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Repository: djstla/novelai-webui-aki-v3
Branch: main
Commit: ed1aef635dfa
Files: 57
Total size: 268.3 KB
Directory structure:
gitextract_f8afe7lc/
├── A用户协议.txt
├── B使用教程+常见问题.txt
├── CODEOWNERS
├── LICENSE.txt
├── README.md
├── cache.json
├── config.json
├── configs/
│ ├── alt-diffusion-inference.yaml
│ ├── instruct-pix2pix.yaml
│ ├── v1-inference.yaml
│ └── v1-inpainting-inference.yaml
├── environment-wsl2.yaml
├── launch.py
├── params.txt
├── requirements.txt
├── requirements_versions.txt
├── script.js
├── scripts/
│ ├── custom_code.py
│ ├── img2imgalt.py
│ ├── loopback.py
│ ├── outpainting_mk_2.py
│ ├── poor_mans_outpainting.py
│ ├── postprocessing_codeformer.py
│ ├── postprocessing_gfpgan.py
│ ├── postprocessing_upscale.py
│ ├── prompt_matrix.py
│ ├── prompts_from_file.py
│ ├── sd_upscale.py
│ └── xyz_grid.py
├── style.css
├── styles.csv
├── tags/
│ └── temp/
│ ├── emb.txt
│ └── wc.txt
├── test/
│ ├── __init__.py
│ ├── basic_features/
│ │ ├── __init__.py
│ │ ├── extras_test.py
│ │ ├── img2img_test.py
│ │ ├── txt2img_test.py
│ │ └── utils_test.py
│ ├── server_poll.py
│ └── test_files/
│ └── empty.pt
├── textual_inversion_templates/
│ ├── hypernetwork.txt
│ ├── none.txt
│ ├── style.txt
│ ├── style_filewords.txt
│ ├── subject.txt
│ └── subject_filewords.txt
├── tmp/
│ ├── stderr.txt
│ ├── stdout.txt
│ └── tagAutocompletePath.txt
├── ui-config.json
├── webui-macos-env.sh
├── webui-user.bat
├── webui-user.sh
├── webui.bat
├── webui.py
└── webui.sh
================================================
FILE CONTENTS
================================================
================================================
FILE: A用户协议.txt
================================================
本整合包仅用作 AIGC 技术学习，基于 Github 上开源项目 Stable Diffusion Webui 制作，提供了算法的运行环境。本整合包并不附带任何生成图像所用的模型。
使用本整合包即代表您已阅读并同意以下用户协议：
[✓] 您不得实施包括但不限于以下行为，也不得为任何违反法律法规的行为提供便利：
反对宪法所规定的基本原则的。
危害国家安全，泄露国家秘密，颠覆国家政权，破坏国家统一的。
损害国家荣誉和利益的。
煽动民族仇恨、民族歧视，破坏民族团结的。
破坏国家宗教政策，宣扬邪教和封建迷信的。
散布谣言，扰乱社会秩序，破坏社会稳定的。
散布淫秽、色情、赌博、暴力、凶杀、恐怖或教唆犯罪的。
侮辱或诽谤他人，侵害他人合法权益的。
实施任何违背“七条底线”的行为。
含有法律、行政法规禁止的其他内容的。
[✓] 因您的数据的产生、收集、处理、使用等任何相关事项存在违反法律法规等情况而造成的全部结果及责任均由您自行承担。
如果您已阅读并同意以上协议内容，请在下方打字写入括号内的字并保存【我已阅读并同意用户协议】。
请在这里的冒号后打入：我已阅读并同意用户协议
================================================
FILE: B使用教程+常见问题.txt
================================================
AI 作图知识库(教程): https://guide.novelai.dev/
标签超市(解析组合): https://tags.novelai.dev/
原图提取标签: https://spell.novelai.dev/
入门参数设置基础：https://guide.novelai.dev/guide/configuration/param-basic
常见安装问题: https://guide.novelai.dev/s/troubleshooting/install
常见跑图问题: https://guide.novelai.dev/s/troubleshooting/generate
怎么写提示词？ https://guide.novelai.dev/advanced/prompt-engineering/
怎么训练模型？ https://guide.novelai.dev/advanced/finetuning/
最新消息: https://guide.novelai.dev/newsfeed
问题速查:
- CUDA out of memory： 炸显存 换启动参数 换显卡
- DefaultCPUAllocator: 炸内存 加虚拟内存 加内存条
- CUDA driver initialization failed: 装CUDA驱动
- Training models with lowvram not possible: 这点显存还想炼丹？
- WinError 5: 建议重装电脑，或者等下一个整合包
训练配置要求:
训练embedding、hypernetwork 6G显存，使用384分辨率 8G以上可以使用512分辨率
训练dreambooth 最少12G显存
================================================
FILE: CODEOWNERS
================================================
* @AUTOMATIC1111
# if you were managing a localization and were removed from this file, this is because
# the intended way to do localizations now is via extensions. See:
# https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions
# Make a repo with your localization and since you are still listed as a collaborator
# you can add it to the wiki page yourself. This change is because some people complained
# the git commit log is cluttered with things unrelated to almost everyone and
# because I believe this is the best overall for the project to handle localizations almost
# entirely without my oversight.
================================================
FILE: LICENSE.txt
================================================
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<https://www.gnu.org/licenses/>.
================================================
FILE: README.md
================================================
# Stable Diffusion web UI
A browser interface based on Gradio library for Stable Diffusion.
## Features
[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
- Original txt2img and img2img modes
- One click install and run script (but you still must install python and git)
- Outpainting
- Inpainting
- Color Sketch
- Prompt Matrix
- Stable Diffusion Upscale
- Attention, specify parts of text that the model should pay more attention to
- a man in a ((tuxedo)) - will pay more attention to tuxedo
- a man in a (tuxedo:1.21) - alternative syntax
- select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
- Loopback, run img2img processing multiple times
- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
- Textual Inversion
- have as many embeddings as you want and use any names you like for them
- use multiple embeddings with different numbers of vectors per token
- works with half precision floating point numbers
- train embeddings on 8GB (also reports of 6GB working)
- Extras tab with:
- GFPGAN, neural network that fixes faces
- CodeFormer, face restoration tool as an alternative to GFPGAN
- RealESRGAN, neural network upscaler
- ESRGAN, neural network upscaler with a lot of third party models
- SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
- LDSR, Latent diffusion super resolution upscaling
- Resizing aspect ratio options
- Sampling method selection
- Adjust sampler eta values (noise multiplier)
- More advanced noise setting options
- Interrupt processing at any time
- 4GB video card support (also reports of 2GB working)
- Correct seeds for batches
- Live prompt token length validation
- Generation parameters
- parameters you used to generate images are saved with that image
- in PNG chunks for PNG, in EXIF for JPEG
- can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
- can be disabled in settings
- drag and drop an image/text-parameters to promptbox
- Read Generation Parameters Button, loads parameters in promptbox to UI
- Settings page
- Running arbitrary python code from UI (must run with --allow-code to enable)
- Mouseover hints for most UI elements
- Possible to change defaults/mix/max/step values for UI elements via text config
- Tiling support, a checkbox to create images that can be tiled like textures
- Progress bar and live image generation preview
- Can use a separate neural network to produce previews with almost none VRAM or compute requirement
- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
- Styles, a way to save part of prompt and easily apply them via dropdown later
- Variations, a way to generate same image but with tiny differences
- Seed resizing, a way to generate same image but at slightly different resolution
- CLIP interrogator, a button that tries to guess prompt from an image
- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
- Batch Processing, process a group of files using img2img
- Img2img Alternative, reverse Euler method of cross attention control
- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
- Reloading checkpoints on the fly
- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
- separate prompts using uppercase `AND`
- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
- DeepDanbooru integration, creates danbooru style tags for anime prompts
- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
- Generate forever option
- Training tab
- hypernetworks and embeddings options
- Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
- Clip skip
- Hypernetworks
- Loras (same as Hypernetworks but more pretty)
- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt.
- Can select to load a different VAE from settings screen
- Estimated completion time in progress bar
- API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
- Now without any bad letters!
- Load checkpoints in safetensors format
- Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
- Now with a license!
- Reorder elements in the UI from settings screen
-
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
Alternatively, use online services (like Google Colab):
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Automatic Installation on Windows
1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
2. Install [git](https://git-scm.com/download/win).
3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
### Automatic Installation on Linux
1. Install the dependencies:
```bash
# Debian-based:
sudo apt install wget git python3 python3-venv
# Red Hat-based:
sudo dnf install wget git python3
# Arch-based:
sudo pacman -S wget git python3
```
2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run:
```bash
bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
```
3. Run `webui.sh`.
### Installation on Apple Silicon
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
## Contributing
Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
## Documentation
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
- CodeFormer - https://github.com/sczhou/CodeFormer
- ESRGAN - https://github.com/xinntao/ESRGAN
- SwinIR - https://github.com/JingyunLiang/SwinIR
- Swin2SR - https://github.com/mv-lab/swin2sr
- LDSR - https://github.com/Hafiidz/latent-diffusion
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- xformers - https://github.com/facebookresearch/xformers
- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
- Security advice - RyotaK
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)
================================================
FILE: cache.json
================================================
{
"hashes": {
"checkpoint/final-prune.ckpt": {
"mtime": 1665122544.3176749,
"sha256": "89d59c3dde4c56c6d5c41da34cc55ce479d93b4007046980934b14db71bdb2a8"
},
"checkpoint/Anything-V3.0-no-ema-full.ckpt": {
"mtime": 1672839247.3145154,
"sha256": "7b66ce8d3a639e13f14761a29de61e252cf5aa489ae967228d96166fc3f55b40"
}
}
}
================================================
FILE: config.json
================================================
{
"samples_save": true,
"samples_format": "png",
"samples_filename_pattern": "",
"save_images_add_number": true,
"grid_save": true,
"grid_format": "png",
"grid_extended_filename": false,
"grid_only_if_multiple": true,
"grid_prevent_empty_spots": false,
"n_rows": -1,
"enable_pnginfo": true,
"save_txt": false,
"save_images_before_face_restoration": false,
"save_images_before_highres_fix": false,
"save_images_before_color_correction": false,
"jpeg_quality": 80,
"export_for_4chan": true,
"use_original_name_batch": true,
"use_upscaler_name_as_suffix": false,
"save_selected_only": true,
"do_not_add_watermark": false,
"temp_dir": "",
"clean_temp_dir_at_start": false,
"outdir_samples": "",
"outdir_txt2img_samples": "outputs/txt2img-images",
"outdir_img2img_samples": "outputs/img2img-images",
"outdir_extras_samples": "outputs/extras-images",
"outdir_grids": "",
"outdir_txt2img_grids": "outputs/txt2img-grids",
"outdir_img2img_grids": "outputs/img2img-grids",
"outdir_save": "log/images",
"save_to_dirs": false,
"grid_save_to_dirs": false,
"use_save_to_dirs_for_ui": false,
"directories_filename_pattern": "[date]",
"directories_max_prompt_words": 8,
"ESRGAN_tile": 192,
"ESRGAN_tile_overlap": 8,
"realesrgan_enabled_models": [
"R-ESRGAN 4x+",
"R-ESRGAN 4x+ Anime6B"
],
"upscaler_for_img2img": null,
"face_restoration_model": null,
"code_former_weight": 0.5,
"face_restoration_unload": false,
"show_warnings": false,
"memmon_poll_rate": 8,
"samples_log_stdout": false,
"multiple_tqdm": true,
"print_hypernet_extra": false,
"unload_models_when_training": true,
"pin_memory": false,
"save_optimizer_state": false,
"save_training_settings_to_txt": true,
"dataset_filename_word_regex": "",
"dataset_filename_join_string": " ",
"training_image_repeats_per_epoch": 1,
"training_write_csv_every": 500,
"training_xattention_optimizations": false,
"training_enable_tensorboard": false,
"training_tensorboard_save_images": false,
"training_tensorboard_flush_every": 120,
"sd_model_checkpoint": "",
"sd_checkpoint_cache": 0,
"sd_vae_checkpoint_cache": 0,
"sd_vae": "animevae.pt",
"sd_vae_as_default": false,
"inpainting_mask_weight": 1.0,
"initial_noise_multiplier": 1.0,
"img2img_color_correction": false,
"img2img_fix_steps": false,
"img2img_background_color": "#ffffff",
"enable_quantization": false,
"enable_emphasis": true,
"enable_batch_seeds": true,
"comma_padding_backtrack": 20,
"CLIP_stop_at_last_layers": 2,
"upcast_attn": false,
"use_old_emphasis_implementation": false,
"use_old_karras_scheduler_sigmas": false,
"use_old_hires_fix_width_height": false,
"interrogate_keep_models_in_memory": false,
"interrogate_return_ranks": false,
"interrogate_clip_num_beams": 1,
"interrogate_clip_min_length": 24,
"interrogate_clip_max_length": 48,
"interrogate_clip_dict_limit": 1500,
"interrogate_clip_skip_categories": [],
"interrogate_deepbooru_score_threshold": 0.7,
"deepbooru_sort_alpha": false,
"deepbooru_use_spaces": false,
"deepbooru_escape": true,
"deepbooru_filter_tags": "",
"extra_networks_default_view": "thumbs",
"extra_networks_default_multiplier": 1.0,
"sd_hypernetwork": "None",
"return_grid": true,
"do_not_show_images": false,
"add_model_hash_to_info": true,
"add_model_name_to_info": true,
"disable_weights_auto_swap": true,
"send_seed": true,
"send_size": true,
"font": "",
"js_modal_lightbox": true,
"js_modal_lightbox_initially_zoomed": true,
"show_progress_in_title": true,
"samplers_in_dropdown": false,
"dimensions_and_batch_together": true,
"keyedit_precision_attention": 0.1,
"keyedit_precision_extra": 0.05,
"quicksettings": "sd_model_checkpoint, sd_vae, CLIP_stop_at_last_layers",
"ui_reorder": "inpaint, sampler, checkboxes, hires_fix, dimensions, cfg, seed, batch, override_settings, scripts",
"ui_extra_networks_tab_reorder": "",
"localization": "zh_CN",
"show_progressbar": true,
"live_previews_enable": true,
"show_progress_grid": true,
"show_progress_every_n_steps": 20,
"show_progress_type": "Approx NN",
"live_preview_content": "Prompt",
"live_preview_refresh_period": 1000,
"hide_samplers": [],
"eta_ddim": 0.0,
"eta_ancestral": 1.0,
"ddim_discretize": "uniform",
"s_churn": 0.0,
"s_tmin": 0.0,
"s_noise": 1.0,
"eta_noise_seed_delta": 31337,
"always_discard_next_to_last_sigma": false,
"postprocessing_enable_in_main_ui": [],
"postprocessing_operation_order": [],
"upscaling_max_images_in_cache": 5,
"disabled_extensions": [],
"sd_checkpoint_hash": "7af57400eb7303877ec35e5b9e03fc29802c44066828165dc3a20b973c439428",
"ldsr_steps": 100,
"ldsr_cached": false,
"SWIN_tile": 192,
"SWIN_tile_overlap": 8,
"sd_lora": "None",
"lora_apply_to_outputs": false,
"tac_tagFile": "danbooru.csv",
"tac_active": true,
"tac_activeIn.txt2img": true,
"tac_activeIn.img2img": true,
"tac_activeIn.negativePrompts": true,
"tac_activeIn.thirdParty": true,
"tac_activeIn.modelList": "",
"tac_activeIn.modelListMode": "Blacklist",
"tac_maxResults": 15.0,
"tac_showAllResults": false,
"tac_resultStepLength": 100.0,
"tac_delayTime": 100.0,
"tac_useWildcards": true,
"tac_useEmbeddings": true,
"tac_useHypernetworks": true,
"tac_useLoras": true,
"tac_showWikiLinks": false,
"tac_replaceUnderscores": true,
"tac_escapeParentheses": true,
"tac_appendComma": true,
"tac_alias.searchByAlias": true,
"tac_alias.onlyShowAlias": false,
"tac_translation.translationFile": "None",
"tac_translation.oldFormat": false,
"tac_translation.searchByTranslation": true,
"tac_extra.extraFile": "extra-quality-tags.csv",
"tac_extra.addMode": "Insert before",
"additional_networks_extra_lora_path": "",
"additional_networks_sort_models_by": "name",
"additional_networks_reverse_sort_order": false,
"additional_networks_model_name_filter": "",
"additional_networks_xy_grid_model_metadata": "",
"additional_networks_hash_thread_count": 1.0,
"additional_networks_back_up_model_when_saving": true,
"additional_networks_show_only_safetensors": false,
"additional_networks_show_only_models_with_metadata": "disabled",
"additional_networks_max_top_tags": 20.0,
"additional_networks_max_dataset_folders": 20.0,
"images_history_preload": false,
"images_record_paths": true,
"images_delete_message": true,
"images_history_page_columns": 6.0,
"images_history_page_rows": 6.0,
"images_history_pages_perload": 20.0,
"img_downscale_threshold": 4.0,
"target_side_length": 4000.0,
"no_dpmpp_sde_batch_determinism": false
}
================================================
FILE: configs/alt-diffusion-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: modules.xlmr.BertSeriesModelWithTransformation
params:
name: "XLMR-Large"
================================================
FILE: configs/instruct-pix2pix.yaml
================================================
# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
# See more details in LICENSE.
model:
base_learning_rate: 1.0e-04
target: modules.models.diffusion.ddpm_edit.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: edited
cond_stage_key: edit
# image_size: 64
# image_size: 32
image_size: 16
channels: 4
cond_stage_trainable: false # Note: different from the one we trained before
conditioning_key: hybrid
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: [ 0 ]
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: 8
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
data:
target: main.DataModuleFromConfig
params:
batch_size: 128
num_workers: 1
wrap: false
validation:
target: edit_dataset.EditDataset
params:
path: data/clip-filtered-dataset
cache_dir: data/
cache_name: data_10k
split: val
min_text_sim: 0.2
min_image_sim: 0.75
min_direction_sim: 0.2
max_samples_per_prompt: 1
min_resize_res: 512
max_resize_res: 512
crop_res: 512
output_as_edit: False
real_input: True
================================================
FILE: configs/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/v1-inpainting-inference.yaml
================================================
model:
base_learning_rate: 7.5e-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 # Note: different from the one we trained before
conditioning_key: hybrid # important
monitor: val/loss_simple_ema
scale_factor: 0.18215
finetune_keys: null
scheduler_config: # 10000 warmup steps
target: ldm.lr_scheduler.LambdaLinearScheduler
params:
warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
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: 9 # 4 data + 4 downscaled image + 1 mask
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: environment-wsl2.yaml
================================================
name: automatic
channels:
- pytorch
- defaults
dependencies:
- python=3.10
- pip=22.2.2
- cudatoolkit=11.3
- pytorch=1.12.1
- torchvision=0.13.1
- numpy=1.23.1
================================================
FILE: launch.py
================================================
# this scripts installs necessary requirements and launches main program in webui.py
import subprocess
import os
import sys
import importlib.util
import shlex
import platform
import argparse
import json
dir_repos = "repositories"
dir_extensions = "extensions"
python = sys.executable
git = os.environ.get('GIT', "git")
index_url = os.environ.get('INDEX_URL', "")
stored_commit_hash = None
skip_install = False
def check_python_version():
is_windows = platform.system() == "Windows"
major = sys.version_info.major
minor = sys.version_info.minor
micro = sys.version_info.micro
if is_windows:
supported_minors = [10]
else:
supported_minors = [7, 8, 9, 10, 11]
if not (major == 3 and minor in supported_minors):
import modules.errors
modules.errors.print_error_explanation(f"""
INCOMPATIBLE PYTHON VERSION
This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
If you encounter an error with "RuntimeError: Couldn't install torch." message,
or any other error regarding unsuccessful package (library) installation,
please downgrade (or upgrade) to the latest version of 3.10 Python
and delete current Python and "venv" folder in WebUI's directory.
You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3109/
{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
Use --skip-python-version-check to suppress this warning.
""")
def commit_hash():
global stored_commit_hash
if stored_commit_hash is not None:
return stored_commit_hash
try:
stored_commit_hash = run(f"{git} rev-parse HEAD").strip()
except Exception:
stored_commit_hash = "<none>"
return stored_commit_hash
def extract_arg(args, name):
return [x for x in args if x != name], name in args
def extract_opt(args, name):
opt = None
is_present = False
if name in args:
is_present = True
idx = args.index(name)
del args[idx]
if idx < len(args) and args[idx][0] != "-":
opt = args[idx]
del args[idx]
return args, is_present, opt
def run(command, desc=None, errdesc=None, custom_env=None, live=False):
if desc is not None:
print(desc)
if live:
result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env)
if result.returncode != 0:
raise RuntimeError(f"""{errdesc or 'Error running command'}.
Command: {command}
Error code: {result.returncode}""")
return ""
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
if result.returncode != 0:
message = f"""{errdesc or 'Error running command'}.
Command: {command}
Error code: {result.returncode}
stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else '<empty>'}
stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else '<empty>'}
"""
raise RuntimeError(message)
return result.stdout.decode(encoding="utf8", errors="ignore")
def check_run(command):
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
return result.returncode == 0
def is_installed(package):
try:
spec = importlib.util.find_spec(package)
except ModuleNotFoundError:
return False
return spec is not None
def repo_dir(name):
return os.path.join(dir_repos, name)
def run_python(code, desc=None, errdesc=None):
return run(f'"{python}" -c "{code}"', desc, errdesc)
def run_pip(args, desc=None):
if skip_install:
return
index_url_line = f' --index-url {index_url}' if index_url != '' else ''
return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
def check_run_python(code):
return check_run(f'"{python}" -c "{code}"')
def git_clone(url, dir, name, commithash=None):
# TODO clone into temporary dir and move if successful
if os.path.exists(dir):
if commithash is None:
return
current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
if current_hash == commithash:
return
run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
return
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
if commithash is not None:
run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
def version_check(commit):
try:
import requests
commits = requests.get('https://api.github.com/repos/AUTOMATIC1111/stable-diffusion-webui/branches/master').json()
if commit != "<none>" and commits['commit']['sha'] != commit:
print("--------------------------------------------------------")
print("| You are not up to date with the most recent release. |")
print("| Consider running `git pull` to update. |")
print("--------------------------------------------------------")
elif commits['commit']['sha'] == commit:
print("You are up to date with the most recent release.")
else:
print("Not a git clone, can't perform version check.")
except Exception as e:
print("version check failed", e)
def run_extension_installer(extension_dir):
path_installer = os.path.join(extension_dir, "install.py")
if not os.path.isfile(path_installer):
return
try:
env = os.environ.copy()
env['PYTHONPATH'] = os.path.abspath(".")
print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
except Exception as e:
print(e, file=sys.stderr)
def list_extensions(settings_file):
settings = {}
try:
if os.path.isfile(settings_file):
with open(settings_file, "r", encoding="utf8") as file:
settings = json.load(file)
except Exception as e:
print(e, file=sys.stderr)
disabled_extensions = set(settings.get('disabled_extensions', []))
return [x for x in os.listdir(dir_extensions) if x not in disabled_extensions]
def run_extensions_installers(settings_file):
if not os.path.isdir(dir_extensions):
return
for dirname_extension in list_extensions(settings_file):
run_extension_installer(os.path.join(dir_extensions, dirname_extension))
def prepare_environment():
global skip_install
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e")
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
sys.argv += shlex.split(commandline_args)
parser = argparse.ArgumentParser(add_help=False)
parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default='config.json')
args, _ = parser.parse_known_args(sys.argv)
sys.argv, _ = extract_arg(sys.argv, '-f')
sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
sys.argv, skip_python_version_check = extract_arg(sys.argv, '--skip-python-version-check')
sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
sys.argv, reinstall_torch = extract_arg(sys.argv, '--reinstall-torch')
sys.argv, update_check = extract_arg(sys.argv, '--update-check')
sys.argv, run_tests, test_dir = extract_opt(sys.argv, '--tests')
sys.argv, skip_install = extract_arg(sys.argv, '--skip-install')
xformers = '--xformers' in sys.argv
ngrok = '--ngrok' in sys.argv
if not skip_python_version_check:
check_python_version()
commit = commit_hash()
print(f"Python {sys.version}")
print(f"Commit hash: {commit}")
if reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
if not skip_torch_cuda_test:
run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
if not is_installed("gfpgan"):
run_pip(f"install {gfpgan_package}", "gfpgan")
if not is_installed("clip"):
run_pip(f"install {clip_package}", "clip")
if not is_installed("open_clip"):
run_pip(f"install {openclip_package}", "open_clip")
if (not is_installed("xformers") or reinstall_xformers) and xformers:
if platform.system() == "Windows":
if platform.python_version().startswith("3.10"):
run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
else:
print("Installation of xformers is not supported in this version of Python.")
print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
if not is_installed("xformers"):
exit(0)
elif platform.system() == "Linux":
run_pip(f"install {xformers_package}", "xformers")
if not is_installed("pyngrok") and ngrok:
run_pip("install pyngrok", "ngrok")
os.makedirs(dir_repos, exist_ok=True)
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
if not is_installed("lpips"):
run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
run_pip(f"install -r {requirements_file}", "requirements for Web UI")
run_extensions_installers(settings_file=args.ui_settings_file)
if update_check:
version_check(commit)
if "--exit" in sys.argv:
print("Exiting because of --exit argument")
exit(0)
if run_tests:
exitcode = tests(test_dir)
exit(exitcode)
def tests(test_dir):
if "--api" not in sys.argv:
sys.argv.append("--api")
if "--ckpt" not in sys.argv:
sys.argv.append("--ckpt")
sys.argv.append("./test/test_files/empty.pt")
if "--skip-torch-cuda-test" not in sys.argv:
sys.argv.append("--skip-torch-cuda-test")
if "--disable-nan-check" not in sys.argv:
sys.argv.append("--disable-nan-check")
print(f"Launching Web UI in another process for testing with arguments: {' '.join(sys.argv[1:])}")
os.environ['COMMANDLINE_ARGS'] = ""
with open('test/stdout.txt', "w", encoding="utf8") as stdout, open('test/stderr.txt', "w", encoding="utf8") as stderr:
proc = subprocess.Popen([sys.executable, *sys.argv], stdout=stdout, stderr=stderr)
import test.server_poll
exitcode = test.server_poll.run_tests(proc, test_dir)
print(f"Stopping Web UI process with id {proc.pid}")
proc.kill()
return exitcode
def start():
print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
import webui
if '--nowebui' in sys.argv:
webui.api_only()
else:
webui.webui()
if __name__ == "__main__":
prepare_environment()
start()
================================================
FILE: params.txt
================================================
masterpiece, best quality, 1girl,
Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 4170070523, Size: 512x512, Model hash: 89d59c3dde, Model: final-prune, Clip skip: 2, ENSD: 31337
================================================
FILE: requirements.txt
================================================
blendmodes
accelerate
basicsr
fonts
font-roboto
gfpgan
gradio==3.16.2
invisible-watermark
numpy
omegaconf
opencv-contrib-python
requests
piexif
Pillow
pytorch_lightning==1.7.7
realesrgan
scikit-image>=0.19
timm==0.4.12
transformers==4.25.1
torch
einops
jsonmerge
clean-fid
resize-right
torchdiffeq
kornia
lark
inflection
GitPython
torchsde
safetensors
psutil
================================================
FILE: requirements_versions.txt
================================================
blendmodes==2022
transformers==4.25.1
accelerate==0.12.0
basicsr==1.4.2
gfpgan==1.3.8
gradio==3.16.2
numpy==1.23.3
Pillow==9.4.0
realesrgan==0.3.0
torch
omegaconf==2.2.3
pytorch_lightning==1.7.6
scikit-image==0.19.2
fonts
font-roboto
timm==0.6.7
piexif==1.1.3
einops==0.4.1
jsonmerge==1.8.0
clean-fid==0.1.29
resize-right==0.0.2
torchdiffeq==0.2.3
kornia==0.6.7
lark==1.1.2
inflection==0.5.1
GitPython==3.1.27
torchsde==0.2.5
safetensors==0.2.7
httpcore<=0.15
fastapi==0.90.1
================================================
FILE: script.js
================================================
function gradioApp() {
const elems = document.getElementsByTagName('gradio-app')
const gradioShadowRoot = elems.length == 0 ? null : elems[0].shadowRoot
return !!gradioShadowRoot ? gradioShadowRoot : document;
}
function get_uiCurrentTab() {
return gradioApp().querySelector('#tabs button:not(.border-transparent)')
}
function get_uiCurrentTabContent() {
return gradioApp().querySelector('.tabitem[id^=tab_]:not([style*="display: none"])')
}
uiUpdateCallbacks = []
uiLoadedCallbacks = []
uiTabChangeCallbacks = []
optionsChangedCallbacks = []
let uiCurrentTab = null
function onUiUpdate(callback){
uiUpdateCallbacks.push(callback)
}
function onUiLoaded(callback){
uiLoadedCallbacks.push(callback)
}
function onUiTabChange(callback){
uiTabChangeCallbacks.push(callback)
}
function onOptionsChanged(callback){
optionsChangedCallbacks.push(callback)
}
function runCallback(x, m){
try {
x(m)
} catch (e) {
(console.error || console.log).call(console, e.message, e);
}
}
function executeCallbacks(queue, m) {
queue.forEach(function(x){runCallback(x, m)})
}
var executedOnLoaded = false;
document.addEventListener("DOMContentLoaded", function() {
var mutationObserver = new MutationObserver(function(m){
if(!executedOnLoaded && gradioApp().querySelector('#txt2img_prompt')){
executedOnLoaded = true;
executeCallbacks(uiLoadedCallbacks);
}
executeCallbacks(uiUpdateCallbacks, m);
const newTab = get_uiCurrentTab();
if ( newTab && ( newTab !== uiCurrentTab ) ) {
uiCurrentTab = newTab;
executeCallbacks(uiTabChangeCallbacks);
}
});
mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
});
/**
* Add a ctrl+enter as a shortcut to start a generation
*/
document.addEventListener('keydown', function(e) {
var handled = false;
if (e.key !== undefined) {
if((e.key == "Enter" && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
} else if (e.keyCode !== undefined) {
if((e.keyCode == 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
}
if (handled) {
button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
if (button) {
button.click();
}
e.preventDefault();
}
})
/**
* checks that a UI element is not in another hidden element or tab content
*/
function uiElementIsVisible(el) {
let isVisible = !el.closest('.\\!hidden');
if ( ! isVisible ) {
return false;
}
while( isVisible = el.closest('.tabitem')?.style.display !== 'none' ) {
if ( ! isVisible ) {
return false;
} else if ( el.parentElement ) {
el = el.parentElement
} else {
break;
}
}
return isVisible;
}
================================================
FILE: scripts/custom_code.py
================================================
import modules.scripts as scripts
import gradio as gr
from modules.processing import Processed
from modules.shared import opts, cmd_opts, state
class Script(scripts.Script):
def title(self):
return "Custom code"
def show(self, is_img2img):
return cmd_opts.allow_code
def ui(self, is_img2img):
code = gr.Textbox(label="Python code", lines=1, elem_id=self.elem_id("code"))
return [code]
def run(self, p, code):
assert cmd_opts.allow_code, '--allow-code option must be enabled'
display_result_data = [[], -1, ""]
def display(imgs, s=display_result_data[1], i=display_result_data[2]):
display_result_data[0] = imgs
display_result_data[1] = s
display_result_data[2] = i
from types import ModuleType
compiled = compile(code, '', 'exec')
module = ModuleType("testmodule")
module.__dict__.update(globals())
module.p = p
module.display = display
exec(compiled, module.__dict__)
return Processed(p, *display_result_data)
================================================
FILE: scripts/img2imgalt.py
================================================
from collections import namedtuple
import numpy as np
from tqdm import trange
import modules.scripts as scripts
import gradio as gr
from modules import processing, shared, sd_samplers, prompt_parser, sd_samplers_common
from modules.processing import Processed
from modules.shared import opts, cmd_opts, state
import torch
import k_diffusion as K
from PIL import Image
from torch import autocast
from einops import rearrange, repeat
def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
x = p.init_latent
s_in = x.new_ones([x.shape[0]])
dnw = K.external.CompVisDenoiser(shared.sd_model)
sigmas = dnw.get_sigmas(steps).flip(0)
shared.state.sampling_steps = steps
for i in trange(1, len(sigmas)):
shared.state.sampling_step += 1
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigmas[i] * s_in] * 2)
cond_in = torch.cat([uncond, cond])
image_conditioning = torch.cat([p.image_conditioning] * 2)
cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
t = dnw.sigma_to_t(sigma_in)
eps = shared.sd_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) * cfg_scale
d = (x - denoised) / sigmas[i]
dt = sigmas[i] - sigmas[i - 1]
x = x + d * dt
sd_samplers_common.store_latent(x)
# This shouldn't be necessary, but solved some VRAM issues
del x_in, sigma_in, cond_in, c_out, c_in, t,
del eps, denoised_uncond, denoised_cond, denoised, d, dt
shared.state.nextjob()
return x / x.std()
Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"])
# Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
x = p.init_latent
s_in = x.new_ones([x.shape[0]])
dnw = K.external.CompVisDenoiser(shared.sd_model)
sigmas = dnw.get_sigmas(steps).flip(0)
shared.state.sampling_steps = steps
for i in trange(1, len(sigmas)):
shared.state.sampling_step += 1
x_in = torch.cat([x] * 2)
sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
cond_in = torch.cat([uncond, cond])
image_conditioning = torch.cat([p.image_conditioning] * 2)
cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
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 = shared.sd_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) * cfg_scale
if i == 1:
d = (x - denoised) / (2 * sigmas[i])
else:
d = (x - denoised) / sigmas[i - 1]
dt = sigmas[i] - sigmas[i - 1]
x = x + d * dt
sd_samplers_common.store_latent(x)
# This shouldn't be necessary, but solved some VRAM issues
del x_in, sigma_in, cond_in, c_out, c_in, t,
del eps, denoised_uncond, denoised_cond, denoised, d, dt
shared.state.nextjob()
return x / sigmas[-1]
class Script(scripts.Script):
def __init__(self):
self.cache = None
def title(self):
return "img2img alternative test"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
info = gr.Markdown('''
* `CFG Scale` should be 2 or lower.
''')
override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True, elem_id=self.elem_id("override_sampler"))
override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True, elem_id=self.elem_id("override_prompt"))
original_prompt = gr.Textbox(label="Original prompt", lines=1, elem_id=self.elem_id("original_prompt"))
original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1, elem_id=self.elem_id("original_negative_prompt"))
override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True, elem_id=self.elem_id("override_steps"))
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50, elem_id=self.elem_id("st"))
override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True, elem_id=self.elem_id("override_strength"))
cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0, elem_id=self.elem_id("cfg"))
randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0, elem_id=self.elem_id("randomness"))
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment"))
return [
info,
override_sampler,
override_prompt, original_prompt, original_negative_prompt,
override_steps, st,
override_strength,
cfg, randomness, sigma_adjustment,
]
def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
# Override
if override_sampler:
p.sampler_name = "Euler"
if override_prompt:
p.prompt = original_prompt
p.negative_prompt = original_negative_prompt
if override_steps:
p.steps = st
if override_strength:
p.denoising_strength = 1.0
def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
lat = (p.init_latent.cpu().numpy() * 10).astype(int)
same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \
and self.cache.original_prompt == original_prompt \
and self.cache.original_negative_prompt == original_negative_prompt \
and self.cache.sigma_adjustment == sigma_adjustment
same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100
if same_everything:
rec_noise = self.cache.noise
else:
shared.state.job_count += 1
cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])
uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])
if sigma_adjustment:
rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st)
else:
rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
sigmas = sampler.model_wrap.get_sigmas(p.steps)
noise_dt = combined_noise - (p.init_latent / sigmas[0])
p.seed = p.seed + 1
return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
p.sample = sample_extra
p.extra_generation_params["Decode prompt"] = original_prompt
p.extra_generation_params["Decode negative prompt"] = original_negative_prompt
p.extra_generation_params["Decode CFG scale"] = cfg
p.extra_generation_params["Decode steps"] = st
p.extra_generation_params["Randomness"] = randomness
p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment
processed = processing.process_images(p)
return processed
================================================
FILE: scripts/loopback.py
================================================
import numpy as np
from tqdm import trange
import modules.scripts as scripts
import gradio as gr
from modules import processing, shared, sd_samplers, images
from modules.processing import Processed
from modules.sd_samplers import samplers
from modules.shared import opts, cmd_opts, state
from modules import deepbooru
class Script(scripts.Script):
def title(self):
return "Loopback"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, elem_id=self.elem_id("denoising_strength_change_factor"))
append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
return [loops, denoising_strength_change_factor, append_interrogation]
def run(self, p, loops, denoising_strength_change_factor, append_interrogation):
processing.fix_seed(p)
batch_count = p.n_iter
p.extra_generation_params = {
"Denoising strength change factor": denoising_strength_change_factor,
}
p.batch_size = 1
p.n_iter = 1
output_images, info = None, None
initial_seed = None
initial_info = None
grids = []
all_images = []
original_init_image = p.init_images
original_prompt = p.prompt
state.job_count = loops * batch_count
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
for n in range(batch_count):
history = []
# Reset to original init image at the start of each batch
p.init_images = original_init_image
for i in range(loops):
p.n_iter = 1
p.batch_size = 1
p.do_not_save_grid = True
if opts.img2img_color_correction:
p.color_corrections = initial_color_corrections
if append_interrogation != "None":
p.prompt = original_prompt + ", " if original_prompt != "" else ""
if append_interrogation == "CLIP":
p.prompt += shared.interrogator.interrogate(p.init_images[0])
elif append_interrogation == "DeepBooru":
p.prompt += deepbooru.model.tag(p.init_images[0])
state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
processed = processing.process_images(p)
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
init_img = processed.images[0]
p.init_images = [init_img]
p.seed = processed.seed + 1
p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1)
history.append(processed.images[0])
grid = images.image_grid(history, rows=1)
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
grids.append(grid)
all_images += history
if opts.return_grid:
all_images = grids + all_images
processed = Processed(p, all_images, initial_seed, initial_info)
return processed
================================================
FILE: scripts/outpainting_mk_2.py
================================================
import math
import numpy as np
import skimage
import modules.scripts as scripts
import gradio as gr
from PIL import Image, ImageDraw
from modules import images, processing, devices
from modules.processing import Processed, process_images
from modules.shared import opts, cmd_opts, state
# this function is taken from https://github.com/parlance-zz/g-diffuser-bot
def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05):
# 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. - 1.) * window_scale_x
for y in range(height):
fy = (y / height * 2. - 1.) * window_scale_y
if mode == 0:
window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std)
else:
window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14) # hey wait a minute that's not gaussian
return window
def _get_masked_window_rgb(np_mask_grey, hardness=1.):
np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
if hardness != 1.:
hardened = np_mask_grey[:] ** hardness
else:
hardened = np_mask_grey[:]
for c in range(3):
np_mask_rgb[:, :, c] = hardened[:]
return np_mask_rgb
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. - np_mask_rgb)
np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.)
img_mask = np_mask_grey > 1e-6
ref_mask = np_mask_grey < 1e-3
windowed_image = _np_src_image * (1. - _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
src_fft = _fft2(windowed_image) # get feature statistics from masked src img
src_dist = np.absolute(src_fft)
src_phase = src_fft / src_dist
# create a generator with a static seed to make outpainting deterministic / only follow global seed
rng = np.random.default_rng(0)
noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise
noise_rgb = rng.random((width, height, num_channels))
noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
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. - 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. # color_variation # todo: temporarily tieing brightness variation to color variation for now
contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
# 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., contrast_adjusted_np_src[ref_mask, :], channel_axis=1)
shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb
matched_noise = shaped_noise[:]
return np.clip(matched_noise, 0., 1.)
class Script(scripts.Script):
def title(self):
return "Outpainting mk2"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
if not is_img2img:
return None
info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>")
pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels"))
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, elem_id=self.elem_id("mask_blur"))
direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction"))
noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0, elem_id=self.elem_id("noise_q"))
color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05, elem_id=self.elem_id("color_variation"))
return [info, pixels, mask_blur, direction, noise_q, color_variation]
def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation):
initial_seed_and_info = [None, None]
process_width = p.width
process_height = p.height
p.mask_blur = mask_blur*4
p.inpaint_full_res = False
p.inpainting_fill = 1
p.do_not_save_samples = True
p.do_not_save_grid = True
left = pixels if "left" in direction else 0
right = pixels if "right" in direction else 0
up = pixels if "up" in direction else 0
down = pixels if "down" in direction else 0
init_img = p.init_images[0]
target_w = math.ceil((init_img.width + left + right) / 64) * 64
target_h = math.ceil((init_img.height + up + down) / 64) * 64
if left > 0:
left = left * (target_w - init_img.width) // (left + right)
if right > 0:
right = target_w - init_img.width - left
if up > 0:
up = up * (target_h - init_img.height) // (up + down)
if down > 0:
down = target_h - init_img.height - up
def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
is_horiz = is_left or is_right
is_vert = is_top or is_bottom
pixels_horiz = expand_pixels if is_horiz else 0
pixels_vert = expand_pixels if is_vert else 0
images_to_process = []
output_images = []
for n in range(count):
res_w = init[n].width + pixels_horiz
res_h = init[n].height + pixels_vert
process_res_w = math.ceil(res_w / 64) * 64
process_res_h = math.ceil(res_h / 64) * 64
img = Image.new("RGB", (process_res_w, process_res_h))
img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
mask = Image.new("RGB", (process_res_w, process_res_h), "white")
draw = ImageDraw.Draw(mask)
draw.rectangle((
expand_pixels + mask_blur if is_left else 0,
expand_pixels + mask_blur if is_top else 0,
mask.width - expand_pixels - mask_blur if is_right else res_w,
mask.height - expand_pixels - mask_blur if is_bottom else res_h,
), fill="black")
np_image = (np.asarray(img) / 255.0).astype(np.float64)
np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB"))
target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width
target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height
p.width = target_width if is_horiz else img.width
p.height = target_height if is_vert else img.height
crop_region = (
0 if is_left else output_images[n].width - target_width,
0 if is_top else output_images[n].height - target_height,
target_width if is_left else output_images[n].width,
target_height if is_top else output_images[n].height,
)
mask = mask.crop(crop_region)
p.image_mask = mask
image_to_process = output_images[n].crop(crop_region)
images_to_process.append(image_to_process)
p.init_images = images_to_process
latent_mask = Image.new("RGB", (p.width, p.height), "white")
draw = ImageDraw.Draw(latent_mask)
draw.rectangle((
expand_pixels + mask_blur * 2 if is_left else 0,
expand_pixels + mask_blur * 2 if is_top else 0,
mask.width - expand_pixels - mask_blur * 2 if is_right else res_w,
mask.height - expand_pixels - mask_blur * 2 if is_bottom else res_h,
), fill="black")
p.latent_mask = latent_mask
proc = process_images(p)
if initial_seed_and_info[0] is None:
initial_seed_and_info[0] = proc.seed
initial_seed_and_info[1] = proc.info
for n in range(count):
output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height))
output_images[n] = output_images[n].crop((0, 0, res_w, res_h))
return output_images
batch_count = p.n_iter
batch_size = p.batch_size
p.n_iter = 1
state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0))
all_processed_images = []
for i in range(batch_count):
imgs = [init_img] * batch_size
state.job = f"Batch {i + 1} out of {batch_count}"
if left > 0:
imgs = expand(imgs, batch_size, left, is_left=True)
if right > 0:
imgs = expand(imgs, batch_size, right, is_right=True)
if up > 0:
imgs = expand(imgs, batch_size, up, is_top=True)
if down > 0:
imgs = expand(imgs, batch_size, down, is_bottom=True)
all_processed_images += imgs
all_images = all_processed_images
combined_grid_image = images.image_grid(all_processed_images)
unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple
if opts.return_grid and not unwanted_grid_because_of_img_count:
all_images = [combined_grid_image] + all_processed_images
res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])
if opts.samples_save:
for img in all_processed_images:
images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
if opts.grid_save and not unwanted_grid_because_of_img_count:
images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
return res
================================================
FILE: scripts/poor_mans_outpainting.py
================================================
import math
import modules.scripts as scripts
import gradio as gr
from PIL import Image, ImageDraw
from modules import images, processing, devices
from modules.processing import Processed, process_images
from modules.shared import opts, cmd_opts, state
class Script(scripts.Script):
def title(self):
return "Poor man's outpainting"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
if not is_img2img:
return None
pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels"))
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id=self.elem_id("mask_blur"))
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", elem_id=self.elem_id("inpainting_fill"))
direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction"))
return [pixels, mask_blur, inpainting_fill, direction]
def run(self, p, pixels, mask_blur, inpainting_fill, direction):
initial_seed = None
initial_info = None
p.mask_blur = mask_blur * 2
p.inpainting_fill = inpainting_fill
p.inpaint_full_res = False
left = pixels if "left" in direction else 0
right = pixels if "right" in direction else 0
up = pixels if "up" in direction else 0
down = pixels if "down" in direction else 0
init_img = p.init_images[0]
target_w = math.ceil((init_img.width + left + right) / 64) * 64
target_h = math.ceil((init_img.height + up + down) / 64) * 64
if left > 0:
left = left * (target_w - init_img.width) // (left + right)
if right > 0:
right = target_w - init_img.width - left
if up > 0:
up = up * (target_h - init_img.height) // (up + down)
if down > 0:
down = target_h - init_img.height - up
img = Image.new("RGB", (target_w, target_h))
img.paste(init_img, (left, up))
mask = Image.new("L", (img.width, img.height), "white")
draw = ImageDraw.Draw(mask)
draw.rectangle((
left + (mask_blur * 2 if left > 0 else 0),
up + (mask_blur * 2 if up > 0 else 0),
mask.width - right - (mask_blur * 2 if right > 0 else 0),
mask.height - down - (mask_blur * 2 if down > 0 else 0)
), fill="black")
latent_mask = Image.new("L", (img.width, img.height), "white")
latent_draw = ImageDraw.Draw(latent_mask)
latent_draw.rectangle((
left + (mask_blur//2 if left > 0 else 0),
up + (mask_blur//2 if up > 0 else 0),
mask.width - right - (mask_blur//2 if right > 0 else 0),
mask.height - down - (mask_blur//2 if down > 0 else 0)
), fill="black")
devices.torch_gc()
grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=pixels)
grid_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
grid_latent_mask = images.split_grid(latent_mask, tile_w=p.width, tile_h=p.height, overlap=pixels)
p.n_iter = 1
p.batch_size = 1
p.do_not_save_grid = True
p.do_not_save_samples = True
work = []
work_mask = []
work_latent_mask = []
work_results = []
for (y, h, row), (_, _, row_mask), (_, _, row_latent_mask) in zip(grid.tiles, grid_mask.tiles, grid_latent_mask.tiles):
for tiledata, tiledata_mask, tiledata_latent_mask in zip(row, row_mask, row_latent_mask):
x, w = tiledata[0:2]
if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:
continue
work.append(tiledata[2])
work_mask.append(tiledata_mask[2])
work_latent_mask.append(tiledata_latent_mask[2])
batch_count = len(work)
print(f"Poor man's outpainting will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)}.")
state.job_count = batch_count
for i in range(batch_count):
p.init_images = [work[i]]
p.image_mask = work_mask[i]
p.latent_mask = work_latent_mask[i]
state.job = f"Batch {i + 1} out of {batch_count}"
processed = process_images(p)
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
p.seed = processed.seed + 1
work_results += processed.images
image_index = 0
for y, h, row in grid.tiles:
for tiledata in row:
x, w = tiledata[0:2]
if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:
continue
tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
image_index += 1
combined_image = images.combine_grid(grid)
if opts.samples_save:
images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.grid_format, info=initial_info, p=p)
processed = Processed(p, [combined_image], initial_seed, initial_info)
return processed
================================================
FILE: scripts/postprocessing_codeformer.py
================================================
from PIL import Image
import numpy as np
from modules import scripts_postprocessing, codeformer_model
import gradio as gr
from modules.ui_components import FormRow
class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing):
name = "CodeFormer"
order = 3000
def ui(self):
with FormRow():
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, elem_id="extras_codeformer_visibility")
codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, elem_id="extras_codeformer_weight")
return {
"codeformer_visibility": codeformer_visibility,
"codeformer_weight": codeformer_weight,
}
def process(self, pp: scripts_postprocessing.PostprocessedImage, codeformer_visibility, codeformer_weight):
if codeformer_visibility == 0:
return
restored_img = codeformer_model.codeformer.restore(np.array(pp.image, dtype=np.uint8), w=codeformer_weight)
res = Image.fromarray(restored_img)
if codeformer_visibility < 1.0:
res = Image.blend(pp.image, res, codeformer_visibility)
pp.image = res
pp.info["CodeFormer visibility"] = round(codeformer_visibility, 3)
pp.info["CodeFormer weight"] = round(codeformer_weight, 3)
================================================
FILE: scripts/postprocessing_gfpgan.py
================================================
from PIL import Image
import numpy as np
from modules import scripts_postprocessing, gfpgan_model
import gradio as gr
from modules.ui_components import FormRow
class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
name = "GFPGAN"
order = 2000
def ui(self):
with FormRow():
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, elem_id="extras_gfpgan_visibility")
return {
"gfpgan_visibility": gfpgan_visibility,
}
def process(self, pp: scripts_postprocessing.PostprocessedImage, gfpgan_visibility):
if gfpgan_visibility == 0:
return
restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image, dtype=np.uint8))
res = Image.fromarray(restored_img)
if gfpgan_visibility < 1.0:
res = Image.blend(pp.image, res, gfpgan_visibility)
pp.image = res
pp.info["GFPGAN visibility"] = round(gfpgan_visibility, 3)
================================================
FILE: scripts/postprocessing_upscale.py
================================================
from PIL import Image
import numpy as np
from modules import scripts_postprocessing, shared
import gradio as gr
from modules.ui_components import FormRow
upscale_cache = {}
class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
name = "Upscale"
order = 1000
def ui(self):
selected_tab = gr.State(value=0)
with gr.Tabs(elem_id="extras_resize_mode"):
with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
with FormRow():
upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
with FormRow():
extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
with FormRow():
extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
return {
"upscale_mode": selected_tab,
"upscale_by": upscaling_resize,
"upscale_to_width": upscaling_resize_w,
"upscale_to_height": upscaling_resize_h,
"upscale_crop": upscaling_crop,
"upscaler_1_name": extras_upscaler_1,
"upscaler_2_name": extras_upscaler_2,
"upscaler_2_visibility": extras_upscaler_2_visibility,
}
def upscale(self, image, info, upscaler, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop):
if upscale_mode == 1:
upscale_by = max(upscale_to_width/image.width, upscale_to_height/image.height)
info["Postprocess upscale to"] = f"{upscale_to_width}x{upscale_to_height}"
else:
info["Postprocess upscale by"] = upscale_by
cache_key = (hash(np.array(image.getdata()).tobytes()), upscaler.name, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
cached_image = upscale_cache.pop(cache_key, None)
if cached_image is not None:
image = cached_image
else:
image = upscaler.scaler.upscale(image, upscale_by, upscaler.data_path)
upscale_cache[cache_key] = image
if len(upscale_cache) > shared.opts.upscaling_max_images_in_cache:
upscale_cache.pop(next(iter(upscale_cache), None), None)
if upscale_mode == 1 and upscale_crop:
cropped = Image.new("RGB", (upscale_to_width, upscale_to_height))
cropped.paste(image, box=(upscale_to_width // 2 - image.width // 2, upscale_to_height // 2 - image.height // 2))
image = cropped
info["Postprocess crop to"] = f"{image.width}x{image.height}"
return image
def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
if upscaler_1_name == "None":
upscaler_1_name = None
upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_1_name]), None)
assert upscaler1 or (upscaler_1_name is None), f'could not find upscaler named {upscaler_1_name}'
if not upscaler1:
return
if upscaler_2_name == "None":
upscaler_2_name = None
upscaler2 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_2_name and x.name != "None"]), None)
assert upscaler2 or (upscaler_2_name is None), f'could not find upscaler named {upscaler_2_name}'
upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
pp.info[f"Postprocess upscaler"] = upscaler1.name
if upscaler2 and upscaler_2_visibility > 0:
second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
upscaled_image = Image.blend(upscaled_image, second_upscale, upscaler_2_visibility)
pp.info[f"Postprocess upscaler 2"] = upscaler2.name
pp.image = upscaled_image
def image_changed(self):
upscale_cache.clear()
class ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale):
name = "Simple Upscale"
order = 900
def ui(self):
with FormRow():
upscaler_name = gr.Dropdown(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
upscale_by = gr.Slider(minimum=0.05, maximum=8.0, step=0.05, label="Upscale by", value=2)
return {
"upscale_by": upscale_by,
"upscaler_name": upscaler_name,
}
def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_by=2.0, upscaler_name=None):
if upscaler_name is None or upscaler_name == "None":
return
upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_name]), None)
assert upscaler1, f'could not find upscaler named {upscaler_name}'
pp.image = self.upscale(pp.image, pp.info, upscaler1, 0, upscale_by, 0, 0, False)
pp.info[f"Postprocess upscaler"] = upscaler1.name
================================================
FILE: scripts/prompt_matrix.py
================================================
import math
from collections import namedtuple
from copy import copy
import random
import modules.scripts as scripts
import gradio as gr
from modules import images
from modules.processing import process_images, Processed
from modules.shared import opts, cmd_opts, state
import modules.sd_samplers
def draw_xy_grid(xs, ys, x_label, y_label, cell):
res = []
ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
first_processed = None
state.job_count = len(xs) * len(ys)
for iy, y in enumerate(ys):
for ix, x in enumerate(xs):
state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
processed = cell(x, y)
if first_processed is None:
first_processed = processed
res.append(processed.images[0])
grid = images.image_grid(res, rows=len(ys))
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
first_processed.images = [grid]
return first_processed
class Script(scripts.Script):
def title(self):
return "Prompt matrix"
def ui(self, is_img2img):
gr.HTML('<br />')
with gr.Row():
with gr.Column():
put_at_start = gr.Checkbox(label='Put variable parts at start of prompt', value=False, elem_id=self.elem_id("put_at_start"))
different_seeds = gr.Checkbox(label='Use different seed for each picture', value=False, elem_id=self.elem_id("different_seeds"))
with gr.Column():
prompt_type = gr.Radio(["positive", "negative"], label="Select prompt", elem_id=self.elem_id("prompt_type"), value="positive")
variations_delimiter = gr.Radio(["comma", "space"], label="Select joining char", elem_id=self.elem_id("variations_delimiter"), value="comma")
with gr.Column():
margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
return [put_at_start, different_seeds, prompt_type, variations_delimiter, margin_size]
def run(self, p, put_at_start, different_seeds, prompt_type, variations_delimiter, margin_size):
modules.processing.fix_seed(p)
# Raise error if promp type is not positive or negative
if prompt_type not in ["positive", "negative"]:
raise ValueError(f"Unknown prompt type {prompt_type}")
# Raise error if variations delimiter is not comma or space
if variations_delimiter not in ["comma", "space"]:
raise ValueError(f"Unknown variations delimiter {variations_delimiter}")
prompt = p.prompt if prompt_type == "positive" else p.negative_prompt
original_prompt = prompt[0] if type(prompt) == list else prompt
positive_prompt = p.prompt[0] if type(p.prompt) == list else p.prompt
delimiter = ", " if variations_delimiter == "comma" else " "
all_prompts = []
prompt_matrix_parts = original_prompt.split("|")
combination_count = 2 ** (len(prompt_matrix_parts) - 1)
for combination_num in range(combination_count):
selected_prompts = [text.strip().strip(',') for n, text in enumerate(prompt_matrix_parts[1:]) if combination_num & (1 << n)]
if put_at_start:
selected_prompts = selected_prompts + [prompt_matrix_parts[0]]
else:
selected_prompts = [prompt_matrix_parts[0]] + selected_prompts
all_prompts.append(delimiter.join(selected_prompts))
p.n_iter = math.ceil(len(all_prompts) / p.batch_size)
p.do_not_save_grid = True
print(f"Prompt matrix will create {len(all_prompts)} images using a total of {p.n_iter} batches.")
if prompt_type == "positive":
p.prompt = all_prompts
else:
p.negative_prompt = all_prompts
p.seed = [p.seed + (i if different_seeds else 0) for i in range(len(all_prompts))]
p.prompt_for_display = positive_prompt
processed = process_images(p)
grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))
grid = images.draw_prompt_matrix(grid, processed.images[0].width, processed.images[1].height, prompt_matrix_parts, margin_size)
processed.images.insert(0, grid)
processed.index_of_first_image = 1
processed.infotexts.insert(0, processed.infotexts[0])
if opts.grid_save:
images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", extension=opts.grid_format, prompt=original_prompt, seed=processed.seed, grid=True, p=p)
return processed
================================================
FILE: scripts/prompts_from_file.py
================================================
import copy
import math
import os
import random
import sys
import traceback
import shlex
import modules.scripts as scripts
import gradio as gr
from modules import sd_samplers
from modules.processing import Processed, process_images
from PIL import Image
from modules.shared import opts, cmd_opts, state
def process_string_tag(tag):
return tag
def process_int_tag(tag):
return int(tag)
def process_float_tag(tag):
return float(tag)
def process_boolean_tag(tag):
return True if (tag == "true") else False
prompt_tags = {
"sd_model": None,
"outpath_samples": process_string_tag,
"outpath_grids": process_string_tag,
"prompt_for_display": process_string_tag,
"prompt": process_string_tag,
"negative_prompt": process_string_tag,
"styles": process_string_tag,
"seed": process_int_tag,
"subseed_strength": process_float_tag,
"subseed": process_int_tag,
"seed_resize_from_h": process_int_tag,
"seed_resize_from_w": process_int_tag,
"sampler_index": process_int_tag,
"sampler_name": process_string_tag,
"batch_size": process_int_tag,
"n_iter": process_int_tag,
"steps": process_int_tag,
"cfg_scale": process_float_tag,
"width": process_int_tag,
"height": process_int_tag,
"restore_faces": process_boolean_tag,
"tiling": process_boolean_tag,
"do_not_save_samples": process_boolean_tag,
"do_not_save_grid": process_boolean_tag
}
def cmdargs(line):
args = shlex.split(line)
pos = 0
res = {}
while pos < len(args):
arg = args[pos]
assert arg.startswith("--"), f'must start with "--": {arg}'
assert pos+1 < len(args), f'missing argument for command line option {arg}'
tag = arg[2:]
if tag == "prompt" or tag == "negative_prompt":
pos += 1
prompt = args[pos]
pos += 1
while pos < len(args) and not args[pos].startswith("--"):
prompt += " "
prompt += args[pos]
pos += 1
res[tag] = prompt
continue
func = prompt_tags.get(tag, None)
assert func, f'unknown commandline option: {arg}'
val = args[pos+1]
if tag == "sampler_name":
val = sd_samplers.samplers_map.get(val.lower(), None)
res[tag] = func(val)
pos += 2
return res
def load_prompt_file(file):
if file is None:
lines = []
else:
lines = [x.strip() for x in file.decode('utf8', errors='ignore').split("\n")]
return None, "\n".join(lines), gr.update(lines=7)
class Script(scripts.Script):
def title(self):
return "Prompts from file or textbox"
def ui(self, is_img2img):
checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False, elem_id=self.elem_id("checkbox_iterate"))
checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False, elem_id=self.elem_id("checkbox_iterate_batch"))
prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1, elem_id=self.elem_id("prompt_txt"))
file = gr.File(label="Upload prompt inputs", type='binary', elem_id=self.elem_id("file"))
file.change(fn=load_prompt_file, inputs=[file], outputs=[file, prompt_txt, prompt_txt])
# We start at one line. When the text changes, we jump to seven lines, or two lines if no \n.
# We don't shrink back to 1, because that causes the control to ignore [enter], and it may
# be unclear to the user that shift-enter is needed.
prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt])
return [checkbox_iterate, checkbox_iterate_batch, prompt_txt]
def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_txt: str):
lines = [x.strip() for x in prompt_txt.splitlines()]
lines = [x for x in lines if len(x) > 0]
p.do_not_save_grid = True
job_count = 0
jobs = []
for line in lines:
if "--" in line:
try:
args = cmdargs(line)
except Exception:
print(f"Error parsing line {line} as commandline:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
args = {"prompt": line}
else:
args = {"prompt": line}
job_count += args.get("n_iter", p.n_iter)
jobs.append(args)
print(f"Will process {len(lines)} lines in {job_count} jobs.")
if (checkbox_iterate or checkbox_iterate_batch) and p.seed == -1:
p.seed = int(random.randrange(4294967294))
state.job_count = job_count
images = []
all_prompts = []
infotexts = []
for n, args in enumerate(jobs):
state.job = f"{state.job_no + 1} out of {state.job_count}"
copy_p = copy.copy(p)
for k, v in args.items():
setattr(copy_p, k, v)
proc = process_images(copy_p)
images += proc.images
if checkbox_iterate:
p.seed = p.seed + (p.batch_size * p.n_iter)
all_prompts += proc.all_prompts
infotexts += proc.infotexts
return Processed(p, images, p.seed, "", all_prompts=all_prompts, infotexts=infotexts)
================================================
FILE: scripts/sd_upscale.py
================================================
import math
import modules.scripts as scripts
import gradio as gr
from PIL import Image
from modules import processing, shared, sd_samplers, images, devices
from modules.processing import Processed
from modules.shared import opts, cmd_opts, state
class Script(scripts.Script):
def title(self):
return "SD upscale"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, elem_id=self.elem_id("overlap"))
scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0, elem_id=self.elem_id("scale_factor"))
upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", elem_id=self.elem_id("upscaler_index"))
return [info, overlap, upscaler_index, scale_factor]
def run(self, p, _, overlap, upscaler_index, scale_factor):
if isinstance(upscaler_index, str):
upscaler_index = [x.name.lower() for x in shared.sd_upscalers].index(upscaler_index.lower())
processing.fix_seed(p)
upscaler = shared.sd_upscalers[upscaler_index]
p.extra_generation_params["SD upscale overlap"] = overlap
p.extra_generation_params["SD upscale upscaler"] = upscaler.name
initial_info = None
seed = p.seed
init_img = p.init_images[0]
init_img = images.flatten(init_img, opts.img2img_background_color)
if upscaler.name != "None":
img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)
else:
img = init_img
devices.torch_gc()
grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=overlap)
batch_size = p.batch_size
upscale_count = p.n_iter
p.n_iter = 1
p.do_not_save_grid = True
p.do_not_save_samples = True
work = []
for y, h, row in grid.tiles:
for tiledata in row:
work.append(tiledata[2])
batch_count = math.ceil(len(work) / batch_size)
state.job_count = batch_count * upscale_count
print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.")
result_images = []
for n in range(upscale_count):
start_seed = seed + n
p.seed = start_seed
work_results = []
for i in range(batch_count):
p.batch_size = batch_size
p.init_images = work[i * batch_size:(i + 1) * batch_size]
state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}"
processed = processing.process_images(p)
if initial_info is None:
initial_info = processed.info
p.seed = processed.seed + 1
work_results += processed.images
image_index = 0
for y, h, row in grid.tiles:
for tiledata in row:
tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
image_index += 1
combined_image = images.combine_grid(grid)
result_images.append(combined_image)
if opts.samples_save:
images.save_image(combined_image, p.outpath_samples, "", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
processed = Processed(p, result_images, seed, initial_info)
return processed
================================================
FILE: scripts/xyz_grid.py
================================================
from collections import namedtuple
from copy import copy
from itertools import permutations, chain
import random
import csv
from io import StringIO
from PIL import Image
import numpy as np
import modules.scripts as scripts
import gradio as gr
from modules import images, paths, sd_samplers, processing, sd_models, sd_vae
from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.sd_samplers
import modules.sd_models
import modules.sd_vae
import glob
import os
import re
from modules.ui_components import ToolButton
fill_values_symbol = "\U0001f4d2" # 📒
AxisInfo = namedtuple('AxisInfo', ['axis', 'values'])
def apply_field(field):
def fun(p, x, xs):
setattr(p, field, x)
return fun
def apply_prompt(p, x, xs):
if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
p.prompt = p.prompt.replace(xs[0], x)
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
def apply_order(p, x, xs):
token_order = []
# Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen
for token in x:
token_order.append((p.prompt.find(token), token))
token_order.sort(key=lambda t: t[0])
prompt_parts = []
# Split the prompt up, taking out the tokens
for _, token in token_order:
n = p.prompt.find(token)
prompt_parts.append(p.prompt[0:n])
p.prompt = p.prompt[n + len(token):]
# Rebuild the prompt with the tokens in the order we want
prompt_tmp = ""
for idx, part in enumerate(prompt_parts):
prompt_tmp += part
prompt_tmp += x[idx]
p.prompt = prompt_tmp + p.prompt
def apply_sampler(p, x, xs):
sampler_name = sd_samplers.samplers_map.get(x.lower(), None)
if sampler_name is None:
raise RuntimeError(f"Unknown sampler: {x}")
p.sampler_name = sampler_name
def confirm_samplers(p, xs):
for x in xs:
if x.lower() not in sd_samplers.samplers_map:
raise RuntimeError(f"Unknown sampler: {x}")
def apply_checkpoint(p, x, xs):
info = modules.sd_models.get_closet_checkpoint_match(x)
if info is None:
raise RuntimeError(f"Unknown checkpoint: {x}")
modules.sd_models.reload_model_weights(shared.sd_model, info)
def confirm_checkpoints(p, xs):
for x in xs:
if modules.sd_models.get_closet_checkpoint_match(x) is None:
raise RuntimeError(f"Unknown checkpoint: {x}")
def apply_clip_skip(p, x, xs):
opts.data["CLIP_stop_at_last_layers"] = x
def apply_upscale_latent_space(p, x, xs):
if x.lower().strip() != '0':
opts.data["use_scale_latent_for_hires_fix"] = True
else:
opts.data["use_scale_latent_for_hires_fix"] = False
def find_vae(name: str):
if name.lower() in ['auto', 'automatic']:
return modules.sd_vae.unspecified
if name.lower() == 'none':
return None
else:
choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
if len(choices) == 0:
print(f"No VAE found for {name}; using automatic")
return modules.sd_vae.unspecified
else:
return modules.sd_vae.vae_dict[choices[0]]
def apply_vae(p, x, xs):
modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
p.styles.extend(x.split(','))
def format_value_add_label(p, opt, x):
if type(x) == float:
x = round(x, 8)
return f"{opt.label}: {x}"
def format_value(p, opt, x):
if type(x) == float:
x = round(x, 8)
return x
def format_value_join_list(p, opt, x):
return ", ".join(x)
def do_nothing(p, x, xs):
pass
def format_nothing(p, opt, x):
return ""
def str_permutations(x):
"""dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
return x
class AxisOption:
def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None):
self.label = label
self.type = type
self.apply = apply
self.format_value = format_value
self.confirm = confirm
self.cost = cost
self.choices = choices
class AxisOptionImg2Img(AxisOption):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_img2img = True
class AxisOptionTxt2Img(AxisOption):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_img2img = False
axis_options = [
AxisOption("Nothing", str, do_nothing, format_value=format_nothing),
AxisOption("Seed", int, apply_field("seed")),
AxisOption("Var. seed", int, apply_field("subseed")),
AxisOption("Var. strength", float, apply_field("subseed_strength")),
AxisOption("Steps", int, apply_field("steps")),
AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_steps")),
AxisOption("CFG Scale", float, apply_field("cfg_scale")),
AxisOptionImg2Img("Image CFG Scale", float, apply_field("image_cfg_scale")),
AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value),
AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),
AxisOption("Sigma Churn", float, apply_field("s_churn")),
AxisOption("Sigma min", float, apply_field("s_tmin")),
AxisOption("Sigma max", float, apply_field("s_tmax")),
AxisOption("Sigma noise", float, apply_field("s_noise")),
AxisOption("Eta", float, apply_field("eta")),
AxisOption("Clip skip", int, apply_clip_skip),
AxisOption("Denoising", float, apply_field("denoising_strength")),
AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),
AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
]
def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size):
hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
title_texts = [[images.GridAnnotation(z)] for z in z_labels]
# Temporary list of all the images that are generated to be populated into the grid.
# Will be filled with empty images for any individual step that fails to process properly
image_cache = [None] * (len(xs) * len(ys) * len(zs))
processed_result = None
cell_mode = "P"
cell_size = (1, 1)
state.job_count = len(xs) * len(ys) * len(zs) * p.n_iter
def process_cell(x, y, z, ix, iy, iz):
nonlocal image_cache, processed_result, cell_mode, cell_size
def index(ix, iy, iz):
return ix + iy * len(xs) + iz * len(xs) * len(ys)
state.job = f"{index(ix, iy, iz) + 1} out of {len(xs) * len(ys) * len(zs)}"
processed: Processed = cell(x, y, z)
try:
# this dereference will throw an exception if the image was not processed
# (this happens in cases such as if the user stops the process from the UI)
processed_image = processed.images[0]
if processed_result is None:
# Use our first valid processed result as a template container to hold our full results
processed_result = copy(processed)
cell_mode = processed_image.mode
cell_size = processed_image.size
processed_result.images = [Image.new(cell_mode, cell_size)]
processed_result.all_prompts = [processed.prompt]
processed_result.all_seeds = [processed.seed]
processed_result.infotexts = [processed.infotexts[0]]
image_cache[index(ix, iy, iz)] = processed_image
if include_lone_images:
processed_result.images.append(processed_image)
processed_result.all_prompts.append(processed.prompt)
processed_result.all_seeds.append(processed.seed)
processed_result.infotexts.append(processed.infotexts[0])
except:
image_cache[index(ix, iy, iz)] = Image.new(cell_mode, cell_size)
if first_axes_processed == 'x':
for ix, x in enumerate(xs):
if second_axes_processed == 'y':
for iy, y in enumerate(ys):
for iz, z in enumerate(zs):
process_cell(x, y, z, ix, iy, iz)
else:
for iz, z in enumerate(zs):
for iy, y in enumerate(ys):
process_cell(x, y, z, ix, iy, iz)
elif first_axes_processed == 'y':
for iy, y in enumerate(ys):
if second_axes_processed == 'x':
for ix, x in enumerate(xs):
for iz, z in enumerate(zs):
process_cell(x, y, z, ix, iy, iz)
else:
for iz, z in enumerate(zs):
for ix, x in enumerate(xs):
process_cell(x, y, z, ix, iy, iz)
elif first_axes_processed == 'z':
for iz, z in enumerate(zs):
if second_axes_processed == 'x':
for ix, x in enumerate(xs):
for iy, y in enumerate(ys):
process_cell(x, y, z, ix, iy, iz)
else:
for iy, y in enumerate(ys):
for ix, x in enumerate(xs):
process_cell(x, y, z, ix, iy, iz)
if not processed_result:
print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
return Processed(p, [])
sub_grids = [None] * len(zs)
for i in range(len(zs)):
start_index = i * len(xs) * len(ys)
end_index = start_index + len(xs) * len(ys)
grid = images.image_grid(image_cache[start_index:end_index], rows=len(ys))
if draw_legend:
grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts, margin_size)
sub_grids[i] = grid
if include_sub_grids and len(zs) > 1:
processed_result.images.insert(i+1, grid)
sub_grid_size = sub_grids[0].size
z_grid = images.image_grid(sub_grids, rows=1)
if draw_legend:
z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
processed_result.images[0] = z_grid
return processed_result, sub_grids
class SharedSettingsStackHelper(object):
def __enter__(self):
self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
self.vae = opts.sd_vae
def __exit__(self, exc_type, exc_value, tb):
opts.data["sd_vae"] = self.vae
modules.sd_models.reload_model_weights()
modules.sd_vae.reload_vae_weights()
opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
class Script(scripts.Script):
def title(self):
return "X/Y/Z plot"
def ui(self, is_img2img):
self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]
with gr.Row():
with gr.Column(scale=19):
with gr.Row():
x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
with gr.Row():
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
with gr.Row():
z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
with gr.Row(variant="compact", elem_id="axis_options"):
with gr.Column():
draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
with gr.Column():
include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id("include_lone_images"))
include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
with gr.Column():
margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
with gr.Row(variant="compact", elem_id="swap_axes"):
swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")
def swap_axes(axis1_type, axis1_values, axis2_type, axis2_values):
return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values
xy_swap_args = [x_type, x_values, y_type, y_values]
swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
yz_swap_args = [y_type, y_values, z_type, z_values]
swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
xz_swap_args = [x_type, x_values, z_type, z_values]
swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
def fill(x_type):
axis = self.current_axis_options[x_type]
return ", ".join(axis.choices()) if axis.choices else gr.update()
fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])
def select_axis(x_type):
return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
self.infotext_fields = (
(x_type, "X Type"),
(x_values, "X Values"),
(y_type, "Y Type"),
(y_values, "Y Values"),
(z_type, "Z Type"),
(z_values, "Z Values"),
)
return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
if not no_fixed_seeds:
modules.processing.fix_seed(p)
if not opts.return_grid:
p.batch_size = 1
def process_axis(opt, vals):
if opt.label == 'Nothing':
return [0]
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]
if opt.type == int:
valslist_ext = []
for val in valslist:
m = re_range.fullmatch(val)
mc = re_range_count.fullmatch(val)
if m is not None:
start = int(m.group(1))
end = int(m.group(2))+1
step = int(m.group(3)) if m.group(3) is not None else 1
valslist_ext += list(range(start, end, step))
elif mc is not None:
start = int(mc.group(1))
end = int(mc.group(2))
num = int(mc.group(3)) if mc.group(3) is not None else 1
valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
else:
valslist_ext.append(val)
valslist = valslist_ext
elif opt.type == float:
valslist_ext = []
for val in valslist:
m = re_range_float.fullmatch(val)
mc = re_range_count_float.fullmatch(val)
if m is not None:
start = float(m.group(1))
end = float(m.group(2))
step = float(m.group(3)) if m.group(3) is not None else 1
valslist_ext += np.arange(start, end + step, step).tolist()
elif mc is not None:
start = float(mc.group(1))
end = float(mc.group(2))
num = int(mc.group(3)) if mc.group(3) is not None else 1
valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
else:
valslist_ext.append(val)
valslist = valslist_ext
elif opt.type == str_permutations:
valslist = list(permutations(valslist))
valslist = [opt.type(x) for x in valslist]
# Confirm options are valid before starting
if opt.confirm:
opt.confirm(p, valslist)
return valslist
x_opt = self.current_axis_options[x_type]
xs = process_axis(x_opt, x_values)
y_opt = self.current_axis_options[y_type]
ys = process_axis(y_opt, y_values)
z_opt = self.current_axis_options[z_type]
zs = process_axis(z_opt, z_values)
def fix_axis_seeds(axis_opt, axis_list):
if axis_opt.label in ['Seed', 'Var. seed']:
return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
else:
return axis_list
if not no_fixed_seeds:
xs = fix_axis_seeds(x_opt, xs)
ys = fix_axis_seeds(y_opt, ys)
zs = fix_axis_seeds(z_opt, zs)
if x_opt.label == 'Steps':
total_steps = sum(xs) * len(ys) * len(zs)
elif y_opt.label == 'Steps':
total_steps = sum(ys) * len(xs) * len(zs)
elif z_opt.label == 'Steps':
total_steps = sum(zs) * len(xs) * len(ys)
else:
total_steps = p.steps * len(xs) * len(ys) * len(zs)
if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
if x_opt.label == "Hires steps":
total_steps += sum(xs) * len(ys) * len(zs)
elif y_opt.label == "Hires steps":
total_steps += sum(ys) * len(xs) * len(zs)
elif z_opt.label == "Hires steps":
total_steps += sum(zs) * len(xs) * len(ys)
elif p.hr_second_pass_steps:
total_steps += p.hr_second_pass_steps * len(xs) * len(ys) * len(zs)
else:
total_steps *= 2
total_steps *= p.n_iter
image_cell_count = p.n_iter * p.batch_size
cell_console_text = f"; {image_cell_count} images per cell" if image_cell_count > 1 else ""
plural_s = 's' if len(zs) > 1 else ''
print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})")
shared.total_tqdm.updateTotal(total_steps)
grid_infotext = [None]
state.xyz_plot_x = AxisInfo(x_opt, xs)
state.xyz_plot_y = AxisInfo(y_opt, ys)
state.xyz_plot_z = AxisInfo(z_opt, zs)
# If one of the axes is very slow to change between (like SD model
# checkpoint), then make sure it is in the outer iteration of the nested
# `for` loop.
first_axes_processed = 'x'
second_axes_processed = 'y'
if x_opt.cost > y_opt.cost and x_opt.cost > z_opt.cost:
first_axes_processed = 'x'
if y_opt.cost > z_opt.cost:
second_axes_processed = 'y'
else:
second_axes_processed = 'z'
elif y_opt.cost > x_opt.cost and y_opt.cost > z_opt.cost:
first_axes_processed = 'y'
if x_opt.cost > z_opt.cost:
second_axes_processed = 'x'
else:
second_axes_processed = 'z'
elif z_opt.cost > x_opt.cost and z_opt.cost > y_opt.cost:
first_axes_processed = 'z'
if x_opt.cost > y_opt.cost:
second_axes_processed = 'x'
else:
second_axes_processed = 'y'
def cell(x, y, z):
if shared.state.interrupted:
return Processed(p, [], p.seed, "")
pc = copy(p)
pc.styles = pc.styles[:]
x_opt.apply(pc, x, xs)
y_opt.apply(pc, y, ys)
z_opt.apply(pc, z, zs)
res = process_images(pc)
if grid_infotext[0] is None:
pc.extra_generation_params = copy(pc.extra_generation_params)
pc.extra_generation_params['Script'] = self.title()
if x_opt.label != 'Nothing':
pc.extra_generation_params["X Type"] = x_opt.label
pc.extra_generation_params["X Values"] = x_values
if x_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
pc.extra_generation_params["Fixed X Values"] = ", ".join([str(x) for x in xs])
if y_opt.label != 'Nothing':
pc.extra_generation_params["Y Type"] = y_opt.label
pc.extra_generation_params["Y Values"] = y_values
if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])
if z_opt.label != 'Nothing':
pc.extra_generation_params["Z Type"] = z_opt.label
pc.extra_generation_params["Z Values"] = z_values
if z_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
pc.extra_generation_params["Fixed Z Values"] = ", ".join([str(z) for z in zs])
grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
return res
with SharedSettingsStackHelper():
processed, sub_grids = draw_xyz_grid(
p,
xs=xs,
ys=ys,
zs=zs,
x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
z_labels=[z_opt.format_value(p, z_opt, z) for z in zs],
cell=cell,
draw_legend=draw_legend,
include_lone_images=include_lone_images,
include_sub_grids=include_sub_grids,
first_axes_processed=first_axes_processed,
second_axes_processed=second_axes_processed,
margin_size=margin_size
)
if opts.grid_save and len(sub_grids) > 1:
for sub_grid in sub_grids:
images.save_image(sub_grid, p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
if opts.grid_save:
images.save_image(processed.images[0], p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
return processed
================================================
FILE: style.css
================================================
.container {
max-width: 100%;
}
.token-counter{
position: absolute;
display: inline-block;
right: 2em;
min-width: 0 !important;
width: auto;
z-index: 100;
}
.token-counter.error span{
box-shadow: 0 0 0.0 0.3em rgba(255,0,0,0.15), inset 0 0 0.6em rgba(255,0,0,0.075);
border: 2px solid rgba(255,0,0,0.4) !important;
}
.token-counter div{
display: inline;
}
.token-counter span{
padding: 0.1em 0.75em;
}
#sh{
min-width: 2em;
min-height: 2em;
max-width: 2em;
max-height: 2em;
flex-grow: 0;
padding-left: 0.25em;
padding-right: 0.25em;
margin: 0.1em 0;
opacity: 0%;
cursor: default;
}
.output-html p {margin: 0 0.5em;}
.row > *,
.row > .gr-form > * {
min-width: min(120px, 100%);
flex: 1 1 0%;
}
.performance {
font-size: 0.85em;
color: #444;
}
.performance p{
display: inline-block;
}
.performance .time {
margin-right: 0;
}
.performance .vram {
}
#txt2img_generate, #img2img_generate {
min-height: 4.5em;
}
@media screen and (min-width: 2500px) {
#txt2img_gallery, #img2img_gallery {
min-height: 768px;
}
}
#txt2img_gallery img, #img2img_gallery img{
object-fit: scale-down;
}
#txt2img_actions_column, #img2img_actions_column {
margin: 0.35rem 0.75rem 0.35rem 0;
}
#script_list {
padding: .625rem .75rem 0 .625rem;
}
.justify-center.overflow-x-scroll {
justify-content: left;
}
.justify-center.overflow-x-scroll button:first-of-type {
margin-left: auto;
}
.justify-center.overflow-x-scroll button:last-of-type {
margin-right: auto;
}
[id$=_random_seed], [id$=_random_subseed], [id$=_reuse_seed], [id$=_reuse_subseed], #open_folder{
min-width: 2.3em;
height: 2.5em;
flex-grow: 0;
padding-left: 0.25em;
padding-right: 0.25em;
}
#hidden_element{
display: none;
}
[id$=_seed_row], [id$=_subseed_row]{
gap: 0.5rem;
padding: 0.6em;
}
[id$=_subseed_show_box]{
min-width: auto;
flex-grow: 0;
}
[id$=_subseed_show_box] > div{
border: 0;
height: 100%;
}
[id$=_subseed_show]{
min-width: auto;
flex-grow: 0;
padding: 0;
}
[id$=_subseed_show] label{
height: 100%;
}
#txt2img_actions_column, #img2img_actions_column{
gap: 0;
margin-right: .75rem;
}
#txt2img_tools, #img2img_tools{
gap: 0.4em;
}
#interrogate_col{
min-width: 0 !important;
max-width: 8em !important;
margin-right: 1em;
gap: 0;
}
#interrogate, #deepbooru{
margin: 0em 0.25em 0.5em 0.25em;
min-width: 8em;
max-width: 8em;
}
#style_pos_col, #style_neg_col{
min-width: 8em !important;
}
#txt2img_styles_row, #img2img_styles_row{
gap: 0.25em;
margin-top: 0.3em;
}
#txt2img_styles_row > button, #img2img_styles_row > button{
margin: 0;
}
#txt2img_styles, #img2img_styles{
padding: 0;
}
#txt2img_styles > label > div, #img2img_styles > label > div{
min-height: 3.2em;
}
ul.list-none{
max-height: 35em;
z-index: 2000;
}
.gr-form{
background: transparent;
}
.my-4{
margin-top: 0;
margin-bottom: 0;
}
#resize_mode{
flex: 1.5;
}
button{
align-self: stretch !important;
}
.overflow-hidden, .gr-panel{
overflow: visible !important;
}
#x_type, #y_type{
max-width: 10em;
}
#txt2img_preview, #img2img_preview, #ti_preview{
position: absolute;
width: 320px;
left: 0;
right: 0;
margin-left: auto;
margin-right: auto;
margin-top: 34px;
z-index: 100;
border: none;
border-top-left-radius: 0;
border-top-right-radius: 0;
}
@media screen and (min-width: 768px) {
#txt2img_preview, #img2img_preview, #ti_preview {
position: absolute;
}
}
@media screen and (max-width: 767px) {
#txt2img_preview, #img2img_preview, #ti_preview {
position: relative;
}
}
#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0, #ti_preview div.left-0.top-0{
display: none;
}
fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block span{
position: absolute;
top: -0.7em;
line-height: 1.2em;
padding: 0;
margin: 0 0.5em;
background-color: white;
box-shadow: 6px 0 6px 0px white, -6px 0 6px 0px white;
z-index: 300;
}
.dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
background-color: rgb(31, 41, 55);
box-shadow: none;
border: 1px solid rgba(128, 128, 128, 0.1);
border-radius: 6px;
padding: 0.1em 0.5em;
}
#txt2img_column_batch, #img2img_column_batch{
min-width: min(13.5em, 100%) !important;
}
#settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{
position: relative;
border: none;
margin-right: 8em;
}
#settings .gr-panel div.flex-col div.justify-between div{
position: relative;
z-index: 200;
}
#settings{
display: block;
}
#settings > div{
border: none;
margin-left: 10em;
}
#settings > div.flex-wrap{
float: left;
display: block;
margin-left: 0;
width: 10em;
}
#settings > div.flex-wrap button{
display: block;
border: none;
text-align: left;
}
#settings_result{
height: 1.4em;
margin: 0 1.2em;
}
input[type="range"]{
margin: 0.5em 0 -0.3em 0;
}
#mask_bug_info {
text-align: center;
display: block;
margin-top: -0.75em;
margin-bottom: -0.75em;
}
#txt2img_negative_prompt, #img2img_negative_prompt{
}
/* gradio 3.8 adds opacity to progressbar which makes it blink; disable it here */
.transition.opacity-20 {
opacity: 1 !important;
}
/* more gradio's garbage cleanup */
.min-h-\[4rem\] { min-height: unset !important; }
.min-h-\[6rem\] { min-height: unset !important; }
.progressDiv{
position: relative;
height: 20px;
background: #b4c0cc;
border-radius: 3px !important;
margin-bottom: -3px;
}
.dark .progressDiv{
background: #424c5b;
}
.progressDiv .progress{
width: 0%;
height: 20px;
background: #0060df;
color: white;
font-weight: bold;
line-height: 20px;
padding: 0 8px 0 0;
text-align: right;
border-radius: 3px;
overflow: visible;
white-space: nowrap;
padding: 0 0.5em;
}
.livePreview{
position: absolute;
z-index: 300;
background-color: white;
margin: -4px;
}
.dark .livePreview{
background-color: rgb(17 24 39 / var(--tw-bg-opacity));
}
.livePreview img{
position: absolute;
object-fit: contain;
width: 100%;
height: 100%;
}
#lightboxModal{
display: none;
position: fixed;
z-index: 1001;
padding-top: 100px;
left: 0;
top: 0;
width: 100%;
height: 100%;
overflow: auto;
background-color: rgba(20, 20, 20, 0.95);
user-select: none;
-webkit-user-select: none;
}
.modalControls {
display: grid;
grid-template-columns: 32px 32px 32px 1fr 32px;
grid-template-areas: "zoom tile save space close";
position: absolute;
top: 0;
left: 0;
right: 0;
padding: 16px;
gap: 16px;
background-color: rgba(0,0,0,0.2);
}
.modalClose {
grid-area: close;
}
.modalZoom {
grid-area: zoom;
}
.modalSave {
grid-area: save;
}
.modalTileImage {
grid-area: tile;
}
.modalClose,
.modalZoom,
.modalTileImage {
color: white;
font-size: 35px;
font-weight: bold;
cursor: pointer;
}
.modalSave {
color: white;
font-size: 28px;
margin-top: 8px;
font-weight: bold;
cursor: pointer;
}
.modalClose:hover,
.modalClose:focus,
.modalSave:hover,
.modalSave:focus,
.modalZoom:hover,
.modalZoom:focus {
color: #999;
text-decoration: none;
cursor: pointer;
}
#modalImage {
display: block;
margin-left: auto;
margin-right: auto;
margin-top: auto;
width: auto;
}
.modalImageFullscreen {
object-fit: contain;
height: 90%;
}
.modalPrev,
.modalNext {
cursor: pointer;
position: absolute;
top: 50%;
width: auto;
padding: 16px;
margin-top: -50px;
color: white;
font-weight: bold;
font-size: 20px;
transition: 0.6s ease;
border-radius: 0 3px 3px 0;
user-select: none;
-webkit-user-select: none;
}
.modalNext {
right: 0;
border-radius: 3px 0 0 3px;
}
.modalPrev:hover,
.modalNext:hover {
background-color: rgba(0, 0, 0, 0.8);
}
#imageARPreview{
position:absolute;
top:0px;
left:0px;
border:2px solid red;
background:rgba(255, 0, 0, 0.3);
z-index: 900;
pointer-events:none;
display:none
}
#txt2img_generate_box, #img2img_generate_box{
position: relative;
}
#txt2img_interrupt, #img2img_interrupt, #txt2img_skip, #img2img_skip{
position: absolute;
width: 50%;
height: 100%;
background: #b4c0cc;
display: none;
}
#txt2img_interrupt, #img2img_interrupt{
left: 0;
border-radius: 0.5rem 0 0 0.5rem;
}
#txt2img_skip, #img2img_skip{
right: 0;
border-radius: 0 0.5rem 0.5rem 0;
}
.red {
color: red;
}
.gallery-item {
--tw-bg-opacity: 0 !important;
}
#context-menu{
z-index:9999;
position:absolute;
display:block;
padding:0px 0;
border:2px solid #a55000;
border-radius:8px;
box-shadow:1px 1px 2px #CE6400;
width: 200px;
}
.context-menu-items{
list-style: none;
margin: 0;
padding: 0;
}
.context-menu-items a{
display:block;
padding:5px;
cursor:pointer;
}
.context-menu-items a:hover{
background: #a55000;
}
#quicksettings {
width: fit-content;
}
#quicksettings > div, #quicksettings > fieldset{
max-width: 24em;
min-width: 24em;
padding: 0;
border: none;
box-shadow: none;
background: none;
margin-right: 10px;
}
#quicksettings > div > div > div > label > span {
position: relative;
margin-right: 9em;
margin-bottom: -1em;
}
canvas[key="mask"] {
z-index: 12 !important;
filter: invert();
mix-blend-mode: multiply;
pointer-events: none;
}
/* gradio 3.4.1 stuff for editable scrollbar values */
.gr-box > div > div > input.gr-text-input{
position: absolute;
right: 0.5em;
top: -0.6em;
z-index: 400;
width: 6em;
}
#quicksettings .gr-box > div > div > input.gr-text-input {
top: -1.12em;
}
.row.gr-compact{
overflow: visible;
}
#img2img_image, #img2img_image > .h-60, #img2img_image > .h-60 > div, #img2img_image > .h-60 > div > img,
#img2img_sketch, #img2img_sketch > .h-60, #img2img_sketch > .h-60 > div, #img2img_sketch > .h-60 > div > img,
#img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img,
#inpaint_sketch, #inpaint_sketch > .h-60, #inpaint_sketch > .h-60 > div, #inpaint_sketch > .h-60 > div > img
{
height: 480px !important;
max-height: 480px !important;
min-height: 480px !important;
}
/* Extensions */
#tab_extensions table{
border-collapse: collapse;
}
#tab_extensions table td, #tab_extensions table th{
border: 1px solid #ccc;
padding: 0.25em 0.5em;
}
#tab_extensions table input[type="checkbox"]{
margin-right: 0.5em;
}
#tab_extensions button{
max-width: 16em;
}
#tab_extensions input[disabled="disabled"]{
opacity: 0.5;
}
.extension-tag{
font-weight: bold;
font-size: 95%;
}
#available_extensions .info{
margin: 0;
}
#available_extensions .date_added{
opacity: 0.85;
font-size: 90%;
}
#image_buttons_txt2img button, #image_buttons_img2img button, #image_buttons_extras button{
min-width: auto;
padding-left: 0.5em;
padding-right: 0.5em;
}
.gr-form{
background-color: white;
}
.dark .gr-form{
background-color: rgb(31 41 55 / var(--tw-bg-opacity));
}
.gr-button-tool, .gr-button-tool-top{
max-width: 2.5em;
min-width: 2.5em !important;
height: 2.4em;
}
.gr-button-tool{
margin: 0.6em 0em 0.55em 0;
}
.gr-button-tool-top, #settings .gr-button-tool{
margin: 1.6em 0.7em 0.55em 0;
}
#modelmerger_results_container{
margin-top: 1em;
overflow: visible;
}
#modelmerger_models{
gap: 0;
}
#quicksettings .gr-button-tool{
margin: 0;
border-color: unset;
background-color: unset;
}
#modelmerger_interp_description>p {
margin: 0!important;
text-align: center;
}
#modelmerger_interp_description {
margin: 0.35rem 0.75rem 1.23rem;
}
#img2img_settings > div.gr-form, #txt2img_settings > div.gr-form {
padding-top: 0.9em;
padding-bottom: 0.9em;
}
#txt2img_settings {
padding-top: 1.16em;
padding-bottom: 0.9em;
}
#img2img_settings {
padding-bottom: 0.9em;
}
#img2img_settings div.gr-form .gr-form, #txt2img_settings div.gr-form .gr-form, #train_tabs div.gr-form .gr-form{
border: none;
padding-bottom: 0.5em;
}
footer {
display: none !important;
}
#footer{
text-align: center;
}
#footer div{
display: inline-block;
}
#footer .versions{
font-size: 85%;
opacity: 0.85;
}
#txtimg_hr_finalres{
min-height: 0 !important;
padding: .625rem .75rem;
margin-left: -0.75em
}
#txtimg_hr_finalres .resolution{
font-weight: bold;
}
#txt2img_checkboxes, #img2img_checkboxes{
margin-bottom: 0.5em;
margin-left: 0em;
}
#txt2img_checkboxes > div, #img2img_checkboxes > div{
flex: 0;
white-space: nowrap;
min-width: auto;
}
#img2img_copy_to_img2img, #img2img_copy_to_sketch, #img2img_copy_to_inpaint, #img2img_copy_to_inpaint_sketch{
margin-left: 0em;
}
#axis_options {
margin-left: 0em;
}
.inactive{
opacity: 0.5;
}
[id*='_prompt_container']{
gap: 0;
}
[id*='_prompt_container'] > div{
margin: -0.4em 0 0 0;
}
.gr-compact {
border: none;
}
.dark .gr-compact{
background-color: rgb(31 41 55 / var(--tw-bg-opacity));
margin-left: 0;
}
.gr-compact{
overflow: visible;
}
.gr-compact > *{
}
.gr-compact .gr-block, .gr-compact .gr-form{
border: none;
box-shadow: none;
}
.gr-compact .gr-box{
border-radius: .5rem !important;
border-width: 1px !important;
}
#mode_img2img > div > div{
gap: 0 !important;
}
[id*='img2img_copy_to_'] {
border: none;
}
[id*='img2img_copy_to_'] > button {
}
[id*='img2img_label_copy_to_'] {
font-size: 1.0em;
font-weight: bold;
text-align: center;
line-height: 2.4em;
}
.extra-networks > div > [id *= '_extra_']{
margin: 0.3em;
}
.extra-network-subdirs{
padding: 0.2em 0.35em;
}
.extra-network-subdirs button{
margin: 0 0.15em;
}
#txt2img_extra_networks .search, #img2img_extra_networks .search{
display: inline-block;
max-width: 16em;
margin: 0.3em;
align-self: center;
}
#txt2img_extra_view, #img2img_extra_view {
width: auto;
}
.extra-network-cards .nocards, .extra-network-thumbs .nocards{
margin: 1.25em 0.5em 0.5em 0.5em;
}
.extra-network-cards .nocards h1, .extra-network-thumbs .nocards h1{
font-size: 1.5em;
margin-bottom: 1em;
}
.extra-network-cards .nocards li, .extra-network-thumbs .nocards li{
margin-left: 0.5em;
}
.extra-network-thumbs {
display: flex;
flex-flow: row wrap;
gap: 10px;
}
.extra-network-thumbs .card {
height: 6em;
width: 6em;
cursor: pointer;
background-image: url('./file=html/card-no-preview.png');
background-size: cover;
background-position: center center;
position: relative;
}
.extra-network-thumbs .card:hover .additional a {
display: block;
}
.extra-network-thumbs .actions .additional a {
background-image: url('./file=html/image-update.svg');
background-repeat: no-repeat;
background-size: cover;
background-position: center center;
position: absolute;
top: 0;
left: 0;
width: 24px;
height: 24px;
display: none;
font-size: 0;
text-align: -9999;
}
.extra-network-thumbs .actions .name {
position: absolute;
bottom: 0;
font-size: 10px;
padding: 3px;
width: 100%;
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
background: rgba(0,0,0,.5);
color: white;
}
.extra-network-thumbs .card:hover .actions .name {
white-space: normal;
word-break: break-all;
}
.extra-network-cards .card{
display: inline-block;
margin: 0.5em;
width: 16em;
height: 24em;
box-shadow: 0 0 5px rgba(128, 128, 128, 0.5);
border-radius: 0.2em;
position: relative;
background-size: auto 100%;
background-position: center;
overflow: hidden;
cursor: pointer;
background-image: url('./file=html/card-no-preview.png')
}
.extra-network-cards .card:hover{
box-shadow: 0 0 2px 0.3em rgba(0, 128, 255, 0.35);
}
.extra-network-cards .card .actions .additional{
display: none;
}
.extra-network-cards .card .actions{
position: absolute;
bottom: 0;
left: 0;
right: 0;
padding: 0.5em;
color: white;
background: rgba(0,0,0,0.5);
box-shadow: 0 0 0.25em 0.25em rgba(0,0,0,0.5);
text-shadow: 0 0 0.2em black;
}
.extra-network-cards .card .actions:hover{
box-shadow: 0 0 0.75em 0.75em rgba(0,0,0,0.5) !important;
}
.extra-network-cards .card .actions .name{
font-size: 1.7em;
font-weight: bold;
line-break: anywhere;
}
.extra-network-cards .card .actions:hover .additional{
display: block;
}
.extra-network-cards .card ul{
margin: 0.25em 0 0.75em 0.25em;
cursor: unset;
}
.extra-network-cards .card ul a{
cursor: pointer;
}
.extra-network-cards .card ul a:hover{
color: red;
}
[id*='_prompt_container'] > div {
margin: 0!important;
}
================================================
FILE: styles.csv
================================================
name,prompt,negative_prompt
None,,
naifu基础起手式,"masterpiece, best quality, ","lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"
================================================
FILE: tags/temp/emb.txt
================================================
================================================
FILE: tags/temp/wc.txt
================================================
================================================
FILE: test/__init__.py
================================================
================================================
FILE: test/basic_features/__init__.py
================================================
================================================
FILE: test/basic_features/extras_test.py
================================================
import unittest
import requests
from gradio.processing_utils import encode_pil_to_base64
from PIL import Image
class TestExtrasWorking(unittest.TestCase):
def setUp(self):
self.url_extras_single = "http://localhost:7860/sdapi/v1/extra-single-image"
self.extras_single = {
"resize_mode": 0,
"show_extras_results": True,
"gfpgan_visibility": 0,
"codeformer_visibility": 0,
"codeformer_weight": 0,
"upscaling_resize": 2,
"upscaling_resize_w": 128,
"upscaling_resize_h": 128,
"upscaling_crop": True,
"upscaler_1": "None",
"upscaler_2": "None",
"extras_upscaler_2_visibility": 0,
"image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))
}
def test_simple_upscaling_performed(self):
self.extras_single["upscaler_1"] = "Lanczos"
self.assertEqual(requests.post(self.url_extras_single, json=self.extras_single).status_code, 200)
class TestPngInfoWorking(unittest.TestCase):
def setUp(self):
self.url_png_info = "http://localhost:7860/sdapi/v1/extra-single-image"
self.png_info = {
"image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))
}
def test_png_info_performed(self):
self.assertEqual(requests.post(self.url_png_info, json=self.png_info).status_code, 200)
class TestInterrogateWorking(unittest.TestCase):
def setUp(self):
self.url_interrogate = "http://localhost:7860/sdapi/v1/extra-single-image"
self.interrogate = {
"image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png")),
"model": "clip"
}
def test_interrogate_performed(self):
self.assertEqual(requests.post(self.url_interrogate, json=self.interrogate).status_code, 200)
if __name__ == "__main__":
unittest.main()
================================================
FILE: test/basic_features/img2img_test.py
================================================
import unittest
import requests
from gradio.processing_utils import encode_pil_to_base64
from PIL import Image
class TestImg2ImgWorking(unittest.TestCase):
def setUp(self):
self.url_img2img = "http://localhost:7860/sdapi/v1/img2img"
self.simple_img2img = {
"init_images": [encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))],
"resize_mode": 0,
"denoising_strength": 0.75,
"mask": None,
"mask_blur": 4,
"inpainting_fill": 0,
"inpaint_full_res": False,
"inpaint_full_res_padding": 0,
"inpainting_mask_invert": False,
"prompt": "example prompt",
"styles": [],
"seed": -1,
"subseed": -1,
"subseed_strength": 0,
"seed_resize_from_h": -1,
"seed_resize_from_w": -1,
"batch_size": 1,
"n_iter": 1,
"steps": 3,
"cfg_scale": 7,
"width": 64,
"height": 64,
"restore_faces": False,
"tiling": False,
"negative_prompt": "",
"eta": 0,
"s_churn": 0,
"s_tmax": 0,
"s_tmin": 0,
"s_noise": 1,
"override_settings": {},
"sampler_index": "Euler a",
"include_init_images": False
}
def test_img2img_simple_performed(self):
self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
def test_inpainting_masked_performed(self):
self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
def test_inpainting_with_inverted_masked_performed(self):
self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
self.simple_img2img["inpainting_mask_invert"] = True
self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
def test_img2img_sd_upscale_performed(self):
self.simple_img2img["script_name"] = "sd upscale"
self.simple_img2img["script_args"] = ["", 8, "Lanczos", 2.0]
self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
if __name__ == "__main__":
unittest.main()
================================================
FILE: test/basic_features/txt2img_test.py
================================================
import unittest
import requests
class TestTxt2ImgWorking(unittest.TestCase):
def setUp(self):
self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
self.simple_txt2img = {
"enable_hr": False,
"denoising_strength": 0,
"firstphase_width": 0,
"firstphase_height": 0,
"prompt": "example prompt",
"styles": [],
"seed": -1,
"subseed": -1,
"subseed_strength": 0,
"seed_resize_from_h": -1,
"seed_resize_from_w": -1,
"batch_size": 1,
"n_iter": 1,
"steps": 3,
"cfg_scale": 7,
"width": 64,
"height": 64,
"restore_faces": False,
"tiling": False,
"negative_prompt": "",
"eta": 0,
"s_churn": 0,
"s_tmax": 0,
"s_tmin": 0,
"s_noise": 1,
"sampler_index": "Euler a"
}
def test_txt2img_simple_performed(self):
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
def test_txt2img_with_negative_prompt_performed(self):
self.simple_txt2img["negative_prompt"] = "example negative prompt"
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
def test_txt2img_with_complex_prompt_performed(self):
self.simple_txt2img["prompt"] = "((emphasis)), (emphasis1:1.1), [to:1], [from::2], [from:to:0.3], [alt|alt1]"
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
def test_txt2img_not_square_image_performed(self):
self.simple_txt2img["height"] = 128
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
def test_txt2img_with_hrfix_performed(self):
self.simple_txt2img["enable_hr"] = True
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
def test_txt2img_with_tiling_performed(self):
self.simple_txt2img["tiling"] = True
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
def test_txt2img_with_restore_faces_performed(self):
self.simple_txt2img["restore_faces"] = True
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
def test_txt2img_with_vanilla_sampler_performed(self):
self.simple_txt2img["sampler_index"] = "PLMS"
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
self.simple_txt2img["sampler_index"] = "DDIM"
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
def test_txt2img_multiple_batches_performed(self):
self.simple_txt2img["n_iter"] = 2
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
def test_txt2img_batch_performed(self):
self.simple_txt2img["batch_size"] = 2
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
if __name__ == "__main__":
unittest.main()
================================================
FILE: test/basic_features/utils_test.py
================================================
import unittest
import requests
class UtilsTests(unittest.TestCase):
def setUp(self):
self.url_options = "http://localhost:7860/sdapi/v1/options"
self.url_cmd_flags = "http://localhost:7860/sdapi/v1/cmd-flags"
self.url_samplers = "http://localhost:7860/sdapi/v1/samplers"
self.url_upscalers = "http://localhost:7860/sdapi/v1/upscalers"
self.url_sd_models = "http://localhost:7860/sdapi/v1/sd-models"
self.url_hypernetworks = "http://localhost:7860/sdapi/v1/hypernetworks"
self.url_face_restorers = "http://localhost:7860/sdapi/v1/face-restorers"
self.url_realesrgan_models = "http://localhost:7860/sdapi/v1/realesrgan-models"
self.url_prompt_styles = "http://localhost:7860/sdapi/v1/prompt-styles"
self.url_embeddings = "http://localhost:7860/sdapi/v1/embeddings"
def test_options_get(self):
self.assertEqual(requests.get(self.url_options).status_code, 200)
def test_options_write(self):
response = requests.get(self.url_options)
self.assertEqual(response.status_code, 200)
pre_value = response.json()["send_seed"]
self.assertEqual(requests.post(self.url_options, json={"send_seed":not pre_value}).status_code, 200)
response = requests.get(self.url_options)
self.assertEqual(response.status_code, 200)
self.assertEqual(response.json()["send_seed"], not pre_value)
requests.post(self.url_options, json={"send_seed": pre_value})
def test_cmd_flags(self):
self.assertEqual(requests.get(self.url_cmd_flags).status_code, 200)
def test_samplers(self):
self.assertEqual(requests.get(self.url_samplers).status_code, 200)
def test_upscalers(self):
self.assertEqual(requests.get(self.url_upscalers).status_code, 200)
def test_sd_models(self):
self.assertEqual(requests.get(self.url_sd_models).status_code, 200)
def test_hypernetworks(self):
self.assertEqual(requests.get(self.url_hypernetworks).status_code, 200)
def test_face_restorers(self):
self.assertEqual(requests.get(self.url_face_restorers).status_code, 200)
def test_realesrgan_models(self):
self.assertEqual(requests.get(self.url_realesrgan_models).status_code, 200)
def test_prompt_styles(self):
self.assertEqual(requests.get(self.url_prompt_styles).status_code, 200)
def test_embeddings(s
gitextract_f8afe7lc/ ├── A用户协议.txt ├── B使用教程+常见问题.txt ├── CODEOWNERS ├── LICENSE.txt ├── README.md ├── cache.json ├── config.json ├── configs/ │ ├── alt-diffusion-inference.yaml │ ├── instruct-pix2pix.yaml │ ├── v1-inference.yaml │ └── v1-inpainting-inference.yaml ├── environment-wsl2.yaml ├── launch.py ├── params.txt ├── requirements.txt ├── requirements_versions.txt ├── script.js ├── scripts/ │ ├── custom_code.py │ ├── img2imgalt.py │ ├── loopback.py │ ├── outpainting_mk_2.py │ ├── poor_mans_outpainting.py │ ├── postprocessing_codeformer.py │ ├── postprocessing_gfpgan.py │ ├── postprocessing_upscale.py │ ├── prompt_matrix.py │ ├── prompts_from_file.py │ ├── sd_upscale.py │ └── xyz_grid.py ├── style.css ├── styles.csv ├── tags/ │ └── temp/ │ ├── emb.txt │ └── wc.txt ├── test/ │ ├── __init__.py │ ├── basic_features/ │ │ ├── __init__.py │ │ ├── extras_test.py │ │ ├── img2img_test.py │ │ ├── txt2img_test.py │ │ └── utils_test.py │ ├── server_poll.py │ └── test_files/ │ └── empty.pt ├── textual_inversion_templates/ │ ├── hypernetwork.txt │ ├── none.txt │ ├── style.txt │ ├── style_filewords.txt │ ├── subject.txt │ └── subject_filewords.txt ├── tmp/ │ ├── stderr.txt │ ├── stdout.txt │ └── tagAutocompletePath.txt ├── ui-config.json ├── webui-macos-env.sh ├── webui-user.bat ├── webui-user.sh ├── webui.bat ├── webui.py └── webui.sh
SYMBOL INDEX (172 symbols across 20 files)
FILE: launch.py
function check_python_version (line 20) | def check_python_version():
function commit_hash (line 51) | def commit_hash():
function extract_arg (line 65) | def extract_arg(args, name):
function extract_opt (line 69) | def extract_opt(args, name):
function run (line 82) | def run(command, desc=None, errdesc=None, custom_env=None, live=False):
function check_run (line 110) | def check_run(command):
function is_installed (line 115) | def is_installed(package):
function repo_dir (line 124) | def repo_dir(name):
function run_python (line 128) | def run_python(code, desc=None, errdesc=None):
function run_pip (line 132) | def run_pip(args, desc=None):
function check_run_python (line 140) | def check_run_python(code):
function git_clone (line 144) | def git_clone(url, dir, name, commithash=None):
function version_check (line 165) | def version_check(commit):
function run_extension_installer (line 182) | def run_extension_installer(extension_dir):
function list_extensions (line 196) | def list_extensions(settings_file):
function run_extensions_installers (line 211) | def run_extensions_installers(settings_file):
function prepare_environment (line 219) | def prepare_environment():
function tests (line 325) | def tests(test_dir):
function start (line 350) | def start():
FILE: script.js
function gradioApp (line 1) | function gradioApp() {
function get_uiCurrentTab (line 7) | function get_uiCurrentTab() {
function get_uiCurrentTabContent (line 11) | function get_uiCurrentTabContent() {
function onUiUpdate (line 21) | function onUiUpdate(callback){
function onUiLoaded (line 24) | function onUiLoaded(callback){
function onUiTabChange (line 27) | function onUiTabChange(callback){
function onOptionsChanged (line 30) | function onOptionsChanged(callback){
function runCallback (line 34) | function runCallback(x, m){
function executeCallbacks (line 41) | function executeCallbacks(queue, m) {
function uiElementIsVisible (line 86) | function uiElementIsVisible(el) {
FILE: scripts/custom_code.py
class Script (line 7) | class Script(scripts.Script):
method title (line 9) | def title(self):
method show (line 12) | def show(self, is_img2img):
method ui (line 15) | def ui(self, is_img2img):
method run (line 21) | def run(self, p, code):
FILE: scripts/img2imgalt.py
function find_noise_for_image (line 21) | def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
function find_noise_for_image_sigma_adjustment (line 68) | def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, st...
class Script (line 118) | class Script(scripts.Script):
method __init__ (line 119) | def __init__(self):
method title (line 122) | def title(self):
method show (line 125) | def show(self, is_img2img):
method ui (line 128) | def ui(self, is_img2img):
method run (line 157) | def run(self, p, _, override_sampler, override_prompt, original_prompt...
FILE: scripts/loopback.py
class Script (line 14) | class Script(scripts.Script):
method title (line 15) | def title(self):
method show (line 18) | def show(self, is_img2img):
method ui (line 21) | def ui(self, is_img2img):
method run (line 28) | def run(self, p, loops, denoising_strength_change_factor, append_inter...
FILE: scripts/outpainting_mk_2.py
function get_matched_noise (line 16) | def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_varia...
class Script (line 121) | class Script(scripts.Script):
method title (line 122) | def title(self):
method show (line 125) | def show(self, is_img2img):
method ui (line 128) | def ui(self, is_img2img):
method run (line 142) | def run(self, p, _, pixels, mask_blur, direction, noise_q, color_varia...
FILE: scripts/poor_mans_outpainting.py
class Script (line 12) | class Script(scripts.Script):
method title (line 13) | def title(self):
method show (line 16) | def show(self, is_img2img):
method ui (line 19) | def ui(self, is_img2img):
method run (line 30) | def run(self, p, pixels, mask_blur, inpainting_fill, direction):
FILE: scripts/postprocessing_codeformer.py
class ScriptPostprocessingCodeFormer (line 10) | class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostpr...
method ui (line 14) | def ui(self):
method process (line 24) | def process(self, pp: scripts_postprocessing.PostprocessedImage, codef...
FILE: scripts/postprocessing_gfpgan.py
class ScriptPostprocessingGfpGan (line 10) | class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostproces...
method ui (line 14) | def ui(self):
method process (line 22) | def process(self, pp: scripts_postprocessing.PostprocessedImage, gfpga...
FILE: scripts/postprocessing_upscale.py
class ScriptPostprocessingUpscale (line 13) | class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostproce...
method ui (line 17) | def ui(self):
method upscale (line 51) | def upscale(self, image, info, upscaler, upscale_mode, upscale_by, up...
method process (line 78) | def process(self, pp: scripts_postprocessing.PostprocessedImage, upsca...
method image_changed (line 105) | def image_changed(self):
class ScriptPostprocessingUpscaleSimple (line 109) | class ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale):
method ui (line 113) | def ui(self):
method process (line 123) | def process(self, pp: scripts_postprocessing.PostprocessedImage, upsca...
FILE: scripts/prompt_matrix.py
function draw_xy_grid (line 15) | def draw_xy_grid(xs, ys, x_label, y_label, cell):
class Script (line 43) | class Script(scripts.Script):
method title (line 44) | def title(self):
method ui (line 47) | def ui(self, is_img2img):
method run (line 61) | def run(self, p, put_at_start, different_seeds, prompt_type, variation...
FILE: scripts/prompts_from_file.py
function process_string_tag (line 18) | def process_string_tag(tag):
function process_int_tag (line 22) | def process_int_tag(tag):
function process_float_tag (line 26) | def process_float_tag(tag):
function process_boolean_tag (line 30) | def process_boolean_tag(tag):
function cmdargs (line 62) | def cmdargs(line):
function load_prompt_file (line 101) | def load_prompt_file(file):
class Script (line 110) | class Script(scripts.Script):
method title (line 111) | def title(self):
method ui (line 114) | def ui(self, is_img2img):
method run (line 129) | def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_txt:...
FILE: scripts/sd_upscale.py
class Script (line 12) | class Script(scripts.Script):
method title (line 13) | def title(self):
method show (line 16) | def show(self, is_img2img):
method ui (line 19) | def ui(self, is_img2img):
method run (line 27) | def run(self, p, _, overlap, upscaler_index, scale_factor):
FILE: scripts/xyz_grid.py
function apply_field (line 31) | def apply_field(field):
function apply_prompt (line 38) | def apply_prompt(p, x, xs):
function apply_order (line 46) | def apply_order(p, x, xs):
function apply_sampler (line 71) | def apply_sampler(p, x, xs):
function confirm_samplers (line 79) | def confirm_samplers(p, xs):
function apply_checkpoint (line 85) | def apply_checkpoint(p, x, xs):
function confirm_checkpoints (line 92) | def confirm_checkpoints(p, xs):
function apply_clip_skip (line 98) | def apply_clip_skip(p, x, xs):
function apply_upscale_latent_space (line 102) | def apply_upscale_latent_space(p, x, xs):
function find_vae (line 109) | def find_vae(name: str):
function apply_vae (line 123) | def apply_vae(p, x, xs):
function apply_styles (line 127) | def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
function format_value_add_label (line 131) | def format_value_add_label(p, opt, x):
function format_value (line 138) | def format_value(p, opt, x):
function format_value_join_list (line 144) | def format_value_join_list(p, opt, x):
function do_nothing (line 148) | def do_nothing(p, x, xs):
function format_nothing (line 152) | def format_nothing(p, opt, x):
function str_permutations (line 156) | def str_permutations(x):
class AxisOption (line 161) | class AxisOption:
method __init__ (line 162) | def __init__(self, label, type, apply, format_value=format_value_add_l...
class AxisOptionImg2Img (line 172) | class AxisOptionImg2Img(AxisOption):
method __init__ (line 173) | def __init__(self, *args, **kwargs):
class AxisOptionTxt2Img (line 177) | class AxisOptionTxt2Img(AxisOption):
method __init__ (line 178) | def __init__(self, *args, **kwargs):
function draw_xyz_grid (line 211) | def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, dra...
class SharedSettingsStackHelper (line 315) | class SharedSettingsStackHelper(object):
method __enter__ (line 316) | def __enter__(self):
method __exit__ (line 320) | def __exit__(self, exc_type, exc_value, tb):
class Script (line 335) | class Script(scripts.Script):
method title (line 336) | def title(self):
method ui (line 339) | def ui(self, is_img2img):
method run (line 410) | def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values,...
FILE: test/basic_features/extras_test.py
class TestExtrasWorking (line 6) | class TestExtrasWorking(unittest.TestCase):
method setUp (line 7) | def setUp(self):
method test_simple_upscaling_performed (line 25) | def test_simple_upscaling_performed(self):
class TestPngInfoWorking (line 30) | class TestPngInfoWorking(unittest.TestCase):
method setUp (line 31) | def setUp(self):
method test_png_info_performed (line 37) | def test_png_info_performed(self):
class TestInterrogateWorking (line 41) | class TestInterrogateWorking(unittest.TestCase):
method setUp (line 42) | def setUp(self):
method test_interrogate_performed (line 49) | def test_interrogate_performed(self):
FILE: test/basic_features/img2img_test.py
class TestImg2ImgWorking (line 7) | class TestImg2ImgWorking(unittest.TestCase):
method setUp (line 8) | def setUp(self):
method test_img2img_simple_performed (line 46) | def test_img2img_simple_performed(self):
method test_inpainting_masked_performed (line 49) | def test_inpainting_masked_performed(self):
method test_inpainting_with_inverted_masked_performed (line 53) | def test_inpainting_with_inverted_masked_performed(self):
method test_img2img_sd_upscale_performed (line 58) | def test_img2img_sd_upscale_performed(self):
FILE: test/basic_features/txt2img_test.py
class TestTxt2ImgWorking (line 5) | class TestTxt2ImgWorking(unittest.TestCase):
method setUp (line 6) | def setUp(self):
method test_txt2img_simple_performed (line 37) | def test_txt2img_simple_performed(self):
method test_txt2img_with_negative_prompt_performed (line 40) | def test_txt2img_with_negative_prompt_performed(self):
method test_txt2img_with_complex_prompt_performed (line 44) | def test_txt2img_with_complex_prompt_performed(self):
method test_txt2img_not_square_image_performed (line 48) | def test_txt2img_not_square_image_performed(self):
method test_txt2img_with_hrfix_performed (line 52) | def test_txt2img_with_hrfix_performed(self):
method test_txt2img_with_tiling_performed (line 56) | def test_txt2img_with_tiling_performed(self):
method test_txt2img_with_restore_faces_performed (line 60) | def test_txt2img_with_restore_faces_performed(self):
method test_txt2img_with_vanilla_sampler_performed (line 64) | def test_txt2img_with_vanilla_sampler_performed(self):
method test_txt2img_multiple_batches_performed (line 70) | def test_txt2img_multiple_batches_performed(self):
method test_txt2img_batch_performed (line 74) | def test_txt2img_batch_performed(self):
FILE: test/basic_features/utils_test.py
class UtilsTests (line 4) | class UtilsTests(unittest.TestCase):
method setUp (line 5) | def setUp(self):
method test_options_get (line 17) | def test_options_get(self):
method test_options_write (line 20) | def test_options_write(self):
method test_cmd_flags (line 34) | def test_cmd_flags(self):
method test_samplers (line 37) | def test_samplers(self):
method test_upscalers (line 40) | def test_upscalers(self):
method test_sd_models (line 43) | def test_sd_models(self):
method test_hypernetworks (line 46) | def test_hypernetworks(self):
method test_face_restorers (line 49) | def test_face_restorers(self):
method test_realesrgan_models (line 52) | def test_realesrgan_models(self):
method test_prompt_styles (line 55) | def test_prompt_styles(self):
method test_embeddings (line 58) | def test_embeddings(self):
FILE: test/server_poll.py
function run_tests (line 6) | def run_tests(proc, test_dir):
FILE: webui.py
function check_versions (line 55) | def check_versions():
function initialize (line 86) | def initialize():
function setup_cors (line 156) | def setup_cors(app):
function create_api (line 165) | def create_api(app):
function wait_on_server (line 171) | def wait_on_server(demo=None):
function api_only (line 182) | def api_only():
function webui (line 195) | def webui():
Condensed preview — 57 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (297K chars).
[
{
"path": "A用户协议.txt",
"chars": 530,
"preview": "本整合包仅用作 AIGC 技术学习，基于 Github 上开源项目 Stable Diffusion Webui 制作，提供了算法的运行环境。本整合包并不附带任何生成图像所用的模型。\r\n使用本整合包即代表您已阅读并同意以下用户协议：\r\n\r\n"
},
{
"path": "B使用教程+常见问题.txt",
"chars": 780,
"preview": "AI 作图知识库(教程): https://guide.novelai.dev/\r\n标签超市(解析组合): https://tags.novelai.dev/\r\n原图提取标签: https://spell.novelai.dev/\r\n\r\n入"
},
{
"path": "CODEOWNERS",
"chars": 657,
"preview": "* @AUTOMATIC1111\r\n\r\n# if you were managing a localization and were removed from this file, this is because\r\n# the "
},
{
"path": "LICENSE.txt",
"chars": 35240,
"preview": " GNU AFFERO GENERAL PUBLIC LICENSE\r\n Version 3, 19 November 2007\r\n\r\n "
},
{
"path": "README.md",
"chars": 10585,
"preview": "# Stable Diffusion web UI\r\nA browser interface based on Gradio library for Stable Diffusion.\r\n\r\n\r\n\r\n#"
},
{
"path": "cache.json",
"chars": 413,
"preview": "{\r\n \"hashes\": {\r\n \"checkpoint/final-prune.ckpt\": {\r\n \"mtime\": 1665122544.3176749,\r\n \"sha"
},
{
"path": "config.json",
"chars": 7251,
"preview": "{\r\n \"samples_save\": true,\r\n \"samples_format\": \"png\",\r\n \"samples_filename_pattern\": \"\",\r\n \"save_images_add_nu"
},
{
"path": "configs/alt-diffusion-inference.yaml",
"chars": 1983,
"preview": "model:\r\n base_learning_rate: 1.0e-04\r\n target: ldm.models.diffusion.ddpm.LatentDiffusion\r\n params:\r\n linear_start:"
},
{
"path": "configs/instruct-pix2pix.yaml",
"chars": 2698,
"preview": "# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).\r\n# See more d"
},
{
"path": "configs/v1-inference.yaml",
"chars": 1943,
"preview": "model:\r\n base_learning_rate: 1.0e-04\r\n target: ldm.models.diffusion.ddpm.LatentDiffusion\r\n params:\r\n linear_start:"
},
{
"path": "configs/v1-inpainting-inference.yaml",
"chars": 2061,
"preview": "model:\r\n base_learning_rate: 7.5e-05\r\n target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion\r\n params:\r\n linear"
},
{
"path": "environment-wsl2.yaml",
"chars": 185,
"preview": "name: automatic\r\nchannels:\r\n - pytorch\r\n - defaults\r\ndependencies:\r\n - python=3.10\r\n - pip=22.2.2\r\n - cudatoolkit=1"
},
{
"path": "launch.py",
"chars": 14352,
"preview": "# this scripts installs necessary requirements and launches main program in webui.py\r\nimport subprocess\r\nimport os\r\nimpo"
},
{
"path": "params.txt",
"chars": 396,
"preview": "masterpiece, best quality, 1girl, \r\nNegative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra"
},
{
"path": "requirements.txt",
"chars": 391,
"preview": "blendmodes\r\naccelerate\r\nbasicsr\r\nfonts\r\nfont-roboto\r\ngfpgan\r\ngradio==3.16.2\r\ninvisible-watermark\r\nnumpy\r\nomegaconf\r\nopen"
},
{
"path": "requirements_versions.txt",
"chars": 506,
"preview": "blendmodes==2022\r\ntransformers==4.25.1\r\naccelerate==0.12.0\r\nbasicsr==1.4.2\r\ngfpgan==1.3.8\r\ngradio==3.16.2\r\nnumpy==1.23.3"
},
{
"path": "script.js",
"chars": 2981,
"preview": "function gradioApp() {\r\n const elems = document.getElementsByTagName('gradio-app')\r\n const gradioShadowRoot = elem"
},
{
"path": "scripts/custom_code.py",
"chars": 1144,
"preview": "import modules.scripts as scripts\r\nimport gradio as gr\r\n\r\nfrom modules.processing import Processed\r\nfrom modules.shared "
},
{
"path": "scripts/img2imgalt.py",
"chars": 9105,
"preview": "from collections import namedtuple\r\n\r\nimport numpy as np\r\nfrom tqdm import trange\r\n\r\nimport modules.scripts as scripts\r\n"
},
{
"path": "scripts/loopback.py",
"chars": 3772,
"preview": "import numpy as np\r\nfrom tqdm import trange\r\n\r\nimport modules.scripts as scripts\r\nimport gradio as gr\r\n\r\nfrom modules im"
},
{
"path": "scripts/outpainting_mk_2.py",
"chars": 13340,
"preview": "import math\r\n\r\nimport numpy as np\r\nimport skimage\r\n\r\nimport modules.scripts as scripts\r\nimport gradio as gr\r\nfrom PIL im"
},
{
"path": "scripts/poor_mans_outpainting.py",
"chars": 5740,
"preview": "import math\r\n\r\nimport modules.scripts as scripts\r\nimport gradio as gr\r\nfrom PIL import Image, ImageDraw\r\n\r\nfrom modules "
},
{
"path": "scripts/postprocessing_codeformer.py",
"chars": 1475,
"preview": "from PIL import Image\r\nimport numpy as np\r\n\r\nfrom modules import scripts_postprocessing, codeformer_model\r\nimport gradio"
},
{
"path": "scripts/postprocessing_gfpgan.py",
"chars": 1058,
"preview": "from PIL import Image\r\nimport numpy as np\r\n\r\nfrom modules import scripts_postprocessing, gfpgan_model\r\nimport gradio as "
},
{
"path": "scripts/postprocessing_upscale.py",
"chars": 6320,
"preview": "from PIL import Image\r\nimport numpy as np\r\n\r\nfrom modules import scripts_postprocessing, shared\r\nimport gradio as gr\r\n\r\n"
},
{
"path": "scripts/prompt_matrix.py",
"chars": 4873,
"preview": "import math\r\nfrom collections import namedtuple\r\nfrom copy import copy\r\nimport random\r\n\r\nimport modules.scripts as scrip"
},
{
"path": "scripts/prompts_from_file.py",
"chars": 5625,
"preview": "import copy\r\nimport math\r\nimport os\r\nimport random\r\nimport sys\r\nimport traceback\r\nimport shlex\r\n\r\nimport modules.scripts"
},
{
"path": "scripts/sd_upscale.py",
"chars": 3984,
"preview": "import math\r\n\r\nimport modules.scripts as scripts\r\nimport gradio as gr\r\nfrom PIL import Image\r\n\r\nfrom modules import proc"
},
{
"path": "scripts/xyz_grid.py",
"chars": 26800,
"preview": "from collections import namedtuple\r\nfrom copy import copy\r\nfrom itertools import permutations, chain\r\nimport random\r\nimp"
},
{
"path": "style.css",
"chars": 18254,
"preview": ".container {\r\n max-width: 100%;\r\n}\r\n\r\n.token-counter{\r\n position: absolute;\r\n display: inline-block;\r\n right"
},
{
"path": "styles.csv",
"chars": 279,
"preview": "name,prompt,negative_prompt\r\nNone,,\r\nnaifu基础起手式,\"masterpiece, best quality, \",\"lowres, bad anatomy, bad hands, text, er"
},
{
"path": "tags/temp/emb.txt",
"chars": 0,
"preview": ""
},
{
"path": "tags/temp/wc.txt",
"chars": 0,
"preview": ""
},
{
"path": "test/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "test/basic_features/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "test/basic_features/extras_test.py",
"chars": 2028,
"preview": "import unittest\r\nimport requests\r\nfrom gradio.processing_utils import encode_pil_to_base64\r\nfrom PIL import Image\r\n\r\ncla"
},
{
"path": "test/basic_features/img2img_test.py",
"chars": 2534,
"preview": "import unittest\r\nimport requests\r\nfrom gradio.processing_utils import encode_pil_to_base64\r\nfrom PIL import Image\r\n\r\n\r\nc"
},
{
"path": "test/basic_features/txt2img_test.py",
"chars": 3357,
"preview": "import unittest\r\nimport requests\r\n\r\n\r\nclass TestTxt2ImgWorking(unittest.TestCase):\r\n def setUp(self):\r\n self.u"
},
{
"path": "test/basic_features/utils_test.py",
"chars": 2481,
"preview": "import unittest\r\nimport requests\r\n\r\nclass UtilsTests(unittest.TestCase):\r\n def setUp(self):\r\n self.url_options = \"ht"
},
{
"path": "test/server_poll.py",
"chars": 782,
"preview": "import unittest\r\nimport requests\r\nimport time\r\n\r\n\r\ndef run_tests(proc, test_dir):\r\n timeout_threshold = 240\r\n star"
},
{
"path": "textual_inversion_templates/hypernetwork.txt",
"chars": 863,
"preview": "a photo of a [filewords]\r\na rendering of a [filewords]\r\na cropped photo of the [filewords]\r\nthe photo of a [filewords]\r\n"
},
{
"path": "textual_inversion_templates/none.txt",
"chars": 9,
"preview": "picture\r\n"
},
{
"path": "textual_inversion_templates/style.txt",
"chars": 607,
"preview": "a painting, art by [name]\r\na rendering, art by [name]\r\na cropped painting, art by [name]\r\nthe painting, art by [name]\r\na"
},
{
"path": "textual_inversion_templates/style_filewords.txt",
"chars": 892,
"preview": "a painting of [filewords], art by [name]\r\na rendering of [filewords], art by [name]\r\na cropped painting of [filewords], "
},
{
"path": "textual_inversion_templates/subject.txt",
"chars": 728,
"preview": "a photo of a [name]\r\na rendering of a [name]\r\na cropped photo of the [name]\r\nthe photo of a [name]\r\na photo of a clean ["
},
{
"path": "textual_inversion_templates/subject_filewords.txt",
"chars": 1079,
"preview": "a photo of a [name], [filewords]\r\na rendering of a [name], [filewords]\r\na cropped photo of the [name], [filewords]\r\nthe "
},
{
"path": "tmp/stderr.txt",
"chars": 2,
"preview": "^C"
},
{
"path": "tmp/stdout.txt",
"chars": 0,
"preview": ""
},
{
"path": "tmp/tagAutocompletePath.txt",
"chars": 42,
"preview": "extensions/a1111-sd-webui-tagcomplete/tags"
},
{
"path": "ui-config.json",
"chars": 53870,
"preview": "{\r\n \"txt2img/Prompt/visible\": true,\r\n \"txt2img/Prompt/value\": \"\",\r\n \"txt2img/Negative prompt/visible\": true,\r\n "
},
{
"path": "webui-macos-env.sh",
"chars": 833,
"preview": "#!/bin/bash\r\n####################################################################\r\n# macOS defa"
},
{
"path": "webui-user.bat",
"chars": 92,
"preview": "@echo off\r\n\r\nset PYTHON=\r\nset GIT=\r\nset VENV_DIR=\r\nset COMMANDLINE_ARGS=\r\n\r\ncall webui.bat\r\n"
},
{
"path": "webui-user.sh",
"chars": 1361,
"preview": "#!/bin/bash\r\n#########################################################\r\n# Uncomment and change the variables below to yo"
},
{
"path": "webui.bat",
"chars": 2115,
"preview": "@echo off\r\n\r\nif not defined PYTHON (set PYTHON=python)\r\nif not defined VENV_DIR (set \"VENV_DIR=%~dp0%venv\")\r\n\r\n\r\nset ERR"
},
{
"path": "webui.py",
"chars": 10871,
"preview": "import os\r\nimport sys\r\nimport time\r\nimport importlib\r\nimport signal\r\nimport re\r\nfrom fastapi import FastAPI\r\nfrom fastap"
},
{
"path": "webui.sh",
"chars": 5499,
"preview": "#!/usr/bin/env bash\r\n#################################################\r\n# Please do not make any changes to this file, "
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the djstla/novelai-webui-aki-v3 GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 57 files (268.3 KB), approximately 72.8k tokens, and a symbol index with 172 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.