steps
                    )
                    / float(st.session_state.sampling_steps)
                )

                if "progress_bar_text" in st.session_state:
                    st.session_state["progress_bar_text"].text(
                        f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps}"
                        f"{percent if percent < 100 else 100}%"
                    )

    if "progress_bar" in st.session_state:
        try:
            st.session_state["progress_bar"].progress(percent if percent < 100 else 100)
        except UnboundLocalError:
            # logger.error(e)
            pass


prompt_parser = re.compile(
    """
    (?P<prompt>                # capture group for 'prompt'
    [^:]+                      # match one or more non ':' characters
    )                          # end 'prompt'
    (?:                        # non-capture group
    :+                         # match one or more ':' characters
    (?P<weight>                # capture group for 'weight'
    -?\\d+(?:\\.\\d+)?            # match positive or negative decimal number
    )?                         # end weight capture group, make optional
    \\s*                        # strip spaces after weight
    |                          # OR
    $                          # else, if no ':' then match end of line
    )                          # end non-capture group
""",
    re.VERBOSE,
)


def split_weighted_subprompts(input_string, normalize=True):
    # grabs all text up to the first occurrence of ':' as sub-prompt
    # takes the value following ':' as weight
    # if ':' has no value defined, defaults to 1.0
    # repeats until no text remaining
    parsed_prompts = [
        (match.group("prompt"), float(match.group("weight") or 1))
        for match in re.finditer(prompt_parser, input_string)
    ]
    if not normalize:
        return parsed_prompts
    # this probably still doesn't handle negative weights very well
    weight_sum = sum(map(lambda x: x[1], parsed_prompts))
    return [(x[0], x[1] / weight_sum) for x in parsed_prompts]


def slerp(device, t, v0: torch.Tensor, v1: torch.Tensor, DOT_THRESHOLD=0.9995):
    v0 = v0.detach().cpu().numpy()
    v1 = v1.detach().cpu().numpy()

    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
    if np.abs(dot) > DOT_THRESHOLD:
        v2 = (1 - t) * v0 + t * v1
    else:
        theta_0 = np.arccos(dot)
        sin_theta_0 = np.sin(theta_0)
        theta_t = theta_0 * t
        sin_theta_t = np.sin(theta_t)
        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
        s1 = sin_theta_t / sin_theta_0
        v2 = s0 * v0 + s1 * v1

    v2 = torch.from_numpy(v2).to(device)

    return v2


#
@st.experimental_memo(persist="disk", show_spinner=False, suppress_st_warning=True)
def optimize_update_preview_frequency(
    current_chunk_speed,
    previous_chunk_speed_list,
    update_preview_frequency,
    update_preview_frequency_list,
):
    """Find the optimal update_preview_frequency value maximizing
    performance while minimizing the time between updates."""
    from statistics import mean

    previous_chunk_avg_speed = mean(previous_chunk_speed_list)

    previous_chunk_speed_list.append(current_chunk_speed)
    current_chunk_avg_speed = mean(previous_chunk_speed_list)

    if current_chunk_avg_speed >= previous_chunk_avg_speed:
        # print(f"{current_chunk_speed} >= {previous_chunk_speed}")
        update_preview_frequency_list.append(update_preview_frequency + 1)
    else:
        # print(f"{current_chunk_speed} <= {previous_chunk_speed}")
        update_preview_frequency_list.append(update_preview_frequency - 1)

    update_preview_frequency = round(mean(update_preview_frequency_list))

    return (
        current_chunk_speed,
        previous_chunk_speed_list,
        update_preview_frequency,
        update_preview_frequency_list,
    )


def get_font(fontsize):
    fonts = ["arial.ttf", "DejaVuSans.ttf"]
    for font_name in fonts:
        try:
            return ImageFont.truetype(font_name, fontsize)
        except OSError:
            pass

    # ImageFont.load_default() is practically unusable as it only supports
    # latin1, so raise an exception instead if no usable font was found
    raise Exception(f"No usable font found (tried {', '.join(fonts)})")


def load_embeddings(fp):
    if fp is not None and hasattr(server_state["model"], "embedding_manager"):
        server_state["model"].embedding_manager.load(fp["name"])


def load_learned_embed_in_clip(
    learned_embeds_path, text_encoder, tokenizer, token=None
):
    loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")

    # separate token and the embeds
    if learned_embeds_path.endswith(".pt"):
        # old format
        # token = * so replace with file directory name when converting
        trained_token = os.path.basename(learned_embeds_path)
        params_dict = {
            trained_token: torch.tensor(
                list(loaded_learned_embeds["string_to_param"].items())[0][1]
            )
        }
        learned_embeds_path = os.path.splitext(learned_embeds_path)[0] + ".bin"
        torch.save(params_dict, learned_embeds_path)
        loaded_learned_embeds = torch.load(learned_embeds_path, map_location="cpu")
        trained_token = list(loaded_learned_embeds.keys())[0]
        embeds = loaded_learned_embeds[trained_token]

    elif learned_embeds_path.endswith(".bin"):
        trained_token = list(loaded_learned_embeds.keys())[0]
        embeds = loaded_learned_embeds[trained_token]

    embeds = loaded_learned_embeds[trained_token]
    # cast to dtype of text_encoder
    dtype = text_encoder.get_input_embeddings().weight.dtype
    embeds.to(dtype)

    # add the token in tokenizer
    token = token if token is not None else trained_token
    tokenizer.add_tokens(token)

    # resize the token embeddings
    text_encoder.resize_token_embeddings(len(tokenizer))

    # get the id for the token and assign the embeds
    token_id = tokenizer.convert_tokens_to_ids(token)
    text_encoder.get_input_embeddings().weight.data[token_id] = embeds
    return token


def image_grid(imgs, batch_size, force_n_rows=None, captions=None):
    # print (len(imgs))
    if force_n_rows is not None:
        rows = force_n_rows
    elif st.session_state["defaults"].general.n_rows > 0:
        rows = st.session_state["defaults"].general.n_rows
    elif st.session_state["defaults"].general.n_rows == 0:
        rows = batch_size
    else:
        rows = math.sqrt(len(imgs))
        rows = round(rows)

    cols = math.ceil(len(imgs) / rows)

    w, h = imgs[0].size
    grid = Image.new("RGB", size=(cols * w, rows * h), color="black")

    try:
        fnt = get_font(30)
    except Exception:
        pass

    for i, img in enumerate(imgs):
        grid.paste(img, box=(i % cols * w, i // cols * h))
        try:
            if captions and i < len(captions):
                d = ImageDraw.Draw(grid)
                size = d.textbbox(
                    (0, 0), captions[i], font=fnt, stroke_width=2, align="center"
                )
                d.multiline_text(
                    (i % cols * w + w / 2, i // cols * h + h - size[3]),
                    captions[i],
                    font=fnt,
                    fill=(255, 255, 255),
                    stroke_width=2,
                    stroke_fill=(0, 0, 0),
                    anchor="mm",
                    align="center",
                )
        except Exception:
            pass
    return grid


def seed_to_int(s):
    if type(s) is int:
        return s
    if s is None or s == "":
        return random.randint(0, 2**32 - 1)

    if "," in s:
        s = s.split(",")

    if type(s) is list:
        seed_list = []
        for seed in s:
            if seed is None or seed == "":
                seed_list.append(random.randint(0, 2**32 - 1))
            else:
                seed_list = s

        return seed_list

    n = abs(int(s) if s.isdigit() else random.Random(s).randint(0, 2**32 - 1))
    while n >= 2**32:
        n = n >> 32
    return n


#
def draw_prompt_matrix(im, width, height, all_prompts):
    def wrap(text, d, font, line_length):
        lines = [""]
        for word in text.split():
            line = f"{lines[-1]} {word}".strip()
            if d.textlength(line, font=font) <= line_length:
                lines[-1] = line
            else:
                lines.append(word)
        return "\n".join(lines)

    def draw_texts(pos, x, y, texts, sizes):
        for i, (text, size) in enumerate(zip(texts, sizes)):
            active = pos & (1 << i) != 0

            if not active:
                text = "\u0336".join(text) + "\u0336"

            d.multiline_text(
                (x, y + size[1] / 2),
                text,
                font=fnt,
                fill=color_active if active else color_inactive,
                anchor="mm",
                align="center",
            )

            y += size[1] + line_spacing

    fontsize = (width + height) // 25
    line_spacing = fontsize // 2
    fnt = get_font(fontsize)
    color_active = (0, 0, 0)
    color_inactive = (153, 153, 153)

    pad_top = height // 4
    pad_left = width * 3 // 4 if len(all_prompts) > 2 else 0

    cols = im.width // width
    rows = im.height // height

    prompts = all_prompts[1:]

    result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
    result.paste(im, (pad_left, pad_top))

    d = ImageDraw.Draw(result)

    boundary = math.ceil(len(prompts) / 2)
    prompts_horiz = [wrap(x, d, fnt, width) for x in prompts[:boundary]]
    prompts_vert = [wrap(x, d, fnt, pad_left) for x in prompts[boundary:]]

    sizes_hor = [
        (x[2] - x[0], x[3] - x[1])
        for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_horiz]
    ]
    sizes_ver = [
        (x[2] - x[0], x[3] - x[1])
        for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_vert]
    ]
    hor_text_height = sum([x[1] + line_spacing for x in sizes_hor]) - line_spacing
    ver_text_height = sum([x[1] + line_spacing for x in sizes_ver]) - line_spacing

    for col in range(cols):
        x = pad_left + width * col + width / 2
        y = pad_top / 2 - hor_text_height / 2

        draw_texts(col, x, y, prompts_horiz, sizes_hor)

    for row in range(rows):
        x = pad_left / 2
        y = pad_top + height * row + height / 2 - ver_text_height / 2

        draw_texts(row, x, y, prompts_vert, sizes_ver)

    return result


#
def enable_minimal_memory_usage(model):
    """Moves only unet to fp16 and to CUDA, while keepping lighter models on CPUs"""
    model.unet.to(torch.float16).to(torch.device("cuda"))
    model.enable_attention_slicing(1)

    torch.cuda.empty_cache()
    torch_gc()


def check_prompt_length(prompt, comments):
    """this function tests if prompt is too long, and if so, adds a message to comments"""

    tokenizer = (
        server_state["model"]
        if not st.session_state["defaults"].general.optimized
        else server_state["modelCS"]
    ).cond_stage_model.tokenizer
    max_length = (
        server_state["model"]
        if not st.session_state["defaults"].general.optimized
        else server_state["modelCS"]
    ).cond_stage_model.max_length

    info = (
        server_state["model"]
        if not st.session_state["defaults"].general.optimized
        else server_state["modelCS"]
    ).cond_stage_model.tokenizer(
        [prompt],
        truncation=True,
        max_length=max_length,
        return_overflowing_tokens=True,
        padding="max_length",
        return_tensors="pt",
    )
    ovf = info["overflowing_tokens"][0]
    overflowing_count = ovf.shape[0]
    if overflowing_count == 0:
        return

    vocab = {v: k for k, v in tokenizer.get_vocab().items()}
    overflowing_words = [vocab.get(int(x), "") for x in ovf]
    overflowing_text = tokenizer.convert_tokens_to_string("".join(overflowing_words))

    comments.append(
        f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n"
    )


#
def custom_models_available():
    with server_state_lock["custom_models"]:
        #
        # Allow for custom models to be used instead of the default one,
        # an example would be Waifu-Diffusion or any other fine tune of stable diffusion
        server_state["custom_models"]: sorted = []

        for root, dirs, files in os.walk(os.path.join("models", "custom")):
            for file in files:
                if os.path.splitext(file)[1] == ".ckpt":
                    server_state["custom_models"].append(os.path.splitext(file)[0])

        with server_state_lock["CustomModel_available"]:
            if len(server_state["custom_models"]) > 0:
                server_state["CustomModel_available"] = True
                server_state["custom_models"].append("Stable Diffusion v1.5")
            else:
                server_state["CustomModel_available"] = False


#
def GFPGAN_available():
    # with server_state_lock["GFPGAN_models"]:
    #

    st.session_state["GFPGAN_models"]: sorted = []
    model = st.session_state["defaults"].model_manager.models.gfpgan

    files_available = 0

    for file in model["files"]:
        if "save_location" in model["files"][file]:
            if os.path.exists(
                os.path.join(
                    model["files"][file]["save_location"],
                    model["files"][file]["file_name"],
                )
            ):
                files_available += 1

        elif os.path.exists(
            os.path.join(model["save_location"], model["files"][file]["file_name"])
        ):
            base_name = os.path.splitext(model["files"][file]["file_name"])[0]
            if "GFPGANv" in base_name:
                st.session_state["GFPGAN_models"].append(base_name)
            files_available += 1

    # we need to show the other models from previous verions that we have on the
    # same directory in case we want to see how they perform vs each other.
    for root, dirs, files in os.walk(st.session_state["defaults"].general.GFPGAN_dir):
        for file in files:
            if os.path.splitext(file)[1] == ".pth":
                if os.path.splitext(file)[0] not in st.session_state["GFPGAN_models"]:
                    st.session_state["GFPGAN_models"].append(os.path.splitext(file)[0])

    if len(st.session_state["GFPGAN_models"]) > 0 and files_available == len(
        model["files"]
    ):
        st.session_state["GFPGAN_available"] = True
    else:
        st.session_state["GFPGAN_available"] = False
        st.session_state["use_GFPGAN"] = False
        st.session_state["GFPGAN_model"] = "GFPGANv1.4"


#
def RealESRGAN_available():
    # with server_state_lock["RealESRGAN_models"]:
    #
    # Allow for custom models to be used instead of the default one,
    # an example would be Waifu-Diffusion or any other fine tune of stable diffusion
    st.session_state["RealESRGAN_models"]: sorted = []
    model = st.session_state["defaults"].model_manager.models.realesrgan
    for file in model["files"]:
        if os.path.exists(
            os.path.join(model["save_location"], model["files"][file]["file_name"])
        ):
            base_name = os.path.splitext(model["files"][file]["file_name"])[0]
            st.session_state["RealESRGAN_models"].append(base_name)

    if len(st.session_state["RealESRGAN_models"]) > 0:
        st.session_state["RealESRGAN_available"] = True
    else:
        st.session_state["RealESRGAN_available"] = False
        st.session_state["use_RealESRGAN"] = False
        st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"


#
def LDSR_available():
    # with server_state_lock["RealESRGAN_models"]:
    #
    # Allow for custom models to be used instead of the default one,
    # an example would be Waifu-Diffusion or any other fine tune of stable diffusion
    st.session_state["LDSR_models"]: sorted = []
    files_available = 0
    model = st.session_state["defaults"].model_manager.models.ldsr
    for file in model["files"]:
        if os.path.exists(
            os.path.join(model["save_location"], model["files"][file]["file_name"])
        ):
            base_name = os.path.splitext(model["files"][file]["file_name"])[0]
            extension = os.path.splitext(model["files"][file]["file_name"])[1]
            if extension == ".ckpt":
                st.session_state["LDSR_models"].append(base_name)
            files_available += 1
    if files_available == len(model["files"]):
        st.session_state["LDSR_available"] = True
    else:
        st.session_state["LDSR_available"] = False
        st.session_state["use_LDSR"] = False
        st.session_state["LDSR_model"] = "model"


def save_sample(
    image,
    sample_path_i,
    filename,
    jpg_sample,
    prompts,
    seeds,
    width,
    height,
    steps,
    cfg_scale,
    normalize_prompt_weights,
    use_GFPGAN,
    write_info_files,
    prompt_matrix,
    init_img,
    uses_loopback,
    uses_random_seed_loopback,
    save_grid,
    sort_samples,
    sampler_name,
    ddim_eta,
    n_iter,
    batch_size,
    i,
    denoising_strength,
    resize_mode,
    save_individual_images,
    model_name,
):
    filename_i = os.path.join(sample_path_i, filename)

    if "defaults" in st.session_state:
        if st.session_state["defaults"].general.save_metadata or write_info_files:
            # toggles differ for txt2img vs. img2img:
            offset = 0 if init_img is None else 2
            toggles = []
            if prompt_matrix:
                toggles.append(0)
            if normalize_prompt_weights:
                toggles.append(1)
            if init_img is not None:
                if uses_loopback:
                    toggles.append(2)
                if uses_random_seed_loopback:
                    toggles.append(3)
            if save_individual_images:
                toggles.append(2 + offset)
            if save_grid:
                toggles.append(3 + offset)
            if sort_samples:
                toggles.append(4 + offset)
            if write_info_files:
                toggles.append(5 + offset)
            if use_GFPGAN:
                toggles.append(6 + offset)
            metadata = dict(
                target="txt2img" if init_img is None else "img2img",
                prompt=prompts[i],
                ddim_steps=steps,
                toggles=toggles,
                sampler_name=sampler_name,
                ddim_eta=ddim_eta,
                n_iter=n_iter,
                batch_size=batch_size,
                cfg_scale=cfg_scale,
                seed=seeds[i],
                width=width,
                height=height,
                normalize_prompt_weights=normalize_prompt_weights,
                model_name=model_name,
            )
            # Not yet any use for these, but they bloat up the files:
            # info_dict["init_img"] = init_img
            # info_dict["init_mask"] = init_mask
            if init_img is not None:
                metadata["denoising_strength"] = str(denoising_strength)
                metadata["resize_mode"] = resize_mode

    if write_info_files:
        with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
            yaml.dump(metadata, f, allow_unicode=True, width=10000)

    if st.session_state["defaults"].general.save_metadata:
        # metadata = {
        # 	"SD:prompt": prompts[i],
        # 	"SD:seed": str(seeds[i]),
        # 	"SD:width": str(width),
        # 	"SD:height": str(height),
        # 	"SD:steps": str(steps),
        # 	"SD:cfg_scale": str(cfg_scale),
        # 	"SD:normalize_prompt_weights": str(normalize_prompt_weights),
        # }
        metadata = {"SD:" + k: v for (k, v) in metadata.items()}

        if save_ext == "png":
            mdata = PngInfo()
            for key in metadata:
                mdata.add_text(key, str(metadata[key]))
            image.save(f"{filename_i}.png", pnginfo=mdata)
        else:
            if jpg_sample:
                image.save(f"{filename_i}.jpg", quality=save_quality, optimize=True)
            elif save_ext == "webp":
                image.save(
                    f"{filename_i}.{save_ext}",
                    "webp",
                    quality=save_quality,
                    lossless=save_lossless,
                )
            else:
                # not sure what file format this is
                image.save(f"{filename_i}.{save_ext}", f"{save_ext}")
            try:
                exif_dict = piexif.load(f"{filename_i}.{save_ext}")
            except:
                exif_dict = {"Exif": dict()}
            exif_dict["Exif"][
                piexif.ExifIFD.UserComment
            ] = piexif.helper.UserComment.dump(json.dumps(metadata), encoding="unicode")
            piexif.insert(piexif.dump(exif_dict), f"{filename_i}.{save_ext}")


def get_next_sequence_number(path, prefix=""):
    """
    Determines and returns the next sequence number to use when saving an
    image in the specified directory.

    If a prefix is given, only consider files whose names start with that
    prefix, and strip the prefix from filenames before extracting their
    sequence number.

    The sequence starts at 0.
    """
    result = -1
    for p in Path(path).iterdir():
        if p.name.endswith((".png", ".jpg")) and p.name.startswith(prefix):
            tmp = p.name[len(prefix) :]
            try:
                result = max(int(tmp.split("-")[0]), result)
            except ValueError:
                pass
    return result + 1


def oxlamon_matrix(prompt, seed, n_iter, batch_size):
    pattern = re.compile(r"(,\s){2,}")

    class PromptItem:
        def __init__(self, text, parts, item):
            self.text = text
            self.parts = parts
            if item:
                self.parts.append(item)

    def clean(txt):
        return re.sub(pattern, ", ", txt)

    def getrowcount(txt):
        for data in re.finditer(".*?\\((.*?)\\).*", txt):
            if data:
                return len(data.group(1).split("|"))
            break
        return None

    def repliter(txt):
        for data in re.finditer(".*?\\((.*?)\\).*", txt):
            if data:
                r = data.span(1)
                for item in data.group(1).split("|"):
                    yield (
                        clean(txt[: r[0] - 1] + item.strip() + txt[r[1] + 1 :]),
                        item.strip(),
                    )
            break

    def iterlist(items):
        outitems = []
        for item in items:
            for newitem, newpart in repliter(item.text):
                outitems.append(PromptItem(newitem, item.parts.copy(), newpart))

        return outitems

    def getmatrix(prompt):
        dataitems = [PromptItem(prompt[1:].strip(), [], None)]
        while True:
            newdataitems = iterlist(dataitems)
            if len(newdataitems) == 0:
                return dataitems
            dataitems = newdataitems

    def classToArrays(items, seed, n_iter):
        texts = []
        parts = []
        seeds = []

        for item in items:
            itemseed = seed
            for i in range(n_iter):
                texts.append(item.text)
                parts.append(f"Seed: {itemseed}\n" + "\n".join(item.parts))
                seeds.append(itemseed)
                itemseed += 1

        return seeds, texts, parts

    all_seeds, all_prompts, prompt_matrix_parts = classToArrays(
        getmatrix(prompt), seed, n_iter
    )
    n_iter = math.ceil(len(all_prompts) / batch_size)

    needrows = getrowcount(prompt)
    if needrows:
        xrows = math.sqrt(len(all_prompts))
        xrows = round(xrows)
        # if columns is to much
        cols = math.ceil(len(all_prompts) / xrows)
        if cols > needrows * 4:
            needrows *= 2

    return all_seeds, n_iter, prompt_matrix_parts, all_prompts, needrows


#
def process_images(
    outpath,
    func_init,
    func_sample,
    prompt,
    seed,
    sampler_name,
    save_grid,
    batch_size,
    n_iter,
    steps,
    cfg_scale,
    width,
    height,
    prompt_matrix,
    use_GFPGAN: bool = True,
    GFPGAN_model: str = "GFPGANv1.4",
    use_RealESRGAN: bool = False,
    realesrgan_model_name: str = "RealESRGAN_x4plus",
    use_LDSR: bool = False,
    LDSR_model_name: str = "model",
    ddim_eta=0.0,
    normalize_prompt_weights=True,
    init_img=None,
    init_mask=None,
    mask_blur_strength=3,
    mask_restore=False,
    denoising_strength=0.75,
    noise_mode=0,
    find_noise_steps=1,
    resize_mode=None,
    uses_loopback=False,
    uses_random_seed_loopback=False,
    sort_samples=True,
    write_info_files=True,
    jpg_sample=False,
    variant_amount=0.0,
    variant_seed=None,
    save_individual_images: bool = True,
):
    """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""

    torch_gc()
    # start time after garbage collection (or before?)
    start_time = time.time()

    # We will use this date here later for the folder name, need to start_time if not need
    datetime.datetime.now()

    mem_mon = MemUsageMonitor("MemMon")
    mem_mon.start()

    if st.session_state.defaults.general.use_sd_concepts_library:
        prompt_tokens = re.findall("<([a-zA-Z0-9-]+)>", prompt)

        if prompt_tokens:
            # compviz
            tokenizer = (
                server_state["model"]
                if not st.session_state["defaults"].general.optimized
                else server_state["modelCS"]
            ).cond_stage_model.tokenizer
            text_encoder = (
                server_state["model"]
                if not st.session_state["defaults"].general.optimized
                else server_state["modelCS"]
            ).cond_stage_model.transformer

            # diffusers
            # tokenizer = pipe.tokenizer
            # text_encoder = pipe.text_encoder

            ext = ("pt", "bin")

            if len(prompt_tokens) > 1:
                for token_name in prompt_tokens:
                    embedding_path = os.path.join(
                        st.session_state["defaults"].general.sd_concepts_library_folder,
                        token_name,
                    )
                    if os.path.exists(embedding_path):
                        for files in os.listdir(embedding_path):
                            if files.endswith(ext):
                                load_learned_embed_in_clip(
                                    f"{os.path.join(embedding_path, files)}",
                                    text_encoder,
                                    tokenizer,
                                    f"<{token_name}>",
                                )
            else:
                embedding_path = os.path.join(
                    st.session_state["defaults"].general.sd_concepts_library_folder,
                    prompt_tokens[0],
                )
                if os.path.exists(embedding_path):
                    for files in os.listdir(embedding_path):
                        if files.endswith(ext):
                            load_learned_embed_in_clip(
                                f"{os.path.join(embedding_path, files)}",
                                text_encoder,
                                tokenizer,
                                f"<{prompt_tokens[0]}>",
                            )

        #

    os.makedirs(outpath, exist_ok=True)

    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)

    if "|" not in prompt and prompt.startswith("@"):
        prompt = prompt[1:]

    negprompt = ""
    if "###" in prompt:
        prompt, negprompt = prompt.split("###", 1)
        prompt = prompt.strip()
        negprompt = negprompt.strip()

    comments = []

    prompt_matrix_parts = []
    simple_templating = False

    if prompt_matrix:
        if prompt.startswith("@"):
            simple_templating = True
            all_seeds, n_iter, prompt_matrix_parts, all_prompts, frows = oxlamon_matrix(
                prompt, seed, n_iter, batch_size
            )
        else:
            all_prompts = []
            prompt_matrix_parts = prompt.split("|")
            combination_count = 2 ** (len(prompt_matrix_parts) - 1)
            for combination_num in range(combination_count):
                current = prompt_matrix_parts[0]

                for n, text in enumerate(prompt_matrix_parts[1:]):
                    if combination_num & (2**n) > 0:
                        current += ("" if text.strip().startswith(",") else ", ") + text

                all_prompts.append(current)

            n_iter = math.ceil(len(all_prompts) / batch_size)
            all_seeds = len(all_prompts) * [seed]

        logger.info(
            f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches."
        )
    else:
        if not st.session_state["defaults"].general.no_verify_input:
            try:
                check_prompt_length(prompt, comments)
            except:
                import traceback

                logger.info("Error verifying input:", file=sys.stderr)
                logger.info(traceback.format_exc(), file=sys.stderr)

        all_prompts = batch_size * n_iter * [prompt]
        all_seeds = [seed + x for x in range(len(all_prompts))]

    precision_scope = (
        autocast
        if st.session_state["defaults"].general.precision == "autocast"
        else nullcontext
    )
    output_images = []
    grid_captions = []
    stats = []
    with torch.no_grad(), precision_scope("cuda"), (
        server_state["model"].ema_scope()
        if not st.session_state["defaults"].general.optimized
        else nullcontext()
    ):
        init_data = func_init()
        time.time()

        # if variant_amount > 0.0 create noise from base seed
        base_x = None
        if variant_amount > 0.0:
            target_seed_randomizer = seed_to_int("")  # random seed
            torch.manual_seed(
                seed
            )  # this has to be the single starting seed (not per-iteration)
            base_x = create_random_tensors(
                [opt_C, height // opt_f, width // opt_f], seeds=[seed]
            )
            # we don't want all_seeds to be sequential from starting seed with variants,
            # since that makes the same variants each time,
            # so we add target_seed_randomizer as a random offset
            for si in range(len(all_seeds)):
                all_seeds[si] += target_seed_randomizer

        for n in range(n_iter):
            logger.info(f"Iteration: {n+1}/{n_iter}")
            prompts = all_prompts[n * batch_size : (n + 1) * batch_size]
            captions = prompt_matrix_parts[n * batch_size : (n + 1) * batch_size]
            seeds = all_seeds[n * batch_size : (n + 1) * batch_size]

            logger.info(prompt)

            if st.session_state["defaults"].general.optimized:
                server_state["modelCS"].to(st.session_state["defaults"].general.gpu)

            uc = (
                server_state["model"]
                if not st.session_state["defaults"].general.optimized
                else server_state["modelCS"]
            ).get_learned_conditioning(len(prompts) * [negprompt])

            if isinstance(prompts, tuple):
                prompts = list(prompts)

            # split the prompt if it has : for weighting
            # TODO for speed it might help to have this occur when all_prompts filled??
            weighted_subprompts = split_weighted_subprompts(
                prompts[0], normalize_prompt_weights
            )

            # sub-prompt weighting used if more than 1
            if len(weighted_subprompts) > 1:
                c = torch.zeros_like(
                    uc
                )  # i dont know if this is correct.. but it works
                for i in range(0, len(weighted_subprompts)):
                    # note if alpha negative, it functions same as torch.sub
                    c = torch.add(
                        c,
                        (
                            server_state["model"]
                            if not st.session_state["defaults"].general.optimized
                            else server_state["modelCS"]
                        ).get_learned_conditioning(weighted_subprompts[i][0]),
                        alpha=weighted_subprompts[i][1],
                    )
            else:  # just behave like usual
                c = (
                    server_state["model"]
                    if not st.session_state["defaults"].general.optimized
                    else server_state["modelCS"]
                ).get_learned_conditioning(prompts)

            shape = [opt_C, height // opt_f, width // opt_f]

            if st.session_state["defaults"].general.optimized:
                mem = torch.cuda.memory_allocated() / 1e6
                server_state["modelCS"].to("cpu")
                while torch.cuda.memory_allocated() / 1e6 >= mem:
                    time.sleep(1)

            if noise_mode == 1 or noise_mode == 3:
                # TODO params for find_noise_to_image
                x = torch.cat(
                    batch_size
                    * [
                        find_noise_for_image(
                            server_state["model"],
                            server_state["device"],
                            init_img.convert("RGB"),
                            "",
                            find_noise_steps,
                            0.0,
                            normalize=True,
                            generation_callback=generation_callback,
                        )
                    ],
                    dim=0,
                )
            else:
                # we manually generate all input noises because each one should have a specific seed
                x = create_random_tensors(shape, seeds=seeds)

            if variant_amount > 0.0:  # we are making variants
                # using variant_seed as sneaky toggle,
                # when not None or '' use the variant_seed
                # otherwise use seeds
                if variant_seed is not None and variant_seed != "":
                    specified_variant_seed = seed_to_int(variant_seed)
                    torch.manual_seed(specified_variant_seed)
                    seeds = [specified_variant_seed]
                # finally, slerp base_x noise to target_x noise for creating a variant
                x = slerp(
                    st.session_state["defaults"].general.gpu,
                    max(0.0, min(1.0, variant_amount)),
                    base_x,
                    x,
                )

            samples_ddim = func_sample(
                init_data=init_data,
                x=x,
                conditioning=c,
                unconditional_conditioning=uc,
                sampler_name=sampler_name,
            )

            if st.session_state["defaults"].general.optimized:
                server_state["modelFS"].to(st.session_state["defaults"].general.gpu)

            x_samples_ddim = (
                server_state["model"]
                if not st.session_state["defaults"].general.optimized
                else server_state["modelFS"]
            ).decode_first_stage(samples_ddim)
            x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

            run_images = []
            for i, x_sample in enumerate(x_samples_ddim):
                sanitized_prompt = slugify(prompts[i])

                percent = i / len(x_samples_ddim)

                if "progress_bar" in st.session_state:
                    st.session_state["progress_bar"].progress(
                        percent if percent < 100 else 100
                    )

                if sort_samples:
                    full_path = os.path.join(os.getcwd(), sample_path, sanitized_prompt)

                    sanitized_prompt = sanitized_prompt[: 120 - len(full_path)]
                    sample_path_i = os.path.join(sample_path, sanitized_prompt)

                    # print(f"output folder length: {len(os.path.join(os.getcwd(), sample_path_i))}")
                    # print(os.path.join(os.getcwd(), sample_path_i))

                    os.makedirs(sample_path_i, exist_ok=True)
                    base_count = get_next_sequence_number(sample_path_i)
                    filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}"
                else:
                    full_path = os.path.join(os.getcwd(), sample_path)
                    sample_path_i = sample_path
                    base_count = get_next_sequence_number(sample_path_i)
                    filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[i]}_{sanitized_prompt}"[
                        : 120 - len(full_path)
                    ]  # same as before

                x_sample = 255.0 * rearrange(x_sample.cpu().numpy(), "c h w -> h w c")
                x_sample = x_sample.astype(np.uint8)
                image = Image.fromarray(x_sample)
                original_filename = filename

                if "preview_image" in st.session_state:
                    st.session_state["preview_image"].image(image)

                #
                if (
                    use_GFPGAN
                    and server_state["GFPGAN"] is not None
                    and not use_RealESRGAN
                    and not use_LDSR
                ):
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running GFPGAN on image %d of %d..."
                            % (i + 1, len(x_samples_ddim))
                        )

                    if server_state["GFPGAN"].name != GFPGAN_model:
                        load_models(
                            use_LDSR=use_LDSR,
                            LDSR_model=LDSR_model_name,
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    torch_gc()

                    with torch.autocast("cuda"):
                        cropped_faces, restored_faces, restored_img = server_state[
                            "GFPGAN"
                        ].enhance(
                            x_sample[:, :, ::-1],
                            has_aligned=False,
                            only_center_face=False,
                            paste_back=True,
                        )

                    gfpgan_sample = restored_img[:, :, ::-1]
                    gfpgan_image = Image.fromarray(gfpgan_sample)

                    # if st.session_state["GFPGAN_strenght"]:
                    # gfpgan_sample = Image.blend(image, gfpgan_image, st.session_state["GFPGAN_strenght"])

                    gfpgan_filename = original_filename + "-gfpgan"

                    save_sample(
                        gfpgan_image,
                        sample_path_i,
                        gfpgan_filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        use_GFPGAN,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        False,
                        server_state["loaded_model"],
                    )

                    output_images.append(gfpgan_image)  # 287
                    run_images.append(gfpgan_image)

                    if simple_templating:
                        grid_captions.append(captions[i] + "\ngfpgan")

                #
                elif (
                    use_RealESRGAN
                    and server_state["RealESRGAN"] is not None
                    and not use_GFPGAN
                ):
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running RealESRGAN on image %d of %d..."
                            % (i + 1, len(x_samples_ddim))
                        )
                    # skip_save = True # #287 >_>
                    torch_gc()

                    if server_state["RealESRGAN"].model.name != realesrgan_model_name:
                        # try_loading_RealESRGAN(realesrgan_model_name)
                        load_models(
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    output, img_mode = server_state["RealESRGAN"].enhance(
                        x_sample[:, :, ::-1]
                    )
                    esrgan_filename = original_filename + "-esrgan4x"
                    esrgan_sample = output[:, :, ::-1]
                    esrgan_image = Image.fromarray(esrgan_sample)

                    # save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
                    # normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
                    # save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)

                    save_sample(
                        esrgan_image,
                        sample_path_i,
                        esrgan_filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        use_GFPGAN,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        False,
                        server_state["loaded_model"],
                    )

                    output_images.append(esrgan_image)  # 287
                    run_images.append(esrgan_image)

                    if simple_templating:
                        grid_captions.append(captions[i] + "\nesrgan")

                #
                elif use_LDSR and "LDSR" in server_state and not use_GFPGAN:
                    logger.info(
                        "Running LDSR on image %d of %d..."
                        % (i + 1, len(x_samples_ddim))
                    )
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running LDSR on image %d of %d..."
                            % (i + 1, len(x_samples_ddim))
                        )
                    # skip_save = True # #287 >_>
                    torch_gc()

                    if server_state["LDSR"].name != LDSR_model_name:
                        # try_loading_RealESRGAN(realesrgan_model_name)
                        load_models(
                            use_LDSR=use_LDSR,
                            LDSR_model=LDSR_model_name,
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    result = server_state["LDSR"].superResolution(
                        image,
                        ddimSteps=st.session_state["ldsr_sampling_steps"],
                        preDownScale=st.session_state["preDownScale"],
                        postDownScale=st.session_state["postDownScale"],
                        downsample_method=st.session_state["downsample_method"],
                    )

                    ldsr_filename = original_filename + "-ldsr4x"
                    # ldsr_sample = result[:,:,::-1]
                    # ldsr_image = Image.fromarray(ldsr_sample)

                    # save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
                    # normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
                    # save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)

                    save_sample(
                        result,
                        sample_path_i,
                        ldsr_filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        use_GFPGAN,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        False,
                        server_state["loaded_model"],
                    )

                    output_images.append(result)  # 287
                    run_images.append(result)

                    if simple_templating:
                        grid_captions.append(captions[i] + "\nldsr")

                #
                elif (
                    use_LDSR
                    and "LDSR" in server_state
                    and use_GFPGAN
                    and "GFPGAN" in server_state
                ):
                    logger.info(
                        "Running GFPGAN+LDSR on image %d of %d..."
                        % (i + 1, len(x_samples_ddim))
                    )
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running GFPGAN+LDSR on image %d of %d..."
                            % (i + 1, len(x_samples_ddim))
                        )

                    if server_state["GFPGAN"].name != GFPGAN_model:
                        load_models(
                            use_LDSR=use_LDSR,
                            LDSR_model=LDSR_model_name,
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    torch_gc()
                    cropped_faces, restored_faces, restored_img = server_state[
                        "GFPGAN"
                    ].enhance(
                        x_sample[:, :, ::-1],
                        has_aligned=False,
                        only_center_face=False,
                        paste_back=True,
                    )

                    gfpgan_sample = restored_img[:, :, ::-1]
                    gfpgan_image = Image.fromarray(gfpgan_sample)

                    if server_state["LDSR"].name != LDSR_model_name:
                        # try_loading_RealESRGAN(realesrgan_model_name)
                        load_models(
                            use_LDSR=use_LDSR,
                            LDSR_model=LDSR_model_name,
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    # LDSR.superResolution(gfpgan_image, ddimSteps=100, preDownScale='None', postDownScale='None', downsample_method="Lanczos")
                    result = server_state["LDSR"].superResolution(
                        gfpgan_image,
                        ddimSteps=st.session_state["ldsr_sampling_steps"],
                        preDownScale=st.session_state["preDownScale"],
                        postDownScale=st.session_state["postDownScale"],
                        downsample_method=st.session_state["downsample_method"],
                    )

                    ldsr_filename = original_filename + "-gfpgan-ldsr2x"
                    # ldsr_sample = result[:,:,::-1]
                    # ldsr_image = Image.fromarray(result)

                    # save_sample(image, sample_path_i, original_filename, jpg_sample, prompts, seeds, width, height, steps, cfg_scale,
                    # normalize_prompt_weights, use_GFPGAN, write_info_files, prompt_matrix, init_img, uses_loopback, uses_random_seed_loopback, skip_save,
                    # save_grid, sort_samples, sampler_name, ddim_eta, n_iter, batch_size, i, denoising_strength, resize_mode)

                    save_sample(
                        result,
                        sample_path_i,
                        ldsr_filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        use_GFPGAN,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        False,
                        server_state["loaded_model"],
                    )

                    output_images.append(result)  # 287
                    run_images.append(result)

                    if simple_templating:
                        grid_captions.append(captions[i] + "\ngfpgan-ldsr")

                elif (
                    use_RealESRGAN
                    and server_state["RealESRGAN"] is not None
                    and use_GFPGAN
                    and server_state["GFPGAN"] is not None
                ):
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running GFPGAN+RealESRGAN on image %d of %d..."
                            % (i + 1, len(x_samples_ddim))
                        )
                    # skip_save = True # #287 >_>
                    torch_gc()
                    cropped_faces, restored_faces, restored_img = server_state[
                        "GFPGAN"
                    ].enhance(
                        x_sample[:, :, ::-1],
                        has_aligned=False,
                        only_center_face=False,
                        paste_back=True,
                    )
                    gfpgan_sample = restored_img[:, :, ::-1]

                    if server_state["RealESRGAN"].model.name != realesrgan_model_name:
                        # try_loading_RealESRGAN(realesrgan_model_name)
                        load_models(
                            use_GFPGAN=use_GFPGAN,
                            use_RealESRGAN=use_RealESRGAN,
                            RealESRGAN_model=realesrgan_model_name,
                        )

                    output, img_mode = server_state["RealESRGAN"].enhance(
                        gfpgan_sample[:, :, ::-1]
                    )
                    gfpgan_esrgan_filename = original_filename + "-gfpgan-esrgan4x"
                    gfpgan_esrgan_sample = output[:, :, ::-1]
                    gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample)

                    save_sample(
                        gfpgan_esrgan_image,
                        sample_path_i,
                        gfpgan_esrgan_filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        False,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        False,
                        server_state["loaded_model"],
                    )

                    output_images.append(gfpgan_esrgan_image)  # 287
                    run_images.append(gfpgan_esrgan_image)

                    if simple_templating:
                        grid_captions.append(captions[i] + "\ngfpgan_esrgan")

                #

                else:
                    output_images.append(image)
                    run_images.append(image)

                if mask_restore and init_mask:
                    # init_mask = init_mask if keep_mask else ImageOps.invert(init_mask)
                    init_mask = init_mask.filter(
                        ImageFilter.GaussianBlur(mask_blur_strength)
                    )
                    init_mask = init_mask.convert("L")
                    init_img = init_img.convert("RGB")
                    image = image.convert("RGB")

                    if use_RealESRGAN and server_state["RealESRGAN"] is not None:
                        if (
                            server_state["RealESRGAN"].model.name
                            != realesrgan_model_name
                        ):
                            # try_loading_RealESRGAN(realesrgan_model_name)
                            load_models(
                                use_GFPGAN=use_GFPGAN,
                                use_RealESRGAN=use_RealESRGAN,
                                RealESRGAN_model=realesrgan_model_name,
                            )

                        output, img_mode = server_state["RealESRGAN"].enhance(
                            np.array(init_img, dtype=np.uint8)
                        )
                        init_img = Image.fromarray(output)
                        init_img = init_img.convert("RGB")

                        output, img_mode = server_state["RealESRGAN"].enhance(
                            np.array(init_mask, dtype=np.uint8)
                        )
                        init_mask = Image.fromarray(output)
                        init_mask = init_mask.convert("L")

                    image = Image.composite(init_img, image, init_mask)

                if save_individual_images:
                    save_sample(
                        image,
                        sample_path_i,
                        filename,
                        jpg_sample,
                        prompts,
                        seeds,
                        width,
                        height,
                        steps,
                        cfg_scale,
                        normalize_prompt_weights,
                        use_GFPGAN,
                        write_info_files,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        save_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        save_individual_images,
                        server_state["loaded_model"],
                    )

                    # if add_original_image or not simple_templating:
                    # output_images.append(image)
                    # if simple_templating:
                    # grid_captions.append( captions[i] )
                if "defaults" in st.session_state:
                    if st.session_state["defaults"].general.optimized:
                        mem = torch.cuda.memory_allocated() / 1e6
                        server_state["modelFS"].to("cpu")
                        while torch.cuda.memory_allocated() / 1e6 >= mem:
                            time.sleep(1)

            if len(run_images) > 1:
                preview_image = image_grid(run_images, n_iter)
            else:
                preview_image = run_images[0]

            # Constrain the final preview image to 1440x900 so we're not sending huge amounts of data
            # to the browser
            preview_image = constrain_image(preview_image, 1440, 900)
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text("Finished!")

            if "preview_image" in st.session_state:
                st.session_state["preview_image"].image(preview_image)

        if prompt_matrix or save_grid:
            if prompt_matrix:
                if simple_templating:
                    grid = image_grid(
                        output_images,
                        n_iter,
                        force_n_rows=frows,
                        captions=grid_captions,
                    )
                else:
                    grid = image_grid(
                        output_images,
                        n_iter,
                        force_n_rows=1 << ((len(prompt_matrix_parts) - 1) // 2),
                    )
                    try:
                        grid = draw_prompt_matrix(
                            grid, width, height, prompt_matrix_parts
                        )
                    except:
                        import traceback

                        logger.error(
                            "Error creating prompt_matrix text:", file=sys.stderr
                        )
                        logger.error(traceback.format_exc(), file=sys.stderr)
            else:
                grid = image_grid(output_images, batch_size)

            if grid and (batch_size > 1 or n_iter > 1):
                output_images.insert(0, grid)

            grid_count = get_next_sequence_number(outpath, "grid-")
            grid_file = f"grid-{grid_count:05}-{seed}_{slugify(prompts[i].replace(' ', '_')[:120-len(full_path)])}.{grid_ext}"
            grid.save(
                os.path.join(outpath, grid_file),
                grid_format,
                quality=grid_quality,
                lossless=grid_lossless,
                optimize=True,
            )

        time.time()

    mem_max_used, mem_total = mem_mon.read_and_stop()
    time_diff = time.time() - start_time

    info = f"""
            {prompt}
            Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', Denoising strength: '+str(denoising_strength) if init_img is not None else ''}{', GFPGAN' if use_GFPGAN and server_state["GFPGAN"] is not None else ''}{', '+realesrgan_model_name if use_RealESRGAN and server_state["RealESRGAN"] is not None else ''}{', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()
    stats = f"""
            Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
            Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%"""

    for comment in comments:
        info += "\n\n" + comment

    # mem_mon.stop()
    # del mem_mon
    torch_gc()

    return output_images, seed, info, stats


def resize_image(resize_mode, im, width, height):
    LANCZOS = (
        Image.Resampling.LANCZOS if hasattr(Image, "Resampling") else Image.LANCZOS
    )
    if resize_mode == 0:
        res = im.resize((width, height), resample=LANCZOS)
    elif resize_mode == 1:
        ratio = width / height
        src_ratio = im.width / im.height

        src_w = width if ratio > src_ratio else im.width * height // im.height
        src_h = height if ratio <= src_ratio else im.height * width // im.width

        resized = im.resize((src_w, src_h), resample=LANCZOS)
        res = Image.new("RGBA", (width, height))
        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
    else:
        ratio = width / height
        src_ratio = im.width / im.height

        src_w = width if ratio < src_ratio else im.width * height // im.height
        src_h = height if ratio >= src_ratio else im.height * width // im.width

        resized = im.resize((src_w, src_h), resample=LANCZOS)
        res = Image.new("RGBA", (width, height))
        res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))

        if ratio < src_ratio:
            fill_height = height // 2 - src_h // 2
            res.paste(
                resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)
            )
            res.paste(
                resized.resize(
                    (width, fill_height), box=(0, resized.height, width, resized.height)
                ),
                box=(0, fill_height + src_h),
            )
        elif ratio > src_ratio:
            fill_width = width // 2 - src_w // 2
            res.paste(
                resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)
            )
            res.paste(
                resized.resize(
                    (fill_width, height), box=(resized.width, 0, resized.width, height)
                ),
                box=(fill_width + src_w, 0),
            )

    return res


def constrain_image(img, max_width, max_height):
    ratio = max(img.width / max_width, img.height / max_height)
    if ratio <= 1:
        return img
    resampler = (
        Image.Resampling.LANCZOS if hasattr(Image, "Resampling") else Image.LANCZOS
    )
    resized = img.resize(
        (int(img.width / ratio), int(img.height / ratio)), resample=resampler
    )
    return resized


def convert_pt_to_bin_and_load(input_file, text_encoder, tokenizer, placeholder_token):
    x = torch.load(input_file, map_location=torch.device("cpu"))

    params_dict = {
        placeholder_token: torch.tensor(list(x["string_to_param"].items())[0][1])
    }
    torch.save(params_dict, "learned_embeds.bin")
    load_learned_embed_in_clip(
        "learned_embeds.bin", text_encoder, tokenizer, placeholder_token
    )
    logger.info("loaded", placeholder_token)


@logger.catch(reraise=True)
def run_bridge(
    interval,
    api_key,
    horde_name,
    horde_url,
    priority_usernames,
    horde_max_pixels,
    horde_nsfw,
    horde_censor_nsfw,
    horde_blacklist,
    horde_censorlist,
):
    current_id = None
    current_payload = None
    loop_retry = 0
    # load the model for stable horde if its not in memory already
    # we should load it after we get the request from the API in
    # case the model is different from the loaded in memory but
    # for now we can load it here so its read right away.
    load_models(use_GFPGAN=True)
    while True:
        if loop_retry > 10 and current_id:
            logger.info(
                f"Exceeded retry count {loop_retry} for generation id {current_id}. Aborting generation!"
            )
            current_id = None
            current_payload = None
            current_generation = None
            loop_retry = 0
        elif current_id:
            logger.info(f"Retrying ({loop_retry}/10) for generation id {current_id}...")
        gen_dict = {
            "name": horde_name,
            "max_pixels": horde_max_pixels,
            "priority_usernames": priority_usernames,
            "nsfw": horde_nsfw,
            "blacklist": horde_blacklist,
            "models": ["stable_diffusion"],
        }
        headers = {"apikey": api_key}
        if current_id:
            loop_retry += 1
        else:
            try:
                pop_req = requests.post(
                    horde_url + "/api/v2/generate/pop", json=gen_dict, headers=headers
                )
            except requests.exceptions.ConnectionError:
                logger.warning(
                    f"Server {horde_url} unavailable during pop. Waiting 10 seconds..."
                )
                time.sleep(10)
                continue
            except requests.exceptions.JSONDecodeError():
                logger.warning(
                    f"Server {horde_url} unavailable during pop. Waiting 10 seconds..."
                )
                time.sleep(10)
                continue
            try:
                pop = pop_req.json()
            except json.decoder.JSONDecodeError:
                logger.warning(
                    f"Could not decode response from {horde_url} as json. Please inform its administrator!"
                )
                time.sleep(interval)
                continue
            if pop is None:
                logger.warning(
                    f"Something has gone wrong with {horde_url}. Please inform its administrator!"
                )
                time.sleep(interval)
                continue
            if not pop_req.ok:
                pop["message"]
                logger.warning(
                    f"During gen pop, server {horde_url} responded with status code {pop_req.status_code}: {pop['message']}. Waiting for 10 seconds..."
                )
                if "errors" in pop:
                    logger.debug(f"Detailed Request Errors: {pop['errors']}")
                time.sleep(10)
                continue
            if not pop.get("id"):
                skipped_info = pop.get("skipped")
                if skipped_info and len(skipped_info):
                    skipped_info = f" Skipped Info: {skipped_info}."
                else:
                    skipped_info = ""
                logger.info(
                    f"Server {horde_url} has no valid generations to do for us.{skipped_info}"
                )
                time.sleep(interval)
                continue
            current_id = pop["id"]
            logger.info(f"Request with id {current_id} picked up. Initiating work...")
            current_payload = pop["payload"]
            if "toggles" in current_payload and current_payload["toggles"] is None:
                logger.error(f"Received Bad payload: {pop}")
                current_id = None
                current_payload = None
                current_generation = None
                loop_retry = 0
                time.sleep(10)
                continue

        logger.debug(current_payload)
        current_payload["toggles"] = current_payload.get("toggles", [1, 4])
        # In bridge-mode, matrix is prepared on the horde and split in multiple nodes
        if 0 in current_payload["toggles"]:
            current_payload["toggles"].remove(0)
        if 8 not in current_payload["toggles"]:
            if horde_censor_nsfw and not horde_nsfw:
                current_payload["toggles"].append(8)
            elif any(word in current_payload["prompt"] for word in horde_censorlist):
                current_payload["toggles"].append(8)

        from txt2img import txt2img

        """{'prompt': 'Centred Husky, inside spiral with circular patterns, trending on dribbble, knotwork, spirals, key patterns,
        zoomorphics, ', 'ddim_steps': 30, 'n_iter': 1, 'sampler_name': 'DDIM', 'cfg_scale': 16.0, 'seed': '3405278433', 'height': 512, 'width': 512}"""

        # images, seed, info, stats = txt2img(**current_payload)
        images, seed, info, stats = txt2img(
            str(current_payload["prompt"]),
            int(current_payload["ddim_steps"]),
            str(current_payload["sampler_name"]),
            int(current_payload["n_iter"]),
            1,
            float(current_payload["cfg_scale"]),
            str(current_payload["seed"]),
            int(current_payload["height"]),
            int(current_payload["width"]),
            save_grid=False,
            group_by_prompt=False,
            save_individual_images=False,
            write_info_files=False,
        )

        buffer = BytesIO()
        # We send as WebP to avoid using all the horde bandwidth
        images[0].save(buffer, format="WebP", quality=90)
        # logger.info(info)
        submit_dict = {
            "id": current_id,
            "generation": base64.b64encode(buffer.getvalue()).decode("utf8"),
            "api_key": api_key,
            "seed": seed,
            "max_pixels": horde_max_pixels,
        }
        current_generation = seed
        while current_id and current_generation is not None:
            try:
                submit_req = requests.post(
                    horde_url + "/api/v2/generate/submit",
                    json=submit_dict,
                    headers=headers,
                )
                try:
                    submit = submit_req.json()
                except json.decoder.JSONDecodeError:
                    logger.error(
                        f"Something has gone wrong with {horde_url} during submit. Please inform its administrator!  (Retry {loop_retry}/10)"
                    )
                    time.sleep(interval)
                    continue
                if submit_req.status_code == 404:
                    logger.info(
                        "The generation we were working on got stale. Aborting!"
                    )
                elif not submit_req.ok:
                    logger.error(
                        f"During gen submit, server {horde_url} responded with status code {submit_req.status_code}: {submit['message']}. Waiting for 10 seconds...  (Retry {loop_retry}/10)"
                    )
                    if "errors" in submit:
                        logger.debug(f"Detailed Request Errors: {submit['errors']}")
                    time.sleep(10)
                    continue
                else:
                    logger.info(
                        f'Submitted generation with id {current_id} and contributed for {submit_req.json()["reward"]}'
                    )
                current_id = None
                current_payload = None
                current_generation = None
                loop_retry = 0
            except requests.exceptions.ConnectionError:
                logger.warning(
                    f"Server {horde_url} unavailable during submit. Waiting 10 seconds...  (Retry {loop_retry}/10)"
                )
                time.sleep(10)
                continue
        time.sleep(interval)


#


================================================
FILE: scripts/stable_diffusion_pipeline.py
================================================
import inspect
from typing import Callable, List, Optional, Union
from pathlib import Path
from torchvision.transforms.functional import pil_to_tensor
import librosa
from PIL import Image
from torchvision.io import write_video
import numpy as np
import time
import json

import torch
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import (
    StableDiffusionSafetyChecker,
)
from diffusers.utils import deprecate, logging
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput

from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from torch import nn

from sd_utils import RealESRGANModel

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


def get_timesteps_arr(audio_filepath, offset, duration, fps=30, margin=1.0, smooth=0.0):
    y, sr = librosa.load(audio_filepath, offset=offset, duration=duration)

    # librosa.stft hardcoded defaults...
    # n_fft defaults to 2048
    # hop length is win_length // 4
    # win_length defaults to n_fft
    D = librosa.stft(y, n_fft=2048, hop_length=2048 // 4, win_length=2048)

    # Extract percussive elements
    D_harmonic, D_percussive = librosa.decompose.hpss(D, margin=margin)
    y_percussive = librosa.istft(D_percussive, length=len(y))

    # Get normalized melspectrogram
    spec_raw = librosa.feature.melspectrogram(y=y_percussive, sr=sr)
    spec_max = np.amax(spec_raw, axis=0)
    spec_norm = (spec_max - np.min(spec_max)) / np.ptp(spec_max)

    # Resize cumsum of spec norm to our desired number of interpolation frames
    x_norm = np.linspace(0, spec_norm.shape[-1], spec_norm.shape[-1])
    y_norm = np.cumsum(spec_norm)
    y_norm /= y_norm[-1]
    x_resize = np.linspace(0, y_norm.shape[-1], int(duration * fps))

    T = np.interp(x_resize, x_norm, y_norm)

    # Apply smoothing
    return T * (1 - smooth) + np.linspace(0.0, 1.0, T.shape[0]) * smooth


def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
    """helper function to spherically interpolate two arrays v1 v2"""

    if not isinstance(v0, np.ndarray):
        inputs_are_torch = True
        input_device = v0.device
        v0 = v0.cpu().numpy()
        v1 = v1.cpu().numpy()

    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
    if np.abs(dot) > DOT_THRESHOLD:
        v2 = (1 - t) * v0 + t * v1
    else:
        theta_0 = np.arccos(dot)
        sin_theta_0 = np.sin(theta_0)
        theta_t = theta_0 * t
        sin_theta_t = np.sin(theta_t)
        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
        s1 = sin_theta_t / sin_theta_0
        v2 = s0 * v0 + s1 * v1

    if inputs_are_torch:
        v2 = torch.from_numpy(v2).to(input_device)

    return v2


def make_video_pyav(
    frames_or_frame_dir: Union[str, Path, torch.Tensor],
    audio_filepath: Union[str, Path] = None,
    fps: int = 30,
    audio_offset: int = 0,
    audio_duration: int = 2,
    sr: int = 22050,
    output_filepath: Union[str, Path] = "output.mp4",
    glob_pattern: str = "*.png",
):
    """
    TODO - docstring here

    frames_or_frame_dir: (Union[str, Path, torch.Tensor]):
        Either a directory of images, or a tensor of shape (T, C, H, W) in range [0, 255].
    """

    # Torchvision write_video doesn't support pathlib paths
    output_filepath = str(output_filepath)

    if isinstance(frames_or_frame_dir, (str, Path)):
        frames = None
        for img in sorted(Path(frames_or_frame_dir).glob(glob_pattern)):
            frame = pil_to_tensor(Image.open(img)).unsqueeze(0)
            frames = frame if frames is None else torch.cat([frames, frame])
    else:
        frames = frames_or_frame_dir

    # TCHW -> THWC
    frames = frames.permute(0, 2, 3, 1)

    if audio_filepath:
        # Read audio, convert to tensor
        audio, sr = librosa.load(
            audio_filepath,
            sr=sr,
            mono=True,
            offset=audio_offset,
            duration=audio_duration,
        )
        audio_tensor = torch.tensor(audio).unsqueeze(0)

        write_video(
            output_filepath,
            frames,
            fps=fps,
            audio_array=audio_tensor,
            audio_fps=sr,
            audio_codec="aac",
            options={"crf": "10", "pix_fmt": "yuv420p"},
        )
    else:
        write_video(
            output_filepath,
            frames,
            fps=fps,
            options={"crf": "10", "pix_fmt": "yuv420p"},
        )

    return output_filepath


class StableDiffusionWalkPipeline(DiffusionPipeline):
    r"""
    Pipeline for generating videos by interpolating  Stable Diffusion's latent space.
    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
        feature_extractor ([`CLIPFeatureExtractor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPFeatureExtractor,
    ):
        super().__init__()

        if (
            hasattr(scheduler.config, "steps_offset")
            and scheduler.config.steps_offset != 1
        ):
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                " file"
            )
            deprecate(
                "steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False
            )
            new_config = dict(scheduler.config)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

        if safety_checker is None:
            logger.warn(
                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
            )

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )

    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
        r"""
        Enable sliced attention computation.
        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
        in several steps. This is useful to save some memory in exchange for a small speed decrease.
        Args:
            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
                `attention_head_dim` must be a multiple of `slice_size`.
        """
        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            slice_size = self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(slice_size)

    def disable_attention_slicing(self):
        r"""
        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
        back to computing attention in one step.
        """
        # set slice_size = `None` to disable `attention slicing`
        self.enable_attention_slicing(None)

    @torch.no_grad()
    def __call__(
        self,
        prompt: Optional[Union[str, List[str]]] = None,
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        text_embeddings: Optional[torch.FloatTensor] = None,
        **kwargs,
    ):
        r"""
        Function invoked when calling the pipeline for generation.
        Args:
            prompt (`str` or `List[str]`, *optional*, defaults to `None`):
                The prompt or prompts to guide the image generation. If not provided, `text_embeddings` is required.
            height (`int`, *optional*, defaults to 512):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to 512):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
                if `guidance_scale` is less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator`, *optional*):
                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            text_embeddings (`torch.FloatTensor`, *optional*, defaults to `None`):
                Pre-generated text embeddings to be used as inputs for image generation. Can be used in place of
                `prompt` to avoid re-computing the embeddings. If not provided, the embeddings will be generated from
                the supplied `prompt`.
        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """

        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(
                f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
            )

        if (callback_steps is None) or (
            callback_steps is not None
            and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        if text_embeddings is None:
            if isinstance(prompt, str):
                batch_size = 1
            elif isinstance(prompt, list):
                batch_size = len(prompt)
            else:
                raise ValueError(
                    f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
                )

            # get prompt text embeddings
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                removed_text = self.tokenizer.batch_decode(
                    text_input_ids[:, self.tokenizer.model_max_length :]
                )
                print(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )
                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
            text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
        else:
            batch_size = text_embeddings.shape[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        bs_embed, seq_len, _ = text_embeddings.shape
        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
        text_embeddings = text_embeddings.view(
            bs_embed * num_images_per_prompt, seq_len, -1
        )

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""]
            elif type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            max_length = self.tokenizer.model_max_length
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )
            uncond_embeddings = self.text_encoder(
                uncond_input.input_ids.to(self.device)
            )[0]

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = uncond_embeddings.shape[1]
            uncond_embeddings = uncond_embeddings.repeat(
                batch_size, num_images_per_prompt, 1
            )
            uncond_embeddings = uncond_embeddings.view(
                batch_size * num_images_per_prompt, seq_len, -1
            )

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        latents_shape = (
            batch_size * num_images_per_prompt,
            self.unet.in_channels,
            height // 8,
            width // 8,
        )
        latents_dtype = text_embeddings.dtype
        if latents is None:
            if self.device.type == "mps":
                # randn does not exist on mps
                latents = torch.randn(
                    latents_shape,
                    generator=generator,
                    device="cpu",
                    dtype=latents_dtype,
                ).to(self.device)
            else:
                latents = torch.randn(
                    latents_shape,
                    generator=generator,
                    device=self.device,
                    dtype=latents_dtype,
                )
        else:
            if latents.shape != latents_shape:
                raise ValueError(
                    f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"
                )
            latents = latents.to(self.device)

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps)

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        timesteps_tensor = self.scheduler.timesteps.to(self.device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        accepts_eta = "eta" in set(
            inspect.signature(self.scheduler.step).parameters.keys()
        )
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
            # expand the latents if we are doing classifier free guidance
            latent_model_input = (
                torch.cat([latents] * 2) if do_classifier_free_guidance else latents
            )
            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

            # predict the noise residual
            noise_pred = self.unet(
                latent_model_input, t, encoder_hidden_states=text_embeddings
            ).sample

            # perform guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (
                    noise_pred_text - noise_pred_uncond
                )

            # compute the previous noisy sample x_t -> x_t-1
            latents = self.scheduler.step(
                noise_pred, t, latents, **extra_step_kwargs
            ).prev_sample

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                callback(i, t, latents)

        latents = 1 / 0.18215 * latents
        image = self.vae.decode(latents).sample

        image = (image / 2 + 0.5).clamp(0, 1)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()

        if self.safety_checker is not None:
            safety_checker_input = self.feature_extractor(
                self.numpy_to_pil(image), return_tensors="pt"
            ).to(self.device)
            image, has_nsfw_concept = self.safety_checker(
                images=image,
                clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype),
            )
        else:
            has_nsfw_concept = None

        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(
            images=image, nsfw_content_detected=has_nsfw_concept
        )

    def generate_inputs(
        self, prompt_a, prompt_b, seed_a, seed_b, noise_shape, T, batch_size
    ):
        embeds_a = self.embed_text(prompt_a)
        embeds_b = self.embed_text(prompt_b)

        latents_a = self.init_noise(seed_a, noise_shape)
        latents_b = self.init_noise(seed_b, noise_shape)

        batch_idx = 0
        embeds_batch, noise_batch = None, None
        for i, t in enumerate(T):
            embeds = torch.lerp(embeds_a, embeds_b, t)
            noise = slerp(float(t), latents_a, latents_b)

            embeds_batch = (
                embeds if embeds_batch is None else torch.cat([embeds_batch, embeds])
            )
            noise_batch = (
                noise if noise_batch is None else torch.cat([noise_batch, noise])
            )
            batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == T.shape[0]
            if not batch_is_ready:
                continue
            yield batch_idx, embeds_batch, noise_batch
            batch_idx += 1
            del embeds_batch, noise_batch
            torch.cuda.empty_cache()
            embeds_batch, noise_batch = None, None

    def make_clip_frames(
        self,
        prompt_a: str,
        prompt_b: str,
        seed_a: int,
        seed_b: int,
        num_interpolation_steps: int = 5,
        save_path: Union[str, Path] = "outputs/",
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        eta: float = 0.0,
        height: int = 512,
        width: int = 512,
        upsample: bool = False,
        batch_size: int = 1,
        image_file_ext: str = ".png",
        T: np.ndarray = None,
        skip: int = 0,
    ):
        save_path = Path(save_path)
        save_path.mkdir(parents=True, exist_ok=True)

        T = T if T is not None else np.linspace(0.0, 1.0, num_interpolation_steps)
        if T.shape[0] != num_interpolation_steps:
            raise ValueError(
                f"Unexpected T shape, got {T.shape}, expected dim 0 to be {num_interpolation_steps}"
            )

        if upsample:
            if getattr(self, "upsampler", None) is None:
                self.upsampler = RealESRGANModel.from_pretrained("nateraw/real-esrgan")
            self.upsampler.to(self.device)

        batch_generator = self.generate_inputs(
            prompt_a,
            prompt_b,
            seed_a,
            seed_b,
            (1, self.unet.in_channels, height // 8, width // 8),
            T[skip:],
            batch_size,
        )

        frame_index = skip
        for _, embeds_batch, noise_batch in batch_generator:
            with torch.autocast("cuda"):
                outputs = self(
                    latents=noise_batch,
                    text_embeddings=embeds_batch,
                    height=height,
                    width=width,
                    guidance_scale=guidance_scale,
                    eta=eta,
                    num_inference_steps=num_inference_steps,
                    output_type="pil" if not upsample else "numpy",
                )["images"]

                for image in outputs:
                    frame_filepath = save_path / (
                        f"frame%06d{image_file_ext}" % frame_index
                    )
                    image = image if not upsample else self.upsampler(image)
                    image.save(frame_filepath)
                    frame_index += 1

    def walk(
        self,
        prompts: Optional[List[str]] = None,
        seeds: Optional[List[int]] = None,
        num_interpolation_steps: Optional[
            Union[int, List[int]]
        ] = 5,  # int or list of int
        output_dir: Optional[str] = "./dreams",
        name: Optional[str] = None,
        image_file_ext: Optional[str] = ".png",
        fps: Optional[int] = 30,
        num_inference_steps: Optional[int] = 50,
        guidance_scale: Optional[float] = 7.5,
        eta: Optional[float] = 0.0,
        height: Optional[int] = 512,
        width: Optional[int] = 512,
        upsample: Optional[bool] = False,
        batch_size: Optional[int] = 1,
        resume: Optional[bool] = False,
        audio_filepath: str = None,
        audio_start_sec: Optional[Union[int, float]] = None,
        margin: Optional[float] = 1.0,
        smooth: Optional[float] = 0.0,
    ):
        """Generate a video from a sequence of prompts and seeds. Optionally, add audio to the
        video to interpolate to the intensity of the audio.

        Args:
            prompts (Optional[List[str]], optional):
                list of text prompts. Defaults to None.
            seeds (Optional[List[int]], optional):
                list of random seeds corresponding to prompts. Defaults to None.
            num_interpolation_steps (Union[int, List[int]], *optional*):
                How many interpolation steps between each prompt. Defaults to None.
            output_dir (Optional[str], optional):
                Where to save the video. Defaults to './dreams'.
            name (Optional[str], optional):
                Name of the subdirectory of output_dir. Defaults to None.
            image_file_ext (Optional[str], *optional*, defaults to '.png'):
                The extension to use when writing video frames.
            fps (Optional[int], *optional*, defaults to 30):
                The frames per second in the resulting output videos.
            num_inference_steps (Optional[int], *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (Optional[float], *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            eta (Optional[float], *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            height (Optional[int], *optional*, defaults to 512):
                height of the images to generate.
            width (Optional[int], *optional*, defaults to 512):
                width of the images to generate.
            upsample (Optional[bool], *optional*, defaults to False):
                When True, upsamples images with realesrgan.
            batch_size (Optional[int], *optional*, defaults to 1):
                Number of images to generate at once.
            resume (Optional[bool], *optional*, defaults to False):
                When True, resumes from the last frame in the output directory based
                on available prompt config. Requires you to provide the `name` argument.
            audio_filepath (str, *optional*, defaults to None):
                Optional path to an audio file to influence the interpolation rate.
            audio_start_sec (Optional[Union[int, float]], *optional*, defaults to 0):
                Global start time of the provided audio_filepath.
            margin (Optional[float], *optional*, defaults to 1.0):
                Margin from librosa hpss to use for audio interpolation.
            smooth (Optional[float], *optional*, defaults to 0.0):
                Smoothness of the audio interpolation. 1.0 means linear interpolation.

        This function will create sub directories for each prompt and seed pair.

        For example, if you provide the following prompts and seeds:

        ```
        prompts = ['a dog', 'a cat', 'a bird']
        seeds = [1, 2, 3]
        num_interpolation_steps = 5
        output_dir = 'output_dir'
        name = 'name'
        fps = 5
        ```

        Then the following directories will be created:

        ```
        output_dir
        ├── name
        │   ├── name_000000
        │   │   ├── frame000000.png
        │   │   ├── ...
        │   │   ├── frame000004.png
        │   │   ├── name_000000.mp4
        │   ├── name_000001
        │   │   ├── frame000000.png
        │   │   ├── ...
        │   │   ├── frame000004.png
        │   │   ├── name_000001.mp4
        │   ├── ...
        │   ├── name.mp4
        |   |── prompt_config.json
        ```

        Returns:
            str: The resulting video filepath. This video includes all sub directories' video clips.
        """

        output_path = Path(output_dir)

        name = name or time.strftime("%Y%m%d-%H%M%S")
        save_path_root = output_path / name
        save_path_root.mkdir(parents=True, exist_ok=True)

        # Where the final video of all the clips combined will be saved
        output_filepath = save_path_root / f"{name}.mp4"

        # If using same number of interpolation steps between, we turn into list
        if not resume and isinstance(num_interpolation_steps, int):
            num_interpolation_steps = [num_interpolation_steps] * (len(prompts) - 1)

        if not resume:
            audio_start_sec = audio_start_sec or 0

        # Save/reload prompt config
        prompt_config_path = save_path_root / "prompt_config.json"
        if not resume:
            prompt_config_path.write_text(
                json.dumps(
                    dict(
                        prompts=prompts,
                        seeds=seeds,
                        num_interpolation_steps=num_interpolation_steps,
                        fps=fps,
                        num_inference_steps=num_inference_steps,
                        guidance_scale=guidance_scale,
                        eta=eta,
                        upsample=upsample,
                        height=height,
                        width=width,
                        audio_filepath=audio_filepath,
                        audio_start_sec=audio_start_sec,
                    ),
                    indent=2,
                    sort_keys=False,
                )
            )
        else:
            data = json.load(open(prompt_config_path))
            prompts = data["prompts"]
            seeds = data["seeds"]
            num_interpolation_steps = data["num_interpolation_steps"]
            fps = data["fps"]
            num_inference_steps = data["num_inference_steps"]
            guidance_scale = data["guidance_scale"]
            eta = data["eta"]
            upsample = data["upsample"]
            height = data["height"]
            width = data["width"]
            audio_filepath = data["audio_filepath"]
            audio_start_sec = data["audio_start_sec"]

        for i, (prompt_a, prompt_b, seed_a, seed_b, num_step) in enumerate(
            zip(prompts, prompts[1:], seeds, seeds[1:], num_interpolation_steps)
        ):
            # {name}_000000 / {name}_000001 / ...
            save_path = save_path_root / f"{name}_{i:06d}"

            # Where the individual clips will be saved
            step_output_filepath = save_path / f"{name}_{i:06d}.mp4"

            # Determine if we need to resume from a previous run
            skip = 0
            if resume:
                if step_output_filepath.exists():
                    print(f"Skipping {save_path} because frames already exist")
                    continue

                existing_frames = sorted(save_path.glob(f"*{image_file_ext}"))
                if existing_frames:
                    skip = int(existing_frames[-1].stem[-6:]) + 1
                    if skip + 1 >= num_step:
                        print(f"Skipping {save_path} because frames already exist")
                        continue
                    print(f"Resuming {save_path.name} from frame {skip}")

            audio_offset = audio_start_sec + sum(num_interpolation_steps[:i]) / fps
            audio_duration = num_step / fps

            self.make_clip_frames(
                prompt_a,
                prompt_b,
                seed_a,
                seed_b,
                num_interpolation_steps=num_step,
                save_path=save_path,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
                eta=eta,
                height=height,
                width=width,
                upsample=upsample,
                batch_size=batch_size,
                skip=skip,
                T=get_timesteps_arr(
                    audio_filepath,
                    offset=audio_offset,
                    duration=audio_duration,
                    fps=fps,
                    margin=margin,
                    smooth=smooth,
                )
                if audio_filepath
                else None,
            )
            make_video_pyav(
                save_path,
                audio_filepath=audio_filepath,
                fps=fps,
                output_filepath=step_output_filepath,
                glob_pattern=f"*{image_file_ext}",
                audio_offset=audio_offset,
                audio_duration=audio_duration,
                sr=44100,
            )

        return make_video_pyav(
            save_path_root,
            audio_filepath=audio_filepath,
            fps=fps,
            audio_offset=audio_start_sec,
            audio_duration=sum(num_interpolation_steps) / fps,
            output_filepath=output_filepath,
            glob_pattern=f"**/*{image_file_ext}",
            sr=44100,
        )

    def embed_text(self, text):
        """Helper to embed some text"""
        with torch.autocast("cuda"):
            text_input = self.tokenizer(
                text,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            with torch.no_grad():
                embed = self.text_encoder(text_input.input_ids.to(self.device))[0]
        return embed

    def init_noise(self, seed, noise_shape):
        """Helper to initialize noise"""
        # randn does not exist on mps, so we create noise on CPU here and move it to the device after initialization
        if self.device.type == "mps":
            noise = torch.randn(
                noise_shape,
                device="cpu",
                generator=torch.Generator(device="cpu").manual_seed(seed),
            ).to(self.device)
        else:
            noise = torch.randn(
                noise_shape,
                device=self.device,
                generator=torch.Generator(device=self.device).manual_seed(seed),
            )
        return noise

    @classmethod
    def from_pretrained(cls, *args, tiled=False, **kwargs):
        """Same as diffusers `from_pretrained` but with tiled option, which makes images tilable"""
        if tiled:

            def patch_conv(**patch):
                cls = nn.Conv2d
                init = cls.__init__

                def __init__(self, *args, **kwargs):
                    return init(self, *args, **kwargs, **patch)

                cls.__init__ = __init__

            patch_conv(padding_mode="circular")

        pipeline = super().from_pretrained(*args, **kwargs)
        pipeline.tiled = tiled
        return pipeline


================================================
FILE: scripts/textual_inversion.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import st, set_page_title, seed_to_int

# streamlit imports
from streamlit.runtime.scriptrunner import StopException
from streamlit_tensorboard import st_tensorboard

# streamlit components section
from streamlit_server_state import server_state

# other imports
from transformers import CLIPTextModel, CLIPTokenizer

# Temp imports

import itertools
import math
import os
import random

# import datetime
# from pathlib import Path
# from typing import Optional

import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.utils.data import Dataset

import PIL
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    LMSDiscreteScheduler,
    StableDiffusionPipeline,
    UNet2DConditionModel,
)  # , PNDMScheduler
from diffusers.optimization import get_scheduler

# from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from pipelines.stable_diffusion.no_check import NoCheck
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from slugify import slugify
import json
import os  # , subprocess

# from io import StringIO


# end of imports
# ---------------------------------------------------------------------------------------------------------------

logger = get_logger(__name__)

imagenet_templates_small = [
    "a photo of a {}",
    "a rendering of a {}",
    "a cropped photo of the {}",
    "the photo of a {}",
    "a photo of a clean {}",
    "a photo of a dirty {}",
    "a dark photo of the {}",
    "a photo of my {}",
    "a photo of the cool {}",
    "a close-up photo of a {}",
    "a bright photo of the {}",
    "a cropped photo of a {}",
    "a photo of the {}",
    "a good photo of the {}",
    "a photo of one {}",
    "a close-up photo of the {}",
    "a rendition of the {}",
    "a photo of the clean {}",
    "a rendition of a {}",
    "a photo of a nice {}",
    "a good photo of a {}",
    "a photo of the nice {}",
    "a photo of the small {}",
    "a photo of the weird {}",
    "a photo of the large {}",
    "a photo of a cool {}",
    "a photo of a small {}",
]

imagenet_style_templates_small = [
    "a painting in the style of {}",
    "a rendering in the style of {}",
    "a cropped painting in the style of {}",
    "the painting in the style of {}",
    "a clean painting in the style of {}",
    "a dirty painting in the style of {}",
    "a dark painting in the style of {}",
    "a picture in the style of {}",
    "a cool painting in the style of {}",
    "a close-up painting in the style of {}",
    "a bright painting in the style of {}",
    "a cropped painting in the style of {}",
    "a good painting in the style of {}",
    "a close-up painting in the style of {}",
    "a rendition in the style of {}",
    "a nice painting in the style of {}",
    "a small painting in the style of {}",
    "a weird painting in the style of {}",
    "a large painting in the style of {}",
]


class TextualInversionDataset(Dataset):
    def __init__(
        self,
        data_root,
        tokenizer,
        learnable_property="object",  # [object, style]
        size=512,
        repeats=100,
        interpolation="bicubic",
        set="train",
        placeholder_token="*",
        center_crop=False,
        templates=None,
    ):
        self.data_root = data_root
        self.tokenizer = tokenizer
        self.learnable_property = learnable_property
        self.size = size
        self.placeholder_token = placeholder_token
        self.center_crop = center_crop

        self.image_paths = [
            os.path.join(self.data_root, file_path)
            for file_path in os.listdir(self.data_root)
            if file_path.lower().endswith((".png", ".jpg", ".jpeg"))
        ]

        self.num_images = len(self.image_paths)
        self._length = self.num_images

        if set == "train":
            self._length = self.num_images * repeats

        self.interpolation = {
            "linear": PIL.Image.LINEAR,
            "bilinear": PIL.Image.Resampling.BILINEAR,
            "bicubic": PIL.Image.Resampling.BICUBIC,
            "lanczos": PIL.Image.Resampling.LANCZOS,
        }[interpolation]

        self.templates = templates
        self.cache = {}
        self.tokenized_templates = [
            self.tokenizer(
                text.format(self.placeholder_token),
                padding="max_length",
                truncation=True,
                max_length=self.tokenizer.model_max_length,
                return_tensors="pt",
            ).input_ids[0]
            for text in self.templates
        ]

    def __len__(self):
        return self._length

    def get_example(self, image_path, flipped):
        if image_path in self.cache:
            return self.cache[image_path]

        example = {}
        image = Image.open(image_path)

        if not image.mode == "RGB":
            image = image.convert("RGB")

        # default to score-sde preprocessing
        img = np.array(image).astype(np.uint8)
        if self.center_crop:
            crop = min(img.shape[0], img.shape[1])
            (
                h,
                w,
            ) = (
                img.shape[0],
                img.shape[1],
            )
            img = img[
                (h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2
            ]
        image = Image.fromarray(img)
        image = image.resize((self.size, self.size), resample=self.interpolation)
        image = transforms.RandomHorizontalFlip(p=1 if flipped else 0)(image)
        image = np.array(image).astype(np.uint8)
        image = (image / 127.5 - 1.0).astype(np.float32)
        example["key"] = "-".join([image_path, "-", str(flipped)])
        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)

        self.cache[image_path] = example
        return example

    def __getitem__(self, i):
        flipped = random.choice([False, True])
        example = self.get_example(self.image_paths[i % self.num_images], flipped)
        example["input_ids"] = random.choice(self.tokenized_templates)
        return example


def freeze_params(params):
    for param in params:
        param.requires_grad = False


def save_resume_file(basepath, extra={}, config=""):
    info = {"args": config["args"]}
    info["args"].update(extra)

    with open(f"{os.path.join(basepath, 'resume.json')}", "w") as f:
        # print (info)
        json.dump(info, f, indent=4)

    with open(f"{basepath}/token_identifier.txt", "w") as f:
        f.write(f"{config['args']['placeholder_token']}")

    with open(f"{basepath}/type_of_concept.txt", "w") as f:
        f.write(f"{config['args']['learnable_property']}")

    config["args"] = info["args"]

    return config["args"]


class Checkpointer:
    def __init__(
        self,
        accelerator,
        vae,
        unet,
        tokenizer,
        placeholder_token,
        placeholder_token_id,
        templates,
        output_dir,
        random_sample_batches,
        sample_batch_size,
        stable_sample_batches,
        seed,
    ):
        self.accelerator = accelerator
        self.vae = vae
        self.unet = unet
        self.tokenizer = tokenizer
        self.placeholder_token = placeholder_token
        self.placeholder_token_id = placeholder_token_id
        self.templates = templates
        self.output_dir = output_dir
        self.seed = seed
        self.random_sample_batches = random_sample_batches
        self.sample_batch_size = sample_batch_size
        self.stable_sample_batches = stable_sample_batches

    @torch.no_grad()
    def checkpoint(self, step, text_encoder, save_samples=True, path=None):
        print("Saving checkpoint for step %d..." % step)
        with torch.autocast("cuda"):
            if path is None:
                checkpoints_path = f"{self.output_dir}/checkpoints"
                os.makedirs(checkpoints_path, exist_ok=True)

            unwrapped = self.accelerator.unwrap_model(text_encoder)

            # Save a checkpoint
            learned_embeds = unwrapped.get_input_embeddings().weight[
                self.placeholder_token_id
            ]
            learned_embeds_dict = {
                self.placeholder_token: learned_embeds.detach().cpu()
            }

            filename = "%s_%d.bin" % (slugify(self.placeholder_token), step)
            if path is not None:
                torch.save(learned_embeds_dict, path)
            else:
                torch.save(learned_embeds_dict, f"{checkpoints_path}/{filename}")
                torch.save(learned_embeds_dict, f"{checkpoints_path}/last.bin")

            del unwrapped
            del learned_embeds

    @torch.no_grad()
    def save_samples(
        self,
        step,
        text_encoder,
        height,
        width,
        guidance_scale,
        eta,
        num_inference_steps,
    ):
        samples_path = f"{self.output_dir}/concept_images"
        os.makedirs(samples_path, exist_ok=True)

        # if "checker" not in server_state['textual_inversion']:
        # with server_state_lock['textual_inversion']["checker"]:
        server_state["textual_inversion"]["checker"] = NoCheck()

        # if "unwrapped" not in server_state['textual_inversion']:
        # 	with server_state_lock['textual_inversion']["unwrapped"]:
        server_state["textual_inversion"]["unwrapped"] = self.accelerator.unwrap_model(
            text_encoder
        )

        # if "pipeline" not in server_state['textual_inversion']:
        # 	with server_state_lock['textual_inversion']["pipeline"]:
        # Save a sample image
        server_state["textual_inversion"]["pipeline"] = StableDiffusionPipeline(
            text_encoder=server_state["textual_inversion"]["unwrapped"],
            vae=self.vae,
            unet=self.unet,
            tokenizer=self.tokenizer,
            scheduler=LMSDiscreteScheduler(
                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
            ),
            safety_checker=NoCheck(),
            feature_extractor=CLIPFeatureExtractor.from_pretrained(
                "openai/clip-vit-base-patch32"
            ),
        ).to("cuda")

        server_state["textual_inversion"]["pipeline"].enable_attention_slicing()

        if self.stable_sample_batches > 0:
            stable_latents = torch.randn(
                (
                    self.sample_batch_size,
                    server_state["textual_inversion"]["pipeline"].unet.in_channels,
                    height // 8,
                    width // 8,
                ),
                device=server_state["textual_inversion"]["pipeline"].device,
                generator=torch.Generator(
                    device=server_state["textual_inversion"]["pipeline"].device
                ).manual_seed(self.seed),
            )

            stable_prompts = [
                choice.format(self.placeholder_token)
                for choice in (self.templates * self.sample_batch_size)[
                    : self.sample_batch_size
                ]
            ]

            # Generate and save stable samples
            for i in range(0, self.stable_sample_batches):
                samples = server_state["textual_inversion"]["pipeline"](
                    prompt=stable_prompts,
                    height=384,
                    latents=stable_latents,
                    width=384,
                    guidance_scale=guidance_scale,
                    eta=eta,
                    num_inference_steps=num_inference_steps,
                    output_type="pil",
                )["sample"]

                for idx, im in enumerate(samples):
                    filename = "stable_sample_%d_%d_step_%d.png" % (
                        i + 1,
                        idx + 1,
                        step,
                    )
                    im.save(f"{samples_path}/{filename}")
                del samples
            del stable_latents

        prompts = [
            choice.format(self.placeholder_token)
            for choice in random.choices(self.templates, k=self.sample_batch_size)
        ]
        # Generate and save random samples
        for i in range(0, self.random_sample_batches):
            samples = server_state["textual_inversion"]["pipeline"](
                prompt=prompts,
                height=384,
                width=384,
                guidance_scale=guidance_scale,
                eta=eta,
                num_inference_steps=num_inference_steps,
                output_type="pil",
            )["sample"]
            for idx, im in enumerate(samples):
                filename = "step_%d_sample_%d_%d.png" % (step, i + 1, idx + 1)
                im.save(f"{samples_path}/{filename}")
            del samples

        del server_state["textual_inversion"]["checker"]
        del server_state["textual_inversion"]["unwrapped"]
        del server_state["textual_inversion"]["pipeline"]
        torch.cuda.empty_cache()


# @retry(RuntimeError, tries=5)
def textual_inversion(config):
    print("Running textual inversion.")

    # if "pipeline" in server_state["textual_inversion"]:
    # del server_state['textual_inversion']["checker"]
    # del server_state['textual_inversion']["unwrapped"]
    # del server_state['textual_inversion']["pipeline"]
    # torch.cuda.empty_cache()

    global_step_offset = 0

    # print(config['args']['resume_from'])
    if config["args"]["resume_from"]:
        try:
            basepath = f"{config['args']['resume_from']}"

            with open(f"{basepath}/resume.json", "r") as f:
                state = json.load(f)

            global_step_offset = state["args"].get("global_step", 0)

            print("Resuming state from %s" % config["args"]["resume_from"])
            print("We've trained %d steps so far" % global_step_offset)

        except json.decoder.JSONDecodeError:
            pass
    else:
        basepath = f"{config['args']['output_dir']}/{slugify(config['args']['placeholder_token'])}"
        os.makedirs(basepath, exist_ok=True)

    accelerator = Accelerator(
        gradient_accumulation_steps=config["args"]["gradient_accumulation_steps"],
        mixed_precision=config["args"]["mixed_precision"],
    )

    # If passed along, set the training seed.
    if config["args"]["seed"]:
        set_seed(config["args"]["seed"])

    # if "tokenizer" not in server_state["textual_inversion"]:
    # Load the tokenizer and add the placeholder token as a additional special token
    # with server_state_lock['textual_inversion']["tokenizer"]:
    if config["args"]["tokenizer_name"]:
        server_state["textual_inversion"]["tokenizer"] = CLIPTokenizer.from_pretrained(
            config["args"]["tokenizer_name"]
        )
    elif config["args"]["pretrained_model_name_or_path"]:
        server_state["textual_inversion"]["tokenizer"] = CLIPTokenizer.from_pretrained(
            config["args"]["pretrained_model_name_or_path"] + "/tokenizer"
        )

    # Add the placeholder token in tokenizer
    num_added_tokens = server_state["textual_inversion"]["tokenizer"].add_tokens(
        config["args"]["placeholder_token"]
    )
    if num_added_tokens == 0:
        st.error(
            f"The tokenizer already contains the token {config['args']['placeholder_token']}. Please pass a different"
            " `placeholder_token` that is not already in the tokenizer."
        )

    # Convert the initializer_token, placeholder_token to ids
    token_ids = server_state["textual_inversion"]["tokenizer"].encode(
        config["args"]["initializer_token"], add_special_tokens=False
    )
    # Check if initializer_token is a single token or a sequence of tokens
    if len(token_ids) > 1:
        st.error("The initializer token must be a single token.")

    initializer_token_id = token_ids[0]
    placeholder_token_id = server_state["textual_inversion"][
        "tokenizer"
    ].convert_tokens_to_ids(config["args"]["placeholder_token"])

    # if "text_encoder" not in server_state['textual_inversion']:
    # Load models and create wrapper for stable diffusion
    # with server_state_lock['textual_inversion']["text_encoder"]:
    server_state["textual_inversion"]["text_encoder"] = CLIPTextModel.from_pretrained(
        config["args"]["pretrained_model_name_or_path"] + "/text_encoder",
    )

    # if "vae" not in server_state['textual_inversion']:
    # with server_state_lock['textual_inversion']["vae"]:
    server_state["textual_inversion"]["vae"] = AutoencoderKL.from_pretrained(
        config["args"]["pretrained_model_name_or_path"] + "/vae",
    )

    # if "unet" not in server_state['textual_inversion']:
    # with server_state_lock['textual_inversion']["unet"]:
    server_state["textual_inversion"]["unet"] = UNet2DConditionModel.from_pretrained(
        config["args"]["pretrained_model_name_or_path"] + "/unet",
    )

    base_templates = (
        imagenet_style_templates_small
        if config["args"]["learnable_property"] == "style"
        else imagenet_templates_small
    )
    if config["args"]["custom_templates"]:
        templates = config["args"]["custom_templates"].split(";")
    else:
        templates = base_templates

    slice_size = (
        server_state["textual_inversion"]["unet"].config.attention_head_dim // 2
    )
    server_state["textual_inversion"]["unet"].set_attention_slice(slice_size)

    # Resize the token embeddings as we are adding new special tokens to the tokenizer
    server_state["textual_inversion"]["text_encoder"].resize_token_embeddings(
        len(server_state["textual_inversion"]["tokenizer"])
    )

    # Initialise the newly added placeholder token with the embeddings of the initializer token
    token_embeds = (
        server_state["textual_inversion"]["text_encoder"]
        .get_input_embeddings()
        .weight.data
    )

    if "resume_checkpoint" in config["args"]:
        if config["args"]["resume_checkpoint"] is not None:
            token_embeds[placeholder_token_id] = torch.load(
                config["args"]["resume_checkpoint"]
            )[config["args"]["placeholder_token"]]
    else:
        token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]

    # Freeze vae and unet
    freeze_params(server_state["textual_inversion"]["vae"].parameters())
    freeze_params(server_state["textual_inversion"]["unet"].parameters())
    # Freeze all parameters except for the token embeddings in text encoder
    params_to_freeze = itertools.chain(
        server_state["textual_inversion"][
            "text_encoder"
        ].text_model.encoder.parameters(),
        server_state["textual_inversion"][
            "text_encoder"
        ].text_model.final_layer_norm.parameters(),
        server_state["textual_inversion"][
            "text_encoder"
        ].text_model.embeddings.position_embedding.parameters(),
    )
    freeze_params(params_to_freeze)

    checkpointer = Checkpointer(
        accelerator=accelerator,
        vae=server_state["textual_inversion"]["vae"],
        unet=server_state["textual_inversion"]["unet"],
        tokenizer=server_state["textual_inversion"]["tokenizer"],
        placeholder_token=config["args"]["placeholder_token"],
        placeholder_token_id=placeholder_token_id,
        templates=templates,
        output_dir=basepath,
        sample_batch_size=config["args"]["sample_batch_size"],
        random_sample_batches=config["args"]["random_sample_batches"],
        stable_sample_batches=config["args"]["stable_sample_batches"],
        seed=config["args"]["seed"],
    )

    if config["args"]["scale_lr"]:
        config["args"]["learning_rate"] = (
            config["args"]["learning_rate"]
            * config["args"]["gradient_accumulation_steps"]
            * config["args"]["train_batch_size"]
            * accelerator.num_processes
        )

    # Initialize the optimizer
    optimizer = torch.optim.AdamW(
        server_state["textual_inversion"]["text_encoder"]
        .get_input_embeddings()
        .parameters(),  # only optimize the embeddings
        lr=config["args"]["learning_rate"],
        betas=(config["args"]["adam_beta1"], config["args"]["adam_beta2"]),
        weight_decay=config["args"]["adam_weight_decay"],
        eps=config["args"]["adam_epsilon"],
    )

    # TODO (patil-suraj): load scheduler using args
    noise_scheduler = DDPMScheduler(
        beta_start=0.00085,
        beta_end=0.012,
        beta_schedule="scaled_linear",
        num_train_timesteps=1000,
        tensor_format="pt",
    )

    train_dataset = TextualInversionDataset(
        data_root=config["args"]["train_data_dir"],
        tokenizer=server_state["textual_inversion"]["tokenizer"],
        size=config["args"]["resolution"],
        placeholder_token=config["args"]["placeholder_token"],
        repeats=config["args"]["repeats"],
        learnable_property=config["args"]["learnable_property"],
        center_crop=config["args"]["center_crop"],
        set="train",
        templates=templates,
    )
    train_dataloader = torch.utils.data.DataLoader(
        train_dataset, batch_size=config["args"]["train_batch_size"], shuffle=True
    )

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(
        len(train_dataloader) / config["args"]["gradient_accumulation_steps"]
    )
    if config["args"]["max_train_steps"] is None:
        config["args"]["max_train_steps"] = (
            config["args"]["num_train_epochs"] * num_update_steps_per_epoch
        )
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        config["args"]["lr_scheduler"],
        optimizer=optimizer,
        num_warmup_steps=config["args"]["lr_warmup_steps"]
        * config["args"]["gradient_accumulation_steps"],
        num_training_steps=config["args"]["max_train_steps"]
        * config["args"]["gradient_accumulation_steps"],
    )

    (
        server_state["textual_inversion"]["text_encoder"],
        optimizer,
        train_dataloader,
        lr_scheduler,
    ) = accelerator.prepare(
        server_state["textual_inversion"]["text_encoder"],
        optimizer,
        train_dataloader,
        lr_scheduler,
    )

    # Move vae and unet to device
    server_state["textual_inversion"]["vae"].to(accelerator.device)
    server_state["textual_inversion"]["unet"].to(accelerator.device)

    # Keep vae and unet in eval mode as we don't train these
    server_state["textual_inversion"]["vae"].eval()
    server_state["textual_inversion"]["unet"].eval()

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(
        len(train_dataloader) / config["args"]["gradient_accumulation_steps"]
    )
    if overrode_max_train_steps:
        config["args"]["max_train_steps"] = (
            config["args"]["num_train_epochs"] * num_update_steps_per_epoch
        )
    # Afterwards we recalculate our number of training epochs
    config["args"]["num_train_epochs"] = math.ceil(
        config["args"]["max_train_steps"] / num_update_steps_per_epoch
    )

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if accelerator.is_main_process:
        accelerator.init_trackers("textual_inversion", config=config["args"])

    # Train!
    total_batch_size = (
        config["args"]["train_batch_size"]
        * accelerator.num_processes
        * st.session_state["textual_inversion"]["args"]["gradient_accumulation_steps"]
    )

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {config['args']['num_train_epochs']}")
    logger.info(
        f"  Instantaneous batch size per device = {config['args']['train_batch_size']}"
    )
    logger.info(
        f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
    )
    logger.info(
        f"  Gradient Accumulation steps = {config['args']['gradient_accumulation_steps']}"
    )
    logger.info(f"  Total optimization steps = {config['args']['max_train_steps']}")
    # Only show the progress bar once on each machine.
    progress_bar = tqdm(
        range(config["args"]["max_train_steps"]),
        disable=not accelerator.is_local_main_process,
    )
    progress_bar.set_description("Steps")
    global_step = 0
    encoded_pixel_values_cache = {}

    try:
        for epoch in range(config["args"]["num_train_epochs"]):
            server_state["textual_inversion"]["text_encoder"].train()
            for step, batch in enumerate(train_dataloader):
                with accelerator.accumulate(
                    server_state["textual_inversion"]["text_encoder"]
                ):
                    # Convert images to latent space
                    key = "|".join(batch["key"])
                    if encoded_pixel_values_cache.get(key, None) is None:
                        encoded_pixel_values_cache[key] = (
                            server_state["textual_inversion"]["vae"]
                            .encode(batch["pixel_values"])
                            .latent_dist
                        )
                    latents = (
                        encoded_pixel_values_cache[key].sample().detach().half()
                        * 0.18215
                    )

                    # Sample noise that we'll add to the latents
                    noise = torch.randn(latents.shape).to(latents.device)
                    bsz = latents.shape[0]
                    # Sample a random timestep for each image
                    timesteps = torch.randint(
                        0,
                        noise_scheduler.num_train_timesteps,
                        (bsz,),
                        device=latents.device,
                    ).long()

                    # Add noise to the latents according to the noise magnitude at each timestep
                    # (this is the forward diffusion process)
                    noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

                    # Get the text embedding for conditioning
                    encoder_hidden_states = server_state["textual_inversion"][
                        "text_encoder"
                    ](batch["input_ids"])[0]

                    # Predict the noise residual
                    noise_pred = server_state["textual_inversion"]["unet"](
                        noisy_latents, timesteps, encoder_hidden_states
                    ).sample

                    loss = (
                        F.mse_loss(noise_pred, noise, reduction="none")
                        .mean([1, 2, 3])
                        .mean()
                    )
                    accelerator.backward(loss)

                    # Zero out the gradients for all token embeddings except the newly added
                    # embeddings for the concept, as we only want to optimize the concept embeddings
                    if accelerator.num_processes > 1:
                        grads = (
                            server_state["textual_inversion"]["text_encoder"]
                            .module.get_input_embeddings()
                            .weight.grad
                        )
                    else:
                        grads = (
                            server_state["textual_inversion"]["text_encoder"]
                            .get_input_embeddings()
                            .weight.grad
                        )
                    # Get the index for tokens that we want to zero the grads for
                    index_grads_to_zero = (
                        torch.arange(
                            len(server_state["textual_inversion"]["tokenizer"])
                        )
                        != placeholder_token_id
                    )
                    grads.data[index_grads_to_zero, :] = grads.data[
                        index_grads_to_zero, :
                    ].fill_(0)

                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

                # try:
                # Checks if the accelerator has performed an optimization step behind the scenes
                if accelerator.sync_gradients:
                    progress_bar.update(1)
                    global_step += 1

                    if (
                        global_step % config["args"]["checkpoint_frequency"] == 0
                        and global_step > 0
                        and accelerator.is_main_process
                    ):
                        checkpointer.checkpoint(
                            global_step + global_step_offset,
                            server_state["textual_inversion"]["text_encoder"],
                        )
                        save_resume_file(
                            basepath,
                            {
                                "global_step": global_step + global_step_offset,
                                "resume_checkpoint": f"{basepath}/checkpoints/last.bin",
                            },
                            config,
                        )

                        checkpointer.save_samples(
                            global_step + global_step_offset,
                            server_state["textual_inversion"]["text_encoder"],
                            config["args"]["resolution"],
                            config["args"]["resolution"],
                            7.5,
                            0.0,
                            config["args"]["sample_steps"],
                        )

                        checkpointer.checkpoint(
                            global_step + global_step_offset,
                            server_state["textual_inversion"]["text_encoder"],
                            path=f"{basepath}/learned_embeds.bin",
                        )
                # except KeyError:
                # raise StopException

                logs = {
                    "loss": loss.detach().item(),
                    "lr": lr_scheduler.get_last_lr()[0],
                }
                progress_bar.set_postfix(**logs)

                # accelerator.log(logs, step=global_step)

                # try:
                if global_step >= config["args"]["max_train_steps"]:
                    break
                # except:
                # pass

            accelerator.wait_for_everyone()

        # Create the pipeline using the trained modules and save it.
        if accelerator.is_main_process:
            print("Finished! Saving final checkpoint and resume state.")
            checkpointer.checkpoint(
                global_step + global_step_offset,
                server_state["textual_inversion"]["text_encoder"],
                path=f"{basepath}/learned_embeds.bin",
            )

            save_resume_file(
                basepath,
                {
                    "global_step": global_step + global_step_offset,
                    "resume_checkpoint": f"{basepath}/checkpoints/last.bin",
                },
                config,
            )

            accelerator.end_training()

    except (KeyboardInterrupt, StopException):
        print("Received Streamlit StopException or KeyboardInterrupt")

        if accelerator.is_main_process:
            print("Interrupted, saving checkpoint and resume state...")
            checkpointer.checkpoint(
                global_step + global_step_offset,
                server_state["textual_inversion"]["text_encoder"],
            )

            config["args"] = save_resume_file(
                basepath,
                {
                    "global_step": global_step + global_step_offset,
                    "resume_checkpoint": f"{basepath}/checkpoints/last.bin",
                },
                config,
            )

            checkpointer.checkpoint(
                global_step + global_step_offset,
                server_state["textual_inversion"]["text_encoder"],
                path=f"{basepath}/learned_embeds.bin",
            )

        quit()


def layout():
    with st.form("textual-inversion"):
        # st.info("Under Construction. :construction_worker:")
        # parser = argparse.ArgumentParser(description="Simple example of a training script.")

        set_page_title("Textual Inversion - Stable Diffusion Playground")

        config_tab, output_tab, tensorboard_tab = st.tabs(
            ["Textual Inversion Config", "Ouput", "TensorBoard"]
        )

        with config_tab:
            col1, col2, col3, col4, col5 = st.columns(5, gap="large")

            if "textual_inversion" not in st.session_state:
                st.session_state["textual_inversion"] = {}

            if "textual_inversion" not in server_state:
                server_state["textual_inversion"] = {}

            if "args" not in st.session_state["textual_inversion"]:
                st.session_state["textual_inversion"]["args"] = {}

            with col1:
                st.session_state["textual_inversion"]["args"][
                    "pretrained_model_name_or_path"
                ] = st.text_input(
                    "Pretrained Model Path",
                    value=st.session_state[
                        "defaults"
                    ].textual_inversion.pretrained_model_name_or_path,
                    help="Path to pretrained model or model identifier from huggingface.co/models.",
                )

                st.session_state["textual_inversion"]["args"][
                    "tokenizer_name"
                ] = st.text_input(
                    "Tokenizer Name",
                    value=st.session_state["defaults"].textual_inversion.tokenizer_name,
                    help="Pretrained tokenizer name or path if not the same as model_name",
                )

                st.session_state["textual_inversion"]["args"][
                    "train_data_dir"
                ] = st.text_input(
                    "train_data_dir",
                    value="",
                    help="A folder containing the training data.",
                )

                st.session_state["textual_inversion"]["args"][
                    "placeholder_token"
                ] = st.text_input(
                    "Placeholder Token",
                    value="",
                    help="A token to use as a placeholder for the concept.",
                )

                st.session_state["textual_inversion"]["args"][
                    "initializer_token"
                ] = st.text_input(
                    "Initializer Token",
                    value="",
                    help="A token to use as initializer word.",
                )

                st.session_state["textual_inversion"]["args"][
                    "learnable_property"
                ] = st.selectbox(
                    "Learnable Property",
                    ["object", "style"],
                    index=0,
                    help="Choose between 'object' and 'style'",
                )

                st.session_state["textual_inversion"]["args"]["repeats"] = int(
                    st.text_input(
                        "Number of times to Repeat",
                        value=100,
                        help="How many times to repeat the training data.",
                    )
                )

                with col2:
                    st.session_state["textual_inversion"]["args"][
                        "output_dir"
                    ] = st.text_input(
                        "Output Directory",
                        value=str(os.path.join("outputs", "textual_inversion")),
                        help="The output directory where the model predictions and checkpoints will be written.",
                    )

                    st.session_state["textual_inversion"]["args"]["seed"] = seed_to_int(
                        st.text_input(
                            "Seed",
                            value=0,
                            help="A seed for reproducible training, if left empty a random one will be generated. Default: 0",
                        )
                    )

                    st.session_state["textual_inversion"]["args"]["resolution"] = int(
                        st.text_input(
                            "Resolution",
                            value=512,
                            help="The resolution for input images, all the images in the train/validation dataset will be resized to this resolution",
                        )
                    )

                    st.session_state["textual_inversion"]["args"][
                        "center_crop"
                    ] = st.checkbox(
                        "Center Image",
                        value=True,
                        help="Whether to center crop images before resizing to resolution",
                    )

                    st.session_state["textual_inversion"]["args"][
                        "train_batch_size"
                    ] = int(
                        st.text_input(
                            "Train Batch Size",
                            value=1,
                            help="Batch size (per device) for the training dataloader.",
                        )
                    )

                    st.session_state["textual_inversion"]["args"][
                        "num_train_epochs"
                    ] = int(
                        st.text_input(
                            "Number of Steps to Train",
                            value=100,
                            help="Number of steps to train.",
                        )
                    )

                    st.session_state["textual_inversion"]["args"][
                        "max_train_steps"
                    ] = int(
                        st.text_input(
                            "Max Number of Steps to Train",
                            value=5000,
                            help="Total number of training steps to perform.  If provided, overrides 'Number of Steps to Train'.",
                        )
                    )

                    with col3:
                        st.session_state["textual_inversion"]["args"][
                            "gradient_accumulation_steps"
                        ] = int(
                            st.text_input(
                                "Gradient Accumulation Steps",
                                value=1,
                                help="Number of updates steps to accumulate before performing a backward/update pass.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "learning_rate"
                        ] = float(
                            st.text_input(
                                "Learning Rate",
                                value=5.0e-04,
                                help="Initial learning rate (after the potential warmup period) to use.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "scale_lr"
                        ] = st.checkbox(
                            "Scale Learning Rate",
                            value=True,
                            help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
                        )

                        st.session_state["textual_inversion"]["args"][
                            "lr_scheduler"
                        ] = st.text_input(
                            "Learning Rate Scheduler",
                            value="constant",
                            help=(
                                "The scheduler type to use. Choose between ['linear', 'cosine', 'cosine_with_restarts', 'polynomial',"
                                " 'constant', 'constant_with_warmup']"
                            ),
                        )

                        st.session_state["textual_inversion"]["args"][
                            "lr_warmup_steps"
                        ] = int(
                            st.text_input(
                                "Learning Rate Warmup Steps",
                                value=500,
                                help="Number of steps for the warmup in the lr scheduler.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "adam_beta1"
                        ] = float(
                            st.text_input(
                                "Adam Beta 1",
                                value=0.9,
                                help="The beta1 parameter for the Adam optimizer.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "adam_beta2"
                        ] = float(
                            st.text_input(
                                "Adam Beta 2",
                                value=0.999,
                                help="The beta2 parameter for the Adam optimizer.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "adam_weight_decay"
                        ] = float(
                            st.text_input(
                                "Adam Weight Decay",
                                value=1e-2,
                                help="Weight decay to use.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "adam_epsilon"
                        ] = float(
                            st.text_input(
                                "Adam Epsilon",
                                value=1e-08,
                                help="Epsilon value for the Adam optimizer",
                            )
                        )

                        with col4:
                            st.session_state["textual_inversion"]["args"][
                                "mixed_precision"
                            ] = st.selectbox(
                                "Mixed Precision",
                                ["no", "fp16", "bf16"],
                                index=1,
                                help="Whether to use mixed precision. Choose"
                                "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
                                "and an Nvidia Ampere GPU.",
                            )

                            st.session_state["textual_inversion"]["args"][
                                "local_rank"
                            ] = int(
                                st.text_input(
                                    "Local Rank",
                                    value=1,
                                    help="For distributed training: local_rank",
                                )
                            )

                            st.session_state["textual_inversion"]["args"][
                                "checkpoint_frequency"
                            ] = int(
                                st.text_input(
                                    "Checkpoint Frequency",
                                    value=500,
                                    help="How often to save a checkpoint and sample image",
                                )
                            )

                            # stable_sample_batches is crashing when saving the samples so for now I will disable it util its fixed.
                            # st.session_state["textual_inversion"]["args"]["stable_sample_batches"] = int(st.text_input("Stable Sample Batches",  value=0,
                            # help="Number of fixed seed sample batches to generate per checkpoint"))

                            st.session_state["textual_inversion"]["args"][
                                "stable_sample_batches"
                            ] = 0

                            st.session_state["textual_inversion"]["args"][
                                "random_sample_batches"
                            ] = int(
                                st.text_input(
                                    "Random Sample Batches",
                                    value=2,
                                    help="Number of random seed sample batches to generate per checkpoint",
                                )
                            )

                            st.session_state["textual_inversion"]["args"][
                                "sample_batch_size"
                            ] = int(
                                st.text_input(
                                    "Sample Batch Size",
                                    value=1,
                                    help="Number of samples to generate per batch",
                                )
                            )

                            st.session_state["textual_inversion"]["args"][
                                "sample_steps"
                            ] = int(
                                st.text_input(
                                    "Sample Steps",
                                    value=100,
                                    help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.",
                                )
                            )

                            st.session_state["textual_inversion"]["args"][
                                "custom_templates"
                            ] = st.text_input(
                                "Custom Templates",
                                value="",
                                help="A semicolon-delimited list of custom template to use for samples, using {} as a placeholder for the concept.",
                            )
                            with col5:
                                st.session_state["textual_inversion"]["args"][
                                    "resume"
                                ] = st.checkbox(
                                    label="Resume Previous Run?",
                                    value=False,
                                    help="Resume previous run, if a valid resume.json file is on the output dir \
								                                                                      it will be used, otherwise if the 'Resume From' field bellow contains a valid resume.json file \
								                                                                      that one will be used.",
                                )

                                st.session_state["textual_inversion"]["args"][
                                    "resume_from"
                                ] = st.text_input(
                                    label="Resume From",
                                    help="Path to a directory to resume training from (ie, logs/token_name)",
                                )

                                # st.session_state["textual_inversion"]["args"]["resume_checkpoint"] = st.file_uploader("Resume Checkpoint", type=["bin"],
                                # help="Path to a specific checkpoint to resume training from (ie, logs/token_name/checkpoints/something.bin).")

                                # st.session_state["textual_inversion"]["args"]["st.session_state["textual_inversion"]"] = st.file_uploader("st.session_state["textual_inversion"] File",  type=["json"],
                                # help="Path to a JSON st.session_state["textual_inversion"]uration file containing arguments for invoking this script."
                                # "If resume_from is given, its resume.json takes priority over this.")
            #
            # print (os.path.join(st.session_state["textual_inversion"]["args"]["output_dir"],st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"),"resume.json"))
            # print (os.path.exists(os.path.join(st.session_state["textual_inversion"]["args"]["output_dir"],st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"),"resume.json")))
            if os.path.exists(
                os.path.join(
                    st.session_state["textual_inversion"]["args"]["output_dir"],
                    st.session_state["textual_inversion"]["args"][
                        "placeholder_token"
                    ].strip("<>"),
                    "resume.json",
                )
            ):
                st.session_state["textual_inversion"]["args"][
                    "resume_from"
                ] = os.path.join(
                    st.session_state["textual_inversion"]["args"]["output_dir"],
                    st.session_state["textual_inversion"]["args"][
                        "placeholder_token"
                    ].strip("<>"),
                )
                # print (st.session_state["textual_inversion"]["args"]["resume_from"])

            if os.path.exists(
                os.path.join(
                    st.session_state["textual_inversion"]["args"]["output_dir"],
                    st.session_state["textual_inversion"]["args"][
                        "placeholder_token"
                    ].strip("<>"),
                    "checkpoints",
                    "last.bin",
                )
            ):
                st.session_state["textual_inversion"]["args"][
                    "resume_checkpoint"
                ] = os.path.join(
                    st.session_state["textual_inversion"]["args"]["output_dir"],
                    st.session_state["textual_inversion"]["args"][
                        "placeholder_token"
                    ].strip("<>"),
                    "checkpoints",
                    "last.bin",
                )

            # if "resume_from" in st.session_state["textual_inversion"]["args"]:
            # if st.session_state["textual_inversion"]["args"]["resume_from"]:
            # if os.path.exists(os.path.join(st.session_state["textual_inversion"]['args']['resume_from'], "resume.json")):
            # with open(os.path.join(st.session_state["textual_inversion"]['args']['resume_from'], "resume.json"), 'rt') as f:
            # try:
            # resume_json = json.load(f)["args"]
            # st.session_state["textual_inversion"]["args"] = OmegaConf.merge(st.session_state["textual_inversion"]["args"], resume_json)
            # st.session_state["textual_inversion"]["args"]["resume_from"] = os.path.join(
            # st.session_state["textual_inversion"]["args"]["output_dir"], st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"))
            # except json.decoder.JSONDecodeError:
            # pass

            # print(st.session_state["textual_inversion"]["args"])
            # print(st.session_state["textual_inversion"]["args"]['resume_from'])

            # elif st.session_state["textual_inversion"]["args"]["st.session_state["textual_inversion"]"] is not None:
            # with open(st.session_state["textual_inversion"]["args"]["st.session_state["textual_inversion"]"], 'rt') as f:
            # args = parser.parse_args(namespace=argparse.Namespace(**json.load(f)["args"]))

            env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
            if (
                env_local_rank != -1
                and env_local_rank
                != st.session_state["textual_inversion"]["args"]["local_rank"]
            ):
                st.session_state["textual_inversion"]["args"][
                    "local_rank"
                ] = env_local_rank

            if st.session_state["textual_inversion"]["args"]["train_data_dir"] is None:
                st.error("You must specify --train_data_dir")

            if (
                st.session_state["textual_inversion"]["args"][
                    "pretrained_model_name_or_path"
                ]
                is None
            ):
                st.error("You must specify --pretrained_model_name_or_path")

            if (
                st.session_state["textual_inversion"]["args"]["placeholder_token"]
                is None
            ):
                st.error("You must specify --placeholder_token")

            if (
                st.session_state["textual_inversion"]["args"]["initializer_token"]
                is None
            ):
                st.error("You must specify --initializer_token")

            if st.session_state["textual_inversion"]["args"]["output_dir"] is None:
                st.error("You must specify --output_dir")

            # add a spacer and the submit button for the form.

            st.session_state["textual_inversion"]["message"] = st.empty()
            st.session_state["textual_inversion"]["progress_bar"] = st.empty()

            st.write("---")

            submit = st.form_submit_button("Run", help="")
            if submit:
                if "pipe" in st.session_state:
                    del st.session_state["pipe"]
                if "model" in st.session_state:
                    del st.session_state["model"]

                set_page_title("Running Textual Inversion - Stable Diffusion WebUI")
                # st.session_state["textual_inversion"]["message"].info("Textual Inversion Running. For more info check the progress on your console or the Ouput Tab.")

                try:
                    # try:
                    # run textual inversion.
                    config = st.session_state["textual_inversion"]
                    textual_inversion(config)
                    # except RuntimeError:
                    # if "pipeline" in server_state["textual_inversion"]:
                    # del server_state['textual_inversion']["checker"]
                    # del server_state['textual_inversion']["unwrapped"]
                    # del server_state['textual_inversion']["pipeline"]

                    # run textual inversion.
                    # config = st.session_state['textual_inversion']
                    # textual_inversion(config)

                    set_page_title("Textual Inversion - Stable Diffusion WebUI")

                except StopException:
                    set_page_title("Textual Inversion - Stable Diffusion WebUI")
                    print("Received Streamlit StopException")

                st.session_state["textual_inversion"]["message"].empty()

        #
        with output_tab:
            st.info("Under Construction. :construction_worker:")

            # st.info("Nothing to show yet. Maybe try running some training first.")

            # st.session_state["textual_inversion"]["preview_image"] = st.empty()
            # st.session_state["textual_inversion"]["progress_bar"] = st.empty()

        with tensorboard_tab:
            # st.info("Under Construction. :construction_worker:")

            # Start TensorBoard
            st_tensorboard(
                logdir=os.path.join("outputs", "textual_inversion"), port=8888
            )


================================================
FILE: scripts/txt2img.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import (
    st,
    MemUsageMonitor,
    server_state,
    no_rerun,
    get_next_sequence_number,
    check_prompt_length,
    torch_gc,
    save_sample,
    generation_callback,
    process_images,
    KDiffusionSampler,
    custom_models_available,
    RealESRGAN_available,
    GFPGAN_available,
    LDSR_available,
    load_models,
    hc,
    seed_to_int,
    logger,
    set_page_title,
)

# streamlit imports
from streamlit.runtime.scriptrunner import StopException

# streamlit components section

# from streamlit.elements import image as STImage
import streamlit.components.v1 as components

# from streamlit.runtime.media_file_manager  import media_file_manager
from streamlit.elements.image import image_to_url

# other imports

import base64, uuid
import os, sys, datetime, time
from PIL import Image
import requests
from slugify import slugify
from ldm.models.diffusion.ddim import DDIMSampler
from typing import Union
from io import BytesIO
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler


# streamlit components
from custom_components import sygil_suggestions

# Temp imports


# end of imports
# ---------------------------------------------------------------------------------------------------------------

sygil_suggestions.init()

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging

    logging.set_verbosity_error()
except:
    pass

#
# Dev mode (server)
# _component_func = components.declare_component(
#         "sd-gallery",
#         url="http://localhost:3001",
#     )

# Init Vuejs component
_component_func = components.declare_component(
    "sd-gallery", "./frontend/dists/sd-gallery/dist"
)


def sdGallery(images=[], key=None):
    component_value = _component_func(images=imgsToGallery(images), key=key, default="")
    return component_value


def imgsToGallery(images):
    urls = []
    for i in images:
        # random string for id
        random_id = str(uuid.uuid4())
        url = image_to_url(
            image=i,
            image_id=random_id,
            width=i.width,
            clamp=False,
            channels="RGB",
            output_format="PNG",
        )
        # image_io = BytesIO()
        # i.save(image_io, 'PNG')
        # width, height = i.size
        # image_id = "%s" % (str(images.index(i)))
        # (data, mimetype) = STImage._normalize_to_bytes(image_io.getvalue(), width, 'auto')
        # this_file = media_file_manager.add(data, mimetype, image_id)
        # img_str = this_file.url
        urls.append(url)

    return urls


class plugin_info:
    plugname = "txt2img"
    description = "Text to Image"
    isTab = True
    displayPriority = 1


@logger.catch(reraise=True)
def stable_horde(
    outpath,
    prompt,
    seed,
    sampler_name,
    save_grid,
    batch_size,
    n_iter,
    steps,
    cfg_scale,
    width,
    height,
    prompt_matrix,
    use_GFPGAN,
    GFPGAN_model,
    use_RealESRGAN,
    realesrgan_model_name,
    use_LDSR,
    LDSR_model_name,
    ddim_eta,
    normalize_prompt_weights,
    save_individual_images,
    sort_samples,
    write_info_files,
    jpg_sample,
    variant_amount,
    variant_seed,
    api_key,
    nsfw=True,
    censor_nsfw=False,
):
    log = []

    log.append("Generating image with Stable Horde.")

    st.session_state["progress_bar_text"].code("\n".join(log), language="")

    # start time after garbage collection (or before?)
    start_time = time.time()

    # We will use this date here later for the folder name, need to start_time if not need
    datetime.datetime.now()

    mem_mon = MemUsageMonitor("MemMon")
    mem_mon.start()

    os.makedirs(outpath, exist_ok=True)

    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)

    params = {
        "sampler_name": "k_euler",
        "toggles": [1, 4],
        "cfg_scale": cfg_scale,
        "seed": str(seed),
        "width": width,
        "height": height,
        "seed_variation": variant_seed if variant_seed else 1,
        "steps": int(steps),
        "n": int(n_iter)
        # You can put extra params here if you wish
    }

    final_submit_dict = {
        "prompt": prompt,
        "params": params,
        "nsfw": nsfw,
        "censor_nsfw": censor_nsfw,
        "trusted_workers": True,
        "workers": [],
    }
    log.append(final_submit_dict)

    headers = {"apikey": api_key}
    logger.debug(final_submit_dict)
    st.session_state["progress_bar_text"].code("\n".join(str(log)), language="")

    horde_url = "https://stablehorde.net"

    submit_req = requests.post(
        f"{horde_url}/api/v2/generate/async", json=final_submit_dict, headers=headers
    )
    if submit_req.ok:
        submit_results = submit_req.json()
        logger.debug(submit_results)

        log.append(submit_results)
        st.session_state["progress_bar_text"].code("".join(str(log)), language="")

        req_id = submit_results["id"]
        is_done = False
        while not is_done:
            chk_req = requests.get(f"{horde_url}/api/v2/generate/check/{req_id}")
            if not chk_req.ok:
                logger.error(chk_req.text)
                return
            chk_results = chk_req.json()
            logger.info(chk_results)
            is_done = chk_results["done"]
            time.sleep(1)
        retrieve_req = requests.get(f"{horde_url}/api/v2/generate/status/{req_id}")
        if not retrieve_req.ok:
            logger.error(retrieve_req.text)
            return
        results_json = retrieve_req.json()
        # logger.debug(results_json)
        results = results_json["generations"]

        output_images = []
        comments = []

        if not st.session_state["defaults"].general.no_verify_input:
            try:
                check_prompt_length(prompt, comments)
            except:
                import traceback

                logger.info("Error verifying input:", file=sys.stderr)
                logger.info(traceback.format_exc(), file=sys.stderr)

        all_prompts = batch_size * n_iter * [prompt]
        all_seeds = [seed + x for x in range(len(all_prompts))]

        for iter in range(len(results)):
            b64img = results[iter]["img"]
            base64_bytes = b64img.encode("utf-8")
            img_bytes = base64.b64decode(base64_bytes)
            img = Image.open(BytesIO(img_bytes))

            sanitized_prompt = slugify(prompt)

            prompts = all_prompts[iter * batch_size : (iter + 1) * batch_size]
            # captions = prompt_matrix_parts[n * batch_size:(n + 1) * batch_size]
            seeds = all_seeds[iter * batch_size : (iter + 1) * batch_size]

            if sort_samples:
                full_path = os.path.join(os.getcwd(), sample_path, sanitized_prompt)

                sanitized_prompt = sanitized_prompt[: 200 - len(full_path)]
                sample_path_i = os.path.join(sample_path, sanitized_prompt)

                # print(f"output folder length: {len(os.path.join(os.getcwd(), sample_path_i))}")
                # print(os.path.join(os.getcwd(), sample_path_i))

                os.makedirs(sample_path_i, exist_ok=True)
                base_count = get_next_sequence_number(sample_path_i)
                filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[iter]}"
            else:
                full_path = os.path.join(os.getcwd(), sample_path)
                sample_path_i = sample_path
                base_count = get_next_sequence_number(sample_path_i)
                filename = (
                    f"{base_count:05}-{steps}_{sampler_name}_{seed}_{sanitized_prompt}"[
                        : 200 - len(full_path)
                    ]
                )  # same as before

            save_sample(
                img,
                sample_path_i,
                filename,
                jpg_sample,
                prompts,
                seeds,
                width,
                height,
                steps,
                cfg_scale,
                normalize_prompt_weights,
                use_GFPGAN,
                write_info_files,
                prompt_matrix,
                init_img=None,
                denoising_strength=0.75,
                resize_mode=None,
                uses_loopback=False,
                uses_random_seed_loopback=False,
                save_grid=save_grid,
                sort_samples=sampler_name,
                sampler_name=sampler_name,
                ddim_eta=ddim_eta,
                n_iter=n_iter,
                batch_size=batch_size,
                i=iter,
                save_individual_images=save_individual_images,
                model_name="Stable Diffusion v1.5",
            )

            output_images.append(img)

            # update image on the UI so we can see the progress
            if "preview_image" in st.session_state:
                st.session_state["preview_image"].image(img)

            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].empty()

            # if len(results) > 1:
            # final_filename = f"{iter}_{filename}"
            # img.save(final_filename)
            # logger.info(f"Saved {final_filename}")
    else:
        if "progress_bar_text" in st.session_state:
            st.session_state["progress_bar_text"].error(submit_req.text)

        logger.error(submit_req.text)

    mem_max_used, mem_total = mem_mon.read_and_stop()
    time_diff = time.time() - start_time

    info = f"""
            {prompt}
            Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN else ''}{', '+realesrgan_model_name if use_RealESRGAN else ''}
            {', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()

    stats = f"""
            Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
            Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%"""

    for comment in comments:
        info += "\n\n" + comment

    # mem_mon.stop()
    # del mem_mon
    torch_gc()

    return output_images, seed, info, stats


#
@logger.catch(reraise=True)
def txt2img(
    prompt: str,
    ddim_steps: int,
    sampler_name: str,
    n_iter: int,
    batch_size: int,
    cfg_scale: float,
    seed: Union[int, str, None],
    height: int,
    width: int,
    separate_prompts: bool = False,
    normalize_prompt_weights: bool = True,
    save_individual_images: bool = True,
    save_grid: bool = True,
    group_by_prompt: bool = True,
    save_as_jpg: bool = True,
    use_GFPGAN: bool = True,
    GFPGAN_model: str = "GFPGANv1.3",
    use_RealESRGAN: bool = False,
    RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B",
    use_LDSR: bool = True,
    LDSR_model: str = "model",
    fp=None,
    variant_amount: float = 0.0,
    variant_seed: int = None,
    ddim_eta: float = 0.0,
    write_info_files: bool = True,
    use_stable_horde: bool = False,
    stable_horde_key: str = "0000000000",
):
    outpath = st.session_state["defaults"].general.outdir_txt2img

    seed = seed_to_int(seed)

    if not use_stable_horde:
        if sampler_name == "PLMS":
            sampler = PLMSSampler(server_state["model"])
        elif sampler_name == "DDIM":
            sampler = DDIMSampler(server_state["model"])
        elif sampler_name == "k_dpm_2_a":
            sampler = KDiffusionSampler(server_state["model"], "dpm_2_ancestral")
        elif sampler_name == "k_dpm_2":
            sampler = KDiffusionSampler(server_state["model"], "dpm_2")
        elif sampler_name == "k_dpmpp_2m":
            sampler = KDiffusionSampler(server_state["model"], "dpmpp_2m")
        elif sampler_name == "k_euler_a":
            sampler = KDiffusionSampler(server_state["model"], "euler_ancestral")
        elif sampler_name == "k_euler":
            sampler = KDiffusionSampler(server_state["model"], "euler")
        elif sampler_name == "k_heun":
            sampler = KDiffusionSampler(server_state["model"], "heun")
        elif sampler_name == "k_lms":
            sampler = KDiffusionSampler(server_state["model"], "lms")
        else:
            raise Exception("Unknown sampler: " + sampler_name)

        def init():
            pass

        def sample(
            init_data, x, conditioning, unconditional_conditioning, sampler_name
        ):
            samples_ddim, _ = sampler.sample(
                S=ddim_steps,
                conditioning=conditioning,
                batch_size=int(x.shape[0]),
                shape=x[0].shape,
                verbose=False,
                unconditional_guidance_scale=cfg_scale,
                unconditional_conditioning=unconditional_conditioning,
                eta=ddim_eta,
                x_T=x,
                img_callback=generation_callback
                if not server_state["bridge"]
                else None,
                log_every_t=int(
                    st.session_state.update_preview_frequency
                    if not server_state["bridge"]
                    else 100
                ),
            )

            return samples_ddim

    if use_stable_horde:
        output_images, seed, info, stats = stable_horde(
            prompt=prompt,
            seed=seed,
            outpath=outpath,
            sampler_name=sampler_name,
            save_grid=save_grid,
            batch_size=batch_size,
            n_iter=n_iter,
            steps=ddim_steps,
            cfg_scale=cfg_scale,
            width=width,
            height=height,
            prompt_matrix=separate_prompts,
            use_GFPGAN=use_GFPGAN,
            GFPGAN_model=GFPGAN_model,
            use_RealESRGAN=use_RealESRGAN,
            realesrgan_model_name=RealESRGAN_model,
            use_LDSR=use_LDSR,
            LDSR_model_name=LDSR_model,
            ddim_eta=ddim_eta,
            normalize_prompt_weights=normalize_prompt_weights,
            save_individual_images=save_individual_images,
            sort_samples=group_by_prompt,
            write_info_files=write_info_files,
            jpg_sample=save_as_jpg,
            variant_amount=variant_amount,
            variant_seed=variant_seed,
            api_key=stable_horde_key,
        )
    else:
        # try:
        output_images, seed, info, stats = process_images(
            outpath=outpath,
            func_init=init,
            func_sample=sample,
            prompt=prompt,
            seed=seed,
            sampler_name=sampler_name,
            save_grid=save_grid,
            batch_size=batch_size,
            n_iter=n_iter,
            steps=ddim_steps,
            cfg_scale=cfg_scale,
            width=width,
            height=height,
            prompt_matrix=separate_prompts,
            use_GFPGAN=use_GFPGAN,
            GFPGAN_model=GFPGAN_model,
            use_RealESRGAN=use_RealESRGAN,
            realesrgan_model_name=RealESRGAN_model,
            use_LDSR=use_LDSR,
            LDSR_model_name=LDSR_model,
            ddim_eta=ddim_eta,
            normalize_prompt_weights=normalize_prompt_weights,
            save_individual_images=save_individual_images,
            sort_samples=group_by_prompt,
            write_info_files=write_info_files,
            jpg_sample=save_as_jpg,
            variant_amount=variant_amount,
            variant_seed=variant_seed,
        )

        del sampler

    return output_images, seed, info, stats

    # except RuntimeError as e:
    # err = e
    # err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
    # stats = err_msg
    # return [], seed, 'err', stats


#
@logger.catch(reraise=True)
def layout():
    with st.form("txt2img-inputs"):
        st.session_state["generation_mode"] = "txt2img"

        input_col1, generate_col1 = st.columns([10, 1])

        with input_col1:
            # prompt = st.text_area("Input Text","")
            placeholder = "A corgi wearing a top hat as an oil painting."
            prompt = st.text_area("Input Text", "", placeholder=placeholder, height=54)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].general.enable_suggestions:
                    sygil_suggestions.suggestion_area(placeholder)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].admin.global_negative_prompt:
                    prompt += f"### {st.session_state['defaults'].admin.global_negative_prompt}"

            # print(prompt)

        # creating the page layout using columns
        col1, col2, col3 = st.columns([2, 5, 2], gap="large")

        with col1:
            width = st.slider(
                "Width:",
                min_value=st.session_state["defaults"].txt2img.width.min_value,
                max_value=st.session_state["defaults"].txt2img.width.max_value,
                value=st.session_state["defaults"].txt2img.width.value,
                step=st.session_state["defaults"].txt2img.width.step,
            )
            height = st.slider(
                "Height:",
                min_value=st.session_state["defaults"].txt2img.height.min_value,
                max_value=st.session_state["defaults"].txt2img.height.max_value,
                value=st.session_state["defaults"].txt2img.height.value,
                step=st.session_state["defaults"].txt2img.height.step,
            )
            cfg_scale = st.number_input(
                "CFG (Classifier Free Guidance Scale):",
                min_value=st.session_state["defaults"].txt2img.cfg_scale.min_value,
                value=st.session_state["defaults"].txt2img.cfg_scale.value,
                step=st.session_state["defaults"].txt2img.cfg_scale.step,
                help="How strongly the image should follow the prompt.",
            )

            seed = st.text_input(
                "Seed:",
                value=st.session_state["defaults"].txt2img.seed,
                help=" The seed to use, if left blank a random seed will be generated.",
            )

            with st.expander("Batch Options"):
                # batch_count = st.slider("Batch count.", min_value=st.session_state['defaults'].txt2img.batch_count.min_value, max_value=st.session_state['defaults'].txt2img.batch_count.max_value,
                # value=st.session_state['defaults'].txt2img.batch_count.value, step=st.session_state['defaults'].txt2img.batch_count.step,
                # help="How many iterations or batches of images to generate in total.")

                # batch_size = st.slider("Batch size", min_value=st.session_state['defaults'].txt2img.batch_size.min_value, max_value=st.session_state['defaults'].txt2img.batch_size.max_value,
                # value=st.session_state.defaults.txt2img.batch_size.value, step=st.session_state.defaults.txt2img.batch_size.step,
                # help="How many images are at once in a batch.\
                # It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
                # Default: 1")

                st.session_state["batch_count"] = st.number_input(
                    "Batch count.",
                    value=st.session_state["defaults"].txt2img.batch_count.value,
                    help="How many iterations or batches of images to generate in total.",
                )

                st.session_state["batch_size"] = st.number_input(
                    "Batch size",
                    value=st.session_state.defaults.txt2img.batch_size.value,
                    help="How many images are at once in a batch.\
                                                                   It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes \
                                                                   to finish generation as more images are generated at once.\
                                                                   Default: 1",
                )

            with st.expander("Preview Settings"):
                st.session_state["update_preview"] = st.session_state[
                    "defaults"
                ].general.update_preview
                st.session_state["update_preview_frequency"] = st.number_input(
                    "Update Image Preview Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].txt2img.update_preview_frequency,
                    help="Frequency in steps at which the the preview image is updated. By default the frequency \
                                                                               is set to 10 step.",
                )

        with col2:
            preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])

            with preview_tab:
                # st.write("Image")
                # Image for testing
                # image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
                # new_image = image.resize((175, 240))
                # preview_image = st.image(image)

                # create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
                st.session_state["preview_image"] = st.empty()

                st.session_state["progress_bar_text"] = st.empty()
                st.session_state["progress_bar_text"].info(
                    "Nothing but crickets here, try generating something first."
                )

                st.session_state["progress_bar"] = st.empty()

                message = st.empty()

            with gallery_tab:
                st.session_state["gallery"] = st.empty()
                # st.session_state["gallery"].info("Nothing but crickets here, try generating something first.")

        with col3:
            # If we have custom models available on the "models/custom"
            # folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
            custom_models_available()

            if server_state["CustomModel_available"]:
                st.session_state["custom_model"] = st.selectbox(
                    "Custom Model:",
                    server_state["custom_models"],
                    index=server_state["custom_models"].index(
                        st.session_state["defaults"].general.default_model
                    ),
                    help="Select the model you want to use. This option is only available if you have custom models \
                                                                on your 'models/custom' folder. The model name that will be shown here is the same as the name\
                                                                the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
                                                                will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.5",
                )

            st.session_state.sampling_steps = st.number_input(
                "Sampling Steps",
                value=st.session_state.defaults.txt2img.sampling_steps.value,
                min_value=st.session_state.defaults.txt2img.sampling_steps.min_value,
                step=st.session_state["defaults"].txt2img.sampling_steps.step,
                help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)",
            )

            sampler_name_list = [
                "k_lms",
                "k_euler",
                "k_euler_a",
                "k_dpm_2",
                "k_dpm_2_a",
                "k_dpmpp_2m",
                "k_heun",
                "PLMS",
                "DDIM",
            ]
            sampler_name = st.selectbox(
                "Sampling method",
                sampler_name_list,
                index=sampler_name_list.index(
                    st.session_state["defaults"].txt2img.default_sampler
                ),
                help="Sampling method to use. Default: k_euler",
            )

            with st.expander("Advanced"):
                with st.expander("Stable Horde"):
                    use_stable_horde = st.checkbox(
                        "Use Stable Horde",
                        value=False,
                        help="Use the Stable Horde to generate images. More info can be found at https://stablehorde.net/",
                    )
                    stable_horde_key = st.text_input(
                        "Stable Horde Api Key",
                        value=st.session_state["defaults"].general.stable_horde_api,
                        type="password",
                        help="Optional Api Key used for the Stable Horde Bridge, if no api key is added the horde will be used anonymously.",
                    )

                with st.expander("Output Settings"):
                    separate_prompts = st.checkbox(
                        "Create Prompt Matrix.",
                        value=st.session_state["defaults"].txt2img.separate_prompts,
                        help="Separate multiple prompts using the `|` character, and get all combinations of them.",
                    )

                    normalize_prompt_weights = st.checkbox(
                        "Normalize Prompt Weights.",
                        value=st.session_state[
                            "defaults"
                        ].txt2img.normalize_prompt_weights,
                        help="Ensure the sum of all weights add up to 1.0",
                    )

                    save_individual_images = st.checkbox(
                        "Save individual images.",
                        value=st.session_state[
                            "defaults"
                        ].txt2img.save_individual_images,
                        help="Save each image generated before any filter or enhancement is applied.",
                    )

                    save_grid = st.checkbox(
                        "Save grid",
                        value=st.session_state["defaults"].txt2img.save_grid,
                        help="Save a grid with all the images generated into a single image.",
                    )
                    group_by_prompt = st.checkbox(
                        "Group results by prompt",
                        value=st.session_state["defaults"].txt2img.group_by_prompt,
                        help="Saves all the images with the same prompt into the same folder. When using a prompt matrix each prompt combination will have its own folder.",
                    )

                    write_info_files = st.checkbox(
                        "Write Info file",
                        value=st.session_state["defaults"].txt2img.write_info_files,
                        help="Save a file next to the image with informartion about the generation.",
                    )

                    save_as_jpg = st.checkbox(
                        "Save samples as jpg",
                        value=st.session_state["defaults"].txt2img.save_as_jpg,
                        help="Saves the images as jpg instead of png.",
                    )

                # check if GFPGAN, RealESRGAN and LDSR are available.
                # if "GFPGAN_available" not in st.session_state:
                GFPGAN_available()

                # if "RealESRGAN_available" not in st.session_state:
                RealESRGAN_available()

                # if "LDSR_available" not in st.session_state:
                LDSR_available()

                if (
                    st.session_state["GFPGAN_available"]
                    or st.session_state["RealESRGAN_available"]
                    or st.session_state["LDSR_available"]
                ):
                    with st.expander("Post-Processing"):
                        face_restoration_tab, upscaling_tab = st.tabs(
                            ["Face Restoration", "Upscaling"]
                        )
                        with face_restoration_tab:
                            # GFPGAN used for face restoration
                            if st.session_state["GFPGAN_available"]:
                                # with st.expander("Face Restoration"):
                                # if st.session_state["GFPGAN_available"]:
                                # with st.expander("GFPGAN"):
                                st.session_state["use_GFPGAN"] = st.checkbox(
                                    "Use GFPGAN",
                                    value=st.session_state[
                                        "defaults"
                                    ].txt2img.use_GFPGAN,
                                    help="Uses the GFPGAN model to improve faces after the generation.\
                                                                                 This greatly improve the quality and consistency of faces but uses\
                                                                                 extra VRAM. Disable if you need the extra VRAM.",
                                )

                                st.session_state["GFPGAN_model"] = st.selectbox(
                                    "GFPGAN model",
                                    st.session_state["GFPGAN_models"],
                                    index=st.session_state["GFPGAN_models"].index(
                                        st.session_state[
                                            "defaults"
                                        ].general.GFPGAN_model
                                    ),
                                )

                            # st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')

                            else:
                                st.session_state["use_GFPGAN"] = False

                        with upscaling_tab:
                            st.session_state["use_upscaling"] = st.checkbox(
                                "Use Upscaling",
                                value=st.session_state[
                                    "defaults"
                                ].txt2img.use_upscaling,
                            )

                            # RealESRGAN and LDSR used for upscaling.
                            if (
                                st.session_state["RealESRGAN_available"]
                                or st.session_state["LDSR_available"]
                            ):
                                upscaling_method_list = []
                                if st.session_state["RealESRGAN_available"]:
                                    upscaling_method_list.append("RealESRGAN")
                                if st.session_state["LDSR_available"]:
                                    upscaling_method_list.append("LDSR")

                                # print (st.session_state["RealESRGAN_available"])
                                st.session_state["upscaling_method"] = st.selectbox(
                                    "Upscaling Method",
                                    upscaling_method_list,
                                    index=upscaling_method_list.index(
                                        st.session_state[
                                            "defaults"
                                        ].general.upscaling_method
                                    )
                                    if st.session_state[
                                        "defaults"
                                    ].general.upscaling_method
                                    in upscaling_method_list
                                    else 0,
                                )

                                if st.session_state["RealESRGAN_available"]:
                                    with st.expander("RealESRGAN"):
                                        if (
                                            st.session_state["upscaling_method"]
                                            == "RealESRGAN"
                                            and st.session_state["use_upscaling"]
                                        ):
                                            st.session_state["use_RealESRGAN"] = True
                                        else:
                                            st.session_state["use_RealESRGAN"] = False

                                        st.session_state[
                                            "RealESRGAN_model"
                                        ] = st.selectbox(
                                            "RealESRGAN model",
                                            st.session_state["RealESRGAN_models"],
                                            index=st.session_state[
                                                "RealESRGAN_models"
                                            ].index(
                                                st.session_state[
                                                    "defaults"
                                                ].general.RealESRGAN_model
                                            ),
                                        )
                                else:
                                    st.session_state["use_RealESRGAN"] = False
                                    st.session_state[
                                        "RealESRGAN_model"
                                    ] = "RealESRGAN_x4plus"

                                #
                                if st.session_state["LDSR_available"]:
                                    with st.expander("LDSR"):
                                        if (
                                            st.session_state["upscaling_method"]
                                            == "LDSR"
                                            and st.session_state["use_upscaling"]
                                        ):
                                            st.session_state["use_LDSR"] = True
                                        else:
                                            st.session_state["use_LDSR"] = False

                                        st.session_state["LDSR_model"] = st.selectbox(
                                            "LDSR model",
                                            st.session_state["LDSR_models"],
                                            index=st.session_state["LDSR_models"].index(
                                                st.session_state[
                                                    "defaults"
                                                ].general.LDSR_model
                                            ),
                                        )

                                        st.session_state[
                                            "ldsr_sampling_steps"
                                        ] = st.number_input(
                                            "Sampling Steps",
                                            value=st.session_state[
                                                "defaults"
                                            ].txt2img.LDSR_config.sampling_steps,
                                            help="",
                                        )

                                        st.session_state[
                                            "preDownScale"
                                        ] = st.number_input(
                                            "PreDownScale",
                                            value=st.session_state[
                                                "defaults"
                                            ].txt2img.LDSR_config.preDownScale,
                                            help="",
                                        )

                                        st.session_state[
                                            "postDownScale"
                                        ] = st.number_input(
                                            "postDownScale",
                                            value=st.session_state[
                                                "defaults"
                                            ].txt2img.LDSR_config.postDownScale,
                                            help="",
                                        )

                                        downsample_method_list = ["Nearest", "Lanczos"]
                                        st.session_state[
                                            "downsample_method"
                                        ] = st.selectbox(
                                            "Downsample Method",
                                            downsample_method_list,
                                            index=downsample_method_list.index(
                                                st.session_state[
                                                    "defaults"
                                                ].txt2img.LDSR_config.downsample_method
                                            ),
                                        )

                                else:
                                    st.session_state["use_LDSR"] = False
                                    st.session_state["LDSR_model"] = "model"

                with st.expander("Variant"):
                    variant_amount = st.slider(
                        "Variant Amount:",
                        value=st.session_state["defaults"].txt2img.variant_amount.value,
                        min_value=st.session_state[
                            "defaults"
                        ].txt2img.variant_amount.min_value,
                        max_value=st.session_state[
                            "defaults"
                        ].txt2img.variant_amount.max_value,
                        step=st.session_state["defaults"].txt2img.variant_amount.step,
                    )
                    variant_seed = st.text_input(
                        "Variant Seed:",
                        value=st.session_state["defaults"].txt2img.seed,
                        help="The seed to use when generating a variant, if left blank a random seed will be generated.",
                    )

            # galleryCont = st.empty()

        # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
        generate_col1.write("")
        generate_col1.write("")
        generate_button = generate_col1.form_submit_button("Generate")

        #
        if generate_button:
            with col2:
                with no_rerun:
                    if not use_stable_horde:
                        with hc.HyLoader(
                            "Loading Models...", hc.Loaders.standard_loaders, index=[0]
                        ):
                            load_models(
                                use_LDSR=st.session_state["use_LDSR"],
                                LDSR_model=st.session_state["LDSR_model"],
                                use_GFPGAN=st.session_state["use_GFPGAN"],
                                GFPGAN_model=st.session_state["GFPGAN_model"],
                                use_RealESRGAN=st.session_state["use_RealESRGAN"],
                                RealESRGAN_model=st.session_state["RealESRGAN_model"],
                                CustomModel_available=server_state[
                                    "CustomModel_available"
                                ],
                                custom_model=st.session_state["custom_model"],
                            )

                # print(st.session_state['use_RealESRGAN'])
                # print(st.session_state['use_LDSR'])
                try:
                    output_images, seeds, info, stats = txt2img(
                        prompt,
                        st.session_state.sampling_steps,
                        sampler_name,
                        st.session_state["batch_count"],
                        st.session_state["batch_size"],
                        cfg_scale,
                        seed,
                        height,
                        width,
                        separate_prompts,
                        normalize_prompt_weights,
                        save_individual_images,
                        save_grid,
                        group_by_prompt,
                        save_as_jpg,
                        st.session_state["use_GFPGAN"],
                        st.session_state["GFPGAN_model"],
                        use_RealESRGAN=st.session_state["use_RealESRGAN"],
                        RealESRGAN_model=st.session_state["RealESRGAN_model"],
                        use_LDSR=st.session_state["use_LDSR"],
                        LDSR_model=st.session_state["LDSR_model"],
                        variant_amount=variant_amount,
                        variant_seed=variant_seed,
                        write_info_files=write_info_files,
                        use_stable_horde=use_stable_horde,
                        stable_horde_key=stable_horde_key,
                    )

                    message.success(
                        "Render Complete: " + info + "; Stats: " + stats, icon="✅"
                    )

                    with gallery_tab:
                        logger.info(seeds)
                        st.session_state["gallery"].text = ""
                        sdGallery(output_images)

                except (
                    StopException,
                    # KeyError
                ):
                    print("Received Streamlit StopException")

                    # reset the page title so the percent doesnt stay on it confusing the user.
                    set_page_title("Stable Diffusion Playground")

                    # this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
                    # use the current col2 first tab to show the preview_img and update it as its generated.
                    # preview_image.image(output_images)


================================================
FILE: scripts/txt2vid.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.

"""
Implementation of Text to Video based on the
https://github.com/nateraw/stable-diffusion-videos
repo and the original gist script from
https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355
"""
from sd_utils import (
    st,
    MemUsageMonitor,
    server_state,
    no_rerun,
    torch_gc,
    custom_models_available,
    RealESRGAN_available,
    GFPGAN_available,
    LDSR_available,
    hc,
    seed_to_int,
    logger,
    slerp,
    optimize_update_preview_frequency,
    load_learned_embed_in_clip,
    load_GFPGAN,
    RealESRGANModel,
    set_page_title,
)


# streamlit imports
from streamlit.runtime.scriptrunner import StopException

# from streamlit.elements import image as STImage

# streamlit components section
from streamlit_server_state import server_state, server_state_lock

# from streamlitextras.threader import lock, trigger_rerun, \
# streamlit_thread, get_thread, \
# last_trigger_time

# other imports

import os, sys, json, re, random, datetime, time, warnings, mimetypes
from PIL import Image
import torch
import numpy as np
import time, inspect, timeit
import torch
from torch import autocast

# from io import BytesIO
import imageio
from slugify import slugify

from diffusers import StableDiffusionPipeline, DiffusionPipeline

# from stable_diffusion_videos import StableDiffusionWalkPipeline

from diffusers.schedulers import (
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    DDPMScheduler,
)

from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion.safety_checker import (
    StableDiffusionSafetyChecker,
)
from diffusers.utils import deprecate
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from typing import Callable, List, Optional, Union
from pathlib import Path
from torchvision.transforms.functional import pil_to_tensor
from torchvision import transforms
import librosa
from PIL import Image
from torchvision.io import write_video
from torchvision import transforms
import torch.nn as nn
from uuid import uuid4


# streamlit components
from custom_components import sygil_suggestions

# Temp imports

# end of imports
# ---------------------------------------------------------------------------------------------------------------

sygil_suggestions.init()

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging

    logging.set_verbosity_error()
except:
    pass

# remove some annoying deprecation warnings that show every now and then.
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)

# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init()
mimetypes.add_type("application/javascript", ".js")


class plugin_info:
    plugname = "txt2vid"
    description = "Text to Image"
    isTab = True
    displayPriority = 1


#
# -----------------------------------------------------------------------------


def txt2vid_generation_callback(step: int, timestep: int, latents: torch.FloatTensor):
    # print ("test")
    # scale and decode the image latents with vae
    cond_latents_2 = 1 / 0.18215 * latents
    image = server_state["pipe"].vae.decode(cond_latents_2)

    # generate output numpy image as uint8
    image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0)
    image2 = transforms.ToPILImage()(image.squeeze_(0))

    st.session_state["preview_image"].image(image2)


def get_timesteps_arr(audio_filepath, offset, duration, fps=30, margin=1.0, smooth=0.0):
    y, sr = librosa.load(audio_filepath, offset=offset, duration=duration)

    # librosa.stft hardcoded defaults...
    # n_fft defaults to 2048
    # hop length is win_length // 4
    # win_length defaults to n_fft
    D = librosa.stft(y, n_fft=2048, hop_length=2048 // 4, win_length=2048)

    # Extract percussive elements
    D_harmonic, D_percussive = librosa.decompose.hpss(D, margin=margin)
    y_percussive = librosa.istft(D_percussive, length=len(y))

    # Get normalized melspectrogram
    spec_raw = librosa.feature.melspectrogram(y=y_percussive, sr=sr)
    spec_max = np.amax(spec_raw, axis=0)
    spec_norm = (spec_max - np.min(spec_max)) / np.ptp(spec_max)

    # Resize cumsum of spec norm to our desired number of interpolation frames
    x_norm = np.linspace(0, spec_norm.shape[-1], spec_norm.shape[-1])
    y_norm = np.cumsum(spec_norm)
    y_norm /= y_norm[-1]
    x_resize = np.linspace(0, y_norm.shape[-1], int(duration * fps))

    T = np.interp(x_resize, x_norm, y_norm)

    # Apply smoothing
    return T * (1 - smooth) + np.linspace(0.0, 1.0, T.shape[0]) * smooth


#
def make_video_pyav(
    frames_or_frame_dir: Union[str, Path, torch.Tensor],
    audio_filepath: Union[str, Path] = None,
    fps: int = 30,
    audio_offset: int = 0,
    audio_duration: int = 2,
    sr: int = 22050,
    output_filepath: Union[str, Path] = "output.mp4",
    glob_pattern: str = "*.png",
):
    """
    TODO - docstring here

    frames_or_frame_dir: (Union[str, Path, torch.Tensor]):
        Either a directory of images, or a tensor of shape (T, C, H, W) in range [0, 255].
    """

    # Torchvision write_video doesn't support pathlib paths
    output_filepath = str(output_filepath)

    if isinstance(frames_or_frame_dir, (str, Path)):
        frames = None
        for img in sorted(Path(frames_or_frame_dir).glob(glob_pattern)):
            frame = pil_to_tensor(Image.open(img)).unsqueeze(0)
            frames = frame if frames is None else torch.cat([frames, frame])
    else:
        frames = frames_or_frame_dir

    # TCHW -> THWC
    frames = frames.permute(0, 2, 3, 1)

    if audio_filepath:
        # Read audio, convert to tensor
        audio, sr = librosa.load(
            audio_filepath,
            sr=sr,
            mono=True,
            offset=audio_offset,
            duration=audio_duration,
        )
        audio_tensor = torch.tensor(audio).unsqueeze(0)

        write_video(
            output_filepath,
            frames,
            fps=fps,
            audio_array=audio_tensor,
            audio_fps=sr,
            audio_codec="aac",
            options={"crf": "10", "pix_fmt": "yuv420p"},
        )
    else:
        write_video(
            output_filepath,
            frames,
            fps=fps,
            options={"crf": "10", "pix_fmt": "yuv420p"},
        )

    return output_filepath


class StableDiffusionWalkPipeline(DiffusionPipeline):
    r"""
    Pipeline for generating videos by interpolating  Stable Diffusion's latent space.
    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
        feature_extractor ([`CLIPFeatureExtractor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPFeatureExtractor,
    ):
        super().__init__()

        if (
            hasattr(scheduler.config, "steps_offset")
            and scheduler.config.steps_offset != 1
        ):
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                " file"
            )
            deprecate(
                "steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False
            )
            new_config = dict(scheduler.config)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )

    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
        r"""
        Enable sliced attention computation.
        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
        in several steps. This is useful to save some memory in exchange for a small speed decrease.
        Args:
            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
                `attention_head_dim` must be a multiple of `slice_size`.
        """
        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            slice_size = self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(slice_size)

    def disable_attention_slicing(self):
        r"""
        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
        back to computing attention in one step.
        """
        # set slice_size = `None` to disable `attention slicing`
        self.enable_attention_slicing(None)

    @torch.no_grad()
    def __call__(
        self,
        prompt: Optional[Union[str, List[str]]] = None,
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        text_embeddings: Optional[torch.FloatTensor] = None,
        **kwargs,
    ):
        r"""
        Function invoked when calling the pipeline for generation.
        Args:
            prompt (`str` or `List[str]`, *optional*, defaults to `None`):
                The prompt or prompts to guide the image generation. If not provided, `text_embeddings` is required.
            height (`int`, *optional*, defaults to 512):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to 512):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
                if `guidance_scale` is less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator`, *optional*):
                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            text_embeddings (`torch.FloatTensor`, *optional*, defaults to `None`):
                Pre-generated text embeddings to be used as inputs for image generation. Can be used in place of
                `prompt` to avoid re-computing the embeddings. If not provided, the embeddings will be generated from
                the supplied `prompt`.
        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """

        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(
                f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
            )

        if (callback_steps is None) or (
            callback_steps is not None
            and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        if text_embeddings is None:
            if isinstance(prompt, str):
                batch_size = 1
            elif isinstance(prompt, list):
                batch_size = len(prompt)
            else:
                raise ValueError(
                    f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
                )

            # get prompt text embeddings
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                removed_text = self.tokenizer.batch_decode(
                    text_input_ids[:, self.tokenizer.model_max_length :]
                )
                print(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )
                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
            text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
        else:
            batch_size = text_embeddings.shape[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        bs_embed, seq_len, _ = text_embeddings.shape
        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
        text_embeddings = text_embeddings.view(
            bs_embed * num_images_per_prompt, seq_len, -1
        )

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""]
            elif type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            max_length = self.tokenizer.model_max_length
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )
            uncond_embeddings = self.text_encoder(
                uncond_input.input_ids.to(self.device)
            )[0]

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = uncond_embeddings.shape[1]
            uncond_embeddings = uncond_embeddings.repeat(
                batch_size, num_images_per_prompt, 1
            )
            uncond_embeddings = uncond_embeddings.view(
                batch_size * num_images_per_prompt, seq_len, -1
            )

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        latents_shape = (
            batch_size * num_images_per_prompt,
            self.unet.in_channels,
            height // 8,
            width // 8,
        )
        latents_dtype = text_embeddings.dtype
        if latents is None:
            if self.device.type == "mps":
                # randn does not exist on mps
                latents = torch.randn(
                    latents_shape,
                    generator=generator,
                    device="cpu",
                    dtype=latents_dtype,
                ).to(self.device)
            else:
                latents = torch.randn(
                    latents_shape,
                    generator=generator,
                    device=self.device,
                    dtype=latents_dtype,
                )
        else:
            if latents.shape != latents_shape:
                raise ValueError(
                    f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"
                )
            latents = latents.to(self.device)

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps)

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        timesteps_tensor = self.scheduler.timesteps.to(self.device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        accepts_eta = "eta" in set(
            inspect.signature(self.scheduler.step).parameters.keys()
        )
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
            # expand the latents if we are doing classifier free guidance
            latent_model_input = (
                torch.cat([latents] * 2) if do_classifier_free_guidance else latents
            )
            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

            # predict the noise residual
            noise_pred = self.unet(
                latent_model_input, t, encoder_hidden_states=text_embeddings
            ).sample

            # perform guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (
                    noise_pred_text - noise_pred_uncond
                )

            # compute the previous noisy sample x_t -> x_t-1
            latents = self.scheduler.step(
                noise_pred, t, latents, **extra_step_kwargs
            ).prev_sample

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                callback(i, t, latents)
            print("test")

        latents = 1 / 0.18215 * latents
        image = self.vae.decode(latents).sample

        image = (image / 2 + 0.5).clamp(0, 1)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()

        if self.safety_checker is not None:
            safety_checker_input = self.feature_extractor(
                self.numpy_to_pil(image), return_tensors="pt"
            ).to(self.device)
            image, has_nsfw_concept = self.safety_checker(
                images=image,
                clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype),
            )
        else:
            has_nsfw_concept = None

        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(
            images=image, nsfw_content_detected=has_nsfw_concept
        )

    def generate_inputs(
        self, prompt_a, prompt_b, seed_a, seed_b, noise_shape, T, batch_size
    ):
        embeds_a = self.embed_text(prompt_a)
        embeds_b = self.embed_text(prompt_b)

        latents_a = self.init_noise(seed_a, noise_shape)
        latents_b = self.init_noise(seed_b, noise_shape)

        batch_idx = 0
        embeds_batch, noise_batch = None, None
        for i, t in enumerate(T):
            embeds = torch.lerp(embeds_a, embeds_b, t)
            noise = slerp(
                device="cuda",
                t=float(t),
                v0=latents_a,
                v1=latents_b,
                DOT_THRESHOLD=0.9995,
            )

            embeds_batch = (
                embeds if embeds_batch is None else torch.cat([embeds_batch, embeds])
            )
            noise_batch = (
                noise if noise_batch is None else torch.cat([noise_batch, noise])
            )
            batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == T.shape[0]
            if not batch_is_ready:
                continue
            yield batch_idx, embeds_batch, noise_batch
            batch_idx += 1
            del embeds_batch, noise_batch
            torch.cuda.empty_cache()
            embeds_batch, noise_batch = None, None

    def make_clip_frames(
        self,
        prompt_a: str,
        prompt_b: str,
        seed_a: int,
        seed_b: int,
        num_interpolation_steps: int = 5,
        save_path: Union[str, Path] = "outputs/",
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        eta: float = 0.0,
        height: int = 512,
        width: int = 512,
        upsample: bool = False,
        batch_size: int = 1,
        image_file_ext: str = ".png",
        T: np.ndarray = None,
        skip: int = 0,
        callback=None,
        callback_steps: int = 1,
    ):
        save_path = Path(save_path)
        save_path.mkdir(parents=True, exist_ok=True)

        T = T if T is not None else np.linspace(0.0, 1.0, num_interpolation_steps)
        if T.shape[0] != num_interpolation_steps:
            raise ValueError(
                f"Unexpected T shape, got {T.shape}, expected dim 0 to be {num_interpolation_steps}"
            )

        if upsample:
            if getattr(self, "upsampler", None) is None:
                self.upsampler = RealESRGANModel.from_pretrained("nateraw/real-esrgan")
            self.upsampler.to(self.device)

        batch_generator = self.generate_inputs(
            prompt_a,
            prompt_b,
            seed_a,
            seed_b,
            (1, self.unet.in_channels, height // 8, width // 8),
            T[skip:],
            batch_size,
        )

        frame_index = skip
        for _, embeds_batch, noise_batch in batch_generator:
            with torch.autocast("cuda"):
                outputs = self(
                    latents=noise_batch,
                    text_embeddings=embeds_batch,
                    height=height,
                    width=width,
                    guidance_scale=guidance_scale,
                    eta=eta,
                    num_inference_steps=num_inference_steps,
                    output_type="pil" if not upsample else "numpy",
                    callback=callback,
                    callback_steps=callback_steps,
                )["images"]

                for image in outputs:
                    frame_filepath = save_path / (
                        f"frame%06d{image_file_ext}" % frame_index
                    )
                    image = image if not upsample else self.upsampler(image)
                    image.save(frame_filepath)
                    frame_index += 1

    def walk(
        self,
        prompt: Optional[List[str]] = None,
        seeds: Optional[List[int]] = None,
        num_interpolation_steps: Optional[
            Union[int, List[int]]
        ] = 5,  # int or list of int
        output_dir: Optional[str] = "./dreams",
        name: Optional[str] = None,
        image_file_ext: Optional[str] = ".png",
        fps: Optional[int] = 30,
        num_inference_steps: Optional[int] = 50,
        guidance_scale: Optional[float] = 7.5,
        eta: Optional[float] = 0.0,
        height: Optional[int] = 512,
        width: Optional[int] = 512,
        upsample: Optional[bool] = False,
        batch_size: Optional[int] = 1,
        resume: Optional[bool] = False,
        audio_filepath: str = None,
        audio_start_sec: Optional[Union[int, float]] = None,
        margin: Optional[float] = 1.0,
        smooth: Optional[float] = 0.0,
        callback=None,
        callback_steps=1,
    ):
        """Generate a video from a sequence of prompts and seeds. Optionally, add audio to the
        video to interpolate to the intensity of the audio.

        Args:
            prompts (Optional[List[str]], optional):
                list of text prompts. Defaults to None.
            seeds (Optional[List[int]], optional):
                list of random seeds corresponding to prompts. Defaults to None.
            num_interpolation_steps (Union[int, List[int]], *optional*):
                How many interpolation steps between each prompt. Defaults to None.
            output_dir (Optional[str], optional):
                Where to save the video. Defaults to './dreams'.
            name (Optional[str], optional):
                Name of the subdirectory of output_dir. Defaults to None.
            image_file_ext (Optional[str], *optional*, defaults to '.png'):
                The extension to use when writing video frames.
            fps (Optional[int], *optional*, defaults to 30):
                The frames per second in the resulting output videos.
            num_inference_steps (Optional[int], *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (Optional[float], *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            eta (Optional[float], *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            height (Optional[int], *optional*, defaults to 512):
                height of the images to generate.
            width (Optional[int], *optional*, defaults to 512):
                width of the images to generate.
            upsample (Optional[bool], *optional*, defaults to False):
                When True, upsamples images with realesrgan.
            batch_size (Optional[int], *optional*, defaults to 1):
                Number of images to generate at once.
            resume (Optional[bool], *optional*, defaults to False):
                When True, resumes from the last frame in the output directory based
                on available prompt config. Requires you to provide the `name` argument.
            audio_filepath (str, *optional*, defaults to None):
                Optional path to an audio file to influence the interpolation rate.
            audio_start_sec (Optional[Union[int, float]], *optional*, defaults to 0):
                Global start time of the provided audio_filepath.
            margin (Optional[float], *optional*, defaults to 1.0):
                Margin from librosa hpss to use for audio interpolation.
            smooth (Optional[float], *optional*, defaults to 0.0):
                Smoothness of the audio interpolation. 1.0 means linear interpolation.

        This function will create sub directories for each prompt and seed pair.

        For example, if you provide the following prompts and seeds:

        ```
        prompts = ['a dog', 'a cat', 'a bird']
        seeds = [1, 2, 3]
        num_interpolation_steps = 5
        output_dir = 'output_dir'
        name = 'name'
        fps = 5
        ```

        Then the following directories will be created:

        ```
        output_dir
        ├── name
        │   ├── name_000000
        │   │   ├── frame000000.png
        │   │   ├── ...
        │   │   ├── frame000004.png
        │   │   ├── name_000000.mp4
        │   ├── name_000001
        │   │   ├── frame000000.png
        │   │   ├── ...
        │   │   ├── frame000004.png
        │   │   ├── name_000001.mp4
        │   ├── ...
        │   ├── name.mp4
        |   |── prompt_config.json
        ```

        Returns:
            str: The resulting video filepath. This video includes all sub directories' video clips.
        """
        if (callback_steps is None) or (
            callback_steps is not None
            and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        # init the output dir
        if type(prompts) == str:
            sanitized_prompt = slugify(prompts)
        else:
            sanitized_prompt = slugify(prompts[0])

        full_path = os.path.join(str(output_dir), str(sanitized_prompt))

        if len(full_path) > 220:
            sanitized_prompt = sanitized_prompt[: 220 - len(full_path)]
            full_path = os.path.join(output_dir, sanitized_prompt)

        os.makedirs(full_path, exist_ok=True)

        # Where the final video of all the clips combined will be saved
        output_filepath = os.path.join(full_path, f"{sanitized_prompt}.mp4")

        # If using same number of interpolation steps between, we turn into list
        if not resume and isinstance(num_interpolation_steps, int):
            num_interpolation_steps = [num_interpolation_steps] * (len(prompts) - 1)

        if not resume:
            audio_start_sec = audio_start_sec or 0

        # Save/reload prompt config
        prompt_config_path = Path(os.path.join(full_path, "prompt_config.json"))
        if not resume:
            prompt_config_path.write_text(
                json.dumps(
                    dict(
                        prompts=prompts,
                        seeds=seeds,
                        num_interpolation_steps=num_interpolation_steps,
                        fps=fps,
                        num_inference_steps=num_inference_steps,
                        guidance_scale=guidance_scale,
                        eta=eta,
                        upsample=upsample,
                        height=height,
                        width=width,
                        audio_filepath=audio_filepath,
                        audio_start_sec=audio_start_sec,
                    ),
                    indent=2,
                    sort_keys=False,
                )
            )
        else:
            data = json.load(open(prompt_config_path))
            prompts = data["prompts"]
            seeds = data["seeds"]
            num_interpolation_steps = data["num_interpolation_steps"]
            fps = data["fps"]
            num_inference_steps = data["num_inference_steps"]
            guidance_scale = data["guidance_scale"]
            eta = data["eta"]
            upsample = data["upsample"]
            height = data["height"]
            width = data["width"]
            audio_filepath = data["audio_filepath"]
            audio_start_sec = data["audio_start_sec"]

        for i, (prompt_a, prompt_b, seed_a, seed_b, num_step) in enumerate(
            zip(prompts, prompts[1:], seeds, seeds[1:], num_interpolation_steps)
        ):
            # {name}_000000 / {name}_000001 / ...
            save_path = Path(f"{full_path}/{name}_{i:06d}")

            # Where the individual clips will be saved
            step_output_filepath = Path(f"{save_path}/{name}_{i:06d}.mp4")

            # Determine if we need to resume from a previous run
            skip = 0
            if resume:
                if step_output_filepath.exists():
                    print(f"Skipping {save_path} because frames already exist")
                    continue

                existing_frames = sorted(save_path.glob(f"*{image_file_ext}"))
                if existing_frames:
                    skip = int(existing_frames[-1].stem[-6:]) + 1
                    if skip + 1 >= num_step:
                        print(f"Skipping {save_path} because frames already exist")
                        continue
                    print(f"Resuming {save_path.name} from frame {skip}")

            audio_offset = audio_start_sec + sum(num_interpolation_steps[:i]) / fps
            audio_duration = num_step / fps

            self.make_clip_frames(
                prompt_a,
                prompt_b,
                seed_a,
                seed_b,
                num_interpolation_steps=num_step,
                save_path=save_path,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
                eta=eta,
                height=height,
                width=width,
                upsample=upsample,
                batch_size=batch_size,
                skip=skip,
                T=get_timesteps_arr(
                    audio_filepath,
                    offset=audio_offset,
                    duration=audio_duration,
                    fps=fps,
                    margin=margin,
                    smooth=smooth,
                    callback=callback,
                    callback_steps=callback_steps,
                )
                if audio_filepath
                else None,
            )
            make_video_pyav(
                save_path,
                audio_filepath=audio_filepath,
                fps=fps,
                output_filepath=step_output_filepath,
                glob_pattern=f"*{image_file_ext}",
                audio_offset=audio_offset,
                audio_duration=audio_duration,
                sr=44100,
            )

        return make_video_pyav(
            full_path,
            audio_filepath=audio_filepath,
            fps=fps,
            audio_offset=audio_start_sec,
            audio_duration=sum(num_interpolation_steps) / fps,
            output_filepath=output_filepath,
            glob_pattern=f"**/*{image_file_ext}",
            sr=44100,
        )

    def embed_text(self, text):
        """Helper to embed some text"""
        with torch.autocast("cuda"):
            text_input = self.tokenizer(
                text,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            with torch.no_grad():
                embed = self.text_encoder(text_input.input_ids.to(self.device))[0]
        return embed

    def init_noise(self, seed, noise_shape):
        """Helper to initialize noise"""
        # randn does not exist on mps, so we create noise on CPU here and move it to the device after initialization
        if self.device.type == "mps":
            noise = torch.randn(
                noise_shape,
                device="cpu",
                generator=torch.Generator(device="cpu").manual_seed(seed),
            ).to(self.device)
        else:
            noise = torch.randn(
                noise_shape,
                device=self.device,
                generator=torch.Generator(device=self.device).manual_seed(seed),
            )
        return noise

    @classmethod
    def from_pretrained(cls, *args, tiled=False, **kwargs):
        """Same as diffusers `from_pretrained` but with tiled option, which makes images tilable"""
        if tiled:

            def patch_conv(**patch):
                cls = nn.Conv2d
                init = cls.__init__

                def __init__(self, *args, **kwargs):
                    return init(self, *args, **kwargs, **patch)

                cls.__init__ = __init__

            patch_conv(padding_mode="circular")

        pipeline = super().from_pretrained(*args, **kwargs)
        pipeline.tiled = tiled
        return pipeline


@torch.no_grad()
def diffuse(
    pipe,
    cond_embeddings,  # text conditioning, should be (1, 77, 768)
    cond_latents,  # image conditioning, should be (1, 4, 64, 64)
    num_inference_steps,
    cfg_scale,
    eta,
    fps=30,
):
    torch_device = cond_latents.get_device()

    # classifier guidance: add the unconditional embedding
    max_length = cond_embeddings.shape[1]  # 77
    uncond_input = pipe.tokenizer(
        [""], padding="max_length", max_length=max_length, return_tensors="pt"
    )
    uncond_embeddings = pipe.text_encoder(uncond_input.input_ids.to(torch_device))[0]
    text_embeddings = torch.cat([uncond_embeddings, cond_embeddings])

    # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
    if isinstance(pipe.scheduler, LMSDiscreteScheduler):
        cond_latents = cond_latents * pipe.scheduler.sigmas[0]

    # init the scheduler
    accepts_offset = "offset" in set(
        inspect.signature(pipe.scheduler.set_timesteps).parameters.keys()
    )
    extra_set_kwargs = {}
    if accepts_offset:
        extra_set_kwargs["offset"] = 1

    pipe.scheduler.set_timesteps(
        num_inference_steps + st.session_state.sampling_steps, **extra_set_kwargs
    )
    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
    # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
    # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
    # and should be between [0, 1]
    accepts_eta = "eta" in set(inspect.signature(pipe.scheduler.step).parameters.keys())
    extra_step_kwargs = {}
    if accepts_eta:
        extra_step_kwargs["eta"] = eta

    step_counter = 0
    inference_counter = 0

    if "current_chunk_speed" not in st.session_state:
        st.session_state["current_chunk_speed"] = 0

    if "previous_chunk_speed_list" not in st.session_state:
        st.session_state["previous_chunk_speed_list"] = [0]
        st.session_state["previous_chunk_speed_list"].append(
            st.session_state["current_chunk_speed"]
        )

    if "update_preview_frequency_list" not in st.session_state:
        st.session_state["update_preview_frequency_list"] = [0]
        st.session_state["update_preview_frequency_list"].append(
            st.session_state["update_preview_frequency"]
        )

    try:
        # diffuse!
        for i, t in enumerate(pipe.scheduler.timesteps):
            start = timeit.default_timer()

            # status_text.text(f"Running step: {step_counter}{total_number_steps} {percent} | {duration:.2f}{speed}")

            # expand the latents for classifier free guidance
            latent_model_input = torch.cat([cond_latents] * 2)
            if isinstance(pipe.scheduler, LMSDiscreteScheduler):
                sigma = pipe.scheduler.sigmas[i]
                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)

            # predict the noise residual
            noise_pred = pipe.unet(
                latent_model_input, t, encoder_hidden_states=text_embeddings
            )["sample"]

            # cfg
            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
            noise_pred = noise_pred_uncond + cfg_scale * (
                noise_pred_text - noise_pred_uncond
            )

            # compute the previous noisy sample x_t -> x_t-1
            if isinstance(pipe.scheduler, LMSDiscreteScheduler):
                cond_latents = pipe.scheduler.step(
                    noise_pred, i, cond_latents, **extra_step_kwargs
                )["prev_sample"]
            else:
                cond_latents = pipe.scheduler.step(
                    noise_pred, t, cond_latents, **extra_step_kwargs
                )["prev_sample"]

            # update the preview image if it is enabled and the frequency matches the step_counter
            if st.session_state["update_preview"]:
                step_counter += 1

                if step_counter == st.session_state["update_preview_frequency"]:
                    if st.session_state.dynamic_preview_frequency:
                        st.session_state["current_chunk_speed"],
                        st.session_state["previous_chunk_speed_list"],
                        st.session_state["update_preview_frequency"],
                        st.session_state[
                            "avg_update_preview_frequency"
                        ] = optimize_update_preview_frequency(
                            st.session_state["current_chunk_speed"],
                            st.session_state["previous_chunk_speed_list"],
                            st.session_state["update_preview_frequency"],
                            st.session_state["update_preview_frequency_list"],
                        )

                    # scale and decode the image latents with vae
                    cond_latents_2 = 1 / 0.18215 * cond_latents
                    image = pipe.vae.decode(cond_latents_2)

                    # generate output numpy image as uint8
                    image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0)
                    image2 = transforms.ToPILImage()(image.squeeze_(0))

                    st.session_state["preview_image"].image(image2)

                    step_counter = 0

            duration = timeit.default_timer() - start

            st.session_state["current_chunk_speed"] = duration

            if duration >= 1:
                speed = "s/it"
            else:
                speed = "it/s"
                duration = 1 / duration

            total_frames = st.session_state.max_duration_in_seconds * fps
            st.session_state.sampling_steps + st.session_state.num_inference_steps

            if i > st.session_state.sampling_steps:
                inference_counter += 1
                inference_percent = int(
                    100
                    * float(
                        inference_counter + 1
                        if inference_counter < num_inference_steps
                        else num_inference_steps
                    )
                    / float(num_inference_steps)
                )
                inference_progress = f"{inference_counter + 1 if inference_counter < num_inference_steps else num_inference_steps}/{num_inference_steps} {inference_percent}% "
            else:
                inference_progress = ""

            total_percent = int(
                100
                * float(
                    i + 1
                    if i + 1 < (num_inference_steps + st.session_state.sampling_steps)
                    else (num_inference_steps + st.session_state.sampling_steps)
                )
                / float((num_inference_steps + st.session_state.sampling_steps))
            )

            percent = int(
                100
                * float(
                    i + 1
                    if i + 1 < num_inference_steps
                    else st.session_state.sampling_steps
                )
                / float(st.session_state.sampling_steps)
            )
            frames_percent = int(
                100
                * float(
                    st.session_state.current_frame
                    if st.session_state.current_frame < total_frames
                    else total_frames
                )
                / float(total_frames)
            )

            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps} "
                    f"{percent if percent < 100 else 100}% {inference_progress}{duration:.2f}{speed} | "
                    f"Frame: {st.session_state.current_frame + 1 if st.session_state.current_frame < total_frames else total_frames}/{total_frames} "
                    f"{frames_percent if frames_percent < 100 else 100}% {st.session_state.frame_duration:.2f}{st.session_state.frame_speed}"
                )

            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    total_percent if total_percent < 100 else 100
                )

                if st.session_state["defaults"].general.show_percent_in_tab_title:
                    set_page_title(
                        f"({percent if percent < 100 else 100}%) Stable Diffusion Playground"
                    )

    except KeyError:
        raise StopException

    # scale and decode the image latents with vae
    cond_latents_2 = 1 / 0.18215 * cond_latents
    image = pipe.vae.decode(cond_latents_2)

    # generate output numpy image as uint8
    image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0)
    image2 = transforms.ToPILImage()(image.squeeze_(0))

    return image2


#
def load_diffusers_model(weights_path, torch_device):
    with server_state_lock["model"]:
        if "model" in server_state:
            del server_state["model"]

    if "textual_inversion" in st.session_state:
        del st.session_state["textual_inversion"]

    try:
        with server_state_lock["pipe"]:
            if "pipe" not in server_state:
                if (
                    "weights_path" in st.session_state
                    and st.session_state["weights_path"] != weights_path
                ):
                    del st.session_state["weights_path"]

                st.session_state["weights_path"] = weights_path
                server_state["float16"] = st.session_state[
                    "defaults"
                ].general.use_float16
                server_state["no_half"] = st.session_state["defaults"].general.no_half
                server_state["optimized"] = st.session_state[
                    "defaults"
                ].general.optimized

                # if folder "models/diffusers/stable-diffusion-v1-4" exists, load the model from there
                if weights_path == "CompVis/stable-diffusion-v1-4":
                    model_path = os.path.join(
                        "models", "diffusers", "stable-diffusion-v1-4"
                    )

                if weights_path == "runwayml/stable-diffusion-v1-5":
                    model_path = os.path.join(
                        "models", "diffusers", "stable-diffusion-v1-5"
                    )
                else:
                    model_path = weights_path

                if not os.path.exists(model_path + "/model_index.json"):
                    server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
                        weights_path,
                        # use_local_file=True,
                        use_auth_token=st.session_state[
                            "defaults"
                        ].general.huggingface_token,
                        torch_dtype=torch.float16
                        if st.session_state["defaults"].general.use_float16
                        else None,
                        revision="fp16"
                        if not st.session_state["defaults"].general.no_half
                        else None,
                        safety_checker=None,  # Very important for videos...lots of false positives while interpolating
                        # custom_pipeline="interpolate_stable_diffusion",
                    )

                    StableDiffusionPipeline.save_pretrained(
                        server_state["pipe"], model_path
                    )
                else:
                    server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
                        model_path,
                        # use_local_file=True,
                        torch_dtype=torch.float16
                        if st.session_state["defaults"].general.use_float16
                        else None,
                        revision="fp16"
                        if not st.session_state["defaults"].general.no_half
                        else None,
                        safety_checker=None,  # Very important for videos...lots of false positives while interpolating
                        # custom_pipeline="interpolate_stable_diffusion",
                    )

                server_state["pipe"].unet.to(torch_device)
                server_state["pipe"].vae.to(torch_device)
                server_state["pipe"].text_encoder.to(torch_device)

                # if st.session_state.defaults.general.enable_attention_slicing:
                server_state["pipe"].enable_attention_slicing()

                if st.session_state.defaults.general.enable_minimal_memory_usage:
                    server_state["pipe"].enable_minimal_memory_usage()

                logger.info("Tx2Vid Model Loaded")
            else:
                # if the float16 or no_half options have changed since the last time the model was loaded then we need to reload the model.
                if (
                    (
                        "float16" in server_state
                        and server_state["float16"]
                        != st.session_state["defaults"].general.use_float16
                    )
                    or (
                        "no_half" in server_state
                        and server_state["no_half"]
                        != st.session_state["defaults"].general.no_half
                    )
                    or (
                        "optimized" in server_state
                        and server_state["optimized"]
                        != st.session_state["defaults"].general.optimized
                    )
                ):
                    del server_state["float16"]
                    del server_state["no_half"]
                    with server_state_lock["pipe"]:
                        del server_state["pipe"]
                        torch_gc()

                    del server_state["optimized"]

                    server_state["float16"] = st.session_state[
                        "defaults"
                    ].general.use_float16
                    server_state["no_half"] = st.session_state[
                        "defaults"
                    ].general.no_half
                    server_state["optimized"] = st.session_state[
                        "defaults"
                    ].general.optimized

                    # with no_rerun:
                    load_diffusers_model(weights_path, torch_device)
                else:
                    logger.info("Tx2Vid Model already Loaded")

    except (EnvironmentError, OSError) as e:
        if (
            "huggingface_token" not in st.session_state
            or st.session_state["defaults"].general.huggingface_token == "None"
        ):
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].error(
                    "You need a huggingface token in order to use the Text to Video tab. Use the Settings page to add your token under the Huggingface section. "
                    "Make sure you save your settings after adding it."
                )
            raise OSError(
                "You need a huggingface token in order to use the Text to Video tab. Use the Settings page to add your token under the Huggingface section. "
                "Make sure you save your settings after adding it."
            )
        else:
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].error(e)


#
def save_video_to_disk(
    frames, seeds, sanitized_prompt, fps=30, save_video=True, outdir="outputs"
):
    if save_video:
        # write video to memory
        # output = io.BytesIO()
        # writer = imageio.get_writer(os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid"), im, extension=".mp4", fps=30)
        # try:
        video_path = os.path.join(
            os.getcwd(),
            outdir,
            "txt2vid",
            f"{seeds}_{sanitized_prompt}{datetime.datetime.now().strftime('%Y%m-%d%H-%M%S-') + str(uuid4())[:8]}.mp4",
        )
        writer = imageio.get_writer(video_path, fps=fps)
        for frame in frames:
            writer.append_data(frame)

        writer.close()
        # except:
        # 	print("Can't save video, skipping.")

    return video_path


#
def txt2vid(
    # --------------------------------------
    # args you probably want to change
    prompts=["blueberry spaghetti", "strawberry spaghetti"],  # prompt to dream about
    gpu: int = st.session_state["defaults"].general.gpu,  # id of the gpu to run on
    # name:str = 'test', # name of this project, for the output directory
    # rootdir:str = st.session_state['defaults'].general.outdir,
    num_steps: int = 200,  # number of steps between each pair of sampled points
    max_duration_in_seconds: int = 30,  # number of frames to write and then exit the script
    num_inference_steps: int = 50,  # more (e.g. 100, 200 etc) can create slightly better images
    cfg_scale: float = 5.0,  # can depend on the prompt. usually somewhere between 3-10 is good
    save_video=True,
    save_video_on_stop=False,
    outdir="outputs",
    do_loop=False,
    use_lerp_for_text=False,
    seeds=None,
    quality: int = 100,  # for jpeg compression of the output images
    eta: float = 0.0,
    width: int = 256,
    height: int = 256,
    weights_path="runwayml/stable-diffusion-v1-5",
    scheduler="klms",  # choices: default, ddim, klms
    disable_tqdm=False,
    # -----------------------------------------------
    beta_start=0.0001,
    beta_end=0.00012,
    beta_schedule="scaled_linear",
    starting_image=None,
    # -----------------------------------------------
    # from new version
    image_file_ext: Optional[str] = ".png",
    fps: Optional[int] = 30,
    upsample: Optional[bool] = False,
    batch_size: Optional[int] = 1,
    resume: Optional[bool] = False,
    audio_filepath: str = None,
    audio_start_sec: Optional[Union[int, float]] = None,
    margin: Optional[float] = 1.0,
    smooth: Optional[float] = 0.0,
):
    """
    prompt = ["blueberry spaghetti", "strawberry spaghetti"], # prompt to dream about
    gpu:int = st.session_state['defaults'].general.gpu, # id of the gpu to run on
    #name:str = 'test', # name of this project, for the output directory
    #rootdir:str = st.session_state['defaults'].general.outdir,
    num_steps:int = 200, # number of steps between each pair of sampled points
    max_duration_in_seconds:int = 10000, # number of frames to write and then exit the script
    num_inference_steps:int = 50, # more (e.g. 100, 200 etc) can create slightly better images
    cfg_scale:float = 5.0, # can depend on the prompt. usually somewhere between 3-10 is good
    do_loop = False,
    use_lerp_for_text = False,
    seed = None,
    quality:int = 100, # for jpeg compression of the output images
    eta:float = 0.0,
    width:int = 256,
    height:int = 256,
    weights_path = "runwayml/stable-diffusion-v1-5",
    scheduler="klms",  # choices: default, ddim, klms
    disable_tqdm = False,
    beta_start = 0.0001,
    beta_end = 0.00012,
    beta_schedule = "scaled_linear"
    """
    mem_mon = MemUsageMonitor("MemMon")
    mem_mon.start()

    seeds = seed_to_int(seeds)

    # We add an extra frame because most
    # of the time the first frame is just the noise.
    # max_duration_in_seconds +=1

    assert torch.cuda.is_available()
    assert height % 8 == 0 and width % 8 == 0
    torch.manual_seed(seeds)
    torch_device = f"cuda:{gpu}"

    if type(seeds) == list:
        prompts = [prompts] * len(seeds)
    else:
        seeds = [seeds, random.randint(0, 2**32 - 1)]

    if type(prompts) == list:
        # init the output dir
        sanitized_prompt = slugify(prompts[0])
    else:
        # init the output dir
        sanitized_prompt = slugify(prompts)

    full_path = os.path.join(
        os.getcwd(),
        st.session_state["defaults"].general.outdir,
        "txt2vid",
        "samples",
        sanitized_prompt,
    )

    if len(full_path) > 220:
        sanitized_prompt = sanitized_prompt[: 220 - len(full_path)]
        full_path = os.path.join(
            os.getcwd(),
            st.session_state["defaults"].general.outdir,
            "txt2vid",
            "samples",
            sanitized_prompt,
        )

    os.makedirs(full_path, exist_ok=True)

    # Write prompt info to file in output dir so we can keep track of what we did
    if st.session_state.write_info_files:
        with open(
            os.path.join(
                full_path,
                f"{slugify(str(seeds))}_config.json"
                if len(prompts) > 1
                else "prompts_config.json",
            ),
            "w",
        ) as outfile:
            outfile.write(
                json.dumps(
                    dict(
                        prompts=prompts,
                        gpu=gpu,
                        num_steps=num_steps,
                        max_duration_in_seconds=max_duration_in_seconds,
                        num_inference_steps=num_inference_steps,
                        cfg_scale=cfg_scale,
                        do_loop=do_loop,
                        use_lerp_for_text=use_lerp_for_text,
                        seeds=seeds,
                        quality=quality,
                        eta=eta,
                        width=width,
                        height=height,
                        weights_path=weights_path,
                        scheduler=scheduler,
                        disable_tqdm=disable_tqdm,
                        beta_start=beta_start,
                        beta_end=beta_end,
                        beta_schedule=beta_schedule,
                    ),
                    indent=2,
                    sort_keys=False,
                )
            )

    # print(scheduler)
    default_scheduler = PNDMScheduler(
        beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    )
    # ------------------------------------------------------------------------------
    # Schedulers
    ddim_scheduler = DDIMScheduler(
        beta_start=beta_start,
        beta_end=beta_end,
        beta_schedule=beta_schedule,
        clip_sample=False,
        set_alpha_to_one=False,
    )

    klms_scheduler = LMSDiscreteScheduler(
        beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    )

    # flaxddims_scheduler = FlaxDDIMScheduler(
    # beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    # )

    # flaxddpms_scheduler = FlaxDDPMScheduler(
    # beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    # )

    # flaxpndms_scheduler = FlaxPNDMScheduler(
    # beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    # )

    ddpms_scheduler = DDPMScheduler(
        beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    )

    SCHEDULERS = dict(
        default=default_scheduler,
        ddim=ddim_scheduler,
        klms=klms_scheduler,
        ddpms=ddpms_scheduler,
        # flaxddims=flaxddims_scheduler,
        # flaxddpms=flaxddpms_scheduler,
        # flaxpndms=flaxpndms_scheduler,
    )

    with no_rerun:
        with st.session_state["progress_bar_text"].container():
            with hc.HyLoader(
                "Loading Models...", hc.Loaders.standard_loaders, index=[0]
            ):
                load_diffusers_model(weights_path, torch_device)

    if "pipe" not in server_state:
        logger.error("wtf")

    server_state["pipe"].scheduler = SCHEDULERS[scheduler]

    server_state["pipe"].use_multiprocessing_for_evaluation = False
    server_state["pipe"].use_multiprocessed_decoding = False

    # if do_loop:
    ##Makes the last prompt loop back to first prompt
    # prompts = [prompts, prompts]
    # seeds = [seeds, seeds]
    # first_seed, *seeds = seeds
    # prompts.append(prompts)
    # seeds.append(first_seed)

    with torch.autocast("cuda"):
        # get the conditional text embeddings based on the prompt
        text_input = server_state["pipe"].tokenizer(
            prompts,
            padding="max_length",
            max_length=server_state["pipe"].tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        cond_embeddings = server_state["pipe"].text_encoder(
            text_input.input_ids.to(torch_device)
        )[0]

    #
    if st.session_state.defaults.general.use_sd_concepts_library:
        prompt_tokens = re.findall("<([a-zA-Z0-9-]+)>", str(prompts))

        if prompt_tokens:
            # compviz
            # tokenizer = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.tokenizer
            # text_encoder = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.transformer

            # diffusers
            tokenizer = st.session_state.pipe.tokenizer
            text_encoder = st.session_state.pipe.text_encoder

            ext = ("pt", "bin")
            # print (prompt_tokens)

            if len(prompt_tokens) > 1:
                for token_name in prompt_tokens:
                    embedding_path = os.path.join(
                        st.session_state["defaults"].general.sd_concepts_library_folder,
                        token_name,
                    )
                    if os.path.exists(embedding_path):
                        for files in os.listdir(embedding_path):
                            if files.endswith(ext):
                                load_learned_embed_in_clip(
                                    f"{os.path.join(embedding_path, files)}",
                                    text_encoder,
                                    tokenizer,
                                    f"<{token_name}>",
                                )
            else:
                embedding_path = os.path.join(
                    st.session_state["defaults"].general.sd_concepts_library_folder,
                    prompt_tokens[0],
                )
                if os.path.exists(embedding_path):
                    for files in os.listdir(embedding_path):
                        if files.endswith(ext):
                            load_learned_embed_in_clip(
                                f"{os.path.join(embedding_path, files)}",
                                text_encoder,
                                tokenizer,
                                f"<{prompt_tokens[0]}>",
                            )

    # sample a source
    init1 = torch.randn(
        (1, server_state["pipe"].unet.in_channels, height // 8, width // 8),
        device=torch_device,
    )

    # iterate the loop
    frames = []
    frame_index = 0

    st.session_state["total_frames_avg_duration"] = []
    st.session_state["total_frames_avg_speed"] = []

    try:
        # code for the new StableDiffusionWalkPipeline implementation.
        start = timeit.default_timer()

        # preview image works but its not the right way to use this, this also do not work properly as it only makes one image and then exits.
        # with torch.autocast("cuda"):
        # StableDiffusionWalkPipeline.__call__(self=server_state["pipe"],
        # prompt=prompts, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=cfg_scale,
        # negative_prompt="", num_images_per_prompt=1, eta=0.0,
        # callback=txt2vid_generation_callback, callback_steps=1,
        # num_interpolation_steps=num_steps,
        # fps=30,
        # image_file_ext = ".png",
        # output_dir=full_path,        # Where images/videos will be saved
        ##name='animals_test',        # Subdirectory of output_dir where images/videos will be saved
        # upsample = False,
        ##do_loop=do_loop,           # Change to True if you want last prompt to loop back to first prompt
        # resume = False,
        # audio_filepath = None,
        # audio_start_sec = None,
        # margin = 1.0,
        # smooth = 0.0,                                                             )

        # works correctly generating all frames but do not show the preview image
        # we also do not have control over the generation and cant stop it until the end of it.
        # with torch.autocast("cuda"):
        # print (prompts)
        # video_path = server_state["pipe"].walk(
        # prompt=prompts,
        # seeds=seeds,
        # num_interpolation_steps=num_steps,
        # height=height,  # use multiples of 64 if > 512. Multiples of 8 if < 512.
        # width=width,   # use multiples of 64 if > 512. Multiples of 8 if < 512.
        # batch_size=4,
        # fps=30,
        # image_file_ext = ".png",
        # eta = 0.0,
        # output_dir=full_path,        # Where images/videos will be saved
        ##name='test',        # Subdirectory of output_dir where images/videos will be saved
        # guidance_scale=cfg_scale,         # Higher adheres to prompt more, lower lets model take the wheel
        # num_inference_steps=num_inference_steps,     # Number of diffusion steps per image generated. 50 is good default
        # upsample = False,
        ##do_loop=do_loop,           # Change to True if you want last prompt to loop back to first prompt
        # resume = False,
        # audio_filepath = None,
        # audio_start_sec = None,
        # margin = 1.0,
        # smooth = 0.0,
        # callback=txt2vid_generation_callback, # our callback function will be called with the arguments callback(step, timestep, latents)
        # callback_steps=1 # our callback function will be called once this many steps are processed in a single frame
        # )

        # old code
        total_frames = st.session_state.max_duration_in_seconds * fps

        while frame_index + 1 <= total_frames:
            st.session_state["frame_duration"] = 0
            st.session_state["frame_speed"] = 0
            st.session_state["current_frame"] = frame_index

            # print(f"Second: {second_count+1}/{max_duration_in_seconds}")

            # sample the destination
            init2 = torch.randn(
                (1, server_state["pipe"].unet.in_channels, height // 8, width // 8),
                device=torch_device,
            )

            for i, t in enumerate(np.linspace(0, 1, num_steps)):
                start = timeit.default_timer()
                logger.info(f"COUNT: {frame_index+1}/{total_frames}")

                if use_lerp_for_text:
                    init = torch.lerp(init1, init2, float(t))
                else:
                    init = slerp(gpu, float(t), init1, init2)

                # init = slerp(gpu, float(t), init1, init2)

                with autocast("cuda"):
                    image = diffuse(
                        server_state["pipe"],
                        cond_embeddings,
                        init,
                        num_inference_steps,
                        cfg_scale,
                        eta,
                        fps=fps,
                    )

                if (
                    st.session_state["save_individual_images"]
                    and not st.session_state["use_GFPGAN"]
                    and not st.session_state["use_RealESRGAN"]
                ):
                    # im = Image.fromarray(image)
                    outpath = os.path.join(full_path, "frame%06d.png" % frame_index)
                    image.save(outpath, quality=quality)

                    # send the image to the UI to update it
                    # st.session_state["preview_image"].image(im)

                    # append the frames to the frames list so we can use them later.
                    frames.append(np.asarray(image))

                #
                # try:
                # if st.session_state["use_GFPGAN"] and server_state["GFPGAN"] is not None and not st.session_state["use_RealESRGAN"]:
                if (
                    st.session_state["use_GFPGAN"]
                    and server_state["GFPGAN"] is not None
                ):
                    # print("Running GFPGAN on image ...")
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running GFPGAN on image ..."
                        )
                    # skip_save = True # #287 >_>
                    torch_gc()
                    cropped_faces, restored_faces, restored_img = server_state[
                        "GFPGAN"
                    ].enhance(
                        np.array(image)[:, :, ::-1],
                        has_aligned=False,
                        only_center_face=False,
                        paste_back=True,
                    )
                    gfpgan_sample = restored_img[:, :, ::-1]
                    gfpgan_image = Image.fromarray(gfpgan_sample)

                    outpath = os.path.join(full_path, "frame%06d.png" % frame_index)
                    gfpgan_image.save(outpath, quality=quality)

                    # append the frames to the frames list so we can use them later.
                    frames.append(np.asarray(gfpgan_image))
                    try:
                        st.session_state["preview_image"].image(gfpgan_image)
                    except KeyError:
                        logger.error("Cant get session_state, skipping image preview.")
                # except (AttributeError, KeyError):
                # print("Cant perform GFPGAN, skipping.")

                # increase frame_index counter.
                frame_index += 1

                st.session_state["current_frame"] = frame_index

                duration = timeit.default_timer() - start

                if duration >= 1:
                    speed = "s/it"
                else:
                    speed = "it/s"
                    duration = 1 / duration

                st.session_state["frame_duration"] = duration
                st.session_state["frame_speed"] = speed
                if frame_index + 1 > total_frames:
                    break

            init1 = init2

        # save the video after the generation is done.
        video_path = save_video_to_disk(
            frames, seeds, sanitized_prompt, save_video=save_video, outdir=outdir
        )

    except StopException:
        # reset the page title so the percent doesnt stay on it confusing the user.
        set_page_title("Stable Diffusion Playground")

        if save_video_on_stop:
            logger.info("Streamlit Stop Exception Received. Saving video")
            video_path = save_video_to_disk(
                frames, seeds, sanitized_prompt, save_video=save_video, outdir=outdir
            )
        else:
            video_path = None

    # if video_path and "preview_video" in st.session_state:
    ## show video preview on the UI
    # st.session_state["preview_video"].video(open(video_path, 'rb').read())

    mem_max_used, mem_total = mem_mon.read_and_stop()
    time_diff = time.time() - start

    info = f"""
        {prompts}
        Sampling Steps: {num_steps}, Sampler: {scheduler}, CFG scale: {cfg_scale}, Seed: {seeds}, Max Duration In Seconds: {max_duration_in_seconds}""".strip()
    stats = f"""
        Took { round(time_diff, 2) }s total ({ round(time_diff/(max_duration_in_seconds),2) }s per image)
        Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%"""

    return video_path, seeds, info, stats


#
def layout():
    with st.form("txt2vid-inputs"):
        st.session_state["generation_mode"] = "txt2vid"

        input_col1, generate_col1 = st.columns([10, 1])
        with input_col1:
            # prompt = st.text_area("Input Text","")
            placeholder = "A corgi wearing a top hat as an oil painting."
            prompt = st.text_area("Input Text", "", placeholder=placeholder, height=54)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].general.enable_suggestions:
                    sygil_suggestions.suggestion_area(placeholder)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].admin.global_negative_prompt:
                    prompt += f"### {st.session_state['defaults'].admin.global_negative_prompt}"

        # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
        generate_col1.write("")
        generate_col1.write("")
        generate_button = generate_col1.form_submit_button("Generate")

        # creating the page layout using columns
        col1, col2, col3 = st.columns([2, 5, 2], gap="large")

        with col1:
            width = st.slider(
                "Width:",
                min_value=st.session_state["defaults"].txt2vid.width.min_value,
                max_value=st.session_state["defaults"].txt2vid.width.max_value,
                value=st.session_state["defaults"].txt2vid.width.value,
                step=st.session_state["defaults"].txt2vid.width.step,
            )
            height = st.slider(
                "Height:",
                min_value=st.session_state["defaults"].txt2vid.height.min_value,
                max_value=st.session_state["defaults"].txt2vid.height.max_value,
                value=st.session_state["defaults"].txt2vid.height.value,
                step=st.session_state["defaults"].txt2vid.height.step,
            )
            cfg_scale = st.number_input(
                "CFG (Classifier Free Guidance Scale):",
                min_value=st.session_state["defaults"].txt2vid.cfg_scale.min_value,
                value=st.session_state["defaults"].txt2vid.cfg_scale.value,
                step=st.session_state["defaults"].txt2vid.cfg_scale.step,
                help="How strongly the image should follow the prompt.",
            )

            # uploaded_images = st.file_uploader("Upload Image", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp"],
            # help="Upload an image which will be used for the image to image generation.")
            seed = st.text_input(
                "Seed:",
                value=st.session_state["defaults"].txt2vid.seed,
                help=" The seed to use, if left blank a random seed will be generated.",
            )
            # batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2vid.batch_count,
            # step=1, help="How many iterations or batches of images to generate in total.")
            # batch_size = st.slider("Batch size", min_value=1, max_value=250, value=st.session_state['defaults'].txt2vid.batch_size, step=1,
            # help="How many images are at once in a batch.\
            # It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
            # Default: 1")

            st.session_state["max_duration_in_seconds"] = st.number_input(
                "Max Duration In Seconds:",
                value=st.session_state["defaults"].txt2vid.max_duration_in_seconds,
                help="Specify the max duration in seconds you want your video to be.",
            )

            st.session_state["fps"] = st.number_input(
                "Frames per Second (FPS):",
                value=st.session_state["defaults"].txt2vid.fps,
                help="Specify the frame rate of the video.",
            )

            with st.expander("Preview Settings"):
                # st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2vid.update_preview,
                # help="If enabled the image preview will be updated during the generation instead of at the end. \
                # You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
                # By default this is enabled and the frequency is set to 1 step.")

                st.session_state["update_preview"] = st.session_state[
                    "defaults"
                ].general.update_preview
                st.session_state["update_preview_frequency"] = st.number_input(
                    "Update Image Preview Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].txt2vid.update_preview_frequency,
                    help="Frequency in steps at which the the preview image is updated. By default the frequency \
				                                                               is set to 1 step.",
                )

                st.session_state["dynamic_preview_frequency"] = st.checkbox(
                    "Dynamic Preview Frequency",
                    value=st.session_state[
                        "defaults"
                    ].txt2vid.dynamic_preview_frequency,
                    help="This option tries to find the best value at which we can update \
					                                               the preview image during generation while minimizing the impact it has in performance. Default: True",
                )

            #

    with col2:
        preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])

        with preview_tab:
            # st.write("Image")
            # Image for testing
            # image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
            # new_image = image.resize((175, 240))
            # preview_image = st.image(image)

            # create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
            st.session_state["preview_image"] = st.empty()

            st.session_state["loading"] = st.empty()

            st.session_state["progress_bar_text"] = st.empty()
            st.session_state["progress_bar"] = st.empty()

            # generate_video = st.empty()
            st.session_state["preview_video"] = st.empty()
            preview_video = st.session_state["preview_video"]

            message = st.empty()

        with gallery_tab:
            st.write(
                "Here should be the image gallery, if I could make a grid in streamlit."
            )

    with col3:
        # If we have custom models available on the "models/custom"
        # folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
        custom_models_available()
        if server_state["CustomModel_available"]:
            custom_model = st.selectbox(
                "Custom Model:",
                st.session_state["defaults"].txt2vid.custom_models_list,
                index=st.session_state["defaults"].txt2vid.custom_models_list.index(
                    st.session_state["defaults"].txt2vid.default_model
                ),
                help="Select the model you want to use. This option is only available if you have custom models \
				                            on your 'models/custom' folder. The model name that will be shown here is the same as the name\
				                            the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
				                        will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.5",
            )
        else:
            custom_model = "runwayml/stable-diffusion-v1-5"

        # st.session_state["weights_path"] = custom_model
        # else:
        # custom_model = "runwayml/stable-diffusion-v1-5"
        # st.session_state["weights_path"] = f"CompVis/{slugify(custom_model.lower())}"

        st.session_state.sampling_steps = st.number_input(
            "Sampling Steps",
            value=st.session_state["defaults"].txt2vid.sampling_steps.value,
            min_value=st.session_state["defaults"].txt2vid.sampling_steps.min_value,
            step=st.session_state["defaults"].txt2vid.sampling_steps.step,
            help="Number of steps between each pair of sampled points",
        )

        st.session_state.num_inference_steps = st.number_input(
            "Inference Steps:",
            value=st.session_state["defaults"].txt2vid.num_inference_steps.value,
            min_value=st.session_state[
                "defaults"
            ].txt2vid.num_inference_steps.min_value,
            step=st.session_state["defaults"].txt2vid.num_inference_steps.step,
            help="Higher values (e.g. 100, 200 etc) can create better images.",
        )

        # sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a",  "k_heun", "PLMS", "DDIM"]
        # sampler_name = st.selectbox("Sampling method", sampler_name_list,
        # index=sampler_name_list.index(st.session_state['defaults'].txt2vid.default_sampler), help="Sampling method to use. Default: k_euler")
        scheduler_name_list = [
            "klms",
            "ddim",
            "ddpms",
            # "flaxddims", "flaxddpms", "flaxpndms"
        ]
        scheduler_name = st.selectbox(
            "Scheduler:",
            scheduler_name_list,
            index=scheduler_name_list.index(
                st.session_state["defaults"].txt2vid.scheduler_name
            ),
            help="Scheduler to use. Default: klms",
        )

        beta_scheduler_type_list = ["scaled_linear", "linear"]
        beta_scheduler_type = st.selectbox(
            "Beta Schedule Type:",
            beta_scheduler_type_list,
            index=beta_scheduler_type_list.index(
                st.session_state["defaults"].txt2vid.beta_scheduler_type
            ),
            help="Schedule Type to use. Default: linear",
        )

        # basic_tab, advanced_tab = st.tabs(["Basic", "Advanced"])

        # with basic_tab:
        # summit_on_enter = st.radio("Submit on enter?", ("Yes", "No"), horizontal=True,
        # help="Press the Enter key to summit, when 'No' is selected you can use the Enter key to write multiple lines.")

        with st.expander("Advanced"):
            with st.expander("Output Settings"):
                st.session_state["separate_prompts"] = st.checkbox(
                    "Create Prompt Matrix.",
                    value=st.session_state["defaults"].txt2vid.separate_prompts,
                    help="Separate multiple prompts using the `|` character, and get all combinations of them.",
                )
                st.session_state["normalize_prompt_weights"] = st.checkbox(
                    "Normalize Prompt Weights.",
                    value=st.session_state["defaults"].txt2vid.normalize_prompt_weights,
                    help="Ensure the sum of all weights add up to 1.0",
                )

                st.session_state["save_individual_images"] = st.checkbox(
                    "Save individual images.",
                    value=st.session_state["defaults"].txt2vid.save_individual_images,
                    help="Save each image generated before any filter or enhancement is applied.",
                )

                st.session_state["save_video"] = st.checkbox(
                    "Save video",
                    value=st.session_state["defaults"].txt2vid.save_video,
                    help="Save a video with all the images generated as frames at the end of the generation.",
                )

                save_video_on_stop = st.checkbox(
                    "Save video on Stop",
                    value=st.session_state["defaults"].txt2vid.save_video_on_stop,
                    help="Save a video with all the images generated as frames when we hit the stop button during a generation.",
                )

                st.session_state["group_by_prompt"] = st.checkbox(
                    "Group results by prompt",
                    value=st.session_state["defaults"].txt2vid.group_by_prompt,
                    help="Saves all the images with the same prompt into the same folder. When using a prompt \
				                                                  matrix each prompt combination will have its own folder.",
                )

                st.session_state["write_info_files"] = st.checkbox(
                    "Write Info file",
                    value=st.session_state["defaults"].txt2vid.write_info_files,
                    help="Save a file next to the image with informartion about the generation.",
                )

                st.session_state["do_loop"] = st.checkbox(
                    "Do Loop",
                    value=st.session_state["defaults"].txt2vid.do_loop,
                    help="Loop the prompt making two prompts from a single one.",
                )

                st.session_state["use_lerp_for_text"] = st.checkbox(
                    "Use Lerp Instead of Slerp",
                    value=st.session_state["defaults"].txt2vid.use_lerp_for_text,
                    help="Uses torch.lerp() instead of slerp. When interpolating between related prompts. \
				                                                    e.g. 'a lion in a grassy meadow' -> 'a bear in a grassy meadow' tends to keep the meadow \
				                                                    the whole way through when lerped, but slerping will often find a path where the meadow \
				                                                    disappears in the middle",
                )

                st.session_state["save_as_jpg"] = st.checkbox(
                    "Save samples as jpg",
                    value=st.session_state["defaults"].txt2vid.save_as_jpg,
                    help="Saves the images as jpg instead of png.",
                )

            #
            if "GFPGAN_available" not in st.session_state:
                GFPGAN_available()

            if "RealESRGAN_available" not in st.session_state:
                RealESRGAN_available()

            if "LDSR_available" not in st.session_state:
                LDSR_available()

            if (
                st.session_state["GFPGAN_available"]
                or st.session_state["RealESRGAN_available"]
                or st.session_state["LDSR_available"]
            ):
                with st.expander("Post-Processing"):
                    face_restoration_tab, upscaling_tab = st.tabs(
                        ["Face Restoration", "Upscaling"]
                    )
                    with face_restoration_tab:
                        # GFPGAN used for face restoration
                        if st.session_state["GFPGAN_available"]:
                            # with st.expander("Face Restoration"):
                            # if st.session_state["GFPGAN_available"]:
                            # with st.expander("GFPGAN"):
                            st.session_state["use_GFPGAN"] = st.checkbox(
                                "Use GFPGAN",
                                value=st.session_state["defaults"].txt2vid.use_GFPGAN,
                                help="Uses the GFPGAN model to improve faces after the generation.\
						                                                                 This greatly improve the quality and consistency of faces but uses\
						                                                                 extra VRAM. Disable if you need the extra VRAM.",
                            )

                            st.session_state["GFPGAN_model"] = st.selectbox(
                                "GFPGAN model",
                                st.session_state["GFPGAN_models"],
                                index=st.session_state["GFPGAN_models"].index(
                                    st.session_state["defaults"].general.GFPGAN_model
                                ),
                            )

                            # st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')

                        else:
                            st.session_state["use_GFPGAN"] = False

                    with upscaling_tab:
                        st.session_state["us_upscaling"] = st.checkbox(
                            "Use Upscaling",
                            value=st.session_state["defaults"].txt2vid.use_upscaling,
                        )
                        # RealESRGAN and LDSR used for upscaling.
                        if (
                            st.session_state["RealESRGAN_available"]
                            or st.session_state["LDSR_available"]
                        ):
                            upscaling_method_list = []
                            if st.session_state["RealESRGAN_available"]:
                                upscaling_method_list.append("RealESRGAN")
                            if st.session_state["LDSR_available"]:
                                upscaling_method_list.append("LDSR")

                            st.session_state["upscaling_method"] = st.selectbox(
                                "Upscaling Method",
                                upscaling_method_list,
                                index=upscaling_method_list.index(
                                    st.session_state[
                                        "defaults"
                                    ].general.upscaling_method
                                )
                                if st.session_state["defaults"].general.upscaling_method
                                in upscaling_method_list
                                else 0,
                            )

                            if st.session_state["RealESRGAN_available"]:
                                with st.expander("RealESRGAN"):
                                    if (
                                        st.session_state["upscaling_method"]
                                        == "RealESRGAN"
                                        and st.session_state["us_upscaling"]
                                    ):
                                        st.session_state["use_RealESRGAN"] = True
                                    else:
                                        st.session_state["use_RealESRGAN"] = False

                                    st.session_state["RealESRGAN_model"] = st.selectbox(
                                        "RealESRGAN model",
                                        st.session_state["RealESRGAN_models"],
                                        index=st.session_state[
                                            "RealESRGAN_models"
                                        ].index(
                                            st.session_state[
                                                "defaults"
                                            ].general.RealESRGAN_model
                                        ),
                                    )
                            else:
                                st.session_state["use_RealESRGAN"] = False
                                st.session_state[
                                    "RealESRGAN_model"
                                ] = "RealESRGAN_x4plus"

                            #
                            if st.session_state["LDSR_available"]:
                                with st.expander("LDSR"):
                                    if (
                                        st.session_state["upscaling_method"] == "LDSR"
                                        and st.session_state["us_upscaling"]
                                    ):
                                        st.session_state["use_LDSR"] = True
                                    else:
                                        st.session_state["use_LDSR"] = False

                                    st.session_state["LDSR_model"] = st.selectbox(
                                        "LDSR model",
                                        st.session_state["LDSR_models"],
                                        index=st.session_state["LDSR_models"].index(
                                            st.session_state[
                                                "defaults"
                                            ].general.LDSR_model
                                        ),
                                    )

                                    st.session_state[
                                        "ldsr_sampling_steps"
                                    ] = st.number_input(
                                        "Sampling Steps",
                                        value=st.session_state[
                                            "defaults"
                                        ].txt2vid.LDSR_config.sampling_steps,
                                        help="",
                                    )

                                    st.session_state["preDownScale"] = st.number_input(
                                        "PreDownScale",
                                        value=st.session_state[
                                            "defaults"
                                        ].txt2vid.LDSR_config.preDownScale,
                                        help="",
                                    )

                                    st.session_state["postDownScale"] = st.number_input(
                                        "postDownScale",
                                        value=st.session_state[
                                            "defaults"
                                        ].txt2vid.LDSR_config.postDownScale,
                                        help="",
                                    )

                                    downsample_method_list = ["Nearest", "Lanczos"]
                                    st.session_state[
                                        "downsample_method"
                                    ] = st.selectbox(
                                        "Downsample Method",
                                        downsample_method_list,
                                        index=downsample_method_list.index(
                                            st.session_state[
                                                "defaults"
                                            ].txt2vid.LDSR_config.downsample_method
                                        ),
                                    )

                            else:
                                st.session_state["use_LDSR"] = False
                                st.session_state["LDSR_model"] = "model"

            with st.expander("Variant"):
                st.session_state["variant_amount"] = st.number_input(
                    "Variant Amount:",
                    value=st.session_state["defaults"].txt2vid.variant_amount.value,
                    min_value=st.session_state[
                        "defaults"
                    ].txt2vid.variant_amount.min_value,
                    max_value=st.session_state[
                        "defaults"
                    ].txt2vid.variant_amount.max_value,
                    step=st.session_state["defaults"].txt2vid.variant_amount.step,
                )

                st.session_state["variant_seed"] = st.text_input(
                    "Variant Seed:",
                    value=st.session_state["defaults"].txt2vid.seed,
                    help="The seed to use when generating a variant, if left blank a random seed will be generated.",
                )

            # st.session_state["beta_start"] = st.slider("Beta Start:", value=st.session_state['defaults'].txt2vid.beta_start.value,
            # min_value=st.session_state['defaults'].txt2vid.beta_start.min_value,
            # max_value=st.session_state['defaults'].txt2vid.beta_start.max_value,
            # step=st.session_state['defaults'].txt2vid.beta_start.step, format=st.session_state['defaults'].txt2vid.beta_start.format)
            # st.session_state["beta_end"] = st.slider("Beta End:", value=st.session_state['defaults'].txt2vid.beta_end.value,
            # min_value=st.session_state['defaults'].txt2vid.beta_end.min_value, max_value=st.session_state['defaults'].txt2vid.beta_end.max_value,
            # step=st.session_state['defaults'].txt2vid.beta_end.step, format=st.session_state['defaults'].txt2vid.beta_end.format)

    if generate_button:
        # print("Loading models")
        # load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
        # load_models(False, st.session_state["use_GFPGAN"], True, st.session_state["RealESRGAN_model"])
        # with no_rerun:
        if st.session_state["use_GFPGAN"]:
            if "GFPGAN" in server_state:
                logger.info("GFPGAN already loaded")
            else:
                with col2:
                    with hc.HyLoader(
                        "Loading Models...", hc.Loaders.standard_loaders, index=[0]
                    ):
                        # Load GFPGAN
                        if os.path.exists(
                            st.session_state["defaults"].general.GFPGAN_dir
                        ):
                            try:
                                load_GFPGAN()
                                logger.info("Loaded GFPGAN")
                            except Exception:
                                import traceback

                                logger.error("Error loading GFPGAN:", file=sys.stderr)
                                logger.error(traceback.format_exc(), file=sys.stderr)
        else:
            if "GFPGAN" in server_state:
                del server_state["GFPGAN"]

        # try:
        # run video generation
        video, seed, info, stats = txt2vid(
            prompts=prompt,
            gpu=st.session_state["defaults"].general.gpu,
            num_steps=st.session_state.sampling_steps,
            max_duration_in_seconds=st.session_state.max_duration_in_seconds,
            num_inference_steps=st.session_state.num_inference_steps,
            cfg_scale=cfg_scale,
            save_video_on_stop=save_video_on_stop,
            outdir=st.session_state["defaults"].general.outdir,
            do_loop=st.session_state["do_loop"],
            use_lerp_for_text=st.session_state["use_lerp_for_text"],
            seeds=seed,
            quality=100,
            eta=0.0,
            width=width,
            height=height,
            weights_path=custom_model,
            scheduler=scheduler_name,
            disable_tqdm=False,
            beta_start=st.session_state["defaults"].txt2vid.beta_start.value,
            beta_end=st.session_state["defaults"].txt2vid.beta_end.value,
            beta_schedule=beta_scheduler_type,
            starting_image=None,
            fps=st.session_state.fps,
        )

        if video and save_video_on_stop:
            if os.path.exists(video):  # temporary solution to bypass exception
                # show video preview on the UI after we hit the stop button
                # currently not working as session_state is cleared on StopException
                preview_video.video(open(video, "rb").read())

        # message.success('Done!', icon="✅")
        message.success("Render Complete: " + info + "; Stats: " + stats, icon="✅")

        # history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']

        # if 'latestVideos' in st.session_state:
        # for i in video:
        ##push the new image to the list of latest images and remove the oldest one
        ##remove the last index from the list\
        # st.session_state['latestVideos'].pop()
        ##add the new image to the start of the list
        # st.session_state['latestVideos'].insert(0, i)
        # PlaceHolder.empty()

        # with PlaceHolder.container():
        # col1, col2, col3 = st.columns(3)
        # col1_cont = st.container()
        # col2_cont = st.container()
        # col3_cont = st.container()

        # with col1_cont:
        # with col1:
        # st.image(st.session_state['latestVideos'][0])
        # st.image(st.session_state['latestVideos'][3])
        # st.image(st.session_state['latestVideos'][6])
        # with col2_cont:
        # with col2:
        # st.image(st.session_state['latestVideos'][1])
        # st.image(st.session_state['latestVideos'][4])
        # st.image(st.session_state['latestVideos'][7])
        # with col3_cont:
        # with col3:
        # st.image(st.session_state['latestVideos'][2])
        # st.image(st.session_state['latestVideos'][5])
        # st.image(st.session_state['latestVideos'][8])
        # historyGallery = st.empty()

        ## check if output_images length is the same as seeds length
        # with gallery_tab:
        # st.markdown(createHTMLGallery(video,seed), unsafe_allow_html=True)

        # st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]

        # except (StopException, KeyError):
        # print(f"Received Streamlit StopException")


================================================
FILE: scripts/webui.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
import argparse, os, sys, re, requests, json, time

import cv2

from logger import logger, set_logger_verbosity, quiesce_logger
from perlin import perlinNoise
from frontend.frontend import draw_gradio_ui
from frontend.job_manager import JobManager, JobInfo
from frontend.image_metadata import ImageMetadata
from frontend.ui_functions import resize_image

parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
    "--ckpt",
    type=str,
    default="models/ldm/stable-diffusion-v1/model.ckpt",
    help="path to checkpoint of model",
)
parser.add_argument(
    "--cli",
    type=str,
    help="don't launch web server, take Python function kwargs from this file.",
    default=None,
)
parser.add_argument(
    "--config",
    type=str,
    default="configs/stable-diffusion/v1-inference.yaml",
    help="path to config which constructs model",
)
parser.add_argument(
    "--defaults",
    type=str,
    help="path to configuration file providing UI defaults, uses same format as cli parameter",
    default="configs/webui/webui.yaml",
)
parser.add_argument(
    "--esrgan-cpu", action="store_true", help="run ESRGAN on cpu", default=False
)
parser.add_argument(
    "--esrgan-gpu",
    type=int,
    help="run ESRGAN on specific gpu (overrides --gpu)",
    default=0,
)
parser.add_argument(
    "--extra-models-cpu",
    action="store_true",
    help="run extra models (GFGPAN/ESRGAN) on cpu",
    default=False,
)
parser.add_argument(
    "--extra-models-gpu",
    action="store_true",
    help="run extra models (GFGPAN/ESRGAN) on gpu",
    default=False,
)
parser.add_argument(
    "--gfpgan-cpu", action="store_true", help="run GFPGAN on cpu", default=False
)
parser.add_argument(
    "--gfpgan-dir",
    type=str,
    help="GFPGAN directory",
    default=("./models/gfpgan" if os.path.exists("./models/gfpgan") else "./GFPGAN"),
)  # i disagree with where you're putting it but since all guidefags are doing it this way, there you go
parser.add_argument(
    "--gfpgan-gpu",
    type=int,
    help="run GFPGAN on specific gpu (overrides --gpu) ",
    default=0,
)
parser.add_argument(
    "--gpu", type=int, help="choose which GPU to use if you have multiple", default=0
)
parser.add_argument(
    "--grid-format",
    type=str,
    help="png for lossless png files; jpg:quality for lossy jpeg; webp:quality for lossy webp, or webp:-compression for lossless webp",
    default="jpg:95",
)
parser.add_argument(
    "--inbrowser",
    action="store_true",
    help="automatically launch the interface in a new tab on the default browser",
    default=False,
)
parser.add_argument(
    "--ldsr-dir",
    type=str,
    help="LDSR directory",
    default=("./models/ldsr" if os.path.exists("./models/ldsr") else "./LDSR"),
)
parser.add_argument(
    "--n_rows",
    type=int,
    default=-1,
    help="rows in the grid; use -1 for autodetect and 0 for n_rows to be same as batch_size (default: -1)",
)
parser.add_argument(
    "--no-half",
    action="store_true",
    help="do not switch the model to 16-bit floats",
    default=False,
)
parser.add_argument(
    "--no-progressbar-hiding",
    action="store_true",
    help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware accleration in browser)",
    default=False,
)
parser.add_argument(
    "--no-verify-input",
    action="store_true",
    help="do not verify input to check if it's too long",
    default=False,
)
parser.add_argument(
    "--optimized-turbo",
    action="store_true",
    help="alternative optimization mode that does not save as much VRAM but runs siginificantly faster",
)
parser.add_argument(
    "--optimized",
    action="store_true",
    help="load the model onto the device piecemeal instead of all at once to reduce VRAM usage at the cost of performance",
)
parser.add_argument(
    "--outdir_scn2img",
    type=str,
    nargs="?",
    help="dir to write scn2img results to (overrides --outdir)",
    default=None,
)
parser.add_argument(
    "--outdir_img2img",
    type=str,
    nargs="?",
    help="dir to write img2img results to (overrides --outdir)",
    default=None,
)
parser.add_argument(
    "--outdir_imglab",
    type=str,
    nargs="?",
    help="dir to write imglab results to (overrides --outdir)",
    default=None,
)
parser.add_argument(
    "--outdir_txt2img",
    type=str,
    nargs="?",
    help="dir to write txt2img results to (overrides --outdir)",
    default=None,
)
parser.add_argument(
    "--outdir", type=str, nargs="?", help="dir to write results to", default=None
)
parser.add_argument(
    "--filename_format", type=str, nargs="?", help="filenames format", default=None
)
parser.add_argument(
    "--port",
    type=int,
    help="choose the port for the gradio webserver to use",
    default=7860,
)
parser.add_argument(
    "--precision",
    type=str,
    help="evaluate at this precision",
    choices=["full", "autocast"],
    default="autocast",
)
parser.add_argument(
    "--realesrgan-dir",
    type=str,
    help="RealESRGAN directory",
    default=(
        "./models/realesrgan"
        if os.path.exists("./models/realesrgan")
        else "./RealESRGAN"
    ),
)
parser.add_argument(
    "--realesrgan-model",
    type=str,
    help="Upscaling model for RealESRGAN",
    default=("RealESRGAN_x4plus"),
)
parser.add_argument(
    "--save-metadata",
    action="store_true",
    help="Store generation parameters in the output png. Drop saved png into Image Lab to read parameters",
    default=False,
)
parser.add_argument(
    "--share-password",
    type=str,
    help="Sharing is open by default, use this to set a password. Username: webui",
    default=None,
)
parser.add_argument(
    "--share",
    action="store_true",
    help="Should share your server on gradio.app, this allows you to use the UI from your mobile app",
    default=False,
)
parser.add_argument(
    "--skip-grid",
    action="store_true",
    help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
    default=False,
)
parser.add_argument(
    "--skip-save",
    action="store_true",
    help="do not save indiviual samples. For speed measurements.",
    default=False,
)
parser.add_argument(
    "--no-job-manager",
    action="store_true",
    help="Don't use the experimental job manager on top of gradio",
    default=False,
)
parser.add_argument(
    "--max-jobs",
    type=int,
    help="Maximum number of concurrent 'generate' commands",
    default=1,
)
parser.add_argument(
    "--tiling", action="store_true", help="Generate tiling images", default=False
)
parser.add_argument(
    "-v",
    "--verbosity",
    action="count",
    default=0,
    help="The default logging level is ERROR or higher. This value increases the amount of logging seen in your screen",
)
parser.add_argument(
    "-q",
    "--quiet",
    action="count",
    default=0,
    help="The default logging level is ERROR or higher. This value decreases the amount of logging seen in your screen",
)
parser.add_argument(
    "--bridge",
    action="store_true",
    help="don't launch web server, but make this instance into a Horde bridge.",
    default=False,
)
parser.add_argument(
    "--horde_api_key",
    action="store",
    required=False,
    type=str,
    help="The API key corresponding to the owner of this Horde instance",
)
parser.add_argument(
    "--horde_name",
    action="store",
    required=False,
    type=str,
    help="The server name for the Horde. It will be shown to the world and there can be only one.",
)
parser.add_argument(
    "--horde_url",
    action="store",
    required=False,
    type=str,
    help="The SH Horde URL. Where the bridge will pickup prompts and send the finished generations.",
)
parser.add_argument(
    "--horde_priority_usernames",
    type=str,
    action="append",
    required=False,
    help="Usernames which get priority use in this horde instance. The owner's username is always in this list.",
)
parser.add_argument(
    "--horde_max_power",
    type=int,
    required=False,
    help="How much power this instance has to generate pictures. Min: 2",
)
parser.add_argument(
    "--horde_sfw",
    action="store_true",
    required=False,
    help="Set to true if you do not want this worker generating NSFW images.",
)
parser.add_argument(
    "--horde_blacklist",
    nargs="+",
    required=False,
    help="List the words that you want to blacklist.",
)
parser.add_argument(
    "--horde_censorlist",
    nargs="+",
    required=False,
    help="List the words that you want to censor.",
)
parser.add_argument(
    "--horde_censor_nsfw",
    action="store_true",
    required=False,
    help="Set to true if you want this bridge worker to censor NSFW images.",
)
opt = parser.parse_args()

# Should not be needed anymore
# os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"   # see issue #152
# all selected gpus, can probably be done nicer
# if opt.extra_models_gpu:
#    gpus = set([opt.gpu, opt.esrgan_gpu, opt.gfpgan_gpu])
#    os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(g) for g in set(gpus))
# else:
#    os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.gpu)

import gradio as gr
import k_diffusion as K
import math
import mimetypes
import numpy as np
import pynvml
import random
import threading, asyncio
import time
import torch
import torch.nn as nn
import yaml
import copy
from typing import List, Union, Callable
from pathlib import Path
from collections import namedtuple
from functools import partial

# tell the user which GPU the code is actually using
if os.getenv("SD_WEBUI_DEBUG", "False").lower() in ("true", "1", "y"):
    gpu_in_use = opt.gpu
    # prioritize --esrgan-gpu and --gfpgan-gpu over --gpu, as stated in the option info
    if opt.esrgan_gpu != opt.gpu:
        gpu_in_use = opt.esrgan_gpu
    elif opt.gfpgan_gpu != opt.gpu:
        gpu_in_use = opt.gfpgan_gpu
    print(
        "Starting on GPU {selected_gpu_name}".format(
            selected_gpu_name=torch.cuda.get_device_name(gpu_in_use)
        )
    )

from contextlib import nullcontext
from einops import rearrange, repeat
from itertools import islice
from omegaconf import OmegaConf
from PIL import Image, ImageFont, ImageDraw, ImageFilter, ImageOps, ImageChops
from io import BytesIO
import base64
import re
from torch import autocast
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from ldm.util import instantiate_from_config


# add global options to models
def patch_conv(**patch):
    cls = torch.nn.Conv2d
    init = cls.__init__

    def __init__(self, *args, **kwargs):
        return init(self, *args, **kwargs, **patch)

    cls.__init__ = __init__


if opt.tiling:
    patch_conv(padding_mode="circular")
    print("patched for tiling")

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.

    from transformers import logging

    logging.set_verbosity_error()
except:
    pass

from diffusers.pipelines.stable_diffusion.safety_checker import (
    StableDiffusionSafetyChecker,
)
from transformers import AutoFeatureExtractor

# load safety model
safety_model_id = "CompVis/stable-diffusion-safety-checker"
safety_feature_extractor = None
safety_checker = None

# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init()
mimetypes.add_type("application/javascript", ".js")

# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
opt_f = 8

LANCZOS = Image.Resampling.LANCZOS if hasattr(Image, "Resampling") else Image.LANCZOS
invalid_filename_chars = '<>:"/\|?*\n'

GFPGAN_dir = opt.gfpgan_dir
RealESRGAN_dir = opt.realesrgan_dir
LDSR_dir = opt.ldsr_dir

if opt.optimized_turbo:
    opt.optimized = True

if opt.no_job_manager:
    job_manager = None
else:
    job_manager = JobManager(opt.max_jobs)
    opt.max_jobs += 1  # Leave a free job open for button clicks

# should probably be moved to a settings menu in the UI at some point
grid_format = [s.lower() for s in opt.grid_format.split(":")]
grid_lossless = False
grid_quality = 100
if grid_format[0] == "png":
    grid_ext = "png"
    grid_format = "png"
elif grid_format[0] in ["jpg", "jpeg"]:
    grid_quality = int(grid_format[1]) if len(grid_format) > 1 else 100
    grid_ext = "jpg"
    grid_format = "jpeg"
elif grid_format[0] == "webp":
    grid_quality = int(grid_format[1]) if len(grid_format) > 1 else 100
    grid_ext = "webp"
    grid_format = "webp"
    if grid_quality < 0:  # e.g. webp:-100 for lossless mode
        grid_lossless = True
        grid_quality = abs(grid_quality)


def chunk(it, size):
    it = iter(it)
    return iter(lambda: tuple(islice(it, size)), ())


def load_model_from_config(config, ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    if len(m) > 0 and verbose:
        print("missing keys:")
        print(m)
    if len(u) > 0 and verbose:
        print("unexpected keys:")
        print(u)

    model.cuda()
    model.eval()
    return model


def load_sd_from_config(ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    return sd


def crash(e, s):
    global model
    global device

    print(s, "\n", e)
    try:
        del model
        del device
    except:
        try:
            del device
        except:
            pass
        pass

    print("exiting...calling os._exit(0)")
    t = threading.Timer(0.25, os._exit, args=[0])
    t.start()


class MemUsageMonitor(threading.Thread):
    stop_flag = False
    max_usage = 0
    total = -1

    def __init__(self, name):
        threading.Thread.__init__(self)
        self.name = name

    def run(self):
        try:
            pynvml.nvmlInit()
        except:
            print(
                f"[{self.name}] Unable to initialize NVIDIA management. No memory stats. \n"
            )
            return
        print(f"[{self.name}] Recording max memory usage...\n")
        # check if we're using a scoped-down GPU environment (pynvml does not listen to CUDA_VISIBLE_DEVICES)
        # so that we can measure memory on the correct GPU
        try:
            isinstance(int(os.environ["CUDA_VISIBLE_DEVICES"]), int)
            handle = pynvml.nvmlDeviceGetHandleByIndex(
                int(os.environ["CUDA_VISIBLE_DEVICES"])
            )
        except (KeyError, ValueError) as pynvmlHandleError:
            if os.getenv("SD_WEBUI_DEBUG", "False").lower() in ("true", "1", "y"):
                print("[MemMon][WARNING]", pynvmlHandleError)
                print(
                    "[MemMon][INFO]",
                    "defaulting to monitoring memory on the default gpu (set via --gpu flag)",
                )
            handle = pynvml.nvmlDeviceGetHandleByIndex(opt.gpu)
        self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
        while not self.stop_flag:
            m = pynvml.nvmlDeviceGetMemoryInfo(handle)
            self.max_usage = max(self.max_usage, m.used)
            # print(self.max_usage)
            time.sleep(0.1)
        print(f"[{self.name}] Stopped recording.\n")
        pynvml.nvmlShutdown()

    def read(self):
        return self.max_usage, self.total

    def stop(self):
        self.stop_flag = True

    def read_and_stop(self):
        self.stop_flag = True
        return self.max_usage, self.total


class CFGMaskedDenoiser(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.inner_model = model

    def forward(self, x, sigma, uncond, cond, cond_scale, mask, x0, xi):
        x_in = x
        x_in = torch.cat([x_in] * 2)
        sigma_in = torch.cat([sigma] * 2)
        cond_in = torch.cat([uncond, cond])
        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
        denoised = uncond + (cond - uncond) * cond_scale

        if mask is not None:
            assert x0 is not None
            img_orig = x0
            mask_inv = 1.0 - mask
            denoised = (img_orig * mask_inv) + (mask * denoised)

        return denoised


class CFGDenoiser(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.inner_model = model

    def forward(self, x, sigma, uncond, cond, cond_scale):
        x_in = torch.cat([x] * 2)
        sigma_in = torch.cat([sigma] * 2)
        cond_in = torch.cat([uncond, cond])
        uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
        return uncond + (cond - uncond) * cond_scale


class KDiffusionSampler:
    def __init__(self, m, sampler):
        self.model = m
        self.model_wrap = K.external.CompVisDenoiser(m)
        self.schedule = sampler

    def get_sampler_name(self):
        return self.schedule

    def sample(
        self,
        S,
        conditioning,
        batch_size,
        shape,
        verbose,
        unconditional_guidance_scale,
        unconditional_conditioning,
        eta,
        x_T,
        img_callback: Callable = None,
    ):
        sigmas = self.model_wrap.get_sigmas(S)
        x = x_T * sigmas[0]
        model_wrap_cfg = CFGDenoiser(self.model_wrap)

        samples_ddim = K.sampling.__dict__[f"sample_{self.schedule}"](
            model_wrap_cfg,
            x,
            sigmas,
            extra_args={
                "cond": conditioning,
                "uncond": unconditional_conditioning,
                "cond_scale": unconditional_guidance_scale,
            },
            disable=False,
            callback=partial(KDiffusionSampler.img_callback_wrapper, img_callback),
        )

        return samples_ddim, None

    @classmethod
    def img_callback_wrapper(cls, callback: Callable, *args):
        """Converts a KDiffusion callback to the standard img_callback"""
        if callback:
            arg_dict = args[0]
            callback(image_sample=arg_dict["denoised"], iter_num=arg_dict["i"])


def create_random_tensors(shape, seeds):
    xs = []
    for seed in seeds:
        torch.manual_seed(seed)

        # randn results depend on device; gpu and cpu get different results for same seed;
        # the way I see it, it's better to do this on CPU, so that everyone gets same result;
        # but the original script had it like this so i do not dare change it for now because
        # it will break everyone's seeds.
        xs.append(torch.randn(shape, device=device))
    x = torch.stack(xs)
    return x


def torch_gc():
    torch.cuda.empty_cache()
    torch.cuda.ipc_collect()


def load_LDSR(checking=False):
    model_name = "model"
    yaml_name = "project"
    model_path = os.path.join(LDSR_dir, model_name + ".ckpt")
    yaml_path = os.path.join(LDSR_dir, yaml_name + ".yaml")
    if not os.path.isfile(model_path):
        raise Exception("LDSR model not found at path " + model_path)
    if not os.path.isfile(yaml_path):
        raise Exception("LDSR model not found at path " + yaml_path)
    if checking is True:
        return True

    sys.path.append(os.path.abspath(LDSR_dir))
    from LDSR import LDSR

    LDSRObject = LDSR(model_path, yaml_path)
    return LDSRObject


def load_GFPGAN(checking=False):
    model_name = "GFPGANv1.3"
    model_path = os.path.join(GFPGAN_dir, model_name + ".pth")
    if not os.path.isfile(model_path):
        raise Exception("GFPGAN model not found at path " + model_path)
    if checking is True:
        return True
    sys.path.append(os.path.abspath(GFPGAN_dir))
    from gfpgan import GFPGANer

    if opt.gfpgan_cpu or opt.extra_models_cpu:
        instance = GFPGANer(
            model_path=model_path,
            upscale=1,
            arch="clean",
            channel_multiplier=2,
            bg_upsampler=None,
            device=torch.device("cpu"),
        )
    elif opt.extra_models_gpu:
        instance = GFPGANer(
            model_path=model_path,
            upscale=1,
            arch="clean",
            channel_multiplier=2,
            bg_upsampler=None,
            device=torch.device(f"cuda:{opt.gfpgan_gpu}"),
        )
    else:
        instance = GFPGANer(
            model_path=model_path,
            upscale=1,
            arch="clean",
            channel_multiplier=2,
            bg_upsampler=None,
            device=torch.device(f"cuda:{opt.gpu}"),
        )
    return instance


def load_RealESRGAN(model_name: str, checking=False):
    from basicsr.archs.rrdbnet_arch import RRDBNet

    RealESRGAN_models = {
        "RealESRGAN_x4plus": RRDBNet(
            num_in_ch=3,
            num_out_ch=3,
            num_feat=64,
            num_block=23,
            num_grow_ch=32,
            scale=4,
        ),
        "RealESRGAN_x4plus_anime_6B": RRDBNet(
            num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4
        ),
    }

    model_path = os.path.join(RealESRGAN_dir, model_name + ".pth")
    if not os.path.isfile(model_path):
        raise Exception(model_name + ".pth not found at path " + model_path)
    if checking is True:
        return True
    sys.path.append(os.path.abspath(RealESRGAN_dir))
    from realesrgan import RealESRGANer

    if opt.esrgan_cpu or opt.extra_models_cpu:
        instance = RealESRGANer(
            scale=2,
            model_path=model_path,
            model=RealESRGAN_models[model_name],
            pre_pad=0,
            half=False,
        )  # cpu does not support half
        instance.device = torch.device("cpu")
        instance.model.to("cpu")
    elif opt.extra_models_gpu:
        instance = RealESRGANer(
            scale=2,
            model_path=model_path,
            model=RealESRGAN_models[model_name],
            pre_pad=0,
            half=not opt.no_half,
            gpu_id=opt.esrgan_gpu,
        )
    else:
        instance = RealESRGANer(
            scale=2,
            model_path=model_path,
            model=RealESRGAN_models[model_name],
            pre_pad=0,
            half=not opt.no_half,
        )
    instance.model.name = model_name
    return instance


GFPGAN = None
if os.path.exists(GFPGAN_dir):
    try:
        GFPGAN = load_GFPGAN(checking=True)
        print("Found GFPGAN")
    except Exception:
        import traceback

        print("Error loading GFPGAN:", file=sys.stderr)
        print(traceback.format_exc(), file=sys.stderr)

RealESRGAN = None


def try_loading_RealESRGAN(model_name: str, checking=False):
    global RealESRGAN
    if os.path.exists(RealESRGAN_dir):
        try:
            RealESRGAN = load_RealESRGAN(
                model_name, checking
            )  # TODO: Should try to load both models before giving up
            if checking is True:
                print("Found RealESRGAN")
                return True
            print("Loaded RealESRGAN with model " + RealESRGAN.model.name)
        except Exception:
            import traceback

            print("Error loading RealESRGAN:", file=sys.stderr)
            print(traceback.format_exc(), file=sys.stderr)


try_loading_RealESRGAN("RealESRGAN_x4plus", checking=True)

LDSR = None


def try_loading_LDSR(model_name: str, checking=False):
    global LDSR
    if os.path.exists(LDSR_dir):
        try:
            LDSR = load_LDSR(
                checking=True
            )  # TODO: Should try to load both models before giving up
            if checking is True:
                print("Found LDSR")
                return True
            print("Latent Diffusion Super Sampling (LDSR) model loaded")
        except Exception:
            import traceback

            print("Error loading LDSR:", file=sys.stderr)
            print(traceback.format_exc(), file=sys.stderr)
    else:
        print(
            "LDSR not found at path, please make sure you have cloned the LDSR repo to ./models/ldsr/"
        )


try_loading_LDSR("model", checking=True)


def load_SD_model():
    if opt.optimized:
        sd = load_sd_from_config(opt.ckpt)
        li, lo = [], []
        for key, v_ in sd.items():
            sp = key.split(".")
            if (sp[0]) == "model":
                if "input_blocks" in sp:
                    li.append(key)
                elif "middle_block" in sp:
                    li.append(key)
                elif "time_embed" in sp:
                    li.append(key)
                else:
                    lo.append(key)
        for key in li:
            sd["model1." + key[6:]] = sd.pop(key)
        for key in lo:
            sd["model2." + key[6:]] = sd.pop(key)

        config = OmegaConf.load("optimizedSD/v1-inference.yaml")
        device = (
            torch.device(f"cuda:{opt.gpu}")
            if torch.cuda.is_available()
            else torch.device("cpu")
        )

        model = instantiate_from_config(config.modelUNet)
        _, _ = model.load_state_dict(sd, strict=False)
        model.cuda()
        model.eval()
        model.turbo = opt.optimized_turbo

        modelCS = instantiate_from_config(config.modelCondStage)
        _, _ = modelCS.load_state_dict(sd, strict=False)
        modelCS.cond_stage_model.device = device
        modelCS.eval()

        modelFS = instantiate_from_config(config.modelFirstStage)
        _, _ = modelFS.load_state_dict(sd, strict=False)
        modelFS.eval()

        del sd

        if not opt.no_half:
            model = model.half()
            modelCS = modelCS.half()
            modelFS = modelFS.half()
        return model, modelCS, modelFS, device, config
    else:
        config = OmegaConf.load(opt.config)
        model = load_model_from_config(config, opt.ckpt)

        device = (
            torch.device(f"cuda:{opt.gpu}")
            if torch.cuda.is_available()
            else torch.device("cpu")
        )
        model = (model if opt.no_half else model.half()).to(device)
    return model, device, config


if opt.optimized:
    model, modelCS, modelFS, device, config = load_SD_model()
else:
    model, device, config = load_SD_model()


def load_embeddings(fp):
    if fp is not None and hasattr(model, "embedding_manager"):
        model.embedding_manager.load(fp.name)


def get_font(fontsize):
    fonts = ["arial.ttf", "DejaVuSans.ttf"]
    for font_name in fonts:
        try:
            return ImageFont.truetype(font_name, fontsize)
        except OSError:
            pass

    # ImageFont.load_default() is practically unusable as it only supports
    # latin1, so raise an exception instead if no usable font was found
    raise Exception(f"No usable font found (tried {', '.join(fonts)})")


def image_grid(imgs, batch_size, force_n_rows=None, captions=None):
    if force_n_rows is not None:
        rows = force_n_rows
    elif opt.n_rows > 0:
        rows = opt.n_rows
    elif opt.n_rows == 0:
        rows = batch_size
    else:
        rows = math.sqrt(len(imgs))
        rows = round(rows)

    cols = math.ceil(len(imgs) / rows)

    w, h = imgs[0].size
    grid = Image.new("RGB", size=(cols * w, rows * h), color="black")

    fnt = get_font(30)

    for i, img in enumerate(imgs):
        grid.paste(img, box=(i % cols * w, i // cols * h))
        if captions and i < len(captions):
            d = ImageDraw.Draw(grid)
            size = d.textbbox(
                (0, 0), captions[i], font=fnt, stroke_width=2, align="center"
            )
            d.multiline_text(
                (i % cols * w + w / 2, i // cols * h + h - size[3]),
                captions[i],
                font=fnt,
                fill=(255, 255, 255),
                stroke_width=2,
                stroke_fill=(0, 0, 0),
                anchor="mm",
                align="center",
            )

    return grid


def seed_to_int(s):
    if type(s) is int:
        return s
    if s is None or s == "":
        return random.randint(0, 2**32 - 1)
    n = abs(int(s) if s.isdigit() else random.Random(s).randint(0, 2**32 - 1))
    while n >= 2**32:
        n = n >> 32
    return n


def draw_prompt_matrix(im, width, height, all_prompts):
    def wrap(text, d, font, line_length):
        lines = [""]
        for word in text.split():
            line = f"{lines[-1]} {word}".strip()
            if d.textlength(line, font=font) <= line_length:
                lines[-1] = line
            else:
                lines.append(word)
        return "\n".join(lines)

    def draw_texts(pos, x, y, texts, sizes):
        for i, (text, size) in enumerate(zip(texts, sizes)):
            active = pos & (1 << i) != 0

            if not active:
                text = "\u0336".join(text) + "\u0336"

            d.multiline_text(
                (x, y + size[1] / 2),
                text,
                font=fnt,
                fill=color_active if active else color_inactive,
                anchor="mm",
                align="center",
            )

            y += size[1] + line_spacing

    fontsize = (width + height) // 25
    line_spacing = fontsize // 2
    fnt = get_font(fontsize)
    color_active = (0, 0, 0)
    color_inactive = (153, 153, 153)

    pad_top = height // 4
    pad_left = width * 3 // 4 if len(all_prompts) > 2 else 0

    cols = im.width // width
    rows = im.height // height

    prompts = all_prompts[1:]

    result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
    result.paste(im, (pad_left, pad_top))

    d = ImageDraw.Draw(result)

    boundary = math.ceil(len(prompts) / 2)
    prompts_horiz = [wrap(x, d, fnt, width) for x in prompts[:boundary]]
    prompts_vert = [wrap(x, d, fnt, pad_left) for x in prompts[boundary:]]

    sizes_hor = [
        (x[2] - x[0], x[3] - x[1])
        for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_horiz]
    ]
    sizes_ver = [
        (x[2] - x[0], x[3] - x[1])
        for x in [d.multiline_textbbox((0, 0), x, font=fnt) for x in prompts_vert]
    ]
    hor_text_height = sum([x[1] + line_spacing for x in sizes_hor]) - line_spacing
    ver_text_height = sum([x[1] + line_spacing for x in sizes_ver]) - line_spacing

    for col in range(cols):
        x = pad_left + width * col + width / 2
        y = pad_top / 2 - hor_text_height / 2

        draw_texts(col, x, y, prompts_horiz, sizes_hor)

    for row in range(rows):
        x = pad_left / 2
        y = pad_top + height * row + height / 2 - ver_text_height / 2

        draw_texts(row, x, y, prompts_vert, sizes_ver)

    return result


def check_prompt_length(prompt, comments):
    """this function tests if prompt is too long, and if so, adds a message to comments"""

    tokenizer = (model if not opt.optimized else modelCS).cond_stage_model.tokenizer
    max_length = (model if not opt.optimized else modelCS).cond_stage_model.max_length

    info = (model if not opt.optimized else modelCS).cond_stage_model.tokenizer(
        [prompt],
        truncation=True,
        max_length=max_length,
        return_overflowing_tokens=True,
        padding="max_length",
        return_tensors="pt",
    )
    ovf = info["overflowing_tokens"][0]
    overflowing_count = ovf.shape[0]
    if overflowing_count == 0:
        return

    vocab = {v: k for k, v in tokenizer.get_vocab().items()}
    overflowing_words = [vocab.get(int(x), "") for x in ovf]
    overflowing_text = tokenizer.convert_tokens_to_string("".join(overflowing_words))

    comments.append(
        f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n"
    )


def save_sample(
    image,
    sample_path_i,
    filename,
    jpg_sample,
    write_info_files,
    write_sample_info_to_log_file,
    prompt_matrix,
    init_img,
    uses_loopback,
    uses_random_seed_loopback,
    skip_save,
    skip_grid,
    sort_samples,
    sampler_name,
    ddim_eta,
    n_iter,
    batch_size,
    i,
    denoising_strength,
    resize_mode,
    skip_metadata=False,
):
    """saves the image according to selected parameters. Expects to find generation parameters on image, set by ImageMetadata.set_on_image()"""
    metadata = ImageMetadata.get_from_image(image)
    if not skip_metadata and metadata is None:
        print(
            "No metadata passed in to save. Set metadata on the image before calling save_sample using the ImageMetadata.set_on_image() function."
        )
        skip_metadata = True
    filename_i = os.path.join(sample_path_i, filename)
    if not jpg_sample:
        if opt.save_metadata and not skip_metadata:
            image.save(f"{filename_i}.png", pnginfo=metadata.as_png_info())
        else:
            image.save(f"{filename_i}.png")
    else:
        image.save(f"{filename_i}.jpg", "jpeg", quality=100, optimize=True)
    if write_info_files or write_sample_info_to_log_file:
        # toggles differ for txt2img vs. img2img:
        offset = 0 if init_img is None else 2
        toggles = []
        if prompt_matrix:
            toggles.append(0)
        if metadata.normalize_prompt_weights:
            toggles.append(1)
        if init_img is not None:
            if uses_loopback:
                toggles.append(2)
            if uses_random_seed_loopback:
                toggles.append(3)
        if not skip_save:
            toggles.append(2 + offset)
        if not skip_grid:
            toggles.append(3 + offset)
        if sort_samples:
            toggles.append(4 + offset)
        if write_info_files:
            toggles.append(5 + offset)
        if write_sample_info_to_log_file:
            toggles.append(6 + offset)
        if metadata.GFPGAN:
            toggles.append(7 + offset)

        info_dict = dict(
            target="txt2img" if init_img is None else "img2img",
            prompt=metadata.prompt,
            ddim_steps=metadata.steps,
            toggles=toggles,
            sampler_name=sampler_name,
            ddim_eta=ddim_eta,
            n_iter=n_iter,
            batch_size=batch_size,
            cfg_scale=metadata.cfg_scale,
            seed=metadata.seed,
            width=metadata.width,
            height=metadata.height,
        )
        if init_img is not None:
            # Not yet any use for these, but they bloat up the files:
            # info_dict["init_img"] = init_img
            # info_dict["init_mask"] = init_mask
            info_dict["denoising_strength"] = denoising_strength
            info_dict["resize_mode"] = resize_mode
        if write_info_files:
            with open(f"{filename_i}.yaml", "w", encoding="utf8") as f:
                yaml.dump(info_dict, f, allow_unicode=True, width=10000)

        if write_sample_info_to_log_file:
            ignore_list = ["prompt", "target", "toggles", "ddim_eta", "batch_size"]
            rename_dict = {
                "ddim_steps": "steps",
                "n_iter": "number",
                "sampler_name": "sampler",
            }  # changes the name of parameters to match with dynamic parameters
            sample_log_path = os.path.join(sample_path_i, "log.yaml")
            log_dump = info_dict.get(
                "prompt"
            )  # making sure the first item that is listed in the txt is the prompt text
            for key, value in info_dict.items():
                if key in ignore_list:
                    continue
                found_key = rename_dict.get(key)

                if (
                    key == "cfg_scale"
                ):  # adds zeros to to cfg_scale necessary for dynamic params
                    value = str(value).zfill(2)

                if found_key:
                    key = found_key
                log_dump += f" {key} {value}"

            log_dump = (
                log_dump + " \n"
            )  # space at the end for dynamic params to accept the last param
            with open(sample_log_path, "a", encoding="utf8") as log_file:
                log_file.write(log_dump)


def get_next_sequence_number(path, prefix=""):
    """
    Determines and returns the next sequence number to use when saving an
    image in the specified directory.

    If a prefix is given, only consider files whose names start with that
    prefix, and strip the prefix from filenames before extracting their
    sequence number.

    The sequence starts at 0.
    """
    # Because when running in bridge-mode, we do not have a dir
    if opt.bridge:
        return 0
    result = -1
    for p in Path(path).iterdir():
        if p.name.endswith((".png", ".jpg")) and p.name.startswith(prefix):
            tmp = p.name[len(prefix) :]
            try:
                result = max(int(tmp.split("-")[0]), result)
            except ValueError:
                pass
    return result + 1


def oxlamon_matrix(prompt, seed, n_iter, batch_size):
    pattern = re.compile(r"(,\s){2,}")

    class PromptItem:
        def __init__(self, text, parts, item):
            self.text = text
            self.parts = parts
            if item:
                self.parts.append(item)

    def clean(txt):
        return re.sub(pattern, ", ", txt)

    def getrowcount(txt):
        for data in re.finditer(".*?\\((.*?)\\).*", txt):
            if data:
                return len(data.group(1).split("|"))
            break
        return None

    def repliter(txt):
        for data in re.finditer(".*?\\((.*?)\\).*", txt):
            if data:
                r = data.span(1)
                for item in data.group(1).split("|"):
                    yield (
                        clean(txt[: r[0] - 1] + item.strip() + txt[r[1] + 1 :]),
                        item.strip(),
                    )
            break

    def iterlist(items):
        outitems = []
        for item in items:
            for newitem, newpart in repliter(item.text):
                outitems.append(PromptItem(newitem, item.parts.copy(), newpart))

        return outitems

    def getmatrix(prompt):
        dataitems = [PromptItem(prompt[1:].strip(), [], None)]
        while True:
            newdataitems = iterlist(dataitems)
            if len(newdataitems) == 0:
                return dataitems
            dataitems = newdataitems

    def classToArrays(items, seed, n_iter):
        texts = []
        parts = []
        seeds = []

        for item in items:
            itemseed = seed
            for i in range(n_iter):
                texts.append(item.text)
                parts.append(f"Seed: {itemseed}\n" + "\n".join(item.parts))
                seeds.append(itemseed)
                itemseed += 1

        return seeds, texts, parts

    all_seeds, all_prompts, prompt_matrix_parts = classToArrays(
        getmatrix(prompt), seed, n_iter
    )
    n_iter = math.ceil(len(all_prompts) / batch_size)

    needrows = getrowcount(prompt)
    if needrows:
        xrows = math.sqrt(len(all_prompts))
        xrows = round(xrows)
        # if columns is to much
        cols = math.ceil(len(all_prompts) / xrows)
        if cols > needrows * 4:
            needrows *= 2

    return all_seeds, n_iter, prompt_matrix_parts, all_prompts, needrows


def perform_masked_image_restoration(
    image,
    init_img,
    init_mask,
    mask_blur_strength,
    mask_restore,
    use_RealESRGAN,
    RealESRGAN,
):
    if not mask_restore:
        return image
    else:
        init_mask = init_mask.filter(ImageFilter.GaussianBlur(mask_blur_strength))
        init_mask = init_mask.convert("L")
        init_img = init_img.convert("RGB")
        image = image.convert("RGB")

        if use_RealESRGAN and RealESRGAN is not None:
            output, img_mode = RealESRGAN.enhance(np.array(init_mask, dtype=np.uint8))
            init_mask = Image.fromarray(output)
            init_mask = init_mask.convert("L")

            output, img_mode = RealESRGAN.enhance(np.array(init_img, dtype=np.uint8))
            init_img = Image.fromarray(output)
            init_img = init_img.convert("RGB")

        image = Image.composite(init_img, image, init_mask)

        return image


def perform_color_correction(img_rgb, correction_target_lab, do_color_correction):
    try:
        from skimage import exposure
    except:
        print("Install scikit-image to perform color correction")
        return img_rgb

    if not do_color_correction:
        return img_rgb
    if correction_target_lab is None:
        return img_rgb

    return Image.fromarray(
        cv2.cvtColor(
            exposure.match_histograms(
                cv2.cvtColor(np.asarray(img_rgb), cv2.COLOR_RGB2LAB),
                correction_target_lab,
                channel_axis=2,
            ),
            cv2.COLOR_LAB2RGB,
        ).astype("uint8")
    )


def process_images(
    outpath,
    func_init,
    func_sample,
    prompt,
    seed,
    sampler_name,
    skip_grid,
    skip_save,
    batch_size,
    n_iter,
    steps,
    cfg_scale,
    width,
    height,
    prompt_matrix,
    filter_nsfw,
    use_GFPGAN,
    use_RealESRGAN,
    realesrgan_model_name,
    fp,
    ddim_eta=0.0,
    do_not_save_grid=False,
    normalize_prompt_weights=True,
    init_img=None,
    init_mask=None,
    keep_mask=False,
    mask_blur_strength=3,
    mask_restore=False,
    denoising_strength=0.75,
    resize_mode=None,
    uses_loopback=False,
    uses_random_seed_loopback=False,
    sort_samples=True,
    write_info_files=True,
    write_sample_info_to_log_file=False,
    jpg_sample=False,
    variant_amount=0.0,
    variant_seed=None,
    imgProcessorTask=False,
    job_info: JobInfo = None,
    do_color_correction=False,
    correction_target=None,
):
    """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""

    def numpy_to_pil(images):
        """
        Convert a numpy image or a batch of images to a PIL image.
        """
        if images.ndim == 3:
            images = images[None, ...]
        images = (images * 255).round().astype("uint8")
        pil_images = [Image.fromarray(image) for image in images]

        return pil_images

    # load replacement of nsfw content
    def load_replacement(x):
        try:
            hwc = x.shape
            y = Image.open("images/nsfw.jpeg").convert("RGB").resize((hwc[1], hwc[0]))
            y = (np.array(y) / 255.0).astype(x.dtype)
            assert y.shape == x.shape
            return y
        except Exception:
            return x

    # check and replace nsfw content
    def check_safety(x_image):
        global safety_feature_extractor, safety_checker
        if safety_feature_extractor is None:
            safety_feature_extractor = AutoFeatureExtractor.from_pretrained(
                safety_model_id
            )
            safety_checker = StableDiffusionSafetyChecker.from_pretrained(
                safety_model_id
            )
        safety_checker_input = safety_feature_extractor(
            numpy_to_pil(x_image), return_tensors="pt"
        )
        x_checked_image, has_nsfw_concept = safety_checker(
            images=x_image, clip_input=safety_checker_input.pixel_values
        )
        for i in range(len(has_nsfw_concept)):
            if has_nsfw_concept[i]:
                x_checked_image[i] = load_replacement(x_checked_image[i])
        return x_checked_image, has_nsfw_concept

    prompt = prompt or ""
    torch_gc()
    # start time after garbage collection (or before?)
    start_time = time.time()

    mem_mon = MemUsageMonitor("MemMon")
    mem_mon.start()

    if hasattr(model, "embedding_manager"):
        load_embeddings(fp)

    if not opt.bridge:
        os.makedirs(outpath, exist_ok=True)

    sample_path = os.path.join(outpath, "samples")
    if not opt.bridge:
        os.makedirs(sample_path, exist_ok=True)

    if "|" not in prompt and prompt.startswith("@"):
        prompt = prompt[1:]

    negprompt = ""
    if "###" in prompt:
        prompt, negprompt = prompt.split("###", 1)
        prompt = prompt.strip()
        negprompt = negprompt.strip()

    comments = []

    prompt_matrix_parts = []
    simple_templating = False
    add_original_image = True
    if prompt_matrix:
        if prompt.startswith("@"):
            simple_templating = True
            add_original_image = not (use_RealESRGAN or use_GFPGAN)
            all_seeds, n_iter, prompt_matrix_parts, all_prompts, frows = oxlamon_matrix(
                prompt, seed, n_iter, batch_size
            )
        else:
            all_prompts = []
            prompt_matrix_parts = prompt.split("|")
            combination_count = 2 ** (len(prompt_matrix_parts) - 1)
            for combination_num in range(combination_count):
                current = prompt_matrix_parts[0]

                for n, text in enumerate(prompt_matrix_parts[1:]):
                    if combination_num & (2**n) > 0:
                        current += ("" if text.strip().startswith(",") else ", ") + text

                all_prompts.append(current)

            n_iter = math.ceil(len(all_prompts) / batch_size)
            all_seeds = len(all_prompts) * [seed]

        print(
            f"Prompt matrix will create {len(all_prompts)} images using a total of {n_iter} batches."
        )
    else:
        if not opt.no_verify_input:
            try:
                check_prompt_length(prompt, comments)
            except:
                import traceback

                print("Error verifying input:", file=sys.stderr)
                print(traceback.format_exc(), file=sys.stderr)

        all_prompts = batch_size * n_iter * [prompt]
        all_seeds = [seed + x for x in range(len(all_prompts))]
    original_seeds = all_seeds.copy()

    precision_scope = autocast if opt.precision == "autocast" else nullcontext
    if job_info:
        output_images = job_info.images
    else:
        output_images = []
    grid_captions = []
    stats = []
    with torch.no_grad(), precision_scope("cuda"), (
        model.ema_scope() if not opt.optimized else nullcontext()
    ):
        init_data = func_init()
        time.time()

        # if variant_amount > 0.0 create noise from base seed
        base_x = None
        if variant_amount > 0.0:
            target_seed_randomizer = seed_to_int("")  # random seed
            torch.manual_seed(
                seed
            )  # this has to be the single starting seed (not per-iteration)
            base_x = create_random_tensors(
                [opt_C, height // opt_f, width // opt_f], seeds=[seed]
            )
            # we don't want all_seeds to be sequential from starting seed with variants,
            # since that makes the same variants each time,
            # so we add target_seed_randomizer as a random offset
            for si in range(len(all_seeds)):
                all_seeds[si] += target_seed_randomizer

        for n in range(n_iter):
            if job_info and job_info.should_stop.is_set():
                print("Early exit requested")
                break

            print(f"Iteration: {n+1}/{n_iter}")
            prompts = all_prompts[n * batch_size : (n + 1) * batch_size]
            captions = prompt_matrix_parts[n * batch_size : (n + 1) * batch_size]
            seeds = all_seeds[n * batch_size : (n + 1) * batch_size]
            current_seeds = original_seeds[n * batch_size : (n + 1) * batch_size]

            if job_info:
                job_info.job_status = (
                    f"Processing Iteration {n+1}/{n_iter}. Batch size {batch_size}"
                )
                job_info.rec_steps_imgs.clear()
                for idx, (p, s) in enumerate(zip(prompts, seeds)):
                    job_info.job_status += f"\nItem {idx}: Seed {s}\nPrompt: {p}"
                    print(f"Current prompt: {p}")

            if opt.optimized:
                modelCS.to(device)
            uc = (model if not opt.optimized else modelCS).get_learned_conditioning(
                len(prompts) * [negprompt]
            )
            if isinstance(prompts, tuple):
                prompts = list(prompts)

            # split the prompt if it has : for weighting
            # TODO for speed it might help to have this occur when all_prompts filled??
            weighted_subprompts = split_weighted_subprompts(
                prompts[0], normalize_prompt_weights
            )

            # sub-prompt weighting used if more than 1
            if len(weighted_subprompts) > 1:
                c = torch.zeros_like(
                    uc
                )  # i dont know if this is correct.. but it works
                for i in range(0, len(weighted_subprompts)):
                    # note if alpha negative, it functions same as torch.sub
                    c = torch.add(
                        c,
                        (
                            model if not opt.optimized else modelCS
                        ).get_learned_conditioning(weighted_subprompts[i][0]),
                        alpha=weighted_subprompts[i][1],
                    )
            else:  # just behave like usual
                c = (model if not opt.optimized else modelCS).get_learned_conditioning(
                    prompts
                )

            shape = [opt_C, height // opt_f, width // opt_f]

            if opt.optimized:
                mem = torch.cuda.memory_allocated() / 1e6
                modelCS.to("cpu")
                while torch.cuda.memory_allocated() / 1e6 >= mem:
                    time.sleep(1)

            cur_variant_amount = variant_amount
            if variant_amount == 0.0:
                # we manually generate all input noises because each one should have a specific seed
                x = create_random_tensors(shape, seeds=seeds)
            else:  # we are making variants
                # using variant_seed as sneaky toggle,
                # when not None or '' use the variant_seed
                # otherwise use seeds
                if variant_seed is not None and variant_seed != "":
                    specified_variant_seed = seed_to_int(variant_seed)
                    torch.manual_seed(specified_variant_seed)
                    target_x = create_random_tensors(
                        shape, seeds=[specified_variant_seed]
                    )
                    # with a variant seed we would end up with the same variant as the basic seed
                    # does not change. But we can increase the steps to get an interesting result
                    # that shows more and more deviation of the original image and let us adjust
                    # how far we will go (using 10 iterations with variation amount set to 0.02 will
                    # generate an icreasingly variated image which is very interesting for movies)
                    cur_variant_amount += n * variant_amount
                else:
                    target_x = create_random_tensors(shape, seeds=seeds)
                # finally, slerp base_x noise to target_x noise for creating a variant
                x = slerp(
                    device, max(0.0, min(1.0, cur_variant_amount)), base_x, target_x
                )

            # If optimized then use first stage for preview and store it on cpu until needed
            if opt.optimized:
                step_preview_model = modelFS
                step_preview_model.cpu()
            else:
                step_preview_model = model

            def sample_iteration_callback(image_sample: torch.Tensor, iter_num: int):
                """Called from the sampler every iteration"""
                if job_info:
                    job_info.active_iteration_cnt = iter_num
                    record_periodic_image = job_info.rec_steps_enabled and (
                        0 == iter_num % job_info.rec_steps_intrvl
                    )
                    if (
                        record_periodic_image
                        or job_info.refresh_active_image_requested.is_set()
                    ):
                        preview_start_time = time.time()
                        if opt.optimized:
                            step_preview_model.to(device)

                        decoded_batch: List[torch.Tensor] = []
                        # Break up batch to save VRAM
                        for sample in image_sample:
                            sample = sample[
                                None, :
                            ]  # expands the tensor as if it still had a batch dimension
                            decoded_sample = step_preview_model.decode_first_stage(
                                sample
                            )[0]
                            decoded_sample = torch.clamp(
                                (decoded_sample + 1.0) / 2.0, min=0.0, max=1.0
                            )
                            decoded_sample = decoded_sample.cpu()
                            decoded_batch.append(decoded_sample)

                        batch_size = len(decoded_batch)

                        if opt.optimized:
                            step_preview_model.cpu()

                        images: List[Image.Image] = []
                        # Convert tensor to image (copied from code below)
                        for ddim in decoded_batch:
                            x_sample = 255.0 * rearrange(ddim.numpy(), "c h w -> h w c")
                            x_sample = x_sample.astype(np.uint8)
                            image = Image.fromarray(x_sample)
                            images.append(image)

                        caption = f"Iter {iter_num}"
                        grid = image_grid(
                            images,
                            len(images),
                            force_n_rows=1,
                            captions=[caption] * len(images),
                        )

                        # Save the images if recording steps, and append existing saved steps
                        if job_info.rec_steps_enabled:
                            gallery_img_size = tuple(
                                int(0.25 * dim) for dim in images[0].size
                            )
                            job_info.rec_steps_imgs.append(
                                grid.resize(gallery_img_size)
                            )

                        # Notify the requester that the image is updated
                        if job_info.refresh_active_image_requested.is_set():
                            if job_info.rec_steps_enabled:
                                grid_rows = (
                                    None
                                    if batch_size == 1
                                    else len(job_info.rec_steps_imgs)
                                )
                                grid = image_grid(
                                    imgs=job_info.rec_steps_imgs[::-1],
                                    batch_size=1,
                                    force_n_rows=grid_rows,
                                )
                            job_info.active_image = grid
                            job_info.refresh_active_image_done.set()
                            job_info.refresh_active_image_requested.clear()

                        preview_elapsed_timed = time.time() - preview_start_time
                        if preview_elapsed_timed / job_info.rec_steps_intrvl > 1:
                            print(
                                f"Warning: Preview generation is slowing image generation. It took {preview_elapsed_timed:.2f}s to generate progress images for batch of {batch_size} images!"
                            )

                    # Interrupt current iteration?
                    if job_info.stop_cur_iter.is_set():
                        job_info.stop_cur_iter.clear()
                        raise StopIteration()

            try:
                samples_ddim = func_sample(
                    init_data=init_data,
                    x=x,
                    conditioning=c,
                    unconditional_conditioning=uc,
                    sampler_name=sampler_name,
                    img_callback=sample_iteration_callback,
                )
            except StopIteration:
                print("Skipping iteration")
                job_info.job_status = "Skipping iteration"
                continue

            if opt.optimized:
                modelFS.to(device)

            for i in range(len(samples_ddim)):
                x_samples_ddim = (
                    model if not opt.optimized else modelFS
                ).decode_first_stage(samples_ddim[i].unsqueeze(0))
                x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)

                if filter_nsfw:
                    x_samples_ddim_numpy = x_sample.cpu().permute(0, 2, 3, 1).numpy()
                    x_checked_image, has_nsfw_concept = check_safety(
                        x_samples_ddim_numpy
                    )
                    x_sample = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)

                sanitized_prompt = (
                    prompts[i]
                    .replace(" ", "_")
                    .translate({ord(x): "" for x in invalid_filename_chars})
                )
                if variant_seed is not None and variant_seed != "":
                    if variant_amount == 0.0:
                        seed_used = f"{current_seeds[i]}-{variant_seed}"
                    else:
                        seed_used = f"{seed}-{variant_seed}"
                else:
                    seed_used = f"{current_seeds[i]}"
                if sort_samples:
                    sanitized_prompt = sanitized_prompt[:128]  # 200 is too long
                    sample_path_i = os.path.join(sample_path, sanitized_prompt)
                    if not opt.bridge:
                        os.makedirs(sample_path_i, exist_ok=True)
                    base_count = get_next_sequence_number(sample_path_i)
                    filename = (
                        opt.filename_format
                        or "[STEPS]_[SAMPLER]_[SEED]_[VARIANT_AMOUNT]"
                    )
                else:
                    sample_path_i = sample_path
                    base_count = get_next_sequence_number(sample_path_i)
                    filename = (
                        opt.filename_format
                        or "[STEPS]_[SAMPLER]_[SEED]_[VARIANT_AMOUNT]_[PROMPT]"
                    )

                # Add new filenames tags here
                filename = f"{base_count:05}-" + filename
                filename = filename.replace("[STEPS]", str(steps))
                filename = filename.replace("[CFG]", str(cfg_scale))
                filename = filename.replace("[PROMPT]", sanitized_prompt[:128])
                filename = filename.replace(
                    "[PROMPT_SPACES]",
                    prompts[i].translate({ord(x): "" for x in invalid_filename_chars})[
                        :128
                    ],
                )
                filename = filename.replace("[WIDTH]", str(width))
                filename = filename.replace("[HEIGHT]", str(height))
                filename = filename.replace("[SAMPLER]", sampler_name)
                filename = filename.replace("[SEED]", seed_used)
                filename = filename.replace(
                    "[VARIANT_AMOUNT]", f"{cur_variant_amount:.2f}"
                )

                x_sample = 255.0 * rearrange(
                    x_sample[0].cpu().numpy(), "c h w -> h w c"
                )
                x_sample = x_sample.astype(np.uint8)
                metadata = ImageMetadata(
                    prompt=prompts[i],
                    seed=seeds[i],
                    height=height,
                    width=width,
                    steps=steps,
                    cfg_scale=cfg_scale,
                    normalize_prompt_weights=normalize_prompt_weights,
                    denoising_strength=denoising_strength,
                    GFPGAN=use_GFPGAN,
                )
                image = Image.fromarray(x_sample)
                image = perform_color_correction(
                    image, correction_target, do_color_correction
                )
                ImageMetadata.set_on_image(image, metadata)

                original_sample = x_sample
                original_filename = filename
                if use_GFPGAN and GFPGAN is not None and not use_RealESRGAN:
                    skip_save = True  # #287 >_>
                    torch_gc()
                    cropped_faces, restored_faces, restored_img = GFPGAN.enhance(
                        original_sample[:, :, ::-1],
                        has_aligned=False,
                        only_center_face=False,
                        paste_back=True,
                    )
                    gfpgan_sample = restored_img[:, :, ::-1]
                    gfpgan_image = Image.fromarray(gfpgan_sample)
                    gfpgan_image = perform_color_correction(
                        gfpgan_image, correction_target, do_color_correction
                    )
                    gfpgan_image = perform_masked_image_restoration(
                        gfpgan_image,
                        init_img,
                        init_mask,
                        mask_blur_strength,
                        mask_restore,
                        use_RealESRGAN=False,
                        RealESRGAN=None,
                    )
                    gfpgan_metadata = copy.copy(metadata)
                    gfpgan_metadata.GFPGAN = True
                    ImageMetadata.set_on_image(gfpgan_image, gfpgan_metadata)
                    gfpgan_filename = original_filename + "-gfpgan"
                    save_sample(
                        gfpgan_image,
                        sample_path_i,
                        gfpgan_filename,
                        jpg_sample,
                        write_info_files,
                        write_sample_info_to_log_file,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        skip_save,
                        skip_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        skip_metadata=False,
                    )
                    output_images.append(gfpgan_image)  # 287
                    # if simple_templating:
                    #    grid_captions.append( captions[i] + "\ngfpgan" )

                if use_RealESRGAN and RealESRGAN is not None and not use_GFPGAN:
                    skip_save = True  # #287 >_>
                    torch_gc()
                    output, img_mode = RealESRGAN.enhance(original_sample[:, :, ::-1])
                    esrgan_filename = original_filename + "-esrgan4x"
                    esrgan_sample = output[:, :, ::-1]
                    esrgan_image = Image.fromarray(esrgan_sample)
                    esrgan_image = perform_color_correction(
                        esrgan_image, correction_target, do_color_correction
                    )
                    esrgan_image = perform_masked_image_restoration(
                        esrgan_image,
                        init_img,
                        init_mask,
                        mask_blur_strength,
                        mask_restore,
                        use_RealESRGAN,
                        RealESRGAN,
                    )
                    ImageMetadata.set_on_image(esrgan_image, metadata)
                    save_sample(
                        esrgan_image,
                        sample_path_i,
                        esrgan_filename,
                        jpg_sample,
                        write_info_files,
                        write_sample_info_to_log_file,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        skip_save,
                        skip_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        skip_metadata=False,
                    )
                    output_images.append(esrgan_image)  # 287
                    # if simple_templating:
                    #    grid_captions.append( captions[i] + "\nesrgan" )

                if (
                    use_RealESRGAN
                    and RealESRGAN is not None
                    and use_GFPGAN
                    and GFPGAN is not None
                ):
                    skip_save = True  # #287 >_>
                    torch_gc()
                    cropped_faces, restored_faces, restored_img = GFPGAN.enhance(
                        x_sample[:, :, ::-1],
                        has_aligned=False,
                        only_center_face=False,
                        paste_back=True,
                    )
                    gfpgan_sample = restored_img[:, :, ::-1]
                    output, img_mode = RealESRGAN.enhance(gfpgan_sample[:, :, ::-1])
                    gfpgan_esrgan_filename = original_filename + "-gfpgan-esrgan4x"
                    gfpgan_esrgan_sample = output[:, :, ::-1]
                    gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample)
                    gfpgan_esrgan_image = perform_color_correction(
                        gfpgan_esrgan_image, correction_target, do_color_correction
                    )
                    gfpgan_esrgan_image = perform_masked_image_restoration(
                        gfpgan_esrgan_image,
                        init_img,
                        init_mask,
                        mask_blur_strength,
                        mask_restore,
                        use_RealESRGAN,
                        RealESRGAN,
                    )
                    ImageMetadata.set_on_image(gfpgan_esrgan_image, metadata)
                    save_sample(
                        gfpgan_esrgan_image,
                        sample_path_i,
                        gfpgan_esrgan_filename,
                        jpg_sample,
                        write_info_files,
                        write_sample_info_to_log_file,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        skip_save,
                        skip_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        skip_metadata=False,
                    )
                    output_images.append(gfpgan_esrgan_image)  # 287
                    # if simple_templating:
                    #    grid_captions.append( captions[i] + "\ngfpgan_esrgan" )

                # this flag is used for imgProcessorTasks like GoBig, will return the image without saving it
                if imgProcessorTask is True:
                    output_images.append(image)

                image = perform_masked_image_restoration(
                    image,
                    init_img,
                    init_mask,
                    mask_blur_strength,
                    mask_restore,
                    # RealESRGAN image already processed in if-case above.
                    use_RealESRGAN=False,
                    RealESRGAN=None,
                )

                if not skip_save:
                    save_sample(
                        image,
                        sample_path_i,
                        filename,
                        jpg_sample,
                        write_info_files,
                        write_sample_info_to_log_file,
                        prompt_matrix,
                        init_img,
                        uses_loopback,
                        uses_random_seed_loopback,
                        skip_save,
                        skip_grid,
                        sort_samples,
                        sampler_name,
                        ddim_eta,
                        n_iter,
                        batch_size,
                        i,
                        denoising_strength,
                        resize_mode,
                        False,
                    )
                if add_original_image or not simple_templating:
                    output_images.append(image)
                    if simple_templating:
                        grid_captions.append(captions[i])

            # Save the progress images?
            if job_info:
                if job_info.rec_steps_enabled and (
                    job_info.rec_steps_to_file or job_info.rec_steps_to_gallery
                ):
                    steps_grid = image_grid(job_info.rec_steps_imgs, 1)
                    if job_info.rec_steps_to_gallery:
                        gallery_img_size = tuple(2 * dim for dim in image.size)
                        output_images.append(steps_grid.resize(gallery_img_size))
                    if job_info.rec_steps_to_file:
                        steps_grid_filename = f"{original_filename}_step_grid"
                        save_sample(
                            steps_grid,
                            sample_path_i,
                            steps_grid_filename,
                            jpg_sample,
                            write_info_files,
                            write_sample_info_to_log_file,
                            prompt_matrix,
                            init_img,
                            uses_loopback,
                            uses_random_seed_loopback,
                            skip_save,
                            skip_grid,
                            sort_samples,
                            sampler_name,
                            ddim_eta,
                            n_iter,
                            batch_size,
                            i,
                            denoising_strength,
                            resize_mode,
                            False,
                        )

            if opt.optimized:
                mem = torch.cuda.memory_allocated() / 1e6
                modelFS.to("cpu")
                while torch.cuda.memory_allocated() / 1e6 >= mem:
                    time.sleep(1)

        if (prompt_matrix or not skip_grid) and not do_not_save_grid:
            grid = None
            if prompt_matrix:
                if simple_templating:
                    grid = image_grid(
                        output_images,
                        batch_size,
                        force_n_rows=frows,
                        captions=grid_captions,
                    )
                else:
                    grid = image_grid(
                        output_images,
                        batch_size,
                        force_n_rows=1 << ((len(prompt_matrix_parts) - 1) // 2),
                    )
                    try:
                        grid = draw_prompt_matrix(
                            grid, width, height, prompt_matrix_parts
                        )
                    except:
                        import traceback

                        print("Error creating prompt_matrix text:", file=sys.stderr)
                        print(traceback.format_exc(), file=sys.stderr)
            elif len(output_images) > 0 and (batch_size > 1 or n_iter > 1):
                grid = image_grid(output_images, batch_size)
            if grid is not None:
                grid_count = get_next_sequence_number(outpath, "grid-")
                grid_file = f"grid-{grid_count:05}-{seed}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.{grid_ext}"
                grid.save(
                    os.path.join(outpath, grid_file),
                    grid_format,
                    quality=grid_quality,
                    lossless=grid_lossless,
                    optimize=True,
                )
                if prompt_matrix:
                    output_images.append(grid)

        time.time()

    mem_max_used, mem_total = mem_mon.read_and_stop()
    time_diff = time.time() - start_time
    args_and_names = {
        "seed": seed,
        "width": width,
        "height": height,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "sampler": sampler_name,
    }

    full_string = f"{prompt}\n" + " ".join([f"{k}:" for k, v in args_and_names.items()])
    info = {
        "text": full_string,
        "entities": [
            {
                "entity": str(v),
                "start": full_string.find(f"{k}:"),
                "end": full_string.find(f"{k}:") + len(f"{k} "),
            }
            for k, v in args_and_names.items()
        ],
    }
    #     info = f"""
    # {prompt} --seed {seed} --W {width} --H {height}  -s {steps} -C {cfg_scale} --sampler {sampler_name}  {', Denoising strength: '+str(denoising_strength) if init_img is not None else ''}{', GFPGAN' if use_GFPGAN and GFPGAN is not None else ''}{', '+realesrgan_model_name if use_RealESRGAN and RealESRGAN is not None else ''}{', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()
    stats = f"""
Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%"""

    for comment in comments:
        info["text"] += "\n\n" + comment

    # mem_mon.stop()
    # del mem_mon
    torch_gc()

    return output_images, seed, info, stats


def txt2img(
    prompt: str,
    ddim_steps: int = 50,
    sampler_name: str = "k_lms",
    toggles: List[int] = [1, 4],
    realesrgan_model_name: str = "",
    ddim_eta: float = 0.0,
    n_iter: int = 1,
    batch_size: int = 1,
    cfg_scale: float = 5.0,
    seed: Union[int, str, None] = None,
    height: int = 512,
    width: int = 512,
    fp=None,
    variant_amount: float = 0.0,
    variant_seed: int = None,
    job_info: JobInfo = None,
):
    outpath = opt.outdir_txt2img or opt.outdir or "outputs/txt2img-samples"
    err = False
    seed = seed_to_int(seed)
    prompt_matrix = 0 in toggles
    normalize_prompt_weights = 1 in toggles
    skip_save = 2 not in toggles
    skip_grid = 3 not in toggles
    sort_samples = 4 in toggles
    write_info_files = 5 in toggles
    write_to_one_file = 6 in toggles
    jpg_sample = 7 in toggles
    filter_nsfw = 8 in toggles
    use_GFPGAN = 9 in toggles
    use_RealESRGAN = 10 in toggles

    do_color_correction = False
    correction_target = None

    ModelLoader(["model"], True, False)
    if use_GFPGAN and not use_RealESRGAN:
        ModelLoader(["GFPGAN"], True, False)
        ModelLoader(["RealESRGAN"], False, True)
    if use_RealESRGAN and not use_GFPGAN:
        ModelLoader(["GFPGAN"], False, True)
        ModelLoader(["RealESRGAN"], True, False, realesrgan_model_name)
    if use_RealESRGAN and use_GFPGAN:
        ModelLoader(["GFPGAN", "RealESRGAN"], True, False, realesrgan_model_name)
    if sampler_name == "PLMS":
        sampler = PLMSSampler(model)
    elif sampler_name == "DDIM":
        sampler = DDIMSampler(model)
    elif sampler_name == "k_dpm_2_a":
        sampler = KDiffusionSampler(model, "dpm_2_ancestral")
    elif sampler_name == "k_dpm_2":
        sampler = KDiffusionSampler(model, "dpm_2")
    elif sampler_name == "k_euler_a":
        sampler = KDiffusionSampler(model, "euler_ancestral")
    elif sampler_name == "k_euler":
        sampler = KDiffusionSampler(model, "euler")
    elif sampler_name == "k_heun":
        sampler = KDiffusionSampler(model, "heun")
    elif sampler_name == "k_lms":
        sampler = KDiffusionSampler(model, "lms")
    else:
        raise Exception("Unknown sampler: " + sampler_name)

    def init():
        pass

    def sample(
        init_data,
        x,
        conditioning,
        unconditional_conditioning,
        sampler_name,
        img_callback: Callable = None,
    ):
        samples_ddim, _ = sampler.sample(
            S=ddim_steps,
            conditioning=conditioning,
            batch_size=int(x.shape[0]),
            shape=x[0].shape,
            verbose=False,
            unconditional_guidance_scale=cfg_scale,
            unconditional_conditioning=unconditional_conditioning,
            eta=ddim_eta,
            x_T=x,
            img_callback=img_callback,
        )
        return samples_ddim

    try:
        output_images, seed, info, stats = process_images(
            outpath=outpath,
            func_init=init,
            func_sample=sample,
            prompt=prompt,
            seed=seed,
            sampler_name=sampler_name,
            skip_save=skip_save,
            skip_grid=skip_grid,
            batch_size=batch_size,
            n_iter=n_iter,
            steps=ddim_steps,
            cfg_scale=cfg_scale,
            width=width,
            height=height,
            prompt_matrix=prompt_matrix,
            filter_nsfw=filter_nsfw,
            use_GFPGAN=use_GFPGAN,
            use_RealESRGAN=use_RealESRGAN,
            realesrgan_model_name=realesrgan_model_name,
            fp=fp,
            ddim_eta=ddim_eta,
            normalize_prompt_weights=normalize_prompt_weights,
            sort_samples=sort_samples,
            write_info_files=write_info_files,
            write_sample_info_to_log_file=write_to_one_file,
            jpg_sample=jpg_sample,
            variant_amount=variant_amount,
            variant_seed=variant_seed,
            job_info=job_info,
            do_color_correction=do_color_correction,
            correction_target=correction_target,
        )

        del sampler

        return output_images, seed, info, stats
    except RuntimeError as e:
        err = e
        err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
        stats = err_msg
        return [], seed, "err", stats
    finally:
        if err:
            crash(err, "!!Runtime error (txt2img)!!")


class Flagging(gr.FlaggingCallback):
    def setup(self, components, flagging_dir: str):
        pass

    def flag(self, flag_data, flag_option=None, flag_index=None, username=None):
        import csv

        os.makedirs("log/images", exist_ok=True)

        # those must match the "txt2img" function !! + images, seed, comment, stats !! NOTE: changes to UI output must be reflected here too
        (
            prompt,
            ddim_steps,
            sampler_name,
            toggles,
            ddim_eta,
            n_iter,
            batch_size,
            cfg_scale,
            seed,
            height,
            width,
            fp,
            variant_amount,
            variant_seed,
            images,
            seed,
            comment,
            stats,
        ) = flag_data

        filenames = []

        with open("log/log.csv", "a", encoding="utf8", newline="") as file:
            import time
            import base64

            at_start = file.tell() == 0
            writer = csv.writer(file)
            if at_start:
                writer.writerow(["sep=,"])
                writer.writerow(
                    [
                        "prompt",
                        "seed",
                        "width",
                        "height",
                        "sampler",
                        "toggles",
                        "n_iter",
                        "n_samples",
                        "cfg_scale",
                        "steps",
                        "filename",
                    ]
                )

            filename_base = str(int(time.time() * 1000))
            for i, filedata in enumerate(images):
                filename = (
                    "log/images/"
                    + filename_base
                    + ("" if len(images) == 1 else "-" + str(i + 1))
                    + ".png"
                )

                if filedata.startswith("data:image/png;base64,"):
                    filedata = filedata[len("data:image/png;base64,") :]

                with open(filename, "wb") as imgfile:
                    imgfile.write(base64.decodebytes(filedata.encode("utf-8")))

                filenames.append(filename)

            writer.writerow(
                [
                    prompt,
                    seed,
                    width,
                    height,
                    sampler_name,
                    toggles,
                    n_iter,
                    batch_size,
                    cfg_scale,
                    ddim_steps,
                    filenames[0],
                ]
            )

        print("Logged:", filenames[0])


def blurArr(a, r=8):
    im1 = Image.fromarray((a * 255).astype(np.int8), "L")
    im2 = im1.filter(ImageFilter.GaussianBlur(radius=r))
    out = np.array(im2) / 255
    return out


def img2img(
    prompt: str,
    image_editor_mode: str,
    mask_mode: str,
    mask_blur_strength: int,
    mask_restore: bool,
    ddim_steps: int,
    sampler_name: str,
    toggles: List[int],
    realesrgan_model_name: str,
    n_iter: int,
    cfg_scale: float,
    denoising_strength: float,
    seed: int,
    height: int,
    width: int,
    resize_mode: int,
    init_info: any = None,
    init_info_mask: any = None,
    fp=None,
    job_info: JobInfo = None,
):
    # print([prompt, image_editor_mode, init_info, init_info_mask, mask_mode,
    #                               mask_blur_strength, ddim_steps, sampler_name, toggles,
    #                               realesrgan_model_name, n_iter, cfg_scale,
    #                               denoising_strength, seed, height, width, resize_mode,
    #                               fp])
    outpath = opt.outdir_img2img or opt.outdir or "outputs/img2img-samples"
    seed = seed_to_int(seed)

    batch_size = 1

    prompt_matrix = 0 in toggles
    normalize_prompt_weights = 1 in toggles
    loopback = 2 in toggles
    random_seed_loopback = 3 in toggles
    skip_save = 4 not in toggles
    skip_grid = 5 not in toggles
    sort_samples = 6 in toggles
    write_info_files = 7 in toggles
    write_sample_info_to_log_file = 8 in toggles
    jpg_sample = 9 in toggles
    do_color_correction = 10 in toggles
    filter_nsfw = 11 in toggles
    use_GFPGAN = 12 in toggles
    use_RealESRGAN = 13 in toggles
    ModelLoader(["model"], True, False)
    if use_GFPGAN and not use_RealESRGAN:
        ModelLoader(["GFPGAN"], True, False)
        ModelLoader(["RealESRGAN"], False, True)
    if use_RealESRGAN and not use_GFPGAN:
        ModelLoader(["GFPGAN"], False, True)
        ModelLoader(["RealESRGAN"], True, False, realesrgan_model_name)
    if use_RealESRGAN and use_GFPGAN:
        ModelLoader(["GFPGAN", "RealESRGAN"], True, False, realesrgan_model_name)
    if sampler_name == "DDIM":
        sampler = DDIMSampler(model)
    elif sampler_name == "k_dpm_2_a":
        sampler = KDiffusionSampler(model, "dpm_2_ancestral")
    elif sampler_name == "k_dpm_2":
        sampler = KDiffusionSampler(model, "dpm_2")
    elif sampler_name == "k_euler_a":
        sampler = KDiffusionSampler(model, "euler_ancestral")
    elif sampler_name == "k_euler":
        sampler = KDiffusionSampler(model, "euler")
    elif sampler_name == "k_heun":
        sampler = KDiffusionSampler(model, "heun")
    elif sampler_name == "k_lms":
        sampler = KDiffusionSampler(model, "lms")
    else:
        raise Exception("Unknown sampler: " + sampler_name)

    if image_editor_mode == "Mask":
        init_img = init_info_mask["image"]
        init_img_transparency = (
            ImageOps.invert(init_img.split()[-1])
            .convert("L")
            .point(lambda x: 255 if x > 0 else 0, mode="1")
        )
        init_img = init_img.convert("RGB")
        init_img = resize_image(resize_mode, init_img, width, height)
        init_img = init_img.convert("RGB")
        init_mask = init_info_mask["mask"]
        init_mask = ImageChops.lighter(
            init_img_transparency, init_mask.convert("L")
        ).convert("RGBA")
        init_mask = init_mask.convert("RGB")
        init_mask = resize_image(resize_mode, init_mask, width, height)
        init_mask = init_mask.convert("RGB")
        keep_mask = mask_mode == 0
        init_mask = init_mask if keep_mask else ImageOps.invert(init_mask)
    else:
        init_img = init_info
        init_mask = None
        keep_mask = False

    assert 0.0 <= denoising_strength <= 1.0, "can only work with strength in [0.0, 1.0]"
    t_enc = int(denoising_strength * ddim_steps)

    def init():
        image = init_img.convert("RGB")
        image = resize_image(resize_mode, image, width, height)
        # image = image.convert("RGB")
        image = np.array(image).astype(np.float32) / 255.0
        image = image[None].transpose(0, 3, 1, 2)
        image = torch.from_numpy(image)

        mask_channel = None
        if image_editor_mode == "Mask":
            alpha = init_mask.convert("RGBA")
            alpha = resize_image(resize_mode, alpha, width // 8, height // 8)
            mask_channel = alpha.split()[1]

        mask = None
        if mask_channel is not None:
            mask = np.array(mask_channel).astype(np.float32) / 255.0
            mask = 1 - mask
            mask = np.tile(mask, (4, 1, 1))
            mask = mask[None].transpose(0, 1, 2, 3)
            mask = torch.from_numpy(mask).to(device)
        if opt.optimized:
            modelFS.to(device)

        # let's try and find where init_image is 0's
        # shape is probably (3,width,height)?

        if image_editor_mode == "Uncrop":
            _image = image.numpy()[0]
            _mask = np.ones((_image.shape[1], _image.shape[2]))

            # compute bounding box
            cmax = np.max(_image, axis=0)
            rowmax = np.max(cmax, axis=0)
            colmax = np.max(cmax, axis=1)
            rowwhere = np.where(rowmax > 0)[0]
            colwhere = np.where(colmax > 0)[0]
            rowstart = rowwhere[0]
            rowend = rowwhere[-1] + 1
            colstart = colwhere[0]
            colend = colwhere[-1] + 1
            print("bounding box: ", rowstart, rowend, colstart, colend)

            # this is where noise will get added
            PAD_IMG = 16
            boundingbox = np.zeros(shape=(height, width))
            boundingbox[
                colstart + PAD_IMG : colend - PAD_IMG,
                rowstart + PAD_IMG : rowend - PAD_IMG,
            ] = 1
            boundingbox = blurArr(boundingbox, 4)

            # this is the mask for outpainting
            PAD_MASK = 24
            boundingbox2 = np.zeros(shape=(height, width))
            boundingbox2[
                colstart + PAD_MASK : colend - PAD_MASK,
                rowstart + PAD_MASK : rowend - PAD_MASK,
            ] = 1
            boundingbox2 = blurArr(boundingbox2, 4)

            # noise=np.random.randn(*_image.shape)
            noise = np.array(
                [perlinNoise(height, width, height / 64, width / 64) for i in range(3)]
            )
            _mask *= 1 - boundingbox2

            # convert 0,1 to -1,1
            _image = 2.0 * _image - 1.0

            # add noise
            boundingbox = np.tile(boundingbox, (3, 1, 1))
            _image = _image * boundingbox + noise * (1 - boundingbox)

            # resize mask
            _mask = (
                np.array(
                    resize_image(
                        resize_mode,
                        Image.fromarray(_mask * 255),
                        width // 8,
                        height // 8,
                    )
                )
                / 255
            )

            # convert back to torch tensor
            init_image = torch.from_numpy(
                np.expand_dims(_image, axis=0).astype(np.float32)
            ).to(device)
            mask = torch.from_numpy(_mask.astype(np.float32)).to(device)

        else:
            init_image = 2.0 * image - 1.0

        init_image = init_image.to(device)
        init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
        init_latent = (
            model if not opt.optimized else modelFS
        ).get_first_stage_encoding(
            (model if not opt.optimized else modelFS).encode_first_stage(init_image)
        )  # move to latent space

        if opt.optimized:
            mem = torch.cuda.memory_allocated() / 1e6
            modelFS.to("cpu")
            while torch.cuda.memory_allocated() / 1e6 >= mem:
                time.sleep(1)

        return (
            init_latent,
            mask,
        )

    def sample(
        init_data,
        x,
        conditioning,
        unconditional_conditioning,
        sampler_name,
        img_callback: Callable = None,
    ):
        t_enc_steps = t_enc
        obliterate = False
        if ddim_steps == t_enc_steps:
            t_enc_steps = t_enc_steps - 1
            obliterate = True

        if sampler_name != "DDIM":
            x0, z_mask = init_data

            sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
            noise = x * sigmas[ddim_steps - t_enc_steps - 1]

            xi = x0 + noise

            # Obliterate masked image
            if z_mask is not None and obliterate:
                random = torch.randn(z_mask.shape, device=xi.device)
                xi = (z_mask * noise) + ((1 - z_mask) * xi)

            sigma_sched = sigmas[ddim_steps - t_enc_steps - 1 :]
            model_wrap_cfg = CFGMaskedDenoiser(sampler.model_wrap)
            samples_ddim = K.sampling.__dict__[f"sample_{sampler.get_sampler_name()}"](
                model_wrap_cfg,
                xi,
                sigma_sched,
                extra_args={
                    "cond": conditioning,
                    "uncond": unconditional_conditioning,
                    "cond_scale": cfg_scale,
                    "mask": z_mask,
                    "x0": x0,
                    "xi": xi,
                },
                disable=False,
                callback=partial(KDiffusionSampler.img_callback_wrapper, img_callback),
            )
        else:
            x0, z_mask = init_data

            sampler.make_schedule(
                ddim_num_steps=ddim_steps, ddim_eta=0.0, verbose=False
            )
            z_enc = sampler.stochastic_encode(
                x0, torch.tensor([t_enc_steps] * batch_size).to(device)
            )

            # Obliterate masked image
            if z_mask is not None and obliterate:
                random = torch.randn(z_mask.shape, device=z_enc.device)
                z_enc = (z_mask * random) + ((1 - z_mask) * z_enc)

                # decode it
            samples_ddim = sampler.decode(
                z_enc,
                conditioning,
                t_enc_steps,
                unconditional_guidance_scale=cfg_scale,
                unconditional_conditioning=unconditional_conditioning,
                z_mask=z_mask,
                x0=x0,
            )
        return samples_ddim

    correction_target = None
    if loopback:
        output_images, info = None, None
        history = []
        initial_seed = None

        # turn on color correction for loopback to prevent known issue of color drift
        do_color_correction = True

        for i in range(n_iter):
            if do_color_correction and i == 0:
                correction_target = cv2.cvtColor(
                    np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB
                )

            output_images, seed, info, stats = process_images(
                outpath=outpath,
                func_init=init,
                func_sample=sample,
                prompt=prompt,
                seed=seed,
                sampler_name=sampler_name,
                skip_save=skip_save,
                skip_grid=skip_grid,
                batch_size=1,
                n_iter=1,
                steps=ddim_steps,
                cfg_scale=cfg_scale,
                width=width,
                height=height,
                prompt_matrix=prompt_matrix,
                filter_nsfw=filter_nsfw,
                use_GFPGAN=use_GFPGAN,
                use_RealESRGAN=False,  # Forcefully disable upscaling when using loopback
                realesrgan_model_name=realesrgan_model_name,
                fp=fp,
                do_not_save_grid=True,
                normalize_prompt_weights=normalize_prompt_weights,
                init_img=init_img,
                init_mask=init_mask,
                keep_mask=keep_mask,
                mask_blur_strength=mask_blur_strength,
                mask_restore=mask_restore,
                denoising_strength=denoising_strength,
                resize_mode=resize_mode,
                uses_loopback=loopback,
                uses_random_seed_loopback=random_seed_loopback,
                sort_samples=sort_samples,
                write_info_files=write_info_files,
                write_sample_info_to_log_file=write_sample_info_to_log_file,
                jpg_sample=jpg_sample,
                job_info=job_info,
                do_color_correction=do_color_correction,
                correction_target=correction_target,
            )

            if initial_seed is None:
                initial_seed = seed

            init_img = output_images[0]

            if not random_seed_loopback:
                seed = seed + 1
            else:
                seed = seed_to_int(None)
            denoising_strength = max(denoising_strength * 0.95, 0.1)
            history.append(init_img)

        if not skip_grid:
            grid_count = get_next_sequence_number(outpath, "grid-")
            grid = image_grid(history, batch_size, force_n_rows=1)
            grid_file = f"grid-{grid_count:05}-{seed}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.{grid_ext}"
            grid.save(
                os.path.join(outpath, grid_file),
                grid_format,
                quality=grid_quality,
                lossless=grid_lossless,
                optimize=True,
            )

        output_images = history
        seed = initial_seed

    else:
        if do_color_correction:
            correction_target = cv2.cvtColor(
                np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB
            )

        output_images, seed, info, stats = process_images(
            outpath=outpath,
            func_init=init,
            func_sample=sample,
            prompt=prompt,
            seed=seed,
            sampler_name=sampler_name,
            skip_save=skip_save,
            skip_grid=skip_grid,
            batch_size=batch_size,
            n_iter=n_iter,
            steps=ddim_steps,
            cfg_scale=cfg_scale,
            width=width,
            height=height,
            prompt_matrix=prompt_matrix,
            filter_nsfw=filter_nsfw,
            use_GFPGAN=use_GFPGAN,
            use_RealESRGAN=use_RealESRGAN,
            realesrgan_model_name=realesrgan_model_name,
            fp=fp,
            normalize_prompt_weights=normalize_prompt_weights,
            init_img=init_img,
            init_mask=init_mask,
            keep_mask=keep_mask,
            mask_blur_strength=mask_blur_strength,
            denoising_strength=denoising_strength,
            mask_restore=mask_restore,
            resize_mode=resize_mode,
            uses_loopback=loopback,
            sort_samples=sort_samples,
            write_info_files=write_info_files,
            write_sample_info_to_log_file=write_sample_info_to_log_file,
            jpg_sample=jpg_sample,
            job_info=job_info,
            do_color_correction=do_color_correction,
            correction_target=correction_target,
        )

    del sampler

    return output_images, seed, info, stats


prompt_parser = re.compile(
    """
    (?P<prompt>     # capture group for 'prompt'
    (?:\\\:|[^:])+  # match one or more non ':' characters or escaped colons '\:'
    )               # end 'prompt'
    (?:             # non-capture group
    :+              # match one or more ':' characters
    (?P<weight>     # capture group for 'weight'
    -?\d*\.{0,1}\d+ # match positive or negative integer or decimal number
    )?              # end weight capture group, make optional
    \s*             # strip spaces after weight
    |               # OR
    $               # else, if no ':' then match end of line
    )               # end non-capture group
""",
    re.VERBOSE,
)


# grabs all text up to the first occurrence of ':' as sub-prompt
# takes the value following ':' as weight
# if ':' has no value defined, defaults to 1.0
# repeats until no text remaining
def split_weighted_subprompts(input_string, normalize=True):
    parsed_prompts = [
        (match.group("prompt").replace("\\:", ":"), float(match.group("weight") or 1))
        for match in re.finditer(prompt_parser, input_string)
    ]
    if not normalize:
        return parsed_prompts
    weight_sum = sum(map(lambda x: x[1], parsed_prompts))
    if weight_sum == 0:
        print(
            "Warning: Subprompt weights add up to zero. Discarding and using even weights instead."
        )
        equal_weight = 1 / (len(parsed_prompts) or 1)
        return [(x[0], equal_weight) for x in parsed_prompts]
    return [(x[0], x[1] / weight_sum) for x in parsed_prompts]


def slerp(device, t, v0: torch.Tensor, v1: torch.Tensor, DOT_THRESHOLD=0.9995):
    v0 = v0.detach().cpu().numpy()
    v1 = v1.detach().cpu().numpy()

    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
    if np.abs(dot) > DOT_THRESHOLD:
        v2 = (1 - t) * v0 + t * v1
    else:
        theta_0 = np.arccos(dot)
        sin_theta_0 = np.sin(theta_0)
        theta_t = theta_0 * t
        sin_theta_t = np.sin(theta_t)
        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
        s1 = sin_theta_t / sin_theta_0
        v2 = s0 * v0 + s1 * v1

    v2 = torch.from_numpy(v2).to(device)

    return v2


def imgproc(
    image,
    image_batch,
    imgproc_prompt,
    imgproc_toggles,
    imgproc_upscale_toggles,
    imgproc_realesrgan_model_name,
    imgproc_sampling,
    imgproc_steps,
    imgproc_height,
    imgproc_width,
    imgproc_cfg,
    imgproc_denoising,
    imgproc_seed,
    imgproc_gfpgan_strength,
    imgproc_ldsr_steps,
    imgproc_ldsr_pre_downSample,
    imgproc_ldsr_post_downSample,
):
    outpath = opt.outdir_imglab or opt.outdir or "outputs/imglab-samples"
    output = []
    images = []

    def processGFPGAN(image, strength):
        image = image.convert("RGB")
        metadata = ImageMetadata.get_from_image(image)
        cropped_faces, restored_faces, restored_img = GFPGAN.enhance(
            np.array(image, dtype=np.uint8),
            has_aligned=False,
            only_center_face=False,
            paste_back=True,
        )
        result = Image.fromarray(restored_img)
        if metadata:
            metadata.GFPGAN = True
            ImageMetadata.set_on_image(image, metadata)

        if strength < 1.0:
            result = Image.blend(image, result, strength)

        return result

    def processRealESRGAN(image):
        if "x2" in imgproc_realesrgan_model_name:
            # downscale to 1/2 size
            modelMode = imgproc_realesrgan_model_name.replace("x2", "x4")
        else:
            modelMode = imgproc_realesrgan_model_name
        image = image.convert("RGB")
        metadata = ImageMetadata.get_from_image(image)
        RealESRGAN = load_RealESRGAN(modelMode)
        result, res = RealESRGAN.enhance(np.array(image, dtype=np.uint8))
        result = Image.fromarray(result)
        ImageMetadata.set_on_image(result, metadata)
        if "x2" in imgproc_realesrgan_model_name:
            # downscale to 1/2 size
            result = result.resize((result.width // 2, result.height // 2), LANCZOS)

        return result

    def processGoBig(image):
        metadata = ImageMetadata.get_from_image(image)
        result = processRealESRGAN(
            image,
        )
        if "x4" in imgproc_realesrgan_model_name:
            # downscale to 1/2 size
            result = result.resize((result.width // 2, result.height // 2), LANCZOS)

        # make sense of parameters
        n_iter = 1
        batch_size = 1
        seed = seed_to_int(imgproc_seed)
        ddim_steps = int(imgproc_steps)
        resize_mode = 0  # need to add resize mode to form, or infer correct resolution from file name
        width = int(imgproc_width)
        height = int(imgproc_height)
        cfg_scale = float(imgproc_cfg)
        denoising_strength = float(imgproc_denoising)
        skip_save = True
        skip_grid = True
        prompt = imgproc_prompt
        t_enc = int(denoising_strength * ddim_steps)
        sampler_name = imgproc_sampling

        if sampler_name == "DDIM":
            sampler = DDIMSampler(model)
        elif sampler_name == "k_dpm_2_a":
            sampler = KDiffusionSampler(model, "dpm_2_ancestral")
        elif sampler_name == "k_dpm_2":
            sampler = KDiffusionSampler(model, "dpm_2")
        elif sampler_name == "k_euler_a":
            sampler = KDiffusionSampler(model, "euler_ancestral")
        elif sampler_name == "k_euler":
            sampler = KDiffusionSampler(model, "euler")
        elif sampler_name == "k_heun":
            sampler = KDiffusionSampler(model, "heun")
        elif sampler_name == "k_lms":
            sampler = KDiffusionSampler(model, "lms")
        else:
            raise Exception("Unknown sampler: " + sampler_name)
            pass
        init_img = result
        mask_restore = False
        assert (
            0.0 <= denoising_strength <= 1.0
        ), "can only work with strength in [0.0, 1.0]"

        def init():
            image = init_img.convert("RGB")
            image = resize_image(resize_mode, image, width, height)
            image = np.array(image).astype(np.float32) / 255.0
            image = image[None].transpose(0, 3, 1, 2)
            image = torch.from_numpy(image)

            if opt.optimized:
                modelFS.to(device)

            init_image = 2.0 * image - 1.0
            init_image = init_image.to(device)
            init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
            init_latent = (
                model if not opt.optimized else modelFS
            ).get_first_stage_encoding(
                (model if not opt.optimized else modelFS).encode_first_stage(init_image)
            )  # move to latent space

            if opt.optimized:
                mem = torch.cuda.memory_allocated() / 1e6
                modelFS.to("cpu")
                while torch.cuda.memory_allocated() / 1e6 >= mem:
                    time.sleep(1)

            return (init_latent,)

        def sample(
            init_data,
            x,
            conditioning,
            unconditional_conditioning,
            sampler_name,
            img_callback: Callable = None,
        ):
            if sampler_name != "DDIM":
                (x0,) = init_data

                sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
                noise = x * sigmas[ddim_steps - t_enc - 1]

                xi = x0 + noise
                sigma_sched = sigmas[ddim_steps - t_enc - 1 :]
                model_wrap_cfg = CFGDenoiser(sampler.model_wrap)
                samples_ddim = K.sampling.__dict__[
                    f"sample_{sampler.get_sampler_name()}"
                ](
                    model_wrap_cfg,
                    xi,
                    sigma_sched,
                    extra_args={
                        "cond": conditioning,
                        "uncond": unconditional_conditioning,
                        "cond_scale": cfg_scale,
                    },
                    disable=False,
                    callback=partial(
                        KDiffusionSampler.img_callback_wrapper, img_callback
                    ),
                )
            else:
                (x0,) = init_data
                sampler.make_schedule(
                    ddim_num_steps=ddim_steps, ddim_eta=0.0, verbose=False
                )
                z_enc = sampler.stochastic_encode(
                    x0, torch.tensor([t_enc] * batch_size).to(device)
                )
                # decode it
                samples_ddim = sampler.decode(
                    z_enc,
                    conditioning,
                    t_enc,
                    unconditional_guidance_scale=cfg_scale,
                    unconditional_conditioning=unconditional_conditioning,
                )
            return samples_ddim

        def split_grid(image, tile_w=512, tile_h=512, overlap=64):
            Grid = namedtuple(
                "Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"]
            )
            w = image.width
            h = image.height

            now = tile_w - overlap  # non-overlap width
            noh = tile_h - overlap

            cols = math.ceil((w - overlap) / now)
            rows = math.ceil((h - overlap) / noh)

            grid = Grid([], tile_w, tile_h, w, h, overlap)
            for row in range(rows):
                row_images = []

                y = row * noh

                if y + tile_h >= h:
                    y = h - tile_h

                for col in range(cols):
                    x = col * now

                    if x + tile_w >= w:
                        x = w - tile_w

                    tile = image.crop((x, y, x + tile_w, y + tile_h))

                    row_images.append([x, tile_w, tile])

                grid.tiles.append([y, tile_h, row_images])

            return grid

        def combine_grid(grid):
            def make_mask_image(r):
                r = r * 255 / grid.overlap
                r = r.astype(np.uint8)
                return Image.fromarray(r, "L")

            mask_w = make_mask_image(
                np.arange(grid.overlap, dtype=np.float)
                .reshape((1, grid.overlap))
                .repeat(grid.tile_h, axis=0)
            )
            mask_h = make_mask_image(
                np.arange(grid.overlap, dtype=np.float)
                .reshape((grid.overlap, 1))
                .repeat(grid.image_w, axis=1)
            )

            combined_image = Image.new("RGB", (grid.image_w, grid.image_h))
            for y, h, row in grid.tiles:
                combined_row = Image.new("RGB", (grid.image_w, h))
                for x, w, tile in row:
                    if x == 0:
                        combined_row.paste(tile, (0, 0))
                        continue

                    combined_row.paste(
                        tile.crop((0, 0, grid.overlap, h)), (x, 0), mask=mask_w
                    )
                    combined_row.paste(
                        tile.crop((grid.overlap, 0, w, h)), (x + grid.overlap, 0)
                    )

                if y == 0:
                    combined_image.paste(combined_row, (0, 0))
                    continue

                combined_image.paste(
                    combined_row.crop((0, 0, combined_row.width, grid.overlap)),
                    (0, y),
                    mask=mask_h,
                )
                combined_image.paste(
                    combined_row.crop((0, grid.overlap, combined_row.width, h)),
                    (0, y + grid.overlap),
                )

            return combined_image

        grid = split_grid(result, tile_w=width, tile_h=height, overlap=64)
        work = []
        work_results = []

        for y, h, row in grid.tiles:
            for tiledata in row:
                work.append(tiledata[2])
        batch_count = math.ceil(len(work) / batch_size)
        print(
            f"GoBig upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} in a total of {batch_count} batches."
        )
        for i in range(batch_count):
            init_img = work[i * batch_size : (i + 1) * batch_size][0]
            output_images, seed, info, stats = process_images(
                outpath=outpath,
                func_init=init,
                func_sample=sample,
                prompt=prompt,
                seed=seed,
                sampler_name=sampler_name,
                skip_save=skip_save,
                skip_grid=skip_grid,
                batch_size=batch_size,
                n_iter=n_iter,
                steps=ddim_steps,
                cfg_scale=cfg_scale,
                width=width,
                height=height,
                prompt_matrix=None,
                filter_nsfw=False,
                use_GFPGAN=None,
                use_RealESRGAN=None,
                realesrgan_model_name=None,
                fp=None,
                normalize_prompt_weights=False,
                init_img=init_img,
                init_mask=None,
                keep_mask=False,
                mask_blur_strength=None,
                denoising_strength=denoising_strength,
                mask_restore=mask_restore,
                resize_mode=resize_mode,
                uses_loopback=False,
                sort_samples=True,
                write_info_files=True,
                write_sample_info_to_log_file=False,
                jpg_sample=False,
                imgProcessorTask=True,
            )
            # if initial_seed is None:
            #    initial_seed = seed
            # seed = seed + 1

            work_results.append(output_images[0])
        image_index = 0
        for y, h, row in grid.tiles:
            for tiledata in row:
                tiledata[2] = work_results[image_index]
                image_index += 1

        combined_image = combine_grid(grid)
        len(os.listdir(outpath)) - 1
        del sampler

        torch.cuda.empty_cache()
        ImageMetadata.set_on_image(combined_image, metadata)
        return combined_image

    def processLDSR(image):
        metadata = ImageMetadata.get_from_image(image)
        result = LDSR.superResolution(
            image,
            int(imgproc_ldsr_steps),
            str(imgproc_ldsr_pre_downSample),
            str(imgproc_ldsr_post_downSample),
        )
        ImageMetadata.set_on_image(result, metadata)
        return result

    if image_batch is not None:
        if image is not None:
            print(
                "Batch detected and single image detected, please only use one of the two. Aborting."
            )
            return None
        # convert file to pillow image
        for img in image_batch:
            image = Image.fromarray(np.array(Image.open(img)))
            images.append(image)

    elif image is not None:
        if image_batch is not None:
            print(
                "Batch detected and single image detected, please only use one of the two. Aborting."
            )
            return None
        else:
            images.append(image)

    if len(images) > 0:
        print("Processing images...")
        # pre load models not in loop
        if 0 in imgproc_toggles:
            ModelLoader(["RealESGAN", "LDSR"], False, True)  # Unload unused models
            ModelLoader(["GFPGAN"], True, False)  # Load used models
        if 1 in imgproc_toggles:
            if imgproc_upscale_toggles == 0:
                ModelLoader(["GFPGAN", "LDSR"], False, True)  # Unload unused models
                ModelLoader(
                    ["RealESGAN"], True, False, imgproc_realesrgan_model_name
                )  # Load used models
            elif imgproc_upscale_toggles == 1:
                ModelLoader(["GFPGAN", "LDSR"], False, True)  # Unload unused models
                ModelLoader(["RealESGAN", "model"], True, False)  # Load used models
            elif imgproc_upscale_toggles == 2:
                ModelLoader(
                    ["model", "GFPGAN", "RealESGAN"], False, True
                )  # Unload unused models
                ModelLoader(["LDSR"], True, False)  # Load used models
            elif imgproc_upscale_toggles == 3:
                ModelLoader(["GFPGAN", "LDSR"], False, True)  # Unload unused models
                ModelLoader(
                    ["RealESGAN", "model"], True, False, imgproc_realesrgan_model_name
                )  # Load used models
        for image in images:
            metadata = ImageMetadata.get_from_image(image)
            if 0 in imgproc_toggles:
                # recheck if GFPGAN is loaded since it's the only model that can be loaded in the loop as well
                ModelLoader(["GFPGAN"], True, False)  # Load used models
                image = processGFPGAN(image, imgproc_gfpgan_strength)
                if metadata:
                    metadata.GFPGAN = True
                ImageMetadata.set_on_image(image, metadata)
                outpathDir = os.path.join(outpath, "GFPGAN")
                os.makedirs(outpathDir, exist_ok=True)
                batchNumber = get_next_sequence_number(outpathDir)
                outFilename = str(batchNumber) + "-" + "result"

                if 1 not in imgproc_toggles:
                    output.append(image)
                    save_sample(
                        image,
                        outpathDir,
                        outFilename,
                        False,
                        None,
                        None,
                        None,
                        None,
                        None,
                        False,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        False,
                    )
            if 1 in imgproc_toggles:
                if imgproc_upscale_toggles == 0:
                    image = processRealESRGAN(image)
                    ImageMetadata.set_on_image(image, metadata)
                    outpathDir = os.path.join(outpath, "RealESRGAN")
                    os.makedirs(outpathDir, exist_ok=True)
                    batchNumber = get_next_sequence_number(outpathDir)
                    outFilename = str(batchNumber) + "-" + "result"
                    output.append(image)
                    save_sample(
                        image,
                        outpathDir,
                        outFilename,
                        False,
                        None,
                        None,
                        None,
                        None,
                        None,
                        False,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        False,
                    )

                elif imgproc_upscale_toggles == 1:
                    image = processGoBig(image)
                    ImageMetadata.set_on_image(image, metadata)
                    outpathDir = os.path.join(outpath, "GoBig")
                    os.makedirs(outpathDir, exist_ok=True)
                    batchNumber = get_next_sequence_number(outpathDir)
                    outFilename = str(batchNumber) + "-" + "result"
                    output.append(image)
                    save_sample(
                        image,
                        outpathDir,
                        outFilename,
                        False,
                        None,
                        None,
                        None,
                        None,
                        None,
                        False,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        False,
                    )

                elif imgproc_upscale_toggles == 2:
                    image = processLDSR(image)
                    ImageMetadata.set_on_image(image, metadata)
                    outpathDir = os.path.join(outpath, "LDSR")
                    os.makedirs(outpathDir, exist_ok=True)
                    batchNumber = get_next_sequence_number(outpathDir)
                    outFilename = str(batchNumber) + "-" + "result"
                    output.append(image)
                    save_sample(
                        image,
                        outpathDir,
                        outFilename,
                        False,
                        None,
                        None,
                        None,
                        None,
                        None,
                        False,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        False,
                    )

                elif imgproc_upscale_toggles == 3:
                    image = processGoBig(image)
                    ModelLoader(
                        ["model", "GFPGAN", "RealESGAN"], False, True
                    )  # Unload unused models
                    ModelLoader(["LDSR"], True, False)  # Load used models
                    image = processLDSR(image)
                    ImageMetadata.set_on_image(image, metadata)
                    outpathDir = os.path.join(outpath, "GoLatent")
                    os.makedirs(outpathDir, exist_ok=True)
                    batchNumber = get_next_sequence_number(outpathDir)
                    outFilename = str(batchNumber) + "-" + "result"
                    output.append(image)

                    save_sample(
                        image,
                        outpathDir,
                        outFilename,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        False,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        False,
                    )

    # LDSR is always unloaded to avoid memory issues
    # ModelLoader(['LDSR'],False,True)
    # print("Reloading default models...")
    # ModelLoader(['model','RealESGAN','GFPGAN'],True,False) # load back models
    print("Done.")
    return output


def ModelLoader(
    models, load=False, unload=False, imgproc_realesrgan_model_name="RealESRGAN_x4plus"
):
    # get global variables
    global_vars = globals()
    # check if m is in globals
    if unload:
        for m in models:
            if m in global_vars:
                # if it is, delete it
                del global_vars[m]
                if opt.optimized:
                    if m == "model":
                        del global_vars[m + "FS"]
                        del global_vars[m + "CS"]
                if m == "model":
                    m = "Stable Diffusion"
                print("Unloaded " + m)
    if load:
        for m in models:
            if (
                m not in global_vars
                or m in global_vars
                and type(global_vars[m]) == bool
            ):
                # if it isn't, load it
                if m == "GFPGAN":
                    global_vars[m] = load_GFPGAN()
                elif m == "model":
                    sdLoader = load_SD_model()
                    global_vars[m] = sdLoader[0]
                    if opt.optimized:
                        global_vars[m + "CS"] = sdLoader[1]
                        global_vars[m + "FS"] = sdLoader[2]
                elif m == "RealESRGAN":
                    global_vars[m] = load_RealESRGAN(imgproc_realesrgan_model_name)
                elif m == "LDSR":
                    global_vars[m] = load_LDSR()
                if m == "model":
                    m = "Stable Diffusion"
                print("Loaded " + m)
    torch_gc()


def run_GFPGAN(image, strength):
    ModelLoader(["LDSR", "RealESRGAN"], False, True)
    ModelLoader(["GFPGAN"], True, False)
    metadata = ImageMetadata.get_from_image(image)
    image = image.convert("RGB")

    cropped_faces, restored_faces, restored_img = GFPGAN.enhance(
        np.array(image, dtype=np.uint8),
        has_aligned=False,
        only_center_face=False,
        paste_back=True,
    )
    res = Image.fromarray(restored_img)
    metadata.GFPGAN = True
    ImageMetadata.set_on_image(res, metadata)

    if strength < 1.0:
        res = Image.blend(image, res, strength)

    return res


def run_RealESRGAN(image, model_name: str):
    ModelLoader(["GFPGAN", "LDSR"], False, True)
    ModelLoader(["RealESRGAN"], True, False)
    if RealESRGAN.model.name != model_name:
        try_loading_RealESRGAN(model_name)

    metadata = ImageMetadata.get_from_image(image)
    image = image.convert("RGB")

    output, img_mode = RealESRGAN.enhance(np.array(image, dtype=np.uint8))
    res = Image.fromarray(output)
    ImageMetadata.set_on_image(res, metadata)

    return res


if opt.defaults is not None and os.path.isfile(opt.defaults):
    try:
        with open(opt.defaults, "r", encoding="utf8") as f:
            user_defaults = yaml.safe_load(f)
    except (OSError, yaml.YAMLError) as e:
        print(f"Error loading defaults file {opt.defaults}:", e, file=sys.stderr)
        print("Falling back to program defaults.", file=sys.stderr)
        user_defaults = {}
else:
    user_defaults = {}

# make sure these indicies line up at the top of txt2img()
txt2img_toggles = [
    "Create prompt matrix (separate multiple prompts using |, and get all combinations of them)",
    "Normalize Prompt Weights (ensure sum of weights add up to 1.0)",
    "Save individual images",
    "Save grid",
    "Sort samples by prompt",
    "Write sample info files",
    "write sample info to log file",
    "jpg samples",
    "Filter NSFW content",
]

if GFPGAN is not None:
    txt2img_toggles.append("Fix faces using GFPGAN")
if RealESRGAN is not None:
    txt2img_toggles.append("Upscale images using RealESRGAN")

txt2img_defaults = {
    "prompt": "",
    "ddim_steps": 50,
    "toggles": [1, 2, 3],
    "sampler_name": "k_lms",
    "ddim_eta": 0.0,
    "n_iter": 1,
    "batch_size": 1,
    "cfg_scale": 7.5,
    "seed": "",
    "height": 512,
    "width": 512,
    "fp": None,
    "variant_amount": 0.0,
    "variant_seed": "",
    "submit_on_enter": "Yes",
    "realesrgan_model_name": "RealESRGAN_x4plus",
}

if "txt2img" in user_defaults:
    txt2img_defaults.update(user_defaults["txt2img"])

txt2img_toggle_defaults = [txt2img_toggles[i] for i in txt2img_defaults["toggles"]]

imgproc_defaults = {
    "prompt": "",
    "ddim_steps": 50,
    "sampler_name": "k_lms",
    "cfg_scale": 7.5,
    "seed": "",
    "height": 512,
    "width": 512,
    "denoising_strength": 0.30,
}
imgproc_mode_toggles = ["Fix Faces", "Upscale"]

# sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
# sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
sample_img2img = None
# make sure these indicies line up at the top of img2img()
img2img_toggles = [
    "Create prompt matrix (separate multiple prompts using |, and get all combinations of them)",
    "Normalize Prompt Weights (ensure sum of weights add up to 1.0)",
    "Loopback (use images from previous batch when creating next batch)",
    "Random loopback seed",
    "Save individual images",
    "Save grid",
    "Sort samples by prompt",
    "Write sample info files",
    "Write sample info to one file",
    "jpg samples",
    "Color correction (always enabled on loopback mode)",
    "Filter NSFW content",
]
# removed for now becuase of Image Lab implementation
if GFPGAN is not None:
    img2img_toggles.append("Fix faces using GFPGAN")
if RealESRGAN is not None:
    img2img_toggles.append("Upscale images using RealESRGAN")

img2img_mask_modes = [
    "Keep masked area",
    "Regenerate only masked area",
]

img2img_resize_modes = [
    "Just resize",
    "Crop and resize",
    "Resize and fill",
]

img2img_defaults = {
    "prompt": "",
    "ddim_steps": 50,
    "toggles": [1, 4, 5],
    "sampler_name": "k_lms",
    "ddim_eta": 0.0,
    "n_iter": 1,
    "batch_size": 1,
    "cfg_scale": 5.0,
    "denoising_strength": 0.75,
    "mask_mode": 0,
    "mask_restore": False,
    "resize_mode": 0,
    "seed": "",
    "height": 512,
    "width": 512,
    "fp": None,
    "mask_blur_strength": 1,
    "realesrgan_model_name": "RealESRGAN_x4plus",
    "image_editor_mode": "Mask",
}

if "img2img" in user_defaults:
    img2img_defaults.update(user_defaults["img2img"])

img2img_toggle_defaults = [img2img_toggles[i] for i in img2img_defaults["toggles"]]
img2img_image_mode = "sketch"

from scn2img import get_scn2img, scn2img_define_args

# avoid circular import, by passing all necessary types, functions
# and variables to get_scn2img, which will return scn2img function.
scn2img = get_scn2img(
    MemUsageMonitor,
    save_sample,
    get_next_sequence_number,
    seed_to_int,
    txt2img,
    txt2img_defaults,
    img2img,
    img2img_defaults,
    opt,
)

scn2img_toggles = [
    "Clear Cache",
    "Output intermediate images",
    "Save individual images",
    "Write sample info files",
    "Write sample info to one file",
    "jpg samples",
]
scn2img_defaults = {"prompt": "", "seed": "", "toggles": [1, 2, 3]}

if "scn2img" in user_defaults:
    scn2img_defaults.update(user_defaults["scn2img"])

scn2img_toggle_defaults = [scn2img_toggles[i] for i in scn2img_defaults["toggles"]]

help_text = """
    ## Mask/Crop
    * The masking/cropping is very temperamental.
    * It may take some time for the image to show when switching from Crop to Mask.
    * If the image doesn't appear after switching to Mask, switch back to Crop and then back again to Mask
    * If the mask appears distorted (the brush is weirdly shaped instead of round), switch back to Crop and then back again to Mask.

    ## Advanced Editor
    * For now the button needs to be clicked twice the first time.
    * Once you have edited your image, you _need_ to click the save button for the next step to work.
    * Clear the image from the crop editor (click the x)
    * Click "Get Image from Advanced Editor" to get the image you saved. If it doesn't work, try opening the editor and saving again.

    If it keeps not working, try switching modes again, switch tabs, clear the image or reload.
"""


def show_help():
    return [
        gr.update(visible=False),
        gr.update(visible=True),
        gr.update(value=help_text),
    ]


def hide_help():
    return [gr.update(visible=True), gr.update(visible=False), gr.update(value="")]


demo = draw_gradio_ui(
    opt,
    user_defaults=user_defaults,
    txt2img=txt2img,
    img2img=img2img,
    imgproc=imgproc,
    scn2img=scn2img,
    txt2img_defaults=txt2img_defaults,
    txt2img_toggles=txt2img_toggles,
    txt2img_toggle_defaults=txt2img_toggle_defaults,
    show_embeddings=hasattr(model, "embedding_manager"),
    img2img_defaults=img2img_defaults,
    img2img_toggles=img2img_toggles,
    img2img_toggle_defaults=img2img_toggle_defaults,
    img2img_mask_modes=img2img_mask_modes,
    img2img_resize_modes=img2img_resize_modes,
    sample_img2img=sample_img2img,
    imgproc_defaults=imgproc_defaults,
    imgproc_mode_toggles=imgproc_mode_toggles,
    scn2img_defaults=scn2img_defaults,
    scn2img_toggles=scn2img_toggles,
    scn2img_toggle_defaults=scn2img_toggle_defaults,
    scn2img_define_args=scn2img_define_args,
    RealESRGAN=RealESRGAN,
    GFPGAN=GFPGAN,
    LDSR=LDSR,
    run_GFPGAN=run_GFPGAN,
    run_RealESRGAN=run_RealESRGAN,
    job_manager=job_manager,
)


class ServerLauncher(threading.Thread):
    def __init__(self, demo):
        threading.Thread.__init__(self)
        self.name = "Gradio Server Thread"
        self.demo = demo

    def run(self):
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        gradio_params = {
            "inbrowser": opt.inbrowser,
            "server_name": "0.0.0.0",
            "server_port": opt.port,
            "share": opt.share,
            "show_error": True,
        }
        if not opt.share:
            demo.queue(concurrency_count=opt.max_jobs)
        if opt.share and opt.share_password:
            gradio_params["auth"] = ("webui", opt.share_password)

        # Check to see if Port 7860 is open
        port_status = 1
        while port_status != 0:
            try:
                self.demo.launch(**gradio_params)
            except OSError:
                print(
                    f"Error: Port: {opt.port} is not open yet. Please wait, this may take upwards of 60 seconds..."
                )
                time.sleep(10)
            else:
                port_status = 0

    def stop(self):
        self.demo.close()  # this tends to hang


def launch_server():
    server_thread = ServerLauncher(demo)
    server_thread.start()

    try:
        while server_thread.is_alive():
            time.sleep(60)
    except (KeyboardInterrupt, OSError) as e:
        crash(e, "Shutting down...")


def run_headless():
    with open(opt.cli, "r", encoding="utf8") as f:
        kwargs = yaml.safe_load(f)
    target = kwargs.pop("target")
    if target == "txt2img":
        target_func = txt2img
    elif target == "img2img":
        target_func = img2img
        raise NotImplementedError()
    else:
        raise ValueError(f"Unknown target: {target}")
    prompts = kwargs.pop("prompt")
    prompts = prompts if type(prompts) is list else [prompts]
    for i, prompt_i in enumerate(prompts):
        print(f"===== Prompt {i+1}/{len(prompts)}: {prompt_i} =====")
        output_images, seed, info, stats = target_func(prompt=prompt_i, **kwargs)
        print(f"Seed: {seed}")
        print(info)
        print(stats)
        print()


@logger.catch
def run_bridge(
    interval,
    api_key,
    horde_name,
    horde_url,
    priority_usernames,
    horde_max_pixels,
    horde_nsfw,
    horde_censor_nsfw,
    horde_blacklist,
    horde_censorlist,
):
    current_id = None
    current_payload = None
    loop_retry = 0
    while True:
        if loop_retry > 10 and current_id:
            logger.error(
                f"Exceeded retry count {loop_retry} for generation id {current_id}. Aborting generation!"
            )
            current_id = None
            current_payload = None
            current_generation = None
            loop_retry = 0
        elif current_id:
            logger.debug(
                f"Retrying ({loop_retry}/10) for generation id {current_id}..."
            )
        gen_dict = {
            "name": horde_name,
            "max_pixels": horde_max_pixels,
            "priority_usernames": priority_usernames,
            "nsfw": horde_nsfw,
            "blacklist": horde_blacklist,
        }
        headers = {"apikey": api_key}
        if current_id:
            loop_retry += 1
        else:
            try:
                pop_req = requests.post(
                    horde_url + "/api/v2/generate/pop", json=gen_dict, headers=headers
                )
            except requests.exceptions.ConnectionError:
                logger.warning(
                    f"Server {horde_url} unavailable during pop. Waiting 10 seconds..."
                )
                time.sleep(10)
                continue
            except requests.exceptions.JSONDecodeError():
                logger.warning(
                    f"Server {horde_url} unavailable during pop. Waiting 10 seconds..."
                )
                time.sleep(10)
                continue
            try:
                pop = pop_req.json()
            except json.decoder.JSONDecodeError:
                logger.error(
                    f"Could not decode response from {horde_url} as json. Please inform its administrator!"
                )
                time.sleep(interval)
                continue
            if pop is None:
                logger.error(
                    f"Something has gone wrong with {horde_url}. Please inform its administrator!"
                )
                time.sleep(interval)
                continue
            if not pop_req.ok:
                pop["message"]
                logger.warning(
                    f"During gen pop, server {horde_url} responded with status code {pop_req.status_code}: {pop['message']}. Waiting for 10 seconds..."
                )
                if "errors" in pop:
                    logger.warning(f"Detailed Request Errors: {pop['errors']}")
                time.sleep(10)
                continue
            if not pop.get("id"):
                skipped_info = pop.get("skipped")
                if skipped_info and len(skipped_info):
                    skipped_info = f" Skipped Info: {skipped_info}."
                else:
                    skipped_info = ""
                logger.debug(
                    f"Server {horde_url} has no valid generations to do for us.{skipped_info}"
                )
                time.sleep(interval)
                continue
            current_id = pop["id"]
            logger.debug(f"Request with id {current_id} picked up. Initiating work...")
            current_payload = pop["payload"]
            if "toggles" in current_payload and current_payload["toggles"] is None:
                logger.error(f"Received Bad payload: {pop}")
                current_id = None
                current_payload = None
                current_generation = None
                loop_retry = 0
                time.sleep(10)
                continue
        current_payload["toggles"] = current_payload.get("toggles", [1, 4])
        # In bridge-mode, matrix is prepared on the horde and split in multiple nodes
        if 0 in current_payload["toggles"]:
            current_payload["toggles"].remove(0)
        if 8 not in current_payload["toggles"]:
            if horde_censor_nsfw and not horde_nsfw:
                current_payload["toggles"].append(8)
            elif any(word in current_payload["prompt"] for word in horde_censorlist):
                current_payload["toggles"].append(8)
        images, seed, info, stats = txt2img(**current_payload)
        buffer = BytesIO()
        # We send as WebP to avoid using all the horde bandwidth
        images[0].save(buffer, format="WebP", quality=90)
        # logger.info(info)
        submit_dict = {
            "id": current_id,
            "generation": base64.b64encode(buffer.getvalue()).decode("utf8"),
            "api_key": api_key,
            "seed": seed,
            "max_pixels": horde_max_pixels,
        }
        current_generation = seed
        while current_id and current_generation is not None:
            try:
                submit_req = requests.post(
                    horde_url + "/api/v2/generate/submit",
                    json=submit_dict,
                    headers=headers,
                )
                try:
                    submit = submit_req.json()
                except json.decoder.JSONDecodeError:
                    logger.error(
                        f"Something has gone wrong with {horde_url} during submit. Please inform its administrator!  (Retry {loop_retry}/10)"
                    )
                    time.sleep(interval)
                    continue
                if submit_req.status_code == 404:
                    logger.warning(
                        "The generation we were working on got stale. Aborting!"
                    )
                elif not submit_req.ok:
                    logger.warning(
                        f"During gen submit, server {horde_url} responded with status code {submit_req.status_code}: {submit['message']}. Waiting for 10 seconds...  (Retry {loop_retry}/10)"
                    )
                    if "errors" in submit:
                        logger.warning(f"Detailed Request Errors: {submit['errors']}")
                    time.sleep(10)
                    continue
                else:
                    logger.info(
                        f'Submitted generation with id {current_id} and contributed for {submit_req.json()["reward"]}'
                    )
                current_id = None
                current_payload = None
                current_generation = None
                loop_retry = 0
            except requests.exceptions.ConnectionError:
                logger.warning(
                    f"Server {horde_url} unavailable during submit. Waiting 10 seconds...  (Retry {loop_retry}/10)"
                )
                time.sleep(10)
                continue
        time.sleep(interval)


if __name__ == "__main__":
    set_logger_verbosity(opt.verbosity)
    quiesce_logger(opt.quiet)
    if opt.cli:
        run_headless()
    if opt.bridge:
        try:
            import bridgeData as cd
        except ModuleNotFoundError as e:
            logger.warning(
                "No bridgeData found. Falling back to default where no CLI args are set."
            )
            logger.warning(str(e))
        except SyntaxError as e:
            logger.warning(
                "bridgeData found, but is malformed. Falling back to default where no CLI args are set."
            )
            logger.warning(str(e))
        except Exception as e:
            logger.warning("No bridgeData found, use default where no CLI args are set")
            logger.warning(str(e))
        finally:
            try:  # check if cd exists (i.e. bridgeData loaded properly)
                cd
            except:  # if not, create defaults

                class temp(object):
                    def __init__(self):
                        random.seed()
                        self.horde_url = "https://stablehorde.net"
                        # Give a cool name to your instance
                        self.horde_name = f"Automated Instance #{random.randint(-100000000, 100000000)}"
                        # The api_key identifies a unique user in the horde
                        self.horde_api_key = "0000000000"
                        # Put other users whose prompts you want to prioritize.
                        # The owner's username is always included so you don't need to add it here, unless you want it to have lower priority than another user
                        self.horde_priority_usernames = []
                        self.horde_max_power = 8
                        self.nsfw = True

                cd = temp()
        horde_api_key = opt.horde_api_key if opt.horde_api_key else cd.horde_api_key
        horde_name = opt.horde_name if opt.horde_name else cd.horde_name
        horde_url = opt.horde_url if opt.horde_url else cd.horde_url
        horde_priority_usernames = (
            opt.horde_priority_usernames
            if opt.horde_priority_usernames
            else cd.horde_priority_usernames
        )
        horde_max_power = (
            opt.horde_max_power if opt.horde_max_power else cd.horde_max_power
        )
        try:
            horde_nsfw = not opt.horde_sfw if opt.horde_sfw else cd.horde_nsfw
        except AttributeError:
            horde_nsfw = True
        try:
            horde_censor_nsfw = (
                opt.horde_censor_nsfw if opt.horde_censor_nsfw else cd.horde_censor_nsfw
            )
        except AttributeError:
            horde_censor_nsfw = False
        try:
            horde_blacklist = (
                opt.horde_blacklist if opt.horde_blacklist else cd.horde_blacklist
            )
        except AttributeError:
            horde_blacklist = []
        try:
            horde_censorlist = (
                opt.horde_censorlist if opt.horde_censorlist else cd.horde_censorlist
            )
        except AttributeError:
            horde_censorlist = []
        if horde_max_power < 2:
            horde_max_power = 2
        horde_max_pixels = 64 * 64 * 8 * horde_max_power
        logger.info(
            f"Joining Horde with parameters: API Key '{horde_api_key}'. Server Name '{horde_name}'. Horde URL '{horde_url}'. Max Pixels {horde_max_pixels}"
        )
        try:
            run_bridge(
                1,
                horde_api_key,
                horde_name,
                horde_url,
                horde_priority_usernames,
                horde_max_pixels,
                horde_nsfw,
                horde_censor_nsfw,
                horde_blacklist,
                horde_censorlist,
            )
        except KeyboardInterrupt:
            logger.info("Keyboard Interrupt Received. Ending Bridge")
    else:
        launch_server()


================================================
FILE: scripts/webui_streamlit.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

# base webui import and utils.
# import streamlit as st

# We import hydralit like this to replace the previous stuff
# we had with native streamlit as it lets ur replace things 1:1
from sd_utils import (
    st,
    hc,
    load_configs,
    load_css,
    # set_logger_verbosity,
    logger,
    # quiesce_logger,
    set_page_title,
    threading,
    random,
)

# streamlit imports

# streamlit components section
# from st_on_hover_tabs import on_hover_tabs
from streamlit_server_state import server_state, server_state_lock

# other imports
import argparse
from sd_utils.bridge import run_bridge

# import custom components
from custom_components import draggable_number_input

# end of imports
# ---------------------------------------------------------------------------------------------------------------

load_configs()

help = """
A double dash (`--`) is used to separate streamlit arguments from app arguments.
As a result using "streamlit run webui_streamlit.py --headless"
will show the help for streamlit itself and not pass any argument to our app,
we need to use "streamlit run webui_streamlit.py -- --headless"
in order to pass a command argument to this app."""
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)

parser.add_argument(
    "--headless",
    action="store_true",
    help="Don't launch web server, util if you just want to run the stable horde bridge.",
    default=False,
)

parser.add_argument(
    "--bridge",
    action="store_true",
    help="don't launch web server, but make this instance into a Horde bridge.",
    default=False,
)
parser.add_argument(
    "--horde_api_key",
    action="store",
    required=False,
    type=str,
    help="The API key corresponding to the owner of this Horde instance",
)
parser.add_argument(
    "--horde_name",
    action="store",
    required=False,
    type=str,
    help="The server name for the Horde. It will be shown to the world and there can be only one.",
)
parser.add_argument(
    "--horde_url",
    action="store",
    required=False,
    type=str,
    help="The SH Horde URL. Where the bridge will pickup prompts and send the finished generations.",
)
parser.add_argument(
    "--horde_priority_usernames",
    type=str,
    action="append",
    required=False,
    help="Usernames which get priority use in this horde instance. The owner's username is always in this list.",
)
parser.add_argument(
    "--horde_max_power",
    type=int,
    required=False,
    help="How much power this instance has to generate pictures. Min: 2",
)
parser.add_argument(
    "--horde_sfw",
    action="store_true",
    required=False,
    help="Set to true if you do not want this worker generating NSFW images.",
)
parser.add_argument(
    "--horde_blacklist",
    nargs="+",
    required=False,
    help="List the words that you want to blacklist.",
)
parser.add_argument(
    "--horde_censorlist",
    nargs="+",
    required=False,
    help="List the words that you want to censor.",
)
parser.add_argument(
    "--horde_censor_nsfw",
    action="store_true",
    required=False,
    help="Set to true if you want this bridge worker to censor NSFW images.",
)
parser.add_argument(
    "--horde_model",
    action="store",
    required=False,
    help="Which model to run on this horde.",
)
parser.add_argument(
    "-v",
    "--verbosity",
    action="count",
    default=0,
    help="The default logging level is ERROR or higher. This value increases the amount of logging seen in your screen",
)
parser.add_argument(
    "-q",
    "--quiet",
    action="count",
    default=0,
    help="The default logging level is ERROR or higher. This value decreases the amount of logging seen in your screen",
)
opt = parser.parse_args()

with server_state_lock["bridge"]:
    server_state["bridge"] = opt.bridge


@logger.catch(reraise=True)
def layout():
    """Layout functions to define all the streamlit layout here."""
    if not st.session_state["defaults"].debug.enable_hydralit:
        st.set_page_config(
            page_title="Stable Diffusion Playground",
            layout="wide",
            initial_sidebar_state="collapsed",
        )

    # app = st.HydraApp(title='Stable Diffusion WebUI', favicon="", sidebar_state="expanded", layout="wide",
    # hide_streamlit_markers=False, allow_url_nav=True , clear_cross_app_sessions=False)

    # load css as an external file, function has an option to local or remote url. Potential use when running from cloud infra that might not have access to local path.
    load_css(True, "frontend/css/streamlit.main.css")

    #
    # specify the primary menu definition
    menu_data = [
        {
            "id": "Stable Diffusion",
            "label": "Stable Diffusion",
            "icon": "bi bi-grid-1x2-fill",
        },
        {
            "id": "Train",
            "label": "Train",
            "icon": "bi bi-lightbulb-fill",
            "submenu": [
                {
                    "id": "Textual Inversion",
                    "label": "Textual Inversion",
                    "icon": "bi bi-lightbulb-fill",
                },
                {
                    "id": "Fine Tunning",
                    "label": "Fine Tunning",
                    "icon": "bi bi-lightbulb-fill",
                },
            ],
        },
        {
            "id": "Model Manager",
            "label": "Model Manager",
            "icon": "bi bi-cloud-arrow-down-fill",
        },
        {
            "id": "Tools",
            "label": "Tools",
            "icon": "bi bi-tools",
            "submenu": [
                {"id": "API Server", "label": "API Server", "icon": "bi bi-server"},
                {
                    "id": "Barfi/BaklavaJS",
                    "label": "Barfi/BaklavaJS",
                    "icon": "bi bi-diagram-3-fill",
                },
                # {'id': 'API Server', 'label': 'API Server', 'icon': 'bi bi-server'},
            ],
        },
        {"id": "Settings", "label": "Settings", "icon": "bi bi-gear-fill"},
    ]

    over_theme = {"txc_inactive": "#FFFFFF", "menu_background": "#000000"}

    menu_id = hc.nav_bar(
        menu_definition=menu_data,
        # home_name='Home',
        # login_name='Logout',
        hide_streamlit_markers=False,
        override_theme=over_theme,
        sticky_nav=True,
        sticky_mode="pinned",
    )

    #
    # if menu_id == "Home":
    # st.info("Under Construction. :construction_worker:")

    if menu_id == "Stable Diffusion":
        # set the page url and title
        # st.experimental_set_query_params(page='stable-diffusion')
        try:
            set_page_title("Stable Diffusion Playground")
        except NameError:
            st.experimental_rerun()

        (
            txt2img_tab,
            img2img_tab,
            txt2vid_tab,
            img2txt_tab,
            post_processing_tab,
            concept_library_tab,
        ) = st.tabs(
            [
                "Text-to-Image",
                "Image-to-Image",
                # "Inpainting",
                "Text-to-Video",
                "Image-To-Text",
                "Post-Processing",
                "Concept Library",
            ]
        )
        # with home_tab:
        # from home import layout
        # layout()

        with txt2img_tab:
            from txt2img import layout

            layout()

        with img2img_tab:
            from img2img import layout

            layout()

        # with inpainting_tab:
        # from inpainting import layout
        # layout()

        with txt2vid_tab:
            from txt2vid import layout

            layout()

        with img2txt_tab:
            from img2txt import layout

            layout()

        with post_processing_tab:
            from post_processing import layout

            layout()

        with concept_library_tab:
            from sd_concept_library import layout

            layout()

    #
    elif menu_id == "Model Manager":
        set_page_title("Model Manager - Stable Diffusion Playground")

        from ModelManager import layout

        layout()

    elif menu_id == "Textual Inversion":
        from textual_inversion import layout

        layout()

    elif menu_id == "Fine Tunning":
        # from textual_inversion import layout
        # layout()
        st.info("Under Construction. :construction_worker:")

    elif menu_id == "API Server":
        set_page_title("API Server - Stable Diffusion Playground")
        from APIServer import layout

        layout()

    elif menu_id == "Barfi/BaklavaJS":
        set_page_title("Barfi/BaklavaJS - Stable Diffusion Playground")
        from barfi_baklavajs import layout

        layout()

    elif menu_id == "Settings":
        set_page_title("Settings - Stable Diffusion Playground")

        from Settings import layout

        layout()

    # calling dragable input component module at the end, so it works on all pages
    draggable_number_input.load()


if __name__ == "__main__":
    # set_logger_verbosity(opt.verbosity)
    # quiesce_logger(opt.quiet)

    if not opt.headless:
        layout()

    with server_state_lock["bridge"]:
        if server_state["bridge"]:
            try:
                import bridgeData as cd
            except ModuleNotFoundError as e:
                logger.warning(
                    "No bridgeData found. Falling back to default where no CLI args are set."
                )
                logger.debug(str(e))
            except SyntaxError as e:
                logger.warning(
                    "bridgeData found, but is malformed. Falling back to default where no CLI args are set."
                )
                logger.debug(str(e))
            except Exception as e:
                logger.warning(
                    "No bridgeData found, use default where no CLI args are set"
                )
                logger.debug(str(e))
            finally:
                try:  # check if cd exists (i.e. bridgeData loaded properly)
                    cd
                except:  # if not, create defaults

                    class temp(object):
                        def __init__(self):
                            random.seed()
                            self.horde_url = "https://stablehorde.net"
                            # Give a cool name to your instance
                            self.horde_name = f"Automated Instance #{random.randint(-100000000, 100000000)}"
                            # The api_key identifies a unique user in the horde
                            self.horde_api_key = "0000000000"
                            # Put other users whose prompts you want to prioritize.
                            # The owner's username is always included so you don't need to add it here, unless you want it to have lower priority than another user
                            self.horde_priority_usernames = []
                            self.horde_max_power = 8
                            self.nsfw = True
                            self.censor_nsfw = False
                            self.blacklist = []
                            self.censorlist = []
                            self.models_to_load = ["stable_diffusion"]

                    cd = temp()
            horde_api_key = opt.horde_api_key if opt.horde_api_key else cd.horde_api_key
            horde_name = opt.horde_name if opt.horde_name else cd.horde_name
            horde_url = opt.horde_url if opt.horde_url else cd.horde_url
            horde_priority_usernames = (
                opt.horde_priority_usernames
                if opt.horde_priority_usernames
                else cd.horde_priority_usernames
            )
            horde_max_power = (
                opt.horde_max_power if opt.horde_max_power else cd.horde_max_power
            )
            # Not used yet
            horde_models = [opt.horde_model] if opt.horde_model else cd.models_to_load
            try:
                horde_nsfw = not opt.horde_sfw if opt.horde_sfw else cd.horde_nsfw
            except AttributeError:
                horde_nsfw = True
            try:
                horde_censor_nsfw = (
                    opt.horde_censor_nsfw
                    if opt.horde_censor_nsfw
                    else cd.horde_censor_nsfw
                )
            except AttributeError:
                horde_censor_nsfw = False
            try:
                horde_blacklist = (
                    opt.horde_blacklist if opt.horde_blacklist else cd.horde_blacklist
                )
            except AttributeError:
                horde_blacklist = []
            try:
                horde_censorlist = (
                    opt.horde_censorlist
                    if opt.horde_censorlist
                    else cd.horde_censorlist
                )
            except AttributeError:
                horde_censorlist = []
            if horde_max_power < 2:
                horde_max_power = 2
            horde_max_pixels = 64 * 64 * 8 * horde_max_power
            logger.info(
                f"Joining Horde with parameters: Server Name '{horde_name}'. Horde URL '{horde_url}'. Max Pixels {horde_max_pixels}"
            )

            try:
                thread = threading.Thread(
                    target=run_bridge(
                        1,
                        horde_api_key,
                        horde_name,
                        horde_url,
                        horde_priority_usernames,
                        horde_max_pixels,
                        horde_nsfw,
                        horde_censor_nsfw,
                        horde_blacklist,
                        horde_censorlist,
                    ),
                    args=(),
                )
                thread.daemon = True
                thread.start()
                # run_bridge(1, horde_api_key, horde_name, horde_url, horde_priority_usernames, horde_max_pixels, horde_nsfw, horde_censor_nsfw, horde_blacklist, horde_censorlist)
            except KeyboardInterrupt:
                print("Keyboard Interrupt Received. Ending Bridge")


================================================
FILE: scripts/webui_streamlit_new.py
================================================


================================================
FILE: setup.py
================================================
from setuptools import setup, find_packages

setup(
    name="sygil-webui",
    version="0.0.1",
    description="",
    packages=find_packages(),
    install_requires=[
        "torch",
        "numpy",
        "tqdm",
    ],
)


================================================
FILE: sidebars.js
================================================
/**
 * Creating a sidebar enables you to:
 - create an ordered group of docs
 - render a sidebar for each doc of that group
 - provide next/previous navigation

 The sidebars can be generated from the filesystem, or explicitly defined here.

 Create as many sidebars as you want.
 */

// @ts-check

/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */
const sidebars = {
  // By default, Docusaurus generates a sidebar from the docs folder structure
  tutorialSidebar: [{type: 'autogenerated', dirName: '.'}],

  // But you can create a sidebar manually
  /*
  tutorialSidebar: [
    'intro',
    'hello',
    {
      type: 'category',
      label: 'Tutorial',
      items: ['tutorial-basics/create-a-document'],
    },
  ],
   */
};

module.exports = sidebars;


================================================
FILE: streamlit_webview.py
================================================
import os, webview
from streamlit.web import bootstrap
from streamlit import config as _config

webview.create_window(
    "Sygil", "http://localhost:8501", width=1000, height=800, min_size=(500, 500)
)
webview.start()

dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, "scripts/webui_streamlit.py")

_config.set_option("server.headless", True)
args = []

# streamlit.cli.main_run(filename, args)
bootstrap.run(filename, "", args, flag_options={})


================================================
FILE: webui/flet/assets/manifest.json
================================================
{
  "name": "Sygil",
  "short_name": "Sygil",
  "start_url": ".",
  "display": "standalone",
  "background_color": "#ffffff",
  "theme_color": "#5175C2",
  "description": "This is a customized Flet app description.",
  "orientation": "natural",
  "prefer_related_applications": false,
  "icons": [
    {
      "src": "icons/icon-192.png",
      "sizes": "192x192",
      "type": "image/png"
    },
    {
      "src": "icons/icon-512.png",
      "sizes": "512x512",
      "type": "image/png"
    },
    {
      "src": "icons/icon-maskable-192.png",
      "sizes": "192x192",
      "type": "image/png",
      "purpose": "maskable"
    },
    {
      "src": "icons/icon-maskable-512.png",
      "sizes": "512x512",
      "type": "image/png",
      "purpose": "maskable"
    }
  ]
}


================================================
FILE: webui/flet/scripts/__init__.py
================================================
# other imports


================================================
FILE: webui/flet/scripts/flet_asset_manager.py
================================================
# flet_layer_manager.py

# Flet imports
import flet as ft

# utils imports


class AssetManager(ft.Container):
    def setup(self):
        self.width = self.page.left_panel_width
        self.bgcolor = self.page.primary_color
        self.padding = self.page.container_padding
        self.margin = self.page.container_margin

        self.set_tab_text_size(self.page.text_size)
        self.set_tab_bgcolor(self.page.secondary_color)
        self.set_tab_padding(self.page.container_padding)
        self.set_tab_margin(self.page.container_margin)

        self.dragbar.content.width = self.page.vertical_divider_width
        self.dragbar.content.color = self.page.tertiary_color

    def on_page_change(self):
        self.width = self.page.left_panel_width
        self.bgcolor = self.page.primary_color
        self.padding = self.page.container_padding
        self.margin = self.page.container_margin

        self.set_tab_text_size(self.page.text_size)
        self.set_tab_bgcolor(self.page.secondary_color)
        self.set_tab_padding(self.page.container_padding)
        self.set_tab_margin(self.page.container_margin)

        self.dragbar.content.width = self.page.vertical_divider_width
        self.dragbar.content.color = self.page.tertiary_color

        if self.page.active_layer is not None:
            self.page.active_layer.handle.color = self.page.tertiary_color

    def add_image_as_layer(self, image):
        return self.layer_panel.add_image_as_layer(image)

    def add_images_as_layers(self, images):
        return self.layer_panel.add_images_as_layers(images)

    def set_tab_text_size(self, size):
        for tab in self.tabs:
            tab.tab_content.size = size

    def set_tab_bgcolor(self, color):
        for tab in self.tabs:
            tab.content.bgcolor = color

    def set_tab_padding(self, padding):
        for tab in self.tabs:
            tab.content.padding = padding

    def set_tab_margin(self, margin):
        for tab in self.tabs:
            tab.content.margin = margin

    def resize_asset_manager(self, e: ft.DragUpdateEvent):
        self.page.left_panel_width = max(250, self.page.left_panel_width + e.delta_x)
        self.width = self.page.left_panel_width
        self.page.update()

    def refresh_layers(self):
        self.layer_panel.refresh_layers()


class AssetPanel(ft.Container):
    pass


class LayerPanel(ft.Container):
    def refresh_layers(self):
        self.layers = self.content.content.controls
        self.refresh_layer_indexes()
        self.refresh_visible_layers()
        self.update()

    def refresh_layer_indexes(self):
        count = 0
        for layer in self.layers:
            layer.index = count
            count += 1

    def refresh_visible_layers(self):
        self.page.visible_layers = []
        for layer in self.layers:
            if not layer.disabled:
                self.page.visible_layers.append(layer)

    def refresh_layer_name(self, e):
        self.page.refresh_layers()

    def make_layer_active(self, index):
        for i, layer in enumerate(self.layers):
            layer.active = False
            layer.handle.color = None
            if i == index:
                layer.active = True
                layer.handle.color = self.page.tertiary_color
                self.page.set_active_layer(layer)

    def add_layer_slot(self, image):
        label = ft.TextField(
            value=image.filename,
            focused_border_color=self.page.tertiary_color,
            text_size=self.page.text_size,
            content_padding=ft.padding.only(left=12, top=0, right=0, bottom=0),
            expand=True,
            on_submit=self.refresh_layer_name,
        )
        handle = ft.Icon(
            name=ft.icons.DRAG_HANDLE,
            color=None,
            tooltip="drag to move",
        )
        layer_slot = LayerSlot(
            content=ft.Row(
                controls=[
                    label,
                    handle,
                ],
                expand=True,
            ),
            height=self.page.layer_height,
            padding=0,
            margin=0,
        )
        layer_slot.label = label
        layer_slot.handle = handle
        layer_slot.index = 0
        layer_slot.disabled = False
        layer_slot.active = False
        layer_slot.image = image
        layer_slot.layer_image = None
        self.content.content.controls.insert(0, layer_slot)
        self.layers = self.content.content.controls
        self.refresh_layer_indexes()
        self.make_layer_active(0)
        return layer_slot

    def add_image_as_layer(self, image):
        return self.add_layer_slot(image)

    def add_images_as_layers(self, images):
        layer_slots = []
        for image in images:
            layer_slots.append(self.add_image_as_layer(image))
        return layer_slots

    def get_layer_index_from_position(self, pos):
        index = int(pos / self.page.layer_height)
        return index

    def move_layer(self, layer, index):
        if index > len(self.layers):
            layer = self.layers.pop(layer.index)
            self.layers.append(layer)
        if layer.index < index:
            index -= 1
        layer = self.layers.pop(layer.index)
        self.layers.insert(index, layer)
        self.page.refresh_layers()

    def delete_layer(self, layer):
        if not layer:
            return
        self.layers.pop(layer.index)


class LayerSlot(ft.Container):
    pass


class LayerActionMenu(ft.Card):
    def show_menu(self):
        self.visible = True
        self.update()

    def hide_menu(self):
        self.visible = False
        self.update()


def close_menu(e):
    layer_action_menu.hide_menu()


def show_hide_layer(e):
    e.page.active_layer.disabled = False if e.page.active_layer.disabled else True
    e.page.refresh_layers()
    close_menu(e)


def move_layer_to_top(e):
    layer_panel.move_layer(e.page.active_layer, 0)
    close_menu(e)


def move_layer_up(e):
    layer_panel.move_layer(e.page.active_layer, e.page.active_layer.index - 1)
    close_menu(e)


def move_layer_down(e):
    layer_panel.move_layer(e.page.active_layer, e.page.active_layer.index + 2)
    close_menu(e)


def delete_layer(e):
    layer_panel.delete_layer(e.page.active_layer)
    e.page.active_layer = None
    e.page.refresh_layers()
    close_menu(e)


class LayerAction:
    def __init__(self, text, on_click):
        self.text = text
        self.on_click = on_click


layer_action_list = [
    LayerAction("Show/Hide Layer", show_hide_layer),
    LayerAction("Move Layer To Top", move_layer_to_top),
    LayerAction("Move Layer Up", move_layer_up),
    LayerAction("Move Layer Down", move_layer_down),
    LayerAction("Delete Layer", delete_layer),
]


def make_action_buttons(action_list):
    button_list = []
    for action in action_list:
        button_list.append(
            ft.TextButton(
                text=action.text,
                on_click=action.on_click,
            )
        )
    return button_list


# LayerActionMenu == ft.Card
layer_action_menu = LayerActionMenu(
    content=ft.GestureDetector(
        content=ft.Column(
            controls=make_action_buttons(layer_action_list),
            expand=False,
            spacing=0,
            alignment="start",
            tight=True,
        ),
        on_exit=close_menu,
    ),
    margin=0,
    visible=False,
)


def layer_left_click(e: ft.TapEvent):
    index = layer_panel.get_layer_index_from_position(e.local_y)
    if index >= len(layer_panel.layers):
        return
    layer_panel.make_layer_active(index)
    layer_panel.update()


def layer_right_click(e: ft.TapEvent):
    index = layer_panel.get_layer_index_from_position(e.local_y)
    if index >= len(layer_panel.layers):
        return
    layer_panel.make_layer_active(index)
    layer_panel.update()
    layer_action_menu.left = e.global_x
    layer_action_menu.top = e.global_y
    layer_action_menu.show_menu()


def pickup_layer(e: ft.DragStartEvent):
    index = layer_panel.get_layer_index_from_position(e.local_y)
    if index >= len(layer_panel.layers):
        return
    layer_panel.layer_being_moved = layer_panel.layers[index]
    layer_panel.make_layer_active(layer_panel.layer_being_moved.index)
    layer_panel.update()


def on_layer_drag(e: ft.DragUpdateEvent):
    if not layer_panel.layer_being_moved:
        return
    index = layer_panel.get_layer_index_from_position(e.local_y)
    if index == layer_panel.layer_being_moved.index:
        return
    layer_panel.move_layer(layer_panel.layer_being_moved, index)


def drop_layer(e: ft.DragEndEvent):
    e.page.refresh_layers()
    layer_panel.layer_being_moved = None


# LayerPanel == ft.Container
layer_panel = LayerPanel(
    content=ft.GestureDetector(
        content=ft.Column(
            controls=[],
            alignment="start",
            expand=True,
            spacing=0,
            scroll="hidden",
        ),
        drag_interval=10,
        on_tap_down=layer_left_click,
        on_secondary_tap_down=layer_right_click,
        on_vertical_drag_start=pickup_layer,
        on_vertical_drag_update=on_layer_drag,
        on_vertical_drag_end=drop_layer,
    ),
)

layer_panel.layers = []
layer_panel.layer_being_moved = None
layer_panel.layer_last_index = 0


# AssetPanel == ft.Container
asset_panel = AssetPanel(
    content=ft.Column(
        controls=[
            ft.Text("Under Construction"),
        ],
    ),
)


def resize_asset_manager(e):
    asset_manager.resize_asset_manager(e)


def realign_canvas(e):
    e.page.align_canvas()


asset_manager_dragbar = ft.GestureDetector(
    mouse_cursor=ft.MouseCursor.RESIZE_COLUMN,
    drag_interval=50,
    on_pan_update=resize_asset_manager,
    on_pan_end=realign_canvas,
    content=ft.VerticalDivider(),
)


# AssetManager == ft.Container
asset_manager = AssetManager(
    content=ft.Row(
        controls=[
            ft.Column(
                controls=[
                    ft.Tabs(
                        selected_index=0,
                        animation_duration=300,
                        tabs=[
                            ft.Tab(
                                content=layer_panel,
                                tab_content=ft.Text(
                                    value="Layers",
                                ),
                            ),
                            ft.Tab(
                                content=asset_panel,
                                tab_content=ft.Text(
                                    value="Assets",
                                ),
                            ),
                        ],
                    ),
                ],
                alignment="start",
                expand=True,
            ),
            asset_manager_dragbar,
        ],
        expand=True,
    ),
    clip_behavior="antiAlias",
)

asset_manager.tabs = asset_manager.content.controls[0].controls[0].tabs
asset_manager.layer_panel = layer_panel
asset_manager.asset_panel = asset_panel
asset_manager.dragbar = asset_manager_dragbar

"""
	# keep track of which layers are visible
	def show_hide_layer(self, e):
		parent = e.control.data['parent']
		if parent.data['visible']:
			parent.data['visible'] = False
			parent.opacity = 0.5
			e.control.icon = ft.icons.VISIBILITY_OFF
		else:
			parent.data['visible'] = True
			parent.opacity = 1.0
			e.control.icon = ft.icons.VISIBILITY
		self.update_visible_layer_list()
		parent.update()
		self.page.refresh_canvas()

	def update_visible_layer_list(self):
		self.page.visible_layer_list = []
		layer_list = self.page.layer_list
		for layer in layer_list:
			if layer.data['type'] == 'slot':
				if layer.content.content.controls[1].data['visible']:
					self.page.visible_layer_list.append(layer)

	# keep track of which layers are active
	def lock_unlock_layer(self, e):
		parent = e.control.data['parent']
		if parent.data['locked']:
			parent.data['locked'] = False
			e.control.icon = ft.icons.LOCK_OPEN_OUTLINED
		else:
			parent.data['locked'] = True
			e.control.icon = ft.icons.LOCK_OUTLINED
		self.update_active_layer_list()
		parent.update()

	def update_active_layer_list(self):
		self.page.active_layer_list = []
		layer_list = self.page.layer_list
		for layer in layer_list:
			if layer.data['type'] == 'slot':
				if not layer.content.content.controls[1].data['locked']:
					self.page.active_layer_list.append(layer)


"""


================================================
FILE: webui/flet/scripts/flet_canvas.py
================================================
# flet_canvas.py

# Flet imports
import flet as ft

# utils imports
from scripts import flet_utils


class Canvas(ft.Container):
    def setup(self):
        self.bgcolor = self.page.secondary_color
        self.padding = self.page.container_padding
        self.margin = self.page.container_margin

        self.overlay.tools.center.icon_size = self.page.icon_size
        self.overlay.tools.zoom_in.icon_size = self.page.icon_size
        self.overlay.tools.zoom_out.icon_size = self.page.icon_size

        self.overlay.size_display.content.color = self.page.text_color
        self.overlay.size_display.content.size = self.page.text_size
        self.add_canvas_background()
        self.center_canvas()
        self.refresh_canvas()

    def on_page_change(self):
        self.bgcolor = self.page.secondary_color
        self.padding = self.page.container_padding
        self.margin = self.page.container_margin

        self.overlay.tools.center.icon_size = self.page.icon_size
        self.overlay.tools.zoom_in.icon_size = self.page.icon_size
        self.overlay.tools.zoom_out.icon_size = self.page.icon_size

        self.overlay.size_display.content.color = self.page.text_color
        self.overlay.size_display.content.size = self.page.text_size
        self.refresh_canvas()

    def refresh_canvas(self):
        self.image_stack.refresh_stack()
        self.align_canvas()
        self.overlay.refresh_canvas_overlay()

    def set_current_tool(self, tool):
        self.page.current_tool = tool

    def add_canvas_background(self):
        self.image_stack.add_canvas_background()

    def add_layer_image(self, image):
        return self.image_stack.add_layer_image(image)

    def get_image_stack_preview(self):
        return self.image_stack.get_preview()

    def center_canvas(self):
        width, height = self.page.get_viewport_size()
        self.image_stack.offset_x = 0
        self.image_stack.offset_y = 0
        self.image_stack.left = (width * 0.5) - (self.image_stack.width * 0.5)
        self.image_stack.top = (height * 0.5) - (self.image_stack.height * 0.5)
        self.overlay.frame.left = self.image_stack.left
        self.overlay.frame.top = self.image_stack.top
        self.update()

    def align_canvas(self):
        width, height = self.page.get_viewport_size()
        self.image_stack.left = (
            (width * 0.5) - (self.image_stack.width * 0.5) + self.image_stack.offset_x
        )
        self.image_stack.top = (
            (height * 0.5) - (self.image_stack.height * 0.5) + self.image_stack.offset_y
        )
        self.overlay.frame.left = self.image_stack.left
        self.overlay.frame.top = self.image_stack.top
        self.overlay.frame.scale = self.image_stack.scale
        self.update()

    def pan_canvas(self, e: ft.DragUpdateEvent):
        self.image_stack.offset_x += e.delta_x
        self.image_stack.offset_y += e.delta_y
        width, height = self.page.get_viewport_size()
        self.image_stack.offset_x = max(
            self.image_stack.offset_x, (width - self.image_stack.width) * 0.5
        )
        self.image_stack.offset_y = max(
            self.image_stack.offset_y, (height - self.image_stack.height) * 0.5
        )
        self.image_stack.offset_x = min(
            self.image_stack.offset_x, (self.image_stack.width - width) * 0.5
        )
        self.image_stack.offset_y = min(
            self.image_stack.offset_y, (self.image_stack.height - height) * 0.5
        )
        self.align_canvas()

    def zoom_in(self, e):
        if self.image_stack.scale >= 4.0:
            self.image_stack.scale = 4.0
        else:
            self.image_stack.scale += 0.05
        self.image_stack.get_scaled_size()
        self.overlay.frame.scale = self.image_stack.scale
        self.align_canvas()

    def zoom_out(self, e):
        if self.image_stack.scale <= 0.1:
            self.image_stack.scale = 0.1
        else:
            self.image_stack.scale -= 0.05
        self.overlay.frame.scale = self.image_stack.scale
        self.image_stack.get_scaled_size()
        self.align_canvas()

    def clear_tools(self):
        self.overlay.clear_tools()

    def set_current_tool(self, tool):
        if tool == "pan":
            self.overlay.controls.pop(2)
            self.overlay.controls.insert(2, pan_tool)
        elif tool == "move":
            self.overlay.controls.pop(2)
            self.overlay.controls.insert(2, move_tool)
        elif tool == "box_select":
            self.overlay.controls.pop(2)
            self.overlay.controls.insert(2, box_select_tool)
        elif tool == "brush":
            self.overlay.controls.pop(2)
            self.overlay.controls.insert(2, brush_tool)
        elif tool == "fill":
            self.overlay.controls.pop(2)
            self.overlay.controls.insert(2, fill_tool)
        else:
            pass
        self.update()


class ImageStack(ft.Container):
    def add_canvas_background(self):
        image = self.page.canvas_background
        canvas_bg = LayerImage(
            left=0,
            top=0,
            width=self.width,
            height=self.height,
            content=ft.Image(
                src_base64=flet_utils.convert_image_to_base64(image),
                width=256,
                height=256,
                repeat="repeat",
                gapless_playback=True,
            ),
        )
        canvas_bg.image = image
        canvas_bg.offset_x = 0
        canvas_bg.offset_y = 0
        self.canvas_bg = canvas_bg
        self.content.controls.append(canvas_bg)

    def add_layer_image(self, image):
        layer_image = None
        if image.path is None:
            layer_image = LayerImage(
                left=0,
                top=0,
                width=image.width,
                height=image.height,
                content=ft.Image(
                    src_base64=flet_utils.convert_image_to_base64(image),
                    width=image.width,
                    height=image.height,
                    gapless_playback=True,
                ),
            )
        else:
            layer_image = LayerImage(
                left=0,
                top=0,
                width=image.width,
                height=image.height,
                content=ft.Image(
                    src=f"{image.path}",
                    width=image.width,
                    height=image.height,
                    gapless_playback=True,
                ),
            )
        layer_image.image = image
        layer_image.offset_x = 0
        layer_image.offset_y = 0
        self.center_layer(layer_image)
        self.content.controls.append(layer_image)
        return layer_image

    def get_preview(self):
        stack = self.content.controls
        return flet_utils.get_preview_from_stack(self.page.canvas_size, stack)

    def refresh_stack(self):
        self.content.controls.clear()
        for slot in self.page.visible_layers:
            self.content.controls.insert(0, slot.layer_image)
        self.content.controls.insert(0, self.canvas_bg)
        self.update()

    def get_scaled_size(self):
        self.scaled_width = self.width * self.scale
        self.scaled_height = self.height * self.scale

    def center_layer(self, layer_image):
        layer_image.offset_x = 0
        layer_image.offset_y = 0
        layer_image.left = (self.width * 0.5) - (layer_image.width * 0.5)
        layer_image.top = (self.height * 0.5) - (layer_image.height * 0.5)

    def align_layer(self, layer_image):
        layer_image.left = (
            (self.width - layer_image.width) * 0.5
        ) + layer_image.offset_x
        layer_image.top = (
            (self.height - layer_image.height) * 0.5
        ) + layer_image.offset_y

    def move_layer(self, e: ft.DragUpdateEvent):
        layer = self.page.active_layer.layer_image
        layer.offset_x += e.delta_x
        layer.offset_y += e.delta_y
        self.align_layer(layer)
        self.update()

    def finish_move_layer(self, e: ft.DragEndEvent):
        canvas.refresh_canvas()

    def resize_layer(self, e: ft.DragUpdateEvent):
        pass

    def box_select(self, e):
        pass

    def bucket_fill(self, e):
        pass


class LayerImage(ft.Container):
    pass


class CanvasGestures(ft.GestureDetector):
    pass


class CanvasOverlay(ft.Stack):
    def refresh_canvas_overlay(self):
        self.refresh_canvas_size_display()
        self.page.refresh_canvas_preview()

    def refresh_canvas_size_display(self):
        self.size_display.content.value = str(self.page.canvas_size)
        self.update()

    def clear_tools(self):
        for tool in canvas_tools.content.controls:
            tool.selected = False


# ImageStack == ft.Container
image_stack = ImageStack(
    width=4096,
    height=4096,
    left=0,
    top=0,
    scale=1.0,
    content=ft.Stack(),
)

image_stack.offset_x = 0
image_stack.offset_y = 0
image_stack.scaled_width = image_stack.width
image_stack.scaled_height = image_stack.height

canvas_frame = ft.Container(
    width=4096,
    height=4096,
    top=0,
    left=0,
    scale=1.0,
    image_fit="cover",
    alignment=ft.alignment.center,
    content=ft.Image(
        src_base64=flet_utils.get_canvas_frame((512, 512)),
        gapless_playback=True,
    ),
)


# CanvasGestures == ft.GestureDetector
def pan_canvas(e):
    canvas.pan_canvas(e)


pan_tool = CanvasGestures(
    mouse_cursor=ft.MouseCursor.GRAB,
    drag_interval=10,
    on_pan_update=pan_canvas,
)


def select_layer(e):
    pass


def move_layer(e):
    image_stack.move_layer(e)


def finish_move_layer(e):
    image_stack.finish_move_layer(e)


move_tool = CanvasGestures(
    mouse_cursor=ft.MouseCursor.MOVE,
    drag_interval=10,
    on_pan_start=select_layer,
    on_pan_update=move_layer,
    on_pan_end=finish_move_layer,
)


def set_select_start(e):
    pass


def draw_select_box(e):
    pass


def get_box_select(e):
    pass


box_select_tool = CanvasGestures(
    mouse_cursor=ft.MouseCursor.GRAB,
    drag_interval=10,
    on_pan_start=set_select_start,
    on_pan_update=draw_select_box,
    on_pan_end=get_box_select,
)


def draw_on_layer(e):
    pass


brush_tool = CanvasGestures(
    mouse_cursor=ft.MouseCursor.GRAB,
    drag_interval=10,
    on_pan_update=draw_on_layer,
)


def fill_selection(e):
    pass


fill_tool = CanvasGestures(
    mouse_cursor=ft.MouseCursor.GRAB,
    drag_interval=10,
    on_tap=fill_selection,
)

canvas_size_display = ft.Container(
    content=ft.Text(
        value="test",
    ),
    left=4,
    bottom=4,
    padding=4,
    border_radius=10,
    opacity=0.5,
    bgcolor="black",
)


def center_canvas(e):
    canvas.center_canvas()


center_canvas_button = ft.IconButton(
    content=ft.Icon(ft.icons.FILTER_CENTER_FOCUS_OUTLINED),
    tooltip="center canvas",
    on_click=center_canvas,
)


def set_pan_tool(e):
    e.page.set_current_tool(e)


pan_canvas_button = ft.IconButton(
    content=ft.Icon(ft.icons.PAN_TOOL_OUTLINED),
    tooltip="pan canvas",
    on_click=set_pan_tool,
    selected=True,
    data={"label": "pan"},
)


def zoom_in_canvas(e):
    canvas.zoom_in(e)


zoom_in_button = ft.IconButton(
    content=ft.Icon(ft.icons.ZOOM_IN_OUTLINED),
    tooltip="zoom in canvas",
    on_click=zoom_in_canvas,
)


def zoom_out_canvas(e):
    canvas.zoom_out(e)


zoom_out_button = ft.IconButton(
    content=ft.Icon(ft.icons.ZOOM_OUT_OUTLINED),
    tooltip="zoom out canvas",
    on_click=zoom_out_canvas,
)

canvas_tools = ft.Container(
    content=ft.Column(
        controls=[
            center_canvas_button,
            pan_canvas_button,
            zoom_in_button,
            zoom_out_button,
        ],
        horizontal_alignment="end",
    ),
    top=4,
    right=4,
    padding=4,
    border_radius=10,
    opacity=0.5,
    bgcolor="black",
    disabled=False,
)

canvas_tools.center = center_canvas_button
canvas_tools.zoom_in = zoom_in_button
canvas_tools.zoom_out = zoom_out_button


# CanvasOverlay == ft.Stack
canvas_overlay = CanvasOverlay(
    [
        canvas_frame,
        pan_tool,
        canvas_size_display,
        canvas_tools,
    ],
)

canvas_overlay.frame = canvas_frame
canvas_overlay.size_display = canvas_size_display
canvas_overlay.tools = canvas_tools


# Canvas = ft.Container
canvas = Canvas(
    content=ft.Stack(
        [
            image_stack,
            canvas_overlay,
        ],
    ),
    clip_behavior="antiAlias",
    alignment=ft.alignment.center,
    expand=True,
)

canvas.image_stack = image_stack
canvas.overlay = canvas_overlay


================================================
FILE: webui/flet/scripts/flet_file_manager.py
================================================
# flet_file_manager.py

# Flet imports
import flet as ft

# other imports
from typing import Dict

# utils imports
from scripts import flet_utils


class UploadWindow(ft.AlertDialog):
    def upload_file(self, e):
        if file_picker.result is not None and file_picker.result.files is not None:
            file_list = []
            for f in file_picker.result.files:
                upload_url = e.page.get_upload_url(f.name, 600)
                img = ft.FilePickerUploadFile(f.name, upload_url)
                file_list.append(img)
            file_picker.upload(file_list)

    def upload_complete(self, e):
        self.progress_bars.clear()
        self.selected_files.controls.clear()
        e.page.close_uploads(e)
        e.page.add_images_as_layers(file_picker.images)
        file_picker.images.clear()
        e.page.message("File upload(s) complete.")

    def get_image_from_uploads(self, name):
        return flet_utils.get_image_from_uploads(name)

    def get_file_display(self, name, progress):
        display = ft.Row(
            controls=[
                progress,
                ft.Text(name),
            ],
        )
        return display


class ImportWindow(ft.AlertDialog):
    pass


selected_files = ft.Column(
    scroll="auto",
    tight=True,
    controls=[],
)

progress_bars: Dict[str, ft.ProgressBar] = {}


def upload_file(e):
    uploads.upload_file(e)


def close_upload_window(e):
    e.page.close_uploads(e)


uploads = UploadWindow(
    title=ft.Text("Confirm file upload(s)"),
    content=selected_files,
    actions_alignment="center",
    actions=[
        ft.ElevatedButton("UPLOAD", on_click=upload_file),
        ft.TextButton("CANCEL", on_click=close_upload_window),
    ],
)

uploads.selected_files = selected_files
uploads.progress_bars = progress_bars


def import_file(e):
    e.page.close_imports(e)


def close_import_window(e):
    e.page.close_imports(e)


imports = ImportWindow(
    title=ft.Text("Confirm file import(s)"),
    content=selected_files,
    actions_alignment="center",
    actions=[
        ft.ElevatedButton("IMPORT", on_click=import_file),
        ft.TextButton("CANCEL", on_click=close_import_window),
    ],
)

imports.selected_files = selected_files
imports.progress_bars = progress_bars


def pick_images(e: ft.FilePickerResultEvent):
    progress_bars.clear()
    selected_files.controls.clear()
    file_picker.images.clear()
    # check to see if files or directory were chosen
    if e.files is not None and e.path is None:
        for f in e.files:
            prog = ft.ProgressRing(
                width=12,
                height=12,
                stroke_width=2,
                value=0,
                color="blue",
            )
            progress_bars[f.name] = prog
            selected_files.controls.append(uploads.get_file_display(f.name, prog))
            file_picker.pending += 1
        # upload if remote, import if local
        if e.page.web:
            e.page.open_uploads(e)
        else:
            e.page.open_imports(e)


def on_image_upload(e: ft.FilePickerUploadEvent):
    if e.error:
        e.page.message(f"Upload error occurred! Failed to fetch '{e.file_name}'.", 1)
        file_picker.pending -= 1
    else:
        # update progress bars
        progress_bars[e.file_name].value = e.progress
        progress_bars[e.file_name].update()
        if e.progress >= 1:
            file_picker.pending -= 1
            file_picker.images.append(uploads.get_image_from_uploads(e.file_name))
        if file_picker.pending <= 0:
            file_picker.pending = 0
            uploads.upload_complete(e)


file_picker = ft.FilePicker(on_result=pick_images, on_upload=on_image_upload)

file_picker.pending = 0
file_picker.images = []


================================================
FILE: webui/flet/scripts/flet_gallery_window.py
================================================
# flet_gallery_window.py

# Flet imports
import flet as ft

# utils imports
from scripts import flet_utils


class GalleryWindow(ft.AlertDialog):
    def setup(self):
        self.refresh_galleries()

    def refresh_galleries(self):
        self.refresh_gallery("uploads")
        self.refresh_gallery("outputs")

    def refresh_gallery(self, gallery_name):
        if gallery_name == "uploads":
            self.uploads_gallery.get_gallery_display(gallery_name)
        elif gallery_name == "outputs":
            self.outputs_gallery.get_gallery_display(gallery_name)
        else:
            page.message(f"{gallery_name} gallery not found.", 1)
            return None

    def get_gallery_images(self, gallery_name):
        return flet_utils.get_gallery_images(gallery_name)

    def select_image(self, e):
        if e.control.border:
            e.control.border = None
            if e.control.image in self.selected_images:
                self.selected_images.remove(e.control.image)
            e.control.update()
        else:
            e.control.border = ft.border.all(2, e.page.tertiary_color)
            self.selected_images.append(e.control.image)
            e.control.update()


class GalleryDisplay(ft.Container):
    def get_gallery_display(self, gallery_name):
        self.content = ft.GridView(
            controls=None,
            padding=0,
            runs_count=3,
            run_spacing=12,
            spacing=12,
            expand=True,
        )
        gallery = gallery_window.get_gallery_images(gallery_name)
        if not gallery:
            self.content.controls.append(
                ft.Image(
                    src="/images/chickens.jpg",
                    tooltip="Nothing here but us chickens!",
                    gapless_playback=True,
                )
            )
            return

        for image in gallery:
            gallery_image = GalleryImage(
                content=ft.Image(
                    src=image.path,
                    tooltip=image.filename,
                    width=image.width,
                    height=image.height,
                    gapless_playback=True,
                ),
                image_fit="contain",
                height=image.height,
                width=image.width,
                padding=0,
                margin=0,
                border=None,
                on_click=gallery_window.select_image,
            )
            gallery_image.image = image
            self.content.controls.append(gallery_image)


class GalleryImage(ft.Container):
    pass


def add_as_new_layer(e):
    if gallery_window.selected_images:
        e.page.add_images_as_layers(gallery_window.selected_images)
        gallery_window.selected_images.clear()
        for tab in gallery_window.content.content.tabs:
            for image in tab.content.content.controls:
                image.border = None
                image.update()


def save_to_disk(e):
    pass


def remove_from_gallery(e):
    pass


uploads_gallery = GalleryDisplay(
    content=None,
    clip_behavior="antiAlias",
)

outputs_gallery = GalleryDisplay(
    content=None,
    clip_behavior="antiAlias",
)

# GalleryWindow == ft.AlertDialog
gallery_window = GalleryWindow(
    title=ft.Text("Gallery"),
    content=ft.Container(
        content=ft.Tabs(
            selected_index=0,
            animation_duration=300,
            tabs=[
                ft.Tab(
                    text="Uploads",
                    content=uploads_gallery,
                ),
                ft.Tab(
                    text="Outputs",
                    content=outputs_gallery,
                ),
            ],
        ),
    ),
    actions=[
        ft.ElevatedButton(
            text="Add As New Layer(s)",
            icon=ft.icons.ADD_OUTLINED,
            on_click=add_as_new_layer,
        ),
        ft.ElevatedButton(
            text="Save",
            icon=ft.icons.SAVE_OUTLINED,
            on_click=save_to_disk,
        ),
        ft.ElevatedButton(
            text="Discard",
            icon=ft.icons.DELETE_OUTLINED,
            on_click=remove_from_gallery,
        ),
    ],
    actions_alignment="end",
)

gallery_window.uploads_gallery = uploads_gallery
gallery_window.outputs_gallery = outputs_gallery
gallery_window.selected_images = []


================================================
FILE: webui/flet/scripts/flet_messages.py
================================================
# flet_messages.py

# Flet imports
import flet as ft

# utils imports
from scripts import flet_utils


class Messages(ft.Container):
    def setup(self):
        self.height = self.page.bottom_panel_height
        self.bgcolor = self.page.primary_color
        self.padding = self.page.container_padding
        self.margin = self.page.container_margin

        self.set_tab_text_size(self.page.text_size)
        self.set_tab_bgcolor(self.page.secondary_color)
        self.set_tab_padding(self.page.container_padding)
        self.set_tab_margin(self.page.container_margin)

        self.dragbar.content.height = self.page.divider_height
        self.dragbar.content.color = self.page.tertiary_color

    def on_page_change(self):
        self.height = self.page.bottom_panel_height
        self.bgcolor = self.page.primary_color
        self.padding = self.page.container_padding
        self.margin = self.page.container_margin

        self.set_tab_text_size(self.page.text_size)
        self.set_tab_bgcolor(self.page.secondary_color)
        self.set_tab_padding(self.page.container_padding)
        self.set_tab_margin(self.page.container_margin)

        self.dragbar.content.height = self.page.divider_height
        self.dragbar.content.color = self.page.tertiary_color

    def set_tab_text_size(self, size):
        for tab in self.tabs:
            tab.tab_content.size = size

    def set_tab_bgcolor(self, color):
        for tab in self.tabs:
            tab.content.bgcolor = color

    def set_tab_padding(self, padding):
        for tab in self.tabs:
            tab.content.padding = padding

    def set_tab_margin(self, margin):
        for tab in self.tabs:
            tab.content.margin = margin

    def resize_messages(self, e: ft.DragUpdateEvent):
        self.page.bottom_panel_height = max(
            100, self.page.bottom_panel_height - e.delta_y
        )
        self.height = self.page.bottom_panel_height
        self.page.update()

    def message(self, text, err=0):
        if err:
            text = "ERROR:  " + text
        self.add_message_to_messages(err, text)
        flet_utils.log_message(text)

    def prune_messages(self):
        if len(message_list.controls) > self.page.max_message_history:
            message_list.controls.pop(0)
        message_list.update()

    def add_message_to_messages(self, err, text):
        if err:
            msg = ft.Text(value=text, color=ft.colors.RED)
        else:
            msg = ft.Text(value=text)
        message_list.controls.append(msg)
        self.prune_messages()


message_list = ft.ListView(
    spacing=4,
    auto_scroll=True,
    controls=[],
)

messages_panel = ft.Container(
    content=message_list,
)

video_editor_panel = ft.Column(expand=True, controls=[ft.Text("Under Construction")])


def resize_messages(e):
    messages.resize_messages(e)


def realign_canvas(e):
    e.page.align_canvas()


messages_dragbar = ft.GestureDetector(
    mouse_cursor=ft.MouseCursor.RESIZE_ROW,
    drag_interval=50,
    on_pan_update=resize_messages,
    on_pan_end=realign_canvas,
    content=ft.Divider(),
)

messages = Messages(
    content=ft.Stack(
        controls=[
            messages_dragbar,
            ft.Tabs(
                selected_index=0,
                animation_duration=300,
                tabs=[
                    ft.Tab(
                        content=messages_panel,
                        tab_content=ft.Text(
                            value="Messages",
                        ),
                    ),
                    ft.Tab(
                        content=video_editor_panel,
                        tab_content=ft.Text(
                            value="Video Editor",
                        ),
                    ),
                ],
            ),
        ],
    ),
    clip_behavior="antiAlias",
)

messages.dragbar = messages_dragbar
messages.tabs = messages.content.controls[1].tabs
messages.messages_panel = messages_panel
messages.video_editor_panel = video_editor_panel
messages.message_list = message_list


================================================
FILE: webui/flet/scripts/flet_property_manager.py
================================================
# flet_property_manager.py

# Flet imports
import flet as ft

# utils imports
from scripts import flet_utils


class PropertyManager(ft.Container):
    def setup(self):
        self.width = self.page.right_panel_width
        self.bgcolor = self.page.primary_color
        self.padding = self.page.container_padding
        self.margin = self.page.container_margin
        self.set_tab_text_size(self.page.text_size)
        self.set_tab_bgcolor(self.page.secondary_color)
        self.set_tab_padding(self.page.container_padding)
        self.set_tab_margin(self.page.container_margin)
        self.dragbar.content.width = self.page.vertical_divider_width
        self.dragbar.content.color = self.page.tertiary_color
        self.property_panel.preview.width = self.page.right_panel_width
        self.property_panel.preview_dragbar.content.content.height = (
            self.page.divider_height
        )
        self.property_panel.preview_dragbar.content.content.color = (
            self.page.tertiary_color
        )
        self.property_panel.canvas_properties_divider.content.height = (
            self.page.divider_height
        )
        self.property_panel.canvas_properties_divider.content.color = (
            self.page.tertiary_color
        )
        self.property_panel.layer_properties_divider.content.height = (
            self.page.divider_height
        )
        self.property_panel.layer_properties_divider.content.color = (
            self.page.tertiary_color
        )

        self.page.refresh_canvas_preview()
        self.refresh_canvas_properties()

    def on_page_change(self):
        self.width = self.page.right_panel_width
        self.bgcolor = self.page.primary_color
        self.padding = self.page.container_padding
        self.margin = self.page.container_margin
        self.set_tab_text_size(self.page.text_size)
        self.set_tab_bgcolor(self.page.secondary_color)
        self.set_tab_padding(self.page.container_padding)
        self.set_tab_margin(self.page.container_margin)
        self.dragbar.content.width = self.page.vertical_divider_width
        self.dragbar.content.color = self.page.tertiary_color
        self.property_panel.preview.width = self.page.right_panel_width
        self.property_panel.preview_dragbar.content.content.height = (
            self.page.divider_height
        )
        self.property_panel.preview_dragbar.content.content.color = (
            self.page.tertiary_color
        )
        self.property_panel.canvas_properties_divider.content.height = (
            self.page.divider_height
        )
        self.property_panel.canvas_properties_divider.content.color = (
            self.page.tertiary_color
        )
        self.property_panel.layer_properties_divider.content.height = (
            self.page.divider_height
        )
        self.property_panel.layer_properties_divider.content.color = (
            self.page.tertiary_color
        )

    def set_tab_text_size(self, size):
        for tab in self.tabs:
            tab.tab_content.size = size

    def set_tab_bgcolor(self, color):
        for tab in self.tabs:
            tab.content.content.bgcolor = color

    def set_tab_padding(self, padding):
        for tab in self.tabs:
            tab.content.padding = padding

    def set_tab_margin(self, margin):
        for tab in self.tabs:
            tab.content.margin = margin

    def set_preview_size(self, width):
        self.property_panel.preview.width = width

    def set_preview_image(self, image):
        self.property_panel.preview.content.src_base64 = (
            flet_utils.convert_image_to_base64(image)
        )
        self.property_panel.update()

    def refresh_canvas_properties(self):
        self.property_panel.refresh_canvas_properties()

    def refresh_layer_properties(self):
        self.property_panel.refresh_layer_properties()

    def resize_property_manager(self, e: ft.DragUpdateEvent):
        self.page.right_panel_width = max(250, self.page.right_panel_width - e.delta_x)
        self.width = self.page.right_panel_width
        self.property_panel.preview.width = self.page.right_panel_width
        self.page.update()


class PropertyPanel(ft.Container):
    def resize_preview(self, e):
        self.preview.height = max(200, self.preview.height + e.delta_y)
        self.update()

    def refresh_canvas_properties(self):
        self.canvas_properties.controls[0].controls[1].value = self.page.canvas_size[0]
        self.canvas_properties.controls[0].controls[3].value = self.page.canvas_size[1]
        self.canvas_properties.update()

    def refresh_layer_properties(self):
        active = True if self.page.active_layer else False
        if active:
            self.layer_property_header.disabled = False
            self.layer_property_header.open = True
            self.layer_property_header.icon = ft.icons.ARROW_DROP_DOWN
            self.layer_property_header.icon_color = self.page.tertiary_color
            self.layer_properties.visible = True
            self.layer_properties.controls[0].controls[
                0
            ].value = self.page.active_layer.label.value
            self.layer_properties.controls[1].controls[
                0
            ].value = self.page.active_layer.image.width
            self.layer_properties.controls[1].controls[
                1
            ].value = self.page.active_layer.image.height
        else:
            self.layer_property_header.disabled = True
            self.layer_property_header.open = False
            self.layer_property_header.icon = ft.icons.ARROW_RIGHT
            self.layer_property_header.icon_color = None
            self.layer_properties.visible = False
        self.update()


preview_pane = ft.Container(
    content=ft.Image(
        src_base64=None,
        gapless_playback=True,
    ),
    image_fit="contain",
    bgcolor="black",
    height=200,
    padding=0,
    margin=0,
)


def resize_preview(e):
    property_panel.resize_preview(e)


preview_dragbar = ft.GestureDetector(
    mouse_cursor=ft.MouseCursor.RESIZE_ROW,
    drag_interval=50,
    on_pan_update=resize_preview,
    content=ft.Container(
        content=ft.Divider(),
        margin=0,
        padding=0,
    ),
)


def open_close_canvas_properties(e):
    if canvas_property_header.open:
        e.control.icon = ft.icons.ARROW_RIGHT
        e.control.icon_color = None
        canvas_property_header.open = False
        canvas_properties.visible = False
        property_panel.update()
    else:
        e.control.icon = ft.icons.ARROW_DROP_DOWN
        e.control.icon_color = e.page.tertiary_color
        canvas_property_header.open = True
        canvas_properties.visible = True
        property_panel.update()


canvas_property_header = ft.Column(
    controls=[
        ft.TextButton(
            text="Canvas Properties",
            icon=ft.icons.ARROW_RIGHT,
            on_click=open_close_canvas_properties,
        ),
    ],
)

canvas_property_header.open = False

canvas_properties = ft.Column(
    visible=False,
    controls=[
        ft.Row(
            controls=[
                ft.Text(
                    value="Width:",
                    text_align="center",
                    no_wrap=True,
                    expand=2,
                ),
                ft.Text(
                    value=0,
                    text_align="start",
                    expand=1,
                ),
                ft.Text(
                    value="Height:",
                    text_align="start",
                    no_wrap=True,
                    expand=2,
                ),
                ft.Text(
                    value=0,
                    text_align="center",
                    expand=1,
                ),
            ],
        ),
    ],
)

canvas_property_divider = ft.Container(
    content=ft.Divider(),
    margin=0,
    padding=0,
)


def open_close_layer_properties(e):
    if layer_property_header.open:
        e.control.icon = ft.icons.ARROW_RIGHT
        e.control.icon_color = None
        layer_property_header.open = False
        layer_properties.visible = False
        property_panel.update()
    else:
        e.control.icon = ft.icons.ARROW_DROP_DOWN
        e.control.icon_color = e.page.tertiary_color
        layer_property_header.open = True
        layer_properties.visible = True
        property_panel.update()


layer_property_header = ft.TextButton(
    text="Layer Properties",
    icon=ft.icons.ARROW_RIGHT,
    on_click=open_close_layer_properties,
    disabled=True,
)

layer_property_header.open = False


def update_layer_name(e):
    e.page.active_layer.label.value = e.control.value
    e.page.asset_manager.update()


layer_properties = ft.Column(
    visible=False,
    controls=[
        ft.Row(
            controls=[
                ft.TextField(
                    label="Layer Name",
                    value="",
                    text_align="center",
                    content_padding=0,
                    expand=1,
                    on_submit=update_layer_name,
                ),
            ],
        ),
        ft.Row(
            controls=[
                ft.TextField(
                    label="Width",
                    value=0,
                    text_align="center",
                    content_padding=0,
                    expand=1,
                ),
                ft.TextField(
                    label="Height",
                    value=0,
                    text_align="center",
                    content_padding=0,
                    expand=1,
                ),
            ],
        ),
    ],
)

layer_property_divider = ft.Container(
    content=ft.Divider(),
    margin=0,
    padding=0,
)


property_panel = PropertyPanel(
    content=ft.Column(
        controls=[
            preview_pane,
            preview_dragbar,
            canvas_property_header,
            canvas_properties,
            canvas_property_divider,
            layer_property_header,
            layer_properties,
            layer_property_divider,
        ],
    ),
)

property_panel.preview = preview_pane
property_panel.preview_dragbar = preview_dragbar
property_panel.canvas_property_header = canvas_property_header
property_panel.canvas_properties_divider = canvas_property_divider
property_panel.canvas_properties = canvas_properties
property_panel.layer_property_header = layer_property_header
property_panel.layer_properties = layer_properties
property_panel.layer_properties_divider = layer_property_divider

output_panel = PropertyPanel(
    content=ft.Column(
        controls=[
            ft.Text("Under Construction."),
        ],
    ),
)


def resize_property_manager(e):
    property_manager.resize_property_manager(e)


def realign_canvas(e):
    e.page.align_canvas()


property_manager_dragbar = ft.GestureDetector(
    mouse_cursor=ft.MouseCursor.RESIZE_COLUMN,
    drag_interval=50,
    on_pan_update=resize_property_manager,
    on_pan_end=realign_canvas,
    content=ft.VerticalDivider(),
)

property_manager = PropertyManager(
    content=ft.Row(
        controls=[
            property_manager_dragbar,
            ft.Column(
                controls=[
                    ft.Tabs(
                        selected_index=0,
                        animation_duration=300,
                        tabs=[
                            ft.Tab(
                                content=property_panel,
                                tab_content=ft.Text(
                                    value="Properties",
                                ),
                            ),
                            ft.Tab(
                                content=output_panel,
                                tab_content=ft.Text(
                                    value="Output",
                                ),
                            ),
                        ],
                    ),
                ],
                alignment="start",
                expand=True,
            ),
            ft.VerticalDivider(
                width=4,
                opacity=0,
            ),
        ],
        expand=True,
    ),
    clip_behavior="antiAlias",
)

property_manager.tabs = property_manager.content.controls[1].controls[0].tabs
property_manager.dragbar = property_manager_dragbar
property_manager.property_panel = property_panel
property_manager.output_panel = output_panel


================================================
FILE: webui/flet/scripts/flet_settings_window.py
================================================
# flet_settings_window.py

# Flet imports
import flet as ft

# utils imports


class SettingsWindow(ft.AlertDialog):
    def setup(self, settings):
        self.get_settings_window_tabs(settings)

    def get_settings_window_tab_page_setting_slider(
        self, settings, section, setting, display_width
    ):
        setting_slider = []
        setting_value = None
        if settings[setting]["value_type"] == "int":
            setting_value = int(settings[setting]["value"])
        elif settings[setting]["value_type"] == "float":
            setting_value = float(settings[setting]["value"])
        else:
            setting_value = settings[setting]["value"]
        label = ft.Text(
            value=setting,
            text_align="center",
        )
        row = SettingsDisplay(
            width=display_width,
            data=[self, section, setting],
            controls=[],
        )
        slider = ft.Slider(
            value=setting_value,
            label="{value}",
            min=settings[setting]["min"],
            max=settings[setting]["max"],
            divisions=int(
                (settings[setting]["max"] - settings[setting]["min"])
                / settings[setting]["step"]
            ),
            on_change=row.settings_window_tab_slider_changed,
            data=row,
            expand=4,
        )
        value = ft.TextField(
            value=setting_value,
            on_submit=row.settings_window_tab_slider_changed,
            data=row,
            content_padding=10,
            expand=1,
        )
        row.controls.extend([slider, value])
        setting_slider.extend([label, row])
        return setting_slider

    def get_settings_window_tab_settings(self, settings, section):
        settings = settings[section]
        section_settings = [ft.Divider(height=10, color="gray")]
        display_width = (self.content.width * 0.5) - 5
        for setting in settings:
            if "value" not in settings[setting]:
                continue
            new_row = SettingsDisplay()
            new_row
            display = None
            display_type = settings[setting]["display"]
            if display_type == "dropdown":
                option_list = []
                for i in range(len(settings[setting]["option_list"])):
                    item = ft.dropdown.Option(text=settings[setting]["option_list"][i])
                    option_list.append(item)
                display = ft.Dropdown(
                    label=setting,
                    value=settings[setting]["value"],
                    options=option_list,
                    on_change=new_row.settings_window_tab_setting_changed,
                    data=section,
                    content_padding=10,
                    width=display_width,
                )
            elif display_type == "textinput":
                display = ft.TextField(
                    label=setting,
                    value=settings[setting]["value"],
                    on_submit=new_row.settings_window_tab_setting_changed,
                    data=section,
                    content_padding=10,
                    width=display_width,
                )
            elif display_type == "bool":
                display = ft.Switch(
                    label=setting,
                    value=settings[setting]["value"],
                    on_change=new_row.settings_window_tab_setting_changed,
                    data=section,
                    width=display_width,
                )
            elif display_type == "slider":
                display = ft.Column(
                    controls=self.get_settings_window_tab_page_setting_slider(
                        settings, section, setting, display_width
                    ),
                )
            else:
                continue
            new_row.data = [self, section, setting]
            new_row.controls.append(display)
            section_settings.append(new_row)
        return section_settings

    def get_settings_window_tab_page(self, settings, section):
        settings_window_tab_page = ft.Column(
            alignment="start",
            scroll="auto",
            controls=self.get_settings_window_tab_settings(settings, section),
        )
        return settings_window_tab_page

    def get_settings_window_tabs(self, settings):
        tabs = []
        for section in settings:
            if section.endswith("_page"):
                tab = ft.Tab(
                    text=section.split("_")[0],
                    content=self.get_settings_window_tab_page(settings, section),
                )
                tabs.append(tab)
        self.content.content.tabs = tabs

    def update_settings_window_tab(self, section):
        settings = self.page.session.get("settings")
        for i, tab in enumerate(self.content.content.tabs):
            if section.startswith(tab.text):
                self.content.content.tabs[
                    i
                ].content = self.get_settings_window_tab_page(settings, section)
                return

    def update_settings_window(self):
        self.get_settings_window_tabs(self.page.session.get("settings"))
        self.page.update()


class SettingsDisplay(ft.Row):
    def settings_window_tab_setting_changed(self, e):
        settings = self.page.session.get("settings")
        settings[e.control.data][e.control.label]["value"] = e.control.value
        update_settings_window_tab(e.control.data)
        self.page.update()

    def settings_window_tab_slider_changed(self, e):
        settings = self.page.session.get("settings")
        parent = e.control.data
        setting = settings[parent.data[1]][parent.data[2]]
        setting_value = None
        if setting["value_type"] == "int":
            setting_value = int(e.control.value)
        elif setting["value_type"] == "float":
            setting_value = float(e.control.value)
        else:
            setting_value = e.control.value
        setting["value"] = setting_value
        parent.controls[0].value = setting_value
        parent.controls[1].value = str(setting_value)
        parent.data[0].update_settings_window_tab(parent.data[1])
        self.page.update()


def apply_settings(e):
    settings_window.update_settings_window()


def save_settings(e):
    save_settings_to_config()
    settings_window.update_settings_window()


def reset_settings(e):
    reset_settings_from_config()
    settings_window.update_settings_window()


# SettingsWindow == ft.AlertDialog
settings_window = SettingsWindow(
    title=ft.Text("Settings"),
    content=ft.Container(
        content=ft.Tabs(
            selected_index=0,
            animation_duration=300,
            tabs=None,
        ),
    ),
    actions=[
        ft.ElevatedButton(
            text="Apply",
            icon=ft.icons.CHECK_CIRCLE,
            on_click=apply_settings,
        ),
        ft.ElevatedButton(
            text="Save",
            icon=ft.icons.SAVE,
            on_click=save_settings,
        ),
        ft.ElevatedButton(
            text="Restore Defaults",
            icon=ft.icons.RESTORE_FROM_TRASH_ROUNDED,
            on_click=reset_settings,
        ),
    ],
    actions_alignment="end",
)


================================================
FILE: webui/flet/scripts/flet_titlebar.py
================================================
# flet_appbar.py

# Flet imports
import flet as ft

# utils imports


class TitleBar(ft.Container):
    def setup(self):
        self.width = self.page.width
        self.height = self.page.titlebar_height

        self.title.size = self.page.titlebar_height * 0.5
        self.title.color = self.page.tertiary_color

        self.prompt.text_size = max(12, self.page.titlebar_height * 0.25)
        self.prompt.focused_border_color = self.page.tertiary_color

        self.layout_menu.controls[0].text_size = self.page.text_size

        self.theme_switcher.size = self.page.titlebar_height
        self.theme_switcher.icon_size = self.page.titlebar_height * 0.5
        self.theme_switcher.tooltip = f"Click to change between the light and dark themes. Current {'(Light theme)' if self.page.theme_mode == 'light' else '(Dark theme)'}"
        self.theme_switcher.on_click = self.page.change_theme_mode

        self.settings_button.size = self.page.titlebar_height
        self.settings_button.icon_size = self.page.titlebar_height * 0.5
        self.settings_button.on_click = self.page.open_settings

    def on_page_change(self):
        self.width = self.page.width
        self.height = self.page.titlebar_height

        self.title.size = self.page.titlebar_height * 0.5
        self.title.color = self.page.tertiary_color

        self.prompt.text_size = max(12, self.page.titlebar_height * 0.25)
        self.prompt.focused_border_color = self.page.tertiary_color

        self.layout_menu.controls[0].text_size = self.page.text_size

        self.theme_switcher.size = self.page.titlebar_height
        self.theme_switcher.icon_size = self.page.titlebar_height * 0.5
        self.theme_switcher.tooltip = f"Click to change between the light and dark themes. Current {'(Light theme)' if self.page.theme_mode == 'light' else '(Dark theme)'}"

        self.settings_button.size = self.page.titlebar_height
        self.settings_button.icon_size = self.page.titlebar_height * 0.5


title = ft.Text(
    value="  Sygil  ",
    text_align="center",
)

prompt = ft.TextField(
    value="",
    min_lines=1,
    max_lines=1,
    content_padding=ft.padding.only(left=12, top=0, right=0, bottom=0),
    shift_enter=True,
    autofocus=True,
    expand=True,
    tooltip="Prompt to use for generation.",
    hint_text="A corgi wearing a top hat as an oil painting.",
)

generate_button = ft.ElevatedButton(
    text="Generate",
    on_click=None,
)


def set_layout(e):
    e.page.set_layout(e)


layout_menu = ft.Row(
    alignment="start",
    controls=[
        ft.Dropdown(
            options=[
                ft.dropdown.Option(text="Default"),
                ft.dropdown.Option(text="Textual Inversion"),
                ft.dropdown.Option(text="Node Editor"),
            ],
            value="Default",
            text_size=20,
            # 						alignment = ft.alignment.center,
            content_padding=ft.padding.only(left=12, top=0, right=0, bottom=0),
            tooltip="Switch between different workspaces",
            on_change=set_layout,
        )
    ],
)

layout_menu.text_size = layout_menu.controls[0].text_size

theme_switcher = ft.IconButton(
    ft.icons.WB_SUNNY_OUTLINED,
)

settings_button = ft.IconButton(
    icon=ft.icons.SETTINGS,
)

option_list = ft.Row(
    controls=[
        ft.Container(content=layout_menu),
        ft.Container(content=theme_switcher),
        ft.Container(content=settings_button),
    ],
    alignment="end",
)


# TitleBar == ft.Container
titlebar = TitleBar(
    content=ft.Row(
        controls=[
            title,
            prompt,
            # generate_button,
            option_list,
        ],
    ),
)

titlebar.title = title
titlebar.prompt = prompt
titlebar.generate_button = generate_button
titlebar.layout_menu = layout_menu
titlebar.theme_switcher = theme_switcher
titlebar.settings_button = settings_button


================================================
FILE: webui/flet/scripts/flet_tool_manager.py
================================================
# flet_tool_manager.py

# Flet imports
import flet as ft

# utils imports


def open_gallery(e):
    e.page.open_gallery(e)


def blank_layer(e):
    e.page.add_blank_layer()


def load_images(e):
    e.page.load_images()


def tool_select(e):
    e.page.set_current_tool(e)


class Action:
    def __init__(self, label, icon, tooltip, on_click):
        self.label = label
        self.icon = icon
        self.tooltip = tooltip
        self.on_click = on_click
        self.disabled = False


action_list = [
    Action("gallery", ft.icons.DASHBOARD_OUTLINED, "Gallery", open_gallery),
    Action("blank layer", ft.icons.ADD_OUTLINED, "Add blank layer", blank_layer),
    Action("load image", ft.icons.IMAGE_OUTLINED, "Load image as layer", load_images),
]


class Tool:
    def __init__(self, label, icon, tooltip):
        self.label = label
        self.icon = icon
        self.tooltip = tooltip
        self.on_click = tool_select
        self.disabled = True


tool_list = [
    Tool("move", ft.icons.OPEN_WITH_OUTLINED, "Move layer(s)"),
    Tool("select", ft.icons.HIGHLIGHT_ALT_OUTLINED, "Select tool"),
    Tool("brush", ft.icons.BRUSH_OUTLINED, "Brush tool"),
    Tool("fill", ft.icons.FORMAT_COLOR_FILL_OUTLINED, "Fill tool"),
]


class ToolManager(ft.Container):
    def setup(self):
        self.toolbox.get_tools()
        self.width = self.page.tool_manager_width
        self.bgcolor = self.page.primary_color
        self.padding = self.page.container_padding
        self.margin = self.page.container_margin

        self.toolbox.bgcolor = self.page.secondary_color
        self.toolbox.padding = self.page.container_padding
        self.toolbox.margin = self.page.container_margin

        self.tool_divider.height = self.page.divider_height
        self.tool_divider.color = self.page.tertiary_color

        self.tool_properties.bgcolor = self.page.secondary_color
        self.tool_properties.padding = self.page.container_padding
        self.tool_properties.margin = self.page.container_margin

        self.dragbar.width = self.page.vertical_divider_width
        self.dragbar.color = self.page.tertiary_color

    def on_page_change(self):
        self.width = self.page.tool_manager_width
        self.bgcolor = self.page.primary_color
        self.padding = self.page.container_padding
        self.margin = self.page.container_margin

        self.toolbox.bgcolor = self.page.secondary_color
        self.toolbox.padding = self.page.container_padding
        self.toolbox.margin = self.page.container_margin

        self.tool_divider.height = self.page.divider_height
        self.tool_divider.color = self.page.tertiary_color

        self.tool_properties.bgcolor = self.page.secondary_color
        self.tool_properties.padding = self.page.container_padding
        self.tool_properties.margin = self.page.container_margin

        self.dragbar.width = self.page.vertical_divider_width
        self.dragbar.color = self.page.tertiary_color

    def resize_tool_manager(self, e: ft.DragUpdateEvent):
        self.page.tool_manager_width = max(50, self.page.tool_manager_width + e.delta_x)
        tool_manager.width = self.page.tool_manager_width
        self.page.update()

    def resize_toolbox(self, e: ft.DragUpdateEvent):
        min_height = self.page.tool_manager_button_size * 2
        self.page.toolbox_height = max(min_height, self.page.toolbox_height + e.delta_y)
        toolbox.height = self.page.toolbox_height
        self.update()

    def enable_tools(self):
        for tool in self.toolbox.content.controls:
            try:
                if tool.on_click == tool_select:
                    tool.disabled = False
            except AttributeError:
                continue  # is divider
        self.update()

    def disable_tools(self):
        for tool in self.toolbox.content.controls:
            try:
                if tool.on_click == tool_select:
                    tool.disabled = True
            except AttributeError:
                continue  # is divider
        self.update()

    def clear_tools(self):
        self.toolbox.clear_tools()


class ToolBox(ft.Container):
    def get_tools(self):
        for action in action_list:
            self.content.controls.append(self.make_button(action))
        divider = ft.Container(
            content=ft.Divider(
                height=self.page.divider_height,
                color=self.page.tertiary_color,
            ),
            margin=0,
            padding=ft.padding.only(left=10, top=0, right=0, bottom=0),
        )
        self.content.controls.append(divider)
        for tool in tool_list:
            self.content.controls.append(self.make_button(tool))
        tool_manager.update()

    def make_button(self, button_info):
        button = ft.IconButton(
            width=self.page.icon_size * 2,
            icon_size=self.page.icon_size,
            content=ft.Icon(button_info.icon),
            selected=False,
            selected_icon_color=self.page.tertiary_color,
            tooltip=button_info.tooltip,
            data={"label": button_info.label},
            on_click=button_info.on_click,
            disabled=button_info.disabled,
        )
        return button

    def clear_tools(self):
        for control in self.content.controls:
            control.selected = False


class ToolPropertyPanel(ft.Container):
    pass


# ToolBox == ft.Container
toolbox = ToolBox(
    clip_behavior="antiAlias",
    content=ft.Row(
        alignment="start",
        wrap=True,
        spacing=0,
        run_spacing=0,
        controls=[],
    ),
    margin=0,
    padding=ft.padding.only(left=15, top=0, right=0, bottom=0),
)


def resize_toolbox(e):
    tool_manager.resize_toolbox(e)


tool_divider = ft.GestureDetector(
    mouse_cursor=ft.MouseCursor.RESIZE_ROW,
    drag_interval=50,
    on_pan_update=resize_toolbox,
    content=ft.Container(
        content=ft.Divider(),
        margin=0,
        padding=ft.padding.only(left=10, top=0, right=0, bottom=0),
    ),
)


# ToolPropertyPanel == ft.Container
tool_properties = ToolPropertyPanel(
    content=ft.Column(
        controls=[],
    )
)


def resize_tool_manager(e):
    tool_manager.resize_tool_manager(e)


def realign_canvas(e):
    e.page.align_canvas()


tool_manager_dragbar = ft.GestureDetector(
    mouse_cursor=ft.MouseCursor.RESIZE_COLUMN,
    drag_interval=50,
    on_pan_update=resize_tool_manager,
    on_pan_end=realign_canvas,
    content=ft.VerticalDivider(),
)


# ToolManager = ft.Container
tool_manager = ToolManager(
    content=ft.Row(
        controls=[
            ft.Column(
                controls=[
                    toolbox,
                    tool_divider,
                    tool_properties,
                ],
                alignment="start",
                expand=True,
            ),
            tool_manager_dragbar,
        ],
        expand=True,
    ),
    clip_behavior="antiAlias",
)

tool_manager.toolbox = toolbox
tool_manager.tool_divider = tool_divider.content.content
tool_manager.tool_properties = tool_properties
tool_manager.dragbar = tool_manager_dragbar.content


================================================
FILE: webui/flet/scripts/flet_utils.py
================================================
# flet_utils.py

# imports
import os, yaml, base64
from PIL import Image
from io import BytesIO
from datetime import datetime


# logging
def log_message(message):
    log_file = None
    # get time and format message
    prefix = datetime.now()
    msg_prefix = prefix.strftime("%Y/%m/%d %H:%M:%S")
    message = msg_prefix + "  " + message
    # check to see if we're appending to current logfile or making a new one'
    try:
        log_file = log_message.log
    except AttributeError:
        log_prefix = prefix.strftime("%Y%m%d_%H%M%S")
        os.makedirs("log/webui/flet", exist_ok=True)
        log_message.log = os.path.join("log/webui/flet", log_prefix + "webui_flet.log")
        log_file = log_message.log
    # write message to logfile
    with open(log_file, "a+") as log:
        log.write(message)


# settings
path_to_default_config = "configs/webui/webui_flet.yaml"
path_to_user_config = "configs/webui/userconfig_flet.yaml"


def get_default_settings_from_config():
    with open(path_to_default_config) as f:
        default_settings = yaml.safe_load(f)
    return default_settings


def get_user_settings_from_config():
    # get default settings
    settings = get_default_settings_from_config()
    # check to see if userconfig exists
    if os.path.exists(path_to_user_config):
        # compare to see which is newer
        default_time = os.path.getmtime(path_to_default_config)
        user_time = os.path.getmtime(path_to_user_config)
        # if default is newer, save over userconfig and exit early
        if default_time > user_time:
            save_user_settings_to_config(settings)
            return settings
        # else, load userconfig
        with open(path_to_user_config) as f:
            user_settings = yaml.safe_load(f)
        settings.update(user_settings)
    # regardless, return settings as dict
    return settings


def save_user_settings_to_config(settings):
    with open(path_to_user_config, "w+") as f:
        yaml.dump(settings, f, default_flow_style=False)


# image handling
path_to_assets = "webui/flet/assets"
path_to_uploads = "webui/flet/assets/uploads"
path_to_outputs = "webui/flet/assets/outputs"


# creates blank image   (to do: take size as arg)
def create_blank_image(size):
    try:
        create_blank_image.count += 1
    except AttributeError:
        create_blank_image.count = 1
    name = "blank_layer_" + str(create_blank_image.count).zfill(2)
    img = Image.new("RGBA", size, (0, 0, 0, 0))
    img.filename = name
    img.path = None
    return img


# takes name of image
# returns dict
#   name of image : image handle
def get_image_from_uploads(name):
    path_to_image = os.path.join(path_to_uploads, name)
    if os.path.exists(path_to_image):
        image = Image.open(path_to_image)
        image = image.convert("RGBA")
        image.filename = name
        image.path = path_to_image
        return image
    else:
        log_message(f'image not found: "{name}"')
        return None


def get_canvas_background(path):
    image = Image.open(path)
    image = image.convert("RGBA")
    return image


# make canvas frame
def get_canvas_frame(canvas_size):
    image = Image.new("RGBA", (4096, 4096), (0, 0, 0, 127))
    canvas_width = canvas_size[0]
    canvas_height = canvas_size[1]
    x0 = int((image.width - canvas_width) * 0.5)
    y0 = int((image.height - canvas_height) * 0.5)
    x1 = x0 + canvas_width
    y1 = y0 + canvas_height
    box = (x0, y0, x1, y1)
    image.paste((0, 0, 0, 0), box)
    return convert_image_to_base64(image)


# takes list of Image(s) as arg
# returns single composite of all images
def get_preview_from_stack(size, stack):
    preview = Image.new("RGBA", size, (0, 0, 0, 0))
    canvas_width = size[0]
    canvas_height = size[1]
    for layer in stack:
        image = layer.image
        # need to crop images for composite
        x0 = ((image.width - canvas_width) * 0.5) - layer.offset_x
        y0 = ((image.height - canvas_height) * 0.5) - layer.offset_y
        x1 = x0 + canvas_width
        y1 = y0 + canvas_height
        box = (x0, y0, x1, y1)
        cropped_image = image.crop(box)
        preview = Image.alpha_composite(preview, cropped_image)
    return preview


# converts Image to base64 string
def convert_image_to_base64(image):
    image_buffer = BytesIO()
    image.save(image_buffer, format="PNG")
    image_buffer.seek(0)
    image_bytes = image_buffer.getvalue()
    return base64.b64encode(image_bytes).decode()


# takes name of gallery as arg ('assets','output','uploads')
# returns list of dicts
#       name of image:
#           'img_path' : path_to_image
#           'info_path' : path_to_yaml
def get_gallery_images(gallery_name):
    path_to_gallery = None
    images = []
    files = []
    if gallery_name == "uploads":
        path_to_gallery = path_to_uploads
    elif gallery_name == "outputs":
        path_to_gallery = path_to_outputs
    else:
        log_message(f'gallery not found: "{gallery_name}"')
        return images
    if os.path.exists(path_to_gallery):
        files = os.listdir(path_to_gallery)
    else:
        return None
    for f in files:
        if f.endswith((".jpg", ".jpeg", ".png")):
            image = Image.open(os.path.join(path_to_gallery, f))
            image = image.convert("RGBA")
            image.filename = f
            image.path = os.path.join(gallery_name, f)
            images.append(image)
    return images


# textual inversion
textual_inversion_grid_row_list = [
    "model",
    "medium",
    "artist",
    "trending",
    "movement",
    "flavors",
    "techniques",
    "tags",
]


def run_textual_inversion(args):
    pass


================================================
FILE: webui/flet/webui_flet.py
================================================
# Flet imports
import flet as ft

# other imports
import os
from loguru import logger

# logging.basicConfig(level=logging.DEBUG)

# utils imports
from scripts import flet_utils
from scripts.flet_settings_window import settings_window
from scripts.flet_gallery_window import gallery_window
from scripts.flet_file_manager import file_picker, uploads, imports
from scripts.flet_titlebar import titlebar
from scripts.flet_tool_manager import tool_manager
from scripts.flet_asset_manager import asset_manager, layer_action_menu
from scripts.flet_canvas import canvas
from scripts.flet_messages import messages
from scripts.flet_property_manager import property_manager

# for debugging

os.environ["FLET_WS_MAX_MESSAGE_SIZE"] = "8000000"


# 	main ###############################################################
@logger.catch(reraise=True)
def main(page: ft.Page):
    # 	init ###############################################################
    # messages
    page.messages = messages
    page.message = messages.message
    page.max_message_history = 50

    # ui
    page.current_layout = "Default"
    page.titlebar_height = 50
    page.bottom_panel_height = page.height * 0.2
    page.toolbox_height = 250
    page.tool_manager_width = 50
    page.tool_manager_button_size = 40
    page.left_panel_width = 200
    page.right_panel_width = 250

    page.background_color = None
    page.primary_color = None
    page.secondary_color = "white_10"
    page.tertiary_color = "blue"

    page.text_color = None
    page.text_size = 14
    page.icon_size = 20

    page.padding = 0
    page.margin = 0
    page.container_padding = 0
    page.container_margin = 0

    page.tab_color = "white_10"
    page.tab_padding = ft.padding.only(left=2, top=12, right=2, bottom=8)
    page.tab_margin = 0

    page.divider_height = 10
    page.vertical_divider_width = 10

    # titlebar
    page.titlebar = titlebar

    def change_theme_mode(e):
        page.theme_mode = "dark" if page.theme_mode == "light" else "light"

        if "(Light theme)" in titlebar.theme_switcher.tooltip:
            titlebar.theme_switcher.tooltip = titlebar.theme_switcher.tooltip.replace(
                "(Light theme)", ""
            )

        if "(Dark theme)" in titlebar.theme_switcher.tooltip:
            titlebar.theme_switcher.tooltip = titlebar.theme_switcher.tooltip.replace(
                "(Dark theme)", ""
            )

        titlebar.theme_switcher.tooltip += (
            "(Light theme)" if page.theme_mode == "light" else "(Dark theme)"
        )
        page.update()

    page.change_theme_mode = change_theme_mode

    # tools
    page.tool_manager = tool_manager
    page.current_tool = "pan"

    def enable_tools():
        page.tool_manager.enable_tools()

    page.enable_tools = enable_tools

    def disable_tools():
        page.tool_manager.disable_tools()

    page.disable_tools = disable_tools

    def set_current_tool(e):
        page.tool_manager.clear_tools()
        page.canvas.clear_tools()
        e.control.selected = True
        page.current_tool = e.control.data["label"]
        page.canvas.set_current_tool(e.control.data["label"])
        page.update()

    page.set_current_tool = set_current_tool

    # asset manager
    page.asset_manager = asset_manager
    page.active_layer = None
    page.visible_layers = []
    page.layer_height = 50

    def set_active_layer(layer_slot):
        if page.active_layer == layer_slot:
            return
        page.active_layer = layer_slot
        page.enable_tools()
        page.property_manager.refresh_layer_properties()

    page.set_active_layer = set_active_layer

    def add_blank_layer():
        image = flet_utils.create_blank_image(page.canvas_size)
        layer_slot = page.asset_manager.add_image_as_layer(image)
        layer_slot.layer_image = page.canvas.add_layer_image(image)
        page.message("added blank layer to canvas")
        page.refresh_layers()

    page.add_blank_layer = add_blank_layer

    def add_images_as_layers(images):
        layer_slots = page.asset_manager.add_images_as_layers(images)
        for slot in layer_slots:
            slot.layer_image = page.canvas.add_layer_image(slot.image)
            page.message(f'added "{slot.image.filename}" as layer')
        page.refresh_layers()

    page.add_images_as_layers = add_images_as_layers

    def load_images():
        page.file_picker.pick_files(file_type="image", allow_multiple=True)

    page.load_images = load_images

    # canvas
    page.canvas = canvas
    page.canvas_background = flet_utils.get_canvas_background(
        "webui/flet/assets/images/default_grid_texture.png"
    )
    page.canvas_size = [512, 512]

    def get_viewport_size():
        viewport_width = page.width - (
            page.tool_manager_width
            + (page.vertical_divider_width * 3)
            + page.left_panel_width
            + page.right_panel_width
        )
        viewport_height = (
            page.height - (page.titlebar_height * 2) - page.bottom_panel_height
        )
        return viewport_width, viewport_height

    page.get_viewport_size = get_viewport_size

    def align_canvas():
        page.canvas.align_canvas()

    page.align_canvas = align_canvas

    # property manager
    page.property_manager = property_manager

    def refresh_canvas_preview():
        preview = page.canvas.get_image_stack_preview()
        page.property_manager.set_preview_image(preview)

    page.refresh_canvas_preview = refresh_canvas_preview

    def refresh_layers():
        if page.active_layer is None:
            page.disable_tools()
        else:
            page.enable_tools()
        page.asset_manager.refresh_layers()
        page.canvas.refresh_canvas()
        page.refresh_canvas_preview()
        page.property_manager.refresh_layer_properties()
        page.update()

    page.refresh_layers = refresh_layers

    # layouts
    def set_layout(e):
        page.current_layout = e.control.value
        page.update()

    page.set_layout = set_layout

    def on_page_change(e):
        page.titlebar.on_page_change()
        page.tool_manager.on_page_change()
        page.asset_manager.on_page_change()
        page.canvas.on_page_change()
        page.messages.on_page_change()
        page.property_manager.on_page_change()
        full_page.width = page.width
        full_page.height = page.height
        page.update()

    page.on_resize = on_page_change

    def on_window_change(e):
        if e.data == "minimize" or e.data == "close":
            return
        else:
            page.on_page_change(e)

    page.on_window_event = on_window_change

    # settings
    def load_settings():
        settings = flet_utils.get_user_settings_from_config()
        page.session.set("settings", settings)
        page.update()

    def save_settings_to_config():
        settings = page.session.get("settings")
        flet_utils.save_user_settings_to_config(settings)

    def reset_settings_from_config():
        settings = flet_utils.get_default_settings_from_config()
        page.session.remove("settings")
        page.session.set("settings", settings)
        save_settings_to_config()

    if not page.session.contains_key("settings"):
        load_settings()
        settings = page.session.get("settings")
        try:
            ui_settings = settings["webui_page"]
            page.theme_mode = ui_settings["default_theme"]["value"]
            ui_settings["max_message_history"]["value"]
        except AttributeError:
            page.message("Config load error: missing setting.", 1)

        page.session.set("layout", "default")

    # 	settings window ####################################################

    def close_settings_window(e):
        settings_window.open = False
        page.update()

    page.close_settings = close_settings_window

    def open_settings_window(e):
        page.dialog = settings_window
        settings_window.open = True
        page.update()

    page.open_settings = open_settings_window

    page.settings_window = settings_window
    settings_window.content.width = page.width * 0.50
    settings_window.content.bgcolor = page.primary_color
    settings_window.content.padding = page.container_padding
    settings_window.content.margin = page.container_margin

    # 	gallery window #####################################################

    def close_gallery_window(e):
        gallery_window.open = False
        page.update()

    page.close_gallery = close_gallery_window

    def open_gallery_window(e):
        page.dialog = gallery_window
        gallery_window.open = True
        page.update()

    page.open_gallery = open_gallery_window

    page.gallery_window = gallery_window
    page.refresh_gallery = gallery_window.refresh_gallery
    gallery_window.content.width = page.width * 0.5
    gallery_window.content.bgcolor = page.primary_color
    gallery_window.content.padding = page.container_padding
    gallery_window.content.margin = page.container_margin

    gallery_window.outputs_gallery.height = page.height * 0.75
    gallery_window.outputs_gallery.bgcolor = page.primary_color
    gallery_window.outputs_gallery.padding = page.container_padding
    gallery_window.outputs_gallery.margin = page.container_margin

    gallery_window.uploads_gallery.height = page.height * 0.75
    gallery_window.uploads_gallery.bgcolor = page.primary_color
    gallery_window.uploads_gallery.padding = page.container_padding
    gallery_window.uploads_gallery.margin = page.container_margin

    # 	file manager #######################################################

    def close_upload_window(e):
        uploads.open = False
        page.update()

    page.close_uploads = close_upload_window

    def open_upload_window(e):
        page.dialog = uploads
        uploads.open = True
        page.update()

    page.open_uploads = open_upload_window

    def close_import_window(e):
        imports.open = False
        page.update()

    page.close_imports = close_import_window

    def open_import_window(e):
        page.dialog = imports
        imports.open = True
        page.update()

    page.open_imports = open_import_window

    page.uploads = uploads
    page.imports = imports
    page.file_picker = file_picker
    page.overlay.append(file_picker)

    # 	center panel #############################################################

    text_editor = ft.Container(
        content=ft.Text("Under Construction."),
        bgcolor=page.secondary_color,
        expand=True,
    )

    viewport = ft.Container(
        bgcolor=page.primary_color,
        padding=page.container_padding,
        margin=page.container_margin,
        content=ft.Tabs(
            selected_index=0,
            animation_duration=300,
            tabs=[
                ft.Tab(
                    content=canvas,
                    tab_content=ft.Text(
                        value="Canvas",
                        size=page.text_size,
                    ),
                ),
                ft.Tab(
                    text="Text Editor",
                    content=text_editor,
                    tab_content=ft.Text(
                        value="Text Editor",
                        size=page.text_size,
                    ),
                ),
            ],
        ),
        expand=True,
    )

    center_panel = ft.Container(
        content=ft.Column(
            controls=[
                viewport,
                messages,
            ],
        ),
        bgcolor=page.primary_color,
        padding=page.container_padding,
        margin=page.container_margin,
        expand=True,
    )

    # 	layouts ############################################################

    default_layout = ft.Row(
        controls=[
            tool_manager,
            asset_manager,
            center_panel,
            property_manager,
        ],
        expand=True,
    )

    current_layout = default_layout

    # 	workspace ##########################################################

    workspace = ft.Column(
        controls=[
            titlebar,
            current_layout,
        ],
        expand=True,
    )

    page.workspace = workspace

    full_page = ft.Stack(
        expand=True,
        controls=[
            workspace,
            layer_action_menu,
        ],
        height=page.height,
        width=page.width,
    )

    page.full_page = full_page

    page.title = "Stable Diffusion Playground"
    page.add(full_page)

    page.settings_window.setup(page.session.get("settings"))
    page.gallery_window.setup()
    page.titlebar.setup()
    page.tool_manager.setup()
    page.asset_manager.setup()
    page.canvas.setup()
    page.messages.setup()
    page.property_manager.setup()
    page.update()


ft.app(
    target=main,
    route_url_strategy="path",
    port=8505,
    assets_dir="assets",
    upload_dir="assets/uploads",
)


================================================
FILE: webui/streamlit/frontend/css/streamlit.main.css
================================================
/*
This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

Copyright 2022 Sygil-Dev team.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
*/

/***********************************************************
* Additional CSS for streamlit builtin components          *
************************************************************/

/* Tab name (e.g. Text-to-Image) //improve legibility*/
button[data-baseweb="tab"] {
  font-size: 25px;
}

/* Image Container (only appear after run finished)//center the image, especially better looks in wide screen */
.css-1kyxreq{
	justify-content: center;
}


/* Streamlit header */
.css-1avcm0n {
  background-color: transparent;
}

/* Main streamlit container (below header) //reduce the empty spaces*/
.css-18e3th9 {
  padding-top: 1rem;
}



/***********************************************************
* Additional CSS for streamlit custom/3rd party components *
************************************************************/
/* For stream_on_hover */
section[data-testid="stSidebar"] > div:nth-of-type(1) {
  background-color: #111;
}

button[kind="header"] {
  background-color: transparent;
  color: rgb(180, 167, 141);
}

@media (hover) {
  /* header element */
  header[data-testid="stHeader"] {
    /* display: none;*/ /*suggested behavior by streamlit hover components*/
    pointer-events: none; /* disable interaction of the transparent background */
  }

  /* The button on the streamlit navigation menu  */
  button[kind="header"] {
    /* display: none;*/ /*suggested behavior by streamlit hover components*/
    pointer-events: auto; /* enable interaction of the button even if parents intereaction disabled */
  }

  /* added to avoid main sectors (all element to the right of sidebar from) moving */
  section[data-testid="stSidebar"] {
    width: 3.5% !important;
    min-width: 3.5% !important;
  }

  /* The navigation menu specs and size */
  section[data-testid="stSidebar"] > div {
    height: 100%;
    width: 2% !important;
    min-width: 100% !important;
    position: relative;
    z-index: 1;
    top: 0;
    left: 0;
    background-color: #111;
    overflow-x: hidden;
    transition: 0.5s ease-in-out;
    padding-top: 0px;
    white-space: nowrap;
  }

  /* The navigation menu open and close on hover and size */
  section[data-testid="stSidebar"] > div:hover {
    width: 300px !important;
  }
}

@media (max-width: 272px) {
  section[data-testid="stSidebar"] > div {
    width: 15rem;
  }
}

/***********************************************************
*  Additional CSS for other elements
************************************************************/
button[data-baseweb="tab"] {
  font-size: 20px;
}

@media (min-width: 1200px){
h1 {
    font-size: 1.75rem;
}
}
#tabs-1-tabpanel-0 > div:nth-child(1) > div > div.stTabs.css-0.exp6ofz0 {
  width: 50rem;
  align-self: center;
}
div.gallery:hover {
  border: 1px solid #777;
}
.css-dg4u6x p {
  font-size: 0.8rem;
  text-align: center;
  position: relative;
  top: 6px;
}

.row-widget.stButton {
  text-align: center;
}

/********************************************************************
    Hide anchor links on titles
*********************************************************************/
/*
.css-15zrgzn {
	display: none
	}
.css-eczf16 {
	display: none
	}
.css-jn99sy {
	display: none
	}

/* Make the text area widget have a similar height as the text input field */
.st-dy{
	height: 54px;
	min-height: 25px;
}
.css-17useex{
	gap: 3px;

}

/* Remove some empty spaces to make the UI more compact. */
.css-18e3th9{
	padding-left: 10px;
	padding-right: 30px;
	position: unset !important; /* Fixes the layout/page going up when an expander or another item is expanded and then collapsed */
}
.css-k1vhr4{
	padding-top: initial;
}
.css-ret2ud{
	padding-left: 10px;
	padding-right: 30px;
	gap: initial;
	display: initial;
}

.css-w5z5an{
	gap: 1px;
}


================================================
FILE: webui/streamlit/scripts/APIServer.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
# from sd_utils import *
from sd_utils import st

# streamlit imports

# streamlit components section

# other imports
# from fastapi import FastAPI
# import uvicorn

# Temp imports

# end of imports
# ---------------------------------------------------------------------------------------------------------------


def layout():
    st.info("Under Construction. :construction_worker:")


================================================
FILE: webui/streamlit/scripts/ModelManager.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import st, logger

# streamlit imports


# other imports
import os, requests
from requests.auth import HTTPBasicAuth
from requests import HTTPError
from stqdm import stqdm

# Temp imports


# end of imports
# ---------------------------------------------------------------------------------------------------------------
def download_file(file_name, file_path, file_url):
    if not os.path.exists(file_path):
        os.makedirs(file_path)

    if not os.path.exists(os.path.join(file_path, file_name)):
        print("Downloading " + file_name + "...")
        # TODO - add progress bar in streamlit
        # download file with `requests``
        if file_name == "Stable Diffusion v1.5":
            if (
                "huggingface_token" not in st.session_state
                or st.session_state["defaults"].general.huggingface_token == "None"
            ):
                if "progress_bar_text" in st.session_state:
                    st.session_state["progress_bar_text"].error(
                        "You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token."
                    )
                raise OSError(
                    "You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token."
                )

        try:
            with requests.get(
                file_url,
                auth=HTTPBasicAuth(
                    "token", st.session_state.defaults.general.huggingface_token
                )
                if "huggingface.co" in file_url
                else None,
                stream=True,
            ) as r:
                r.raise_for_status()
                with open(os.path.join(file_path, file_name), "wb") as f:
                    for chunk in stqdm(
                        r.iter_content(chunk_size=8192), backend=True, unit="kb"
                    ):
                        f.write(chunk)
        except HTTPError as e:
            if "huggingface.co" in file_url:
                if "resolve" in file_url:
                    repo_url = file_url.split("resolve")[0]

                    st.session_state["progress_bar_text"].error(
                        f"You need to accept the license for the model in order to be able to download it. "
                        f"Please visit {repo_url} and accept the lincense there, then try again to download the model."
                    )

            logger.error(e)

    else:
        print(file_name + " already exists.")


def download_model(models, model_name):
    """Download all files from model_list[model_name]"""
    for file in models[model_name]:
        download_file(file["file_name"], file["file_path"], file["file_url"])
    return


def layout():
    # search = st.text_input(label="Search", placeholder="Type the name of the model you want to search for.", help="")

    colms = st.columns((1, 3, 3, 5, 5))
    columns = ["№", "Model Name", "Save Location", "Download", "Download Link"]

    models = st.session_state["defaults"].model_manager.models

    for col, field_name in zip(colms, columns):
        # table header
        col.write(field_name)

    for x, model_name in enumerate(models):
        col1, col2, col3, col4, col5 = st.columns((1, 3, 3, 3, 6))
        col1.write(x)  # index
        col2.write(models[model_name]["model_name"])
        col3.write(models[model_name]["save_location"])
        with col4:
            files_exist = 0
            for file in models[model_name]["files"]:
                if "save_location" in models[model_name]["files"][file]:
                    os.path.exists(
                        os.path.join(
                            models[model_name]["files"][file]["save_location"],
                            models[model_name]["files"][file]["file_name"],
                        )
                    )
                    files_exist += 1
                elif os.path.exists(
                    os.path.join(
                        models[model_name]["save_location"],
                        models[model_name]["files"][file]["file_name"],
                    )
                ):
                    files_exist += 1
            files_needed = []
            for file in models[model_name]["files"]:
                if "save_location" in models[model_name]["files"][file]:
                    if not os.path.exists(
                        os.path.join(
                            models[model_name]["files"][file]["save_location"],
                            models[model_name]["files"][file]["file_name"],
                        )
                    ):
                        files_needed.append(file)
                elif not os.path.exists(
                    os.path.join(
                        models[model_name]["save_location"],
                        models[model_name]["files"][file]["file_name"],
                    )
                ):
                    files_needed.append(file)
            if len(files_needed) > 0:
                if st.button(
                    "Download",
                    key=models[model_name]["model_name"],
                    help="Download " + models[model_name]["model_name"],
                ):
                    for file in files_needed:
                        if "save_location" in models[model_name]["files"][file]:
                            download_file(
                                models[model_name]["files"][file]["file_name"],
                                models[model_name]["files"][file]["save_location"],
                                models[model_name]["files"][file]["download_link"],
                            )
                        else:
                            download_file(
                                models[model_name]["files"][file]["file_name"],
                                models[model_name]["save_location"],
                                models[model_name]["files"][file]["download_link"],
                            )
                    st.experimental_rerun()
                else:
                    st.empty()
            else:
                st.write("✅")

        #


================================================
FILE: webui/streamlit/scripts/Settings.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import st, custom_models_available, logger, human_readable_size

# streamlit imports

# streamlit components section
from streamlit_server_state import server_state

# other imports
from omegaconf import OmegaConf
import torch
import os, toml

# end of imports
# ---------------------------------------------------------------------------------------------------------------


@logger.catch(reraise=True)
def layout():
    # st.header("Settings")

    with st.form("Settings"):
        (
            general_tab,
            txt2img_tab,
            img2img_tab,
            img2txt_tab,
            txt2vid_tab,
            image_processing,
            textual_inversion_tab,
            concepts_library_tab,
        ) = st.tabs(
            [
                "General",
                "Text-To-Image",
                "Image-To-Image",
                "Image-To-Text",
                "Text-To-Video",
                "Image processing",
                "Textual Inversion",
                "Concepts Library",
            ]
        )

        with general_tab:
            col1, col2, col3, col4, col5 = st.columns(5, gap="large")

            device_list = []
            device_properties = [
                (i, torch.cuda.get_device_properties(i))
                for i in range(torch.cuda.device_count())
            ]
            for device in device_properties:
                id = device[0]
                name = device[1].name
                total_memory = device[1].total_memory

                device_list.append(
                    f"{id}: {name} ({human_readable_size(total_memory, decimal_places=0)})"
                )

            with col1:
                st.title("General")
                st.session_state["defaults"].general.gpu = int(
                    st.selectbox(
                        "GPU",
                        device_list,
                        index=st.session_state["defaults"].general.gpu,
                        help=f"Select which GPU to use. Default: {device_list[0]}",
                    ).split(":")[0]
                )

                st.session_state["defaults"].general.outdir = str(
                    st.text_input(
                        "Output directory",
                        value=st.session_state["defaults"].general.outdir,
                        help="Relative directory on which the output images after a generation will be placed. Default: 'outputs'",
                    )
                )

                # If we have custom models available on the "models/custom"
                # folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
                custom_models_available()

                if server_state["CustomModel_available"]:
                    st.session_state.defaults.general.default_model = st.selectbox(
                        "Default Model:",
                        server_state["custom_models"],
                        index=server_state["custom_models"].index(
                            st.session_state["defaults"].general.default_model
                        ),
                        help="Select the model you want to use. If you have placed custom models \
                                                                  on your 'models/custom' folder they will be shown here as well. The model name that will be shown here \
                                                                  is the same as the name the file for the model has on said folder, \
                                                                  it is recommended to give the .ckpt file a name that \
                                                                  will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4",
                    )
                else:
                    st.session_state.defaults.general.default_model = st.selectbox(
                        "Default Model:",
                        [st.session_state["defaults"].general.default_model],
                        help="Select the model you want to use. If you have placed custom models \
                                                                  on your 'models/custom' folder they will be shown here as well. \
                                                                  The model name that will be shown here is the same as the name\
                                                                  the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
                                                                  will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4",
                    )

                st.session_state[
                    "defaults"
                ].general.default_model_config = st.text_input(
                    "Default Model Config",
                    value=st.session_state["defaults"].general.default_model_config,
                    help="Default model config file for inference. Default: 'configs/stable-diffusion/v1-inference.yaml'",
                )

                st.session_state["defaults"].general.default_model_path = st.text_input(
                    "Default Model Config",
                    value=st.session_state["defaults"].general.default_model_path,
                    help="Default model path. Default: 'models/ldm/stable-diffusion-v1/model.ckpt'",
                )

                st.session_state["defaults"].general.GFPGAN_dir = st.text_input(
                    "Default GFPGAN directory",
                    value=st.session_state["defaults"].general.GFPGAN_dir,
                    help="Default GFPGAN directory. Default: './models/gfpgan'",
                )

                st.session_state["defaults"].general.RealESRGAN_dir = st.text_input(
                    "Default RealESRGAN directory",
                    value=st.session_state["defaults"].general.RealESRGAN_dir,
                    help="Default GFPGAN directory. Default: './models/realesrgan'",
                )

                RealESRGAN_model_list = [
                    "RealESRGAN_x4plus",
                    "RealESRGAN_x4plus_anime_6B",
                ]
                st.session_state["defaults"].general.RealESRGAN_model = st.selectbox(
                    "RealESRGAN model",
                    RealESRGAN_model_list,
                    index=RealESRGAN_model_list.index(
                        st.session_state["defaults"].general.RealESRGAN_model
                    ),
                    help="Default RealESRGAN model. Default: 'RealESRGAN_x4plus'",
                )
                Upscaler_list = ["RealESRGAN", "LDSR"]
                st.session_state["defaults"].general.upscaling_method = st.selectbox(
                    "Upscaler",
                    Upscaler_list,
                    index=Upscaler_list.index(
                        st.session_state["defaults"].general.upscaling_method
                    ),
                    help="Default upscaling method. Default: 'RealESRGAN'",
                )

            with col2:
                st.title("Performance")

                st.session_state["defaults"].general.gfpgan_cpu = st.checkbox(
                    "GFPGAN - CPU",
                    value=st.session_state["defaults"].general.gfpgan_cpu,
                    help="Run GFPGAN on the cpu. Default: False",
                )

                st.session_state["defaults"].general.esrgan_cpu = st.checkbox(
                    "ESRGAN - CPU",
                    value=st.session_state["defaults"].general.esrgan_cpu,
                    help="Run ESRGAN on the cpu. Default: False",
                )

                st.session_state["defaults"].general.extra_models_cpu = st.checkbox(
                    "Extra Models - CPU",
                    value=st.session_state["defaults"].general.extra_models_cpu,
                    help="Run extra models (GFGPAN/ESRGAN) on cpu. Default: False",
                )

                st.session_state["defaults"].general.extra_models_gpu = st.checkbox(
                    "Extra Models - GPU",
                    value=st.session_state["defaults"].general.extra_models_gpu,
                    help="Run extra models (GFGPAN/ESRGAN) on gpu. \
                                                                                    Check and save in order to be able to select the GPU that each model will use. Default: False",
                )
                if st.session_state["defaults"].general.extra_models_gpu:
                    st.session_state["defaults"].general.gfpgan_gpu = int(
                        st.selectbox(
                            "GFGPAN GPU",
                            device_list,
                            index=st.session_state["defaults"].general.gfpgan_gpu,
                            help=f"Select which GPU to use. Default: {device_list[st.session_state['defaults'].general.gfpgan_gpu]}",
                            key="gfpgan_gpu",
                        ).split(":")[0]
                    )

                    st.session_state["defaults"].general.esrgan_gpu = int(
                        st.selectbox(
                            "ESRGAN - GPU",
                            device_list,
                            index=st.session_state["defaults"].general.esrgan_gpu,
                            help=f"Select which GPU to use. Default: {device_list[st.session_state['defaults'].general.esrgan_gpu]}",
                            key="esrgan_gpu",
                        ).split(":")[0]
                    )

                st.session_state["defaults"].general.no_half = st.checkbox(
                    "No Half",
                    value=st.session_state["defaults"].general.no_half,
                    help="DO NOT switch the model to 16-bit floats. Default: False",
                )

                st.session_state["defaults"].general.use_cudnn = st.checkbox(
                    "Use cudnn",
                    value=st.session_state["defaults"].general.use_cudnn,
                    help="Switch the pytorch backend to use cudnn, this should help with fixing Nvidia 16xx cards getting"
                    "a black or green image. Default: False",
                )

                st.session_state["defaults"].general.use_float16 = st.checkbox(
                    "Use float16",
                    value=st.session_state["defaults"].general.use_float16,
                    help="Switch the model to 16-bit floats. Default: False",
                )

                precision_list = ["full", "autocast"]
                st.session_state["defaults"].general.precision = st.selectbox(
                    "Precision",
                    precision_list,
                    index=precision_list.index(
                        st.session_state["defaults"].general.precision
                    ),
                    help="Evaluates at this precision. Default: autocast",
                )

                st.session_state["defaults"].general.optimized = st.checkbox(
                    "Optimized Mode",
                    value=st.session_state["defaults"].general.optimized,
                    help="Loads the model onto the device piecemeal instead of all at once to reduce VRAM usage\
																		   at the cost of performance. Default: False",
                )

                st.session_state["defaults"].general.optimized_turbo = st.checkbox(
                    "Optimized Turbo Mode",
                    value=st.session_state["defaults"].general.optimized_turbo,
                    help="Alternative optimization mode that does not save as much VRAM but \
                                                                                   runs siginificantly faster. Default: False",
                )

                st.session_state["defaults"].general.optimized_config = st.text_input(
                    "Optimized Config",
                    value=st.session_state["defaults"].general.optimized_config,
                    help="Loads alternative optimized configuration for inference. \
                                                                                      Default: optimizedSD/v1-inference.yaml",
                )

                st.session_state[
                    "defaults"
                ].general.enable_attention_slicing = st.checkbox(
                    "Enable Attention Slicing",
                    value=st.session_state["defaults"].general.enable_attention_slicing,
                    help="Enable sliced attention computation. When this option is enabled, the attention module will \
                                                                                            split the input tensor in slices, to compute attention in several steps. This is useful to save some \
                                                                                            memory in exchange for a small speed decrease. Only works the txt2vid tab right now. Default: False",
                )

                st.session_state[
                    "defaults"
                ].general.enable_minimal_memory_usage = st.checkbox(
                    "Enable Minimal Memory Usage",
                    value=st.session_state[
                        "defaults"
                    ].general.enable_minimal_memory_usage,
                    help="Moves only unet to fp16 and to CUDA, while keepping lighter models on CPUs \
                                                                                               (Not properly implemented and currently not working, check this \
                                                                                               link 'https://github.com/huggingface/diffusers/pull/537' for more information on it ). Default: False",
                )

                # st.session_state["defaults"].general.update_preview = st.checkbox("Update Preview Image", value=st.session_state['defaults'].general.update_preview,
                # help="Enables the preview image to be updated and shown to the user on the UI during the generation.\
                # If checked, once you save the settings an option to specify the frequency at which the image is updated\
                # in steps will be shown, this is helpful to reduce the negative effect this option has on performance. \
                # Default: True")
                st.session_state["defaults"].general.update_preview = True
                st.session_state[
                    "defaults"
                ].general.update_preview_frequency = st.number_input(
                    "Update Preview Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].general.update_preview_frequency,
                    help="Specify the frequency at which the image is updated in steps, this is helpful to reduce the \
                                                                                                negative effect updating the preview image has on performance. Default: 10",
                )

            with col3:
                st.title("Others")
                st.session_state[
                    "defaults"
                ].general.use_sd_concepts_library = st.checkbox(
                    "Use the Concepts Library",
                    value=st.session_state["defaults"].general.use_sd_concepts_library,
                    help="Use the embeds Concepts Library, if checked, once the settings are saved an option will\
                                                                                           appear to specify the directory where the concepts are stored. Default: True)",
                )

                if st.session_state["defaults"].general.use_sd_concepts_library:
                    st.session_state[
                        "defaults"
                    ].general.sd_concepts_library_folder = st.text_input(
                        "Concepts Library Folder",
                        value=st.session_state[
                            "defaults"
                        ].general.sd_concepts_library_folder,
                        help="Relative folder on which the concepts library embeds are stored. \
                                                                                                    Default: 'models/custom/sd-concepts-library'",
                    )

                st.session_state["defaults"].general.LDSR_dir = st.text_input(
                    "LDSR Folder",
                    value=st.session_state["defaults"].general.LDSR_dir,
                    help="Folder where LDSR is located. Default: './models/ldsr'",
                )

                st.session_state["defaults"].general.save_metadata = st.checkbox(
                    "Save Metadata",
                    value=st.session_state["defaults"].general.save_metadata,
                    help="Save metadata on the output image. Default: True",
                )
                save_format_list = ["png", "jpg", "jpeg", "webp"]
                st.session_state["defaults"].general.save_format = st.selectbox(
                    "Save Format",
                    save_format_list,
                    index=save_format_list.index(
                        st.session_state["defaults"].general.save_format
                    ),
                    help="Format that will be used whens saving the output images. Default: 'png'",
                )

                st.session_state["defaults"].general.skip_grid = st.checkbox(
                    "Skip Grid",
                    value=st.session_state["defaults"].general.skip_grid,
                    help="Skip saving the grid output image. Default: False",
                )
                if not st.session_state["defaults"].general.skip_grid:
                    st.session_state["defaults"].general.grid_quality = st.number_input(
                        "Grid Quality",
                        value=st.session_state["defaults"].general.grid_quality,
                        help="Format for saving the grid output image. Default: 95",
                    )

                st.session_state["defaults"].general.skip_save = st.checkbox(
                    "Skip Save",
                    value=st.session_state["defaults"].general.skip_save,
                    help="Skip saving the output image. Default: False",
                )

                st.session_state["defaults"].general.n_rows = st.number_input(
                    "Number of Grid Rows",
                    value=st.session_state["defaults"].general.n_rows,
                    help="Number of rows the grid wil have when saving the grid output image. Default: '-1'",
                )

                st.session_state["defaults"].general.no_verify_input = st.checkbox(
                    "Do not Verify Input",
                    value=st.session_state["defaults"].general.no_verify_input,
                    help="Do not verify input to check if it's too long. Default: False",
                )

                st.session_state[
                    "defaults"
                ].general.show_percent_in_tab_title = st.checkbox(
                    "Show Percent in tab title",
                    value=st.session_state[
                        "defaults"
                    ].general.show_percent_in_tab_title,
                    help="Add the progress percent value to the page title on the tab on your browser. "
                    "This is useful in case you need to know how the generation is going while doign something else"
                    "in another tab on your browser. Default: True",
                )

                st.session_state["defaults"].general.enable_suggestions = st.checkbox(
                    "Enable Suggestions Box",
                    value=st.session_state["defaults"].general.enable_suggestions,
                    help="Adds a suggestion box under the prompt when clicked. Default: True",
                )

                st.session_state[
                    "defaults"
                ].daisi_app.running_on_daisi_io = st.checkbox(
                    "Running on Daisi.io?",
                    value=st.session_state["defaults"].daisi_app.running_on_daisi_io,
                    help="Specify if we are running on app.Daisi.io . Default: False",
                )

            with col4:
                st.title("Streamlit Config")

                default_theme_list = ["light", "dark"]
                st.session_state["defaults"].general.default_theme = st.selectbox(
                    "Default Theme",
                    default_theme_list,
                    index=default_theme_list.index(
                        st.session_state["defaults"].general.default_theme
                    ),
                    help="Defaut theme to use as base for streamlit. Default: dark",
                )
                st.session_state["streamlit_config"]["theme"][
                    "base"
                ] = st.session_state["defaults"].general.default_theme

                if not st.session_state["defaults"].admin.hide_server_setting:
                    with st.expander("Server", True):
                        st.session_state["streamlit_config"]["server"][
                            "headless"
                        ] = st.checkbox(
                            "Run Headless",
                            help="If false, will attempt to open a browser window on start.  \
                                                                                                 Default: false unless (1) we are on a Linux box where DISPLAY is unset, \
                                                                                                 or (2) we are running in the Streamlit Atom plugin.",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "port"
                        ] = st.number_input(
                            "Port",
                            value=st.session_state["streamlit_config"]["server"][
                                "port"
                            ],
                            help="The port where the server will listen for browser connections. Default: 8501",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "baseUrlPath"
                        ] = st.text_input(
                            "Base Url Path",
                            value=st.session_state["streamlit_config"]["server"][
                                "baseUrlPath"
                            ],
                            help="The base path for the URL where Streamlit should be served from. Default: '' ",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "enableCORS"
                        ] = st.checkbox(
                            "Enable CORS",
                            value=st.session_state["streamlit_config"]["server"][
                                "enableCORS"
                            ],
                            help="Enables support for Cross-Origin Request Sharing (CORS) protection, for added security. \
                                                                                                   Due to conflicts between CORS and XSRF, if `server.enableXsrfProtection` is on and `server.enableCORS` \
                                                                                                   is off at the same time, we will prioritize `server.enableXsrfProtection`. Default: true",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "enableXsrfProtection"
                        ] = st.checkbox(
                            "Enable Xsrf Protection",
                            value=st.session_state["streamlit_config"]["server"][
                                "enableXsrfProtection"
                            ],
                            help="Enables support for Cross-Site Request Forgery (XSRF) protection, \
                                                                                                             for added security. Due to conflicts between CORS and XSRF, \
                                                                                                             if `server.enableXsrfProtection` is on and `server.enableCORS` is off at \
                                                                                                             the same time, we will prioritize `server.enableXsrfProtection`. Default: true",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "maxUploadSize"
                        ] = st.number_input(
                            "Max Upload Size",
                            value=st.session_state["streamlit_config"]["server"][
                                "maxUploadSize"
                            ],
                            help="Max size, in megabytes, for files uploaded with the file_uploader. Default: 200",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "maxMessageSize"
                        ] = st.number_input(
                            "Max Message Size",
                            value=st.session_state["streamlit_config"]["server"][
                                "maxUploadSize"
                            ],
                            help="Max size, in megabytes, of messages that can be sent via the WebSocket connection. Default: 200",
                        )

                        st.session_state["streamlit_config"]["server"][
                            "enableWebsocketCompression"
                        ] = st.checkbox(
                            "Enable Websocket Compression",
                            value=st.session_state["streamlit_config"]["server"][
                                "enableWebsocketCompression"
                            ],
                            help=" Enables support for websocket compression. Default: false",
                        )
                if not st.session_state["defaults"].admin.hide_browser_setting:
                    with st.expander("Browser", expanded=True):
                        st.session_state["streamlit_config"]["browser"][
                            "serverAddress"
                        ] = st.text_input(
                            "Server Address",
                            value=st.session_state["streamlit_config"]["browser"][
                                "serverAddress"
                            ]
                            if "serverAddress" in st.session_state["streamlit_config"]
                            else "localhost",
                            help="Internet address where users should point their browsers in order \
                                                                                                       to connect to the app. Can be IP address or DNS name and path.\
                                                                                                       This is used to: - Set the correct URL for CORS and XSRF protection purposes. \
                                                                                                       - Show the URL on the terminal - Open the browser. Default: 'localhost'",
                        )

                        st.session_state[
                            "defaults"
                        ].general.streamlit_telemetry = st.checkbox(
                            "Enable Telemetry",
                            value=st.session_state[
                                "defaults"
                            ].general.streamlit_telemetry,
                            help="Enables or Disables streamlit telemetry. Default: False",
                        )
                        st.session_state["streamlit_config"]["browser"][
                            "gatherUsageStats"
                        ] = st.session_state["defaults"].general.streamlit_telemetry

                        st.session_state["streamlit_config"]["browser"][
                            "serverPort"
                        ] = st.number_input(
                            "Server Port",
                            value=st.session_state["streamlit_config"]["browser"][
                                "serverPort"
                            ],
                            help="Port where users should point their browsers in order to connect to the app. \
                                                                                                 This is used to: - Set the correct URL for CORS and XSRF protection purposes. \
                                                                                                 - Show the URL on the terminal - Open the browser \
                                                                                                 Default: whatever value is set in server.port.",
                        )

            with col5:
                st.title("Huggingface")
                st.session_state["defaults"].general.huggingface_token = st.text_input(
                    "Huggingface Token",
                    value=st.session_state["defaults"].general.huggingface_token,
                    type="password",
                    help="Your Huggingface Token, it's used to download the model for the diffusers library which \
                                                                                       is used on the Text To Video tab. This token will be saved to your user config file\
                                                                                       and WILL NOT be share with us or anyone. You can get your access token \
                                                                                       at https://huggingface.co/settings/tokens. Default: None",
                )

                st.title("Stable Horde")
                st.session_state["defaults"].general.stable_horde_api = st.text_input(
                    "Stable Horde Api",
                    value=st.session_state["defaults"].general.stable_horde_api,
                    type="password",
                    help="First Register an account at https://stablehorde.net/register which will generate for you \
                                                                                      an API key. Store that key somewhere safe. \n \
                                                                                      If you do not want to register, you can use `0000000000` as api_key to connect anonymously.\
                                                                                      However anonymous accounts have the lowest priority when there's too many concurrent requests! \
                                                                                      To increase your priority you will need a unique API key and then to increase your Kudos \
                                                                                      read more about them at https://dbzer0.com/blog/the-kudos-based-economy-for-the-koboldai-horde/.",
                )

        with txt2img_tab:
            col1, col2, col3, col4, col5 = st.columns(5, gap="medium")

            with col1:
                st.title("Slider Parameters")

                # Width
                st.session_state["defaults"].txt2img.width.value = st.number_input(
                    "Default Image Width",
                    value=st.session_state["defaults"].txt2img.width.value,
                    help="Set the default width for the generated image. Default is: 512",
                )

                st.session_state["defaults"].txt2img.width.min_value = st.number_input(
                    "Minimum Image Width",
                    value=st.session_state["defaults"].txt2img.width.min_value,
                    help="Set the default minimum value for the width slider. Default is: 64",
                )

                st.session_state["defaults"].txt2img.width.max_value = st.number_input(
                    "Maximum Image Width",
                    value=st.session_state["defaults"].txt2img.width.max_value,
                    help="Set the default maximum value for the width slider. Default is: 2048",
                )

                # Height
                st.session_state["defaults"].txt2img.height.value = st.number_input(
                    "Default Image Height",
                    value=st.session_state["defaults"].txt2img.height.value,
                    help="Set the default height for the generated image. Default is: 512",
                )

                st.session_state["defaults"].txt2img.height.min_value = st.number_input(
                    "Minimum Image Height",
                    value=st.session_state["defaults"].txt2img.height.min_value,
                    help="Set the default minimum value for the height slider. Default is: 64",
                )

                st.session_state["defaults"].txt2img.height.max_value = st.number_input(
                    "Maximum Image Height",
                    value=st.session_state["defaults"].txt2img.height.max_value,
                    help="Set the default maximum value for the height slider. Default is: 2048",
                )

                with col2:
                    # CFG
                    st.session_state[
                        "defaults"
                    ].txt2img.cfg_scale.value = st.number_input(
                        "Default CFG Scale",
                        value=st.session_state["defaults"].txt2img.cfg_scale.value,
                        help="Set the default value for the CFG Scale. Default is: 7.5",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2img.cfg_scale.min_value = st.number_input(
                        "Minimum CFG Scale Value",
                        value=st.session_state["defaults"].txt2img.cfg_scale.min_value,
                        help="Set the default minimum value for the CFG scale slider. Default is: 1",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2img.cfg_scale.step = st.number_input(
                        "CFG Slider Steps",
                        value=st.session_state["defaults"].txt2img.cfg_scale.step,
                        help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5",
                    )
                    # Sampling Steps
                    st.session_state[
                        "defaults"
                    ].txt2img.sampling_steps.value = st.number_input(
                        "Default Sampling Steps",
                        value=st.session_state["defaults"].txt2img.sampling_steps.value,
                        help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2img.sampling_steps.min_value = st.number_input(
                        "Minimum Sampling Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2img.sampling_steps.min_value,
                        help="Set the default minimum value for the sampling steps slider. Default is: 1",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2img.sampling_steps.step = st.number_input(
                        "Sampling Slider Steps",
                        value=st.session_state["defaults"].txt2img.sampling_steps.step,
                        help="Set the default value for the number of steps on the sampling steps slider. Default is: 10",
                    )

            with col3:
                st.title("General Parameters")

                # Batch Count
                st.session_state[
                    "defaults"
                ].txt2img.batch_count.value = st.number_input(
                    "Batch count",
                    value=st.session_state["defaults"].txt2img.batch_count.value,
                    help="How many iterations or batches of images to generate in total.",
                )

                st.session_state["defaults"].txt2img.batch_size.value = st.number_input(
                    "Batch size",
                    value=st.session_state.defaults.txt2img.batch_size.value,
                    help="How many images are at once in a batch.\
                                                                                        It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
                                                                                        takes to finish generation as more images are generated at once.\
                                                                                        Default: 1",
                )

                default_sampler_list = [
                    "k_lms",
                    "k_euler",
                    "k_euler_a",
                    "k_dpm_2",
                    "k_dpm_2_a",
                    "k_heun",
                    "PLMS",
                    "DDIM",
                ]
                st.session_state["defaults"].txt2img.default_sampler = st.selectbox(
                    "Default Sampler",
                    default_sampler_list,
                    index=default_sampler_list.index(
                        st.session_state["defaults"].txt2img.default_sampler
                    ),
                    help="Defaut sampler to use for txt2img. Default: k_euler",
                )

                st.session_state["defaults"].txt2img.seed = st.text_input(
                    "Default Seed",
                    value=st.session_state["defaults"].txt2img.seed,
                    help="Default seed.",
                )

            with col4:
                st.session_state["defaults"].txt2img.separate_prompts = st.checkbox(
                    "Separate Prompts",
                    value=st.session_state["defaults"].txt2img.separate_prompts,
                    help="Separate Prompts. Default: False",
                )

                st.session_state[
                    "defaults"
                ].txt2img.normalize_prompt_weights = st.checkbox(
                    "Normalize Prompt Weights",
                    value=st.session_state["defaults"].txt2img.normalize_prompt_weights,
                    help="Choose to normalize prompt weights. Default: True",
                )

                st.session_state[
                    "defaults"
                ].txt2img.save_individual_images = st.checkbox(
                    "Save Individual Images",
                    value=st.session_state["defaults"].txt2img.save_individual_images,
                    help="Choose to save individual images. Default: True",
                )

                st.session_state["defaults"].txt2img.save_grid = st.checkbox(
                    "Save Grid Images",
                    value=st.session_state["defaults"].txt2img.save_grid,
                    help="Choose to save the grid images. Default: True",
                )

                st.session_state["defaults"].txt2img.group_by_prompt = st.checkbox(
                    "Group By Prompt",
                    value=st.session_state["defaults"].txt2img.group_by_prompt,
                    help="Choose to save images grouped by their prompt. Default: False",
                )

                st.session_state["defaults"].txt2img.save_as_jpg = st.checkbox(
                    "Save As JPG",
                    value=st.session_state["defaults"].txt2img.save_as_jpg,
                    help="Choose to save images as jpegs. Default: False",
                )

                st.session_state["defaults"].txt2img.write_info_files = st.checkbox(
                    "Write Info Files For Images",
                    value=st.session_state["defaults"].txt2img.write_info_files,
                    help="Choose to write the info files along with the generated images. Default: True",
                )

                st.session_state["defaults"].txt2img.use_GFPGAN = st.checkbox(
                    "Use GFPGAN",
                    value=st.session_state["defaults"].txt2img.use_GFPGAN,
                    help="Choose to use GFPGAN. Default: False",
                )

                st.session_state["defaults"].txt2img.use_upscaling = st.checkbox(
                    "Use Upscaling",
                    value=st.session_state["defaults"].txt2img.use_upscaling,
                    help="Choose to turn on upscaling by default. Default: False",
                )

                st.session_state["defaults"].txt2img.update_preview = True
                st.session_state[
                    "defaults"
                ].txt2img.update_preview_frequency = st.number_input(
                    "Preview Image Update Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].txt2img.update_preview_frequency,
                    help="Set the default value for the frrquency of the preview image updates. Default is: 10",
                )

            with col5:
                st.title("Variation Parameters")

                st.session_state[
                    "defaults"
                ].txt2img.variant_amount.value = st.number_input(
                    "Default Variation Amount",
                    value=st.session_state["defaults"].txt2img.variant_amount.value,
                    help="Set the default variation to use. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2img.variant_amount.min_value = st.number_input(
                    "Minimum Variation Amount",
                    value=st.session_state["defaults"].txt2img.variant_amount.min_value,
                    help="Set the default minimum value for the variation slider. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2img.variant_amount.max_value = st.number_input(
                    "Maximum Variation Amount",
                    value=st.session_state["defaults"].txt2img.variant_amount.max_value,
                    help="Set the default maximum value for the variation slider. Default is: 1.0",
                )

                st.session_state[
                    "defaults"
                ].txt2img.variant_amount.step = st.number_input(
                    "Variation Slider Steps",
                    value=st.session_state["defaults"].txt2img.variant_amount.step,
                    help="Set the default value for the number of steps on the variation slider. Default is: 1",
                )

                st.session_state["defaults"].txt2img.variant_seed = st.text_input(
                    "Default Variation Seed",
                    value=st.session_state["defaults"].txt2img.variant_seed,
                    help="Default variation seed.",
                )

        with img2img_tab:
            col1, col2, col3, col4, col5 = st.columns(5, gap="medium")

            with col1:
                st.title("Image Editing")

                # Denoising
                st.session_state[
                    "defaults"
                ].img2img.denoising_strength.value = st.number_input(
                    "Default Denoising Amount",
                    value=st.session_state["defaults"].img2img.denoising_strength.value,
                    help="Set the default denoising to use. Default is: 0.75",
                )

                st.session_state[
                    "defaults"
                ].img2img.denoising_strength.min_value = st.number_input(
                    "Minimum Denoising Amount",
                    value=st.session_state[
                        "defaults"
                    ].img2img.denoising_strength.min_value,
                    help="Set the default minimum value for the denoising slider. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].img2img.denoising_strength.max_value = st.number_input(
                    "Maximum Denoising Amount",
                    value=st.session_state[
                        "defaults"
                    ].img2img.denoising_strength.max_value,
                    help="Set the default maximum value for the denoising slider. Default is: 1.0",
                )

                st.session_state[
                    "defaults"
                ].img2img.denoising_strength.step = st.number_input(
                    "Denoising Slider Steps",
                    value=st.session_state["defaults"].img2img.denoising_strength.step,
                    help="Set the default value for the number of steps on the denoising slider. Default is: 0.01",
                )

                # Masking
                st.session_state["defaults"].img2img.mask_mode = st.number_input(
                    "Default Mask Mode",
                    value=st.session_state["defaults"].img2img.mask_mode,
                    help="Set the default mask mode to use. 0 = Keep Masked Area, 1 = Regenerate Masked Area. Default is: 0",
                )

                st.session_state["defaults"].img2img.mask_restore = st.checkbox(
                    "Default Mask Restore",
                    value=st.session_state["defaults"].img2img.mask_restore,
                    help="Mask Restore. Default: False",
                )

                st.session_state["defaults"].img2img.resize_mode = st.number_input(
                    "Default Resize Mode",
                    value=st.session_state["defaults"].img2img.resize_mode,
                    help="Set the default resizing mode. 0 = Just Resize, 1 = Crop and Resize, 3 = Resize and Fill. Default is: 0",
                )

            with col2:
                st.title("Slider Parameters")

                # Width
                st.session_state["defaults"].img2img.width.value = st.number_input(
                    "Default Outputted Image Width",
                    value=st.session_state["defaults"].img2img.width.value,
                    help="Set the default width for the generated image. Default is: 512",
                )

                st.session_state["defaults"].img2img.width.min_value = st.number_input(
                    "Minimum Outputted Image Width",
                    value=st.session_state["defaults"].img2img.width.min_value,
                    help="Set the default minimum value for the width slider. Default is: 64",
                )

                st.session_state["defaults"].img2img.width.max_value = st.number_input(
                    "Maximum Outputted Image Width",
                    value=st.session_state["defaults"].img2img.width.max_value,
                    help="Set the default maximum value for the width slider. Default is: 2048",
                )

                # Height
                st.session_state["defaults"].img2img.height.value = st.number_input(
                    "Default Outputted Image Height",
                    value=st.session_state["defaults"].img2img.height.value,
                    help="Set the default height for the generated image. Default is: 512",
                )

                st.session_state["defaults"].img2img.height.min_value = st.number_input(
                    "Minimum Outputted Image Height",
                    value=st.session_state["defaults"].img2img.height.min_value,
                    help="Set the default minimum value for the height slider. Default is: 64",
                )

                st.session_state["defaults"].img2img.height.max_value = st.number_input(
                    "Maximum Outputted Image Height",
                    value=st.session_state["defaults"].img2img.height.max_value,
                    help="Set the default maximum value for the height slider. Default is: 2048",
                )

                # CFG
                st.session_state["defaults"].img2img.cfg_scale.value = st.number_input(
                    "Default Img2Img CFG Scale",
                    value=st.session_state["defaults"].img2img.cfg_scale.value,
                    help="Set the default value for the CFG Scale. Default is: 7.5",
                )

                st.session_state[
                    "defaults"
                ].img2img.cfg_scale.min_value = st.number_input(
                    "Minimum Img2Img CFG Scale Value",
                    value=st.session_state["defaults"].img2img.cfg_scale.min_value,
                    help="Set the default minimum value for the CFG scale slider. Default is: 1",
                )

                with col3:
                    st.session_state[
                        "defaults"
                    ].img2img.cfg_scale.step = st.number_input(
                        "Img2Img CFG Slider Steps",
                        value=st.session_state["defaults"].img2img.cfg_scale.step,
                        help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5",
                    )

                    # Sampling Steps
                    st.session_state[
                        "defaults"
                    ].img2img.sampling_steps.value = st.number_input(
                        "Default Img2Img Sampling Steps",
                        value=st.session_state["defaults"].img2img.sampling_steps.value,
                        help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)",
                    )

                    st.session_state[
                        "defaults"
                    ].img2img.sampling_steps.min_value = st.number_input(
                        "Minimum Img2Img Sampling Steps",
                        value=st.session_state[
                            "defaults"
                        ].img2img.sampling_steps.min_value,
                        help="Set the default minimum value for the sampling steps slider. Default is: 1",
                    )

                    st.session_state[
                        "defaults"
                    ].img2img.sampling_steps.step = st.number_input(
                        "Img2Img Sampling Slider Steps",
                        value=st.session_state["defaults"].img2img.sampling_steps.step,
                        help="Set the default value for the number of steps on the sampling steps slider. Default is: 10",
                    )

                    # Batch Count
                    st.session_state[
                        "defaults"
                    ].img2img.batch_count.value = st.number_input(
                        "Img2img Batch count",
                        value=st.session_state["defaults"].img2img.batch_count.value,
                        help="How many iterations or batches of images to generate in total.",
                    )

                    st.session_state[
                        "defaults"
                    ].img2img.batch_size.value = st.number_input(
                        "Img2img Batch size",
                        value=st.session_state["defaults"].img2img.batch_size.value,
                        help="How many images are at once in a batch.\
                                                                                            It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
                                                                                            takes to finish generation as more images are generated at once.\
                                                                                            Default: 1",
                    )
                    with col4:
                        # Inference Steps
                        st.session_state[
                            "defaults"
                        ].img2img.num_inference_steps.value = st.number_input(
                            "Default Inference Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.num_inference_steps.value,
                            help="Set the default number of inference steps to use. Default is: 200",
                        )

                        st.session_state[
                            "defaults"
                        ].img2img.num_inference_steps.min_value = st.number_input(
                            "Minimum Sampling Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.num_inference_steps.min_value,
                            help="Set the default minimum value for the inference steps slider. Default is: 10",
                        )

                        st.session_state[
                            "defaults"
                        ].img2img.num_inference_steps.max_value = st.number_input(
                            "Maximum Sampling Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.num_inference_steps.max_value,
                            help="Set the default maximum value for the inference steps slider. Default is: 500",
                        )

                        st.session_state[
                            "defaults"
                        ].img2img.num_inference_steps.step = st.number_input(
                            "Inference Slider Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.num_inference_steps.step,
                            help="Set the default value for the number of steps on the inference steps slider.\
                                                                                                        Default is: 10",
                        )

                        # Find Noise Steps
                        st.session_state[
                            "defaults"
                        ].img2img.find_noise_steps.value = st.number_input(
                            "Default Find Noise Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.find_noise_steps.value,
                            help="Set the default number of find noise steps to use. Default is: 100",
                        )

                        st.session_state[
                            "defaults"
                        ].img2img.find_noise_steps.min_value = st.number_input(
                            "Minimum Find Noise Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.find_noise_steps.min_value,
                            help="Set the default minimum value for the find noise steps slider. Default is: 0",
                        )

                        st.session_state[
                            "defaults"
                        ].img2img.find_noise_steps.step = st.number_input(
                            "Find Noise Slider Steps",
                            value=st.session_state[
                                "defaults"
                            ].img2img.find_noise_steps.step,
                            help="Set the default value for the number of steps on the find noise steps slider. \
                                                                                                     Default is: 100",
                        )

            with col5:
                st.title("General Parameters")

                default_sampler_list = [
                    "k_lms",
                    "k_euler",
                    "k_euler_a",
                    "k_dpm_2",
                    "k_dpm_2_a",
                    "k_heun",
                    "PLMS",
                    "DDIM",
                ]
                st.session_state["defaults"].img2img.sampler_name = st.selectbox(
                    "Default Img2Img Sampler",
                    default_sampler_list,
                    index=default_sampler_list.index(
                        st.session_state["defaults"].img2img.sampler_name
                    ),
                    help="Defaut sampler to use for img2img. Default: k_euler",
                )

                st.session_state["defaults"].img2img.seed = st.text_input(
                    "Default Img2Img Seed",
                    value=st.session_state["defaults"].img2img.seed,
                    help="Default seed.",
                )

                st.session_state["defaults"].img2img.separate_prompts = st.checkbox(
                    "Separate Img2Img Prompts",
                    value=st.session_state["defaults"].img2img.separate_prompts,
                    help="Separate Prompts. Default: False",
                )

                st.session_state[
                    "defaults"
                ].img2img.normalize_prompt_weights = st.checkbox(
                    "Normalize Img2Img Prompt Weights",
                    value=st.session_state["defaults"].img2img.normalize_prompt_weights,
                    help="Choose to normalize prompt weights. Default: True",
                )

                st.session_state[
                    "defaults"
                ].img2img.save_individual_images = st.checkbox(
                    "Save Individual Img2Img Images",
                    value=st.session_state["defaults"].img2img.save_individual_images,
                    help="Choose to save individual images. Default: True",
                )

                st.session_state["defaults"].img2img.save_grid = st.checkbox(
                    "Save Img2Img Grid Images",
                    value=st.session_state["defaults"].img2img.save_grid,
                    help="Choose to save the grid images. Default: True",
                )

                st.session_state["defaults"].img2img.group_by_prompt = st.checkbox(
                    "Group By Img2Img Prompt",
                    value=st.session_state["defaults"].img2img.group_by_prompt,
                    help="Choose to save images grouped by their prompt. Default: False",
                )

                st.session_state["defaults"].img2img.save_as_jpg = st.checkbox(
                    "Save Img2Img As JPG",
                    value=st.session_state["defaults"].img2img.save_as_jpg,
                    help="Choose to save images as jpegs. Default: False",
                )

                st.session_state["defaults"].img2img.write_info_files = st.checkbox(
                    "Write Info Files For Img2Img Images",
                    value=st.session_state["defaults"].img2img.write_info_files,
                    help="Choose to write the info files along with the generated images. Default: True",
                )

                st.session_state["defaults"].img2img.use_GFPGAN = st.checkbox(
                    "Img2Img Use GFPGAN",
                    value=st.session_state["defaults"].img2img.use_GFPGAN,
                    help="Choose to use GFPGAN. Default: False",
                )

                st.session_state["defaults"].img2img.use_RealESRGAN = st.checkbox(
                    "Img2Img Use RealESRGAN",
                    value=st.session_state["defaults"].img2img.use_RealESRGAN,
                    help="Choose to use RealESRGAN. Default: False",
                )

                st.session_state["defaults"].img2img.update_preview = True
                st.session_state[
                    "defaults"
                ].img2img.update_preview_frequency = st.number_input(
                    "Img2Img Preview Image Update Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].img2img.update_preview_frequency,
                    help="Set the default value for the frrquency of the preview image updates. Default is: 10",
                )

                st.title("Variation Parameters")

                st.session_state["defaults"].img2img.variant_amount = st.number_input(
                    "Default Img2Img Variation Amount",
                    value=st.session_state["defaults"].img2img.variant_amount,
                    help="Set the default variation to use. Default is: 0.0",
                )

                # I THINK THESE ARE MISSING FROM THE CONFIG FILE
                # st.session_state["defaults"].img2img.variant_amount.min_value = st.number_input("Minimum Img2Img Variation Amount",
                # value=st.session_state['defaults'].img2img.variant_amount.min_value, help="Set the default minimum value for the variation slider. Default is: 0.0"))

                # st.session_state["defaults"].img2img.variant_amount.max_value = st.number_input("Maximum Img2Img Variation Amount",
                # value=st.session_state['defaults'].img2img.variant_amount.max_value, help="Set the default maximum value for the variation slider. Default is: 1.0"))

                # st.session_state["defaults"].img2img.variant_amount.step = st.number_input("Img2Img Variation Slider Steps",
                # value=st.session_state['defaults'].img2img.variant_amount.step, help="Set the default value for the number of steps on the variation slider. Default is: 1"))

                st.session_state["defaults"].img2img.variant_seed = st.text_input(
                    "Default Img2Img Variation Seed",
                    value=st.session_state["defaults"].img2img.variant_seed,
                    help="Default variation seed.",
                )

        with img2txt_tab:
            col1 = st.columns(1, gap="large")

            st.title("Image-To-Text")

            st.session_state["defaults"].img2txt.batch_size = st.number_input(
                "Default Img2Txt Batch Size",
                value=st.session_state["defaults"].img2txt.batch_size,
                help="Set the default batch size for Img2Txt. Default is: 420?",
            )

            st.session_state["defaults"].img2txt.blip_image_eval_size = st.number_input(
                "Default Blip Image Size Evaluation",
                value=st.session_state["defaults"].img2txt.blip_image_eval_size,
                help="Set the default value for the blip image evaluation size. Default is: 512",
            )

        with txt2vid_tab:
            col1, col2, col3, col4, col5 = st.columns(5, gap="medium")

            with col1:
                st.title("Slider Parameters")

                # Width
                st.session_state["defaults"].txt2vid.width.value = st.number_input(
                    "Default txt2vid Image Width",
                    value=st.session_state["defaults"].txt2vid.width.value,
                    help="Set the default width for the generated image. Default is: 512",
                )

                st.session_state["defaults"].txt2vid.width.min_value = st.number_input(
                    "Minimum txt2vid Image Width",
                    value=st.session_state["defaults"].txt2vid.width.min_value,
                    help="Set the default minimum value for the width slider. Default is: 64",
                )

                st.session_state["defaults"].txt2vid.width.max_value = st.number_input(
                    "Maximum txt2vid Image Width",
                    value=st.session_state["defaults"].txt2vid.width.max_value,
                    help="Set the default maximum value for the width slider. Default is: 2048",
                )

                # Height
                st.session_state["defaults"].txt2vid.height.value = st.number_input(
                    "Default txt2vid Image Height",
                    value=st.session_state["defaults"].txt2vid.height.value,
                    help="Set the default height for the generated image. Default is: 512",
                )

                st.session_state["defaults"].txt2vid.height.min_value = st.number_input(
                    "Minimum txt2vid Image Height",
                    value=st.session_state["defaults"].txt2vid.height.min_value,
                    help="Set the default minimum value for the height slider. Default is: 64",
                )

                st.session_state["defaults"].txt2vid.height.max_value = st.number_input(
                    "Maximum txt2vid Image Height",
                    value=st.session_state["defaults"].txt2vid.height.max_value,
                    help="Set the default maximum value for the height slider. Default is: 2048",
                )

                # CFG
                st.session_state["defaults"].txt2vid.cfg_scale.value = st.number_input(
                    "Default txt2vid CFG Scale",
                    value=st.session_state["defaults"].txt2vid.cfg_scale.value,
                    help="Set the default value for the CFG Scale. Default is: 7.5",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.cfg_scale.min_value = st.number_input(
                    "Minimum txt2vid CFG Scale Value",
                    value=st.session_state["defaults"].txt2vid.cfg_scale.min_value,
                    help="Set the default minimum value for the CFG scale slider. Default is: 1",
                )

                st.session_state["defaults"].txt2vid.cfg_scale.step = st.number_input(
                    "txt2vid CFG Slider Steps",
                    value=st.session_state["defaults"].txt2vid.cfg_scale.step,
                    help="Set the default value for the number of steps on the CFG scale slider. Default is: 0.5",
                )

                with col2:
                    # Sampling Steps
                    st.session_state[
                        "defaults"
                    ].txt2vid.sampling_steps.value = st.number_input(
                        "Default txt2vid Sampling Steps",
                        value=st.session_state["defaults"].txt2vid.sampling_steps.value,
                        help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2vid.sampling_steps.min_value = st.number_input(
                        "Minimum txt2vid Sampling Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2vid.sampling_steps.min_value,
                        help="Set the default minimum value for the sampling steps slider. Default is: 1",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2vid.sampling_steps.step = st.number_input(
                        "txt2vid Sampling Slider Steps",
                        value=st.session_state["defaults"].txt2vid.sampling_steps.step,
                        help="Set the default value for the number of steps on the sampling steps slider. Default is: 10",
                    )

                    # Batch Count
                    st.session_state[
                        "defaults"
                    ].txt2vid.batch_count.value = st.number_input(
                        "txt2vid Batch count",
                        value=st.session_state["defaults"].txt2vid.batch_count.value,
                        help="How many iterations or batches of images to generate in total.",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2vid.batch_size.value = st.number_input(
                        "txt2vid Batch size",
                        value=st.session_state.defaults.txt2vid.batch_size.value,
                        help="How many images are at once in a batch.\
                                                                                            It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
                                                                                            takes to finish generation as more images are generated at once.\
                                                                                            Default: 1",
                    )

                    # Inference Steps
                    st.session_state[
                        "defaults"
                    ].txt2vid.num_inference_steps.value = st.number_input(
                        "Default Txt2Vid Inference Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2vid.num_inference_steps.value,
                        help="Set the default number of inference steps to use. Default is: 200",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2vid.num_inference_steps.min_value = st.number_input(
                        "Minimum Txt2Vid Sampling Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2vid.num_inference_steps.min_value,
                        help="Set the default minimum value for the inference steps slider. Default is: 10",
                    )

                    st.session_state[
                        "defaults"
                    ].txt2vid.num_inference_steps.max_value = st.number_input(
                        "Maximum Txt2Vid Sampling Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2vid.num_inference_steps.max_value,
                        help="Set the default maximum value for the inference steps slider. Default is: 500",
                    )
                    st.session_state[
                        "defaults"
                    ].txt2vid.num_inference_steps.step = st.number_input(
                        "Txt2Vid Inference Slider Steps",
                        value=st.session_state[
                            "defaults"
                        ].txt2vid.num_inference_steps.step,
                        help="Set the default value for the number of steps on the inference steps slider. Default is: 10",
                    )

            with col3:
                st.title("General Parameters")

                st.session_state["defaults"].txt2vid.default_model = st.text_input(
                    "Default Txt2Vid Model",
                    value=st.session_state["defaults"].txt2vid.default_model,
                    help="Default: CompVis/stable-diffusion-v1-4",
                )

                # INSERT CUSTOM_MODELS_LIST HERE

                default_sampler_list = [
                    "k_lms",
                    "k_euler",
                    "k_euler_a",
                    "k_dpm_2",
                    "k_dpm_2_a",
                    "k_heun",
                    "PLMS",
                    "DDIM",
                ]
                st.session_state["defaults"].txt2vid.default_sampler = st.selectbox(
                    "Default txt2vid Sampler",
                    default_sampler_list,
                    index=default_sampler_list.index(
                        st.session_state["defaults"].txt2vid.default_sampler
                    ),
                    help="Defaut sampler to use for txt2vid. Default: k_euler",
                )

                st.session_state["defaults"].txt2vid.seed = st.text_input(
                    "Default txt2vid Seed",
                    value=st.session_state["defaults"].txt2vid.seed,
                    help="Default seed.",
                )

                st.session_state["defaults"].txt2vid.scheduler_name = st.text_input(
                    "Default Txt2Vid Scheduler",
                    value=st.session_state["defaults"].txt2vid.scheduler_name,
                    help="Default scheduler.",
                )

                st.session_state["defaults"].txt2vid.separate_prompts = st.checkbox(
                    "Separate txt2vid Prompts",
                    value=st.session_state["defaults"].txt2vid.separate_prompts,
                    help="Separate Prompts. Default: False",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.normalize_prompt_weights = st.checkbox(
                    "Normalize txt2vid Prompt Weights",
                    value=st.session_state["defaults"].txt2vid.normalize_prompt_weights,
                    help="Choose to normalize prompt weights. Default: True",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.save_individual_images = st.checkbox(
                    "Save Individual txt2vid Images",
                    value=st.session_state["defaults"].txt2vid.save_individual_images,
                    help="Choose to save individual images. Default: True",
                )

                st.session_state["defaults"].txt2vid.save_video = st.checkbox(
                    "Save Txt2Vid Video",
                    value=st.session_state["defaults"].txt2vid.save_video,
                    help="Choose to save the Txt2Vid video. Default: True",
                )

                st.session_state["defaults"].txt2vid.save_video_on_stop = st.checkbox(
                    "Save video on Stop",
                    value=st.session_state["defaults"].txt2vid.save_video_on_stop,
                    help="Save a video with all the images generated as frames when we hit the stop button \
																					  during a generation.",
                )

                st.session_state["defaults"].txt2vid.group_by_prompt = st.checkbox(
                    "Group By txt2vid Prompt",
                    value=st.session_state["defaults"].txt2vid.group_by_prompt,
                    help="Choose to save images grouped by their prompt. Default: False",
                )

                st.session_state["defaults"].txt2vid.save_as_jpg = st.checkbox(
                    "Save txt2vid As JPG",
                    value=st.session_state["defaults"].txt2vid.save_as_jpg,
                    help="Choose to save images as jpegs. Default: False",
                )

                # Need more info for the Help dialog...
                st.session_state["defaults"].txt2vid.do_loop = st.checkbox(
                    "Loop Generations",
                    value=st.session_state["defaults"].txt2vid.do_loop,
                    help="Choose to loop or something, IDK.... Default: False",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.max_duration_in_seconds = st.number_input(
                    "Txt2Vid Max Duration in Seconds",
                    value=st.session_state["defaults"].txt2vid.max_duration_in_seconds,
                    help="Set the default value for the max duration in seconds for the video generated. Default is: 30",
                )

                st.session_state["defaults"].txt2vid.write_info_files = st.checkbox(
                    "Write Info Files For txt2vid Images",
                    value=st.session_state["defaults"].txt2vid.write_info_files,
                    help="Choose to write the info files along with the generated images. Default: True",
                )

                st.session_state["defaults"].txt2vid.use_GFPGAN = st.checkbox(
                    "txt2vid Use GFPGAN",
                    value=st.session_state["defaults"].txt2vid.use_GFPGAN,
                    help="Choose to use GFPGAN. Default: False",
                )

                st.session_state["defaults"].txt2vid.use_RealESRGAN = st.checkbox(
                    "txt2vid Use RealESRGAN",
                    value=st.session_state["defaults"].txt2vid.use_RealESRGAN,
                    help="Choose to use RealESRGAN. Default: False",
                )

                st.session_state["defaults"].txt2vid.update_preview = True
                st.session_state[
                    "defaults"
                ].txt2vid.update_preview_frequency = st.number_input(
                    "txt2vid Preview Image Update Frequency",
                    value=st.session_state["defaults"].txt2vid.update_preview_frequency,
                    help="Set the default value for the frrquency of the preview image updates. Default is: 10",
                )

            with col4:
                st.title("Variation Parameters")

                st.session_state[
                    "defaults"
                ].txt2vid.variant_amount.value = st.number_input(
                    "Default txt2vid Variation Amount",
                    value=st.session_state["defaults"].txt2vid.variant_amount.value,
                    help="Set the default variation to use. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.variant_amount.min_value = st.number_input(
                    "Minimum txt2vid Variation Amount",
                    value=st.session_state["defaults"].txt2vid.variant_amount.min_value,
                    help="Set the default minimum value for the variation slider. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.variant_amount.max_value = st.number_input(
                    "Maximum txt2vid Variation Amount",
                    value=st.session_state["defaults"].txt2vid.variant_amount.max_value,
                    help="Set the default maximum value for the variation slider. Default is: 1.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.variant_amount.step = st.number_input(
                    "txt2vid Variation Slider Steps",
                    value=st.session_state["defaults"].txt2vid.variant_amount.step,
                    help="Set the default value for the number of steps on the variation slider. Default is: 1",
                )

                st.session_state["defaults"].txt2vid.variant_seed = st.text_input(
                    "Default txt2vid Variation Seed",
                    value=st.session_state["defaults"].txt2vid.variant_seed,
                    help="Default variation seed.",
                )

            with col5:
                st.title("Beta Parameters")

                # Beta Start
                st.session_state["defaults"].txt2vid.beta_start.value = st.number_input(
                    "Default txt2vid Beta Start Value",
                    value=st.session_state["defaults"].txt2vid.beta_start.value,
                    help="Set the default variation to use. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.beta_start.min_value = st.number_input(
                    "Minimum txt2vid Beta Start Amount",
                    value=st.session_state["defaults"].txt2vid.beta_start.min_value,
                    help="Set the default minimum value for the variation slider. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.beta_start.max_value = st.number_input(
                    "Maximum txt2vid Beta Start Amount",
                    value=st.session_state["defaults"].txt2vid.beta_start.max_value,
                    help="Set the default maximum value for the variation slider. Default is: 1.0",
                )

                st.session_state["defaults"].txt2vid.beta_start.step = st.number_input(
                    "txt2vid Beta Start Slider Steps",
                    value=st.session_state["defaults"].txt2vid.beta_start.step,
                    help="Set the default value for the number of steps on the variation slider. Default is: 1",
                )

                st.session_state["defaults"].txt2vid.beta_start.format = st.text_input(
                    "Default txt2vid Beta Start Format",
                    value=st.session_state["defaults"].txt2vid.beta_start.format,
                    help="Set the default Beta Start Format. Default is: %.5\f",
                )

                # Beta End
                st.session_state["defaults"].txt2vid.beta_end.value = st.number_input(
                    "Default txt2vid Beta End Value",
                    value=st.session_state["defaults"].txt2vid.beta_end.value,
                    help="Set the default variation to use. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.beta_end.min_value = st.number_input(
                    "Minimum txt2vid Beta End Amount",
                    value=st.session_state["defaults"].txt2vid.beta_end.min_value,
                    help="Set the default minimum value for the variation slider. Default is: 0.0",
                )

                st.session_state[
                    "defaults"
                ].txt2vid.beta_end.max_value = st.number_input(
                    "Maximum txt2vid Beta End Amount",
                    value=st.session_state["defaults"].txt2vid.beta_end.max_value,
                    help="Set the default maximum value for the variation slider. Default is: 1.0",
                )

                st.session_state["defaults"].txt2vid.beta_end.step = st.number_input(
                    "txt2vid Beta End Slider Steps",
                    value=st.session_state["defaults"].txt2vid.beta_end.step,
                    help="Set the default value for the number of steps on the variation slider. Default is: 1",
                )

                st.session_state["defaults"].txt2vid.beta_end.format = st.text_input(
                    "Default txt2vid Beta End Format",
                    value=st.session_state["defaults"].txt2vid.beta_start.format,
                    help="Set the default Beta Start Format. Default is: %.5\f",
                )

        with image_processing:
            col1, col2, col3, col4, col5 = st.columns(5, gap="large")

            with col1:
                st.title("GFPGAN")

                st.session_state["defaults"].gfpgan.strength = st.number_input(
                    "Default Img2Txt Batch Size",
                    value=st.session_state["defaults"].gfpgan.strength,
                    help="Set the default global strength for GFPGAN. Default is: 100",
                )
            with col2:
                st.title("GoBig")
            with col3:
                st.title("RealESRGAN")
            with col4:
                st.title("LDSR")
            with col5:
                st.title("GoLatent")

        with textual_inversion_tab:
            st.title("Textual Inversion")

            st.session_state[
                "defaults"
            ].textual_inversion.pretrained_model_name_or_path = st.text_input(
                "Default Textual Inversion Model Path",
                value=st.session_state[
                    "defaults"
                ].textual_inversion.pretrained_model_name_or_path,
                help="Default: models/ldm/stable-diffusion-v1-4",
            )

            st.session_state[
                "defaults"
            ].textual_inversion.tokenizer_name = st.text_input(
                "Default Img2Img Variation Seed",
                value=st.session_state["defaults"].textual_inversion.tokenizer_name,
                help="Default tokenizer seed.",
            )

        with concepts_library_tab:
            st.title("Concepts Library")
            # st.info("Under Construction. :construction_worker:")
            col1, col2, col3, col4, col5 = st.columns(5, gap="large")
            with col1:
                st.session_state[
                    "defaults"
                ].concepts_library.concepts_per_page = st.number_input(
                    "Concepts Per Page",
                    value=st.session_state[
                        "defaults"
                    ].concepts_library.concepts_per_page,
                    help="Number of concepts per page to show on the Concepts Library. Default: '12'",
                )

        # add space for the buttons at the bottom
        st.markdown("---")

        # We need a submit button to save the Settings
        # as well as one to reset them to the defaults, just in case.
        _, _, save_button_col, reset_button_col, _, _ = st.columns(
            [1, 1, 1, 1, 1, 1], gap="large"
        )
        with save_button_col:
            save_button = st.form_submit_button("Save")

        with reset_button_col:
            reset_button = st.form_submit_button("Reset")

        if save_button:
            OmegaConf.save(
                config=st.session_state.defaults,
                f="configs/webui/userconfig_streamlit.yaml",
            )
            loaded = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")
            assert st.session_state.defaults == loaded

            #
            if os.path.exists(".streamlit/config.toml"):
                with open(".streamlit/config.toml", "w") as toml_file:
                    toml.dump(st.session_state["streamlit_config"], toml_file)

        if reset_button:
            st.session_state["defaults"] = OmegaConf.load(
                "configs/webui/webui_streamlit.yaml"
            )
            st.experimental_rerun()


================================================
FILE: webui/streamlit/scripts/barfi_baklavajs.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sandbox-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
# from sd_utils import *
from sd_utils import st

# streamlit imports

# streamlit components section

# other imports
from barfi import st_barfi, barfi_schemas, Block

# Temp imports

# end of imports
# ---------------------------------------------------------------------------------------------------------------


def layout():
    # st.info("Under Construction. :construction_worker:")

    # from barfi import st_barfi, Block

    # add = Block(name='Addition')
    # sub = Block(name='Subtraction')
    # mul = Block(name='Multiplication')
    # div = Block(name='Division')

    # barfi_result = st_barfi(base_blocks= [add, sub, mul, div])
    # or if you want to use a category to organise them in the frontend sub-menu
    # barfi_result = st_barfi(base_blocks= {'Op 1': [add, sub], 'Op 2': [mul, div]})

    col1, col2, col3 = st.columns([1, 8, 1])

    with col2:
        feed = Block(name="Feed")
        feed.add_output()

        def feed_func(self):
            self.set_interface(name="Output 1", value=4)

        feed.add_compute(feed_func)

        splitter = Block(name="Splitter")
        splitter.add_input()
        splitter.add_output()
        splitter.add_output()

        def splitter_func(self):
            in_1 = self.get_interface(name="Input 1")
            value = in_1 / 2
            self.set_interface(name="Output 1", value=value)
            self.set_interface(name="Output 2", value=value)

        splitter.add_compute(splitter_func)

        mixer = Block(name="Mixer")
        mixer.add_input()
        mixer.add_input()
        mixer.add_output()

        def mixer_func(self):
            in_1 = self.get_interface(name="Input 1")
            in_2 = self.get_interface(name="Input 2")
            value = in_1 + in_2
            self.set_interface(name="Output 1", value=value)

        mixer.add_compute(mixer_func)

        result = Block(name="Result")
        result.add_input()

        def result_func(self):
            self.get_interface(name="Input 1")

        result.add_compute(result_func)

        load_schema = st.selectbox("Select a saved schema:", barfi_schemas())

        compute_engine = st.checkbox("Activate barfi compute engine", value=False)

        barfi_result = st_barfi(
            base_blocks=[feed, result, mixer, splitter],
            compute_engine=compute_engine,
            load_schema=load_schema,
        )

        if barfi_result:
            st.write(barfi_result)


================================================
FILE: webui/streamlit/scripts/custom_components/dragable_number_input/index.html
================================================
<html>
	<head>
		<style>
			/* our style while dragging */
			.value-dragging
			{
				background-color: lightblue;
			}
		</style>
	</head>
	<body>
		<!-- our fields -->
		<input type="number" value="0" min=0 max=200 step=10>
		<input type="number" value="0" min=-50 max=200 step=10>

		<script>
			// iframe parent
			// var parentDoc = window.parent.document

			// check for mouse pointer locking support, not a requirement but improves the overall experience
			var havePointerLock = 'pointerLockElement' in document ||
			  'mozPointerLockElement' in document ||
			  'webkitPointerLockElement' in document;

			// the pointer locking exit function
			document.exitPointerLock = document.exitPointerLock || document.mozExitPointerLock || document.webkitExitPointerLock;

			// how far should the mouse travel for a step 50 pixel
			var pixelPerStep = 50;

			// how many steps did the mouse move in as float
			var movementDelta = 0.0;
			// value when drag started
			var lockedValue = 0;
			// minimum value from field
			var lockedMin = 0;
			// maximum value from field
			var lockedMax = 0;
			// how big should the field steps be
			var lockedStep = 0;
			// the currently locked in field
			var lockedField = null;

			// register events and pointer locking on field
			RegisterField = (field) => {
				if(havePointerLock)
					field.requestPointerLock = field.requestPointerLock || field.mozRequestPointerLock || field.webkitRequestPointerLock;

				field.title = "Click and hold middle mouse button\nmove mouse left to decrease\nmove right to increase";

				field.addEventListener('mousedown', (e) => {
				  onDragStart(e)
				});
			}

			onDragStart = (e) => {
				// if middle mouse is down
				if(e.button === 1)
				{
					// save current field
					lockedField = e.target;
					// add class for styling
					lockedField.classList.add("value-dragging");
					// reset movement delta
					movementDelta = 0.0;
					// set to 0 if field is empty
					if(lockedField.value === '')
						lockedField.value = '0';

					// save current field value
					lockedValue = parseInt(lockedField.value);

					if(lockedField.min === '')
						lockedField.min = '-99999999';

					if(lockedField.max === '')
						lockedField.max = '99999999';

					if(lockedField.step === '')
						lockedField.step = '10';

					lockedMin = parseInt(lockedField.min);
					lockedMax = parseInt(lockedField.max);

					lockedStep = parseInt(lockedField.step);

					// lock pointer if available
					if(havePointerLock)
						lockedField.requestPointerLock();

					// add drag event
					document.addEventListener("mousemove", onDrag, false);
					// prevent event propagation
					e.preventDefault();
				}
			};

			onDrag = (e) => {
				if(lockedField !== null)
				{
					// add movement to delta
					movementDelta += e.movementX / pixelPerStep;
					// set new value
					let value = lockedValue + Math.floor(Math.abs(movementDelta)) * lockedStep * Math.sign(movementDelta);
					lockedField.value = Math.min(Math.max(value, lockedMin), lockedMax);
				}
			};

			document.addEventListener('mouseup', (e) => {
				// if middle mouse is up
				if(e.button === 1)
				{
					// release pointer lock if available
					if(havePointerLock)
						document.exitPointerLock();

					if(lockedField !== null)
					{
						// stop drag event
						document.removeEventListener("mousemove", onDrag, false);
						// remove class for styling
						lockedField.classList.remove("value-dragging");
						// remove reference
						lockedField = null;
					}
				}
			});

			// find and register all input fields of type=number
			var list = document.querySelectorAll('input[type="number"]');
			list.forEach(RegisterField);
		</script>
	</body>
</html>


================================================
FILE: webui/streamlit/scripts/custom_components/draggable_number_input/__init__.py
================================================
import os
import streamlit.components.v1 as components


def load(pixel_per_step=50):
    parent_dir = os.path.dirname(os.path.abspath(__file__))
    file = os.path.join(parent_dir, "main.js")

    with open(file) as f:
        javascript_main = f.read()
        javascript_main = javascript_main.replace(
            "%%pixelPerStep%%", str(pixel_per_step)
        )
        components.html(f"<script>{javascript_main}</script>")


================================================
FILE: webui/streamlit/scripts/custom_components/draggable_number_input/main.js
================================================
// iframe parent
var parentDoc = window.parent.document

// check for mouse pointer locking support, not a requirement but improves the overall experience
var havePointerLock = 'pointerLockElement' in parentDoc ||
  'mozPointerLockElement' in parentDoc ||
  'webkitPointerLockElement' in parentDoc;

// the pointer locking exit function
parentDoc.exitPointerLock = parentDoc.exitPointerLock || parentDoc.mozExitPointerLock || parentDoc.webkitExitPointerLock;

// how far should the mouse travel for a step in pixel
var pixelPerStep = %%pixelPerStep%%;
// how many steps did the mouse move in as float
var movementDelta = 0.0;
// value when drag started
var lockedValue = 0.0;
// minimum value from field
var lockedMin = 0.0;
// maximum value from field
var lockedMax = 0.0;
// how big should the field steps be
var lockedStep = 0.0;
// the currently locked in field
var lockedField = null;

// lock box to just request pointer lock for one element
var lockBox = document.createElement("div");
lockBox.classList.add("lockbox");
parentDoc.body.appendChild(lockBox);
lockBox.requestPointerLock = lockBox.requestPointerLock || lockBox.mozRequestPointerLock || lockBox.webkitRequestPointerLock;

function Lock(field)
{
	var rect = field.getBoundingClientRect();
	lockBox.style.left = (rect.left-2.5)+"px";
	lockBox.style.top = (rect.top-2.5)+"px";

	lockBox.style.width = (rect.width+2.5)+"px";
	lockBox.style.height = (rect.height+5)+"px";

	lockBox.requestPointerLock();
}

function Unlock()
{
	parentDoc.exitPointerLock();
	lockBox.style.left = "0px";
	lockBox.style.top = "0px";

	lockBox.style.width = "0px";
	lockBox.style.height = "0px";
	lockedField.focus();
}

parentDoc.addEventListener('mousedown', (e) => {
	// if middle is down
	if(e.button === 1)
	{
		if(e.target.tagName === 'INPUT' && e.target.type === 'number')
		{
			e.preventDefault();
			var field = e.target;
			if(havePointerLock)
				Lock(field);

			// save current field
			lockedField = e.target;
			// add class for styling
			lockedField.classList.add("value-dragging");
			// reset movement delta
			movementDelta = 0.0;
			// set to 0 if field is empty
			if(lockedField.value === '')
				lockedField.value = 0.0;

			// save current field value
			lockedValue = parseFloat(lockedField.value);

			if(lockedField.min === '' || lockedField.min === '-Infinity')
				lockedMin = -99999999.0;
			else
				lockedMin = parseFloat(lockedField.min);

			if(lockedField.max === '' || lockedField.max === 'Infinity')
				lockedMax = 99999999.0;
			else
				lockedMax = parseFloat(lockedField.max);

			if(lockedField.step === '' || lockedField.step === 'Infinity')
				lockedStep = 1.0;
			else
				lockedStep = parseFloat(lockedField.step);

			// lock pointer if available
			if(havePointerLock)
				Lock(lockedField);

			// add drag event
			parentDoc.addEventListener("mousemove", onDrag, false);
		}
	}
});

function onDrag(e)
{
	if(lockedField !== null)
	{
		// add movement to delta
		movementDelta += e.movementX / pixelPerStep;
		if(lockedField === NaN)
			return;
		// set new value
		let value = lockedValue + Math.floor(Math.abs(movementDelta)) * lockedStep * Math.sign(movementDelta);
		lockedField.focus();
		lockedField.select();
		parentDoc.execCommand('insertText', false /*no UI*/, Math.min(Math.max(value, lockedMin), lockedMax));
	}
}

parentDoc.addEventListener('mouseup', (e) => {
	// if mouse is up
	if(e.button === 1)
	{
		// release pointer lock if available
		if(havePointerLock)
			Unlock();

		if(lockedField !== null && lockedField !== NaN)
		{
			// stop drag event
			parentDoc.removeEventListener("mousemove", onDrag, false);
			// remove class for styling
			lockedField.classList.remove("value-dragging");
			// remove reference
			lockedField = null;
		}
	}
});

// only execute once (even though multiple iframes exist)
if(!parentDoc.hasOwnProperty("dragableInitialized"))
{
	var parentCSS =
`
/* Make input-instruction not block mouse events */
.input-instructions,.input-instructions > *{
	pointer-events: none;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
}

.lockbox {
	background-color: transparent;
	position: absolute;
	pointer-events: none;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
	border-left: dotted 2px rgb(255,75,75);
	border-top: dotted 2px rgb(255,75,75);
	border-bottom: dotted 2px rgb(255,75,75);
	border-right: dotted 1px rgba(255,75,75,0.2);
	border-top-left-radius: 0.25rem;
	border-bottom-left-radius: 0.25rem;
	z-index: 1000;
}
`;

	// get parent document head
	var head = parentDoc.getElementsByTagName('head')[0];
	// add style tag
	var s = document.createElement('style');
    // set type attribute
	s.setAttribute('type', 'text/css');
    // add css forwarded from python
	if (s.styleSheet) {   // IE
        s.styleSheet.cssText = parentCSS;
    } else {                // the world
        s.appendChild(document.createTextNode(parentCSS));
    }
	// add style to head
    head.appendChild(s);
	// set flag so this only runs once
	parentDoc["dragableInitialized"] = true;
}


================================================
FILE: webui/streamlit/scripts/custom_components/sygil_suggestions/__init__.py
================================================
import os
import streamlit.components.v1 as components

# where to save the downloaded key_phrases
key_phrases_file = "data/tags/key_phrases.json"
# the loaded key phrase json as text
key_phrases_json = ""
# where to save the downloaded key_phrases
thumbnails_file = "data/tags/thumbnails.json"
# the loaded key phrase json as text
thumbnails_json = ""


def init():
    global key_phrases_json, thumbnails_json
    with open(key_phrases_file) as f:
        key_phrases_json = f.read()
    with open(thumbnails_file) as f:
        thumbnails_json = f.read()


def suggestion_area(placeholder):
    # get component path
    parent_dir = os.path.dirname(os.path.abspath(__file__))
    # get file paths
    javascript_file = os.path.join(parent_dir, "main.js")
    stylesheet_file = os.path.join(parent_dir, "main.css")
    parent_stylesheet_file = os.path.join(parent_dir, "parent.css")

    # load file texts
    with open(javascript_file) as f:
        javascript_main = f.read()
    with open(stylesheet_file) as f:
        stylesheet_main = f.read()
    with open(parent_stylesheet_file) as f:
        parent_stylesheet = f.read()

    # add suggestion area div box
    html = "<div id='scroll_area' class='st-bg'><div id='suggestion_area'>javascript failed</div></div>"
    # add loaded style
    html += f"<style>{stylesheet_main}</style>"
    # set default variables
    html += f"<script>var thumbnails = {thumbnails_json};\nvar keyPhrases = {key_phrases_json};\nvar parentCSS = `{parent_stylesheet}`;\nvar placeholder='{placeholder}';</script>"
    # add main java script
    html += f"\n<script>{javascript_main}</script>"
    # add component to site
    components.html(html, width=None, height=None, scrolling=True)


================================================
FILE: webui/streamlit/scripts/custom_components/sygil_suggestions/main.css
================================================
*
{
	padding: 0px;
	margin: 0px;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
}

body
{
	width: 100%;
	height: 100%;
	padding-left: calc( 1em - 1px );
	padding-top: calc( 1em - 1px );
	overflow: hidden;
}

/* width */
::-webkit-scrollbar {
  width: 7px;
}

/* Track */
::-webkit-scrollbar-track {
  background: rgb(10, 13, 19);
}

/* Handle */
::-webkit-scrollbar-thumb {
  background: #6c6e72;
  border-radius: 3px;
}

/* Handle on hover */
::-webkit-scrollbar-thumb:hover {
  background: #6c6e72;
}

#scroll_area
{
	display: flex;
	overflow-x: hidden;
	overflow-y: auto;
}

#suggestion_area
{
	overflow-x: hidden;
	width: calc( 100% - 2em - 2px );
	margin-bottom: calc( 1em + 13px );
	min-height: 50px;
}

span
{
	border: 1px solid rgba(250, 250, 250, 0.2);
	border-radius: 0.25rem;
	font-size: 1rem;
	font-family: "Source Sans Pro", sans-serif;

	background-color: rgb(38, 39, 48);
	color: white;
	display: inline-block;
	padding: 0.5rem;
	margin-right: 3px;
	cursor: pointer;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
}

span:hover
{
	color: rgb(255,75,75);
	border-color: rgb(255,75,75);
}


================================================
FILE: webui/streamlit/scripts/custom_components/sygil_suggestions/main.js
================================================

// parent document
var parentDoc = window.parent.document;
// iframe element in parent document
var frame = window.frameElement;
// the area to put the suggestions in
var suggestionArea = document.getElementById('suggestion_area');
var scrollArea = document.getElementById('scroll_area');
// button height is read when the first button gets created
var buttonHeight = -1;
// the maximum size of the iframe in buttons (3 x buttons height)
var maxHeightInButtons = 3;
// the prompt field connected to this iframe
var promptField = null;
// the category of suggestions
var activeCategory = contextCategory;

var conditionalButtons = null;

var contextCategory = "[context]";

var frameHeight = "calc( 3em - 3px + {} )";

var filterGroups = {nsfw_mild: "nsfw_mild", nsfw_basic: "nsfw_basic", nsfw_strict: "nsfw_strict", gore_mild: "gore_mild", gore_basic: "gore_basic", gore_strict: "gore_strict"};
var activeFilters = [filterGroups.nsfw_mild, filterGroups.nsfw_basic, filterGroups.gore_mild];

var triggers = {empty: "empty", nsfw: "nsfw", nude: "nude"};
var activeContext = [];

var triggerIndex = {};

var wordMap = {};
var tagMap = {};

// could pass in an array of specific stylesheets for optimization
function getAllCSSVariableNames(styleSheets = parentDoc.styleSheets){
   var cssVars = [];
   // loop each stylesheet
   for(var i = 0; i < styleSheets.length; i++){
      // loop stylesheet's cssRules
      try{ // try/catch used because 'hasOwnProperty' doesn't work
         for( var j = 0; j < styleSheets[i].cssRules.length; j++){
            try{
				//console.log(styleSheets[i].cssRules[j].selectorText);
               // loop stylesheet's cssRules' style (property names)
               for(var k = 0; k < styleSheets[i].cssRules[j].style.length; k++){
                  let name = styleSheets[i].cssRules[j].style[k];
                  // test name for css variable signiture and uniqueness
                  if(name.startsWith('--') && cssVars.indexOf(name) == -1){
                     cssVars.push(name);
                  }
               }
            } catch (error) {}
         }
      } catch (error) {}
   }
   return cssVars;
}

function currentFrameAbsolutePosition() {
  let currentWindow = window;
  let currentParentWindow;
  let positions = [];
  let rect;

  while (currentWindow !== window.top) {
    currentParentWindow = currentWindow.parent;
    for (let idx = 0; idx < currentParentWindow.frames.length; idx++)
      if (currentParentWindow.frames[idx] === currentWindow) {
        for (let frameElement of currentParentWindow.document.getElementsByTagName('iframe')) {
          if (frameElement.contentWindow === currentWindow) {
            rect = frameElement.getBoundingClientRect();
            positions.push({x: rect.x, y: rect.y});
          }
        }
        currentWindow = currentParentWindow;
        break;
      }
  }
  return positions.reduce((accumulator, currentValue) => {
    return {
      x: accumulator.x + currentValue.x,
      y: accumulator.y + currentValue.y
    };
  }, { x: 0, y: 0 });
}

// check if element is visible
function isVisible(e) {
    return !!( e.offsetWidth || e.offsetHeight || e.getClientRects().length );
}

// remove everything from the suggestion area
function ClearSuggestionArea(text = "")
{
	suggestionArea.innerHTML = text;
	conditionalButtons = [];
}

// update iframe size depending on button rows
function UpdateSize()
{
	// calculate maximum height
	var maxHeight = buttonHeight * maxHeightInButtons;

	var height = suggestionArea.lastChild.offsetTop + buttonHeight;
	// apply height to iframe
	frame.style.height = frameHeight.replace("{}", Math.min(height,maxHeight)+"px");
	scrollArea.style.height = frame.style.height;
}

// add a button to the suggestion area
function AddButton(label, action, dataTooltip = null, tooltipImage = null, pattern = null, data = null)
{
	// create span
	var button = document.createElement("span");
	// label it
	button.innerHTML = label;
	if(data != null)
	{
		// add category attribute to button, will be read on click
		button.setAttribute("data",data);
	}
	if(pattern != null)
	{
		// add category attribute to button, will be read on click
		button.setAttribute("pattern",pattern);
	}
	if(dataTooltip != null)
	{
		// add category attribute to button, will be read on click
		button.setAttribute("tooltip-text",dataTooltip);
	}
	if(tooltipImage != null)
	{
		// add category attribute to button, will be read on click
		button.setAttribute("tooltip-image",tooltipImage);
	}
	// add button function
	button.addEventListener('click', action, false);
	button.addEventListener('mouseover', ButtonHoverEnter);
	button.addEventListener('mouseout', ButtonHoverExit);
	// add button to suggestion area
	suggestionArea.appendChild(button);
	// get buttonHeight if not set
	if(buttonHeight < 0)
		buttonHeight = button.offsetHeight;
	return button;
}

// find visible prompt field to connect to this iframe
function GetPromptField()
{
	// get all prompt fields, the %% placeholder %% is set in python
	var all = parentDoc.querySelectorAll('textarea[placeholder="'+placeholder+'"]');
	// filter visible
	for(var i = 0; i < all.length; i++)
	{
		if(isVisible(all[i]))
		{
			promptField = all[i];
			promptField.addEventListener('input', OnChange, false);
			promptField.addEventListener('click', OnClick, false);
			promptField.addEventListener('keyup', OnKey, false);
			break;
		}
	}
}

function OnChange(e)
{
	ButtonConditions();
	ButtonUpdateContext(true);
}

function OnClick(e)
{
	ButtonUpdateContext(true);
}

function OnKey(e)
{
	if (e.keyCode == '37' || e.keyCode == '38' || e.keyCode == '39' || e.keyCode == '40') {
		ButtonUpdateContext(false);
	}
}

function getCaretPosition(ctrl) {
    // IE < 9 Support
    if (document.selection) {
        ctrl.focus();
        var range = document.selection.createRange();
        var rangelen = range.text.length;
        range.moveStart('character', -ctrl.value.length);
        var start = range.text.length - rangelen;
        return {
            'start': start,
            'end': start + rangelen
        };
    } // IE >=9 and other browsers
    else if (ctrl.selectionStart || ctrl.selectionStart == '0') {
        return {
            'start': ctrl.selectionStart,
            'end': ctrl.selectionEnd
        };
    } else {
        return {
            'start': 0,
            'end': 0
        };
    }
}

function setCaretPosition(ctrl, start, end) {
    // IE >= 9 and other browsers
    if (ctrl.setSelectionRange) {
        ctrl.focus();
        ctrl.setSelectionRange(start, end);
    }
    // IE < 9
    else if (ctrl.createTextRange) {
        var range = ctrl.createTextRange();
        range.collapse(true);
        range.moveEnd('character', end);
        range.moveStart('character', start);
        range.select();
    }
}

function isEmptyOrSpaces(str){
    return str === null || str.match(/^ *$/) !== null;
}

function ButtonUpdateContext(changeCategory)
{
	let targetCategory = contextCategory;
	let text = promptField.value;
	if(document.activeElement === promptField)
	{
		var pos = getCaretPosition(promptField).end;
		text = promptField.value.slice(0, pos);
	}

	activeContext = [];

	var parts = text.split(/[\.?!,]/);
	if(activeCategory == "Artists" && !isEmptyOrSpaces(parts[parts.length-1]))
	{
		return;
	}
	if(text == "")
	{
		activeContext.push(triggers.empty);
	}
	if(text.endsWith("by"))
	{
		changeCategory = true;
		targetCategory = "Artists";
		activeContext.push("Artists");
	}
	else
	{
		var parts = text.split(/[\.,!?;]/);
		parts = parts.reverse();

		parts.forEach( part =>
		{
			var words = part.split(" ");
			words = words.reverse();
			words.forEach( word =>
			{
				word = word.replace(/[^a-zA-Z0-9 \._\-]/g, '').trim().toLowerCase();
				word = WordToKey(word);
				if(wordMap.hasOwnProperty(word))
				{
					activeContext = activeContext.concat(wordMap[word]).unique();
				}
			});
		});
	}

	if(activeContext.length == 0)
	{
		if(activeCategory == contextCategory)
		{
			activeCategory = "";
			ShowMenu();
		}
	}
	else if(changeCategory)
	{
		activeCategory = targetCategory;
		ShowMenu();
	}
	else if(activeCategory == contextCategory)
		ShowMenu();
}

// when pressing a button, give the focus back to the prompt field
function KeepFocus(e)
{
	e.preventDefault();
	promptField.focus();
}

function selectCategory(e)
{
	KeepFocus(e);
	// set category from attribute
	activeCategory = e.target.getAttribute("data");
	// rebuild menu
	ShowMenu();
}

function leaveCategory(e)
{
	KeepFocus(e);
	activeCategory = "";
	// rebuild menu
	ShowMenu();
}

// [...]=block "..."=requirement ...=add {|}=cursor {}=insert .,!?;=start
// [{} {|}]
// [,by {}{|}]["by "* and by {}{|}]
// [, {}{|}]

function PatternWalk(text, pattern)
{
	var parts = text.split(/[\,!?;]/);
	var part = parts[parts.length - 1];

	var indent = 0;
	var outPattern = "";
	var requirement = ""
	var mode = "";
	var patternFailed = false;
	var partIndex = 0;
	for( let i = 0; i < pattern.length; i++)
	{
		if(mode == "")
		{
			if(pattern[i] == "[")
			{
				indent++;
				mode = "pattern";
				console.log("pattern start:");
			}
		}
		else if(indent > 0)
		{
			if(pattern[i] == "[")
			{
				indent++;
			}
			else if(mode == "pattern")
			{
				if(patternFailed)
				{
					if(pattern[i] == "]")
					{
						indent--;
						if(indent == 0)
						{
							mode = "";
							outPattern = "";
							partIndex = 0;
							patternFailed = false;
							part = parts[parts.length - 1];
						}
					}
					else
					{
					}
				}
				else
				{
					if(pattern[i] == "\"")
					{
						mode = "requirement";
					}
					else if(pattern[i] == "]")
					{
						indent--;
						if(indent == 0)
						{
							mode = "";
							return outPattern;
						}
					}
					else if(pattern[i] == "," || pattern[i] == "!" || pattern[i] == "?" || pattern[i] == ";" )
					{
						let textToCheck = (text+outPattern).trim();

						if(textToCheck.endsWith("and"))
						{
							outPattern += "{_}";
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("with"))
						{
							outPattern += "{_}";
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("of"))
						{
							outPattern += "{_}";
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("at"))
						{
							outPattern += "{_}";
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("and a"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("with a"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("of a"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("at a"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("and an"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("with an"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("of an"))
						{
							part = "";
							partIndex = 0;
						}
						else if(textToCheck.endsWith("at an"))
						{
							part = "";
							partIndex = 0;
						}
						else if(!textToCheck.endsWith(pattern[i]))
						{
							outPattern += pattern[i];
							part = "";
							partIndex = 0;
						}
					}
					else if(pattern[i] == "{")
					{
						outPattern += pattern[i];
						mode = "write";
					}
					else if(pattern[i] == "." && pattern[i+1] == "*" || pattern[i] == "*")
					{
						let minLength = false;
						if(pattern[i] == "." && pattern[i+1] == "*")
						{
							minLength = true;
							i++;
						}
						var o = pattern.slice(i+1).search(/[^\w\s]/);
						var subpattern = pattern.slice(i+1,i+1+o);

						var index = part.lastIndexOf(subpattern);
						var subPatternIndex = subpattern.length;
						while(index == -1)
						{
							if(subPatternIndex <= 1)
							{
								patternFailed = true;
								break;
							}

							subPatternIndex--;
							var slice = subpattern.slice(0,subPatternIndex);
							index = part.lastIndexOf(slice);
						}
						if(!patternFailed)
						{
							if(minLength && index == 0)
							{
								patternFailed = true;
							}
							partIndex += index;
						}
						else
						{
						}
					}
					else
					{
						if(partIndex >= part.length)
						{
							outPattern += pattern[i];
						}
						else if(part[partIndex] == pattern[i])
						{
							partIndex++;
						}
						else
						{
							patternFailed = true;
						}
					}
				}
			}
			else if(mode == "requirement")
			{
				if(pattern[i] == "\"")
				{
					if(!part.includes(requirement))
					{
						patternFailed = true;
					}
					else
					{
						partIndex = part.indexOf(requirement)+requirement.length;
					}
					mode = "pattern";
					requirement = "";
				}
				else
				{
					requirement += pattern[i];
				}
			}
			else if(mode == "write")
			{
				if(pattern[i] == "}")
				{
					outPattern += pattern[i];
					mode = "pattern";
				}
				else
				{
					outPattern += pattern[i];
				}
			}
		}
		else if(pattern[i] == "[")
			indent++;
	}
	// fallback
	return ", {}";
}

function InsertPhrase(phrase, pattern)
{
	var text = promptField.value ?? "";
	if(document.activeElement === promptField)
	{
		var pos = getCaretPosition(promptField).end;
		text = promptField.value.slice(0, pos);
	}
	var insert = PatternWalk(text,pattern);
	insert = insert.replace('{}',phrase);

	let firstLetter = phrase.trim()[0];

	if(firstLetter == "a" || firstLetter == "e" || firstLetter == "i" || firstLetter == "o" || firstLetter == "u")
		insert = insert.replace('{_}',"an");
	else
		insert = insert.replace('{_}',"a");

	insert = insert.replace(/{[^|]/,"");
	insert = insert.replace(/[^|]}/,"");

	var caret = (text+insert).indexOf("{|}");
	insert = insert.replace('{|}',"");
	// inserting via execCommand is required, this triggers all native browser functionality as if the user wrote into the prompt field.
	parentDoc.execCommand('insertText', false, insert);
	setCaretPosition(promptField, caret, caret);
}

function SelectPhrase(e)
{
	KeepFocus(e);
	var pattern = e.target.getAttribute("pattern");
	var phrase = e.target.getAttribute("data");

	InsertPhrase(phrase,pattern);
}

function CheckButtonCondition(condition)
{
	var pos = getCaretPosition(promptField).end;
	var text = promptField.value.slice(0, pos);
	if(condition === "empty")
	{
		return text == "";
	}
}

function ButtonConditions()
{
	conditionalButtons.forEach(entry =>
	{
		let filtered = !CheckButtonCondition(entry.condition);

		if(entry.filterGroup != null)
		{
			entry.filterGroup.split(",").forEach( (group) =>
			{

				if(activeFilters.includes(group.trim().toLowerCase()))
				{
					filtered = filtered || true;
					return;
				}
			});
		}
		if(filtered)
			entry.element.style.display = "none";
		else
			entry.element.style.display = "inline-block";
	});
}

function ButtonHoverEnter(e)
{
	var text = e.target.getAttribute("tooltip-text");
	var image = e.target.getAttribute("tooltip-image");
	ShowTooltip(text, e.target, image)
}

function ButtonHoverExit(e)
{
	HideTooltip();
}

function ShowTooltip(text, target, image = "")
{
	var cleanedName = image == null? null : image.replace(/[^a-zA-Z0-9 \._\-]/g, '');
	if((text == "" || text == null) && (image == "" || image == null || thumbnails[cleanedName] === undefined))
		return;

	var currentFramePosition = currentFrameAbsolutePosition();
	var rect = target.getBoundingClientRect();
	var element = parentDoc["phraseTooltip"];
	element.innerText = text;
	if(image != "" && image != null && thumbnails[cleanedName] !== undefined)
	{

		var img = parentDoc.createElement('img');
		img.src = GetThumbnailURL(cleanedName);
		element.appendChild(img)
	}
	element.style.display = "flex";
	element.style.top = (rect.bottom+currentFramePosition.y)+"px";
	element.style.left = (rect.right+currentFramePosition.x)+"px";
	element.style.width = "inherit";
	element.style.height = "inherit";
}

function base64toBlob(base64Data, contentType) {
    contentType = contentType || '';
    var sliceSize = 1024;
    var byteCharacters = atob(base64Data);
    var bytesLength = byteCharacters.length;
    var slicesCount = Math.ceil(bytesLength / sliceSize);
    var byteArrays = new Array(slicesCount);

    for (var sliceIndex = 0; sliceIndex < slicesCount; ++sliceIndex) {
        var begin = sliceIndex * sliceSize;
        var end = Math.min(begin + sliceSize, bytesLength);

        var bytes = new Array(end - begin);
        for (var offset = begin, i = 0; offset < end; ++i, ++offset) {
            bytes[i] = byteCharacters[offset].charCodeAt(0);
        }
        byteArrays[sliceIndex] = new Uint8Array(bytes);
    }
    return new Blob(byteArrays, { type: contentType });
}

function GetThumbnailURL(image)
{
	if(parentDoc["keyPhraseSuggestionsLoadedBlobs"].hasOwnProperty(image))
	{
		return parentDoc["keyPhraseSuggestionsLoadedBlobs"][image];
	}
	else
	{
		let url = URL.createObjectURL(GetThumbnail(image));
		parentDoc["keyPhraseSuggestionsLoadedBlobs"][image] = url;
		return url;
	}
}

function GetThumbnail(image)
{
	return base64toBlob(thumbnails[image], 'image/webp');
}

function HideTooltip()
{
	var element = parentDoc["phraseTooltip"];
	element.style.display= "none";
	element.innerHTML = "";
	element.style.top = "0px";
	element.style.left = "0px";
	element.style.width = "0px";
	element.style.height = "0px";
}

function RemoveDouble(str, symbol)
{
	let doubleSymbole = symbol+symbol;
	while(str.includes(doubleSymbole))
	{
		str = str.replace(doubleSymbole, symbol);
	}
	return str;
}

function ReplaceAll(str, toReplace, seperator, symbol)
{
	toReplace.split(seperator).forEach( (replaceSymbol) =>
	{
		str = str.replace(replaceSymbol, symbol);
	});
	return str;
}

function WordToKey(word)
{
	if(word.endsWith("s"))
		word = word.slice(0, -1);
	word = word.replace("'", "");
	if(word.endsWith("s"))
		word = word.slice(0, -1);
	word = ReplaceAll(word, "sch;sh;ch;ll;gg;r;l;j;g", ';', 'h');
	word = ReplaceAll(word, "sw;ss;zz;qu;kk;k;z;q;s;x", ';','c');
	word = ReplaceAll(word, "pp;bb;tt;th;ff;p;t;b;f;v", ';','d');
	word = ReplaceAll(word, "yu;yo;oo;u;y;w", ';','o');
	word = ReplaceAll(word, "ee;ie;a;i", ';','e');
	word = ReplaceAll(word, "mm;nn;n", ';','n');
	word = RemoveDouble(word, "l");
	word = RemoveDouble(word, "c");
	word = RemoveDouble(word, "e");
	word = RemoveDouble(word, "m");
	word = RemoveDouble(word, "j");
	word = RemoveDouble(word, "o");
	word = RemoveDouble(word, "d");
	word = RemoveDouble(word, "f");
	return word;
}

Array.prototype.unique = function() {
    var a = this.concat();
    for(var i=0; i<a.length; ++i) {
        for(var j=i+1; j<a.length; ++j) {
            if(a[i] == a[j])
                a.splice(j--, 1);
        }
    }

    return a;
};


function BuildTriggerIndex()
{
	triggerIndex = {};
	wordMap = {};
	tagMap = {};
	for (let category in keyPhrases)
	{
		let count = keyPhrases[category]["entries"].length;
		for(let i = 0; i < count; i++)
		{
			let entry = keyPhrases[category]["entries"][i];
			if(entry["trigger"] != null && entry["trigger"] != "")
			{
				let entryTriggers = entry["trigger"].split(",");
				entryTriggers.forEach( trigger =>
				{
					trigger = trigger.replace(/[^a-zA-Z0-9 \._\-]/g, '').trim().toLowerCase();
					if(!triggers.hasOwnProperty(trigger))
					{
						trigger = WordToKey(trigger);
					}
					if(triggerIndex.hasOwnProperty(trigger))
					{
						triggerIndex[trigger].push( { category: category, index: i });
					}
					else
					{
						triggerIndex[trigger] = [];
						triggerIndex[trigger].push( { category: category, index: i });
					}
				});
			}

			/*let words = entry["phrase"].split(" ");
			let wordCount = words.length;
			for(let e = 0; e < wordCount; e++)
			{
				let wordKey = WordToKey(words[e].replace(/[^a-zA-Z0-9 \._\-]/g, '').trim().toLowerCase());

				if(wordKey.length < 2)
					continue;

				if(!wordMap.hasOwnProperty(wordKey))
				{
					wordMap[wordKey] = [];
				}

				let entrySearchTags = entry["search_tags"].split(",");
				entrySearchTags.push(category);
				entrySearchTags.forEach( search_tag =>
				{
					if(search_tag != null && search_tag != "")
					{
						if(search_tag.endsWith("'s"))
							search_tag = search_tag.slice(0, -2);
						if(search_tag.endsWith("s"))
							search_tag = search_tag.slice(0, -1);
						search_tag = search_tag.replace(/[^a-zA-Z0-9 \._\-]/g, '').trim().toLowerCase();
						wordMap[wordKey].push(search_tag);
						if(!tagMap.hasOwnProperty(search_tag))
						{
							tagMap[search_tag] = [];
						}
						tagMap[search_tag].push({ category: category, index: i });
						tagMap[search_tag] = tagMap[search_tag].unique();
					}
				});
				wordMap[wordKey] = wordMap[wordKey].unique();
			}*/
		}
	}
}

function ConditionalButton(entry, button)
{
	if(entry["show_if"] != "" || entry["filter_group"] != "")
		conditionalButtons.push({element:button,condition:entry["show_if"], filterGroup:entry["filter_group"]});
}

// generate menu in suggestion area
function ShowMenu()
{
	// clear all buttons from menu
	ClearSuggestionArea();
	HideTooltip();

	// if no chategory is selected
	if(activeCategory == "")
	{
		if(activeContext.length != 0)
		{
			AddButton("Context", selectCategory, "A dynamicly updating category based on the current prompt.", null, null, contextCategory);
		}
		for (var category in keyPhrases)
		{
			AddButton(category, selectCategory, keyPhrases[category]["description"], null, null, category);
		}
		// change iframe size after buttons have been added
		UpdateSize();
	}
	else if(activeCategory == contextCategory)
	{
		// add a button to leave the chategory
		var backbutton = AddButton("&#x2191; back", leaveCategory);
		activeContext.forEach( context =>
		{
			if(tagMap.hasOwnProperty(context))
			{
				var words = tagMap[context].unique();
				words.forEach( word =>
				{
					var entry = keyPhrases[word.category]["entries"][word.index];
					var tempPattern = keyPhrases[word.category]["pattern"];

					if(entry["pattern_override"] != "")
					{
						tempPattern = entry["pattern_override"];
					}

					var button = AddButton(entry["phrase"], SelectPhrase, entry["description"], entry["phrase"],tempPattern, entry["phrase"]);

					ConditionalButton(entry, button);
				});
			}
			if(triggerIndex.hasOwnProperty(context))
			{
				var triggered = triggerIndex[context];
				triggered.forEach( trigger =>
				{
					var entry = keyPhrases[trigger.category]["entries"][trigger.index];
					var tempPattern = keyPhrases[trigger.category]["pattern"];

					if(entry["pattern_override"] != "")
					{
						tempPattern = entry["pattern_override"];
					}

					var button = AddButton(entry["phrase"], SelectPhrase, entry["description"], entry["phrase"],tempPattern, entry["phrase"]);

					ConditionalButton(entry, button);
				});
			}
		});

		ButtonConditions();
		// change iframe size after buttons have been added
		UpdateSize();
	}
	// if a chategory is selected
	else
	{
		// add a button to leave the chategory
		var backbutton = AddButton("&#x2191; back", leaveCategory);
		var pattern = keyPhrases[activeCategory]["pattern"];
		keyPhrases[activeCategory]["entries"].forEach(entry =>
		{
			var tempPattern = pattern;
			if(entry["pattern_override"] != "")
			{
				tempPattern = entry["pattern_override"];
			}

			var button = AddButton(entry["phrase"], SelectPhrase, entry["description"], entry["phrase"],tempPattern, entry["phrase"]);

			ConditionalButton(entry, button);
		});
		ButtonConditions();
		// change iframe size after buttons have been added
		UpdateSize();
	}
}

// listen for clicks on the prompt field
parentDoc.addEventListener("click", (e) =>
{
	// skip if this frame is not visible
	if(!isVisible(frame))
		return;

	// if the iframes prompt field is not set, get it and set it
	if(promptField === null)
	{
		GetPromptField();
		ButtonUpdateContext(true);
	}

	// get the field with focus
	var target = parentDoc.activeElement;

	// if the field with focus is a prompt field, the %% placeholder %% is set in python
	if(	target.placeholder === placeholder)
	{
		// generate menu
		ShowMenu();
		frame.style.borderBottomWidth = '13px';
	}
	else
	{
		// else hide the iframe
		frame.style.height = "0px";
		frame.style.borderBottomWidth = '0px';
	}
});

function AppendStyle(targetDoc, id, content)
{
	  // get parent document head
	var head = targetDoc.getElementsByTagName('head')[0];

	// add style tag
	var style = targetDoc.createElement('style');
    // set type attribute
	style.setAttribute('type', 'text/css');
	style.id = id;
    // add css forwarded from python
	if (style.styleSheet) {   // IE
        style.styleSheet.cssText = content;
    } else {                // the world
        style.appendChild(parentDoc.createTextNode(content));
    }
	// add style to head
    head.appendChild(style);
}

// Transfer all styles
var head = document.getElementsByTagName("head")[0];
var parentStyle = parentDoc.getElementsByTagName("style");
for (var i = 0; i < parentStyle.length; i++)
	head.appendChild(parentStyle[i].cloneNode(true));
var parentLinks = parentDoc.querySelectorAll('link[rel="stylesheet"]');
for (var i = 0; i < parentLinks.length; i++)
	head.appendChild(parentLinks[i].cloneNode(true));

// add custom style to iframe
frame.classList.add("suggestion-frame");
// clear suggestion area to remove the "javascript failed" message
ClearSuggestionArea();
// collapse the iframe by default
frame.style.height = "0px";
frame.style.borderBottomWidth = '0px';

BuildTriggerIndex();

// only execute once (even though multiple iframes exist)
if(!parentDoc.hasOwnProperty('keyPhraseSuggestionsInitialized'))
{
	AppendStyle(parentDoc, "key-phrase-suggestions", parentCSS);

	var tooltip = parentDoc.createElement('div');
	tooltip.id = "phrase-tooltip";
	parentDoc.body.appendChild(tooltip);
	parentDoc["phraseTooltip"] = tooltip;
	// set flag so this only runs once
	parentDoc["keyPhraseSuggestionsLoadedBlobs"] = {};
	parentDoc["keyPhraseSuggestionsInitialized"] = true;

	var cssVars = getAllCSSVariableNames();
	computedStyle = getComputedStyle(parentDoc.documentElement);

	parentDoc["keyPhraseSuggestionsCSSvariables"] = ":root{";

	cssVars.forEach( (rule) =>
	{
		parentDoc["keyPhraseSuggestionsCSSvariables"] += rule+": "+computedStyle.getPropertyValue(rule)+";";
	});
	parentDoc["keyPhraseSuggestionsCSSvariables"] += "}";
}

AppendStyle(document, "variables", parentDoc["keyPhraseSuggestionsCSSvariables"]);


================================================
FILE: webui/streamlit/scripts/custom_components/sygil_suggestions/parent.css
================================================
.suggestion-frame
{
	position: absolute;

	/* make as small as possible */
	margin: 0px;
	padding: 0px;
	min-height: 0px;
	line-height: 0;

	/* animate transitions of the height property */
	-webkit-transition: height 1s;
	-moz-transition: height 1s;
	-ms-transition: height 1s;
	-o-transition: height 1s;
	transition: height 1s, border-bottom-width 1s;

	/* block selection */
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;

	z-index: 700;

	outline: 1px solid rgba(250, 250, 250, 0.2);
	outline-offset: 0px;
	border-radius: 0.25rem;
	background: rgb(14, 17, 23);

	box-sizing: border-box;
    -moz-box-sizing: border-box;
    -webkit-box-sizing: border-box;
	border-bottom: solid 13px rgb(14, 17, 23) !important;
	border-left: solid 13px rgb(14, 17, 23) !important;
}

#phrase-tooltip
{
	display: none;
	pointer-events: none;
	position: absolute;
	border-bottom-left-radius: 0.5rem;
	border-top-right-radius: 0.5rem;
	border-bottom-right-radius: 0.5rem;
	border: solid rgb(255,75,75) 2px;
	background-color: rgb(38, 39, 48);
	color: rgb(255,75,75);
	font-size: 1rem;
	font-family: "Source Sans Pro", sans-serif;
	padding: 0.5rem;

	cursor: default;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
	z-index: 1000;
}

#phrase-tooltip:has(img)
{
	transform: scale(1.25, 1.25);
	-ms-transform: scale(1.25, 1.25);
	-webkit-transform: scale(1.25, 1.25);
}

#phrase-tooltip>img
{
	pointer-events: none;
	border-bottom-left-radius: 0.5rem;
	border-top-right-radius: 0.5rem;
	border-bottom-right-radius: 0.5rem;

	cursor: default;
	user-select: none;
	-moz-user-select: none;
	-khtml-user-select: none;
	-webkit-user-select: none;
	-o-user-select: none;
	z-index: 1500;
}


================================================
FILE: webui/streamlit/scripts/img2img.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import (
    st,
    server_state,
    no_rerun,
    custom_models_available,
    RealESRGAN_available,
    GFPGAN_available,
    LDSR_available,
)

# generation_callback, process_images, KDiffusionSampler, \
# load_models, hc, seed_to_int, logger, \
# resize_image, get_matched_noise, CFGMaskedDenoiser, ImageFilter, set_page_title

# streamlit imports
from streamlit.runtime.scriptrunner import StopException

# other imports
import cv2
from PIL import Image, ImageOps
import torch
import k_diffusion as K
import numpy as np
import time
import torch
import skimage
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler

# streamlit components
from custom_components import sygil_suggestions
from streamlit_drawable_canvas import st_canvas

# Temp imports


# end of imports
# ---------------------------------------------------------------------------------------------------------------

sygil_suggestions.init()

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging

    logging.set_verbosity_error()
except:
    pass


def img2img(
    prompt: str = "",
    init_info: any = None,
    init_info_mask: any = None,
    mask_mode: int = 0,
    mask_blur_strength: int = 3,
    mask_restore: bool = False,
    ddim_steps: int = 50,
    sampler_name: str = "DDIM",
    n_iter: int = 1,
    cfg_scale: float = 7.5,
    denoising_strength: float = 0.8,
    seed: int = -1,
    noise_mode: int = 0,
    find_noise_steps: str = "",
    height: int = 512,
    width: int = 512,
    resize_mode: int = 0,
    fp=None,
    variant_amount: float = 0.0,
    variant_seed: int = None,
    ddim_eta: float = 0.0,
    write_info_files: bool = True,
    separate_prompts: bool = False,
    normalize_prompt_weights: bool = True,
    save_individual_images: bool = True,
    save_grid: bool = True,
    group_by_prompt: bool = True,
    save_as_jpg: bool = True,
    use_GFPGAN: bool = True,
    GFPGAN_model: str = "GFPGANv1.4",
    use_RealESRGAN: bool = True,
    RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B",
    use_LDSR: bool = True,
    LDSR_model: str = "model",
    loopback: bool = False,
    random_seed_loopback: bool = False,
):
    outpath = st.session_state["defaults"].general.outdir_img2img
    seed = seed_to_int(seed)

    batch_size = 1

    if sampler_name == "PLMS":
        sampler = PLMSSampler(server_state["model"])
    elif sampler_name == "DDIM":
        sampler = DDIMSampler(server_state["model"])
    elif sampler_name == "k_dpm_2_a":
        sampler = KDiffusionSampler(server_state["model"], "dpm_2_ancestral")
    elif sampler_name == "k_dpm_2":
        sampler = KDiffusionSampler(server_state["model"], "dpm_2")
    elif sampler_name == "k_dpmpp_2m":
        sampler = KDiffusionSampler(server_state["model"], "dpmpp_2m")
    elif sampler_name == "k_euler_a":
        sampler = KDiffusionSampler(server_state["model"], "euler_ancestral")
    elif sampler_name == "k_euler":
        sampler = KDiffusionSampler(server_state["model"], "euler")
    elif sampler_name == "k_heun":
        sampler = KDiffusionSampler(server_state["model"], "heun")
    elif sampler_name == "k_lms":
        sampler = KDiffusionSampler(server_state["model"], "lms")
    else:
        raise Exception("Unknown sampler: " + sampler_name)

    def process_init_mask(init_mask: Image):
        if init_mask.mode == "RGBA":
            init_mask = init_mask.convert("RGBA")
            background = Image.new("RGBA", init_mask.size, (0, 0, 0))
            init_mask = Image.alpha_composite(background, init_mask)
            init_mask = init_mask.convert("RGB")
        return init_mask

    init_img = init_info
    init_mask = None
    if mask_mode == 0:
        if init_info_mask:
            init_mask = process_init_mask(init_info_mask)
    elif mask_mode == 1:
        if init_info_mask:
            init_mask = process_init_mask(init_info_mask)
            init_mask = ImageOps.invert(init_mask)
    elif mask_mode == 2:
        init_img_transparency = init_img.split()[-1].convert(
            "L"
        )  # .point(lambda x: 255 if x > 0 else 0, mode='1')
        init_mask = init_img_transparency
        init_mask = init_mask.convert("RGB")
        init_mask = resize_image(resize_mode, init_mask, width, height)
        init_mask = init_mask.convert("RGB")

    assert 0.0 <= denoising_strength <= 1.0, "can only work with strength in [0.0, 1.0]"
    t_enc = int(denoising_strength * ddim_steps)

    if (
        init_mask is not None
        and (noise_mode == 2 or noise_mode == 3)
        and init_img is not None
    ):
        noise_q = 0.99
        color_variation = 0.0
        mask_blend_factor = 1.0

        np_init = (np.asarray(init_img.convert("RGB")) / 255.0).astype(
            np.float64
        )  # annoyingly complex mask fixing
        np_mask_rgb = 1.0 - (
            np.asarray(ImageOps.invert(init_mask).convert("RGB")) / 255.0
        ).astype(np.float64)
        np_mask_rgb -= np.min(np_mask_rgb)
        np_mask_rgb /= np.max(np_mask_rgb)
        np_mask_rgb = 1.0 - np_mask_rgb
        np_mask_rgb_hardened = 1.0 - (np_mask_rgb < 0.99).astype(np.float64)
        blurred = skimage.filters.gaussian(
            np_mask_rgb_hardened[:], sigma=16.0, channel_axis=2, truncate=32.0
        )
        blurred2 = skimage.filters.gaussian(
            np_mask_rgb_hardened[:], sigma=16.0, channel_axis=2, truncate=32.0
        )
        # np_mask_rgb_dilated = np_mask_rgb + blurred  # fixup mask todo: derive magic constants
        # np_mask_rgb = np_mask_rgb + blurred
        np_mask_rgb_dilated = np.clip((np_mask_rgb + blurred2) * 0.7071, 0.0, 1.0)
        np_mask_rgb = np.clip((np_mask_rgb + blurred) * 0.7071, 0.0, 1.0)

        noise_rgb = get_matched_noise(np_init, np_mask_rgb, noise_q, color_variation)
        blend_mask_rgb = np.clip(np_mask_rgb_dilated, 0.0, 1.0) ** (mask_blend_factor)
        noised = noise_rgb[:]
        blend_mask_rgb **= 2.0
        noised = np_init[:] * (1.0 - blend_mask_rgb) + noised * blend_mask_rgb

        np_mask_grey = np.sum(np_mask_rgb, axis=2) / 3.0
        ref_mask = np_mask_grey < 1e-3

        all_mask = np.ones((height, width), dtype=bool)
        noised[all_mask, :] = skimage.exposure.match_histograms(
            noised[all_mask, :] ** 1.0, noised[ref_mask, :], channel_axis=1
        )

        init_img = Image.fromarray(
            np.clip(noised * 255.0, 0.0, 255.0).astype(np.uint8), mode="RGB"
        )
        st.session_state["editor_image"].image(init_img)  # debug

    def init():
        image = init_img.convert("RGB")
        image = np.array(image).astype(np.float32) / 255.0
        image = image[None].transpose(0, 3, 1, 2)
        image = torch.from_numpy(image)

        mask_channel = None
        if init_mask:
            alpha = resize_image(resize_mode, init_mask, width // 8, height // 8)
            mask_channel = alpha.split()[-1]

        mask = None
        if mask_channel is not None:
            mask = np.array(mask_channel).astype(np.float32) / 255.0
            mask = 1 - mask
            mask = np.tile(mask, (4, 1, 1))
            mask = mask[None].transpose(0, 1, 2, 3)
            mask = torch.from_numpy(mask).to(server_state["device"])

        if st.session_state["defaults"].general.optimized:
            server_state["modelFS"].to(server_state["device"])

        init_image = 2.0 * image - 1.0
        init_image = init_image.to(server_state["device"])
        init_latent = (
            server_state["model"]
            if not st.session_state["defaults"].general.optimized
            else server_state["modelFS"]
        ).get_first_stage_encoding(
            (
                server_state["model"]
                if not st.session_state["defaults"].general.optimized
                else server_state["modelFS"]
            ).encode_first_stage(init_image)
        )  # move to latent space

        if st.session_state["defaults"].general.optimized:
            mem = torch.cuda.memory_allocated() / 1e6
            server_state["modelFS"].to("cpu")
            while torch.cuda.memory_allocated() / 1e6 >= mem:
                time.sleep(1)

        return (
            init_latent,
            mask,
        )

    def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
        t_enc_steps = t_enc
        obliterate = False
        if ddim_steps == t_enc_steps:
            t_enc_steps = t_enc_steps - 1
            obliterate = True

        if sampler_name != "DDIM":
            x0, z_mask = init_data

            sigmas = sampler.model_wrap.get_sigmas(ddim_steps)
            noise = x * sigmas[ddim_steps - t_enc_steps - 1]

            xi = x0 + noise

            # Obliterate masked image
            if z_mask is not None and obliterate:
                random = torch.randn(z_mask.shape, device=xi.device)
                xi = (z_mask * noise) + ((1 - z_mask) * xi)

            sigma_sched = sigmas[ddim_steps - t_enc_steps - 1 :]
            model_wrap_cfg = CFGMaskedDenoiser(sampler.model_wrap)
            samples_ddim = K.sampling.__dict__[f"sample_{sampler.get_sampler_name()}"](
                model_wrap_cfg,
                xi,
                sigma_sched,
                extra_args={
                    "cond": conditioning,
                    "uncond": unconditional_conditioning,
                    "cond_scale": cfg_scale,
                    "mask": z_mask,
                    "x0": x0,
                    "xi": xi,
                },
                disable=False,
                callback=generation_callback if not server_state["bridge"] else None,
            )
        else:
            x0, z_mask = init_data

            sampler.make_schedule(
                ddim_num_steps=ddim_steps, ddim_eta=0.0, verbose=False
            )
            z_enc = sampler.stochastic_encode(
                x0, torch.tensor([t_enc_steps] * batch_size).to(server_state["device"])
            )

            # Obliterate masked image
            if z_mask is not None and obliterate:
                random = torch.randn(z_mask.shape, device=z_enc.device)
                z_enc = (z_mask * random) + ((1 - z_mask) * z_enc)

                # decode it
            samples_ddim = sampler.decode(
                z_enc,
                conditioning,
                t_enc_steps,
                unconditional_guidance_scale=cfg_scale,
                unconditional_conditioning=unconditional_conditioning,
                z_mask=z_mask,
                x0=x0,
            )
        return samples_ddim

    if loopback:
        output_images, info = None, None
        history = []
        initial_seed = None

        do_color_correction = False
        try:
            from skimage import exposure

            do_color_correction = True
        except:
            logger.error("Install scikit-image to perform color correction on loopback")

        for i in range(n_iter):
            if do_color_correction and i == 0:
                correction_target = cv2.cvtColor(
                    np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB
                )

            # RealESRGAN can only run on the final iteration
            is_final_iteration = i == n_iter - 1

            output_images, seed, info, stats = process_images(
                outpath=outpath,
                func_init=init,
                func_sample=sample,
                prompt=prompt,
                seed=seed,
                sampler_name=sampler_name,
                save_grid=save_grid,
                batch_size=1,
                n_iter=1,
                steps=ddim_steps,
                cfg_scale=cfg_scale,
                width=width,
                height=height,
                prompt_matrix=separate_prompts,
                use_GFPGAN=use_GFPGAN,
                GFPGAN_model=GFPGAN_model,
                use_RealESRGAN=use_RealESRGAN
                and is_final_iteration,  # Forcefully disable upscaling when using loopback
                realesrgan_model_name=RealESRGAN_model,
                use_LDSR=use_LDSR,
                LDSR_model_name=LDSR_model,
                normalize_prompt_weights=normalize_prompt_weights,
                save_individual_images=save_individual_images,
                init_img=init_img,
                init_mask=init_mask,
                mask_blur_strength=mask_blur_strength,
                mask_restore=mask_restore,
                denoising_strength=denoising_strength,
                noise_mode=noise_mode,
                find_noise_steps=find_noise_steps,
                resize_mode=resize_mode,
                uses_loopback=loopback,
                uses_random_seed_loopback=random_seed_loopback,
                sort_samples=group_by_prompt,
                write_info_files=write_info_files,
                jpg_sample=save_as_jpg,
            )

            if initial_seed is None:
                initial_seed = seed

            input_image = init_img
            init_img = output_images[0]

            if do_color_correction and correction_target is not None:
                init_img = Image.fromarray(
                    cv2.cvtColor(
                        exposure.match_histograms(
                            cv2.cvtColor(np.asarray(init_img), cv2.COLOR_RGB2LAB),
                            correction_target,
                            channel_axis=2,
                        ),
                        cv2.COLOR_LAB2RGB,
                    ).astype("uint8")
                )
                if mask_restore is True and init_mask is not None:
                    color_mask = init_mask.filter(
                        ImageFilter.GaussianBlur(mask_blur_strength)
                    )
                    color_mask = color_mask.convert("L")
                    source_image = input_image.convert("RGB")
                    target_image = init_img.convert("RGB")

                    init_img = Image.composite(source_image, target_image, color_mask)

            if not random_seed_loopback:
                seed = seed + 1
            else:
                seed = seed_to_int(None)

            denoising_strength = max(denoising_strength * 0.95, 0.1)
            history.append(init_img)

        output_images = history
        seed = initial_seed

    else:
        output_images, seed, info, stats = process_images(
            outpath=outpath,
            func_init=init,
            func_sample=sample,
            prompt=prompt,
            seed=seed,
            sampler_name=sampler_name,
            save_grid=save_grid,
            batch_size=batch_size,
            n_iter=n_iter,
            steps=ddim_steps,
            cfg_scale=cfg_scale,
            width=width,
            height=height,
            prompt_matrix=separate_prompts,
            use_GFPGAN=use_GFPGAN,
            GFPGAN_model=GFPGAN_model,
            use_RealESRGAN=use_RealESRGAN,
            realesrgan_model_name=RealESRGAN_model,
            use_LDSR=use_LDSR,
            LDSR_model_name=LDSR_model,
            normalize_prompt_weights=normalize_prompt_weights,
            save_individual_images=save_individual_images,
            init_img=init_img,
            init_mask=init_mask,
            mask_blur_strength=mask_blur_strength,
            denoising_strength=denoising_strength,
            noise_mode=noise_mode,
            find_noise_steps=find_noise_steps,
            mask_restore=mask_restore,
            resize_mode=resize_mode,
            uses_loopback=loopback,
            sort_samples=group_by_prompt,
            write_info_files=write_info_files,
            jpg_sample=save_as_jpg,
        )

    del sampler

    return output_images, seed, info, stats


#
def layout():
    with st.form("img2img-inputs"):
        st.session_state["generation_mode"] = "img2img"

        img2img_input_col, img2img_generate_col = st.columns([10, 1])
        with img2img_input_col:
            # prompt = st.text_area("Input Text","")
            placeholder = "A corgi wearing a top hat as an oil painting."
            prompt = st.text_area("Input Text", "", placeholder=placeholder, height=54)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].general.enable_suggestions:
                    sygil_suggestions.suggestion_area(placeholder)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].admin.global_negative_prompt:
                    prompt += f"### {st.session_state['defaults'].admin.global_negative_prompt}"

        # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
        img2img_generate_col.write("")
        img2img_generate_col.write("")
        generate_button = img2img_generate_col.form_submit_button("Generate")

        # creating the page layout using columns
        col1_img2img_layout, col2_img2img_layout, col3_img2img_layout = st.columns(
            [2, 4, 4], gap="medium"
        )

        with col1_img2img_layout:
            # If we have custom models available on the "models/custom"
            # folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
            custom_models_available()
            if server_state["CustomModel_available"]:
                st.session_state["custom_model"] = st.selectbox(
                    "Custom Model:",
                    server_state["custom_models"],
                    index=server_state["custom_models"].index(
                        st.session_state["defaults"].general.default_model
                    ),
                    help="Select the model you want to use. This option is only available if you have custom models \
							    on your 'models/custom' folder. The model name that will be shown here is the same as the name\
							    the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
							    will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.5",
                )
            else:
                st.session_state["custom_model"] = "Stable Diffusion v1.5"

            st.session_state["sampling_steps"] = st.number_input(
                "Sampling Steps",
                value=st.session_state["defaults"].img2img.sampling_steps.value,
                min_value=st.session_state["defaults"].img2img.sampling_steps.min_value,
                step=st.session_state["defaults"].img2img.sampling_steps.step,
            )

            sampler_name_list = [
                "k_lms",
                "k_euler",
                "k_euler_a",
                "k_dpm_2",
                "k_dpm_2_a",
                "k_dpmpp_2m",
                "k_heun",
                "PLMS",
                "DDIM",
            ]
            st.session_state["sampler_name"] = st.selectbox(
                "Sampling method",
                sampler_name_list,
                index=sampler_name_list.index(
                    st.session_state["defaults"].img2img.sampler_name
                ),
                help="Sampling method to use.",
            )

            width = st.slider(
                "Width:",
                min_value=st.session_state["defaults"].img2img.width.min_value,
                max_value=st.session_state["defaults"].img2img.width.max_value,
                value=st.session_state["defaults"].img2img.width.value,
                step=st.session_state["defaults"].img2img.width.step,
            )
            height = st.slider(
                "Height:",
                min_value=st.session_state["defaults"].img2img.height.min_value,
                max_value=st.session_state["defaults"].img2img.height.max_value,
                value=st.session_state["defaults"].img2img.height.value,
                step=st.session_state["defaults"].img2img.height.step,
            )
            seed = st.text_input(
                "Seed:",
                value=st.session_state["defaults"].img2img.seed,
                help=" The seed to use, if left blank a random seed will be generated.",
            )

            cfg_scale = st.number_input(
                "CFG (Classifier Free Guidance Scale):",
                min_value=st.session_state["defaults"].img2img.cfg_scale.min_value,
                value=st.session_state["defaults"].img2img.cfg_scale.value,
                step=st.session_state["defaults"].img2img.cfg_scale.step,
                help="How strongly the image should follow the prompt.",
            )

            st.session_state["denoising_strength"] = st.slider(
                "Denoising Strength:",
                value=st.session_state["defaults"].img2img.denoising_strength.value,
                min_value=st.session_state[
                    "defaults"
                ].img2img.denoising_strength.min_value,
                max_value=st.session_state[
                    "defaults"
                ].img2img.denoising_strength.max_value,
                step=st.session_state["defaults"].img2img.denoising_strength.step,
            )

            mask_expander = st.empty()
            with mask_expander.expander("Inpainting/Outpainting"):
                mask_mode_list = ["Outpainting", "Inpainting", "Image alpha"]
                mask_mode = st.selectbox(
                    "Painting Mode",
                    mask_mode_list,
                    index=st.session_state["defaults"].img2img.mask_mode,
                    help='Select how you want your image to be masked/painted."Inpainting" modifies the image where the mask is white.\n\
							 "Inverted mask" modifies the image where the mask is black. "Image alpha" modifies the image where the image is transparent.',
                )
                mask_mode = mask_mode_list.index(mask_mode)

                noise_mode_list = [
                    "Seed",
                    "Find Noise",
                    "Matched Noise",
                    "Find+Matched Noise",
                ]
                noise_mode = st.selectbox(
                    "Noise Mode",
                    noise_mode_list,
                    index=noise_mode_list.index(
                        st.session_state["defaults"].img2img.noise_mode
                    ),
                    help="",
                )
                # noise_mode = noise_mode_list.index(noise_mode)
                find_noise_steps = st.number_input(
                    "Find Noise Steps",
                    value=st.session_state["defaults"].img2img.find_noise_steps.value,
                    min_value=st.session_state[
                        "defaults"
                    ].img2img.find_noise_steps.min_value,
                    step=st.session_state["defaults"].img2img.find_noise_steps.step,
                )

                # Specify canvas parameters in application
                drawing_mode = st.selectbox(
                    "Drawing tool:",
                    (
                        "freedraw",
                        "transform",
                        # "line",
                        "rect",
                        "circle",
                        # "polygon",
                    ),
                )

                stroke_width = st.slider("Stroke width: ", 1, 100, 50)
                stroke_color = st.color_picker("Stroke color hex: ", value="#EEEEEE")
                bg_color = st.color_picker("Background color hex: ", "#7B6E6E")

                display_toolbar = st.checkbox("Display toolbar", True)
                # realtime_update = st.checkbox("Update in realtime", True)

            with st.expander("Batch Options"):
                st.session_state["batch_count"] = st.number_input(
                    "Batch count.",
                    value=st.session_state["defaults"].img2img.batch_count.value,
                    help="How many iterations or batches of images to generate in total.",
                )

                st.session_state["batch_size"] = st.number_input(
                    "Batch size",
                    value=st.session_state.defaults.img2img.batch_size.value,
                    help="How many images are at once in a batch.\
										 It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
										 Default: 1",
                )

            with st.expander("Preview Settings"):
                st.session_state["update_preview"] = st.session_state[
                    "defaults"
                ].general.update_preview
                st.session_state["update_preview_frequency"] = st.number_input(
                    "Update Image Preview Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].img2img.update_preview_frequency,
                    help="Frequency in steps at which the the preview image is updated. By default the frequency \
											       is set to 1 step.",
                )
            #
            with st.expander("Advanced"):
                with st.expander("Output Settings"):
                    separate_prompts = st.checkbox(
                        "Create Prompt Matrix.",
                        value=st.session_state["defaults"].img2img.separate_prompts,
                        help="Separate multiple prompts using the `|` character, and get all combinations of them.",
                    )
                    normalize_prompt_weights = st.checkbox(
                        "Normalize Prompt Weights.",
                        value=st.session_state[
                            "defaults"
                        ].img2img.normalize_prompt_weights,
                        help="Ensure the sum of all weights add up to 1.0",
                    )
                    loopback = st.checkbox(
                        "Loopback.",
                        value=st.session_state["defaults"].img2img.loopback,
                        help="Use images from previous batch when creating next batch.",
                    )
                    st.checkbox(
                        "Random loopback seed.",
                        value=st.session_state["defaults"].img2img.random_seed_loopback,
                        help="Random loopback seed",
                    )
                    img2img_mask_restore = st.checkbox(
                        "Only modify regenerated parts of image",
                        value=st.session_state["defaults"].img2img.mask_restore,
                        help="Enable to restore the unmasked parts of the image with the input, may not blend as well but preserves detail",
                    )
                    save_individual_images = st.checkbox(
                        "Save individual images.",
                        value=st.session_state[
                            "defaults"
                        ].img2img.save_individual_images,
                        help="Save each image generated before any filter or enhancement is applied.",
                    )
                    save_grid = st.checkbox(
                        "Save grid",
                        value=st.session_state["defaults"].img2img.save_grid,
                        help="Save a grid with all the images generated into a single image.",
                    )
                    group_by_prompt = st.checkbox(
                        "Group results by prompt",
                        value=st.session_state["defaults"].img2img.group_by_prompt,
                        help="Saves all the images with the same prompt into the same folder. \
						                      When using a prompt matrix each prompt combination will have its own folder.",
                    )
                    write_info_files = st.checkbox(
                        "Write Info file",
                        value=st.session_state["defaults"].img2img.write_info_files,
                        help="Save a file next to the image with informartion about the generation.",
                    )
                    save_as_jpg = st.checkbox(
                        "Save samples as jpg",
                        value=st.session_state["defaults"].img2img.save_as_jpg,
                        help="Saves the images as jpg instead of png.",
                    )

                #
                # check if GFPGAN, RealESRGAN and LDSR are available.
                if "GFPGAN_available" not in st.session_state:
                    GFPGAN_available()

                if "RealESRGAN_available" not in st.session_state:
                    RealESRGAN_available()

                if "LDSR_available" not in st.session_state:
                    LDSR_available()

                if (
                    st.session_state["GFPGAN_available"]
                    or st.session_state["RealESRGAN_available"]
                    or st.session_state["LDSR_available"]
                ):
                    with st.expander("Post-Processing"):
                        face_restoration_tab, upscaling_tab = st.tabs(
                            ["Face Restoration", "Upscaling"]
                        )
                        with face_restoration_tab:
                            # GFPGAN used for face restoration
                            if st.session_state["GFPGAN_available"]:
                                # with st.expander("Face Restoration"):
                                # if st.session_state["GFPGAN_available"]:
                                # with st.expander("GFPGAN"):
                                st.session_state["use_GFPGAN"] = st.checkbox(
                                    "Use GFPGAN",
                                    value=st.session_state[
                                        "defaults"
                                    ].img2img.use_GFPGAN,
                                    help="Uses the GFPGAN model to improve faces after the generation.\
																							 This greatly improve the quality and consistency of faces but uses\
																							 extra VRAM. Disable if you need the extra VRAM.",
                                )

                                st.session_state["GFPGAN_model"] = st.selectbox(
                                    "GFPGAN model",
                                    st.session_state["GFPGAN_models"],
                                    index=st.session_state["GFPGAN_models"].index(
                                        st.session_state[
                                            "defaults"
                                        ].general.GFPGAN_model
                                    ),
                                )

                                # st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')

                            else:
                                st.session_state["use_GFPGAN"] = False

                        with upscaling_tab:
                            st.session_state["us_upscaling"] = st.checkbox(
                                "Use Upscaling",
                                value=st.session_state[
                                    "defaults"
                                ].img2img.use_upscaling,
                            )

                            # RealESRGAN and LDSR used for upscaling.
                            if (
                                st.session_state["RealESRGAN_available"]
                                or st.session_state["LDSR_available"]
                            ):
                                upscaling_method_list = []
                                if st.session_state["RealESRGAN_available"]:
                                    upscaling_method_list.append("RealESRGAN")
                                if st.session_state["LDSR_available"]:
                                    upscaling_method_list.append("LDSR")

                                st.session_state["upscaling_method"] = st.selectbox(
                                    "Upscaling Method",
                                    upscaling_method_list,
                                    index=upscaling_method_list.index(
                                        st.session_state[
                                            "defaults"
                                        ].general.upscaling_method
                                    )
                                    if st.session_state[
                                        "defaults"
                                    ].general.upscaling_method
                                    in upscaling_method_list
                                    else 0,
                                )

                                if st.session_state["RealESRGAN_available"]:
                                    with st.expander("RealESRGAN"):
                                        if (
                                            st.session_state["upscaling_method"]
                                            == "RealESRGAN"
                                            and st.session_state["us_upscaling"]
                                        ):
                                            st.session_state["use_RealESRGAN"] = True
                                        else:
                                            st.session_state["use_RealESRGAN"] = False

                                        st.session_state[
                                            "RealESRGAN_model"
                                        ] = st.selectbox(
                                            "RealESRGAN model",
                                            st.session_state["RealESRGAN_models"],
                                            index=st.session_state[
                                                "RealESRGAN_models"
                                            ].index(
                                                st.session_state[
                                                    "defaults"
                                                ].general.RealESRGAN_model
                                            ),
                                        )
                                else:
                                    st.session_state["use_RealESRGAN"] = False
                                    st.session_state[
                                        "RealESRGAN_model"
                                    ] = "RealESRGAN_x4plus"

                                #
                                if st.session_state["LDSR_available"]:
                                    with st.expander("LDSR"):
                                        if (
                                            st.session_state["upscaling_method"]
                                            == "LDSR"
                                            and st.session_state["us_upscaling"]
                                        ):
                                            st.session_state["use_LDSR"] = True
                                        else:
                                            st.session_state["use_LDSR"] = False

                                        st.session_state["LDSR_model"] = st.selectbox(
                                            "LDSR model",
                                            st.session_state["LDSR_models"],
                                            index=st.session_state["LDSR_models"].index(
                                                st.session_state[
                                                    "defaults"
                                                ].general.LDSR_model
                                            ),
                                        )

                                        st.session_state[
                                            "ldsr_sampling_steps"
                                        ] = st.number_input(
                                            "Sampling Steps",
                                            value=st.session_state[
                                                "defaults"
                                            ].img2img.LDSR_config.sampling_steps,
                                            help="",
                                        )

                                        st.session_state[
                                            "preDownScale"
                                        ] = st.number_input(
                                            "PreDownScale",
                                            value=st.session_state[
                                                "defaults"
                                            ].img2img.LDSR_config.preDownScale,
                                            help="",
                                        )

                                        st.session_state[
                                            "postDownScale"
                                        ] = st.number_input(
                                            "postDownScale",
                                            value=st.session_state[
                                                "defaults"
                                            ].img2img.LDSR_config.postDownScale,
                                            help="",
                                        )

                                        downsample_method_list = ["Nearest", "Lanczos"]
                                        st.session_state[
                                            "downsample_method"
                                        ] = st.selectbox(
                                            "Downsample Method",
                                            downsample_method_list,
                                            index=downsample_method_list.index(
                                                st.session_state[
                                                    "defaults"
                                                ].img2img.LDSR_config.downsample_method
                                            ),
                                        )

                                else:
                                    st.session_state["use_LDSR"] = False
                                    st.session_state["LDSR_model"] = "model"

                with st.expander("Variant"):
                    variant_amount = st.slider(
                        "Variant Amount:",
                        value=st.session_state["defaults"].img2img.variant_amount,
                        min_value=0.0,
                        max_value=1.0,
                        step=0.01,
                    )
                    variant_seed = st.text_input(
                        "Variant Seed:",
                        value=st.session_state["defaults"].img2img.variant_seed,
                        help="The seed to use when generating a variant, if left blank a random seed will be generated.",
                    )

        with col2_img2img_layout:
            st.tabs(["Editor"])

            editor_image = st.empty()
            st.session_state["editor_image"] = editor_image

            st.form_submit_button("Refresh")

            # if "canvas" not in st.session_state:
            st.session_state["canvas"] = st.empty()

            st.empty()
            st.empty()

            uploaded_images = st.file_uploader(
                "Upload Image",
                accept_multiple_files=False,
                type=["png", "jpg", "jpeg", "webp", "jfif"],
                help="Upload an image which will be used for the image to image generation.",
            )
            if uploaded_images:
                image = Image.open(uploaded_images).convert("RGB")
                new_img = image.resize((width, height))
                # image_holder.image(new_img)

            # mask_holder = st.empty()

            # uploaded_masks = st.file_uploader(
            # "Upload Mask", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp", 'jfif'],
            # help="Upload an mask image which will be used for masking the image to image generation.",
            # )

            #
            # Create a canvas component
            with st.session_state["canvas"]:
                st.session_state["uploaded_masks"] = st_canvas(
                    fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
                    stroke_width=stroke_width,
                    stroke_color=stroke_color,
                    background_color=bg_color,
                    background_image=image if uploaded_images else None,
                    update_streamlit=True,
                    width=width,
                    height=height,
                    drawing_mode=drawing_mode,
                    initial_drawing=st.session_state["uploaded_masks"].json_data
                    if "uploaded_masks" in st.session_state
                    else None,
                    display_toolbar=display_toolbar,
                    key="full_app",
                )

                # try:
                ##print (type(st.session_state["uploaded_masks"]))
                # if st.session_state["uploaded_masks"] != None:
                # mask_expander.expander("Mask", expanded=True)
                # mask = Image.fromarray(st.session_state["uploaded_masks"].image_data)

                # st.image(mask)

                # if mask.mode == "RGBA":
                # mask = mask.convert('RGBA')
                # background = Image.new('RGBA', mask.size, (0, 0, 0))
                # mask = Image.alpha_composite(background, mask)
                # mask = mask.resize((width, height))
                # except AttributeError:
                # pass

            with col3_img2img_layout:
                st.tabs(["Result"])

                # create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
                preview_image = st.empty()
                st.session_state["preview_image"] = preview_image

                # st.session_state["loading"] = st.empty()

                st.session_state["progress_bar_text"] = st.empty()
                st.session_state["progress_bar"] = st.empty()

                message = st.empty()

                # if uploaded_images:
                # image = Image.open(uploaded_images).convert('RGB')
                ##img_array = np.array(image) # if you want to pass it to OpenCV
                # new_img = image.resize((width, height))
                # st.image(new_img, use_column_width=True)

        if generate_button:
            # print("Loading models")
            # load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
            with col3_img2img_layout:
                with no_rerun:
                    with hc.HyLoader(
                        "Loading Models...", hc.Loaders.standard_loaders, index=[0]
                    ):
                        load_models(
                            use_LDSR=st.session_state["use_LDSR"],
                            LDSR_model=st.session_state["LDSR_model"],
                            use_GFPGAN=st.session_state["use_GFPGAN"],
                            GFPGAN_model=st.session_state["GFPGAN_model"],
                            use_RealESRGAN=st.session_state["use_RealESRGAN"],
                            RealESRGAN_model=st.session_state["RealESRGAN_model"],
                            CustomModel_available=server_state["CustomModel_available"],
                            custom_model=st.session_state["custom_model"],
                        )

            if uploaded_images:
                # image = Image.fromarray(image).convert('RGBA')
                # new_img = image.resize((width, height))
                ###img_array = np.array(image) # if you want to pass it to OpenCV
                # image_holder.image(new_img)
                new_mask = None

                if st.session_state["uploaded_masks"]:
                    mask = Image.fromarray(
                        st.session_state["uploaded_masks"].image_data
                    )
                    new_mask = mask.resize((width, height))

                    # masked_image_holder.image(new_mask)
                try:
                    output_images, seed, info, stats = img2img(
                        prompt=prompt,
                        init_info=new_img,
                        init_info_mask=new_mask,
                        mask_mode=mask_mode,
                        mask_restore=img2img_mask_restore,
                        ddim_steps=st.session_state["sampling_steps"],
                        sampler_name=st.session_state["sampler_name"],
                        n_iter=st.session_state["batch_count"],
                        cfg_scale=cfg_scale,
                        denoising_strength=st.session_state["denoising_strength"],
                        variant_seed=variant_seed,
                        seed=seed,
                        noise_mode=noise_mode,
                        find_noise_steps=find_noise_steps,
                        width=width,
                        height=height,
                        variant_amount=variant_amount,
                        ddim_eta=st.session_state.defaults.img2img.ddim_eta,
                        write_info_files=write_info_files,
                        separate_prompts=separate_prompts,
                        normalize_prompt_weights=normalize_prompt_weights,
                        save_individual_images=save_individual_images,
                        save_grid=save_grid,
                        group_by_prompt=group_by_prompt,
                        save_as_jpg=save_as_jpg,
                        use_GFPGAN=st.session_state["use_GFPGAN"],
                        GFPGAN_model=st.session_state["GFPGAN_model"],
                        use_RealESRGAN=st.session_state["use_RealESRGAN"],
                        RealESRGAN_model=st.session_state["RealESRGAN_model"],
                        use_LDSR=st.session_state["use_LDSR"],
                        LDSR_model=st.session_state["LDSR_model"],
                        loopback=loopback,
                    )

                    # show a message when the generation is complete.
                    message.success(
                        "Render Complete: " + info + "; Stats: " + stats, icon="✅"
                    )

                except (
                    StopException,
                    # KeyError
                ):
                    logger.info("Received Streamlit StopException")
                    # reset the page title so the percent doesnt stay on it confusing the user.
                    set_page_title("Stable Diffusion Playground")

                # this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
                # use the current col2 first tab to show the preview_img and update it as its generated.
                # preview_image.image(output_images, width=750)


# on import run init


================================================
FILE: webui/streamlit/scripts/img2txt.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

# ---------------------------------------------------------------------------------------------------------------------------------------------------
"""
CLIP Interrogator made by @pharmapsychotic modified to work with our WebUI.

# CLIP Interrogator by @pharmapsychotic
Twitter: https://twitter.com/pharmapsychotic
Github: https://github.com/pharmapsychotic/clip-interrogator

Description:
What do the different OpenAI CLIP models see in an image? What might be a good text prompt to create similar images using CLIP guided diffusion
or another text to image model? The CLIP Interrogator is here to get you answers!

Please consider buying him a coffee via [ko-fi](https://ko-fi.com/pharmapsychotic) or following him on [twitter](https://twitter.com/pharmapsychotic).

And if you're looking for more Ai art tools check out my [Ai generative art tools list](https://pharmapsychotic.com/tools.html).

"""
# ---------------------------------------------------------------------------------------------------------------------------------------------------

# base webui import and utils.
from sd_utils import st, logger, server_state, server_state_lock, random

# streamlit imports

# streamlit components section

# other imports

import clip
import open_clip
import gc
import os
import pandas as pd

# import requests
import torch
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from ldm.models.blip import blip_decoder

# import hashlib

# end of imports
# ---------------------------------------------------------------------------------------------------------------

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
blip_image_eval_size = 512

st.session_state["log"] = []


def load_blip_model():
    logger.info("Loading BLIP Model")
    if "log" not in st.session_state:
        st.session_state["log"] = []

    st.session_state["log"].append("Loading BLIP Model")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )

    if "blip_model" not in server_state:
        with server_state_lock["blip_model"]:
            server_state["blip_model"] = blip_decoder(
                pretrained="models/blip/model__base_caption.pth",
                image_size=blip_image_eval_size,
                vit="base",
                med_config="configs/blip/med_config.json",
            )

            server_state["blip_model"] = server_state["blip_model"].eval()

            server_state["blip_model"] = server_state["blip_model"].to(device).half()

            logger.info("BLIP Model Loaded")
            st.session_state["log"].append("BLIP Model Loaded")
            st.session_state["log_message"].code(
                "\n".join(st.session_state["log"]), language=""
            )
    else:
        logger.info("BLIP Model already loaded")
        st.session_state["log"].append("BLIP Model already loaded")
        st.session_state["log_message"].code(
            "\n".join(st.session_state["log"]), language=""
        )


def generate_caption(pil_image):
    load_blip_model()

    gpu_image = (
        transforms.Compose(
            [  # type: ignore
                transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),  # type: ignore
                transforms.ToTensor(),  # type: ignore
                transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),  # type: ignore
            ]
        )(pil_image)
        .unsqueeze(0)
        .to(device)
        .half()
    )

    with torch.no_grad():
        caption = server_state["blip_model"].generate(
            gpu_image, sample=False, num_beams=3, max_length=20, min_length=5
        )

    return caption[0]


def load_list(filename):
    with open(filename, "r", encoding="utf-8", errors="replace") as f:
        items = [line.strip() for line in f.readlines()]
        return items


def rank(model, image_features, text_array, top_count=1):
    top_count = min(top_count, len(text_array))
    text_tokens = clip.tokenize([text for text in text_array]).cuda()
    with torch.no_grad():
        text_features = model.encode_text(text_tokens).float()
    text_features /= text_features.norm(dim=-1, keepdim=True)

    similarity = torch.zeros((1, len(text_array))).to(device)
    for i in range(image_features.shape[0]):
        similarity += (
            100.0 * image_features[i].unsqueeze(0) @ text_features.T
        ).softmax(dim=-1)
    similarity /= image_features.shape[0]

    top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
    return [
        (text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy() * 100))
        for i in range(top_count)
    ]


def clear_cuda():
    torch.cuda.empty_cache()
    gc.collect()


def batch_rank(
    model,
    image_features,
    text_array,
    batch_size=st.session_state["defaults"].img2txt.batch_size,
):
    batch_size = min(batch_size, len(text_array))
    batch_count = int(len(text_array) / batch_size)
    batches = [
        text_array[i * batch_size : (i + 1) * batch_size] for i in range(batch_count)
    ]
    ranks = []
    for batch in batches:
        ranks += rank(model, image_features, batch)
    return ranks


def interrogate(image, models):
    load_blip_model()

    logger.info("Generating Caption")
    st.session_state["log"].append("Generating Caption")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )
    caption = generate_caption(image)

    if st.session_state["defaults"].general.optimized:
        del server_state["blip_model"]
        clear_cuda()

    logger.info("Caption Generated")
    st.session_state["log"].append("Caption Generated")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )

    if len(models) == 0:
        logger.info(f"\n\n{caption}")
        return

    table = []
    bests = [[("", 0)]] * 7

    logger.info("Ranking Text")
    st.session_state["log"].append("Ranking Text")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )

    for model_name in models:
        with torch.no_grad(), torch.autocast("cuda", dtype=torch.float16):
            logger.info(f"Interrogating with {model_name}...")
            st.session_state["log"].append(f"Interrogating with {model_name}...")
            st.session_state["log_message"].code(
                "\n".join(st.session_state["log"]), language=""
            )

            if model_name not in server_state["clip_models"]:
                if not st.session_state["defaults"].img2txt.keep_all_models_loaded:
                    model_to_delete = []
                    for model in server_state["clip_models"]:
                        if model != model_name:
                            model_to_delete.append(model)
                    for model in model_to_delete:
                        del server_state["clip_models"][model]
                        del server_state["preprocesses"][model]
                        clear_cuda()
                if model_name == "ViT-H-14":
                    (
                        server_state["clip_models"][model_name],
                        _,
                        server_state["preprocesses"][model_name],
                    ) = open_clip.create_model_and_transforms(
                        model_name,
                        pretrained="laion2b_s32b_b79k",
                        cache_dir="models/clip",
                    )
                elif model_name == "ViT-g-14":
                    (
                        server_state["clip_models"][model_name],
                        _,
                        server_state["preprocesses"][model_name],
                    ) = open_clip.create_model_and_transforms(
                        model_name,
                        pretrained="laion2b_s12b_b42k",
                        cache_dir="models/clip",
                    )
                else:
                    (
                        server_state["clip_models"][model_name],
                        server_state["preprocesses"][model_name],
                    ) = clip.load(
                        model_name, device=device, download_root="models/clip"
                    )
                server_state["clip_models"][model_name] = (
                    server_state["clip_models"][model_name].cuda().eval()
                )

            images = server_state["preprocesses"][model_name](image).unsqueeze(0).cuda()

            image_features = (
                server_state["clip_models"][model_name].encode_image(images).float()
            )

            image_features /= image_features.norm(dim=-1, keepdim=True)

            if st.session_state["defaults"].general.optimized:
                clear_cuda()

            ranks = []
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["mediums"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    ["by " + artist for artist in server_state["artists"]],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["trending_list"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["movements"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["flavors"],
                )
            )
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["domains"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["subreddits"]))
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["techniques"],
                )
            )
            ranks.append(
                batch_rank(
                    server_state["clip_models"][model_name],
                    image_features,
                    server_state["tags"],
                )
            )

            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["genres"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["styles"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["subjects"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["colors"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["moods"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["themes"]))
            # ranks.append(batch_rank(server_state["clip_models"][model_name], image_features, server_state["keywords"]))

            # print (bests)
            # print (ranks)

            for i in range(len(ranks)):
                confidence_sum = 0
                for ci in range(len(ranks[i])):
                    confidence_sum += ranks[i][ci][1]
                if confidence_sum > sum(bests[i][t][1] for t in range(len(bests[i]))):
                    bests[i] = ranks[i]

            for best in bests:
                best.sort(key=lambda x: x[1], reverse=True)
                # prune to 3
                best = best[:3]

            row = [model_name]

            for r in ranks:
                row.append(", ".join([f"{x[0]} ({x[1]:0.1f}%)" for x in r]))

            # for rank in ranks:
            #    rank.sort(key=lambda x: x[1], reverse=True)
            #    row.append(f'{rank[0][0]} {rank[0][1]:.2f}%')

            table.append(row)

            if st.session_state["defaults"].general.optimized:
                del server_state["clip_models"][model_name]
                gc.collect()

    st.session_state["prediction_table"][
        st.session_state["processed_image_count"]
    ].dataframe(
        pd.DataFrame(
            table,
            columns=[
                "Model",
                "Medium",
                "Artist",
                "Trending",
                "Movement",
                "Flavors",
                "Techniques",
                "Tags",
            ],
        )
    )

    medium = bests[0][0][0]
    artist = bests[1][0][0]
    trending = bests[2][0][0]
    movement = bests[3][0][0]
    flavors = bests[4][0][0]
    # domains = bests[5][0][0]
    # subreddits = bests[6][0][0]
    techniques = bests[5][0][0]
    tags = bests[6][0][0]

    if caption.startswith(medium):
        st.session_state["text_result"][st.session_state["processed_image_count"]].code(
            f"\n\n{caption} {artist}, {trending}, {movement}, {techniques}, {flavors}, {tags}",
            language="",
        )
    else:
        st.session_state["text_result"][st.session_state["processed_image_count"]].code(
            f"\n\n{caption}, {medium} {artist}, {trending}, {movement}, {techniques}, {flavors}, {tags}",
            language="",
        )

    logger.info("Finished Interrogating.")
    st.session_state["log"].append("Finished Interrogating.")
    st.session_state["log_message"].code(
        "\n".join(st.session_state["log"]), language=""
    )


def img2txt():
    models = []

    if st.session_state["ViT-L/14"]:
        models.append("ViT-L/14")
    if st.session_state["ViT-H-14"]:
        models.append("ViT-H-14")
    if st.session_state["ViT-g-14"]:
        models.append("ViT-g-14")

    if st.session_state["ViTB32"]:
        models.append("ViT-B/32")
    if st.session_state["ViTB16"]:
        models.append("ViT-B/16")

    if st.session_state["ViTL14_336px"]:
        models.append("ViT-L/14@336px")
    if st.session_state["RN101"]:
        models.append("RN101")
    if st.session_state["RN50"]:
        models.append("RN50")
    if st.session_state["RN50x4"]:
        models.append("RN50x4")
    if st.session_state["RN50x16"]:
        models.append("RN50x16")
    if st.session_state["RN50x64"]:
        models.append("RN50x64")

    # if str(image_path_or_url).startswith('http://') or str(image_path_or_url).startswith('https://'):
    # image = Image.open(requests.get(image_path_or_url, stream=True).raw).convert('RGB')
    # else:
    # image = Image.open(image_path_or_url).convert('RGB')

    # thumb = st.session_state["uploaded_image"].image.copy()
    # thumb.thumbnail([blip_image_eval_size, blip_image_eval_size])
    # display(thumb)

    st.session_state["processed_image_count"] = 0

    for i in range(len(st.session_state["uploaded_image"])):
        interrogate(st.session_state["uploaded_image"][i].pil_image, models=models)
        # increase counter.
        st.session_state["processed_image_count"] += 1


#


def layout():
    # set_page_title("Image-to-Text - Stable Diffusion WebUI")
    # st.info("Under Construction. :construction_worker:")
    #
    if "clip_models" not in server_state:
        server_state["clip_models"] = {}
    if "preprocesses" not in server_state:
        server_state["preprocesses"] = {}
    data_path = "data/"
    if "artists" not in server_state:
        server_state["artists"] = load_list(
            os.path.join(data_path, "img2txt", "artists.txt")
        )
    if "flavors" not in server_state:
        server_state["flavors"] = random.choices(
            load_list(os.path.join(data_path, "img2txt", "flavors.txt")), k=2000
        )
    if "mediums" not in server_state:
        server_state["mediums"] = load_list(
            os.path.join(data_path, "img2txt", "mediums.txt")
        )
    if "movements" not in server_state:
        server_state["movements"] = load_list(
            os.path.join(data_path, "img2txt", "movements.txt")
        )
    if "sites" not in server_state:
        server_state["sites"] = load_list(
            os.path.join(data_path, "img2txt", "sites.txt")
        )
    # server_state["domains"] = load_list(os.path.join(data_path, 'img2txt', 'domains.txt'))
    # server_state["subreddits"] = load_list(os.path.join(data_path, 'img2txt', 'subreddits.txt'))
    if "techniques" not in server_state:
        server_state["techniques"] = load_list(
            os.path.join(data_path, "img2txt", "techniques.txt")
        )
    if "tags" not in server_state:
        server_state["tags"] = load_list(os.path.join(data_path, "img2txt", "tags.txt"))
    # server_state["genres"] = load_list(os.path.join(data_path, 'img2txt', 'genres.txt'))
    # server_state["styles"] = load_list(os.path.join(data_path, 'img2txt', 'styles.txt'))
    # server_state["subjects"] = load_list(os.path.join(data_path, 'img2txt', 'subjects.txt'))
    if "trending_list" not in server_state:
        server_state["trending_list"] = [site for site in server_state["sites"]]
        server_state["trending_list"].extend(
            ["trending on " + site for site in server_state["sites"]]
        )
        server_state["trending_list"].extend(
            ["featured on " + site for site in server_state["sites"]]
        )
        server_state["trending_list"].extend(
            [site + " contest winner" for site in server_state["sites"]]
        )
    with st.form("img2txt-inputs"):
        st.session_state["generation_mode"] = "img2txt"

        # st.write("---")
        # creating the page layout using columns
        col1, col2 = st.columns([1, 4], gap="large")

        with col1:
            st.session_state["uploaded_image"] = st.file_uploader(
                "Input Image",
                type=["png", "jpg", "jpeg", "jfif", "webp"],
                accept_multiple_files=True,
            )

            with st.expander("CLIP models", expanded=True):
                st.session_state["ViT-L/14"] = st.checkbox(
                    "ViT-L/14", value=True, help="ViT-L/14 model."
                )
                st.session_state["ViT-H-14"] = st.checkbox(
                    "ViT-H-14", value=False, help="ViT-H-14 model."
                )
                st.session_state["ViT-g-14"] = st.checkbox(
                    "ViT-g-14", value=False, help="ViT-g-14 model."
                )

            with st.expander("Others"):
                st.info(
                    "For DiscoDiffusion and JAX enable all the same models here as you intend to use when generating your images."
                )

                st.session_state["ViTL14_336px"] = st.checkbox(
                    "ViTL14_336px", value=False, help="ViTL14_336px model."
                )
                st.session_state["ViTB16"] = st.checkbox(
                    "ViTB16", value=False, help="ViTB16 model."
                )
                st.session_state["ViTB32"] = st.checkbox(
                    "ViTB32", value=False, help="ViTB32 model."
                )
                st.session_state["RN50"] = st.checkbox(
                    "RN50", value=False, help="RN50 model."
                )
                st.session_state["RN50x4"] = st.checkbox(
                    "RN50x4", value=False, help="RN50x4 model."
                )
                st.session_state["RN50x16"] = st.checkbox(
                    "RN50x16", value=False, help="RN50x16 model."
                )
                st.session_state["RN50x64"] = st.checkbox(
                    "RN50x64", value=False, help="RN50x64 model."
                )
                st.session_state["RN101"] = st.checkbox(
                    "RN101", value=False, help="RN101 model."
                )

            #
            # st.subheader("Logs:")

            st.session_state["log_message"] = st.empty()
            st.session_state["log_message"].code("", language="")

        with col2:
            st.subheader("Image")

            image_col1, image_col2 = st.columns([10, 25])
            with image_col1:
                st.form_submit_button(
                    "Update Preview Image",
                    help="Refresh the image preview to show your uploaded image instead of the default placeholder.",
                )

            if st.session_state["uploaded_image"]:
                # print (type(st.session_state["uploaded_image"]))
                # if len(st.session_state["uploaded_image"]) == 1:
                st.session_state["input_image_preview"] = []
                st.session_state["input_image_preview_container"] = []
                st.session_state["prediction_table"] = []
                st.session_state["text_result"] = []

                for i in range(len(st.session_state["uploaded_image"])):
                    st.session_state["input_image_preview_container"].append(i)
                    st.session_state["input_image_preview_container"][i] = st.empty()

                    with st.session_state["input_image_preview_container"][
                        i
                    ].container():
                        col1_output, col2_output = st.columns([2, 10], gap="medium")
                        with col1_output:
                            st.session_state["input_image_preview"].append(i)
                            st.session_state["input_image_preview"][i] = st.empty()
                            st.session_state["uploaded_image"][
                                i
                            ].pil_image = Image.open(
                                st.session_state["uploaded_image"][i]
                            ).convert(
                                "RGB"
                            )

                            st.session_state["input_image_preview"][i].image(
                                st.session_state["uploaded_image"][i].pil_image,
                                use_column_width=True,
                                clamp=True,
                            )

                    with st.session_state["input_image_preview_container"][
                        i
                    ].container():
                        with col2_output:
                            st.session_state["prediction_table"].append(i)
                            st.session_state["prediction_table"][i] = st.empty()
                            st.session_state["prediction_table"][i].table()

                            st.session_state["text_result"].append(i)
                            st.session_state["text_result"][i] = st.empty()
                            st.session_state["text_result"][i].code("", language="")

            else:
                # st.session_state["input_image_preview"].code('', language="")
                st.image("images/streamlit/img2txt_placeholder.png", clamp=True)

        with image_col2:
            #
            # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
            # generate_col1.title("")
            # generate_col1.title("")
            generate_button = st.form_submit_button(
                "Generate!",
                help="Start interrogating the images to generate a prompt from each of the selected images",
            )

    if generate_button:
        # if model, pipe, RealESRGAN or GFPGAN is in st.session_state remove the model and pipe form session_state so that they are reloaded.
        if "model" in server_state and st.session_state["defaults"].general.optimized:
            del server_state["model"]
        if "pipe" in server_state and st.session_state["defaults"].general.optimized:
            del server_state["pipe"]
        if (
            "RealESRGAN" in server_state
            and st.session_state["defaults"].general.optimized
        ):
            del server_state["RealESRGAN"]
        if "GFPGAN" in server_state and st.session_state["defaults"].general.optimized:
            del server_state["GFPGAN"]

        # run clip interrogator
        img2txt()


================================================
FILE: webui/streamlit/scripts/post_processing.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sandbox-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
# from sd_utils import *
from sd_utils import (
    st,
    server_state,
    RealESRGAN_available,
    GFPGAN_available,
    LDSR_available,
)

# streamlit imports

# streamlit components section
import hydralit_components as hc

# other imports
import os
from PIL import Image
import torch

# Temp imports

# end of imports
# ---------------------------------------------------------------------------------------------------------------


def post_process(
    use_GFPGAN=True,
    GFPGAN_model="",
    use_RealESRGAN=False,
    realesrgan_model_name="",
    use_LDSR=False,
    LDSR_model_name="",
):
    for i in range(len(st.session_state["uploaded_image"])):
        # st.session_state["uploaded_image"][i].pil_image

        if (
            use_GFPGAN
            and server_state["GFPGAN"] is not None
            and not use_RealESRGAN
            and not use_LDSR
        ):
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    "Running GFPGAN on image %d of %d..."
                    % (i + 1, len(st.session_state["uploaded_image"]))
                )

            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    int(
                        100
                        * float(
                            i + 1
                            if i + 1 < len(st.session_state["uploaded_image"])
                            else len(st.session_state["uploaded_image"])
                        )
                        / float(len(st.session_state["uploaded_image"]))
                    )
                )

            if server_state["GFPGAN"].name != GFPGAN_model:
                load_models(
                    use_LDSR=use_LDSR,
                    LDSR_model=LDSR_model_name,
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            torch_gc()

            with torch.autocast("cuda"):
                cropped_faces, restored_faces, restored_img = server_state[
                    "GFPGAN"
                ].enhance(
                    st.session_state["uploaded_image"][i].pil_image,
                    has_aligned=False,
                    only_center_face=False,
                    paste_back=True,
                )

            gfpgan_sample = restored_img[:, :, ::-1]
            gfpgan_image = Image.fromarray(gfpgan_sample)

            # if st.session_state["GFPGAN_strenght"]:
            # gfpgan_sample = Image.blend(image, gfpgan_image, st.session_state["GFPGAN_strenght"])

            gfpgan_filename = (
                st.session_state["uploaded_image"][i].name.split(".")[0] + "-gfpgan"
            )

            gfpgan_image.save(
                os.path.join(
                    st.session_state["defaults"].post_processing.outdir_post_processing,
                    f"{gfpgan_filename}.png",
                )
            )

        #
        elif (
            use_RealESRGAN and server_state["RealESRGAN"] is not None and not use_GFPGAN
        ):
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    "Running RealESRGAN on image %d of %d..."
                    % (i + 1, len(st.session_state["uploaded_image"]))
                )

            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    int(
                        100
                        * float(
                            i + 1
                            if i + 1 < len(st.session_state["uploaded_image"])
                            else len(st.session_state["uploaded_image"])
                        )
                        / float(len(st.session_state["uploaded_image"]))
                    )
                )

            torch_gc()

            if server_state["RealESRGAN"].model.name != realesrgan_model_name:
                # try_loading_RealESRGAN(realesrgan_model_name)
                load_models(
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            output, img_mode = server_state["RealESRGAN"].enhance(
                st.session_state["uploaded_image"][i].pil_image
            )
            esrgan_filename = (
                st.session_state["uploaded_image"][i].name.split(".")[0] + "-esrgan4x"
            )
            esrgan_sample = output[:, :, ::-1]
            esrgan_image = Image.fromarray(esrgan_sample)

            esrgan_image.save(
                os.path.join(
                    st.session_state["defaults"].post_processing.outdir_post_processing,
                    f"{esrgan_filename}.png",
                )
            )

        #
        elif use_LDSR and "LDSR" in server_state and not use_GFPGAN:
            logger.info(
                "Running LDSR on image %d of %d..."
                % (i + 1, len(st.session_state["uploaded_image"]))
            )
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    "Running LDSR on image %d of %d..."
                    % (i + 1, len(st.session_state["uploaded_image"]))
                )
            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    int(
                        100
                        * float(
                            i + 1
                            if i + 1 < len(st.session_state["uploaded_image"])
                            else len(st.session_state["uploaded_image"])
                        )
                        / float(len(st.session_state["uploaded_image"]))
                    )
                )

            torch_gc()

            if server_state["LDSR"].name != LDSR_model_name:
                # try_loading_RealESRGAN(realesrgan_model_name)
                load_models(
                    use_LDSR=use_LDSR,
                    LDSR_model=LDSR_model_name,
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            result = server_state["LDSR"].superResolution(
                st.session_state["uploaded_image"][i].pil_image,
                ddimSteps=st.session_state["ldsr_sampling_steps"],
                preDownScale=st.session_state["preDownScale"],
                postDownScale=st.session_state["postDownScale"],
                downsample_method=st.session_state["downsample_method"],
            )

            ldsr_filename = (
                st.session_state["uploaded_image"][i].name.split(".")[0] + "-ldsr4x"
            )

            result.save(
                os.path.join(
                    st.session_state["defaults"].post_processing.outdir_post_processing,
                    f"{ldsr_filename}.png",
                )
            )

        #
        elif (
            use_LDSR
            and "LDSR" in server_state
            and use_GFPGAN
            and "GFPGAN" in server_state
        ):
            logger.info(
                "Running GFPGAN+LDSR on image %d of %d..."
                % (i + 1, len(st.session_state["uploaded_image"]))
            )
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    "Running GFPGAN+LDSR on image %d of %d..."
                    % (i + 1, len(st.session_state["uploaded_image"]))
                )

            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    int(
                        100
                        * float(
                            i + 1
                            if i + 1 < len(st.session_state["uploaded_image"])
                            else len(st.session_state["uploaded_image"])
                        )
                        / float(len(st.session_state["uploaded_image"]))
                    )
                )

            if server_state["GFPGAN"].name != GFPGAN_model:
                load_models(
                    use_LDSR=use_LDSR,
                    LDSR_model=LDSR_model_name,
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            torch_gc()
            cropped_faces, restored_faces, restored_img = server_state[
                "GFPGAN"
            ].enhance(
                st.session_state["uploaded_image"][i].pil_image,
                has_aligned=False,
                only_center_face=False,
                paste_back=True,
            )

            gfpgan_sample = restored_img[:, :, ::-1]
            gfpgan_image = Image.fromarray(gfpgan_sample)

            if server_state["LDSR"].name != LDSR_model_name:
                # try_loading_RealESRGAN(realesrgan_model_name)
                load_models(
                    use_LDSR=use_LDSR,
                    LDSR_model=LDSR_model_name,
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            # LDSR.superResolution(gfpgan_image, ddimSteps=100, preDownScale='None', postDownScale='None', downsample_method="Lanczos")
            result = server_state["LDSR"].superResolution(
                gfpgan_image,
                ddimSteps=st.session_state["ldsr_sampling_steps"],
                preDownScale=st.session_state["preDownScale"],
                postDownScale=st.session_state["postDownScale"],
                downsample_method=st.session_state["downsample_method"],
            )

            ldsr_filename = (
                st.session_state["uploaded_image"][i].name.split(".")[0]
                + "-gfpgan-ldsr2x"
            )

            result.save(
                os.path.join(
                    st.session_state["defaults"].post_processing.outdir_post_processing,
                    f"{ldsr_filename}.png",
                )
            )

        elif (
            use_RealESRGAN
            and server_state["RealESRGAN"] is not None
            and use_GFPGAN
            and server_state["GFPGAN"] is not None
        ):
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    "Running GFPGAN+RealESRGAN on image %d of %d..."
                    % (i + 1, len(st.session_state["uploaded_image"]))
                )

            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    int(
                        100
                        * float(
                            i + 1
                            if i + 1 < len(st.session_state["uploaded_image"])
                            else len(st.session_state["uploaded_image"])
                        )
                        / float(len(st.session_state["uploaded_image"]))
                    )
                )

            torch_gc()
            cropped_faces, restored_faces, restored_img = server_state[
                "GFPGAN"
            ].enhance(
                st.session_state["uploaded_image"][i].pil_image,
                has_aligned=False,
                only_center_face=False,
                paste_back=True,
            )
            gfpgan_sample = restored_img[:, :, ::-1]

            if server_state["RealESRGAN"].model.name != realesrgan_model_name:
                # try_loading_RealESRGAN(realesrgan_model_name)
                load_models(
                    use_GFPGAN=use_GFPGAN,
                    use_RealESRGAN=use_RealESRGAN,
                    RealESRGAN_model=realesrgan_model_name,
                )

            output, img_mode = server_state["RealESRGAN"].enhance(
                gfpgan_sample[:, :, ::-1]
            )
            gfpgan_esrgan_filename = (
                st.session_state["uploaded_image"][i].name.split(".")[0]
                + "-gfpgan-esrgan4x"
            )
            gfpgan_esrgan_sample = output[:, :, ::-1]
            gfpgan_esrgan_image = Image.fromarray(gfpgan_esrgan_sample)

            gfpgan_esrgan_image.save(
                os.path.join(
                    st.session_state["defaults"].post_processing.outdir_post_processing,
                    f"{gfpgan_esrgan_filename}.png",
                )
            )


def layout():
    # st.info("Under Construction. :construction_worker:")
    st.session_state["progress_bar_text"] = st.empty()
    # st.session_state["progress_bar_text"].info("Nothing but crickets here, try generating something first.")

    st.session_state["progress_bar"] = st.empty()

    with st.form("post-processing-inputs"):
        # creating the page layout using columns
        col1, col2 = st.columns([1, 4], gap="medium")

        with col1:
            st.session_state["uploaded_image"] = st.file_uploader(
                "Input Image",
                type=["png", "jpg", "jpeg", "jfif", "webp"],
                accept_multiple_files=True,
            )

            # check if GFPGAN, RealESRGAN and LDSR are available.
            # if "GFPGAN_available" not in st.session_state:
            GFPGAN_available()

            # if "RealESRGAN_available" not in st.session_state:
            RealESRGAN_available()

            # if "LDSR_available" not in st.session_state:
            LDSR_available()

            if (
                st.session_state["GFPGAN_available"]
                or st.session_state["RealESRGAN_available"]
                or st.session_state["LDSR_available"]
            ):
                face_restoration_tab, upscaling_tab = st.tabs(
                    ["Face Restoration", "Upscaling"]
                )
                with face_restoration_tab:
                    # GFPGAN used for face restoration
                    if st.session_state["GFPGAN_available"]:
                        # with st.expander("Face Restoration"):
                        # if st.session_state["GFPGAN_available"]:
                        # with st.expander("GFPGAN"):
                        st.session_state["use_GFPGAN"] = st.checkbox(
                            "Use GFPGAN",
                            value=st.session_state["defaults"].txt2img.use_GFPGAN,
                            help="Uses the GFPGAN model to improve faces after the generation.\
                                                                         This greatly improve the quality and consistency of faces but uses\
                                                                         extra VRAM. Disable if you need the extra VRAM.",
                        )

                        st.session_state["GFPGAN_model"] = st.selectbox(
                            "GFPGAN model",
                            st.session_state["GFPGAN_models"],
                            index=st.session_state["GFPGAN_models"].index(
                                st.session_state["defaults"].general.GFPGAN_model
                            ),
                        )

                    # st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')

                    else:
                        st.session_state["use_GFPGAN"] = False

                with upscaling_tab:
                    st.session_state["use_upscaling"] = st.checkbox(
                        "Use Upscaling",
                        value=st.session_state["defaults"].txt2img.use_upscaling,
                    )

                    # RealESRGAN and LDSR used for upscaling.
                    if (
                        st.session_state["RealESRGAN_available"]
                        or st.session_state["LDSR_available"]
                    ):
                        upscaling_method_list = []
                        if st.session_state["RealESRGAN_available"]:
                            upscaling_method_list.append("RealESRGAN")
                        if st.session_state["LDSR_available"]:
                            upscaling_method_list.append("LDSR")

                        # print (st.session_state["RealESRGAN_available"])
                        st.session_state["upscaling_method"] = st.selectbox(
                            "Upscaling Method",
                            upscaling_method_list,
                            index=upscaling_method_list.index(
                                st.session_state["defaults"].general.upscaling_method
                            )
                            if st.session_state["defaults"].general.upscaling_method
                            in upscaling_method_list
                            else 0,
                        )

                        if st.session_state["RealESRGAN_available"]:
                            with st.expander("RealESRGAN"):
                                if (
                                    st.session_state["upscaling_method"] == "RealESRGAN"
                                    and st.session_state["use_upscaling"]
                                ):
                                    st.session_state["use_RealESRGAN"] = True
                                else:
                                    st.session_state["use_RealESRGAN"] = False

                                st.session_state["RealESRGAN_model"] = st.selectbox(
                                    "RealESRGAN model",
                                    st.session_state["RealESRGAN_models"],
                                    index=st.session_state["RealESRGAN_models"].index(
                                        st.session_state[
                                            "defaults"
                                        ].general.RealESRGAN_model
                                    ),
                                )
                        else:
                            st.session_state["use_RealESRGAN"] = False
                            st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"

                        #
                        if st.session_state["LDSR_available"]:
                            with st.expander("LDSR"):
                                if (
                                    st.session_state["upscaling_method"] == "LDSR"
                                    and st.session_state["use_upscaling"]
                                ):
                                    st.session_state["use_LDSR"] = True
                                else:
                                    st.session_state["use_LDSR"] = False

                                st.session_state["LDSR_model"] = st.selectbox(
                                    "LDSR model",
                                    st.session_state["LDSR_models"],
                                    index=st.session_state["LDSR_models"].index(
                                        st.session_state["defaults"].general.LDSR_model
                                    ),
                                )

                                st.session_state[
                                    "ldsr_sampling_steps"
                                ] = st.number_input(
                                    "Sampling Steps",
                                    value=st.session_state[
                                        "defaults"
                                    ].txt2img.LDSR_config.sampling_steps,
                                    help="",
                                )

                                st.session_state["preDownScale"] = st.number_input(
                                    "PreDownScale",
                                    value=st.session_state[
                                        "defaults"
                                    ].txt2img.LDSR_config.preDownScale,
                                    help="",
                                )

                                st.session_state["postDownScale"] = st.number_input(
                                    "postDownScale",
                                    value=st.session_state[
                                        "defaults"
                                    ].txt2img.LDSR_config.postDownScale,
                                    help="",
                                )

                                downsample_method_list = ["Nearest", "Lanczos"]
                                st.session_state["downsample_method"] = st.selectbox(
                                    "Downsample Method",
                                    downsample_method_list,
                                    index=downsample_method_list.index(
                                        st.session_state[
                                            "defaults"
                                        ].txt2img.LDSR_config.downsample_method
                                    ),
                                )

                        else:
                            st.session_state["use_LDSR"] = False
                            st.session_state["LDSR_model"] = "model"

            # process = st.form_submit_button("Process Images", help="")

            #
            with st.expander("Output Settings", True):
                # st.session_state['defaults'].post_processing.save_original_images = st.checkbox("Save input images.", value=st.session_state['defaults'].post_processing.save_original_images,
                # help="Save each original/input image next to the Post Processed image. "
                # "This might be helpful for comparing the before and after images.")

                st.session_state[
                    "defaults"
                ].post_processing.outdir_post_processing = st.text_input(
                    "Output Dir",
                    value=st.session_state[
                        "defaults"
                    ].post_processing.outdir_post_processing,
                    help="Folder where the images will be saved after post processing.",
                )

        with col2:
            st.subheader("Image")

            image_col1, image_col2, image_col3 = st.columns([2, 2, 2], gap="small")
            with image_col1:
                st.form_submit_button(
                    "Refresh",
                    help="Refresh the image preview to show your uploaded image.",
                )

            if st.session_state["uploaded_image"]:
                # print (type(st.session_state["uploaded_image"]))
                # if len(st.session_state["uploaded_image"]) == 1:
                st.session_state["input_image_preview"] = []
                st.session_state["input_image_caption"] = []
                st.session_state["output_image_preview"] = []
                st.session_state["output_image_caption"] = []
                st.session_state["input_image_preview_container"] = []
                st.session_state["prediction_table"] = []
                st.session_state["text_result"] = []

                for i in range(len(st.session_state["uploaded_image"])):
                    st.session_state["input_image_preview_container"].append(i)
                    st.session_state["input_image_preview_container"][i] = st.empty()

                    with st.session_state["input_image_preview_container"][
                        i
                    ].container():
                        col1_output, col2_output, col3_output = st.columns(
                            [2, 2, 2], gap="medium"
                        )
                        with col1_output:
                            st.session_state["output_image_caption"].append(i)
                            st.session_state["output_image_caption"][i] = st.empty()
                            # st.session_state["output_image_caption"][i] = st.session_state["uploaded_image"][i].name

                            st.session_state["input_image_caption"].append(i)
                            st.session_state["input_image_caption"][i] = st.empty()
                            # st.session_state["input_image_caption"][i].caption(")

                            st.session_state["input_image_preview"].append(i)
                            st.session_state["input_image_preview"][i] = st.empty()
                            st.session_state["uploaded_image"][
                                i
                            ].pil_image = Image.open(
                                st.session_state["uploaded_image"][i]
                            ).convert(
                                "RGB"
                            )

                            st.session_state["input_image_preview"][i].image(
                                st.session_state["uploaded_image"][i].pil_image,
                                use_column_width=True,
                                clamp=True,
                            )

                        with col2_output:
                            st.session_state["output_image_preview"].append(i)
                            st.session_state["output_image_preview"][i] = st.empty()

                            st.session_state["output_image_preview"][i].image(
                                st.session_state["uploaded_image"][i].pil_image,
                                use_column_width=True,
                                clamp=True,
                            )

                    with st.session_state["input_image_preview_container"][
                        i
                    ].container():
                        with col3_output:
                            # st.session_state["prediction_table"].append(i)
                            # st.session_state["prediction_table"][i] = st.empty()
                            # st.session_state["prediction_table"][i].table(pd.DataFrame(columns=["Model", "Filename", "Progress"]))

                            st.session_state["text_result"].append(i)
                            st.session_state["text_result"][i] = st.empty()
                            st.session_state["text_result"][i].code("", language="")

            # else:
            ##st.session_state["input_image_preview"].code('', language="")
            # st.image("images/streamlit/img2txt_placeholder.png", clamp=True)

        with image_col3:
            # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
            process = st.form_submit_button("Process Images!")

        if process:
            with hc.HyLoader(
                "Loading Models...", hc.Loaders.standard_loaders, index=[0]
            ):
                # load_models(use_LDSR=st.session_state["use_LDSR"], LDSR_model=st.session_state["LDSR_model"],
                # use_GFPGAN=st.session_state["use_GFPGAN"], GFPGAN_model=st.session_state["GFPGAN_model"] ,
                # use_RealESRGAN=st.session_state["use_RealESRGAN"], RealESRGAN_model=st.session_state["RealESRGAN_model"])

                if st.session_state["use_GFPGAN"]:
                    load_GFPGAN(model_name=st.session_state["GFPGAN_model"])

                if st.session_state["use_RealESRGAN"]:
                    load_RealESRGAN(st.session_state["RealESRGAN_model"])

                if st.session_state["use_LDSR"]:
                    load_LDSR(st.session_state["LDSR_model"])

            post_process(
                use_GFPGAN=st.session_state["use_GFPGAN"],
                GFPGAN_model=st.session_state["GFPGAN_model"],
                use_RealESRGAN=st.session_state["use_RealESRGAN"],
                realesrgan_model_name=st.session_state["RealESRGAN_model"],
                use_LDSR=st.session_state["use_LDSR"],
                LDSR_model_name=st.session_state["LDSR_model"],
            )


================================================
FILE: webui/streamlit/scripts/sd_concept_library.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import st

# streamlit imports
import streamlit.components.v1 as components

# other imports

import os, math
from PIL import Image

# Temp imports
# from basicsr.utils.registry import ARCH_REGISTRY


# end of imports
# ---------------------------------------------------------------------------------------------------------------

# Init Vuejs component
_component_func = components.declare_component(
    "sd-concepts-browser", "./frontend/dists/concept-browser/dist"
)


def sdConceptsBrowser(concepts, key=None):
    component_value = _component_func(concepts=concepts, key=key, default="")
    return component_value


@st.experimental_memo(persist="disk", show_spinner=False, suppress_st_warning=True)
def getConceptsFromPath(page, conceptPerPage, searchText=""):
    # print("getConceptsFromPath", "page:", page, "conceptPerPage:", conceptPerPage, "searchText:", searchText)
    # get the path where the concepts are stored
    path = os.path.join(
        os.getcwd(), st.session_state["defaults"].general.sd_concepts_library_folder
    )
    acceptedExtensions = ("jpeg", "jpg", "png")
    concepts = []

    if os.path.exists(path):
        # List all folders (concepts) in the path
        folders = [f for f in os.listdir(path) if os.path.isdir(os.path.join(path, f))]
        filteredFolders = folders

        # Filter the folders by the search text
        if searchText != "":
            filteredFolders = [f for f in folders if searchText.lower() in f.lower()]
    else:
        filteredFolders = []

    conceptIndex = 1
    for folder in filteredFolders:
        # handle pagination
        if conceptIndex > (page * conceptPerPage):
            continue
        if conceptIndex <= ((page - 1) * conceptPerPage):
            conceptIndex += 1
            continue

        concept = {
            "name": folder,
            "token": "<" + folder + ">",
            "images": [],
            "type": "",
        }

        # type of concept is inside type_of_concept.txt
        typePath = os.path.join(path, folder, "type_of_concept.txt")
        binFile = os.path.join(path, folder, "learned_embeds.bin")

        # Continue if the concept is not valid or the download has failed (no type_of_concept.txt or no binFile)
        if not os.path.exists(typePath) or not os.path.exists(binFile):
            continue

        with open(typePath, "r") as f:
            concept["type"] = f.read()

        # List all files in the concept/concept_images folder
        files = [
            f
            for f in os.listdir(os.path.join(path, folder, "concept_images"))
            if os.path.isfile(os.path.join(path, folder, "concept_images", f))
        ]
        # Retrieve only the 4 first images
        for file in files:
            # Skip if we already have 4 images
            if len(concept["images"]) >= 4:
                break

            if file.endswith(acceptedExtensions):
                try:
                    # Add a copy of the image to avoid file locking
                    originalImage = Image.open(
                        os.path.join(path, folder, "concept_images", file)
                    )

                    # Maintain the aspect ratio (max 200x200)
                    resizedImage = originalImage.copy()
                    resizedImage.thumbnail((200, 200), Image.Resampling.LANCZOS)

                    # concept["images"].append(resizedImage)

                    concept["images"].append(imageToBase64(resizedImage))
                    # Close original image
                    originalImage.close()
                except:
                    print(
                        "Error while loading image",
                        file,
                        "in concept",
                        folder,
                        "(The file may be corrupted). Skipping it.",
                    )

        concepts.append(concept)
        conceptIndex += 1
    # print all concepts name
    # print("Results:", [c["name"] for c in concepts])
    return concepts


@st.cache(
    persist=True,
    allow_output_mutation=True,
    show_spinner=False,
    suppress_st_warning=True,
)
def imageToBase64(image):
    import io
    import base64

    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return img_str


@st.experimental_memo(persist="disk", show_spinner=False, suppress_st_warning=True)
def getTotalNumberOfConcepts(searchText=""):
    # get the path where the concepts are stored
    path = os.path.join(
        os.getcwd(), st.session_state["defaults"].general.sd_concepts_library_folder
    )

    if os.path.exists(path):
        # List all folders (concepts) in the path
        folders = [f for f in os.listdir(path) if os.path.isdir(os.path.join(path, f))]
        filteredFolders = folders

        # Filter the folders by the search text
        if searchText != "":
            filteredFolders = [f for f in folders if searchText.lower() in f.lower()]
    else:
        filteredFolders = []
    return len(filteredFolders)


def layout():
    # 2 tabs, one for Concept Library and one for the Download Manager
    tab_library, tab_downloader = st.tabs(["Library", "Download Manager"])

    # Concept Library
    with tab_library:
        downloaded_concepts_count = getTotalNumberOfConcepts()
        concepts_per_page = st.session_state[
            "defaults"
        ].concepts_library.concepts_per_page

        if "results" not in st.session_state:
            st.session_state["results"] = getConceptsFromPath(1, concepts_per_page, "")

        # Pagination controls
        if "cl_current_page" not in st.session_state:
            st.session_state["cl_current_page"] = 1

        # Search
        if "cl_search_text" not in st.session_state:
            st.session_state["cl_search_text"] = ""

        if "cl_search_results_count" not in st.session_state:
            st.session_state["cl_search_results_count"] = downloaded_concepts_count

        # Search bar
        _search_col, _refresh_col = st.columns([10, 2])
        with _search_col:
            search_text_input = st.text_input(
                "Search",
                "",
                placeholder=f"Search for a concept ({downloaded_concepts_count} available)",
                label_visibility="hidden",
            )
            if search_text_input != st.session_state["cl_search_text"]:
                # Search text has changed
                st.session_state["cl_search_text"] = search_text_input
                st.session_state["cl_current_page"] = 1
                st.session_state["cl_search_results_count"] = getTotalNumberOfConcepts(
                    st.session_state["cl_search_text"]
                )
                st.session_state["results"] = getConceptsFromPath(
                    1, concepts_per_page, st.session_state["cl_search_text"]
                )

        with _refresh_col:
            # Super weird fix to align the refresh button with the search bar ( Please streamlit, add css support..  )
            _refresh_col.write("")
            _refresh_col.write("")
            if st.button(
                "Refresh concepts",
                key="refresh_concepts",
                help="Refresh the concepts folders. Use this if you have added new concepts manually or deleted some.",
            ):
                getTotalNumberOfConcepts.clear()
                getConceptsFromPath.clear()
                st.experimental_rerun()

        # Show results
        results_empty = st.empty()

        # Pagination
        pagination_empty = st.empty()

        # Layouts
        with pagination_empty:
            with st.container():
                if len(st.session_state["results"]) > 0:
                    last_page = math.ceil(
                        st.session_state["cl_search_results_count"] / concepts_per_page
                    )
                    (
                        _1,
                        _2,
                        _3,
                        _4,
                        _previous_page,
                        _current_page,
                        _next_page,
                        _9,
                        _10,
                        _11,
                        _12,
                    ) = st.columns([1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1])

                    # Previous page
                    with _previous_page:
                        if st.button("Previous", key="cl_previous_page"):
                            st.session_state["cl_current_page"] -= 1
                            if st.session_state["cl_current_page"] <= 0:
                                st.session_state["cl_current_page"] = last_page
                            st.session_state["results"] = getConceptsFromPath(
                                st.session_state["cl_current_page"],
                                concepts_per_page,
                                st.session_state["cl_search_text"],
                            )

                    # Current page
                    with _current_page:
                        _current_page_container = st.empty()

                    # Next page
                    with _next_page:
                        if st.button("Next", key="cl_next_page"):
                            st.session_state["cl_current_page"] += 1
                            if st.session_state["cl_current_page"] > last_page:
                                st.session_state["cl_current_page"] = 1
                            st.session_state["results"] = getConceptsFromPath(
                                st.session_state["cl_current_page"],
                                concepts_per_page,
                                st.session_state["cl_search_text"],
                            )

                    # Current page
                    with _current_page_container:
                        st.markdown(
                            f'<p style="text-align: center">Page {st.session_state["cl_current_page"]} of {last_page}</p>',
                            unsafe_allow_html=True,
                        )
                        # st.write(f"Page {st.session_state['cl_current_page']} of {last_page}", key="cl_current_page")

        with results_empty:
            with st.container():
                if downloaded_concepts_count == 0:
                    st.write("You don't have any concepts in your library ")
                    st.markdown(
                        "To add concepts to your library, download some from the [sd-concepts-library](https://github.com/Sygil-Dev/sd-concepts-library) \
						repository and save the content of `sd-concepts-library` into ```./models/custom/sd-concepts-library``` or just create your own concepts :wink:.",
                        unsafe_allow_html=False,
                    )
                else:
                    if len(st.session_state["results"]) == 0:
                        st.write(
                            "No concept found in the library matching your search: "
                            + st.session_state["cl_search_text"]
                        )
                    else:
                        # display number of results
                        if st.session_state["cl_search_text"]:
                            st.write(
                                f"Found {st.session_state['cl_search_results_count']} {'concepts' if st.session_state['cl_search_results_count'] > 1 else 'concept' } matching your search"
                            )
                        sdConceptsBrowser(st.session_state["results"], key="results")

    with tab_downloader:
        st.write("Not implemented yet")

    return False


================================================
FILE: webui/streamlit/scripts/sd_utils/__init__.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
# from webui_streamlit import st
import hydralit as st

# streamlit imports
# from streamlit.runtime.scriptrunner import script_run_context

# streamlit components section
from streamlit_server_state import server_state, server_state_lock

# from streamlitextras.threader import lock, trigger_rerun, \
# streamlit_thread, get_thread, \
# last_trigger_time

# other imports

import warnings

import os, time, toml
import torch
import mimetypes
import pynvml
import threading
import torch
from omegaconf import OmegaConf

# from abc import ABC, abstractmethod
from packaging import version
import shutup

# import librosa
from nataili.util.logger import logger


# try:
# from realesrgan import RealESRGANer
# from basicsr.archs.rrdbnet_arch import RRDBNet
# except ImportError as e:
# logger.error("You tried to import realesrgan without having it installed properly. To install Real-ESRGAN, run:\n\n"
# "pip install realesrgan")

# Temp imports
# from basicsr.utils.registry import ARCH_REGISTRY


# end of imports
# ---------------------------------------------------------------------------------------------------------------

# remove all the annoying python warnings.
shutup.please()

# the following lines should help fixing an issue with nvidia 16xx cards.
if "defaults" in st.session_state:
    if st.session_state["defaults"].general.use_cudnn:
        torch.backends.cudnn.benchmark = True
        torch.backends.cudnn.enabled = True

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging

    logging.set_verbosity_error()
except:
    pass

# disable diffusers telemetry
os.environ["DISABLE_TELEMETRY"] = "YES"

# remove some annoying deprecation warnings that show every now and then.
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)

# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init()
mimetypes.add_type("application/javascript", ".js")

# some of those options should not be changed at all because they would break the model, so I removed them from options.
opt_C = 4
opt_f = 8

# The model manager loads and unloads the SD models and has features to download them or find their location
# model_manager = ModelManager()


def load_configs():
    if "defaults" not in st.session_state:
        st.session_state["defaults"] = {}

    st.session_state["defaults"] = OmegaConf.load("configs/webui/webui_streamlit.yaml")

    if os.path.exists("configs/webui/userconfig_streamlit.yaml"):
        user_defaults = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")

        if "version" in user_defaults.general:
            if version.parse(user_defaults.general.version) < version.parse(
                st.session_state["defaults"].general.version
            ):
                logger.error(
                    "The version of the user config file is older than the version on the defaults config file. "
                    "This means there were big changes we made on the config."
                    "We are removing this file and recreating it from the defaults in order to make sure things work properly."
                )
                os.remove("configs/webui/userconfig_streamlit.yaml")
                st.experimental_rerun()
        else:
            logger.error(
                "The version of the user config file is older than the version on the defaults config file. "
                "This means there were big changes we made on the config."
                "We are removing this file and recreating it from the defaults in order to make sure things work properly."
            )
            os.remove("configs/webui/userconfig_streamlit.yaml")
            st.experimental_rerun()

        try:
            st.session_state["defaults"] = OmegaConf.merge(
                st.session_state["defaults"], user_defaults
            )
        except KeyError:
            st.experimental_rerun()
    else:
        OmegaConf.save(
            config=st.session_state.defaults,
            f="configs/webui/userconfig_streamlit.yaml",
        )
        loaded = OmegaConf.load("configs/webui/userconfig_streamlit.yaml")
        assert st.session_state.defaults == loaded

    if os.path.exists(".streamlit/config.toml"):
        st.session_state["streamlit_config"] = toml.load(".streamlit/config.toml")

    # if st.session_state["defaults"].daisi_app.running_on_daisi_io:
    # if os.path.exists("scripts/modeldownload.py"):
    # import modeldownload
    # modeldownload.updateModels()

    if "keep_all_models_loaded" in st.session_state.defaults.general:
        with server_state_lock["keep_all_models_loaded"]:
            server_state["keep_all_models_loaded"] = st.session_state[
                "defaults"
            ].general.keep_all_models_loaded
    else:
        st.session_state["defaults"].general.keep_all_models_loaded = False
        with server_state_lock["keep_all_models_loaded"]:
            server_state["keep_all_models_loaded"] = st.session_state[
                "defaults"
            ].general.keep_all_models_loaded


load_configs()

#
# if st.session_state["defaults"].debug.enable_hydralit:
# navbar_theme = {'txc_inactive': '#FFFFFF','menu_background':'#0e1117','txc_active':'black','option_active':'red'}
# app = st.HydraApp(title='Stable Diffusion WebUI', favicon="", use_cookie_cache=False, sidebar_state="expanded", layout="wide", navbar_theme=navbar_theme,
# hide_streamlit_markers=False, allow_url_nav=True , clear_cross_app_sessions=False, use_loader=False)
# else:
# app = None

#
grid_format = st.session_state["defaults"].general.save_format
grid_lossless = False
grid_quality = st.session_state["defaults"].general.grid_quality
if grid_format == "png":
    grid_ext = "png"
    grid_format = "png"
elif grid_format in ["jpg", "jpeg"]:
    grid_quality = int(grid_format) if len(grid_format) > 1 else 100
    grid_ext = "jpg"
    grid_format = "jpeg"
elif grid_format[0] == "webp":
    grid_quality = int(grid_format) if len(grid_format) > 1 else 100
    grid_ext = "webp"
    grid_format = "webp"
    if grid_quality < 0:  # e.g. webp:-100 for lossless mode
        grid_lossless = True
        grid_quality = abs(grid_quality)

#
save_format = st.session_state["defaults"].general.save_format
save_lossless = False
save_quality = 100
if save_format == "png":
    save_ext = "png"
    save_format = "png"
elif save_format in ["jpg", "jpeg"]:
    save_quality = int(save_format) if len(save_format) > 1 else 100
    save_ext = "jpg"
    save_format = "jpeg"
elif save_format == "webp":
    save_quality = int(save_format) if len(save_format) > 1 else 100
    save_ext = "webp"
    save_format = "webp"
    if save_quality < 0:  # e.g. webp:-100 for lossless mode
        save_lossless = True
        save_quality = abs(save_quality)

# this should force GFPGAN and RealESRGAN onto the selected gpu as well
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"  # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = str(st.session_state["defaults"].general.gpu)


# functions to load css locally OR remotely starts here. Options exist for future flexibility. Called as st.markdown with unsafe_allow_html as css injection
# TODO, maybe look into async loading the file especially for remote fetching
def local_css(file_name):
    with open(file_name) as f:
        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)


def remote_css(url):
    st.markdown(f'<link href="{url}" rel="stylesheet">', unsafe_allow_html=True)


def load_css(isLocal, nameOrURL):
    if isLocal:
        local_css(nameOrURL)
    else:
        remote_css(nameOrURL)


def set_page_title(title):
    """
    Simple function to allows us to change the title dynamically.
    Normally you can use `st.set_page_config` to change the title but it can only be used once per app.
    """

    st.sidebar.markdown(
        unsafe_allow_html=True,
        body=f"""
                            <iframe height=0 srcdoc="<script>
                            const title = window.parent.document.querySelector('title') \

                            const oldObserver = window.parent.titleObserver
                            if (oldObserver) {{
                            oldObserver.disconnect()
                            }} \

                            const newObserver = new MutationObserver(function(mutations) {{
                            const target = mutations[0].target
                            if (target.text !== '{title}') {{
                            target.text = '{title}'
                            }}
                            }}) \

                            newObserver.observe(title, {{ childList: true }})
                            window.parent.titleObserver = newObserver \

                            title.text = '{title}'
                            </script>" />
                            """,
    )


class MemUsageMonitor(threading.Thread):
    stop_flag = False
    max_usage = 0
    total = -1

    def __init__(self, name):
        threading.Thread.__init__(self)
        self.name = name

    def run(self):
        try:
            pynvml.nvmlInit()
        except:
            logger.debug(
                f"[{self.name}] Unable to initialize NVIDIA management. No memory stats. \n"
            )
            return
        logger.info(f"[{self.name}] Recording memory usage...\n")
        # Missing context
        # handle = pynvml.nvmlDeviceGetHandleByIndex(st.session_state['defaults'].general.gpu)
        handle = pynvml.nvmlDeviceGetHandleByIndex(0)
        self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
        while not self.stop_flag:
            m = pynvml.nvmlDeviceGetMemoryInfo(handle)
            self.max_usage = max(self.max_usage, m.used)
            # logger.info(self.max_usage)
            time.sleep(0.1)
        logger.info(f"[{self.name}] Stopped recording.\n")
        pynvml.nvmlShutdown()

    def read(self):
        return self.max_usage, self.total

    def stop(self):
        self.stop_flag = True

    def read_and_stop(self):
        self.stop_flag = True
        return self.max_usage, self.total


#
def custom_models_available():
    with server_state_lock["custom_models"]:
        #
        # Allow for custom models to be used instead of the default one,
        # an example would be Waifu-Diffusion or any other fine tune of stable diffusion
        server_state["custom_models"]: sorted = []

        for root, dirs, files in os.walk(os.path.join("models", "custom")):
            for file in files:
                if os.path.splitext(file)[1] == ".ckpt":
                    server_state["custom_models"].append(os.path.splitext(file)[0])

        with server_state_lock["CustomModel_available"]:
            if len(server_state["custom_models"]) > 0:
                server_state["CustomModel_available"] = True
                server_state["custom_models"].append("Stable Diffusion v1.5")
            else:
                server_state["CustomModel_available"] = False


#
def GFPGAN_available():
    # with server_state_lock["GFPGAN_models"]:
    #

    st.session_state["GFPGAN_models"]: sorted = []
    model = st.session_state["defaults"].model_manager.models.gfpgan

    files_available = 0

    for file in model["files"]:
        if "save_location" in model["files"][file]:
            if os.path.exists(
                os.path.join(
                    model["files"][file]["save_location"],
                    model["files"][file]["file_name"],
                )
            ):
                files_available += 1

        elif os.path.exists(
            os.path.join(model["save_location"], model["files"][file]["file_name"])
        ):
            base_name = os.path.splitext(model["files"][file]["file_name"])[0]
            if "GFPGANv" in base_name:
                st.session_state["GFPGAN_models"].append(base_name)
            files_available += 1

    # we need to show the other models from previous verions that we have on the
    # same directory in case we want to see how they perform vs each other.
    for root, dirs, files in os.walk(st.session_state["defaults"].general.GFPGAN_dir):
        for file in files:
            if os.path.splitext(file)[1] == ".pth":
                if os.path.splitext(file)[0] not in st.session_state["GFPGAN_models"]:
                    st.session_state["GFPGAN_models"].append(os.path.splitext(file)[0])

    if len(st.session_state["GFPGAN_models"]) > 0 and files_available == len(
        model["files"]
    ):
        st.session_state["GFPGAN_available"] = True
    else:
        st.session_state["GFPGAN_available"] = False
        st.session_state["use_GFPGAN"] = False
        st.session_state["GFPGAN_model"] = "GFPGANv1.4"


#
def RealESRGAN_available():
    # with server_state_lock["RealESRGAN_models"]:

    st.session_state["RealESRGAN_models"]: sorted = []
    model = st.session_state["defaults"].model_manager.models.realesrgan
    for file in model["files"]:
        if os.path.exists(
            os.path.join(model["save_location"], model["files"][file]["file_name"])
        ):
            base_name = os.path.splitext(model["files"][file]["file_name"])[0]
            st.session_state["RealESRGAN_models"].append(base_name)

    if len(st.session_state["RealESRGAN_models"]) > 0:
        st.session_state["RealESRGAN_available"] = True
    else:
        st.session_state["RealESRGAN_available"] = False
        st.session_state["use_RealESRGAN"] = False
        st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"


#
def LDSR_available():
    st.session_state["LDSR_models"]: sorted = []
    files_available = 0
    model = st.session_state["defaults"].model_manager.models.ldsr
    for file in model["files"]:
        if os.path.exists(
            os.path.join(model["save_location"], model["files"][file]["file_name"])
        ):
            base_name = os.path.splitext(model["files"][file]["file_name"])[0]
            extension = os.path.splitext(model["files"][file]["file_name"])[1]
            if extension == ".ckpt":
                st.session_state["LDSR_models"].append(base_name)
            files_available += 1
    if files_available == len(model["files"]):
        st.session_state["LDSR_available"] = True
    else:
        st.session_state["LDSR_available"] = False
        st.session_state["use_LDSR"] = False
        st.session_state["LDSR_model"] = "model"


================================================
FILE: webui/streamlit/scripts/sd_utils/bridge.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

# base webui import and utils.
# import streamlit as st

# We import hydralit like this to replace the previous stuff
# we had with native streamlit as it lets ur replace things 1:1
from nataili.util import logger

# streamlit imports

# streamlit components section

# other imports
import requests, time, json, base64
from io import BytesIO

# import custom components


# end of imports
# ---------------------------------------------------------------------------------------------------------------


@logger.catch(reraise=True)
def run_bridge(
    interval,
    api_key,
    horde_name,
    horde_url,
    priority_usernames,
    horde_max_pixels,
    horde_nsfw,
    horde_censor_nsfw,
    horde_blacklist,
    horde_censorlist,
):
    current_id = None
    current_payload = None
    loop_retry = 0
    # load the model for stable horde if its not in memory already
    # we should load it after we get the request from the API in
    # case the model is different from the loaded in memory but
    # for now we can load it here so its read right away.
    load_models(use_GFPGAN=True)
    while True:
        if loop_retry > 10 and current_id:
            logger.info(
                f"Exceeded retry count {loop_retry} for generation id {current_id}. Aborting generation!"
            )
            current_id = None
            current_payload = None
            current_generation = None
            loop_retry = 0
        elif current_id:
            logger.info(f"Retrying ({loop_retry}/10) for generation id {current_id}...")
        gen_dict = {
            "name": horde_name,
            "max_pixels": horde_max_pixels,
            "priority_usernames": priority_usernames,
            "nsfw": horde_nsfw,
            "blacklist": horde_blacklist,
            "models": ["stable_diffusion"],
        }
        headers = {"apikey": api_key}
        if current_id:
            loop_retry += 1
        else:
            try:
                pop_req = requests.post(
                    horde_url + "/api/v2/generate/pop", json=gen_dict, headers=headers
                )
            except requests.exceptions.ConnectionError:
                logger.warning(
                    f"Server {horde_url} unavailable during pop. Waiting 10 seconds..."
                )
                time.sleep(10)
                continue
            except requests.exceptions.JSONDecodeError():
                logger.warning(
                    f"Server {horde_url} unavailable during pop. Waiting 10 seconds..."
                )
                time.sleep(10)
                continue
            try:
                pop = pop_req.json()
            except json.decoder.JSONDecodeError:
                logger.warning(
                    f"Could not decode response from {horde_url} as json. Please inform its administrator!"
                )
                time.sleep(interval)
                continue
            if pop is None:
                logger.warning(
                    f"Something has gone wrong with {horde_url}. Please inform its administrator!"
                )
                time.sleep(interval)
                continue
            if not pop_req.ok:
                pop["message"]
                logger.warning(
                    f"During gen pop, server {horde_url} responded with status code {pop_req.status_code}: {pop['message']}. Waiting for 10 seconds..."
                )
                if "errors" in pop:
                    logger.debug(f"Detailed Request Errors: {pop['errors']}")
                time.sleep(10)
                continue
            if not pop.get("id"):
                skipped_info = pop.get("skipped")
                if skipped_info and len(skipped_info):
                    skipped_info = f" Skipped Info: {skipped_info}."
                else:
                    skipped_info = ""
                logger.info(
                    f"Server {horde_url} has no valid generations to do for us.{skipped_info}"
                )
                time.sleep(interval)
                continue
            current_id = pop["id"]
            logger.info(f"Request with id {current_id} picked up. Initiating work...")
            current_payload = pop["payload"]
            if "toggles" in current_payload and current_payload["toggles"] is None:
                logger.error(f"Received Bad payload: {pop}")
                current_id = None
                current_payload = None
                current_generation = None
                loop_retry = 0
                time.sleep(10)
                continue

        logger.debug(current_payload)
        current_payload["toggles"] = current_payload.get("toggles", [1, 4])
        # In bridge-mode, matrix is prepared on the horde and split in multiple nodes
        if 0 in current_payload["toggles"]:
            current_payload["toggles"].remove(0)
        if 8 not in current_payload["toggles"]:
            if horde_censor_nsfw and not horde_nsfw:
                current_payload["toggles"].append(8)
            elif any(word in current_payload["prompt"] for word in horde_censorlist):
                current_payload["toggles"].append(8)

        from txt2img import txt2img

        """{'prompt': 'Centred Husky, inside spiral with circular patterns, trending on dribbble, knotwork, spirals, key patterns,
        zoomorphics, ', 'ddim_steps': 30, 'n_iter': 1, 'sampler_name': 'DDIM', 'cfg_scale': 16.0, 'seed': '3405278433', 'height': 512, 'width': 512}"""

        # images, seed, info, stats = txt2img(**current_payload)
        images, seed, info, stats = txt2img(
            str(current_payload["prompt"]),
            int(current_payload["ddim_steps"]),
            str(current_payload["sampler_name"]),
            int(current_payload["n_iter"]),
            1,
            float(current_payload["cfg_scale"]),
            str(current_payload["seed"]),
            int(current_payload["height"]),
            int(current_payload["width"]),
            save_grid=False,
            group_by_prompt=False,
            save_individual_images=False,
            write_info_files=False,
        )

        buffer = BytesIO()
        # We send as WebP to avoid using all the horde bandwidth
        images[0].save(buffer, format="WebP", quality=90)
        # logger.info(info)
        submit_dict = {
            "id": current_id,
            "generation": base64.b64encode(buffer.getvalue()).decode("utf8"),
            "api_key": api_key,
            "seed": seed,
            "max_pixels": horde_max_pixels,
        }
        current_generation = seed
        while current_id and current_generation is not None:
            try:
                submit_req = requests.post(
                    horde_url + "/api/v2/generate/submit",
                    json=submit_dict,
                    headers=headers,
                )
                try:
                    submit = submit_req.json()
                except json.decoder.JSONDecodeError:
                    logger.error(
                        f"Something has gone wrong with {horde_url} during submit. Please inform its administrator!  (Retry {loop_retry}/10)"
                    )
                    time.sleep(interval)
                    continue
                if submit_req.status_code == 404:
                    logger.info(
                        "The generation we were working on got stale. Aborting!"
                    )
                elif not submit_req.ok:
                    logger.error(
                        f"During gen submit, server {horde_url} responded with status code {submit_req.status_code}: {submit['message']}. Waiting for 10 seconds...  (Retry {loop_retry}/10)"
                    )
                    if "errors" in submit:
                        logger.debug(f"Detailed Request Errors: {submit['errors']}")
                    time.sleep(10)
                    continue
                else:
                    logger.info(
                        f'Submitted generation with id {current_id} and contributed for {submit_req.json()["reward"]}'
                    )
                current_id = None
                current_payload = None
                current_generation = None
                loop_retry = 0
            except requests.exceptions.ConnectionError:
                logger.warning(
                    f"Server {horde_url} unavailable during submit. Waiting 10 seconds...  (Retry {loop_retry}/10)"
                )
                time.sleep(10)
                continue
        time.sleep(interval)


================================================
FILE: webui/streamlit/scripts/textual_inversion.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import st, set_page_title, seed_to_int

# streamlit imports
from streamlit.runtime.scriptrunner import StopException
from streamlit_tensorboard import st_tensorboard

# streamlit components section
from streamlit_server_state import server_state

# other imports
from transformers import CLIPTextModel, CLIPTokenizer

# Temp imports

import itertools
import math
import os
import random

# import datetime
# from pathlib import Path
# from typing import Optional

import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.utils.data import Dataset

import PIL
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    LMSDiscreteScheduler,
    StableDiffusionPipeline,
    UNet2DConditionModel,
)  # , PNDMScheduler
from diffusers.optimization import get_scheduler

# from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from pipelines.stable_diffusion.no_check import NoCheck
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from slugify import slugify
import json
import os  # , subprocess

# from io import StringIO


# end of imports
# ---------------------------------------------------------------------------------------------------------------

logger = get_logger(__name__)

imagenet_templates_small = [
    "a photo of a {}",
    "a rendering of a {}",
    "a cropped photo of the {}",
    "the photo of a {}",
    "a photo of a clean {}",
    "a photo of a dirty {}",
    "a dark photo of the {}",
    "a photo of my {}",
    "a photo of the cool {}",
    "a close-up photo of a {}",
    "a bright photo of the {}",
    "a cropped photo of a {}",
    "a photo of the {}",
    "a good photo of the {}",
    "a photo of one {}",
    "a close-up photo of the {}",
    "a rendition of the {}",
    "a photo of the clean {}",
    "a rendition of a {}",
    "a photo of a nice {}",
    "a good photo of a {}",
    "a photo of the nice {}",
    "a photo of the small {}",
    "a photo of the weird {}",
    "a photo of the large {}",
    "a photo of a cool {}",
    "a photo of a small {}",
]

imagenet_style_templates_small = [
    "a painting in the style of {}",
    "a rendering in the style of {}",
    "a cropped painting in the style of {}",
    "the painting in the style of {}",
    "a clean painting in the style of {}",
    "a dirty painting in the style of {}",
    "a dark painting in the style of {}",
    "a picture in the style of {}",
    "a cool painting in the style of {}",
    "a close-up painting in the style of {}",
    "a bright painting in the style of {}",
    "a cropped painting in the style of {}",
    "a good painting in the style of {}",
    "a close-up painting in the style of {}",
    "a rendition in the style of {}",
    "a nice painting in the style of {}",
    "a small painting in the style of {}",
    "a weird painting in the style of {}",
    "a large painting in the style of {}",
]


class TextualInversionDataset(Dataset):
    def __init__(
        self,
        data_root,
        tokenizer,
        learnable_property="object",  # [object, style]
        size=512,
        repeats=100,
        interpolation="bicubic",
        set="train",
        placeholder_token="*",
        center_crop=False,
        templates=None,
    ):
        self.data_root = data_root
        self.tokenizer = tokenizer
        self.learnable_property = learnable_property
        self.size = size
        self.placeholder_token = placeholder_token
        self.center_crop = center_crop

        self.image_paths = [
            os.path.join(self.data_root, file_path)
            for file_path in os.listdir(self.data_root)
            if file_path.lower().endswith((".png", ".jpg", ".jpeg"))
        ]

        self.num_images = len(self.image_paths)
        self._length = self.num_images

        if set == "train":
            self._length = self.num_images * repeats

        self.interpolation = {
            "linear": PIL.Image.LINEAR,
            "bilinear": PIL.Image.Resampling.BILINEAR,
            "bicubic": PIL.Image.Resampling.BICUBIC,
            "lanczos": PIL.Image.Resampling.LANCZOS,
        }[interpolation]

        self.templates = templates
        self.cache = {}
        self.tokenized_templates = [
            self.tokenizer(
                text.format(self.placeholder_token),
                padding="max_length",
                truncation=True,
                max_length=self.tokenizer.model_max_length,
                return_tensors="pt",
            ).input_ids[0]
            for text in self.templates
        ]

    def __len__(self):
        return self._length

    def get_example(self, image_path, flipped):
        if image_path in self.cache:
            return self.cache[image_path]

        example = {}
        image = Image.open(image_path)

        if not image.mode == "RGB":
            image = image.convert("RGB")

        # default to score-sde preprocessing
        img = np.array(image).astype(np.uint8)
        if self.center_crop:
            crop = min(img.shape[0], img.shape[1])
            (
                h,
                w,
            ) = (
                img.shape[0],
                img.shape[1],
            )
            img = img[
                (h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2
            ]
        image = Image.fromarray(img)
        image = image.resize((self.size, self.size), resample=self.interpolation)
        image = transforms.RandomHorizontalFlip(p=1 if flipped else 0)(image)
        image = np.array(image).astype(np.uint8)
        image = (image / 127.5 - 1.0).astype(np.float32)
        example["key"] = "-".join([image_path, "-", str(flipped)])
        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)

        self.cache[image_path] = example
        return example

    def __getitem__(self, i):
        flipped = random.choice([False, True])
        example = self.get_example(self.image_paths[i % self.num_images], flipped)
        example["input_ids"] = random.choice(self.tokenized_templates)
        return example


def freeze_params(params):
    for param in params:
        param.requires_grad = False


def save_resume_file(basepath, extra={}, config=""):
    info = {"args": config["args"]}
    info["args"].update(extra)

    with open(f"{os.path.join(basepath, 'resume.json')}", "w") as f:
        # print (info)
        json.dump(info, f, indent=4)

    with open(f"{basepath}/token_identifier.txt", "w") as f:
        f.write(f"{config['args']['placeholder_token']}")

    with open(f"{basepath}/type_of_concept.txt", "w") as f:
        f.write(f"{config['args']['learnable_property']}")

    config["args"] = info["args"]

    return config["args"]


class Checkpointer:
    def __init__(
        self,
        accelerator,
        vae,
        unet,
        tokenizer,
        placeholder_token,
        placeholder_token_id,
        templates,
        output_dir,
        random_sample_batches,
        sample_batch_size,
        stable_sample_batches,
        seed,
    ):
        self.accelerator = accelerator
        self.vae = vae
        self.unet = unet
        self.tokenizer = tokenizer
        self.placeholder_token = placeholder_token
        self.placeholder_token_id = placeholder_token_id
        self.templates = templates
        self.output_dir = output_dir
        self.seed = seed
        self.random_sample_batches = random_sample_batches
        self.sample_batch_size = sample_batch_size
        self.stable_sample_batches = stable_sample_batches

    @torch.no_grad()
    def checkpoint(self, step, text_encoder, save_samples=True, path=None):
        print("Saving checkpoint for step %d..." % step)
        with torch.autocast("cuda"):
            if path is None:
                checkpoints_path = f"{self.output_dir}/checkpoints"
                os.makedirs(checkpoints_path, exist_ok=True)

            unwrapped = self.accelerator.unwrap_model(text_encoder)

            # Save a checkpoint
            learned_embeds = unwrapped.get_input_embeddings().weight[
                self.placeholder_token_id
            ]
            learned_embeds_dict = {
                self.placeholder_token: learned_embeds.detach().cpu()
            }

            filename = "%s_%d.bin" % (slugify(self.placeholder_token), step)
            if path is not None:
                torch.save(learned_embeds_dict, path)
            else:
                torch.save(learned_embeds_dict, f"{checkpoints_path}/{filename}")
                torch.save(learned_embeds_dict, f"{checkpoints_path}/last.bin")

            del unwrapped
            del learned_embeds

    @torch.no_grad()
    def save_samples(
        self,
        step,
        text_encoder,
        height,
        width,
        guidance_scale,
        eta,
        num_inference_steps,
    ):
        samples_path = f"{self.output_dir}/concept_images"
        os.makedirs(samples_path, exist_ok=True)

        # if "checker" not in server_state['textual_inversion']:
        # with server_state_lock['textual_inversion']["checker"]:
        server_state["textual_inversion"]["checker"] = NoCheck()

        # if "unwrapped" not in server_state['textual_inversion']:
        # 	with server_state_lock['textual_inversion']["unwrapped"]:
        server_state["textual_inversion"]["unwrapped"] = self.accelerator.unwrap_model(
            text_encoder
        )

        # if "pipeline" not in server_state['textual_inversion']:
        # 	with server_state_lock['textual_inversion']["pipeline"]:
        # Save a sample image
        server_state["textual_inversion"]["pipeline"] = StableDiffusionPipeline(
            text_encoder=server_state["textual_inversion"]["unwrapped"],
            vae=self.vae,
            unet=self.unet,
            tokenizer=self.tokenizer,
            scheduler=LMSDiscreteScheduler(
                beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
            ),
            safety_checker=NoCheck(),
            feature_extractor=CLIPFeatureExtractor.from_pretrained(
                "openai/clip-vit-base-patch32"
            ),
        ).to("cuda")

        server_state["textual_inversion"]["pipeline"].enable_attention_slicing()

        if self.stable_sample_batches > 0:
            stable_latents = torch.randn(
                (
                    self.sample_batch_size,
                    server_state["textual_inversion"]["pipeline"].unet.in_channels,
                    height // 8,
                    width // 8,
                ),
                device=server_state["textual_inversion"]["pipeline"].device,
                generator=torch.Generator(
                    device=server_state["textual_inversion"]["pipeline"].device
                ).manual_seed(self.seed),
            )

            stable_prompts = [
                choice.format(self.placeholder_token)
                for choice in (self.templates * self.sample_batch_size)[
                    : self.sample_batch_size
                ]
            ]

            # Generate and save stable samples
            for i in range(0, self.stable_sample_batches):
                samples = server_state["textual_inversion"]["pipeline"](
                    prompt=stable_prompts,
                    height=384,
                    latents=stable_latents,
                    width=384,
                    guidance_scale=guidance_scale,
                    eta=eta,
                    num_inference_steps=num_inference_steps,
                    output_type="pil",
                )["sample"]

                for idx, im in enumerate(samples):
                    filename = "stable_sample_%d_%d_step_%d.png" % (
                        i + 1,
                        idx + 1,
                        step,
                    )
                    im.save(f"{samples_path}/{filename}")
                del samples
            del stable_latents

        prompts = [
            choice.format(self.placeholder_token)
            for choice in random.choices(self.templates, k=self.sample_batch_size)
        ]
        # Generate and save random samples
        for i in range(0, self.random_sample_batches):
            samples = server_state["textual_inversion"]["pipeline"](
                prompt=prompts,
                height=384,
                width=384,
                guidance_scale=guidance_scale,
                eta=eta,
                num_inference_steps=num_inference_steps,
                output_type="pil",
            )["sample"]
            for idx, im in enumerate(samples):
                filename = "step_%d_sample_%d_%d.png" % (step, i + 1, idx + 1)
                im.save(f"{samples_path}/{filename}")
            del samples

        del server_state["textual_inversion"]["checker"]
        del server_state["textual_inversion"]["unwrapped"]
        del server_state["textual_inversion"]["pipeline"]
        torch.cuda.empty_cache()


# @retry(RuntimeError, tries=5)
def textual_inversion(config):
    print("Running textual inversion.")

    # if "pipeline" in server_state["textual_inversion"]:
    # del server_state['textual_inversion']["checker"]
    # del server_state['textual_inversion']["unwrapped"]
    # del server_state['textual_inversion']["pipeline"]
    # torch.cuda.empty_cache()

    global_step_offset = 0

    # print(config['args']['resume_from'])
    if config["args"]["resume_from"]:
        try:
            basepath = f"{config['args']['resume_from']}"

            with open(f"{basepath}/resume.json", "r") as f:
                state = json.load(f)

            global_step_offset = state["args"].get("global_step", 0)

            print("Resuming state from %s" % config["args"]["resume_from"])
            print("We've trained %d steps so far" % global_step_offset)

        except json.decoder.JSONDecodeError:
            pass
    else:
        basepath = f"{config['args']['output_dir']}/{slugify(config['args']['placeholder_token'])}"
        os.makedirs(basepath, exist_ok=True)

    accelerator = Accelerator(
        gradient_accumulation_steps=config["args"]["gradient_accumulation_steps"],
        mixed_precision=config["args"]["mixed_precision"],
    )

    # If passed along, set the training seed.
    if config["args"]["seed"]:
        set_seed(config["args"]["seed"])

    # if "tokenizer" not in server_state["textual_inversion"]:
    # Load the tokenizer and add the placeholder token as a additional special token
    # with server_state_lock['textual_inversion']["tokenizer"]:
    if config["args"]["tokenizer_name"]:
        server_state["textual_inversion"]["tokenizer"] = CLIPTokenizer.from_pretrained(
            config["args"]["tokenizer_name"]
        )
    elif config["args"]["pretrained_model_name_or_path"]:
        server_state["textual_inversion"]["tokenizer"] = CLIPTokenizer.from_pretrained(
            config["args"]["pretrained_model_name_or_path"] + "/tokenizer"
        )

    # Add the placeholder token in tokenizer
    num_added_tokens = server_state["textual_inversion"]["tokenizer"].add_tokens(
        config["args"]["placeholder_token"]
    )
    if num_added_tokens == 0:
        st.error(
            f"The tokenizer already contains the token {config['args']['placeholder_token']}. Please pass a different"
            " `placeholder_token` that is not already in the tokenizer."
        )

    # Convert the initializer_token, placeholder_token to ids
    token_ids = server_state["textual_inversion"]["tokenizer"].encode(
        config["args"]["initializer_token"], add_special_tokens=False
    )
    # Check if initializer_token is a single token or a sequence of tokens
    if len(token_ids) > 1:
        st.error("The initializer token must be a single token.")

    initializer_token_id = token_ids[0]
    placeholder_token_id = server_state["textual_inversion"][
        "tokenizer"
    ].convert_tokens_to_ids(config["args"]["placeholder_token"])

    # if "text_encoder" not in server_state['textual_inversion']:
    # Load models and create wrapper for stable diffusion
    # with server_state_lock['textual_inversion']["text_encoder"]:
    server_state["textual_inversion"]["text_encoder"] = CLIPTextModel.from_pretrained(
        config["args"]["pretrained_model_name_or_path"] + "/text_encoder",
    )

    # if "vae" not in server_state['textual_inversion']:
    # with server_state_lock['textual_inversion']["vae"]:
    server_state["textual_inversion"]["vae"] = AutoencoderKL.from_pretrained(
        config["args"]["pretrained_model_name_or_path"] + "/vae",
    )

    # if "unet" not in server_state['textual_inversion']:
    # with server_state_lock['textual_inversion']["unet"]:
    server_state["textual_inversion"]["unet"] = UNet2DConditionModel.from_pretrained(
        config["args"]["pretrained_model_name_or_path"] + "/unet",
    )

    base_templates = (
        imagenet_style_templates_small
        if config["args"]["learnable_property"] == "style"
        else imagenet_templates_small
    )
    if config["args"]["custom_templates"]:
        templates = config["args"]["custom_templates"].split(";")
    else:
        templates = base_templates

    slice_size = (
        server_state["textual_inversion"]["unet"].config.attention_head_dim // 2
    )
    server_state["textual_inversion"]["unet"].set_attention_slice(slice_size)

    # Resize the token embeddings as we are adding new special tokens to the tokenizer
    server_state["textual_inversion"]["text_encoder"].resize_token_embeddings(
        len(server_state["textual_inversion"]["tokenizer"])
    )

    # Initialise the newly added placeholder token with the embeddings of the initializer token
    token_embeds = (
        server_state["textual_inversion"]["text_encoder"]
        .get_input_embeddings()
        .weight.data
    )

    if "resume_checkpoint" in config["args"]:
        if config["args"]["resume_checkpoint"] is not None:
            token_embeds[placeholder_token_id] = torch.load(
                config["args"]["resume_checkpoint"]
            )[config["args"]["placeholder_token"]]
    else:
        token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]

    # Freeze vae and unet
    freeze_params(server_state["textual_inversion"]["vae"].parameters())
    freeze_params(server_state["textual_inversion"]["unet"].parameters())
    # Freeze all parameters except for the token embeddings in text encoder
    params_to_freeze = itertools.chain(
        server_state["textual_inversion"][
            "text_encoder"
        ].text_model.encoder.parameters(),
        server_state["textual_inversion"][
            "text_encoder"
        ].text_model.final_layer_norm.parameters(),
        server_state["textual_inversion"][
            "text_encoder"
        ].text_model.embeddings.position_embedding.parameters(),
    )
    freeze_params(params_to_freeze)

    checkpointer = Checkpointer(
        accelerator=accelerator,
        vae=server_state["textual_inversion"]["vae"],
        unet=server_state["textual_inversion"]["unet"],
        tokenizer=server_state["textual_inversion"]["tokenizer"],
        placeholder_token=config["args"]["placeholder_token"],
        placeholder_token_id=placeholder_token_id,
        templates=templates,
        output_dir=basepath,
        sample_batch_size=config["args"]["sample_batch_size"],
        random_sample_batches=config["args"]["random_sample_batches"],
        stable_sample_batches=config["args"]["stable_sample_batches"],
        seed=config["args"]["seed"],
    )

    if config["args"]["scale_lr"]:
        config["args"]["learning_rate"] = (
            config["args"]["learning_rate"]
            * config["args"]["gradient_accumulation_steps"]
            * config["args"]["train_batch_size"]
            * accelerator.num_processes
        )

    # Initialize the optimizer
    optimizer = torch.optim.AdamW(
        server_state["textual_inversion"]["text_encoder"]
        .get_input_embeddings()
        .parameters(),  # only optimize the embeddings
        lr=config["args"]["learning_rate"],
        betas=(config["args"]["adam_beta1"], config["args"]["adam_beta2"]),
        weight_decay=config["args"]["adam_weight_decay"],
        eps=config["args"]["adam_epsilon"],
    )

    # TODO (patil-suraj): load scheduler using args
    noise_scheduler = DDPMScheduler(
        beta_start=0.00085,
        beta_end=0.012,
        beta_schedule="scaled_linear",
        num_train_timesteps=1000,
        tensor_format="pt",
    )

    train_dataset = TextualInversionDataset(
        data_root=config["args"]["train_data_dir"],
        tokenizer=server_state["textual_inversion"]["tokenizer"],
        size=config["args"]["resolution"],
        placeholder_token=config["args"]["placeholder_token"],
        repeats=config["args"]["repeats"],
        learnable_property=config["args"]["learnable_property"],
        center_crop=config["args"]["center_crop"],
        set="train",
        templates=templates,
    )
    train_dataloader = torch.utils.data.DataLoader(
        train_dataset, batch_size=config["args"]["train_batch_size"], shuffle=True
    )

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(
        len(train_dataloader) / config["args"]["gradient_accumulation_steps"]
    )
    if config["args"]["max_train_steps"] is None:
        config["args"]["max_train_steps"] = (
            config["args"]["num_train_epochs"] * num_update_steps_per_epoch
        )
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        config["args"]["lr_scheduler"],
        optimizer=optimizer,
        num_warmup_steps=config["args"]["lr_warmup_steps"]
        * config["args"]["gradient_accumulation_steps"],
        num_training_steps=config["args"]["max_train_steps"]
        * config["args"]["gradient_accumulation_steps"],
    )

    (
        server_state["textual_inversion"]["text_encoder"],
        optimizer,
        train_dataloader,
        lr_scheduler,
    ) = accelerator.prepare(
        server_state["textual_inversion"]["text_encoder"],
        optimizer,
        train_dataloader,
        lr_scheduler,
    )

    # Move vae and unet to device
    server_state["textual_inversion"]["vae"].to(accelerator.device)
    server_state["textual_inversion"]["unet"].to(accelerator.device)

    # Keep vae and unet in eval mode as we don't train these
    server_state["textual_inversion"]["vae"].eval()
    server_state["textual_inversion"]["unet"].eval()

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(
        len(train_dataloader) / config["args"]["gradient_accumulation_steps"]
    )
    if overrode_max_train_steps:
        config["args"]["max_train_steps"] = (
            config["args"]["num_train_epochs"] * num_update_steps_per_epoch
        )
    # Afterwards we recalculate our number of training epochs
    config["args"]["num_train_epochs"] = math.ceil(
        config["args"]["max_train_steps"] / num_update_steps_per_epoch
    )

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if accelerator.is_main_process:
        accelerator.init_trackers("textual_inversion", config=config["args"])

    # Train!
    total_batch_size = (
        config["args"]["train_batch_size"]
        * accelerator.num_processes
        * st.session_state["textual_inversion"]["args"]["gradient_accumulation_steps"]
    )

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {config['args']['num_train_epochs']}")
    logger.info(
        f"  Instantaneous batch size per device = {config['args']['train_batch_size']}"
    )
    logger.info(
        f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
    )
    logger.info(
        f"  Gradient Accumulation steps = {config['args']['gradient_accumulation_steps']}"
    )
    logger.info(f"  Total optimization steps = {config['args']['max_train_steps']}")
    # Only show the progress bar once on each machine.
    progress_bar = tqdm(
        range(config["args"]["max_train_steps"]),
        disable=not accelerator.is_local_main_process,
    )
    progress_bar.set_description("Steps")
    global_step = 0
    encoded_pixel_values_cache = {}

    try:
        for epoch in range(config["args"]["num_train_epochs"]):
            server_state["textual_inversion"]["text_encoder"].train()
            for step, batch in enumerate(train_dataloader):
                with accelerator.accumulate(
                    server_state["textual_inversion"]["text_encoder"]
                ):
                    # Convert images to latent space
                    key = "|".join(batch["key"])
                    if encoded_pixel_values_cache.get(key, None) is None:
                        encoded_pixel_values_cache[key] = (
                            server_state["textual_inversion"]["vae"]
                            .encode(batch["pixel_values"])
                            .latent_dist
                        )
                    latents = (
                        encoded_pixel_values_cache[key].sample().detach().half()
                        * 0.18215
                    )

                    # Sample noise that we'll add to the latents
                    noise = torch.randn(latents.shape).to(latents.device)
                    bsz = latents.shape[0]
                    # Sample a random timestep for each image
                    timesteps = torch.randint(
                        0,
                        noise_scheduler.num_train_timesteps,
                        (bsz,),
                        device=latents.device,
                    ).long()

                    # Add noise to the latents according to the noise magnitude at each timestep
                    # (this is the forward diffusion process)
                    noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

                    # Get the text embedding for conditioning
                    encoder_hidden_states = server_state["textual_inversion"][
                        "text_encoder"
                    ](batch["input_ids"])[0]

                    # Predict the noise residual
                    noise_pred = server_state["textual_inversion"]["unet"](
                        noisy_latents, timesteps, encoder_hidden_states
                    ).sample

                    loss = (
                        F.mse_loss(noise_pred, noise, reduction="none")
                        .mean([1, 2, 3])
                        .mean()
                    )
                    accelerator.backward(loss)

                    # Zero out the gradients for all token embeddings except the newly added
                    # embeddings for the concept, as we only want to optimize the concept embeddings
                    if accelerator.num_processes > 1:
                        grads = (
                            server_state["textual_inversion"]["text_encoder"]
                            .module.get_input_embeddings()
                            .weight.grad
                        )
                    else:
                        grads = (
                            server_state["textual_inversion"]["text_encoder"]
                            .get_input_embeddings()
                            .weight.grad
                        )
                    # Get the index for tokens that we want to zero the grads for
                    index_grads_to_zero = (
                        torch.arange(
                            len(server_state["textual_inversion"]["tokenizer"])
                        )
                        != placeholder_token_id
                    )
                    grads.data[index_grads_to_zero, :] = grads.data[
                        index_grads_to_zero, :
                    ].fill_(0)

                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

                # try:
                # Checks if the accelerator has performed an optimization step behind the scenes
                if accelerator.sync_gradients:
                    progress_bar.update(1)
                    global_step += 1

                    if (
                        global_step % config["args"]["checkpoint_frequency"] == 0
                        and global_step > 0
                        and accelerator.is_main_process
                    ):
                        checkpointer.checkpoint(
                            global_step + global_step_offset,
                            server_state["textual_inversion"]["text_encoder"],
                        )
                        save_resume_file(
                            basepath,
                            {
                                "global_step": global_step + global_step_offset,
                                "resume_checkpoint": f"{basepath}/checkpoints/last.bin",
                            },
                            config,
                        )

                        checkpointer.save_samples(
                            global_step + global_step_offset,
                            server_state["textual_inversion"]["text_encoder"],
                            config["args"]["resolution"],
                            config["args"]["resolution"],
                            7.5,
                            0.0,
                            config["args"]["sample_steps"],
                        )

                        checkpointer.checkpoint(
                            global_step + global_step_offset,
                            server_state["textual_inversion"]["text_encoder"],
                            path=f"{basepath}/learned_embeds.bin",
                        )
                # except KeyError:
                # raise StopException

                logs = {
                    "loss": loss.detach().item(),
                    "lr": lr_scheduler.get_last_lr()[0],
                }
                progress_bar.set_postfix(**logs)

                # accelerator.log(logs, step=global_step)

                # try:
                if global_step >= config["args"]["max_train_steps"]:
                    break
                # except:
                # pass

            accelerator.wait_for_everyone()

        # Create the pipeline using the trained modules and save it.
        if accelerator.is_main_process:
            print("Finished! Saving final checkpoint and resume state.")
            checkpointer.checkpoint(
                global_step + global_step_offset,
                server_state["textual_inversion"]["text_encoder"],
                path=f"{basepath}/learned_embeds.bin",
            )

            save_resume_file(
                basepath,
                {
                    "global_step": global_step + global_step_offset,
                    "resume_checkpoint": f"{basepath}/checkpoints/last.bin",
                },
                config,
            )

            accelerator.end_training()

    except (KeyboardInterrupt, StopException):
        print("Received Streamlit StopException or KeyboardInterrupt")

        if accelerator.is_main_process:
            print("Interrupted, saving checkpoint and resume state...")
            checkpointer.checkpoint(
                global_step + global_step_offset,
                server_state["textual_inversion"]["text_encoder"],
            )

            config["args"] = save_resume_file(
                basepath,
                {
                    "global_step": global_step + global_step_offset,
                    "resume_checkpoint": f"{basepath}/checkpoints/last.bin",
                },
                config,
            )

            checkpointer.checkpoint(
                global_step + global_step_offset,
                server_state["textual_inversion"]["text_encoder"],
                path=f"{basepath}/learned_embeds.bin",
            )

        quit()


def layout():
    with st.form("textual-inversion"):
        # st.info("Under Construction. :construction_worker:")
        # parser = argparse.ArgumentParser(description="Simple example of a training script.")

        set_page_title("Textual Inversion - Stable Diffusion Playground")

        config_tab, output_tab, tensorboard_tab = st.tabs(
            ["Textual Inversion Config", "Ouput", "TensorBoard"]
        )

        with config_tab:
            col1, col2, col3, col4, col5 = st.columns(5, gap="large")

            if "textual_inversion" not in st.session_state:
                st.session_state["textual_inversion"] = {}

            if "textual_inversion" not in server_state:
                server_state["textual_inversion"] = {}

            if "args" not in st.session_state["textual_inversion"]:
                st.session_state["textual_inversion"]["args"] = {}

            with col1:
                st.session_state["textual_inversion"]["args"][
                    "pretrained_model_name_or_path"
                ] = st.text_input(
                    "Pretrained Model Path",
                    value=st.session_state[
                        "defaults"
                    ].textual_inversion.pretrained_model_name_or_path,
                    help="Path to pretrained model or model identifier from huggingface.co/models.",
                )

                st.session_state["textual_inversion"]["args"][
                    "tokenizer_name"
                ] = st.text_input(
                    "Tokenizer Name",
                    value=st.session_state["defaults"].textual_inversion.tokenizer_name,
                    help="Pretrained tokenizer name or path if not the same as model_name",
                )

                st.session_state["textual_inversion"]["args"][
                    "train_data_dir"
                ] = st.text_input(
                    "train_data_dir",
                    value="",
                    help="A folder containing the training data.",
                )

                st.session_state["textual_inversion"]["args"][
                    "placeholder_token"
                ] = st.text_input(
                    "Placeholder Token",
                    value="",
                    help="A token to use as a placeholder for the concept.",
                )

                st.session_state["textual_inversion"]["args"][
                    "initializer_token"
                ] = st.text_input(
                    "Initializer Token",
                    value="",
                    help="A token to use as initializer word.",
                )

                st.session_state["textual_inversion"]["args"][
                    "learnable_property"
                ] = st.selectbox(
                    "Learnable Property",
                    ["object", "style"],
                    index=0,
                    help="Choose between 'object' and 'style'",
                )

                st.session_state["textual_inversion"]["args"]["repeats"] = int(
                    st.text_input(
                        "Number of times to Repeat",
                        value=100,
                        help="How many times to repeat the training data.",
                    )
                )

                with col2:
                    st.session_state["textual_inversion"]["args"][
                        "output_dir"
                    ] = st.text_input(
                        "Output Directory",
                        value=str(os.path.join("outputs", "textual_inversion")),
                        help="The output directory where the model predictions and checkpoints will be written.",
                    )

                    st.session_state["textual_inversion"]["args"]["seed"] = seed_to_int(
                        st.text_input(
                            "Seed",
                            value=0,
                            help="A seed for reproducible training, if left empty a random one will be generated. Default: 0",
                        )
                    )

                    st.session_state["textual_inversion"]["args"]["resolution"] = int(
                        st.text_input(
                            "Resolution",
                            value=512,
                            help="The resolution for input images, all the images in the train/validation dataset will be resized to this resolution",
                        )
                    )

                    st.session_state["textual_inversion"]["args"][
                        "center_crop"
                    ] = st.checkbox(
                        "Center Image",
                        value=True,
                        help="Whether to center crop images before resizing to resolution",
                    )

                    st.session_state["textual_inversion"]["args"][
                        "train_batch_size"
                    ] = int(
                        st.text_input(
                            "Train Batch Size",
                            value=1,
                            help="Batch size (per device) for the training dataloader.",
                        )
                    )

                    st.session_state["textual_inversion"]["args"][
                        "num_train_epochs"
                    ] = int(
                        st.text_input(
                            "Number of Steps to Train",
                            value=100,
                            help="Number of steps to train.",
                        )
                    )

                    st.session_state["textual_inversion"]["args"][
                        "max_train_steps"
                    ] = int(
                        st.text_input(
                            "Max Number of Steps to Train",
                            value=5000,
                            help="Total number of training steps to perform.  If provided, overrides 'Number of Steps to Train'.",
                        )
                    )

                    with col3:
                        st.session_state["textual_inversion"]["args"][
                            "gradient_accumulation_steps"
                        ] = int(
                            st.text_input(
                                "Gradient Accumulation Steps",
                                value=1,
                                help="Number of updates steps to accumulate before performing a backward/update pass.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "learning_rate"
                        ] = float(
                            st.text_input(
                                "Learning Rate",
                                value=5.0e-04,
                                help="Initial learning rate (after the potential warmup period) to use.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "scale_lr"
                        ] = st.checkbox(
                            "Scale Learning Rate",
                            value=True,
                            help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
                        )

                        st.session_state["textual_inversion"]["args"][
                            "lr_scheduler"
                        ] = st.text_input(
                            "Learning Rate Scheduler",
                            value="constant",
                            help=(
                                "The scheduler type to use. Choose between ['linear', 'cosine', 'cosine_with_restarts', 'polynomial',"
                                " 'constant', 'constant_with_warmup']"
                            ),
                        )

                        st.session_state["textual_inversion"]["args"][
                            "lr_warmup_steps"
                        ] = int(
                            st.text_input(
                                "Learning Rate Warmup Steps",
                                value=500,
                                help="Number of steps for the warmup in the lr scheduler.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "adam_beta1"
                        ] = float(
                            st.text_input(
                                "Adam Beta 1",
                                value=0.9,
                                help="The beta1 parameter for the Adam optimizer.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "adam_beta2"
                        ] = float(
                            st.text_input(
                                "Adam Beta 2",
                                value=0.999,
                                help="The beta2 parameter for the Adam optimizer.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "adam_weight_decay"
                        ] = float(
                            st.text_input(
                                "Adam Weight Decay",
                                value=1e-2,
                                help="Weight decay to use.",
                            )
                        )

                        st.session_state["textual_inversion"]["args"][
                            "adam_epsilon"
                        ] = float(
                            st.text_input(
                                "Adam Epsilon",
                                value=1e-08,
                                help="Epsilon value for the Adam optimizer",
                            )
                        )

                        with col4:
                            st.session_state["textual_inversion"]["args"][
                                "mixed_precision"
                            ] = st.selectbox(
                                "Mixed Precision",
                                ["no", "fp16", "bf16"],
                                index=1,
                                help="Whether to use mixed precision. Choose"
                                "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
                                "and an Nvidia Ampere GPU.",
                            )

                            st.session_state["textual_inversion"]["args"][
                                "local_rank"
                            ] = int(
                                st.text_input(
                                    "Local Rank",
                                    value=1,
                                    help="For distributed training: local_rank",
                                )
                            )

                            st.session_state["textual_inversion"]["args"][
                                "checkpoint_frequency"
                            ] = int(
                                st.text_input(
                                    "Checkpoint Frequency",
                                    value=500,
                                    help="How often to save a checkpoint and sample image",
                                )
                            )

                            # stable_sample_batches is crashing when saving the samples so for now I will disable it util its fixed.
                            # st.session_state["textual_inversion"]["args"]["stable_sample_batches"] = int(st.text_input("Stable Sample Batches",  value=0,
                            # help="Number of fixed seed sample batches to generate per checkpoint"))

                            st.session_state["textual_inversion"]["args"][
                                "stable_sample_batches"
                            ] = 0

                            st.session_state["textual_inversion"]["args"][
                                "random_sample_batches"
                            ] = int(
                                st.text_input(
                                    "Random Sample Batches",
                                    value=2,
                                    help="Number of random seed sample batches to generate per checkpoint",
                                )
                            )

                            st.session_state["textual_inversion"]["args"][
                                "sample_batch_size"
                            ] = int(
                                st.text_input(
                                    "Sample Batch Size",
                                    value=1,
                                    help="Number of samples to generate per batch",
                                )
                            )

                            st.session_state["textual_inversion"]["args"][
                                "sample_steps"
                            ] = int(
                                st.text_input(
                                    "Sample Steps",
                                    value=100,
                                    help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.",
                                )
                            )

                            st.session_state["textual_inversion"]["args"][
                                "custom_templates"
                            ] = st.text_input(
                                "Custom Templates",
                                value="",
                                help="A semicolon-delimited list of custom template to use for samples, using {} as a placeholder for the concept.",
                            )
                            with col5:
                                st.session_state["textual_inversion"]["args"][
                                    "resume"
                                ] = st.checkbox(
                                    label="Resume Previous Run?",
                                    value=False,
                                    help="Resume previous run, if a valid resume.json file is on the output dir \
								                                                                      it will be used, otherwise if the 'Resume From' field bellow contains a valid resume.json file \
								                                                                      that one will be used.",
                                )

                                st.session_state["textual_inversion"]["args"][
                                    "resume_from"
                                ] = st.text_input(
                                    label="Resume From",
                                    help="Path to a directory to resume training from (ie, logs/token_name)",
                                )

                                # st.session_state["textual_inversion"]["args"]["resume_checkpoint"] = st.file_uploader("Resume Checkpoint", type=["bin"],
                                # help="Path to a specific checkpoint to resume training from (ie, logs/token_name/checkpoints/something.bin).")

                                # st.session_state["textual_inversion"]["args"]["st.session_state["textual_inversion"]"] = st.file_uploader("st.session_state["textual_inversion"] File",  type=["json"],
                                # help="Path to a JSON st.session_state["textual_inversion"]uration file containing arguments for invoking this script."
                                # "If resume_from is given, its resume.json takes priority over this.")
            #
            # print (os.path.join(st.session_state["textual_inversion"]["args"]["output_dir"],st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"),"resume.json"))
            # print (os.path.exists(os.path.join(st.session_state["textual_inversion"]["args"]["output_dir"],st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"),"resume.json")))
            if os.path.exists(
                os.path.join(
                    st.session_state["textual_inversion"]["args"]["output_dir"],
                    st.session_state["textual_inversion"]["args"][
                        "placeholder_token"
                    ].strip("<>"),
                    "resume.json",
                )
            ):
                st.session_state["textual_inversion"]["args"][
                    "resume_from"
                ] = os.path.join(
                    st.session_state["textual_inversion"]["args"]["output_dir"],
                    st.session_state["textual_inversion"]["args"][
                        "placeholder_token"
                    ].strip("<>"),
                )
                # print (st.session_state["textual_inversion"]["args"]["resume_from"])

            if os.path.exists(
                os.path.join(
                    st.session_state["textual_inversion"]["args"]["output_dir"],
                    st.session_state["textual_inversion"]["args"][
                        "placeholder_token"
                    ].strip("<>"),
                    "checkpoints",
                    "last.bin",
                )
            ):
                st.session_state["textual_inversion"]["args"][
                    "resume_checkpoint"
                ] = os.path.join(
                    st.session_state["textual_inversion"]["args"]["output_dir"],
                    st.session_state["textual_inversion"]["args"][
                        "placeholder_token"
                    ].strip("<>"),
                    "checkpoints",
                    "last.bin",
                )

            # if "resume_from" in st.session_state["textual_inversion"]["args"]:
            # if st.session_state["textual_inversion"]["args"]["resume_from"]:
            # if os.path.exists(os.path.join(st.session_state["textual_inversion"]['args']['resume_from'], "resume.json")):
            # with open(os.path.join(st.session_state["textual_inversion"]['args']['resume_from'], "resume.json"), 'rt') as f:
            # try:
            # resume_json = json.load(f)["args"]
            # st.session_state["textual_inversion"]["args"] = OmegaConf.merge(st.session_state["textual_inversion"]["args"], resume_json)
            # st.session_state["textual_inversion"]["args"]["resume_from"] = os.path.join(
            # st.session_state["textual_inversion"]["args"]["output_dir"], st.session_state["textual_inversion"]["args"]["placeholder_token"].strip("<>"))
            # except json.decoder.JSONDecodeError:
            # pass

            # print(st.session_state["textual_inversion"]["args"])
            # print(st.session_state["textual_inversion"]["args"]['resume_from'])

            # elif st.session_state["textual_inversion"]["args"]["st.session_state["textual_inversion"]"] is not None:
            # with open(st.session_state["textual_inversion"]["args"]["st.session_state["textual_inversion"]"], 'rt') as f:
            # args = parser.parse_args(namespace=argparse.Namespace(**json.load(f)["args"]))

            env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
            if (
                env_local_rank != -1
                and env_local_rank
                != st.session_state["textual_inversion"]["args"]["local_rank"]
            ):
                st.session_state["textual_inversion"]["args"][
                    "local_rank"
                ] = env_local_rank

            if st.session_state["textual_inversion"]["args"]["train_data_dir"] is None:
                st.error("You must specify --train_data_dir")

            if (
                st.session_state["textual_inversion"]["args"][
                    "pretrained_model_name_or_path"
                ]
                is None
            ):
                st.error("You must specify --pretrained_model_name_or_path")

            if (
                st.session_state["textual_inversion"]["args"]["placeholder_token"]
                is None
            ):
                st.error("You must specify --placeholder_token")

            if (
                st.session_state["textual_inversion"]["args"]["initializer_token"]
                is None
            ):
                st.error("You must specify --initializer_token")

            if st.session_state["textual_inversion"]["args"]["output_dir"] is None:
                st.error("You must specify --output_dir")

            # add a spacer and the submit button for the form.

            st.session_state["textual_inversion"]["message"] = st.empty()
            st.session_state["textual_inversion"]["progress_bar"] = st.empty()

            st.write("---")

            submit = st.form_submit_button("Run", help="")
            if submit:
                if "pipe" in st.session_state:
                    del st.session_state["pipe"]
                if "model" in st.session_state:
                    del st.session_state["model"]

                set_page_title("Running Textual Inversion - Stable Diffusion WebUI")
                # st.session_state["textual_inversion"]["message"].info("Textual Inversion Running. For more info check the progress on your console or the Ouput Tab.")

                try:
                    # try:
                    # run textual inversion.
                    config = st.session_state["textual_inversion"]
                    textual_inversion(config)
                    # except RuntimeError:
                    # if "pipeline" in server_state["textual_inversion"]:
                    # del server_state['textual_inversion']["checker"]
                    # del server_state['textual_inversion']["unwrapped"]
                    # del server_state['textual_inversion']["pipeline"]

                    # run textual inversion.
                    # config = st.session_state['textual_inversion']
                    # textual_inversion(config)

                    set_page_title("Textual Inversion - Stable Diffusion WebUI")

                except StopException:
                    set_page_title("Textual Inversion - Stable Diffusion WebUI")
                    print("Received Streamlit StopException")

                st.session_state["textual_inversion"]["message"].empty()

        #
        with output_tab:
            st.info("Under Construction. :construction_worker:")

            # st.info("Nothing to show yet. Maybe try running some training first.")

            # st.session_state["textual_inversion"]["preview_image"] = st.empty()
            # st.session_state["textual_inversion"]["progress_bar"] = st.empty()

        with tensorboard_tab:
            # st.info("Under Construction. :construction_worker:")

            # Start TensorBoard
            st_tensorboard(
                logdir=os.path.join("outputs", "textual_inversion"), port=8888
            )


================================================
FILE: webui/streamlit/scripts/txt2img.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import (
    st,
    MemUsageMonitor,
    server_state,
    no_rerun,
    logger,
    set_page_title,
    custom_models_available,
    RealESRGAN_available,
    GFPGAN_available,
    LDSR_available,
)

# load_models, hc, seed_to_int, \
# get_next_sequence_number, check_prompt_length, torch_gc, \
# save_sample, generation_callback, process_images, \
# KDiffusionSampler, \

# streamlit imports
from streamlit.runtime.scriptrunner import StopException

# streamlit components section

# from streamlit.elements import image as STImage
import streamlit.components.v1 as components

# from streamlit.runtime.media_file_manager  import media_file_manager
from streamlit.elements.image import image_to_url

# other imports

import base64, uuid
import os, sys, datetime, time
from PIL import Image
import requests
from slugify import slugify
from ldm.models.diffusion.ddim import DDIMSampler
from typing import Union
from io import BytesIO
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler


# streamlit components
from custom_components import sygil_suggestions

# Temp imports


# end of imports
# ---------------------------------------------------------------------------------------------------------------

sygil_suggestions.init()

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging

    logging.set_verbosity_error()
except:
    pass

#
# Dev mode (server)
# _component_func = components.declare_component(
#         "sd-gallery",
#         url="http://localhost:3001",
#     )

# Init Vuejs component
_component_func = components.declare_component(
    "sd-gallery", "./frontend/dists/sd-gallery/dist"
)


def sdGallery(images=[], key=None):
    component_value = _component_func(images=imgsToGallery(images), key=key, default="")
    return component_value


def imgsToGallery(images):
    urls = []
    for i in images:
        # random string for id
        random_id = str(uuid.uuid4())
        url = image_to_url(
            image=i,
            image_id=random_id,
            width=i.width,
            clamp=False,
            channels="RGB",
            output_format="PNG",
        )
        # image_io = BytesIO()
        # i.save(image_io, 'PNG')
        # width, height = i.size
        # image_id = "%s" % (str(images.index(i)))
        # (data, mimetype) = STImage._normalize_to_bytes(image_io.getvalue(), width, 'auto')
        # this_file = media_file_manager.add(data, mimetype, image_id)
        # img_str = this_file.url
        urls.append(url)

    return urls


class plugin_info:
    plugname = "txt2img"
    description = "Text to Image"
    isTab = True
    displayPriority = 1


@logger.catch(reraise=True)
def stable_horde(
    outpath,
    prompt,
    seed,
    sampler_name,
    save_grid,
    batch_size,
    n_iter,
    steps,
    cfg_scale,
    width,
    height,
    prompt_matrix,
    use_GFPGAN,
    GFPGAN_model,
    use_RealESRGAN,
    realesrgan_model_name,
    use_LDSR,
    LDSR_model_name,
    ddim_eta,
    normalize_prompt_weights,
    save_individual_images,
    sort_samples,
    write_info_files,
    jpg_sample,
    variant_amount,
    variant_seed,
    api_key,
    nsfw=True,
    censor_nsfw=False,
):
    log = []

    log.append("Generating image with Stable Horde.")

    st.session_state["progress_bar_text"].code("\n".join(log), language="")

    # start time after garbage collection (or before?)
    start_time = time.time()

    # We will use this date here later for the folder name, need to start_time if not need
    datetime.datetime.now()

    mem_mon = MemUsageMonitor("MemMon")
    mem_mon.start()

    os.makedirs(outpath, exist_ok=True)

    sample_path = os.path.join(outpath, "samples")
    os.makedirs(sample_path, exist_ok=True)

    params = {
        "sampler_name": "k_euler",
        "toggles": [1, 4],
        "cfg_scale": cfg_scale,
        "seed": str(seed),
        "width": width,
        "height": height,
        "seed_variation": variant_seed if variant_seed else 1,
        "steps": int(steps),
        "n": int(n_iter)
        # You can put extra params here if you wish
    }

    final_submit_dict = {
        "prompt": prompt,
        "params": params,
        "nsfw": nsfw,
        "censor_nsfw": censor_nsfw,
        "trusted_workers": True,
        "workers": [],
    }
    log.append(final_submit_dict)

    headers = {"apikey": api_key}
    logger.debug(final_submit_dict)
    st.session_state["progress_bar_text"].code("\n".join(str(log)), language="")

    horde_url = "https://stablehorde.net"

    submit_req = requests.post(
        f"{horde_url}/api/v2/generate/async", json=final_submit_dict, headers=headers
    )
    if submit_req.ok:
        submit_results = submit_req.json()
        logger.debug(submit_results)

        log.append(submit_results)
        st.session_state["progress_bar_text"].code("".join(str(log)), language="")

        req_id = submit_results["id"]
        is_done = False
        while not is_done:
            chk_req = requests.get(f"{horde_url}/api/v2/generate/check/{req_id}")
            if not chk_req.ok:
                logger.error(chk_req.text)
                return
            chk_results = chk_req.json()
            logger.info(chk_results)
            is_done = chk_results["done"]
            time.sleep(1)
        retrieve_req = requests.get(f"{horde_url}/api/v2/generate/status/{req_id}")
        if not retrieve_req.ok:
            logger.error(retrieve_req.text)
            return
        results_json = retrieve_req.json()
        # logger.debug(results_json)
        results = results_json["generations"]

        output_images = []
        comments = []

        if not st.session_state["defaults"].general.no_verify_input:
            try:
                check_prompt_length(prompt, comments)
            except:
                import traceback

                logger.info("Error verifying input:", file=sys.stderr)
                logger.info(traceback.format_exc(), file=sys.stderr)

        all_prompts = batch_size * n_iter * [prompt]
        all_seeds = [seed + x for x in range(len(all_prompts))]

        for iter in range(len(results)):
            b64img = results[iter]["img"]
            base64_bytes = b64img.encode("utf-8")
            img_bytes = base64.b64decode(base64_bytes)
            img = Image.open(BytesIO(img_bytes))

            sanitized_prompt = slugify(prompt)

            prompts = all_prompts[iter * batch_size : (iter + 1) * batch_size]
            # captions = prompt_matrix_parts[n * batch_size:(n + 1) * batch_size]
            seeds = all_seeds[iter * batch_size : (iter + 1) * batch_size]

            if sort_samples:
                full_path = os.path.join(os.getcwd(), sample_path, sanitized_prompt)

                sanitized_prompt = sanitized_prompt[: 200 - len(full_path)]
                sample_path_i = os.path.join(sample_path, sanitized_prompt)

                # print(f"output folder length: {len(os.path.join(os.getcwd(), sample_path_i))}")
                # print(os.path.join(os.getcwd(), sample_path_i))

                os.makedirs(sample_path_i, exist_ok=True)
                base_count = get_next_sequence_number(sample_path_i)
                filename = f"{base_count:05}-{steps}_{sampler_name}_{seeds[iter]}"
            else:
                full_path = os.path.join(os.getcwd(), sample_path)
                sample_path_i = sample_path
                base_count = get_next_sequence_number(sample_path_i)
                filename = (
                    f"{base_count:05}-{steps}_{sampler_name}_{seed}_{sanitized_prompt}"[
                        : 200 - len(full_path)
                    ]
                )  # same as before

            save_sample(
                img,
                sample_path_i,
                filename,
                jpg_sample,
                prompts,
                seeds,
                width,
                height,
                steps,
                cfg_scale,
                normalize_prompt_weights,
                use_GFPGAN,
                write_info_files,
                prompt_matrix,
                init_img=None,
                denoising_strength=0.75,
                resize_mode=None,
                uses_loopback=False,
                uses_random_seed_loopback=False,
                save_grid=save_grid,
                sort_samples=sampler_name,
                sampler_name=sampler_name,
                ddim_eta=ddim_eta,
                n_iter=n_iter,
                batch_size=batch_size,
                i=iter,
                save_individual_images=save_individual_images,
                model_name="Stable Diffusion v1.5",
            )

            output_images.append(img)

            # update image on the UI so we can see the progress
            if "preview_image" in st.session_state:
                st.session_state["preview_image"].image(img)

            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].empty()

            # if len(results) > 1:
            # final_filename = f"{iter}_{filename}"
            # img.save(final_filename)
            # logger.info(f"Saved {final_filename}")
    else:
        if "progress_bar_text" in st.session_state:
            st.session_state["progress_bar_text"].error(submit_req.text)

        logger.error(submit_req.text)

    mem_max_used, mem_total = mem_mon.read_and_stop()
    time_diff = time.time() - start_time

    info = f"""
            {prompt}
            Steps: {steps}, Sampler: {sampler_name}, CFG scale: {cfg_scale}, Seed: {seed}{', GFPGAN' if use_GFPGAN else ''}{', '+realesrgan_model_name if use_RealESRGAN else ''}
            {', Prompt Matrix Mode.' if prompt_matrix else ''}""".strip()

    stats = f"""
            Took { round(time_diff, 2) }s total ({ round(time_diff/(len(all_prompts)),2) }s per image)
            Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%"""

    for comment in comments:
        info += "\n\n" + comment

    # mem_mon.stop()
    # del mem_mon
    torch_gc()

    return output_images, seed, info, stats


#
@logger.catch(reraise=True)
def txt2img(
    prompt: str,
    ddim_steps: int,
    sampler_name: str,
    n_iter: int,
    batch_size: int,
    cfg_scale: float,
    seed: Union[int, str, None],
    height: int,
    width: int,
    separate_prompts: bool = False,
    normalize_prompt_weights: bool = True,
    save_individual_images: bool = True,
    save_grid: bool = True,
    group_by_prompt: bool = True,
    save_as_jpg: bool = True,
    use_GFPGAN: bool = True,
    GFPGAN_model: str = "GFPGANv1.3",
    use_RealESRGAN: bool = False,
    RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B",
    use_LDSR: bool = True,
    LDSR_model: str = "model",
    fp=None,
    variant_amount: float = 0.0,
    variant_seed: int = None,
    ddim_eta: float = 0.0,
    write_info_files: bool = True,
    use_stable_horde: bool = False,
    stable_horde_key: str = "0000000000",
):
    outpath = st.session_state["defaults"].general.outdir_txt2img

    seed = seed_to_int(seed)

    if not use_stable_horde:
        if sampler_name == "PLMS":
            sampler = PLMSSampler(server_state["model"])
        elif sampler_name == "DDIM":
            sampler = DDIMSampler(server_state["model"])
        elif sampler_name == "k_dpm_2_a":
            sampler = KDiffusionSampler(server_state["model"], "dpm_2_ancestral")
        elif sampler_name == "k_dpm_2":
            sampler = KDiffusionSampler(server_state["model"], "dpm_2")
        elif sampler_name == "k_dpmpp_2m":
            sampler = KDiffusionSampler(server_state["model"], "dpmpp_2m")
        elif sampler_name == "k_euler_a":
            sampler = KDiffusionSampler(server_state["model"], "euler_ancestral")
        elif sampler_name == "k_euler":
            sampler = KDiffusionSampler(server_state["model"], "euler")
        elif sampler_name == "k_heun":
            sampler = KDiffusionSampler(server_state["model"], "heun")
        elif sampler_name == "k_lms":
            sampler = KDiffusionSampler(server_state["model"], "lms")
        else:
            raise Exception("Unknown sampler: " + sampler_name)

        def init():
            pass

        def sample(
            init_data, x, conditioning, unconditional_conditioning, sampler_name
        ):
            samples_ddim, _ = sampler.sample(
                S=ddim_steps,
                conditioning=conditioning,
                batch_size=int(x.shape[0]),
                shape=x[0].shape,
                verbose=False,
                unconditional_guidance_scale=cfg_scale,
                unconditional_conditioning=unconditional_conditioning,
                eta=ddim_eta,
                x_T=x,
                img_callback=generation_callback
                if not server_state["bridge"]
                else None,
                log_every_t=int(
                    st.session_state.update_preview_frequency
                    if not server_state["bridge"]
                    else 100
                ),
            )

            return samples_ddim

    if use_stable_horde:
        output_images, seed, info, stats = stable_horde(
            prompt=prompt,
            seed=seed,
            outpath=outpath,
            sampler_name=sampler_name,
            save_grid=save_grid,
            batch_size=batch_size,
            n_iter=n_iter,
            steps=ddim_steps,
            cfg_scale=cfg_scale,
            width=width,
            height=height,
            prompt_matrix=separate_prompts,
            use_GFPGAN=use_GFPGAN,
            GFPGAN_model=GFPGAN_model,
            use_RealESRGAN=use_RealESRGAN,
            realesrgan_model_name=RealESRGAN_model,
            use_LDSR=use_LDSR,
            LDSR_model_name=LDSR_model,
            ddim_eta=ddim_eta,
            normalize_prompt_weights=normalize_prompt_weights,
            save_individual_images=save_individual_images,
            sort_samples=group_by_prompt,
            write_info_files=write_info_files,
            jpg_sample=save_as_jpg,
            variant_amount=variant_amount,
            variant_seed=variant_seed,
            api_key=stable_horde_key,
        )
    else:
        # try:
        output_images, seed, info, stats = process_images(
            outpath=outpath,
            func_init=init,
            func_sample=sample,
            prompt=prompt,
            seed=seed,
            sampler_name=sampler_name,
            save_grid=save_grid,
            batch_size=batch_size,
            n_iter=n_iter,
            steps=ddim_steps,
            cfg_scale=cfg_scale,
            width=width,
            height=height,
            prompt_matrix=separate_prompts,
            use_GFPGAN=use_GFPGAN,
            GFPGAN_model=GFPGAN_model,
            use_RealESRGAN=use_RealESRGAN,
            realesrgan_model_name=RealESRGAN_model,
            use_LDSR=use_LDSR,
            LDSR_model_name=LDSR_model,
            ddim_eta=ddim_eta,
            normalize_prompt_weights=normalize_prompt_weights,
            save_individual_images=save_individual_images,
            sort_samples=group_by_prompt,
            write_info_files=write_info_files,
            jpg_sample=save_as_jpg,
            variant_amount=variant_amount,
            variant_seed=variant_seed,
        )

        del sampler

    return output_images, seed, info, stats

    # except RuntimeError as e:
    # err = e
    # err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
    # stats = err_msg
    # return [], seed, 'err', stats


#
@logger.catch(reraise=True)
def layout():
    with st.form("txt2img-inputs"):
        st.session_state["generation_mode"] = "txt2img"

        input_col1, generate_col1 = st.columns([10, 1])

        with input_col1:
            # prompt = st.text_area("Input Text","")
            placeholder = "A corgi wearing a top hat as an oil painting."
            prompt = st.text_area("Input Text", "", placeholder=placeholder, height=54)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].general.enable_suggestions:
                    sygil_suggestions.suggestion_area(placeholder)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].admin.global_negative_prompt:
                    prompt += f"### {st.session_state['defaults'].admin.global_negative_prompt}"

            # print(prompt)

        # creating the page layout using columns
        col1, col2, col3 = st.columns([2, 5, 2], gap="large")

        with col1:
            width = st.slider(
                "Width:",
                min_value=st.session_state["defaults"].txt2img.width.min_value,
                max_value=st.session_state["defaults"].txt2img.width.max_value,
                value=st.session_state["defaults"].txt2img.width.value,
                step=st.session_state["defaults"].txt2img.width.step,
            )
            height = st.slider(
                "Height:",
                min_value=st.session_state["defaults"].txt2img.height.min_value,
                max_value=st.session_state["defaults"].txt2img.height.max_value,
                value=st.session_state["defaults"].txt2img.height.value,
                step=st.session_state["defaults"].txt2img.height.step,
            )
            cfg_scale = st.number_input(
                "CFG (Classifier Free Guidance Scale):",
                min_value=st.session_state["defaults"].txt2img.cfg_scale.min_value,
                value=st.session_state["defaults"].txt2img.cfg_scale.value,
                step=st.session_state["defaults"].txt2img.cfg_scale.step,
                help="How strongly the image should follow the prompt.",
            )

            seed = st.text_input(
                "Seed:",
                value=st.session_state["defaults"].txt2img.seed,
                help=" The seed to use, if left blank a random seed will be generated.",
            )

            with st.expander("Batch Options"):
                # batch_count = st.slider("Batch count.", min_value=st.session_state['defaults'].txt2img.batch_count.min_value, max_value=st.session_state['defaults'].txt2img.batch_count.max_value,
                # value=st.session_state['defaults'].txt2img.batch_count.value, step=st.session_state['defaults'].txt2img.batch_count.step,
                # help="How many iterations or batches of images to generate in total.")

                # batch_size = st.slider("Batch size", min_value=st.session_state['defaults'].txt2img.batch_size.min_value, max_value=st.session_state['defaults'].txt2img.batch_size.max_value,
                # value=st.session_state.defaults.txt2img.batch_size.value, step=st.session_state.defaults.txt2img.batch_size.step,
                # help="How many images are at once in a batch.\
                # It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
                # Default: 1")

                st.session_state["batch_count"] = st.number_input(
                    "Batch count.",
                    value=st.session_state["defaults"].txt2img.batch_count.value,
                    help="How many iterations or batches of images to generate in total.",
                )

                st.session_state["batch_size"] = st.number_input(
                    "Batch size",
                    value=st.session_state.defaults.txt2img.batch_size.value,
                    help="How many images are at once in a batch.\
                                                                   It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes \
                                                                   to finish generation as more images are generated at once.\
                                                                   Default: 1",
                )

            with st.expander("Preview Settings"):
                st.session_state["update_preview"] = st.session_state[
                    "defaults"
                ].general.update_preview
                st.session_state["update_preview_frequency"] = st.number_input(
                    "Update Image Preview Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].txt2img.update_preview_frequency,
                    help="Frequency in steps at which the the preview image is updated. By default the frequency \
                                                                               is set to 10 step.",
                )

        with col2:
            preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])

            with preview_tab:
                # st.write("Image")
                # Image for testing
                # image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
                # new_image = image.resize((175, 240))
                # preview_image = st.image(image)

                # create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
                st.session_state["preview_image"] = st.empty()

                st.session_state["progress_bar_text"] = st.empty()
                st.session_state["progress_bar_text"].info(
                    "Nothing but crickets here, try generating something first."
                )

                st.session_state["progress_bar"] = st.empty()

                message = st.empty()

            with gallery_tab:
                st.session_state["gallery"] = st.empty()
                # st.session_state["gallery"].info("Nothing but crickets here, try generating something first.")

        with col3:
            # If we have custom models available on the "models/custom"
            # folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
            custom_models_available()

            if server_state["CustomModel_available"]:
                st.session_state["custom_model"] = st.selectbox(
                    "Custom Model:",
                    server_state["custom_models"],
                    index=server_state["custom_models"].index(
                        st.session_state["defaults"].general.default_model
                    ),
                    help="Select the model you want to use. This option is only available if you have custom models \
                                                                on your 'models/custom' folder. The model name that will be shown here is the same as the name\
                                                                the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
                                                                will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.5",
                )

            st.session_state.sampling_steps = st.number_input(
                "Sampling Steps",
                value=st.session_state.defaults.txt2img.sampling_steps.value,
                min_value=st.session_state.defaults.txt2img.sampling_steps.min_value,
                step=st.session_state["defaults"].txt2img.sampling_steps.step,
                help="Set the default number of sampling steps to use. Default is: 30 (with k_euler)",
            )

            sampler_name_list = [
                "k_lms",
                "k_euler",
                "k_euler_a",
                "k_dpm_2",
                "k_dpm_2_a",
                "k_dpmpp_2m",
                "k_heun",
                "PLMS",
                "DDIM",
            ]
            sampler_name = st.selectbox(
                "Sampling method",
                sampler_name_list,
                index=sampler_name_list.index(
                    st.session_state["defaults"].txt2img.default_sampler
                ),
                help="Sampling method to use. Default: k_euler",
            )

            with st.expander("Advanced"):
                with st.expander("Stable Horde"):
                    use_stable_horde = st.checkbox(
                        "Use Stable Horde",
                        value=False,
                        help="Use the Stable Horde to generate images. More info can be found at https://stablehorde.net/",
                    )
                    stable_horde_key = st.text_input(
                        "Stable Horde Api Key",
                        value=st.session_state["defaults"].general.stable_horde_api,
                        type="password",
                        help="Optional Api Key used for the Stable Horde Bridge, if no api key is added the horde will be used anonymously.",
                    )

                with st.expander("Output Settings"):
                    separate_prompts = st.checkbox(
                        "Create Prompt Matrix.",
                        value=st.session_state["defaults"].txt2img.separate_prompts,
                        help="Separate multiple prompts using the `|` character, and get all combinations of them.",
                    )

                    normalize_prompt_weights = st.checkbox(
                        "Normalize Prompt Weights.",
                        value=st.session_state[
                            "defaults"
                        ].txt2img.normalize_prompt_weights,
                        help="Ensure the sum of all weights add up to 1.0",
                    )

                    save_individual_images = st.checkbox(
                        "Save individual images.",
                        value=st.session_state[
                            "defaults"
                        ].txt2img.save_individual_images,
                        help="Save each image generated before any filter or enhancement is applied.",
                    )

                    save_grid = st.checkbox(
                        "Save grid",
                        value=st.session_state["defaults"].txt2img.save_grid,
                        help="Save a grid with all the images generated into a single image.",
                    )
                    group_by_prompt = st.checkbox(
                        "Group results by prompt",
                        value=st.session_state["defaults"].txt2img.group_by_prompt,
                        help="Saves all the images with the same prompt into the same folder. When using a prompt matrix each prompt combination will have its own folder.",
                    )

                    write_info_files = st.checkbox(
                        "Write Info file",
                        value=st.session_state["defaults"].txt2img.write_info_files,
                        help="Save a file next to the image with informartion about the generation.",
                    )

                    save_as_jpg = st.checkbox(
                        "Save samples as jpg",
                        value=st.session_state["defaults"].txt2img.save_as_jpg,
                        help="Saves the images as jpg instead of png.",
                    )

                # check if GFPGAN, RealESRGAN and LDSR are available.
                # if "GFPGAN_available" not in st.session_state:
                GFPGAN_available()

                # if "RealESRGAN_available" not in st.session_state:
                RealESRGAN_available()

                # if "LDSR_available" not in st.session_state:
                LDSR_available()

                if (
                    st.session_state["GFPGAN_available"]
                    or st.session_state["RealESRGAN_available"]
                    or st.session_state["LDSR_available"]
                ):
                    with st.expander("Post-Processing"):
                        face_restoration_tab, upscaling_tab = st.tabs(
                            ["Face Restoration", "Upscaling"]
                        )
                        with face_restoration_tab:
                            # GFPGAN used for face restoration
                            if st.session_state["GFPGAN_available"]:
                                # with st.expander("Face Restoration"):
                                # if st.session_state["GFPGAN_available"]:
                                # with st.expander("GFPGAN"):
                                st.session_state["use_GFPGAN"] = st.checkbox(
                                    "Use GFPGAN",
                                    value=st.session_state[
                                        "defaults"
                                    ].txt2img.use_GFPGAN,
                                    help="Uses the GFPGAN model to improve faces after the generation.\
                                                                                 This greatly improve the quality and consistency of faces but uses\
                                                                                 extra VRAM. Disable if you need the extra VRAM.",
                                )

                                st.session_state["GFPGAN_model"] = st.selectbox(
                                    "GFPGAN model",
                                    st.session_state["GFPGAN_models"],
                                    index=st.session_state["GFPGAN_models"].index(
                                        st.session_state[
                                            "defaults"
                                        ].general.GFPGAN_model
                                    ),
                                )

                            # st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')

                            else:
                                st.session_state["use_GFPGAN"] = False

                        with upscaling_tab:
                            st.session_state["use_upscaling"] = st.checkbox(
                                "Use Upscaling",
                                value=st.session_state[
                                    "defaults"
                                ].txt2img.use_upscaling,
                            )

                            # RealESRGAN and LDSR used for upscaling.
                            if (
                                st.session_state["RealESRGAN_available"]
                                or st.session_state["LDSR_available"]
                            ):
                                upscaling_method_list = []
                                if st.session_state["RealESRGAN_available"]:
                                    upscaling_method_list.append("RealESRGAN")
                                if st.session_state["LDSR_available"]:
                                    upscaling_method_list.append("LDSR")

                                # print (st.session_state["RealESRGAN_available"])
                                st.session_state["upscaling_method"] = st.selectbox(
                                    "Upscaling Method",
                                    upscaling_method_list,
                                    index=upscaling_method_list.index(
                                        st.session_state[
                                            "defaults"
                                        ].general.upscaling_method
                                    )
                                    if st.session_state[
                                        "defaults"
                                    ].general.upscaling_method
                                    in upscaling_method_list
                                    else 0,
                                )

                                if st.session_state["RealESRGAN_available"]:
                                    with st.expander("RealESRGAN"):
                                        if (
                                            st.session_state["upscaling_method"]
                                            == "RealESRGAN"
                                            and st.session_state["use_upscaling"]
                                        ):
                                            st.session_state["use_RealESRGAN"] = True
                                        else:
                                            st.session_state["use_RealESRGAN"] = False

                                        st.session_state[
                                            "RealESRGAN_model"
                                        ] = st.selectbox(
                                            "RealESRGAN model",
                                            st.session_state["RealESRGAN_models"],
                                            index=st.session_state[
                                                "RealESRGAN_models"
                                            ].index(
                                                st.session_state[
                                                    "defaults"
                                                ].general.RealESRGAN_model
                                            ),
                                        )
                                else:
                                    st.session_state["use_RealESRGAN"] = False
                                    st.session_state[
                                        "RealESRGAN_model"
                                    ] = "RealESRGAN_x4plus"

                                #
                                if st.session_state["LDSR_available"]:
                                    with st.expander("LDSR"):
                                        if (
                                            st.session_state["upscaling_method"]
                                            == "LDSR"
                                            and st.session_state["use_upscaling"]
                                        ):
                                            st.session_state["use_LDSR"] = True
                                        else:
                                            st.session_state["use_LDSR"] = False

                                        st.session_state["LDSR_model"] = st.selectbox(
                                            "LDSR model",
                                            st.session_state["LDSR_models"],
                                            index=st.session_state["LDSR_models"].index(
                                                st.session_state[
                                                    "defaults"
                                                ].general.LDSR_model
                                            ),
                                        )

                                        st.session_state[
                                            "ldsr_sampling_steps"
                                        ] = st.number_input(
                                            "Sampling Steps",
                                            value=st.session_state[
                                                "defaults"
                                            ].txt2img.LDSR_config.sampling_steps,
                                            help="",
                                        )

                                        st.session_state[
                                            "preDownScale"
                                        ] = st.number_input(
                                            "PreDownScale",
                                            value=st.session_state[
                                                "defaults"
                                            ].txt2img.LDSR_config.preDownScale,
                                            help="",
                                        )

                                        st.session_state[
                                            "postDownScale"
                                        ] = st.number_input(
                                            "postDownScale",
                                            value=st.session_state[
                                                "defaults"
                                            ].txt2img.LDSR_config.postDownScale,
                                            help="",
                                        )

                                        downsample_method_list = ["Nearest", "Lanczos"]
                                        st.session_state[
                                            "downsample_method"
                                        ] = st.selectbox(
                                            "Downsample Method",
                                            downsample_method_list,
                                            index=downsample_method_list.index(
                                                st.session_state[
                                                    "defaults"
                                                ].txt2img.LDSR_config.downsample_method
                                            ),
                                        )

                                else:
                                    st.session_state["use_LDSR"] = False
                                    st.session_state["LDSR_model"] = "model"

                with st.expander("Variant"):
                    variant_amount = st.slider(
                        "Variant Amount:",
                        value=st.session_state["defaults"].txt2img.variant_amount.value,
                        min_value=st.session_state[
                            "defaults"
                        ].txt2img.variant_amount.min_value,
                        max_value=st.session_state[
                            "defaults"
                        ].txt2img.variant_amount.max_value,
                        step=st.session_state["defaults"].txt2img.variant_amount.step,
                    )
                    variant_seed = st.text_input(
                        "Variant Seed:",
                        value=st.session_state["defaults"].txt2img.seed,
                        help="The seed to use when generating a variant, if left blank a random seed will be generated.",
                    )

            # galleryCont = st.empty()

        # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
        generate_col1.write("")
        generate_col1.write("")
        generate_button = generate_col1.form_submit_button("Generate")

        #
        if generate_button:
            with col2:
                with no_rerun:
                    if not use_stable_horde:
                        with hc.HyLoader(
                            "Loading Models...", hc.Loaders.standard_loaders, index=[0]
                        ):
                            load_models(
                                use_LDSR=st.session_state["use_LDSR"],
                                LDSR_model=st.session_state["LDSR_model"],
                                use_GFPGAN=st.session_state["use_GFPGAN"],
                                GFPGAN_model=st.session_state["GFPGAN_model"],
                                use_RealESRGAN=st.session_state["use_RealESRGAN"],
                                RealESRGAN_model=st.session_state["RealESRGAN_model"],
                                CustomModel_available=server_state[
                                    "CustomModel_available"
                                ],
                                custom_model=st.session_state["custom_model"],
                            )

                # print(st.session_state['use_RealESRGAN'])
                # print(st.session_state['use_LDSR'])
                try:
                    output_images, seeds, info, stats = txt2img(
                        prompt,
                        st.session_state.sampling_steps,
                        sampler_name,
                        st.session_state["batch_count"],
                        st.session_state["batch_size"],
                        cfg_scale,
                        seed,
                        height,
                        width,
                        separate_prompts,
                        normalize_prompt_weights,
                        save_individual_images,
                        save_grid,
                        group_by_prompt,
                        save_as_jpg,
                        st.session_state["use_GFPGAN"],
                        st.session_state["GFPGAN_model"],
                        use_RealESRGAN=st.session_state["use_RealESRGAN"],
                        RealESRGAN_model=st.session_state["RealESRGAN_model"],
                        use_LDSR=st.session_state["use_LDSR"],
                        LDSR_model=st.session_state["LDSR_model"],
                        variant_amount=variant_amount,
                        variant_seed=variant_seed,
                        write_info_files=write_info_files,
                        use_stable_horde=use_stable_horde,
                        stable_horde_key=stable_horde_key,
                    )

                    message.success(
                        "Render Complete: " + info + "; Stats: " + stats, icon="✅"
                    )

                    with gallery_tab:
                        logger.info(seeds)
                        st.session_state["gallery"].text = ""
                        sdGallery(output_images)

                except (
                    StopException,
                    # KeyError
                ):
                    print("Received Streamlit StopException")

                    # reset the page title so the percent doesnt stay on it confusing the user.
                    set_page_title("Stable Diffusion Playground")

                    # this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
                    # use the current col2 first tab to show the preview_img and update it as its generated.
                    # preview_image.image(output_images)


================================================
FILE: webui/streamlit/scripts/txt2vid.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.

"""
Implementation of Text to Video based on the
https://github.com/nateraw/stable-diffusion-videos
repo and the original gist script from
https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355
"""
from sd_utils import (
    st,
    MemUsageMonitor,
    server_state,
    no_rerun,
    custom_models_available,
    RealESRGAN_available,
    GFPGAN_available,
    LDSR_available,
    hc,
    logger,
)

# seed_to_int, logger, slerp, optimize_update_preview_frequency, \
# load_learned_embed_in_clip, load_GFPGAN, RealESRGANModel, set_page_title


# streamlit imports
from streamlit.runtime.scriptrunner import StopException

# from streamlit.elements import image as STImage

# streamlit components section
from streamlit_server_state import server_state, server_state_lock

# from streamlitextras.threader import lock, trigger_rerun, \
# streamlit_thread, get_thread, \
# last_trigger_time

# other imports

import os, sys, json, re, random, datetime, time, warnings, mimetypes
from PIL import Image
import torch
import numpy as np
import time, inspect, timeit
import torch
from torch import autocast

# from io import BytesIO
import imageio
from slugify import slugify

from diffusers import StableDiffusionPipeline, DiffusionPipeline

# from stable_diffusion_videos import StableDiffusionWalkPipeline

from diffusers.schedulers import (
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    DDPMScheduler,
)

from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion.safety_checker import (
    StableDiffusionSafetyChecker,
)
from diffusers.utils import deprecate
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from typing import Callable, List, Optional, Union
from pathlib import Path
from torchvision.transforms.functional import pil_to_tensor
from torchvision import transforms
import librosa
from PIL import Image
from torchvision.io import write_video
from torchvision import transforms
import torch.nn as nn
from uuid import uuid4


# streamlit components
from custom_components import sygil_suggestions

# Temp imports

# end of imports
# ---------------------------------------------------------------------------------------------------------------

sygil_suggestions.init()

try:
    # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
    from transformers import logging

    logging.set_verbosity_error()
except:
    pass

# remove some annoying deprecation warnings that show every now and then.
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=UserWarning)

# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes.init()
mimetypes.add_type("application/javascript", ".js")


class plugin_info:
    plugname = "txt2vid"
    description = "Text to Image"
    isTab = True
    displayPriority = 1


#
# -----------------------------------------------------------------------------


def txt2vid_generation_callback(step: int, timestep: int, latents: torch.FloatTensor):
    # print ("test")
    # scale and decode the image latents with vae
    cond_latents_2 = 1 / 0.18215 * latents
    image = server_state["pipe"].vae.decode(cond_latents_2)

    # generate output numpy image as uint8
    image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0)
    image2 = transforms.ToPILImage()(image.squeeze_(0))

    st.session_state["preview_image"].image(image2)


def get_timesteps_arr(audio_filepath, offset, duration, fps=30, margin=1.0, smooth=0.0):
    y, sr = librosa.load(audio_filepath, offset=offset, duration=duration)

    # librosa.stft hardcoded defaults...
    # n_fft defaults to 2048
    # hop length is win_length // 4
    # win_length defaults to n_fft
    D = librosa.stft(y, n_fft=2048, hop_length=2048 // 4, win_length=2048)

    # Extract percussive elements
    D_harmonic, D_percussive = librosa.decompose.hpss(D, margin=margin)
    y_percussive = librosa.istft(D_percussive, length=len(y))

    # Get normalized melspectrogram
    spec_raw = librosa.feature.melspectrogram(y=y_percussive, sr=sr)
    spec_max = np.amax(spec_raw, axis=0)
    spec_norm = (spec_max - np.min(spec_max)) / np.ptp(spec_max)

    # Resize cumsum of spec norm to our desired number of interpolation frames
    x_norm = np.linspace(0, spec_norm.shape[-1], spec_norm.shape[-1])
    y_norm = np.cumsum(spec_norm)
    y_norm /= y_norm[-1]
    x_resize = np.linspace(0, y_norm.shape[-1], int(duration * fps))

    T = np.interp(x_resize, x_norm, y_norm)

    # Apply smoothing
    return T * (1 - smooth) + np.linspace(0.0, 1.0, T.shape[0]) * smooth


#
def make_video_pyav(
    frames_or_frame_dir: Union[str, Path, torch.Tensor],
    audio_filepath: Union[str, Path] = None,
    fps: int = 30,
    audio_offset: int = 0,
    audio_duration: int = 2,
    sr: int = 22050,
    output_filepath: Union[str, Path] = "output.mp4",
    glob_pattern: str = "*.png",
):
    """
    TODO - docstring here

    frames_or_frame_dir: (Union[str, Path, torch.Tensor]):
        Either a directory of images, or a tensor of shape (T, C, H, W) in range [0, 255].
    """

    # Torchvision write_video doesn't support pathlib paths
    output_filepath = str(output_filepath)

    if isinstance(frames_or_frame_dir, (str, Path)):
        frames = None
        for img in sorted(Path(frames_or_frame_dir).glob(glob_pattern)):
            frame = pil_to_tensor(Image.open(img)).unsqueeze(0)
            frames = frame if frames is None else torch.cat([frames, frame])
    else:
        frames = frames_or_frame_dir

    # TCHW -> THWC
    frames = frames.permute(0, 2, 3, 1)

    if audio_filepath:
        # Read audio, convert to tensor
        audio, sr = librosa.load(
            audio_filepath,
            sr=sr,
            mono=True,
            offset=audio_offset,
            duration=audio_duration,
        )
        audio_tensor = torch.tensor(audio).unsqueeze(0)

        write_video(
            output_filepath,
            frames,
            fps=fps,
            audio_array=audio_tensor,
            audio_fps=sr,
            audio_codec="aac",
            options={"crf": "10", "pix_fmt": "yuv420p"},
        )
    else:
        write_video(
            output_filepath,
            frames,
            fps=fps,
            options={"crf": "10", "pix_fmt": "yuv420p"},
        )

    return output_filepath


class StableDiffusionWalkPipeline(DiffusionPipeline):
    r"""
    Pipeline for generating videos by interpolating  Stable Diffusion's latent space.
    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
        feature_extractor ([`CLIPFeatureExtractor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPFeatureExtractor,
    ):
        super().__init__()

        if (
            hasattr(scheduler.config, "steps_offset")
            and scheduler.config.steps_offset != 1
        ):
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                " file"
            )
            deprecate(
                "steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False
            )
            new_config = dict(scheduler.config)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )

    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
        r"""
        Enable sliced attention computation.
        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
        in several steps. This is useful to save some memory in exchange for a small speed decrease.
        Args:
            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
                `attention_head_dim` must be a multiple of `slice_size`.
        """
        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            slice_size = self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(slice_size)

    def disable_attention_slicing(self):
        r"""
        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
        back to computing attention in one step.
        """
        # set slice_size = `None` to disable `attention slicing`
        self.enable_attention_slicing(None)

    @torch.no_grad()
    def __call__(
        self,
        prompt: Optional[Union[str, List[str]]] = None,
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        text_embeddings: Optional[torch.FloatTensor] = None,
        **kwargs,
    ):
        r"""
        Function invoked when calling the pipeline for generation.
        Args:
            prompt (`str` or `List[str]`, *optional*, defaults to `None`):
                The prompt or prompts to guide the image generation. If not provided, `text_embeddings` is required.
            height (`int`, *optional*, defaults to 512):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to 512):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
                if `guidance_scale` is less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            generator (`torch.Generator`, *optional*):
                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                deterministic.
            latents (`torch.FloatTensor`, *optional*):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            text_embeddings (`torch.FloatTensor`, *optional*, defaults to `None`):
                Pre-generated text embeddings to be used as inputs for image generation. Can be used in place of
                `prompt` to avoid re-computing the embeddings. If not provided, the embeddings will be generated from
                the supplied `prompt`.
        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """

        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(
                f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
            )

        if (callback_steps is None) or (
            callback_steps is not None
            and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        if text_embeddings is None:
            if isinstance(prompt, str):
                batch_size = 1
            elif isinstance(prompt, list):
                batch_size = len(prompt)
            else:
                raise ValueError(
                    f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
                )

            # get prompt text embeddings
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                return_tensors="pt",
            )
            text_input_ids = text_inputs.input_ids

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                removed_text = self.tokenizer.batch_decode(
                    text_input_ids[:, self.tokenizer.model_max_length :]
                )
                print(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )
                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
            text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
        else:
            batch_size = text_embeddings.shape[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        bs_embed, seq_len, _ = text_embeddings.shape
        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
        text_embeddings = text_embeddings.view(
            bs_embed * num_images_per_prompt, seq_len, -1
        )

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""]
            elif type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            max_length = self.tokenizer.model_max_length
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )
            uncond_embeddings = self.text_encoder(
                uncond_input.input_ids.to(self.device)
            )[0]

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = uncond_embeddings.shape[1]
            uncond_embeddings = uncond_embeddings.repeat(
                batch_size, num_images_per_prompt, 1
            )
            uncond_embeddings = uncond_embeddings.view(
                batch_size * num_images_per_prompt, seq_len, -1
            )

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        latents_shape = (
            batch_size * num_images_per_prompt,
            self.unet.in_channels,
            height // 8,
            width // 8,
        )
        latents_dtype = text_embeddings.dtype
        if latents is None:
            if self.device.type == "mps":
                # randn does not exist on mps
                latents = torch.randn(
                    latents_shape,
                    generator=generator,
                    device="cpu",
                    dtype=latents_dtype,
                ).to(self.device)
            else:
                latents = torch.randn(
                    latents_shape,
                    generator=generator,
                    device=self.device,
                    dtype=latents_dtype,
                )
        else:
            if latents.shape != latents_shape:
                raise ValueError(
                    f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"
                )
            latents = latents.to(self.device)

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps)

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        timesteps_tensor = self.scheduler.timesteps.to(self.device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        accepts_eta = "eta" in set(
            inspect.signature(self.scheduler.step).parameters.keys()
        )
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
            # expand the latents if we are doing classifier free guidance
            latent_model_input = (
                torch.cat([latents] * 2) if do_classifier_free_guidance else latents
            )
            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

            # predict the noise residual
            noise_pred = self.unet(
                latent_model_input, t, encoder_hidden_states=text_embeddings
            ).sample

            # perform guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                noise_pred = noise_pred_uncond + guidance_scale * (
                    noise_pred_text - noise_pred_uncond
                )

            # compute the previous noisy sample x_t -> x_t-1
            latents = self.scheduler.step(
                noise_pred, t, latents, **extra_step_kwargs
            ).prev_sample

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                callback(i, t, latents)
            print("test")

        latents = 1 / 0.18215 * latents
        image = self.vae.decode(latents).sample

        image = (image / 2 + 0.5).clamp(0, 1)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
        image = image.cpu().permute(0, 2, 3, 1).float().numpy()

        if self.safety_checker is not None:
            safety_checker_input = self.feature_extractor(
                self.numpy_to_pil(image), return_tensors="pt"
            ).to(self.device)
            image, has_nsfw_concept = self.safety_checker(
                images=image,
                clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype),
            )
        else:
            has_nsfw_concept = None

        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(
            images=image, nsfw_content_detected=has_nsfw_concept
        )

    def generate_inputs(
        self, prompt_a, prompt_b, seed_a, seed_b, noise_shape, T, batch_size
    ):
        embeds_a = self.embed_text(prompt_a)
        embeds_b = self.embed_text(prompt_b)

        latents_a = self.init_noise(seed_a, noise_shape)
        latents_b = self.init_noise(seed_b, noise_shape)

        batch_idx = 0
        embeds_batch, noise_batch = None, None
        for i, t in enumerate(T):
            embeds = torch.lerp(embeds_a, embeds_b, t)
            noise = slerp(
                device="cuda",
                t=float(t),
                v0=latents_a,
                v1=latents_b,
                DOT_THRESHOLD=0.9995,
            )

            embeds_batch = (
                embeds if embeds_batch is None else torch.cat([embeds_batch, embeds])
            )
            noise_batch = (
                noise if noise_batch is None else torch.cat([noise_batch, noise])
            )
            batch_is_ready = embeds_batch.shape[0] == batch_size or i + 1 == T.shape[0]
            if not batch_is_ready:
                continue
            yield batch_idx, embeds_batch, noise_batch
            batch_idx += 1
            del embeds_batch, noise_batch
            torch.cuda.empty_cache()
            embeds_batch, noise_batch = None, None

    def make_clip_frames(
        self,
        prompt_a: str,
        prompt_b: str,
        seed_a: int,
        seed_b: int,
        num_interpolation_steps: int = 5,
        save_path: Union[str, Path] = "outputs/",
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        eta: float = 0.0,
        height: int = 512,
        width: int = 512,
        upsample: bool = False,
        batch_size: int = 1,
        image_file_ext: str = ".png",
        T: np.ndarray = None,
        skip: int = 0,
        callback=None,
        callback_steps: int = 1,
    ):
        save_path = Path(save_path)
        save_path.mkdir(parents=True, exist_ok=True)

        T = T if T is not None else np.linspace(0.0, 1.0, num_interpolation_steps)
        if T.shape[0] != num_interpolation_steps:
            raise ValueError(
                f"Unexpected T shape, got {T.shape}, expected dim 0 to be {num_interpolation_steps}"
            )

        if upsample:
            if getattr(self, "upsampler", None) is None:
                self.upsampler = RealESRGANModel.from_pretrained("nateraw/real-esrgan")
            self.upsampler.to(self.device)

        batch_generator = self.generate_inputs(
            prompt_a,
            prompt_b,
            seed_a,
            seed_b,
            (1, self.unet.in_channels, height // 8, width // 8),
            T[skip:],
            batch_size,
        )

        frame_index = skip
        for _, embeds_batch, noise_batch in batch_generator:
            with torch.autocast("cuda"):
                outputs = self(
                    latents=noise_batch,
                    text_embeddings=embeds_batch,
                    height=height,
                    width=width,
                    guidance_scale=guidance_scale,
                    eta=eta,
                    num_inference_steps=num_inference_steps,
                    output_type="pil" if not upsample else "numpy",
                    callback=callback,
                    callback_steps=callback_steps,
                )["images"]

                for image in outputs:
                    frame_filepath = save_path / (
                        f"frame%06d{image_file_ext}" % frame_index
                    )
                    image = image if not upsample else self.upsampler(image)
                    image.save(frame_filepath)
                    frame_index += 1

    def walk(
        self,
        prompt: Optional[List[str]] = None,
        seeds: Optional[List[int]] = None,
        num_interpolation_steps: Optional[
            Union[int, List[int]]
        ] = 5,  # int or list of int
        output_dir: Optional[str] = "./dreams",
        name: Optional[str] = None,
        image_file_ext: Optional[str] = ".png",
        fps: Optional[int] = 30,
        num_inference_steps: Optional[int] = 50,
        guidance_scale: Optional[float] = 7.5,
        eta: Optional[float] = 0.0,
        height: Optional[int] = 512,
        width: Optional[int] = 512,
        upsample: Optional[bool] = False,
        batch_size: Optional[int] = 1,
        resume: Optional[bool] = False,
        audio_filepath: str = None,
        audio_start_sec: Optional[Union[int, float]] = None,
        margin: Optional[float] = 1.0,
        smooth: Optional[float] = 0.0,
        callback=None,
        callback_steps=1,
    ):
        """Generate a video from a sequence of prompts and seeds. Optionally, add audio to the
        video to interpolate to the intensity of the audio.

        Args:
            prompts (Optional[List[str]], optional):
                list of text prompts. Defaults to None.
            seeds (Optional[List[int]], optional):
                list of random seeds corresponding to prompts. Defaults to None.
            num_interpolation_steps (Union[int, List[int]], *optional*):
                How many interpolation steps between each prompt. Defaults to None.
            output_dir (Optional[str], optional):
                Where to save the video. Defaults to './dreams'.
            name (Optional[str], optional):
                Name of the subdirectory of output_dir. Defaults to None.
            image_file_ext (Optional[str], *optional*, defaults to '.png'):
                The extension to use when writing video frames.
            fps (Optional[int], *optional*, defaults to 30):
                The frames per second in the resulting output videos.
            num_inference_steps (Optional[int], *optional*, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (Optional[float], *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            eta (Optional[float], *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            height (Optional[int], *optional*, defaults to 512):
                height of the images to generate.
            width (Optional[int], *optional*, defaults to 512):
                width of the images to generate.
            upsample (Optional[bool], *optional*, defaults to False):
                When True, upsamples images with realesrgan.
            batch_size (Optional[int], *optional*, defaults to 1):
                Number of images to generate at once.
            resume (Optional[bool], *optional*, defaults to False):
                When True, resumes from the last frame in the output directory based
                on available prompt config. Requires you to provide the `name` argument.
            audio_filepath (str, *optional*, defaults to None):
                Optional path to an audio file to influence the interpolation rate.
            audio_start_sec (Optional[Union[int, float]], *optional*, defaults to 0):
                Global start time of the provided audio_filepath.
            margin (Optional[float], *optional*, defaults to 1.0):
                Margin from librosa hpss to use for audio interpolation.
            smooth (Optional[float], *optional*, defaults to 0.0):
                Smoothness of the audio interpolation. 1.0 means linear interpolation.

        This function will create sub directories for each prompt and seed pair.

        For example, if you provide the following prompts and seeds:

        ```
        prompts = ['a dog', 'a cat', 'a bird']
        seeds = [1, 2, 3]
        num_interpolation_steps = 5
        output_dir = 'output_dir'
        name = 'name'
        fps = 5
        ```

        Then the following directories will be created:

        ```
        output_dir
        ├── name
        │   ├── name_000000
        │   │   ├── frame000000.png
        │   │   ├── ...
        │   │   ├── frame000004.png
        │   │   ├── name_000000.mp4
        │   ├── name_000001
        │   │   ├── frame000000.png
        │   │   ├── ...
        │   │   ├── frame000004.png
        │   │   ├── name_000001.mp4
        │   ├── ...
        │   ├── name.mp4
        |   |── prompt_config.json
        ```

        Returns:
            str: The resulting video filepath. This video includes all sub directories' video clips.
        """
        if (callback_steps is None) or (
            callback_steps is not None
            and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        # init the output dir
        if type(prompts) == str:
            sanitized_prompt = slugify(prompts)
        else:
            sanitized_prompt = slugify(prompts[0])

        full_path = os.path.join(str(output_dir), str(sanitized_prompt))

        if len(full_path) > 220:
            sanitized_prompt = sanitized_prompt[: 220 - len(full_path)]
            full_path = os.path.join(output_dir, sanitized_prompt)

        os.makedirs(full_path, exist_ok=True)

        # Where the final video of all the clips combined will be saved
        output_filepath = os.path.join(full_path, f"{sanitized_prompt}.mp4")

        # If using same number of interpolation steps between, we turn into list
        if not resume and isinstance(num_interpolation_steps, int):
            num_interpolation_steps = [num_interpolation_steps] * (len(prompts) - 1)

        if not resume:
            audio_start_sec = audio_start_sec or 0

        # Save/reload prompt config
        prompt_config_path = Path(os.path.join(full_path, "prompt_config.json"))
        if not resume:
            prompt_config_path.write_text(
                json.dumps(
                    dict(
                        prompts=prompts,
                        seeds=seeds,
                        num_interpolation_steps=num_interpolation_steps,
                        fps=fps,
                        num_inference_steps=num_inference_steps,
                        guidance_scale=guidance_scale,
                        eta=eta,
                        upsample=upsample,
                        height=height,
                        width=width,
                        audio_filepath=audio_filepath,
                        audio_start_sec=audio_start_sec,
                    ),
                    indent=2,
                    sort_keys=False,
                )
            )
        else:
            data = json.load(open(prompt_config_path))
            prompts = data["prompts"]
            seeds = data["seeds"]
            num_interpolation_steps = data["num_interpolation_steps"]
            fps = data["fps"]
            num_inference_steps = data["num_inference_steps"]
            guidance_scale = data["guidance_scale"]
            eta = data["eta"]
            upsample = data["upsample"]
            height = data["height"]
            width = data["width"]
            audio_filepath = data["audio_filepath"]
            audio_start_sec = data["audio_start_sec"]

        for i, (prompt_a, prompt_b, seed_a, seed_b, num_step) in enumerate(
            zip(prompts, prompts[1:], seeds, seeds[1:], num_interpolation_steps)
        ):
            # {name}_000000 / {name}_000001 / ...
            save_path = Path(f"{full_path}/{name}_{i:06d}")

            # Where the individual clips will be saved
            step_output_filepath = Path(f"{save_path}/{name}_{i:06d}.mp4")

            # Determine if we need to resume from a previous run
            skip = 0
            if resume:
                if step_output_filepath.exists():
                    print(f"Skipping {save_path} because frames already exist")
                    continue

                existing_frames = sorted(save_path.glob(f"*{image_file_ext}"))
                if existing_frames:
                    skip = int(existing_frames[-1].stem[-6:]) + 1
                    if skip + 1 >= num_step:
                        print(f"Skipping {save_path} because frames already exist")
                        continue
                    print(f"Resuming {save_path.name} from frame {skip}")

            audio_offset = audio_start_sec + sum(num_interpolation_steps[:i]) / fps
            audio_duration = num_step / fps

            self.make_clip_frames(
                prompt_a,
                prompt_b,
                seed_a,
                seed_b,
                num_interpolation_steps=num_step,
                save_path=save_path,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
                eta=eta,
                height=height,
                width=width,
                upsample=upsample,
                batch_size=batch_size,
                skip=skip,
                T=get_timesteps_arr(
                    audio_filepath,
                    offset=audio_offset,
                    duration=audio_duration,
                    fps=fps,
                    margin=margin,
                    smooth=smooth,
                    callback=callback,
                    callback_steps=callback_steps,
                )
                if audio_filepath
                else None,
            )
            make_video_pyav(
                save_path,
                audio_filepath=audio_filepath,
                fps=fps,
                output_filepath=step_output_filepath,
                glob_pattern=f"*{image_file_ext}",
                audio_offset=audio_offset,
                audio_duration=audio_duration,
                sr=44100,
            )

        return make_video_pyav(
            full_path,
            audio_filepath=audio_filepath,
            fps=fps,
            audio_offset=audio_start_sec,
            audio_duration=sum(num_interpolation_steps) / fps,
            output_filepath=output_filepath,
            glob_pattern=f"**/*{image_file_ext}",
            sr=44100,
        )

    def embed_text(self, text):
        """Helper to embed some text"""
        with torch.autocast("cuda"):
            text_input = self.tokenizer(
                text,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=True,
                return_tensors="pt",
            )
            with torch.no_grad():
                embed = self.text_encoder(text_input.input_ids.to(self.device))[0]
        return embed

    def init_noise(self, seed, noise_shape):
        """Helper to initialize noise"""
        # randn does not exist on mps, so we create noise on CPU here and move it to the device after initialization
        if self.device.type == "mps":
            noise = torch.randn(
                noise_shape,
                device="cpu",
                generator=torch.Generator(device="cpu").manual_seed(seed),
            ).to(self.device)
        else:
            noise = torch.randn(
                noise_shape,
                device=self.device,
                generator=torch.Generator(device=self.device).manual_seed(seed),
            )
        return noise

    @classmethod
    def from_pretrained(cls, *args, tiled=False, **kwargs):
        """Same as diffusers `from_pretrained` but with tiled option, which makes images tilable"""
        if tiled:

            def patch_conv(**patch):
                cls = nn.Conv2d
                init = cls.__init__

                def __init__(self, *args, **kwargs):
                    return init(self, *args, **kwargs, **patch)

                cls.__init__ = __init__

            patch_conv(padding_mode="circular")

        pipeline = super().from_pretrained(*args, **kwargs)
        pipeline.tiled = tiled
        return pipeline


@torch.no_grad()
def diffuse(
    pipe,
    cond_embeddings,  # text conditioning, should be (1, 77, 768)
    cond_latents,  # image conditioning, should be (1, 4, 64, 64)
    num_inference_steps,
    cfg_scale,
    eta,
    fps=30,
):
    torch_device = cond_latents.get_device()

    # classifier guidance: add the unconditional embedding
    max_length = cond_embeddings.shape[1]  # 77
    uncond_input = pipe.tokenizer(
        [""], padding="max_length", max_length=max_length, return_tensors="pt"
    )
    uncond_embeddings = pipe.text_encoder(uncond_input.input_ids.to(torch_device))[0]
    text_embeddings = torch.cat([uncond_embeddings, cond_embeddings])

    # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
    if isinstance(pipe.scheduler, LMSDiscreteScheduler):
        cond_latents = cond_latents * pipe.scheduler.sigmas[0]

    # init the scheduler
    accepts_offset = "offset" in set(
        inspect.signature(pipe.scheduler.set_timesteps).parameters.keys()
    )
    extra_set_kwargs = {}
    if accepts_offset:
        extra_set_kwargs["offset"] = 1

    pipe.scheduler.set_timesteps(
        num_inference_steps + st.session_state.sampling_steps, **extra_set_kwargs
    )
    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
    # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
    # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
    # and should be between [0, 1]
    accepts_eta = "eta" in set(inspect.signature(pipe.scheduler.step).parameters.keys())
    extra_step_kwargs = {}
    if accepts_eta:
        extra_step_kwargs["eta"] = eta

    step_counter = 0
    inference_counter = 0

    if "current_chunk_speed" not in st.session_state:
        st.session_state["current_chunk_speed"] = 0

    if "previous_chunk_speed_list" not in st.session_state:
        st.session_state["previous_chunk_speed_list"] = [0]
        st.session_state["previous_chunk_speed_list"].append(
            st.session_state["current_chunk_speed"]
        )

    if "update_preview_frequency_list" not in st.session_state:
        st.session_state["update_preview_frequency_list"] = [0]
        st.session_state["update_preview_frequency_list"].append(
            st.session_state["update_preview_frequency"]
        )

    try:
        # diffuse!
        for i, t in enumerate(pipe.scheduler.timesteps):
            start = timeit.default_timer()

            # status_text.text(f"Running step: {step_counter}{total_number_steps} {percent} | {duration:.2f}{speed}")

            # expand the latents for classifier free guidance
            latent_model_input = torch.cat([cond_latents] * 2)
            if isinstance(pipe.scheduler, LMSDiscreteScheduler):
                sigma = pipe.scheduler.sigmas[i]
                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)

            # predict the noise residual
            noise_pred = pipe.unet(
                latent_model_input, t, encoder_hidden_states=text_embeddings
            )["sample"]

            # cfg
            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
            noise_pred = noise_pred_uncond + cfg_scale * (
                noise_pred_text - noise_pred_uncond
            )

            # compute the previous noisy sample x_t -> x_t-1
            if isinstance(pipe.scheduler, LMSDiscreteScheduler):
                cond_latents = pipe.scheduler.step(
                    noise_pred, i, cond_latents, **extra_step_kwargs
                )["prev_sample"]
            else:
                cond_latents = pipe.scheduler.step(
                    noise_pred, t, cond_latents, **extra_step_kwargs
                )["prev_sample"]

            # update the preview image if it is enabled and the frequency matches the step_counter
            if st.session_state["update_preview"]:
                step_counter += 1

                if step_counter == st.session_state["update_preview_frequency"]:
                    if st.session_state.dynamic_preview_frequency:
                        st.session_state["current_chunk_speed"],
                        st.session_state["previous_chunk_speed_list"],
                        st.session_state["update_preview_frequency"],
                        st.session_state[
                            "avg_update_preview_frequency"
                        ] = optimize_update_preview_frequency(
                            st.session_state["current_chunk_speed"],
                            st.session_state["previous_chunk_speed_list"],
                            st.session_state["update_preview_frequency"],
                            st.session_state["update_preview_frequency_list"],
                        )

                    # scale and decode the image latents with vae
                    cond_latents_2 = 1 / 0.18215 * cond_latents
                    image = pipe.vae.decode(cond_latents_2)

                    # generate output numpy image as uint8
                    image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0)
                    image2 = transforms.ToPILImage()(image.squeeze_(0))

                    st.session_state["preview_image"].image(image2)

                    step_counter = 0

            duration = timeit.default_timer() - start

            st.session_state["current_chunk_speed"] = duration

            if duration >= 1:
                speed = "s/it"
            else:
                speed = "it/s"
                duration = 1 / duration

            total_frames = st.session_state.max_duration_in_seconds * fps
            st.session_state.sampling_steps + st.session_state.num_inference_steps

            if i > st.session_state.sampling_steps:
                inference_counter += 1
                inference_percent = int(
                    100
                    * float(
                        inference_counter + 1
                        if inference_counter < num_inference_steps
                        else num_inference_steps
                    )
                    / float(num_inference_steps)
                )
                inference_progress = f"{inference_counter + 1 if inference_counter < num_inference_steps else num_inference_steps}/{num_inference_steps} {inference_percent}% "
            else:
                inference_progress = ""

            total_percent = int(
                100
                * float(
                    i + 1
                    if i + 1 < (num_inference_steps + st.session_state.sampling_steps)
                    else (num_inference_steps + st.session_state.sampling_steps)
                )
                / float((num_inference_steps + st.session_state.sampling_steps))
            )

            percent = int(
                100
                * float(
                    i + 1
                    if i + 1 < num_inference_steps
                    else st.session_state.sampling_steps
                )
                / float(st.session_state.sampling_steps)
            )
            frames_percent = int(
                100
                * float(
                    st.session_state.current_frame
                    if st.session_state.current_frame < total_frames
                    else total_frames
                )
                / float(total_frames)
            )

            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].text(
                    f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps} "
                    f"{percent if percent < 100 else 100}% {inference_progress}{duration:.2f}{speed} | "
                    f"Frame: {st.session_state.current_frame + 1 if st.session_state.current_frame < total_frames else total_frames}/{total_frames} "
                    f"{frames_percent if frames_percent < 100 else 100}% {st.session_state.frame_duration:.2f}{st.session_state.frame_speed}"
                )

            if "progress_bar" in st.session_state:
                st.session_state["progress_bar"].progress(
                    total_percent if total_percent < 100 else 100
                )

                if st.session_state["defaults"].general.show_percent_in_tab_title:
                    set_page_title(
                        f"({percent if percent < 100 else 100}%) Stable Diffusion Playground"
                    )

    except KeyError:
        raise StopException

    # scale and decode the image latents with vae
    cond_latents_2 = 1 / 0.18215 * cond_latents
    image = pipe.vae.decode(cond_latents_2)

    # generate output numpy image as uint8
    image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0)
    image2 = transforms.ToPILImage()(image.squeeze_(0))

    return image2


#
def load_diffusers_model(weights_path, torch_device):
    with server_state_lock["model"]:
        if "model" in server_state:
            del server_state["model"]

    if "textual_inversion" in st.session_state:
        del st.session_state["textual_inversion"]

    try:
        with server_state_lock["pipe"]:
            if "pipe" not in server_state:
                if (
                    "weights_path" in st.session_state
                    and st.session_state["weights_path"] != weights_path
                ):
                    del st.session_state["weights_path"]

                st.session_state["weights_path"] = weights_path
                server_state["float16"] = st.session_state[
                    "defaults"
                ].general.use_float16
                server_state["no_half"] = st.session_state["defaults"].general.no_half
                server_state["optimized"] = st.session_state[
                    "defaults"
                ].general.optimized

                # if folder "models/diffusers/stable-diffusion-v1-4" exists, load the model from there
                if weights_path == "CompVis/stable-diffusion-v1-4":
                    model_path = os.path.join(
                        "models", "diffusers", "stable-diffusion-v1-4"
                    )

                if weights_path == "runwayml/stable-diffusion-v1-5":
                    model_path = os.path.join(
                        "models", "diffusers", "stable-diffusion-v1-5"
                    )
                else:
                    model_path = weights_path

                if not os.path.exists(model_path + "/model_index.json"):
                    server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
                        weights_path,
                        # use_local_file=True,
                        use_auth_token=st.session_state[
                            "defaults"
                        ].general.huggingface_token,
                        torch_dtype=torch.float16
                        if st.session_state["defaults"].general.use_float16
                        else None,
                        revision="fp16"
                        if not st.session_state["defaults"].general.no_half
                        else None,
                        safety_checker=None,  # Very important for videos...lots of false positives while interpolating
                        # custom_pipeline="interpolate_stable_diffusion",
                    )

                    StableDiffusionPipeline.save_pretrained(
                        server_state["pipe"], model_path
                    )
                else:
                    server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
                        model_path,
                        # use_local_file=True,
                        torch_dtype=torch.float16
                        if st.session_state["defaults"].general.use_float16
                        else None,
                        revision="fp16"
                        if not st.session_state["defaults"].general.no_half
                        else None,
                        safety_checker=None,  # Very important for videos...lots of false positives while interpolating
                        # custom_pipeline="interpolate_stable_diffusion",
                    )

                server_state["pipe"].unet.to(torch_device)
                server_state["pipe"].vae.to(torch_device)
                server_state["pipe"].text_encoder.to(torch_device)

                # if st.session_state.defaults.general.enable_attention_slicing:
                server_state["pipe"].enable_attention_slicing()

                if st.session_state.defaults.general.enable_minimal_memory_usage:
                    server_state["pipe"].enable_minimal_memory_usage()

                logger.info("Tx2Vid Model Loaded")
            else:
                # if the float16 or no_half options have changed since the last time the model was loaded then we need to reload the model.
                if (
                    (
                        "float16" in server_state
                        and server_state["float16"]
                        != st.session_state["defaults"].general.use_float16
                    )
                    or (
                        "no_half" in server_state
                        and server_state["no_half"]
                        != st.session_state["defaults"].general.no_half
                    )
                    or (
                        "optimized" in server_state
                        and server_state["optimized"]
                        != st.session_state["defaults"].general.optimized
                    )
                ):
                    del server_state["float16"]
                    del server_state["no_half"]
                    with server_state_lock["pipe"]:
                        del server_state["pipe"]
                        torch_gc()

                    del server_state["optimized"]

                    server_state["float16"] = st.session_state[
                        "defaults"
                    ].general.use_float16
                    server_state["no_half"] = st.session_state[
                        "defaults"
                    ].general.no_half
                    server_state["optimized"] = st.session_state[
                        "defaults"
                    ].general.optimized

                    # with no_rerun:
                    load_diffusers_model(weights_path, torch_device)
                else:
                    logger.info("Tx2Vid Model already Loaded")

    except (EnvironmentError, OSError) as e:
        if (
            "huggingface_token" not in st.session_state
            or st.session_state["defaults"].general.huggingface_token == "None"
        ):
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].error(
                    "You need a huggingface token in order to use the Text to Video tab. Use the Settings page to add your token under the Huggingface section. "
                    "Make sure you save your settings after adding it."
                )
            raise OSError(
                "You need a huggingface token in order to use the Text to Video tab. Use the Settings page to add your token under the Huggingface section. "
                "Make sure you save your settings after adding it."
            )
        else:
            if "progress_bar_text" in st.session_state:
                st.session_state["progress_bar_text"].error(e)


#
def save_video_to_disk(
    frames, seeds, sanitized_prompt, fps=30, save_video=True, outdir="outputs"
):
    if save_video:
        # write video to memory
        # output = io.BytesIO()
        # writer = imageio.get_writer(os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid"), im, extension=".mp4", fps=30)
        # try:
        video_path = os.path.join(
            os.getcwd(),
            outdir,
            "txt2vid",
            f"{seeds}_{sanitized_prompt}{datetime.datetime.now().strftime('%Y%m-%d%H-%M%S-') + str(uuid4())[:8]}.mp4",
        )
        writer = imageio.get_writer(video_path, fps=fps)
        for frame in frames:
            writer.append_data(frame)

        writer.close()
        # except:
        # 	print("Can't save video, skipping.")

    return video_path


#
def txt2vid(
    # --------------------------------------
    # args you probably want to change
    prompts=["blueberry spaghetti", "strawberry spaghetti"],  # prompt to dream about
    gpu: int = st.session_state["defaults"].general.gpu,  # id of the gpu to run on
    # name:str = 'test', # name of this project, for the output directory
    # rootdir:str = st.session_state['defaults'].general.outdir,
    num_steps: int = 200,  # number of steps between each pair of sampled points
    max_duration_in_seconds: int = 30,  # number of frames to write and then exit the script
    num_inference_steps: int = 50,  # more (e.g. 100, 200 etc) can create slightly better images
    cfg_scale: float = 5.0,  # can depend on the prompt. usually somewhere between 3-10 is good
    save_video=True,
    save_video_on_stop=False,
    outdir="outputs",
    do_loop=False,
    use_lerp_for_text=False,
    seeds=None,
    quality: int = 100,  # for jpeg compression of the output images
    eta: float = 0.0,
    width: int = 256,
    height: int = 256,
    weights_path="runwayml/stable-diffusion-v1-5",
    scheduler="klms",  # choices: default, ddim, klms
    disable_tqdm=False,
    # -----------------------------------------------
    beta_start=0.0001,
    beta_end=0.00012,
    beta_schedule="scaled_linear",
    starting_image=None,
    # -----------------------------------------------
    # from new version
    image_file_ext: Optional[str] = ".png",
    fps: Optional[int] = 30,
    upsample: Optional[bool] = False,
    batch_size: Optional[int] = 1,
    resume: Optional[bool] = False,
    audio_filepath: str = None,
    audio_start_sec: Optional[Union[int, float]] = None,
    margin: Optional[float] = 1.0,
    smooth: Optional[float] = 0.0,
):
    """
    prompt = ["blueberry spaghetti", "strawberry spaghetti"], # prompt to dream about
    gpu:int = st.session_state['defaults'].general.gpu, # id of the gpu to run on
    #name:str = 'test', # name of this project, for the output directory
    #rootdir:str = st.session_state['defaults'].general.outdir,
    num_steps:int = 200, # number of steps between each pair of sampled points
    max_duration_in_seconds:int = 10000, # number of frames to write and then exit the script
    num_inference_steps:int = 50, # more (e.g. 100, 200 etc) can create slightly better images
    cfg_scale:float = 5.0, # can depend on the prompt. usually somewhere between 3-10 is good
    do_loop = False,
    use_lerp_for_text = False,
    seed = None,
    quality:int = 100, # for jpeg compression of the output images
    eta:float = 0.0,
    width:int = 256,
    height:int = 256,
    weights_path = "runwayml/stable-diffusion-v1-5",
    scheduler="klms",  # choices: default, ddim, klms
    disable_tqdm = False,
    beta_start = 0.0001,
    beta_end = 0.00012,
    beta_schedule = "scaled_linear"
    """
    mem_mon = MemUsageMonitor("MemMon")
    mem_mon.start()

    seeds = seed_to_int(seeds)

    # We add an extra frame because most
    # of the time the first frame is just the noise.
    # max_duration_in_seconds +=1

    assert torch.cuda.is_available()
    assert height % 8 == 0 and width % 8 == 0
    torch.manual_seed(seeds)
    torch_device = f"cuda:{gpu}"

    if type(seeds) == list:
        prompts = [prompts] * len(seeds)
    else:
        seeds = [seeds, random.randint(0, 2**32 - 1)]

    if type(prompts) == list:
        # init the output dir
        sanitized_prompt = slugify(prompts[0])
    else:
        # init the output dir
        sanitized_prompt = slugify(prompts)

    full_path = os.path.join(
        os.getcwd(),
        st.session_state["defaults"].general.outdir,
        "txt2vid",
        "samples",
        sanitized_prompt,
    )

    if len(full_path) > 220:
        sanitized_prompt = sanitized_prompt[: 220 - len(full_path)]
        full_path = os.path.join(
            os.getcwd(),
            st.session_state["defaults"].general.outdir,
            "txt2vid",
            "samples",
            sanitized_prompt,
        )

    os.makedirs(full_path, exist_ok=True)

    # Write prompt info to file in output dir so we can keep track of what we did
    if st.session_state.write_info_files:
        with open(
            os.path.join(
                full_path,
                f"{slugify(str(seeds))}_config.json"
                if len(prompts) > 1
                else "prompts_config.json",
            ),
            "w",
        ) as outfile:
            outfile.write(
                json.dumps(
                    dict(
                        prompts=prompts,
                        gpu=gpu,
                        num_steps=num_steps,
                        max_duration_in_seconds=max_duration_in_seconds,
                        num_inference_steps=num_inference_steps,
                        cfg_scale=cfg_scale,
                        do_loop=do_loop,
                        use_lerp_for_text=use_lerp_for_text,
                        seeds=seeds,
                        quality=quality,
                        eta=eta,
                        width=width,
                        height=height,
                        weights_path=weights_path,
                        scheduler=scheduler,
                        disable_tqdm=disable_tqdm,
                        beta_start=beta_start,
                        beta_end=beta_end,
                        beta_schedule=beta_schedule,
                    ),
                    indent=2,
                    sort_keys=False,
                )
            )

    # print(scheduler)
    default_scheduler = PNDMScheduler(
        beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    )
    # ------------------------------------------------------------------------------
    # Schedulers
    ddim_scheduler = DDIMScheduler(
        beta_start=beta_start,
        beta_end=beta_end,
        beta_schedule=beta_schedule,
        clip_sample=False,
        set_alpha_to_one=False,
    )

    klms_scheduler = LMSDiscreteScheduler(
        beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    )

    # flaxddims_scheduler = FlaxDDIMScheduler(
    # beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    # )

    # flaxddpms_scheduler = FlaxDDPMScheduler(
    # beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    # )

    # flaxpndms_scheduler = FlaxPNDMScheduler(
    # beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    # )

    ddpms_scheduler = DDPMScheduler(
        beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
    )

    SCHEDULERS = dict(
        default=default_scheduler,
        ddim=ddim_scheduler,
        klms=klms_scheduler,
        ddpms=ddpms_scheduler,
        # flaxddims=flaxddims_scheduler,
        # flaxddpms=flaxddpms_scheduler,
        # flaxpndms=flaxpndms_scheduler,
    )

    with no_rerun:
        with st.session_state["progress_bar_text"].container():
            with hc.HyLoader(
                "Loading Models...", hc.Loaders.standard_loaders, index=[0]
            ):
                load_diffusers_model(weights_path, torch_device)

    if "pipe" not in server_state:
        logger.error("wtf")

    server_state["pipe"].scheduler = SCHEDULERS[scheduler]

    server_state["pipe"].use_multiprocessing_for_evaluation = False
    server_state["pipe"].use_multiprocessed_decoding = False

    # if do_loop:
    ##Makes the last prompt loop back to first prompt
    # prompts = [prompts, prompts]
    # seeds = [seeds, seeds]
    # first_seed, *seeds = seeds
    # prompts.append(prompts)
    # seeds.append(first_seed)

    with torch.autocast("cuda"):
        # get the conditional text embeddings based on the prompt
        text_input = server_state["pipe"].tokenizer(
            prompts,
            padding="max_length",
            max_length=server_state["pipe"].tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        cond_embeddings = server_state["pipe"].text_encoder(
            text_input.input_ids.to(torch_device)
        )[0]

    #
    if st.session_state.defaults.general.use_sd_concepts_library:
        prompt_tokens = re.findall("<([a-zA-Z0-9-]+)>", str(prompts))

        if prompt_tokens:
            # compviz
            # tokenizer = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.tokenizer
            # text_encoder = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.transformer

            # diffusers
            tokenizer = st.session_state.pipe.tokenizer
            text_encoder = st.session_state.pipe.text_encoder

            ext = ("pt", "bin")
            # print (prompt_tokens)

            if len(prompt_tokens) > 1:
                for token_name in prompt_tokens:
                    embedding_path = os.path.join(
                        st.session_state["defaults"].general.sd_concepts_library_folder,
                        token_name,
                    )
                    if os.path.exists(embedding_path):
                        for files in os.listdir(embedding_path):
                            if files.endswith(ext):
                                load_learned_embed_in_clip(
                                    f"{os.path.join(embedding_path, files)}",
                                    text_encoder,
                                    tokenizer,
                                    f"<{token_name}>",
                                )
            else:
                embedding_path = os.path.join(
                    st.session_state["defaults"].general.sd_concepts_library_folder,
                    prompt_tokens[0],
                )
                if os.path.exists(embedding_path):
                    for files in os.listdir(embedding_path):
                        if files.endswith(ext):
                            load_learned_embed_in_clip(
                                f"{os.path.join(embedding_path, files)}",
                                text_encoder,
                                tokenizer,
                                f"<{prompt_tokens[0]}>",
                            )

    # sample a source
    init1 = torch.randn(
        (1, server_state["pipe"].unet.in_channels, height // 8, width // 8),
        device=torch_device,
    )

    # iterate the loop
    frames = []
    frame_index = 0

    st.session_state["total_frames_avg_duration"] = []
    st.session_state["total_frames_avg_speed"] = []

    try:
        # code for the new StableDiffusionWalkPipeline implementation.
        start = timeit.default_timer()

        # preview image works but its not the right way to use this, this also do not work properly as it only makes one image and then exits.
        # with torch.autocast("cuda"):
        # StableDiffusionWalkPipeline.__call__(self=server_state["pipe"],
        # prompt=prompts, height=height, width=width, num_inference_steps=num_inference_steps, guidance_scale=cfg_scale,
        # negative_prompt="", num_images_per_prompt=1, eta=0.0,
        # callback=txt2vid_generation_callback, callback_steps=1,
        # num_interpolation_steps=num_steps,
        # fps=30,
        # image_file_ext = ".png",
        # output_dir=full_path,        # Where images/videos will be saved
        ##name='animals_test',        # Subdirectory of output_dir where images/videos will be saved
        # upsample = False,
        ##do_loop=do_loop,           # Change to True if you want last prompt to loop back to first prompt
        # resume = False,
        # audio_filepath = None,
        # audio_start_sec = None,
        # margin = 1.0,
        # smooth = 0.0,                                                             )

        # works correctly generating all frames but do not show the preview image
        # we also do not have control over the generation and cant stop it until the end of it.
        # with torch.autocast("cuda"):
        # print (prompts)
        # video_path = server_state["pipe"].walk(
        # prompt=prompts,
        # seeds=seeds,
        # num_interpolation_steps=num_steps,
        # height=height,  # use multiples of 64 if > 512. Multiples of 8 if < 512.
        # width=width,   # use multiples of 64 if > 512. Multiples of 8 if < 512.
        # batch_size=4,
        # fps=30,
        # image_file_ext = ".png",
        # eta = 0.0,
        # output_dir=full_path,        # Where images/videos will be saved
        ##name='test',        # Subdirectory of output_dir where images/videos will be saved
        # guidance_scale=cfg_scale,         # Higher adheres to prompt more, lower lets model take the wheel
        # num_inference_steps=num_inference_steps,     # Number of diffusion steps per image generated. 50 is good default
        # upsample = False,
        ##do_loop=do_loop,           # Change to True if you want last prompt to loop back to first prompt
        # resume = False,
        # audio_filepath = None,
        # audio_start_sec = None,
        # margin = 1.0,
        # smooth = 0.0,
        # callback=txt2vid_generation_callback, # our callback function will be called with the arguments callback(step, timestep, latents)
        # callback_steps=1 # our callback function will be called once this many steps are processed in a single frame
        # )

        # old code
        total_frames = st.session_state.max_duration_in_seconds * fps

        while frame_index + 1 <= total_frames:
            st.session_state["frame_duration"] = 0
            st.session_state["frame_speed"] = 0
            st.session_state["current_frame"] = frame_index

            # print(f"Second: {second_count+1}/{max_duration_in_seconds}")

            # sample the destination
            init2 = torch.randn(
                (1, server_state["pipe"].unet.in_channels, height // 8, width // 8),
                device=torch_device,
            )

            for i, t in enumerate(np.linspace(0, 1, num_steps)):
                start = timeit.default_timer()
                logger.info(f"COUNT: {frame_index+1}/{total_frames}")

                if use_lerp_for_text:
                    init = torch.lerp(init1, init2, float(t))
                else:
                    init = slerp(gpu, float(t), init1, init2)

                # init = slerp(gpu, float(t), init1, init2)

                with autocast("cuda"):
                    image = diffuse(
                        server_state["pipe"],
                        cond_embeddings,
                        init,
                        num_inference_steps,
                        cfg_scale,
                        eta,
                        fps=fps,
                    )

                if (
                    st.session_state["save_individual_images"]
                    and not st.session_state["use_GFPGAN"]
                    and not st.session_state["use_RealESRGAN"]
                ):
                    # im = Image.fromarray(image)
                    outpath = os.path.join(full_path, "frame%06d.png" % frame_index)
                    image.save(outpath, quality=quality)

                    # send the image to the UI to update it
                    # st.session_state["preview_image"].image(im)

                    # append the frames to the frames list so we can use them later.
                    frames.append(np.asarray(image))

                #
                # try:
                # if st.session_state["use_GFPGAN"] and server_state["GFPGAN"] is not None and not st.session_state["use_RealESRGAN"]:
                if (
                    st.session_state["use_GFPGAN"]
                    and server_state["GFPGAN"] is not None
                ):
                    # print("Running GFPGAN on image ...")
                    if "progress_bar_text" in st.session_state:
                        st.session_state["progress_bar_text"].text(
                            "Running GFPGAN on image ..."
                        )
                    # skip_save = True # #287 >_>
                    torch_gc()
                    cropped_faces, restored_faces, restored_img = server_state[
                        "GFPGAN"
                    ].enhance(
                        np.array(image)[:, :, ::-1],
                        has_aligned=False,
                        only_center_face=False,
                        paste_back=True,
                    )
                    gfpgan_sample = restored_img[:, :, ::-1]
                    gfpgan_image = Image.fromarray(gfpgan_sample)

                    outpath = os.path.join(full_path, "frame%06d.png" % frame_index)
                    gfpgan_image.save(outpath, quality=quality)

                    # append the frames to the frames list so we can use them later.
                    frames.append(np.asarray(gfpgan_image))
                    try:
                        st.session_state["preview_image"].image(gfpgan_image)
                    except KeyError:
                        logger.error("Cant get session_state, skipping image preview.")
                # except (AttributeError, KeyError):
                # print("Cant perform GFPGAN, skipping.")

                # increase frame_index counter.
                frame_index += 1

                st.session_state["current_frame"] = frame_index

                duration = timeit.default_timer() - start

                if duration >= 1:
                    speed = "s/it"
                else:
                    speed = "it/s"
                    duration = 1 / duration

                st.session_state["frame_duration"] = duration
                st.session_state["frame_speed"] = speed
                if frame_index + 1 > total_frames:
                    break

            init1 = init2

        # save the video after the generation is done.
        video_path = save_video_to_disk(
            frames, seeds, sanitized_prompt, save_video=save_video, outdir=outdir
        )

    except StopException:
        # reset the page title so the percent doesnt stay on it confusing the user.
        set_page_title("Stable Diffusion Playground")

        if save_video_on_stop:
            logger.info("Streamlit Stop Exception Received. Saving video")
            video_path = save_video_to_disk(
                frames, seeds, sanitized_prompt, save_video=save_video, outdir=outdir
            )
        else:
            video_path = None

    # if video_path and "preview_video" in st.session_state:
    ## show video preview on the UI
    # st.session_state["preview_video"].video(open(video_path, 'rb').read())

    mem_max_used, mem_total = mem_mon.read_and_stop()
    time_diff = time.time() - start

    info = f"""
        {prompts}
        Sampling Steps: {num_steps}, Sampler: {scheduler}, CFG scale: {cfg_scale}, Seed: {seeds}, Max Duration In Seconds: {max_duration_in_seconds}""".strip()
    stats = f"""
        Took { round(time_diff, 2) }s total ({ round(time_diff/(max_duration_in_seconds),2) }s per image)
        Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%"""

    return video_path, seeds, info, stats


#
def layout():
    with st.form("txt2vid-inputs"):
        st.session_state["generation_mode"] = "txt2vid"

        input_col1, generate_col1 = st.columns([10, 1])
        with input_col1:
            # prompt = st.text_area("Input Text","")
            placeholder = "A corgi wearing a top hat as an oil painting."
            prompt = st.text_area("Input Text", "", placeholder=placeholder, height=54)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].general.enable_suggestions:
                    sygil_suggestions.suggestion_area(placeholder)

            if "defaults" in st.session_state:
                if st.session_state["defaults"].admin.global_negative_prompt:
                    prompt += f"### {st.session_state['defaults'].admin.global_negative_prompt}"

        # Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
        generate_col1.write("")
        generate_col1.write("")
        generate_button = generate_col1.form_submit_button("Generate")

        # creating the page layout using columns
        col1, col2, col3 = st.columns([2, 5, 2], gap="large")

        with col1:
            width = st.slider(
                "Width:",
                min_value=st.session_state["defaults"].txt2vid.width.min_value,
                max_value=st.session_state["defaults"].txt2vid.width.max_value,
                value=st.session_state["defaults"].txt2vid.width.value,
                step=st.session_state["defaults"].txt2vid.width.step,
            )
            height = st.slider(
                "Height:",
                min_value=st.session_state["defaults"].txt2vid.height.min_value,
                max_value=st.session_state["defaults"].txt2vid.height.max_value,
                value=st.session_state["defaults"].txt2vid.height.value,
                step=st.session_state["defaults"].txt2vid.height.step,
            )
            cfg_scale = st.number_input(
                "CFG (Classifier Free Guidance Scale):",
                min_value=st.session_state["defaults"].txt2vid.cfg_scale.min_value,
                value=st.session_state["defaults"].txt2vid.cfg_scale.value,
                step=st.session_state["defaults"].txt2vid.cfg_scale.step,
                help="How strongly the image should follow the prompt.",
            )

            # uploaded_images = st.file_uploader("Upload Image", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp"],
            # help="Upload an image which will be used for the image to image generation.")
            seed = st.text_input(
                "Seed:",
                value=st.session_state["defaults"].txt2vid.seed,
                help=" The seed to use, if left blank a random seed will be generated.",
            )
            # batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2vid.batch_count,
            # step=1, help="How many iterations or batches of images to generate in total.")
            # batch_size = st.slider("Batch size", min_value=1, max_value=250, value=st.session_state['defaults'].txt2vid.batch_size, step=1,
            # help="How many images are at once in a batch.\
            # It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
            # Default: 1")

            st.session_state["max_duration_in_seconds"] = st.number_input(
                "Max Duration In Seconds:",
                value=st.session_state["defaults"].txt2vid.max_duration_in_seconds,
                help="Specify the max duration in seconds you want your video to be.",
            )

            st.session_state["fps"] = st.number_input(
                "Frames per Second (FPS):",
                value=st.session_state["defaults"].txt2vid.fps,
                help="Specify the frame rate of the video.",
            )

            with st.expander("Preview Settings"):
                # st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2vid.update_preview,
                # help="If enabled the image preview will be updated during the generation instead of at the end. \
                # You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
                # By default this is enabled and the frequency is set to 1 step.")

                st.session_state["update_preview"] = st.session_state[
                    "defaults"
                ].general.update_preview
                st.session_state["update_preview_frequency"] = st.number_input(
                    "Update Image Preview Frequency",
                    min_value=0,
                    value=st.session_state["defaults"].txt2vid.update_preview_frequency,
                    help="Frequency in steps at which the the preview image is updated. By default the frequency \
				                                                               is set to 1 step.",
                )

                st.session_state["dynamic_preview_frequency"] = st.checkbox(
                    "Dynamic Preview Frequency",
                    value=st.session_state[
                        "defaults"
                    ].txt2vid.dynamic_preview_frequency,
                    help="This option tries to find the best value at which we can update \
					                                               the preview image during generation while minimizing the impact it has in performance. Default: True",
                )

            #

    with col2:
        preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])

        with preview_tab:
            # st.write("Image")
            # Image for testing
            # image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
            # new_image = image.resize((175, 240))
            # preview_image = st.image(image)

            # create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
            st.session_state["preview_image"] = st.empty()

            st.session_state["loading"] = st.empty()

            st.session_state["progress_bar_text"] = st.empty()
            st.session_state["progress_bar"] = st.empty()

            # generate_video = st.empty()
            st.session_state["preview_video"] = st.empty()
            preview_video = st.session_state["preview_video"]

            message = st.empty()

        with gallery_tab:
            st.write(
                "Here should be the image gallery, if I could make a grid in streamlit."
            )

    with col3:
        # If we have custom models available on the "models/custom"
        # folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
        custom_models_available()
        if server_state["CustomModel_available"]:
            custom_model = st.selectbox(
                "Custom Model:",
                st.session_state["defaults"].txt2vid.custom_models_list,
                index=st.session_state["defaults"].txt2vid.custom_models_list.index(
                    st.session_state["defaults"].txt2vid.default_model
                ),
                help="Select the model you want to use. This option is only available if you have custom models \
				                            on your 'models/custom' folder. The model name that will be shown here is the same as the name\
				                            the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
				                        will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.5",
            )
        else:
            custom_model = "runwayml/stable-diffusion-v1-5"

        # st.session_state["weights_path"] = custom_model
        # else:
        # custom_model = "runwayml/stable-diffusion-v1-5"
        # st.session_state["weights_path"] = f"CompVis/{slugify(custom_model.lower())}"

        st.session_state.sampling_steps = st.number_input(
            "Sampling Steps",
            value=st.session_state["defaults"].txt2vid.sampling_steps.value,
            min_value=st.session_state["defaults"].txt2vid.sampling_steps.min_value,
            step=st.session_state["defaults"].txt2vid.sampling_steps.step,
            help="Number of steps between each pair of sampled points",
        )

        st.session_state.num_inference_steps = st.number_input(
            "Inference Steps:",
            value=st.session_state["defaults"].txt2vid.num_inference_steps.value,
            min_value=st.session_state[
                "defaults"
            ].txt2vid.num_inference_steps.min_value,
            step=st.session_state["defaults"].txt2vid.num_inference_steps.step,
            help="Higher values (e.g. 100, 200 etc) can create better images.",
        )

        # sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a",  "k_heun", "PLMS", "DDIM"]
        # sampler_name = st.selectbox("Sampling method", sampler_name_list,
        # index=sampler_name_list.index(st.session_state['defaults'].txt2vid.default_sampler), help="Sampling method to use. Default: k_euler")
        scheduler_name_list = [
            "klms",
            "ddim",
            "ddpms",
            # "flaxddims", "flaxddpms", "flaxpndms"
        ]
        scheduler_name = st.selectbox(
            "Scheduler:",
            scheduler_name_list,
            index=scheduler_name_list.index(
                st.session_state["defaults"].txt2vid.scheduler_name
            ),
            help="Scheduler to use. Default: klms",
        )

        beta_scheduler_type_list = ["scaled_linear", "linear"]
        beta_scheduler_type = st.selectbox(
            "Beta Schedule Type:",
            beta_scheduler_type_list,
            index=beta_scheduler_type_list.index(
                st.session_state["defaults"].txt2vid.beta_scheduler_type
            ),
            help="Schedule Type to use. Default: linear",
        )

        # basic_tab, advanced_tab = st.tabs(["Basic", "Advanced"])

        # with basic_tab:
        # summit_on_enter = st.radio("Submit on enter?", ("Yes", "No"), horizontal=True,
        # help="Press the Enter key to summit, when 'No' is selected you can use the Enter key to write multiple lines.")

        with st.expander("Advanced"):
            with st.expander("Output Settings"):
                st.session_state["separate_prompts"] = st.checkbox(
                    "Create Prompt Matrix.",
                    value=st.session_state["defaults"].txt2vid.separate_prompts,
                    help="Separate multiple prompts using the `|` character, and get all combinations of them.",
                )
                st.session_state["normalize_prompt_weights"] = st.checkbox(
                    "Normalize Prompt Weights.",
                    value=st.session_state["defaults"].txt2vid.normalize_prompt_weights,
                    help="Ensure the sum of all weights add up to 1.0",
                )

                st.session_state["save_individual_images"] = st.checkbox(
                    "Save individual images.",
                    value=st.session_state["defaults"].txt2vid.save_individual_images,
                    help="Save each image generated before any filter or enhancement is applied.",
                )

                st.session_state["save_video"] = st.checkbox(
                    "Save video",
                    value=st.session_state["defaults"].txt2vid.save_video,
                    help="Save a video with all the images generated as frames at the end of the generation.",
                )

                save_video_on_stop = st.checkbox(
                    "Save video on Stop",
                    value=st.session_state["defaults"].txt2vid.save_video_on_stop,
                    help="Save a video with all the images generated as frames when we hit the stop button during a generation.",
                )

                st.session_state["group_by_prompt"] = st.checkbox(
                    "Group results by prompt",
                    value=st.session_state["defaults"].txt2vid.group_by_prompt,
                    help="Saves all the images with the same prompt into the same folder. When using a prompt \
				                                                  matrix each prompt combination will have its own folder.",
                )

                st.session_state["write_info_files"] = st.checkbox(
                    "Write Info file",
                    value=st.session_state["defaults"].txt2vid.write_info_files,
                    help="Save a file next to the image with informartion about the generation.",
                )

                st.session_state["do_loop"] = st.checkbox(
                    "Do Loop",
                    value=st.session_state["defaults"].txt2vid.do_loop,
                    help="Loop the prompt making two prompts from a single one.",
                )

                st.session_state["use_lerp_for_text"] = st.checkbox(
                    "Use Lerp Instead of Slerp",
                    value=st.session_state["defaults"].txt2vid.use_lerp_for_text,
                    help="Uses torch.lerp() instead of slerp. When interpolating between related prompts. \
				                                                    e.g. 'a lion in a grassy meadow' -> 'a bear in a grassy meadow' tends to keep the meadow \
				                                                    the whole way through when lerped, but slerping will often find a path where the meadow \
				                                                    disappears in the middle",
                )

                st.session_state["save_as_jpg"] = st.checkbox(
                    "Save samples as jpg",
                    value=st.session_state["defaults"].txt2vid.save_as_jpg,
                    help="Saves the images as jpg instead of png.",
                )

            #
            if "GFPGAN_available" not in st.session_state:
                GFPGAN_available()

            if "RealESRGAN_available" not in st.session_state:
                RealESRGAN_available()

            if "LDSR_available" not in st.session_state:
                LDSR_available()

            if (
                st.session_state["GFPGAN_available"]
                or st.session_state["RealESRGAN_available"]
                or st.session_state["LDSR_available"]
            ):
                with st.expander("Post-Processing"):
                    face_restoration_tab, upscaling_tab = st.tabs(
                        ["Face Restoration", "Upscaling"]
                    )
                    with face_restoration_tab:
                        # GFPGAN used for face restoration
                        if st.session_state["GFPGAN_available"]:
                            # with st.expander("Face Restoration"):
                            # if st.session_state["GFPGAN_available"]:
                            # with st.expander("GFPGAN"):
                            st.session_state["use_GFPGAN"] = st.checkbox(
                                "Use GFPGAN",
                                value=st.session_state["defaults"].txt2vid.use_GFPGAN,
                                help="Uses the GFPGAN model to improve faces after the generation.\
						                                                                 This greatly improve the quality and consistency of faces but uses\
						                                                                 extra VRAM. Disable if you need the extra VRAM.",
                            )

                            st.session_state["GFPGAN_model"] = st.selectbox(
                                "GFPGAN model",
                                st.session_state["GFPGAN_models"],
                                index=st.session_state["GFPGAN_models"].index(
                                    st.session_state["defaults"].general.GFPGAN_model
                                ),
                            )

                            # st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')

                        else:
                            st.session_state["use_GFPGAN"] = False

                    with upscaling_tab:
                        st.session_state["us_upscaling"] = st.checkbox(
                            "Use Upscaling",
                            value=st.session_state["defaults"].txt2vid.use_upscaling,
                        )
                        # RealESRGAN and LDSR used for upscaling.
                        if (
                            st.session_state["RealESRGAN_available"]
                            or st.session_state["LDSR_available"]
                        ):
                            upscaling_method_list = []
                            if st.session_state["RealESRGAN_available"]:
                                upscaling_method_list.append("RealESRGAN")
                            if st.session_state["LDSR_available"]:
                                upscaling_method_list.append("LDSR")

                            st.session_state["upscaling_method"] = st.selectbox(
                                "Upscaling Method",
                                upscaling_method_list,
                                index=upscaling_method_list.index(
                                    st.session_state[
                                        "defaults"
                                    ].general.upscaling_method
                                )
                                if st.session_state["defaults"].general.upscaling_method
                                in upscaling_method_list
                                else 0,
                            )

                            if st.session_state["RealESRGAN_available"]:
                                with st.expander("RealESRGAN"):
                                    if (
                                        st.session_state["upscaling_method"]
                                        == "RealESRGAN"
                                        and st.session_state["us_upscaling"]
                                    ):
                                        st.session_state["use_RealESRGAN"] = True
                                    else:
                                        st.session_state["use_RealESRGAN"] = False

                                    st.session_state["RealESRGAN_model"] = st.selectbox(
                                        "RealESRGAN model",
                                        st.session_state["RealESRGAN_models"],
                                        index=st.session_state[
                                            "RealESRGAN_models"
                                        ].index(
                                            st.session_state[
                                                "defaults"
                                            ].general.RealESRGAN_model
                                        ),
                                    )
                            else:
                                st.session_state["use_RealESRGAN"] = False
                                st.session_state[
                                    "RealESRGAN_model"
                                ] = "RealESRGAN_x4plus"

                            #
                            if st.session_state["LDSR_available"]:
                                with st.expander("LDSR"):
                                    if (
                                        st.session_state["upscaling_method"] == "LDSR"
                                        and st.session_state["us_upscaling"]
                                    ):
                                        st.session_state["use_LDSR"] = True
                                    else:
                                        st.session_state["use_LDSR"] = False

                                    st.session_state["LDSR_model"] = st.selectbox(
                                        "LDSR model",
                                        st.session_state["LDSR_models"],
                                        index=st.session_state["LDSR_models"].index(
                                            st.session_state[
                                                "defaults"
                                            ].general.LDSR_model
                                        ),
                                    )

                                    st.session_state[
                                        "ldsr_sampling_steps"
                                    ] = st.number_input(
                                        "Sampling Steps",
                                        value=st.session_state[
                                            "defaults"
                                        ].txt2vid.LDSR_config.sampling_steps,
                                        help="",
                                    )

                                    st.session_state["preDownScale"] = st.number_input(
                                        "PreDownScale",
                                        value=st.session_state[
                                            "defaults"
                                        ].txt2vid.LDSR_config.preDownScale,
                                        help="",
                                    )

                                    st.session_state["postDownScale"] = st.number_input(
                                        "postDownScale",
                                        value=st.session_state[
                                            "defaults"
                                        ].txt2vid.LDSR_config.postDownScale,
                                        help="",
                                    )

                                    downsample_method_list = ["Nearest", "Lanczos"]
                                    st.session_state[
                                        "downsample_method"
                                    ] = st.selectbox(
                                        "Downsample Method",
                                        downsample_method_list,
                                        index=downsample_method_list.index(
                                            st.session_state[
                                                "defaults"
                                            ].txt2vid.LDSR_config.downsample_method
                                        ),
                                    )

                            else:
                                st.session_state["use_LDSR"] = False
                                st.session_state["LDSR_model"] = "model"

            with st.expander("Variant"):
                st.session_state["variant_amount"] = st.number_input(
                    "Variant Amount:",
                    value=st.session_state["defaults"].txt2vid.variant_amount.value,
                    min_value=st.session_state[
                        "defaults"
                    ].txt2vid.variant_amount.min_value,
                    max_value=st.session_state[
                        "defaults"
                    ].txt2vid.variant_amount.max_value,
                    step=st.session_state["defaults"].txt2vid.variant_amount.step,
                )

                st.session_state["variant_seed"] = st.text_input(
                    "Variant Seed:",
                    value=st.session_state["defaults"].txt2vid.seed,
                    help="The seed to use when generating a variant, if left blank a random seed will be generated.",
                )

            # st.session_state["beta_start"] = st.slider("Beta Start:", value=st.session_state['defaults'].txt2vid.beta_start.value,
            # min_value=st.session_state['defaults'].txt2vid.beta_start.min_value,
            # max_value=st.session_state['defaults'].txt2vid.beta_start.max_value,
            # step=st.session_state['defaults'].txt2vid.beta_start.step, format=st.session_state['defaults'].txt2vid.beta_start.format)
            # st.session_state["beta_end"] = st.slider("Beta End:", value=st.session_state['defaults'].txt2vid.beta_end.value,
            # min_value=st.session_state['defaults'].txt2vid.beta_end.min_value, max_value=st.session_state['defaults'].txt2vid.beta_end.max_value,
            # step=st.session_state['defaults'].txt2vid.beta_end.step, format=st.session_state['defaults'].txt2vid.beta_end.format)

    if generate_button:
        # print("Loading models")
        # load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
        # load_models(False, st.session_state["use_GFPGAN"], True, st.session_state["RealESRGAN_model"])
        # with no_rerun:
        if st.session_state["use_GFPGAN"]:
            if "GFPGAN" in server_state:
                logger.info("GFPGAN already loaded")
            else:
                with col2:
                    with hc.HyLoader(
                        "Loading Models...", hc.Loaders.standard_loaders, index=[0]
                    ):
                        # Load GFPGAN
                        if os.path.exists(
                            st.session_state["defaults"].general.GFPGAN_dir
                        ):
                            try:
                                load_GFPGAN()
                                logger.info("Loaded GFPGAN")
                            except Exception:
                                import traceback

                                logger.error("Error loading GFPGAN:", file=sys.stderr)
                                logger.error(traceback.format_exc(), file=sys.stderr)
        else:
            if "GFPGAN" in server_state:
                del server_state["GFPGAN"]

        # try:
        # run video generation
        video, seed, info, stats = txt2vid(
            prompts=prompt,
            gpu=st.session_state["defaults"].general.gpu,
            num_steps=st.session_state.sampling_steps,
            max_duration_in_seconds=st.session_state.max_duration_in_seconds,
            num_inference_steps=st.session_state.num_inference_steps,
            cfg_scale=cfg_scale,
            save_video_on_stop=save_video_on_stop,
            outdir=st.session_state["defaults"].general.outdir,
            do_loop=st.session_state["do_loop"],
            use_lerp_for_text=st.session_state["use_lerp_for_text"],
            seeds=seed,
            quality=100,
            eta=0.0,
            width=width,
            height=height,
            weights_path=custom_model,
            scheduler=scheduler_name,
            disable_tqdm=False,
            beta_start=st.session_state["defaults"].txt2vid.beta_start.value,
            beta_end=st.session_state["defaults"].txt2vid.beta_end.value,
            beta_schedule=beta_scheduler_type,
            starting_image=None,
            fps=st.session_state.fps,
        )

        if video and save_video_on_stop:
            if os.path.exists(video):  # temporary solution to bypass exception
                # show video preview on the UI after we hit the stop button
                # currently not working as session_state is cleared on StopException
                preview_video.video(open(video, "rb").read())

        # message.success('Done!', icon="✅")
        message.success("Render Complete: " + info + "; Stats: " + stats, icon="✅")

        # history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']

        # if 'latestVideos' in st.session_state:
        # for i in video:
        ##push the new image to the list of latest images and remove the oldest one
        ##remove the last index from the list\
        # st.session_state['latestVideos'].pop()
        ##add the new image to the start of the list
        # st.session_state['latestVideos'].insert(0, i)
        # PlaceHolder.empty()

        # with PlaceHolder.container():
        # col1, col2, col3 = st.columns(3)
        # col1_cont = st.container()
        # col2_cont = st.container()
        # col3_cont = st.container()

        # with col1_cont:
        # with col1:
        # st.image(st.session_state['latestVideos'][0])
        # st.image(st.session_state['latestVideos'][3])
        # st.image(st.session_state['latestVideos'][6])
        # with col2_cont:
        # with col2:
        # st.image(st.session_state['latestVideos'][1])
        # st.image(st.session_state['latestVideos'][4])
        # st.image(st.session_state['latestVideos'][7])
        # with col3_cont:
        # with col3:
        # st.image(st.session_state['latestVideos'][2])
        # st.image(st.session_state['latestVideos'][5])
        # st.image(st.session_state['latestVideos'][8])
        # historyGallery = st.empty()

        ## check if output_images length is the same as seeds length
        # with gallery_tab:
        # st.markdown(createHTMLGallery(video,seed), unsafe_allow_html=True)

        # st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]

        # except (StopException, KeyError):
        # print(f"Received Streamlit StopException")


================================================
FILE: webui/streamlit/scripts/webui_streamlit.py
================================================
# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

# base webui import and utils.
# import streamlit as st

# We import hydralit like this to replace the previous stuff
# we had with native streamlit as it lets ur replace things 1:1
from sd_utils import (
    st,
    hc,
    load_configs,
    load_css,
    set_logger_verbosity,
    logger,
    quiesce_logger,
    set_page_title,
)

# streamlit imports

# streamlit components section
# from st_on_hover_tabs import on_hover_tabs
# from streamlit_server_state import server_state, server_state_lock

# other imports
import argparse

# from sd_utils.bridge import run_bridge

# import custom components
from custom_components import draggable_number_input

# end of imports
# ---------------------------------------------------------------------------------------------------------------

load_configs()

help = """
A double dash (`--`) is used to separate streamlit arguments from app arguments.
As a result using "streamlit run webui_streamlit.py --headless"
will show the help for streamlit itself and not pass any argument to our app,
we need to use "streamlit run webui_streamlit.py -- --headless"
in order to pass a command argument to this app."""
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)

parser.add_argument(
    "--headless",
    action="store_true",
    help="Don't launch web server, util if you just want to run the stable horde bridge.",
    default=False,
)

parser.add_argument(
    "--bridge",
    action="store_true",
    help="don't launch web server, but make this instance into a Horde bridge.",
    default=False,
)
parser.add_argument(
    "--horde_api_key",
    action="store",
    required=False,
    type=str,
    help="The API key corresponding to the owner of this Horde instance",
)
parser.add_argument(
    "--horde_name",
    action="store",
    required=False,
    type=str,
    help="The server name for the Horde. It will be shown to the world and there can be only one.",
)
parser.add_argument(
    "--horde_url",
    action="store",
    required=False,
    type=str,
    help="The SH Horde URL. Where the bridge will pickup prompts and send the finished generations.",
)
parser.add_argument(
    "--horde_priority_usernames",
    type=str,
    action="append",
    required=False,
    help="Usernames which get priority use in this horde instance. The owner's username is always in this list.",
)
parser.add_argument(
    "--horde_max_power",
    type=int,
    required=False,
    help="How much power this instance has to generate pictures. Min: 2",
)
parser.add_argument(
    "--horde_sfw",
    action="store_true",
    required=False,
    help="Set to true if you do not want this worker generating NSFW images.",
)
parser.add_argument(
    "--horde_blacklist",
    nargs="+",
    required=False,
    help="List the words that you want to blacklist.",
)
parser.add_argument(
    "--horde_censorlist",
    nargs="+",
    required=False,
    help="List the words that you want to censor.",
)
parser.add_argument(
    "--horde_censor_nsfw",
    action="store_true",
    required=False,
    help="Set to true if you want this bridge worker to censor NSFW images.",
)
parser.add_argument(
    "--horde_model",
    action="store",
    required=False,
    help="Which model to run on this horde.",
)
parser.add_argument(
    "-v",
    "--verbosity",
    action="count",
    default=0,
    help="The default logging level is ERROR or higher. This value increases the amount of logging seen in your screen",
)
parser.add_argument(
    "-q",
    "--quiet",
    action="count",
    default=0,
    help="The default logging level is ERROR or higher. This value decreases the amount of logging seen in your screen",
)
opt = parser.parse_args()

# with server_state_lock["bridge"]:
# server_state["bridge"] = opt.bridge


@logger.catch(reraise=True)
def layout():
    """Layout functions to define all the streamlit layout here."""
    if not st.session_state["defaults"].debug.enable_hydralit:
        st.set_page_config(
            page_title="Stable Diffusion Playground",
            layout="wide",
            initial_sidebar_state="collapsed",
        )

    # app = st.HydraApp(title='Stable Diffusion WebUI', favicon="", sidebar_state="expanded", layout="wide",
    # hide_streamlit_markers=False, allow_url_nav=True , clear_cross_app_sessions=False)

    # load css as an external file, function has an option to local or remote url. Potential use when running from cloud infra that might not have access to local path.
    load_css(True, "frontend/css/streamlit.main.css")

    #
    # specify the primary menu definition
    menu_data = [
        {
            "id": "Stable Diffusion",
            "label": "Stable Diffusion",
            "icon": "bi bi-grid-1x2-fill",
        },
        {
            "id": "Train",
            "label": "Train",
            "icon": "bi bi-lightbulb-fill",
            "submenu": [
                {
                    "id": "Textual Inversion",
                    "label": "Textual Inversion",
                    "icon": "bi bi-lightbulb-fill",
                },
                {
                    "id": "Fine Tunning",
                    "label": "Fine Tunning",
                    "icon": "bi bi-lightbulb-fill",
                },
            ],
        },
        {
            "id": "Model Manager",
            "label": "Model Manager",
            "icon": "bi bi-cloud-arrow-down-fill",
        },
        {
            "id": "Tools",
            "label": "Tools",
            "icon": "bi bi-tools",
            "submenu": [
                {"id": "API Server", "label": "API Server", "icon": "bi bi-server"},
                {
                    "id": "Barfi/BaklavaJS",
                    "label": "Barfi/BaklavaJS",
                    "icon": "bi bi-diagram-3-fill",
                },
                # {'id': 'API Server', 'label': 'API Server', 'icon': 'bi bi-server'},
            ],
        },
        {"id": "Settings", "label": "Settings", "icon": "bi bi-gear-fill"},
    ]

    over_theme = {"txc_inactive": "#FFFFFF", "menu_background": "#000000"}

    menu_id = hc.nav_bar(
        menu_definition=menu_data,
        # home_name='Home',
        # login_name='Logout',
        hide_streamlit_markers=False,
        override_theme=over_theme,
        sticky_nav=True,
        sticky_mode="pinned",
    )

    #
    # if menu_id == "Home":
    # st.info("Under Construction. :construction_worker:")

    if menu_id == "Stable Diffusion":
        # set the page url and title
        # st.experimental_set_query_params(page='stable-diffusion')
        try:
            set_page_title("Stable Diffusion Playground")
        except NameError:
            st.experimental_rerun()

        (
            txt2img_tab,
            img2img_tab,
            txt2vid_tab,
            img2txt_tab,
            post_processing_tab,
            concept_library_tab,
        ) = st.tabs(
            [
                "Text-to-Image",
                "Image-to-Image",
                # "Inpainting",
                "Text-to-Video",
                "Image-To-Text",
                "Post-Processing",
                "Concept Library",
            ]
        )
        # with home_tab:
        # from home import layout
        # layout()

        with txt2img_tab:
            from txt2img import layout

            layout()

        with img2img_tab:
            from img2img import layout

            layout()

        # with inpainting_tab:
        # from inpainting import layout
        # layout()

        with txt2vid_tab:
            from txt2vid import layout

            layout()

        with img2txt_tab:
            from img2txt import layout

            layout()

        with post_processing_tab:
            from post_processing import layout

            layout()

        with concept_library_tab:
            from sd_concept_library import layout

            layout()

    #
    elif menu_id == "Model Manager":
        set_page_title("Model Manager - Stable Diffusion Playground")

        from ModelManager import layout

        layout()

    elif menu_id == "Textual Inversion":
        from textual_inversion import layout

        layout()

    elif menu_id == "Fine Tunning":
        # from textual_inversion import layout
        # layout()
        st.info("Under Construction. :construction_worker:")

    elif menu_id == "API Server":
        set_page_title("API Server - Stable Diffusion Playground")
        from APIServer import layout

        layout()

    elif menu_id == "Barfi/BaklavaJS":
        set_page_title("Barfi/BaklavaJS - Stable Diffusion Playground")
        from barfi_baklavajs import layout

        layout()

    elif menu_id == "Settings":
        set_page_title("Settings - Stable Diffusion Playground")

        from Settings import layout

        layout()

    # calling dragable input component module at the end, so it works on all pages
    draggable_number_input.load()


if __name__ == "__main__":
    set_logger_verbosity(opt.verbosity)
    quiesce_logger(opt.quiet)

    if not opt.headless:
        layout()

    # with server_state_lock["bridge"]:
    # if server_state["bridge"]:
    # try:
    # import bridgeData as cd
    # except ModuleNotFoundError as e:
    # logger.warning("No bridgeData found. Falling back to default where no CLI args are set.")
    # logger.debug(str(e))
    # except SyntaxError as e:
    # logger.warning("bridgeData found, but is malformed. Falling back to default where no CLI args are set.")
    # logger.debug(str(e))
    # except Exception as e:
    # logger.warning("No bridgeData found, use default where no CLI args are set")
    # logger.debug(str(e))
    # finally:
    # try: # check if cd exists (i.e. bridgeData loaded properly)
    # cd
    # except: # if not, create defaults
    # class temp(object):
    # def __init__(self):
    # random.seed()
    # self.horde_url = "https://stablehorde.net"
    ## Give a cool name to your instance
    # self.horde_name = f"Automated Instance #{random.randint(-100000000, 100000000)}"
    ## The api_key identifies a unique user in the horde
    # self.horde_api_key = "0000000000"
    ## Put other users whose prompts you want to prioritize.
    ## The owner's username is always included so you don't need to add it here, unless you want it to have lower priority than another user
    # self.horde_priority_usernames = []
    # self.horde_max_power = 8
    # self.nsfw = True
    # self.censor_nsfw = False
    # self.blacklist = []
    # self.censorlist = []
    # self.models_to_load = ["stable_diffusion"]
    # cd = temp()
    # horde_api_key = opt.horde_api_key if opt.horde_api_key else cd.horde_api_key
    # horde_name = opt.horde_name if opt.horde_name else cd.horde_name
    # horde_url = opt.horde_url if opt.horde_url else cd.horde_url
    # horde_priority_usernames = opt.horde_priority_usernames if opt.horde_priority_usernames else cd.horde_priority_usernames
    # horde_max_power = opt.horde_max_power if opt.horde_max_power else cd.horde_max_power
    ## Not used yet
    # horde_models = [opt.horde_model] if opt.horde_model else cd.models_to_load
    # try:
    # horde_nsfw = not opt.horde_sfw if opt.horde_sfw else cd.horde_nsfw
    # except AttributeError:
    # horde_nsfw = True
    # try:
    # horde_censor_nsfw = opt.horde_censor_nsfw if opt.horde_censor_nsfw else cd.horde_censor_nsfw
    # except AttributeError:
    # horde_censor_nsfw = False
    # try:
    # horde_blacklist = opt.horde_blacklist if opt.horde_blacklist else cd.horde_blacklist
    # except AttributeError:
    # horde_blacklist = []
    # try:
    # horde_censorlist = opt.horde_censorlist if opt.horde_censorlist else cd.horde_censorlist
    # except AttributeError:
    # horde_censorlist = []
    # if horde_max_power < 2:
    # horde_max_power = 2
    # horde_max_pixels = 64*64*8*horde_max_power
    # logger.info(f"Joining Horde with parameters: Server Name '{horde_name}'. Horde URL '{horde_url}'. Max Pixels {horde_max_pixels}")

    # try:
    # thread = threading.Thread(target=run_bridge(1, horde_api_key, horde_name, horde_url,
    # horde_priority_usernames, horde_max_pixels,
    # horde_nsfw, horde_censor_nsfw, horde_blacklist,
    # horde_censorlist), args=())
    # thread.daemon = True
    # thread.start()
    ##run_bridge(1, horde_api_key, horde_name, horde_url, horde_priority_usernames, horde_max_pixels, horde_nsfw, horde_censor_nsfw, horde_blacklist, horde_censorlist)
    # except KeyboardInterrupt:
    # print(f"Keyboard Interrupt Received. Ending Bridge")


================================================
FILE: webui.cmd
================================================
@echo off
:: This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
::
:: Copyright 2022 Sygil-Dev team.
:: This program is free software: you can redistribute it and/or modify
:: it under the terms of the GNU Affero General Public License as published by
:: the Free Software Foundation, either version 3 of the License, or
:: (at your option) any later version.
::
:: This program is distributed in the hope that it will be useful,
:: but WITHOUT ANY WARRANTY; without even the implied warranty of
:: MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
:: GNU Affero General Public License for more details.
::
:: You should have received a copy of the GNU Affero General Public License
:: along with this program.  If not, see <http://www.gnu.org/licenses/>.
:: Run all commands using this script's directory as the working directory
cd %~dp0

:: copy over the first line from environment.yaml, e.g. name: ldm, and take the second word after splitting by ":" delimiter
for /F "tokens=2 delims=: " %%i in (environment.yaml) DO (
  set v_conda_env_name=%%i
  goto EOL
)
:EOL

echo Environment name is set as %v_conda_env_name% as per environment.yaml

:: Put the path to conda directory in a file called "custom-conda-path.txt" if it's installed at non-standard path
IF EXIST custom-conda-path.txt (
  FOR /F %%i IN (custom-conda-path.txt) DO set v_custom_path=%%i
)

set INSTALL_ENV_DIR=%cd%\installer_files\env
set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%

set v_paths=%INSTALL_ENV_DIR%
set v_paths=%v_paths%;%ProgramData%\miniconda3
set v_paths=%v_paths%;%USERPROFILE%\miniconda3
set v_paths=%v_paths%;%ProgramData%\anaconda3
set v_paths=%v_paths%;%USERPROFILE%\anaconda3

for %%a in (%v_paths%) do (
  IF NOT "%v_custom_path%"=="" (
    set v_paths=%v_custom_path%;%v_paths%
  )
)

for %%a in (%v_paths%) do (
  if EXIST "%%a\Scripts\activate.bat" (
    SET v_conda_path=%%a
    echo anaconda3/miniconda3 detected in %%a
    goto :CONDA_FOUND
  )
)

IF "%v_conda_path%"=="" (
  echo anaconda3/miniconda3 not found. Install from here https://docs.conda.io/en/latest/miniconda.html
  pause
  exit /b 1
)

:CONDA_FOUND
echo Stashing local changes and pulling latest update...
git status --porcelain=1 -uno | findstr . && set "HasChanges=1" || set "HasChanges=0"
call git stash
call git pull
IF "%HasChanges%" == "0" GOTO SKIP_RESTORE

set /P restore="Do you want to restore changes you made before updating? (Y/N): "
IF /I "%restore%" == "N" (
  echo Removing changes...
  call git stash drop
  echo Changes removed
) ELSE IF /I "%restore%" == "Y" (
  echo Restoring changes...
  call git stash pop --quiet
  echo Changes restored
)

:SKIP_RESTORE
call "%v_conda_path%\Scripts\activate.bat"

for /f "delims=" %%a in ('git log -1 --format^="%%H" -- environment.yaml')  DO set v_cur_hash=%%a
set /p "v_last_hash="<"z_version_env.tmp"
echo %v_cur_hash%>z_version_env.tmp

echo Current  environment.yaml hash: %v_cur_hash%
echo Previous environment.yaml hash: %v_last_hash%

if "%v_last_hash%" == "%v_cur_hash%" (
  echo environment.yaml unchanged. dependencies should be up to date.
  echo if you still have unresolved dependencies, delete "z_version_env.tmp"
) else (
  echo environment.yaml changed. updating dependencies
  call conda env create --name "%v_conda_env_name%" -f environment.yaml
  call conda env update --name "%v_conda_env_name%" -f environment.yaml
)


call "%v_conda_path%\Scripts\activate.bat" "%v_conda_env_name%"

:PROMPT
set SETUPTOOLS_USE_DISTUTILS=stdlib
IF EXIST "models\ldm\stable-diffusion-v1\Stable Diffusion v1.5.ckpt" (
  python -m streamlit run scripts\webui_streamlit.py --theme.base dark
) ELSE (
  echo Your model file does not exist! Once the WebUI launches please visit the Model Manager page and download the models by using the Download button for each model.
  python -m streamlit run scripts\webui_streamlit.py --theme.base dark
)

::cmd /k


================================================
FILE: webui.sh
================================================
#!/bin/bash -i

# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
# Start the Stable Diffusion WebUI for Linux Users

DIRECTORY="."
ENV_FILE="environment.yaml"
ENV_NAME="ldm"
ENV_MODIFIED=$(date -r $ENV_FILE "+%s")
ENV_MODIFED_FILE=".env_updated"
ENV_UPDATED=0

INSTALL_ENV_DIR="$(pwd)/../installer_files/env" # since linux-sd.sh clones the repo into a subfolder
if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi

# Models used for upscaling
GFPGAN_MODEL="https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth"
LATENT_DIFFUSION_REPO="https://github.com/devilismyfriend/latent-diffusion.git"
LSDR_CONFIG="https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
LSDR_MODEL="https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
REALESRGAN_MODEL="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth"
REALESRGAN_ANIME_MODEL="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth"
SD_CONCEPT_REPO="https://github.com/Sygil-Dev/sd-concepts-library/archive/refs/heads/main.zip"


if [[ -f $ENV_MODIFED_FILE ]]; then
    ENV_MODIFIED_CACHED=$(<${ENV_MODIFED_FILE})
else
    ENV_MODIFIED_CACHED=0
fi

# Setup the Conda env for the project. This will also handle updating the env as needed too.
conda_env_setup () {
    # Set conda path if it is not already in default environment
    CUSTOM_CONDA_PATH=

    # Allow setting custom path via file to allow updates of this script without undoing custom path
    if [ -f custom-conda-path.txt ]; then
        CUSTOM_CONDA_PATH=$(cat custom-conda-path.txt)
    fi

    # If a custom conda isn't specified, and the installer downloaded conda for the user, then use that
    if [ -f "$INSTALL_ENV_DIR/etc/profile.d/conda.sh" ] && [ "$CUSTOM_CONDA_PATH" == "" ]; then
        . "$INSTALL_ENV_DIR/etc/profile.d/conda.sh"
    fi

    # If custom path is set above, try to setup conda environment
    if [ -f "${CUSTOM_CONDA_PATH}/etc/profile.d/conda.sh" ]; then
        . "${CUSTOM_CONDA_PATH}/etc/profile.d/conda.sh"
    elif [ -n "${CUSTOM_CONDA_PATH}" ] && [ -f "${CUSTOM_CONDA_PATH}/bin" ]; then
        export PATH="${CUSTOM_CONDA_PATH}/bin:$PATH"
    fi

    if ! command -v conda >/dev/null; then
        printf "Anaconda3 not found. Install from here https://www.anaconda.com/products/distribution\n"
        exit 1
    fi

    # Create/update conda env if needed
    if ! conda env list | grep ".*${ENV_NAME}.*" >/dev/null 2>&1; then
        printf "Could not find conda env: ${ENV_NAME} ... creating ... \n\n"
        conda env create -f $ENV_FILE
        ENV_UPDATED=1
    elif [[ ! -z $CONDA_FORCE_UPDATE && $CONDA_FORCE_UPDATE == "true" ]] || (( $ENV_MODIFIED > $ENV_MODIFIED_CACHED )); then
        printf "Updating conda env: ${ENV_NAME} ...\n\n"
        PIP_EXISTS_ACTION=w conda env update --file $ENV_FILE --prune
        ENV_UPDATED=1
    fi

    # Clear artifacts from conda after create/update
    if (( $ENV_UPDATED > 0 )); then
        conda clean --all
        echo -n $ENV_MODIFIED > $ENV_MODIFED_FILE
    fi
}

# Activate conda environment
conda_env_activation () {
    conda activate $ENV_NAME
    conda info | grep active
}

# Checks to see if the upscaling models exist in their correct locations. If they do not they will be downloaded as required
post_processor_model_loading () {
    # Check to see if GFPGAN has been added yet, if not it will download it and place it in the proper directory
    if [ -f "$DIRECTORY/models/gfpgan/GFPGANv1.3.pth" ]; then
        printf "GFPGAN already exists. Continuing...\n\n"
    else
        printf "Downloading GFPGAN model. Please wait...\n"
        wget $GFPGAN_MODEL -P $DIRECTORY/models/gfpgan
    fi

    # Check to see if realESRGAN has been added yet, if not it will download it and place it in the proper directory
    if [ -f "$DIRECTORY/models/realesrgan/RealESRGAN_x4plus.pth" ]; then
        printf "realESRGAN already exists. Continuing...\n\n"
    else
        printf "Downloading realESRGAN model. Please wait...\n"
        wget $REALESRGAN_MODEL -P $DIRECTORY/models/realesrgan
        wget $REALESRGAN_ANIME_MODEL -P $DIRECTORY/models/realesrgan
    fi

    # Check to see if LDSR has been added yet, if not it will be cloned and its models downloaded to the correct directory
    if [ -f "$DIRECTORY/models/ldsr/model.ckpt" ]; then
        printf "LDSR already exists. Continuing...\n\n"
    else
        printf "Cloning LDSR and downloading model. Please wait...\n"
        git clone $LATENT_DIFFUSION_REPO
        mv latent-diffusion $DIRECTORY/models/ldsr
        mkdir $DIRECTORY/models/ldsr/experiments
        mkdir $DIRECTORY/models/ldsr
        wget $LSDR_CONFIG -P $DIRECTORY/models/ldsr
        mv $DIRECTORY/models/ldsr/index.html?dl=1 $DIRECTORY/models/ldsr/project.yaml
        wget $LSDR_MODEL -P $DIRECTORY/models/ldsr
        mv $DIRECTORY/models/ldsr/index.html?dl=1 $DIRECTORY/models/ldsr/model.ckpt
    fi

    # Check to see if SD Concepts has been added yet, if not it will download it and place it in the proper directory
    if [ -d "$DIRECTORY/models/custom/sd-concepts-library" ]; then
        printf "SD Concepts Library already exists. Continuing...\n\n"
    else
        printf "Downloading and Extracting SD Concepts Library model. Please wait...\n"
        mkdir $DIRECTORY/models/custom
        wget $SD_CONCEPT_REPO
        if ! command -v unzip &> /dev/null
        then
            printf "Warning: unzip could not be found. \nPlease install 'unzip' from your package manager and rerun this program.\n"
            exit 1
        fi
        unzip main.zip
        mv sd-concepts-library-main/sd-concepts-library $DIRECTORY/models/custom
    fi
}

# Show the user a prompt asking them which version of the WebUI they wish to use, Streamlit or Gradio
launch_webui () {
    # skip the prompt if --bridge command-line argument is detected
    for arg in "$@"; do
        if [ "$arg" == "--bridge" ]; then
           python -u scripts/relauncher.py "$@"
           return
        fi
    done
    printf "\n\n########## LAUNCH USING STREAMLIT OR GRADIO? ##########\n\n"
    printf "Do you wish to run the WebUI using the Gradio or StreamLit Interface?\n\n"
    printf "Streamlit: \nHas A More Modern UI \nMore Features Planned \nWill Be The Main UI Going Forward \nCurrently In Active Development \n\n"
    printf "Gradio: \nCurrently Feature Complete \nUses An Older Interface Style \nWill Not Receive Major Updates\n\n"
    printf "Which Version of the WebUI Interface do you wish to use?\n"
    select yn in "Streamlit" "Gradio"; do
        case $yn in
            Streamlit ) printf "\nStarting Stable Diffusion WebUI: Streamlit Interface. Please Wait...\n"; python -m streamlit run scripts/webui_streamlit.py; break;;
            Gradio ) printf "\nStarting Stable Diffusion WebUI: Gradio Interface. Please Wait...\n"; python scripts/relauncher.py "$@"; break;;
        esac
    done
}

# Function to initialize the other functions
start_initialization () {
    conda_env_setup
    sd_model_loading
    post_processor_model_loading
    conda_env_activation
    if [ ! -e "models/ldm/stable-diffusion-v1/*.ckpt" ]; then
        echo "Your model file does not exist! Streamlit will handle this automatically, however Gradio still requires this file be placed manually. If you intend to use the Gradio interface, place it in 'models/ldm/stable-diffusion-v1' with the name 'model.ckpt'."
        read -p "Once you have sd-v1-4.ckpt in the project root, if you are going to use Gradio, Press Enter...\n\n"
    fi
    launch_webui "$@"

}

start_initialization "$@"


================================================
FILE: webui_legacy.cmd
================================================
@echo off
:: This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).

:: Copyright 2022 Sygil-Dev team.
:: This program is free software: you can redistribute it and/or modify
:: it under the terms of the GNU Affero General Public License as published by
:: the Free Software Foundation, either version 3 of the License, or
:: (at your option) any later version.

:: This program is distributed in the hope that it will be useful,
:: but WITHOUT ANY WARRANTY; without even the implied warranty of
:: MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
:: GNU Affero General Public License for more details.

:: You should have received a copy of the GNU Affero General Public License
:: along with this program.  If not, see <http://www.gnu.org/licenses/>.
:: Run all commands using this script's directory as the working directory
cd %~dp0

:: copy over the first line from environment.yaml, e.g. name: ldm, and take the second word after splitting by ":" delimiter
for /F "tokens=2 delims=: " %%i in (environment.yaml) DO (
  set v_conda_env_name=%%i
  goto EOL
)
:EOL

echo Environment name is set as %v_conda_env_name% as per environment.yaml

:: Put the path to conda directory in a file called "custom-conda-path.txt" if it's installed at non-standard path
IF EXIST custom-conda-path.txt (
  FOR /F %%i IN (custom-conda-path.txt) DO set v_custom_path=%%i
)

set v_paths=%ProgramData%\miniconda3
set v_paths=%v_paths%;%USERPROFILE%\miniconda3
set v_paths=%v_paths%;%ProgramData%\anaconda3
set v_paths=%v_paths%;%USERPROFILE%\anaconda3

for %%a in (%v_paths%) do (
  IF NOT "%v_custom_path%"=="" (
    set v_paths=%v_custom_path%;%v_paths%
  )
)

for %%a in (%v_paths%) do (
  if EXIST "%%a\Scripts\activate.bat" (
    SET v_conda_path=%%a
    echo anaconda3/miniconda3 detected in %%a
    goto :CONDA_FOUND
  )
)

IF "%v_conda_path%"=="" (
  echo anaconda3/miniconda3 not found. Install from here https://docs.conda.io/en/latest/miniconda.html
  pause
  exit /b 1
)

:CONDA_FOUND
echo Stashing local changes and pulling latest update...
git status --porcelain=1 -uno | findstr . && set "HasChanges=1" || set "HasChanges=0"
call git stash
call git pull
IF "%HasChanges%" == "0" GOTO SKIP_RESTORE

set /P restore="Do you want to restore changes you made before updating? (Y/N): "
IF /I "%restore%" == "N" (
  echo Removing changes...
  call git stash drop
  echo Changes removed
) ELSE IF /I "%restore%" == "Y" (
  echo Restoring changes...
  call git stash pop --quiet
  echo Changes restored
)

:SKIP_RESTORE
call "%v_conda_path%\Scripts\activate.bat"

for /f "delims=" %%a in ('git log -1 --format^="%%H" -- environment.yaml')  DO set v_cur_hash=%%a
set /p "v_last_hash="<"z_version_env.tmp"
echo %v_cur_hash%>z_version_env.tmp

echo Current  environment.yaml hash: %v_cur_hash%
echo Previous environment.yaml hash: %v_last_hash%

if "%v_last_hash%" == "%v_cur_hash%" (
  echo environment.yaml unchanged. dependencies should be up to date.
  echo if you still have unresolved dependencies, delete "z_version_env.tmp"
) else (
  echo environment.yaml changed. updating dependencies
  call conda env create --name "%v_conda_env_name%" -f environment.yaml
  call conda env update --name "%v_conda_env_name%" -f environment.yaml
)


call "%v_conda_path%\Scripts\activate.bat" "%v_conda_env_name%"

:PROMPT
set SETUPTOOLS_USE_DISTUTILS=stdlib
IF EXIST "models\ldm\stable-diffusion-v1\model.ckpt" (
  set "PYTHONPATH=%~dp0"
  python scripts\relauncher.py %*
) ELSE (
  echo Your model file does not exist! Place it in 'models\ldm\stable-diffusion-v1' with the name 'model.ckpt'.
  pause
)

::cmd /k